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English Pages 415 [417] Year 2022
Oncogenic Viruses Volume 2 Medical Applications of Viral Oncology Research
Graphical abstract
Moulay Mustapha Ennaji
Oncogenic Viruses Volume 2 Medical Applications of Viral Oncology Research
Edited by
Moulay Mustapha Ennaji Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). MATLABs is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLABs software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLABs software. Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-824156-1 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals
Publisher: Stacy Masucci Acquisitions Editor: Kattie Washington Editorial Project Manager: Tracy I. Tufaga Production Project Manager: Swapna Srinivasan Cover Designer: Matthew Limbert Typeset by MPS Limited, Chennai, India
Contents List of contributors About the editor Preface—Oncogenic Viruses: Up To Recent Knowledge Acknowledgments
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Vitamin D new therapy for breast cancer prevention
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Marwah Labyed, Najwa Hassou, Mohammed El Mzibri and Moulay Mustapha Ennaji
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1.1 Introduction 1.2 Breast cancer 1.2.1 Generality 1.2.2 Anatomy 1.2.3 Symptoms 1.2.4 Breast cancer types 1.2.5 Risk factors 1.3 Viral etiology of breast cancer 1.4 Mouse mammary tumor virus like 1.5 Human papilloma virus 1.6 EpsteinBarr virus 1.7 Vitamin D 1.7.1 Generality 1.7.2 Biosynthesis 1.8 Food needs and sources 1.9 Storage sites 1.10 Vitamin D receptors 1.11 Vitamin D new therapy for breast cancers prevention 1.11.1 Relationship between vitamin D and breast cancer 1.12 Mechanism of action 1.13 Vitamin D and breast cancer prevention 1.14 Conclusion References
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Molecular diagnosis of human papillomavirus related to cervical cancer
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Kaoutar Anouar Tadlaoui and Moulay Mustapha Ennaji 2.1 Introduction
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2.2 Etiopathogenesis of human papillomavirus infection 2.2.1 Human papillomavirus genome structure 2.2.2 Mechanism of human papillomavirus infection in the cervix and carcinogenesis 2.3 Diagnosis of human papillomavirus viral genome 2.3.1 Identification of human papillomavirus without genotyping 2.3.2 Human papillomavirus genotyping 2.3.3 Human papillomavirus E6/E7 mRNA and protein detection 2.4 Conclusion Acknowledgment References
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Risk of the development of cancers induced by the consumption of mussels accumulating metallic trace elements
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Hanaaˆ Bazir, Najwa Hassou, Mohammed Nabil Benchekroun, Hlima Bessi and Moulay Mustapha Ennaji
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3.1 Introduction 3.2 Trace metal elements 3.2.1 Origin and cycle of trace metal elements in the natural environment 3.2.2 Properties of trace metal elements 3.2.3 Transfer of trace metal elements in the trophic chain 3.2.4 Effects of metal toxicity on human health 3.3 Bivalve molluscs 3.3.1 Classification of lamellibranchs (bivalves) 3.3.2 The mytilidae as bioindicators 3.3.3 Response of marine organisms to trace metal elements 3.4 Oxidative stress and cancer 3.5 Conclusion Acknowledgments References
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Oncolytic virus cancer therapeutic options and integration of artificial intelligence into virus cancer research
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Vaishak Kaviarasan, Barath Ragunath and Ramakrishnan Veerabathiran 4.1 4.2 4.3 4.4
Introduction History General properties of oncovirus Oncolytic viral therapy: a new era of treatment
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4.4.1 Cancer immunoediting hypothesis 4.4.2 Pharmacokinetics of oncolytic viral therapy 4.5 Applications of oncolytic viral therapy 4.5.1 Diagnosis 4.5.2 Tumor targeted cell delivery by oncolytic virotherapy 4.5.3 Genetically modified oncolytic virus 4.5.4 Integration of oncolytic viral therapy in radiotherapy 4.5.5 Integration of oncolytic viral therapy in chemotherapy 4.5.6 Integration of oncolytic viral therapy with immune inhibitor checkpoints 4.6 Limitations 4.7 Integration of artificial intelligence or machine learning into cancer research 4.8 Future concerns 4.9 Conclusion Acknowledgment References
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Oncoviruses: future prospects of molecular mechanisms and therapeutic strategies
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Iyshwarya Bhaskar Kalarani, Kamila Thasneem and Ramakrishnan Veerabathiran 5.1 Introduction 5.2 Mechanism of oncovirus 5.3 Types and mechanism of oncoviruses 5.3.1 EpsteinBarr virus 5.3.2 Hepatitis B virus 5.3.3 Aviadenovirus 5.3.4 Human immunodeficiency virus 5.3.5 Human papillomavirus 5.3.6 Polyomavirus 5.3.7 Herpes simplex virus 5.3.8 Parvovirus 5.3.9 Leporipoxvirus 5.3.10 Orthopoxvirus 5.4 Genetics of virus 5.5 Types of treatment 5.5.1 Immunotherapy 5.5.2 Chemotherapy 5.5.3 Targeted therapy 5.5.4 Radiation therapy 5.5.5 Hormonal therapy 5.5.6 Surgery 5.6 Stem cell transplant therapy 5.6.1 Oncotherapy
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5.7 Future of oncotherapy 5.8 Conclusion Acknowledgment References
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Multi-omics methods and tools in dissecting the oncovirus behavior in human host
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Sheik S.S.J. Ahmed, Ramakrishnan Veerabathiran, Mookkandi Sudhan, Harsh Panwar and Prabu Pramasivam
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6.1 Introduction 6.1.1 Definition 6.2 Types of omics 6.2.1 Genomics 6.2.2 Transcriptomics 6.2.3 Proteomics 6.2.4 Metabolomics 6.2.5 Bioinformatics resources 6.2.6 Databases and tools 6.3 Conclusions References
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Role of viral human oncogenesis: recent developments in molecular approaches
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ChandraLekha Saravanan, Mahalakshmi Baskar, Sheik S.S.J. Ahmed and Ramakrishnan Veerabathiran 7.1 Introduction 7.2 Prevalence of oncovirus 7.3 Classification of oncovirus 7.3.1 DNA tumor viruses 7.3.2 RNA tumor viruses 7.4 Molecular tools used for oncovirus detection 7.5 Vaccines available for oncovirus 7.6 Statistical analysis of oncovirus 7.6.1 EpsteinBarr virus 7.6.2 Hepatitis B virus 7.6.3 Human papillomavirus 7.6.4 Hepatitis C virus 7.6.5 Kaposi sarcoma-associated herpesvirus 7.6.6 Human immunodeficiency virus 7.6.7 Human T-cell lymphotropic virus type 1 7.6.8 Merkel cell polyomavirus 7.7 Oncovirus and cancer progression 7.7.1 Human papillomavirus on cancer progression
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7.7.2 Hepatitis B virus on cancer progression 7.7.3 Hepatitis C virus on cancer progression 7.7.4 Human papillomavirus on cancer progression 7.8 Oncolytic virotherapy 7.9 Conclusion Acknowledgment References
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Strategies for the development of hepatitis B virus vaccines
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Fadoua El Battioui, Fatima El Malki and Said Barrijal
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8.1 Introduction 8.2 Virus-like particle-based hepatitis B vaccines 8.3 Therapeutic vaccines 8.4 DNA-based vaccines 8.5 mRNA-based vaccines 8.6 Proteins/peptides vaccines 8.7 Cell-based vaccines 8.8 Nanovaccines 8.9 Efficacy of therapeutic vaccines 8.10 Harmlessness 8.11 Immunization coverage 8.12 Conclusion References
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MYC oncogenes as potential anticancer targets
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ˇ Radostina Alexandrova and Crtomir Podlipnik 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8
Introduction Biological role of MYC genes MYC in normal tissues and cancer MYC signal transduction pathway Structure of MYC The MYCMax interaction MYC as a potential target for antitumor therapy Targeting the MYCMax interaction with small molecule inhibitors 9.9 Indirect targeting of the MYC 9.10 Targeting MYC transcription 9.11 Targeting of MYC expression 9.12 Targeting MYC stability 9.13 Synthetic lethality with MYC 9.14 G-quadruplexes and expression of c-MYC 9.15 Conclusions and perspective Acknowledgments References
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10. Current status of viral biomarkers for oncogenic viruses
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Kazim Yalcin Arga and Medi Kori 10.1 Introduction 10.2 Epstein-Barr virus 10.2.1 Epstein-Barr virus-associated cancers 10.2.2 Epstein Bar virus-associated cancer biomarkers 10.3 Hepatitis B virus and hepatitis C virus 10.3.1 Hepatitis B virus- and hepatitis C virus-associated cancers 10.3.2 Hepatitis B virus-associated cancer biomarkers 10.3.3 Hepatitis C virus-associated cancer biomarkers 10.4 Human T-cell lymphotropic virus-1 10.4.1 HTLV-1-associated cancers 10.4.2 HTLV-1-associated cancer biomarkers 10.5 Human Herpesvirus-8 10.5.1 HHV-8-associated cancers 10.5.2 HHV-8-associated cancer biomarkers 10.6 Human papillomavirus 10.6.1 Human papillomaviruses-associated cancers 10.6.2 Human papillomaviruses-associated cancer biomarkers 10.7 Conclusions References
11. Bioinformatics serving oncoviral studies
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Virupaksha Ajit Bastikar, Pramodkumar Pyarelal Gupta, Alpana Bastikar, Santosh Subhash Chhajed and Santosh Ajabrao Bothe 11.1 Biological database 11.2 Sequence analysis 11.3 Molecular dynamics simulations 11.4 Computer-aided drug discovery 11.5 Systems biology approach 11.6 Artificial intelligence approaches 11.7 Conclusion References
12. QSAR approach for combating cancer cells
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ˇ Said Byadi, Aziz Aboulmouhajir and Crtomir Podlipnik 12.1 12.2 12.3 12.4 12.5
Introduction Handling and curation of chemical and biological data Structures drawing and database building Molecular descriptors Multivariate analysis
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Multiple linear regression analysis Principal component regression Partial least squares Kernel partial least squares Artificial neural network Other methods Classification-based QSAR approaches QSAR model generation Model examination and validation Internal validation External validation Applicability domain Model application for the prediction of compounds activity References
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13. Human papillomaviruses and their carcinogens effect
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12.6 12.7 12.8 12.9 12.10 12.11 12.12 12.13 12.14 12.15 12.16 12.17 12.18
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Elamrani Elhassani Salma and Bahia Bennani 13.1 13.2 13.3 13.4
Introduction Epidemiology of human papillomaviruse Human papillomaviruse classification Human papillomavirus transmission 13.4.1 Vertical transmission 13.4.2 Horizontal transmission 13.5 Structure, genomic organization, and viral proteins 13.5.1 The long control region 13.5.2 The early region 13.5.3 The late region: L1 and L2 13.6 Human papillomavirus replication cycle 13.7 Infection evolution 13.8 Molecular mechanisms of HPV-induced carcinogenesis 13.9 Mechanisms of cell transformation 13.10 Conclusion Acknowledgments References
14. Progress in the development of vaccines against human papillomavirus
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Fadoua El Battioui, Fatima El Malki, Hassan Ghazal and Said Barrijal 14.1 Introduction 14.2 Virus-like particle vaccination strategy 14.3 Vaccines prophylactic against human papillomavirus 14.3.1 Types of vaccines 14.4 Immunization procedures and doses
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14.5 Efficacy and safety of human papillomavirus vaccines 14.5.1 Efficacy 14.5.2 Safety and security 14.6 L2-based human papillomavirus prophylactic vaccines 14.7 Human papillomavirus vaccine coverage 14.8 Factors influencing vaccination coverage 14.9 Therapeutic vaccines 14.9.1 Bacterial vector vaccines 14.9.2 Viral vector vaccines 14.9.3 Vaccinia virus Efficacy and safety 14.9.4 DNA vaccines 14.9.5 RNA-based vaccines 14.9.6 Peptide-based vaccines 14.9.7 Protein vaccines 14.9.8 Cellular vaccines (Dendritic cell-based vaccines) 14.10 Conclusion References
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15. Development and characterization of an electrochemical sensor using molecularly imprinted polymer based on a gold screen-printed electrode for the detection of creatinine and glucose in human urine and saliva
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Benachir Bouchikhi, Alassane Diouf, Moulay Mustapha Ennaji and Nezha El Bari 15.1 Introduction 15.2 Urine and saliva as noninvasive sources of biomarkers 15.3 Biomarkers in the bloodstream can infiltrate the acini and eventually be secreted into the saliva 15.3.1 Recognition of particular compounds as an indicator of diseases 15.4 Current electrochemical sensor devices 15.5 Applications of gas sensors in oncology or virology as tools for the detection of biomarkers 15.6 Experimental 15.6.1 Chemicals and reagents 15.6.2 Polymer synthesis 15.6.3 Electrochemical sensors fabrication steps 15.6.4 Physicochemical characterization 15.6.5 Electrochemical measurements 15.7 Results and discussion 15.7.1 Morphological characterization of the fabricated sensor 15.7.2 Voltammetric array and electrochemical impedance spectroscopy responses
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15.7.3 Repeatability, reproducibility, selectivity, and stability of the sensor 15.7.4 Real samples detection 15.8 Conclusion Acknowledgments Declaration of competing interest References
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16. Detection of triclosan and sodium lauryl sulfate in environmental samples and cosmetic product by electrochemical sensor based on biomimetic recognition combined with electronic nose
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Nezha EL Bari, Soukaina Motia, Moulay Mustapha Ennaji and Benachir Bouchikhi 16.1 Introduction 16.1.1 Wastewater as sources of micropollutants 16.1.2 Current electrochemical sensors for environmental residues 16.1.3 Potential sensors for applications in the fields of virology and oncology 16.1.4 Electronic nose technology 16.2 Experimental 16.2.1 Chemicals and reagents 16.2.2 Polymer synthesis 16.2.3 Electrochemical sensors fabrication steps 16.2.4 Surface morphotogical analysis 16.2.5 Electrochemical measurements 16.2.6 E-nose setup and measurement 16.3 Results and discussion 16.3.1 Morphological characterization of the fabricated sensors 16.3.2 Electrochemical characterization of the sensors’ fabrication stages 16.3.3 Reproducibility, selectivity, and stability of the sensor 16.3.4 Practical application 16.4 Conclusion Acknowledgments Declaration of competing interest Appendix A References Index
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List of contributors Aziz Aboulmouhajir Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa Sheik S.S.J. Ahmed Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Radostina Alexandrova Department of Pathology, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, Sofia, Bulgaria Kaoutar Anouar Tadlaoui Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Kazim Yalcin Arga Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey; Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey Said Barrijal Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco Mahalakshmi Baskar Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Alpana Bastikar Department of Computer-Aided Drug Design, Navin Saxena Research and Technology Pvt. Ltd, Gandhidham, Gujarat, India Virupaksha Ajit Bastikar Amity Institute of Biotechnology, Amity University Mumbai, Maharashtra, India Hanaaˆ Bazir Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Mohammed Nabil Benchekroun Laboratory of Virology, Microbiology, Quality and Biotechnology/Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques–Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
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Bahia Bennani Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco Hlima Bessi Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Iyshwarya Bhaskar Kalarani Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Santosh Ajabrao Bothe Saraswati College, Gaulkhed Road, Shegaon, Dist Buldhana, Maharashtra, India Benachir Bouchikhi Biosensors and Nanotechnology Group, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco; Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Moulay Ismaı¨l University, Meknes, Morocco Said Byadi Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa Santosh Subhash Chhajed Department of Pharmaceutical Chemistry, METs Institute of Pharmacy, Nashik, Maharashtra, India Alassane Diouf Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco Nezha El Bari Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco; Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco Fadoua El Battioui Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco Fatima El Malki Institute of Nursing, Tangier, Morocco Moulay Mustapha Ennaji Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Hassan Ghazal National Center for Scientific and Technical Research (CNRST), Rabat, Morocco Pramodkumar Pyarelal Gupta School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, Maharashtra, India
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Najwa Hassou Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco; Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Vaishak Kaviarasan Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Medi Kori Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey Marwah Labyed Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Soukaina Motia Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco Mohammed El Mzibri Laboratory of Biology and Medical Research, National Center for Energy Sciences and Nuclear Techniques Rabat, Mohammedia, Morocco Harsh Panwar Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab, India ˇ rtomir Podlipnik Faculty of Chemistry and Chemical Technology, University of C Ljubljana, Ljubljana, Slovenia Prabu Pramasivam Department of Neurology, University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, United States Barath Ragunath Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Elamrani Elhassani Salma Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco ChandraLekha Saravanan Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Mookkandi Sudhan Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chennai, Tamil Nadu, India
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Kamila Thasneem Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India
About the editor Prof. Dr. Moulay Mustapha Ennaji is a Moroccan and Canadian citizen and native of Marrakesh (Morocco). He is a scientist specialized in the fields of virology, hygiene, and microbiology. He received his master’s degree in science in 1986 and PhD in virology in 1993 from Armand Frappier Institute, University of Quebec (Canada). Between 1991 and 1993, he completed his postdoctorate at the Canadian Red Cross. From 1993 to 1995, he was a research associate (RA) and from 1995 to 1996 a research officer (RO) at the National Council of Research of Canada (CNRC). He was also a visiting researcher at the University of California, Irvine, United States, and abroad lecturer at the Histochemistry Institutes of Paris, France. He was a guest researcher of the Franklin Foundation in US NIH Bethesda. He was recruited as a lecturer and enabled professor in 1996 to the Faculty of Sciences and Techniques Mohammedia (FSTM), falling under the University Hassan II of Casablanca (UH2C), where he was the head of the biology department from 1997 to 2000. He is currently a professor of Higher Education (PES C) in the same faculty. Being a scientist who is concerned with research development, he has participated in numerous conferences and delivered lectures on virology, cancerology, hygiene, and microbiology since 1986 at Moroccan, Canadian, and American universities. Since 2010, he has been the director of the Laboratory of Virology, Oncology, Biosciences, Environment and New Energies, Faculty of Sciences and Techniques, Mohammedia, UH2C, Morocco; a leader of Virology Oncology and Biotechnology Team; and deputy director of the Research Centre of Health and Biotechnologies of UH2C and will continue to hold the positions until 2024. From 2010 to 2016, he was the director of the Laboratory of Virology, Microbiology, Quality, Biotechnologies/Ecotoxicology and Biodiversity, Faculty of Sciences and Techniques, Mohammedia, UH2C, Morocco; leader of Virology, Oncology, Total Quality and Medical Biotechnology Team; and deputy director of the Research Centre of Natural Resources and Food (Rensa) of UH2C.
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Between 2005 and 2010, he was appointed as the director of Virology, Hygiene, and Microbiology and coordinator of the Consortium of Biomedical and Environmental Sciences laboratories at UH2C-FSTM. He was also responsible for the master’s programs in biotechnology and biomedical technologies (200003), DESA of microbiology and bioengineering (200510), and master of science and technology (MST Microbiology, Applied Virology, and Bio-industry Engineering and MST of Livings) (Immuno-Virology and Applied Microbiology) from 2010 to 2015. He has been a member of the Council of the Center of Doctoral Studies (CEDoc) at FSTM-UH2C since 2008. Between 2005 and 2010, he was the deputy head of the UFR DESA Biomedical Sciences and from 2000 to 2005 deputy leaders of the UFR PhD in health and environment. From 2005 to present, he has been the deputy head of Life and Environment Sciences Doctoral UFR. From 2010 to present, he has been a national expert at CNRST and member of the National Commission for Scholarships. Previously, from 2012 to 2014, he was also a UNESCO expert on governance reform of university systems. Throughout his career, he has been rewarded with 24 awards. He has organized numerous national and international meetings in the fields of virology, microbiology, and hygiene. At present, he is the Vice-President of the Moroccan Association of Biosafety, Cancer, and Microbiology.
Preface—Oncogenic Viruses: Up To Recent Knowledge Several research works concerning the tumorigenesis factors have confirmed the oconogenic viruses as a major cause of some cancers and other neoplastic growths. This field is full of arguments and controversial conclusions. Some of these arguments and conclusions are even related to a single virus. There are different study designs, populations, clinical and pathological criteria, social statuses of probands studied, etc., involved in these studies. Some of these studies even implicated other viruses responsible for cancers. These differences even led to contradictory conclusions, which can be noticed in today’s literature. However, there is a general agreement on the suspected roles played by viruses in initiating the development of cancer. Let me consider one of the most prominent oncogenic viruses: human papilloma virus (HPV). It is responsible for the majority of cervical cancer causalities, especially in the developing countries (World Health Organization, 2016). HPV 16 and 18 contribute to malignancy and are classified under the high-risk genital types with their prevalence in 70% of all cases and 20% of the adult population in western countries (Faridi et al., 2011). Talking about other tumors and oncogenic viruses’ relations, for instance, approximately 4% of HTLV-I-infected individuals develop adult T-cell leukemia (ATL)/lymphoma (ATLL) (Shuh & Beilke, 2005). HTLV-1 clinically causes two major diseases: ATLL and tropical spastic paraparesis/HTLV-1associated myelopathy. This virus was discovered as the first retrovirus that is associated with human diseases, including an aggressive leukemia derived from CD41 T cells (ATL), according to Satou et al. (2011). Another virus associated with lymphoma is EpsteinBarr virus (EBV), and many studies linked it to HIV casualties (Shibata et al., 1993). This association was also linked to HIV and development of non-Hodgkin’s lymphoma (NHL) in HIV-infected patients, with immunodeficiency patients at substantial risk of developing NHL and particularly primary CNS lymphoma, as concluded by Pluda et al. (1993). A positive association between hepatitis C virus (HCV) and risk of NHL was also suggested in the beginning of the second millennium (Matsuo et al., 2004).
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Other studies reported different degrees of association of lymphoma with HCV (Negri et al., 2004), but HCV prevalence in patients with B-cell NHL (B-NHL) has been found to be approximately 15%, higher than that reported in a general population (1.5%), suggesting a role of HCV in the etiology of B-NHL. It is important to note that some clinical and pathologic features of the NHL disease are associated with HCV infection, but the virus does not seem to affect prognosis. In addition, a positive association between hepatitis B virus (HBV) infection and B-NHL raises the probability that HBV may play an oncogenic role in the initiation of B-NHL (Gisbert et al., 2003; Keegan et al., 2005; Marcucci et al., 2006; Vallisa et al., 1999). According to WHO, regions with maximum exposure to human papilloma virus include Eastern Africa, Melanesia, and Southern and Central Africa. Unless cervical cancer prevention and control measures are successfully implemented, it is estimated that by 2030, approximately 800,000 new cases of cervical cancer will be annually diagnosed. The vast majority of these cases will be in developing countries. Since 2009, WHO has been promoting the inclusion of HPV vaccination into national immunization programs in countries where cervical cancer is a public health priority and where cost-effective and sustainable implementation of the vaccine is feasible (WHO, 2020). In the last 2 years, the world suffered from a unique pandemic situation (COVID-19) caused by one of zoonetic viruses, that is, coronavirus. This disease continues to have an impact on human health even after treatment, with new symptoms coming to picture every passing day. This pandemic situation has compelled the scientific community to take care of two important aspects of medical biology: the seriousness of viral infections on human health and the importance of an effective vaccination process. Neutralizing antibodies are crucial for vaccine-mediated protection against viral diseases. They probably act by suppressing the infection, which is then dealt with by cellular immunity. The protective effects of neutralizing antibodies can be achieved by neutralization of free virus particles, and also by several activities directed against infected cells. Several viruses have evolved mechanisms to evade neutralizing-antibody responses, and these viruses pose challenges for vaccine design (Burton, 2002). There are two types of immune system: innate immunity possessed by most organisms and the adaptive system found in vertebrates, which inspires the design of vaccines. The former involve cells such as dendritic cells and macrophages, which also have important roles to play in the adaptive system. The latter are characterized by lymphocytes, which possess specific receptors that recognize foreign antigens (Gordon, 2007). Vaccine as a means of preventing infectious diseases is widely applied and appreciated. Amongst them, vectors based on recombinant viruses have shown great promise and play an important role in the development of new
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vaccines. The ideal viral vector is safe and enables availability of required pathogen-specific antigens to the immune system (Souza et al., 2005). Scientists very well realize the importance of the development of vaccine for cancer treatment. Vaccine therapy for cancer is less toxic than chemotherapy or radiation and could, therefore, be especially effective in older, more frail cancer patients. However, it has been shown that older individuals do not respond to vaccine therapy as well as younger adults (Gravekamp, 2009). In this book, we also focus on some concepts of vaccination against viruses for cancer patients. Based on current shreds of evidence generated by clinical and epidemiological studies of various tumors, and a level of familiarity with oncoviruses’ mechanism of action, oncogenic viruses’ role in the initiation and progression of tumors could be confirmed. In addition, the underlying tumorigenesis mechanisms in a vast majority of human cancers could be accurately identified. For this reason, it is recommended to develop HPV vaccines or other such vaccines aimed at neutralizing oncogenic viruses responsible for human tumors, thus saving uncountable lives every hour of every day. This work, as a book, is dedicated to providing an update on oncogenic virus’s molecular biology and recent findings. It is presented in two volumes: the basic knowledge of fundamentals of oncogenic viruses (Volume 1: Fundamental of Oncovirus) and oncogenic viruses’ applications in mitigating threats posed by human cancer (Volume 2: Medical Applications of Viral Oncology Research). Moulay Mustapha Ennaji
References Burton, D. (2002). Antibodies, viruses and vaccines. Nature Reviews Immunology, 2, 706713. Faridi, R., Zahra, A., Khan, K., et al. (2011). Oncogenic potential of Human Papillomavirus (HPV) and its relation with cervical cancer. Virology Journal, 8, 269. Available from https://doi.org/10.1186/1743-422X-8-269. Gisbert, J. P., Garc´ıa-Buey, L., Pajares, J. M., & Moreno-Otero, R. (2003). Prevalence of hepatitis C virus infection in B-cell non-Hodgkin’s lymphoma: Systematic review and metaanalysis. Gastroenterology, 125(6), 17231732. Gordon, A. (2007). The importance of vaccination. Frontiers in Bioscience: A Journal and Virtual Library, 12, 12781290. Gravekamp, C. (2009). The importance of the age factor in cancer vaccination at older age. Cancer Immunology, Immunotherapy: CII, 58, 1969. Keegan, T. H., Glaser, S. L., Clarke, C. A., Gulley, M. L., Craig, F. E., DiGiuseppe, J. A., . . . Ambinder, R. F. (2005). Epstein-Barr virus as a marker of survival after Hodgkin’s lymphoma: A population-based study. Journal of Clinical Oncology, 23(30), 76047613.
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Marcucci, F., Mele, A., Spada, E., Candido, A., Bianco, E., Pulsoni, A., . . . De Renzo, A. (2006). ). High prevalence of hepatitis B virus infection in B-cell non-Hodgkin’s lymphoma. Haematologica, 91(4), 554557. Matsuo, K., Kusano, A., Sugumar, A., Nakamura, S., Tajima, K., & Mueller, N. E. (2004). Effect of hepatitis C virus infection on the risk of non-Hodgkin’s lymphoma: A meta-analysis of epidemiological studies. Cancer Science, 95(9), 745752. Negri, E., Little, D. A., Boiocchi, M., La Vecchia, C., & Franceschi, S. (2004). B-cell nonHodgkin’s lymphoma and hepatitis C virus infection: a systematic review. International Journal of Cancer, 111(1), 18. Pluda, J. M., Venzon, D. J., Tosato, G., Lietzau, J., Wyvill, K., Nelson, D. L., . . . Yarchoan, R. (1993). ). Parameters affecting the development of non-Hodgkin’s lymphoma in patients with severe human immunodeficiency virus infection receiving antiretroviral therapy. Journal of Clinical Oncology, 11(6), 10991107. Satou, Y., Yasunaga, J. I., Zhao, T., Yoshida, M., Miyazato, P., Takai, K., . . . Yamaguchi, T. (2011). HTLV-1 bZIP factor induces T-cell lymphoma and systemic inflammation in vivo. PLoS Pathogens, 7(2), e1001274. Shuh, M., & Beilke, M. (2005). The human T-cell leukemia virus type 1 (HTLV-1): New insights into the clinical aspects and molecular pathogenesis of adult t-cell leukemia/lymphoma (ATLL) and tropical spastic paraparesis/HTLV-associated myelopathy (TSP/HAM). Microscopy Research and Technique, 68(3-4), 176196. Shibata, D., Weiss, L. M., Hernandez, A. M., Nathwani, B. N., Bernstein, L., & Levine, A. M. (1993). Epstein-Barr virus-associated non-Hodgkin’s lymphoma in patients infected with the human immunodeficiency virus. Blood, 81(8), 21022109. Souza, A. P. D., et al. (2005). Recombinant viruses as vaccines against viral diseases. Brazilian Journal of Medical and Biological Research, 38, 509522. Vallisa, D., Berte`, R., Rocca, A., Civardi, G., Giangregorio, F., Ferrari, B., . . . Cavanna, L. (1999). Association between hepatitis C virus and non-Hodgkin’s lymphoma, and effects of viral infection on histologic subtype and clinical course. The American Journal of Medicine, 106(5), 556560. WHO, Guide to introducing HPV vaccine into national immunization programmes. 2020. Who. org (Accessed on 12 September 2020). World Health Organization. (2016). World health statistics 2016: monitoring health for the SDGs sustainable development goals. World Health Organization.
Acknowledgments If the only prayer you say in your whole life is thank you, that will be enough. Maıˆtre Eckhart
The birth of this book entitled “Oncogenic Viruses: Volume 2, Medical Applications of Viral Oncology Research” would not have taken place without the participation of a group of eminent/prominent scientists, clinicians, and academicians. I would like to acknowledge the extraordinary debt I owe to the authors who have contributed their chapters in this book to share their knowledge with the scientific and technical community working on oncoviruses and the new applications of viruses in the oncological field. We would not be able to complete this book without the continuous support and vision of the reviewers, who have contributed significantly with their expertise towards adding value to the chapters dealing with the current state of the art in the field of applications of viruses in cancer treatment. Special thanks to Elsevier publishing house, and all the Elsevier staff who have contributed significantly to the publication of this book in time. I would also like to thank the members of the editorial board who invested a significant amount of time in the different stages of publication of this book. Moulay Mustapha Ennaji
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Chapter 1
Vitamin D new therapy for breast cancer prevention Marwah Labyed1, Najwa Hassou1, Mohammed El Mzibri2 and Moulay Mustapha Ennaji3 1
Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco, 2Laboratory of Biology and Medical Research, National Center for Energy Sciences and Nuclear Techniques Rabat, Mohammedia, Morocco, 3Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
1.1
Introduction
Breast cancer is a major public health problem worldwide because it accounts for more than half of the cancers that occur in the female population. Its incidence and mortality rates continue to rise each year. This leads us to look for protective factors against this scourge. Vitamin D is important for the development, growth, and repair of bones; the normal absorption of calcium; and the proper functioning of the immune system. However, many recent scientific studies have shown an extra-bony protective role of vitamin D in multiple pathologies. Furthermore, the scientists confirmed a link between vitamin D absorption, that is, significant level of vitamin D in the blood, and a decrease in the risk of developing certain cancers, including breast cancer (Esterle et al., 2014). Other studies have shown that women with vitamin D deficiency are nearly three times more likely to develop the deadliest form of breast cancer than women without vitamin D deficiency. In addition, women in countries in the Northern Hemisphere, which are least exposed to the sun, are among the most affected by breast cancer, as are African women. In the United States, the coloring of the skin is an obstacle to the synthesis of vitamin D in regions of low luminous intensity. Vitamin D has been shown to affect more than 200 genes that influence in particular the proliferation and differentiation of cancer cells (Ebert et al., Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00010-8 Copyright © 2023 Elsevier Inc. All rights reserved.
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Oncogenic Viruses Volume 2
2006). Similarly, they inhibit the growth of certain cancer cells in culture. This could lead us to a new treatment path.
1.2 1.2.1
Breast cancer Generality
Breast cancer is a multifactorial chronic disease that constitutes a major problem of public health in the world. It represents the most common cancer among the female population and presents mostly in women over the age of 50 years. It is the main cause of mortality in women aged between 35 and 65 years. The therapeutic options for breast cancer include surgery, radiotherapy, chemotherapy, hormonal therapy, and biological therapy (targeted therapy). These treatments can be used alone or in combination. The treatment choice depends on several factors such as the cancer stage, tumor type and subtype (including hormone receptor status), patient age, etc.
1.2.2
Anatomy
The breast is an even globular organ placed on the anterior and lateral parts of the thorax, between the clavicle, the armpit, and the middle of the sternum; it is placed on the muscle of the large pectoral and has the main function of producing milk in order to feed a newborn. Each breast contains a mammary gland (itself composed of 1520 compartments separated by fatty tissue) and supporting tissue that contains vessels, fibers, and fat (Fig. 1.1). Each compartment of the mammary gland consists of: 1. Lobules: a group of glands that produce breast milk during pregnancy. There are between 15 and 25 of these;
FIGURE 1.1 Breast anatomy.
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3
FIGURE 1.2 The lymph nodes.
2. Ducts: these convey milk from the lobules to the nipple; 3. Nipple: it is composed of muscle fibers that can contract and harden and is located at the center of the areola, the most pigmented part of the breast. The nipple is used to remove breast milk; 4. Areola: it is the region that surrounds the nipple. It is the most pigmented part of the breast and may be brownish or pinkish. Forming a circle, it contains sebaceous glands called areolar glands; 5. Ligaments: these allow the fixation and support of the breasts to the thorax muscles, and are composed of connective tissue; 6. The lymphatic system: it helps fight infections and is composed of lymph nodes and lymph vessels (Fig. 1.2).
1.2.3
Symptoms
There are approximately 35 lymph nodes around each breast. These are mainly located at the axilla (axillary nodes); above the clavicle (supraclavicular nodes); below the clavicle (subclavian or infraclavicular nodes); and inside the thorax, around the sternum (internal breast nodes). The initial symptoms most commonly seen in breast cancer are detectable abnormalities in the form such as a change in the size or shape of a breast, a lumen/ball in a breast, a hard ganglion in the armpit, a flow by the nipple (especially if it is bloody), or a change in pigmentation or texture. But there are always long delays between the birth of breast cancer and the moment when it becomes clinically detectable. If the cancer is not diagnosed early after the onset of the initial symptoms, the tumor may grow and spread to other parts of the body. The result is the expression of later symptoms such as bone pain, nausea, loss of appetite, weight loss,
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Oncogenic Viruses Volume 2
jaundice, shortness of breath, cough, an accumulation of fluid around the lungs, headache, double vision, and muscle weakness (Bower, 2008).
1.2.4
Breast cancer types
There are several types of breast cancer, although some of them are quite rare. These include noninvasive or in situ cancer, invasive cancer, inflammatory cancer, nipple disease, and triple-negative breast cancer (Caliskan et al., 2008; Kanitakis, 2007) (Table 1.1).
TABLE 1.1 Types of breast cancer. Types of breast cancer
Description
The noninvasive breast cancer or in situ breast cancer
The ductal carcinoma in situ (DCIS)
It is the more current, noninfiltrating type. A DCIS means that the cancer is located only in the walls of the milk ducts. It may not extend to the lymph nodes or to other parts of the body
The lobular carcinoma in situ (LCIS)
Appears when abnormal cells develop in the lobules. It does not spread to the outside walls of the lobules, nor to other parts of the body. Although not fatal, the LCIS indicates that a woman presents an increased risk of developing later an invasive breast cancer in the one or the other of the breasts
The infiltrating ductal carcinoma (IDC)
Also called ductal adenocarcinoma, it is the most current (80%) type of invasive breast cancer. It takes birth in the channels, passes through the wall of the channels, and invades the breast tissue around it. It can extend to the lymph nodes and other parts of the body
The lobular carcinoma infiltrating (LCI)
It represents approximately 10% of invasive breast cancer cases. It takes birth in the lobules, spreads to the outside of the lobules, and invades the breast tissue around it. It can extend to the lymph nodes and other parts of the body
The invasive breast cancer
(Continued )
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TABLE 1.1 (Continued) Types of breast cancer
Description
Inflammatory breast cancer
It is a rare and very aggressive cancer representing approximately 1%3% of all breast cancer cases. It takes birth in the milk ducts and spreads to the lymph vessels. It tends to develop in layers or lamellae and not in the form of solid tumor
The Paget’s disease of the nipple
It is the less evident type of breast cancers and represents less than 4% of all cases. It is more common among adolescents and among women older than 80 years. In this cancer cells develop inside and around the nipple
Triple-negative breast cancer
These types of cancers get their name from the fact that they have no estrogen receptors, no progesterone receptors, and no HER-2 receptors. This means that hormone therapy and targeted anti-HER-2 therapy cannot be used to treat these cancers. Most triple-negative breast cancers are IDCs
1.2.5
Risk factors
Breast cancer is a multifactorial disease. This means that several factors influence the risk of its occurrence, that is, there are many risk factors. We now know a number of risk factors for breast cancer, although there are still uncertainties about the implication and weight of many of these factors.
1.2.5.1 Endogenous hormonal factors 1.2.5.1.1 Early age of first menstruation Many studies show that the onset of the first period before the age of 12 increases the risk of breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer, 1996).
1.2.5.1.2 Late menopause Women who have menopause after 50 years of age are at an increased risk for breast cancer compared to those who stop menstruating early. This risk increases by about 3% for each additional year from the presumed age of menopause (Rossouw, 2002). The mechanism by which late menopause increases the risk of breast cancer appears to be the result of prolonged production of ovarian hormones.
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1.2.5.2 Exogenous hormonal factors 1.2.5.2.1 Oral contraceptives The risk of breast cancer is highly related to the regular use of oral contraceptives. This risk increases by about 25% for women who use them routinely. However, this increase in risk drops as soon as consumption stops. Therefore 10 years after cessation of use, no significant increase in risk is evident (Layde et al., 1989). 1.2.5.2.2
Hormone replacement therapy
Women on hormone replacement therapy (HRT) have an increased risk of breast cancer compared to women who have never used it (Hinkula et al., 2001; Rossouw, 2002). This risk increases with the duration of use.
1.2.5.3 Reproductive factors 1.2.5.3.1 Parity and early age at first motherhood Parity is defined as the number of times a woman has given birth to a child; a close relationship has been established between parity and breast cancer. Women who have had at least one full-term pregnancy before the age of 30 years have, on average, 25% lower risk of breast cancer compared to nulliparous women (Collaborative Group on Hormonal Factors in Breast Cancer, 2002). The protective effect of multiparity seems to increase in proportion to the number of deliveries. Women who have had eight to nine births are about 30% less likely to have breast cancer than women who have had five births (Pavelka et al., 2007). 1.2.5.3.2
Breastfeeding
In general, the risk of breast cancer decreases through breastfeeding. Women who breastfeed for a total of at least 25 months have a 33% lower risk of breast cancer compared to women who have never breastfed (Salehi et al., 2008).
1.2.5.4 Genetic, family, demographic, and health factors 1.2.5.4.1 History of genetic mutations Some genetic mutations are likely to increase the risk of breast cancer. Two genes, BRCA1 and BCRA2, appear to be the most involved (Satagopan et al., 2001; Wolpert et al., 2000). The risk associated with mutations of these genes exceeds 80% for women and 6% for men, when the person carrying these genes reaches the age of 70 years (Parazzini et al., 1991; Schildkraut & Thompson, 1988).
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1.2.5.4.2
7
Family history
The risk of breast cancer in a woman is two or more times greater if she has a first-degree relative (mother, sister, or daughter) who developed the disease before the age of 50 years, and the older the relative when she developed breast cancer, the greater the risk. For example, this risk can increase four to six times if two first-degree relatives develop breast cancer (Little et al., 1999) (Fig. 1.3). 1.2.5.4.3
Age
Age is the most important risk factor for breast cancer (Boyd et al., 1998). This risk increases between the ages of 50 and 75 years (almost two-thirds of breast cancers) (Narod et al., 1994). Breast cancer is rare in women under the age of 30 years. 1.2.5.4.4
Ionizing radiation
Exposure of breast tissue to ionizing radiation before the age of 40 years is likely to cause breast cancer in later years. The risk of breast cancer is similar for single exposure or multiple exposures of equal total intensity (IARC, 2002). Ionizing radiation increases the risk of breast cancer as it damages DNA and its constituents.
FIGURE 1.3 Family tree of a family with genetically inherited breast cancer (Calle et al., 1996).
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1.2.5.4.5
Mammography density
The risk of breast cancer increases with the density of breast tissue in mammography. For women with dense mammogram of breasts, the risk is multiplied by two to six times (Marmot et al., 2007).
1.2.5.5 Lifestyle and nutrition factors 1.2.5.5.1 Overweight Overweight and obesity are known factors to promote the development of cancer (Johnson et al., 2000; Wenten et al., 2002). Women who are overweight by more than 20 kg by the age of 18 have a doubling risk of breast cancer after menopause (Collaborative Group on Hormonal Factors in Breast Cancer, 2002). 1.2.5.5.2 Smoking Cigarette smoke contains chemicals that can damage the genetic structure (DNA) of body cells, and turn the normal cells into cancer cells. This truth does not stop for smokers only because it also concerns passive smokers. For example, passive smoking appears to be associated with an increased risk of breast cancer of about 60%; this risk is tripled in women after menopause (Brown et al., 2001). 1.2.5.5.3
Alcohol
Alcohol is a nutritional factor that increases the risk of breast cancer by about 7% for an average consumption of one alcoholic beverage per day (Collaborative Group on Hormonal Factors in Breast Cancer, 2002).
1.3
Viral etiology of breast cancer
Experimental and biological data now indicate that around 20% of human cancers worldwide can be attributed to viruses (Howley, 2006). This is an important part. It is shown that a wide variety of infectious agents, such as DNA and RNA viruses, constitute one of the main causes of cancer in humans (Pisani et al., 1997). However, virus infection is generally not sufficient for cancer; additional events and host factors, such as immunosuppression, somatic mutations, genetic predisposition, and exposure to carcinogens, must also play a role. Mouse mammary tumor virus like (MMTV-like) and EpsteinBarr virus (EBV) are among the viruses implicated in the etiology of breast cancer.
1.4
Mouse mammary tumor virus like
MMTV belongs to the retrovirus family, β-retrovirus group; in 1936, Bittner demonstrated that spontaneous laboratory mouse mammary tumors were due
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9
to the presence of this virus (Bittner, 1936). MMTV has been extensively implicated in the pathogenesis of breast tumors in mice. It is transmitted either endogenously in germ lines as provirus or exogenously through breast milk as an infectious virion. DNA sequences showing homology with those of the MMTV have been detected in human breast cancer, suggesting that this virus called MMTVlike could be the human form of the mouse breast tumor virus (Wang et al., 1995; Wang et al., 1998). The 9.9-Kb proviral sequence of the MMTV-like virus has been demonstrated by PCR techniques in two cases of human breast cancer, the proviral DNA has 95% homology to the MMTV (Liu, Klimberg, et al., 2001; Liu, Wang, et al., 2001; Melana et al., 2007). Wang et al. detected MMTV env gene sequences in 30%40% of breast cancers in women among various populations (Wang et al., 1995). Other names for this virus include HMTV for human mammary tumor virus (Melana et al., 2002) and HHMMTV for human homolog of the mouse mammary tumor virus (Lawson et al., 2001).
1.5
Human papilloma virus
Human papilloma viruses (HPVs) are small, naked viruses consisting of a 55-nm diameter icosahedric capsid and a bistranded, circular DNA of 8000 base pairs. They belong to the family Papillomavirus (Papillomaviridae). They are classified into types and subtypes, based on the degree of nucleotide sequence homology (Bernard et al., 2010). HPV infection is one of the most common sexually transmitted infections. However, in most cases, the infection disappears or becomes undetectable within 1 or 2 years (Moscicki et al., 2001). High-risk HPVs are present in about 50% of human breast cancers worldwide (Glenn et al., 2012). More than 200 types of HPV are suspected, two of them (types 16, 18) are considered to have high oncogenic potential for their cancers in humans and these are correlated with invasive carcinomas (Glenn et al., 2012; Kroupis et al., 2006). Now, it has been shown that HPV type 16 E6/E7 onco-proteins coat noninvasive and metastatic breast cancer cells in the invasive and metastatic form (Yasmeen et al., 2007).
1.6
EpsteinBarr virus
EBV is a member of the Herpesvirus family (Herpesviridae), a very large family of more than 120 known viruses with a relatively broad host spectrum, including mammals, birds, amphibians, reptiles, and fish (Strauss & Strauss, 2002). It is associated with a number of lymphoma-type cancers (Burkitt and Hodgkin lymphomas) and carcinoma-type cancers (gastric and undifferentiated rhinopharyngeal carcinomas). Ire´ne Joab’s team highlighted
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Oncogenic Viruses Volume 2
the presence of the VBS genome in breast cancer biopsies (Bonnet et al., 1999).
1.7 1.7.1
Vitamin D Generality
Vitamin D was discovered in 1919 by Sir Edward Mellanby; it is not really a vitamin. It is considered a steroid hormone (Mellanby, 1919). It actually contains a set of five different D vitamins, numbered from 1 to 5. Two of which are important for humans: D2 (ergocalciferol) and D3 (cholecalciferol) (Fig. 1.4). Vitamin D2 is synthesized naturally by plants and fungi, thus found in humans through diet. Vitamin D3 is of animal or human origin. It is synthesized in the epidermis by the action of ultraviolet rays (Brown et al., 1999). The object forms can be chemically produced and used as supplements. Vitamins D2 and D3 have different activities depending on the species; however, their activity is similar in humans. It is therefore customary to consider all vitamin D reserves (D2 1 D3) to assess the vitamin D status in humans (Fig. 1.4). Vitamin D presents a double origin: exogenous, which corresponds to the dietary intake but also endogenous, resulting from a neosynthesis at the level of the epidermis (Holick, 2007). Its endogenous synthesis is influenced by season, exposure schedule, and latitude (Holick & Chen, 2008). Vitamin D of endogenous origin is transported in the blood by DBP (vitamin D-binding protein: carrier protein) and vitamin D of exogenous origin is absorbed by the small intestine with bile salts then transported by DBP. These two vitamins are transported to the liver where they are hydroxylated into 25 hydroxyvitamin D, calcidiol, biologically inactive form, then to the kidney for a second hydroxylation into 1,25(OH)2 vitamin D, calcitriol, biologically active form (Souberbielle et al., 2008).
FIGURE 1.4 Structure of vitamins D2 and D3.
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The (1,25(OH)2D) action on its target cells requires the presence of an intracellular specific receptor, the vitamin D receptor (VDR) and the transfer of the 1,25(OH)2DVDR couple to the cell nucleus. In 1988, Minghetti and Norman cataloged the known actions of vitamin D and postulated a key role of vitamin D in the growth and differentiation of mammalian cells (Minghetti & Norman, 1988).
1.7.2
Biosynthesis
The biosynthesis of vitamin D3 is initiated mainly in the skin in the deep layers of the epidermis by the action of ultraviolet radiations (290310 nm), which transform 7-dehydroliterol (provitamin D) into previtamin D3 (Madhok & DeLuca, 1979; Stamp et al., 1977). Previtamine D3, in turn, isomerizes to vitamin D3. Its activation is catalyzed by CYP localized in liver and kidney cells (Fig. 1.5) (Brown et al., 1999). The first step is a hydroxylation of position 25 which leads to the formation of 25-hydroxyvitamin D3 (25(OH)D3), a form of vitamin D3 reserve whose plasma half-life is two to three weeks. This hepatic hydroxylation is performed by CYP located in the endoplasmic reticulum or mitochondria. Today, localized CYP2R1 in microsomes appears as the major candidate in
FIGURE 1.5 Diagram of vitamin metabolism D3 (Tissandie´ et al., 2006).
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Oncogenic Viruses Volume 2
the synthesis of 25(OH)D3. Individuals carrying a mutation in the CYP2R1 gene have an abnormally low circulating rate of 25(OH)D3 (Cheng et al., 2004). 25(OH)D3 is then taken over by the plasma protein DBP to be transported to the kidney. The second stage is a hydroxylation in position 1 by the mitochondrial CYP27B1 which leads to the biologically active 1,25-dihydroxyvitamin D3 (1,25(OH)2D3), with a plasma half-life of about 4 hours. In addition to this major renal production, minor production sites of 1,25 (OH)2D3 have been identified in the placenta, brain, prostate, keratinocytes, osteoblasts, and macrophages that express CYP27B1. However, this extrarenal production does not usually contribute to the formation of 1,25(OH)2D3 plasmatic (Garabe´dian, 2000). Once synthesized, active vitamin D3 diffuses into the body and binds to target tissues by a receptor, VDR, to act on its target organs (Miller & Portale, 2000; Schuessler et al., 2001).
1.8
Food needs and sources
The recommended nutritional requirement for vitamin D may be defined by the RDA (recommended nutritional intake) which is the amount of a nutrient required daily for good health and must not exceed the tolerable upper intake (TMA); they depend on age and stage of life (Table 1.2).
TABLE 1.2 Recommended nutritional intake and tolerable daily maximum intake for vitamin D. Age range
Recommended nutritional intake (RNI) per day
Tolerable daily maximum intake (TDMI) per day
Infants 06 month
400 UI (10 mcg)
1000 UI (25 mcg)
Infants 712 month
400 UI (10 mcg)
1500 UI (38 mcg)
Children 13 years
600 UI (15 mcg)
2500 UI (63 mcg)
Children 48 years
600 UI (15 mcg)
3000 UI (75 mcg)
Children and adults 970 years
600 UI (15 mcg)
4000 UI (100 mcg)
Adults .70 years
800 UI (20 mcg)
4000 UI (100 mcg)
Pregnancy and lactation
600 UI (15 mcg)
4000 UI (100 mcg)
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TABLE 1.3 Foods naturally rich in vitamin D. Food
Quantity
Vitamin D content (IU)
Cod liver oil
15 ml
1400
Fresh wild salmon
100 g
6001000
Farmed salmon
100 g
100250
Sardine, herring, canned tuna
100 g
224332
Dried chiitake mushrooms
100 g
1600
Boletus/dried morels
100 g
130
Margarine
15 mL
65110
Butter
100 g
50
Egg yolk
1
40
Yogurt
100 g
89
Hard cheese
100 g
44
Parmesan cheese
100 g
28
Vitamin D is found in some foods consumed by humans but in tiny doses: egg yolk, yogurt, parmesan, hard cheese, margarine, butter, boletus. And at higher concentrations in dry shiitake mushrooms and fatty marine fish (cod, salmon, etc.) (Table 1.3).
1.9
Storage sites
Vitamin D, like other vitamins, can be stored in different major vitamin D storage tissues in native form or 25(OH)D (Heaney et al., 2009; Landrier et al., 2012) (Table 1.4). Concentrations of 25(OH)D are directly dependent on dietary intake of vitamin D and sun exposure, but are little influenced by age (Bjo¨rkhem & Holmberg, 1978; Heaney et al., 2009). Its dosage is that it reflects the stock and availability of vitamin D in the body. According to official standards, there is vitamin D deficiency if 25(OH) D is at 25 nmoles/L (10 ng/mL), deficiency: between 25 and 75 nmoles/L (1030 ng/mL), normality: between 75 and 250 nmoles/L (30100 ng/mL).
1.10 Vitamin D receptors The discovery of the VDR was made in 1969 by Haussler and Norman (1969). The discovery of this receptor firmly establishes vitamin D as a
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Oncogenic Viruses Volume 2
TABLE 1.4 Major vitamin D storage sites (Garland et al., 2009). Vitamin D (IU)
25(OH)D (IU)
Total (IU)
Adipose tissue
6960
1763
8723
Muscle
1527
1055
2581
Liver
168
214
382
Serum
271
1559
1830
Other
571
578
1149
Total
9496
5169
14,665
member of the steroid hormone family. The human vitamin D receptor was cloned and sequenced in 1988 by Baker et al. (1988). The VDR gene has more than 470 single nucleotide polymorphisms (McCullough et al., 2009; Zmuda et al., 2000). The important and intensively studied one-dimensional polymorphisms of VDR are FokI, VDR-BsmI, VDR-TaqI, VDR ApaI, and Poly (A) (Alimirah et al., 2011). Using biochemical and fluorescent immunohistochemistry analyses, the studies demonstrated that VDR are present in a wide range of tissues and cell types (Pike et al., 1980), such as breast, bone, prostate, intestine, activated B and T lymphocytes, monocytes and keratinocytes with distinct mitochondrial, membranous, cytosolic and perinuclear localization (Gombart et al., 2006; Silvagno et al., 2010).
1.11 Vitamin D new therapy for breast cancers prevention 1.11.1 Relationship between vitamin D and breast cancer Several epidemiological studies have shown that the incidence and mortality rates of certain cancers are lower in people living in southern latitudes, where levels of sun exposure are relatively high, compared to those living in northern latitudes (Grant & Mohr, 2009). Experimental evidence also suggested a strong association between vitamin D and cancer risk. In studies on cancer cells and tumors in mice, they found that vitamin D had several activities that could slow down or prevent the development of cancer, including the promotion of cell differentiation, decreased growth of cancer cells, stimulation of apoptosis, and reduction of angiogenesis (Deeb et al., 2007; Thorne & Campbell, 2008).
1.12 Mechanism of action Vitamin D acts on a range of tumor cells that express VDR in several processes such as innate immunity, apoptosis, proliferation, and cell differentiation
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FIGURE 1.6 Proposed mechanism of action of 1,25(OH) 2D in target cells (McCullough et al., 2009).
through its active form. 1,25(OH)2D is supposed to act locally through multiple mechanisms (Fleet, 2007). A rapid response can occur through a plasma membrane receptor and second messengers involved in regulating various cellular activities, including cell cycle control. Genomic effects are mediated by a 1.25(OH)2D link to the nuclear VDR. VDR then binds to the target DNA sequences as a heterodimer with the retinoid receptor X (RXR). This complex binds to the vitamin D response element (VDRE) to induce or suppress the expression of the target genes. VDREs are located on more than 200 genes and can influence a number of biological processes, including cell proliferation, differentiation, apoptosis, growth factor signaling, inflammation, and immunomodulation (Ebert et al., 2006) (Fig. 1.6).
1.13 Vitamin D and breast cancer prevention Epidemiological studies have shown an inverse correlation between average annual sun exposure and the incidence of breast cancer (Garland et al., 1990;
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Gorham et al., 1990; Moukayed & Grant, 2013), suggesting an association between endogenous vitamin D production and breast cancer. Several epidemiological surveys have analyzed possible associations between circulating levels of 25-(OH) D and the risk of developing cancer (Cui & Rohan, 2006; Garland et al., 2007; Lappe et al., 2007; Gorham et al., 2007; Wactawski-Wende et al., 2006). However, the 25-(OH) D threshold above which the risk of cancer decreases varies from study to study. Generally above 32 ng/mL or 80 nmol/L, the risk of developing cancer decreases, whatever its type (Gorham et al., 2007). Jeong and his colleagues showed that supplementation with 1,25 (OH)2D3 to mice with breast tumor-initiating stem cells significantly delayed tumor onset and growth, while mice fed on diets deficient in vitamin D showed increased tumor growth and appearance (Jeong et al., 2015).
1.14 Conclusion These results indicate that 1,25 (OH)2D3 may be a means of prevention and a useful therapeutic strategy in the targeting of some gyneco-mammary cancer. It is useful to consider this evidence in the context of previous studies. It is also helpful to incorporate the genetic variation of the VDR in people with breast cancer in a review, as it is increasingly clear that vitamin D can work together.
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Schuessler, M., Astecker, N., Herzig, G., Vorisek, G., & Schuster, I. (2001). Skin is an autonomous organ in synthesis, two-step activation and degradation of vitamin D3: CYP27 in epidermis completes the set of essential vitamin D3-hydroxylases. Steroids, 66(35), 399408. Available from https://doi.org/10.1016/S0039-128X(00)00229-4. Silvagno, F., De Vivo, E., Attanasio, A., Gallo, V., Mazzucco, G., Pescarmona, G., & Kowaltowski, A. J. (2010). Mitochondrial localization of vitamin D receptor in human platelets and differentiated megakaryocytes. PLoS One, 5(1), e8670. Available from https://doi. org/10.1371/journal.pone.0008670. Souberbielle, J. C., Prie´, D., Courbebaisse, M., Friedlander, G., Houillier, P., Maruani, G., Cavalier, E., & Cormier, C. (2008). Actualite´ sur les effets de la vitamine D et l’e´valuation du statut vitaminique D. Annales d’Endocrinologie, 69(6), 501510. Available from https:// doi.org/10.1016/j.ando.2008.07.010. Stamp, T. C. B., Haddad, J. G., & Twigg, C. A. (1977). Comparison of oral 25hydroxycholecalciferol, vitamin D, and ultraviolet light as determinants of circulating 25-hydroxyvitamin D. The Lancet, 1, 13411343. Available from https://doi.org/10.1016/ s0140-6736(77)92553-3. Strauss, J. H., & Strauss, E. G. (2002). Viruses and human disease. Elsevier. Thorne, J., & Campbell, M. J. (2008). The vitamin D receptor in cancer. Proceedings of the Nutrition Society, 67((2)), 115127. Available from https://doi.org/10.1017/S0029665108006964. Tissandie´, E., Gue´guen, Y., A.Lobaccaro, J.-M., Aigueperse, J., & Souidi, M. (2006). Vitamine D: Me´tabolisme, re´gulation et maladies associe´es. Me´decine/Sciences, 22(12), 10951100. Available from https://doi.org/10.1051/medsci/200622121095. Wactawski-Wende, J., Morley Kotchen, J., Anderson, G. L., Assaf, A. R., Brunner, R. L., O’Sullivan, M. J., Margolis, K. L., Ockene, J. K., Phillips, L., Pottern, L., Prentice, R. L., Robbins, J., Rohan, T. E., Sarto, G. E., Sharma, S., Stefanick, M. L., Van Horn, L., Wallace, R. B., Whitlock, E., . . . Manson, J. A. E. (2006). Calcium plus vitamin D supplementation and the risk of colorectal cancer. New England Journal of Medicine, 354(7), 684696. Available from https://doi.org/10.1056/NEJMoa055222. Wang, Y., Go, V., Holland, J. F., Melana, S. M., & Pogo, B. G. T. (1998). Expression of mouse mammary tumor virus-like env gene sequences in human breast cancer. Clinical Cancer Research, 4(10), 25652568. Wang, Y., Holland, J. F., Melana, S., Liu, X., Pelisson, I., Stellrecht, K., Mani, S., Pogo, B. G. T., Bleiweiss, I. J., Cantarella, A., & Pogo, B. G. T. (1995). Detection of mammary tumor virus ENV gene-like sequences in human breast cancer. Cancer Research, 55(22), 51735179. Wenten, M., Gilliland, F. D., Baumgartner, K., & Samet, J. M. (2002). Associations of weight, weight change, and body mass with breast cancer risk in Hispanic and Non-Hispanic white women. Annals of Epidemiology, 12(6), 435444. Available from https://doi.org/10.1016/ S1047-2797(01)00293-9. Wolpert, N., Warner, E., Seminsky, M. F., Futreal, A., & Narod, S. A. (2000). Prevalence of BRCA1 and BRCA2 mutations in male breast cancer patients in Canada. Clinical Breast Cancer, 1(1), 5765. Available from https://doi.org/10.3816/cbc.2000.n.005. Yasmeen, A., Bismar, T. A., Kandouz, M., Foulkes, W. D., Desprez, P. Y., & Al Moustafa, A. E. (2007). E6/E7 of HPV type 16 promotes cell invasion and metastasisof human breast cancer cells. Cell Cycle (Georgetown, Tex.), 6(16), 20382042. Available from https://doi. org/10.4161/cc.6.16.4555. Zmuda, J. M., Cauley, J. A., & Ferrell, R. E. (2000). Molecular epidemiology of vitamin D receptor gene variants. Epidemiologic Reviews, 22(2), 203217. Available from https://doi. org/10.1093/oxfordjournals.epirev.a018033.
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Chapter 2
Molecular diagnosis of human papillomavirus related to cervical cancer Kaoutar Anouar Tadlaoui1 and Moulay Mustapha Ennaji2 1
Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
2.1
Introduction
Human papillomavirus (HPV) is a small deoxyribonucleic acid (DNA) virus that belongs to the Papillomaviridae family (Zheng & Baker, 2006). The circular double-stranded viral genome is approximately 8000 base pairs in length (Mirabello et al., 2018). Over the past 20 years, HPV has included more than 200 genotypes (Lizano et al., 2009), of which more than 120 have been identified following complete genome sequencing (De Villiers et al., 2004). Some of these infect skin and mucous cells, and about 50 genotypes cause genitourinary infections (Ljubojevic & Skerlev, 2014), which leads to the development of specific cancers. Among them, more than 13 genotypes (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68) are considered high-risk oncogenic viruses, of which the two main high-risk oncogenic HPV (HR-HPV) are HPV-16 and HPV-18, these are involved in 70% of cervical cancers (CCs) (Graham, 2010). Because of these infections, benign warts appear, such as cervical neoplasia or benign verruciform epidermodysplasia, which can potentially progress to invasive malignant tumors, including precancerous lesions and CC (Tadlaoui et al., 2020). HPV is the most common cause of sexually transmitted genital infections worldwide. It is also the main causal factor of CC, which is one of the most common cancers in women. According to global estimated data in 2012, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00013-3 Copyright © 2023 Elsevier Inc. All rights reserved.
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Oncogenic Viruses Volume 2
530,000 cases of cancer in women (approximately 84% of female cancers) were linked to HPV, and it took a toll of 266,000 deaths (whether 8% of deaths from HPV-related cancers in women) (de Martel et al., 2017). However, not every woman infected with HR-HPV will develop CC. Various factors are involved such as infection persistence, genotype, immunosuppressor, age over 30 years, viral variants, viral load, integration, coinfection, smoking, prophylactic use, long-term use of oral contraceptives, etc. (Melo´n et al., 2013). Oncogenic HPV is also involved in other types of cancer, such as penis, anal, neck, head, or oropharynx cancer (Georgieva et al., 2009; Spano et al., 2005). However, the number of HPV infections in developing and developed countries is considerably increasing, due frequently to late diagnosis and poor prognosis. Following this increase, Kroupis and Vourlidis expected that the number of HPV-related cancer cases will double by 2050, due also to population and life expectancy increases (Kroupis & Vourlidis, 2011). Furthermore, prophylactic vaccination against genotypes 16 and 18, responsible for CC, allows to prevent the course of these infections and eventually CC (Monsonego, 2006) but this is not enough. Further investigations are required to develop a faster and more accurate diagnostic evaluation of HPV.
2.2 2.2.1
Etiopathogenesis of human papillomavirus infection Human papillomavirus genome structure
The HPV virion has a circular double-stranded DNA genome of about 8 kbp (Zheng & Baker, 2006). This small genome contains eight to ten overlapping open reading frames (ORFs) that are present in the early (E) and late (L) regions (Kroupis & Vourlidis, 2011). The viral genome of HPV is divided into three main regions: the early region (E), the late region (L), and the long control region (LCR) within an icosahedral capsid of 4555 nm in size (de Villiers et al., 2005). Thus, the early polyadenylation (PAE) and late polyadenylation (PAL) sites separate these three regions (Zheng & Baker, 2006).
2.2.1.1 The long control region LCR, also known as noncoding upstream regulatory region (URR), is located between L1 and E6/E7 HPV proteins and has a size between 400 and 1000 bp (Tadlaoui et al., 2020). It contains the origin of replication (Ori), the promoters of viral transcription, four DNA-binding regions for E2, and about 20 other binding regions for host transcription factors specific for epithelial cells (Kroupis & Vourlidis, 2011). Therefore, LCR plays a crucial role in the regulation of virus replication, transcription, and assembly (Tadlaoui et al., 2020).
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2.2.1.2 The early control region (E) The early region is approximately 4 kbp in size (half the genome size) and encodes six early nonstructural viral proteins E1, E2, E4, E5, E6, and E7 (Tadlaoui et al., 2020). In some HPV types, E region also encodes two other proteins E3 and E8 (Lambert et al., 1987). Function of E3 is not yet defined. While E8, fused with E2, downregulates genome replication and viral transcription (Han et al., 1998). E1 protein has an ATP-dependent helicase function. Involving the E2 protein, it binds to Ori and initiates the viral genome replication (Wilson et al., 2002). It also allows to maintain the viral genome in episomal form in cells (Wilson et al., 2002). E2 performs other viral functions such as being a viral transcription factor and downregulator of E6 and E7 oncogenes expression (Kim et al., 2000). E4 is expressed as E1^E4 spliced fusion protein and its role is not well understood, since its presence or absence has no effect on viral DNA replication (Wang et al., 2004). However, E4 plays a role in genome amplification and viral synthesis (Doorbar, 2013). E4 is highly expressed in epithelial cells infected with HPV, suggesting other roles in viral release and/or transmission (Doorbar, 2013). Recent studies have shown that the E4 expression correlates with the levels of viral DNA uptake by the host. Thereby, E4 can serve as a potential biomarker of the viral carcinogenesis stages (Yajid et al., 2017). The putative oncoprotein, E5, stimulates the cellular proliferation by interacting with the E7 protein (Pim et al., 1992), abolishes apoptosis, induces pore formation (Kabsch et al., 2004), as well as facilitates the genome amplification involving epidermal growth factor (EGF) receptor and mitogen-activated protein (MAP) kinases activation (Pim et al., 1992; Yajid et al., 2017). As for the HPV’s oncoproteins, E6 and E7 of HR-HPV degrade the proteins tumor suppressor p53 and retinoblastoma protein (pRb), respectively; this disrupts the cell cycle inducing proliferation and transformation of infected cells, thus leading to viral persistence and carcinogenesis (see below) (Tadlaoui et al., 2020). 2.2.1.3 The late region (L) The late region is approximately 3 kb in size and encodes for major and minor structural proteins of the capsid: L1 (5560 kDa) and L2 (70 kDa) (Tadlaoui et al., 2020). L1 and L2 are respectively involved in virion assembly and viral conditioning (Tadlaoui et al., 2020). 2.2.2 Mechanism of human papillomavirus infection in the cervix and carcinogenesis CC is the fourth most common cancer in women. It is mainly due to the persistence of high-risk oncogenic HPV infection in cervical cells, inducing
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precancerous lesions of the cervix, which can develop into squamous cell carcinoma over the years (Tadlaoui et al., 2020). HPV infection and integration of viral particles into epithelial cervical cells are key precocious events in the progression of cervical lesions, while many genetic and cellular events appear to be necessary for cervical carcinogenesis. After the viral genome crosses the epithelial membrane and integrates the host cell nucleus, the E1 and E2 genes are expressed, triggering viral replication (Doorbar, 2006). During host cell differentiation, the E4 and E5 genes regulate the EGF, thus promoting viral genomic production. While the E6 and E7 oncogenes allow the E2F transcription factor to drive cell proliferation processes (Melo´n et al., 2013). PRb, being a tumor suppressor protein, blocks the cell cycle in G1 phase via sequestration of the transcription factor E2F. As a result of the expression of the oncoprotein E7, which acts to bind to regulatory proteins of the cell cycle, especially pRb, the release of E2F1 from pRb and the activation of the cell cycle are induced. Thus, pRb is functionally inactivated from the early stages of cervical carcinogenesis (Griffiths et al., 2006; Leversha et al., 2003). Disruption of the pRb-E2F1 pathway by E7 induces overexpression of p16, which is a tumor suppressor protein, known as a cyclin-dependent kinase inhibitor 2A (CDKN2A), and encoded by the CDKN2A gene (MTS1, INK4A) located on chromosome 9p21 (Kalof & Cooper, 2006; Serrano et al., 1993). It slows down the cell cycle by inhibiting the function of the complex-cdk4 and cdk6cyclin D. This complex phosphorylates and subsequently inactivates the retinoblastoma protein (pRb), which E2F released, thereby preventing binding between E2F and pRb. This means that the CDK4/CDK6cyclin D complex allows the regulation of the G1 phase checkpoint of the cell cycle by activating the transcription of numerous genes favoring the entry of cells into S phase and the subsequent advancement of the cell cycle in this phase (Kalof & Cooper, 2006; Melo´n et al., 2013; Pauck et al., 2014). This correlation between p16 and pRb regulates negatively the progression of the cell cycle. Therefore, the intervention of the E6 and E7 genes deletes two main cellular defense mechanisms and leads the cell replication machinery to produce new viral particles, which instigate oncogenesis of cervical carcinoma.
2.3
Diagnosis of human papillomavirus viral genome
The diagnosis of HPV infection is essentially based on viral genome screening. Considering the specific character of this virus which is difficult to cultivate in cell culture and is undetectable by serological methods, the qualitative and quantitative viral detection is based on molecular biology techniques.
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Using these molecular techniques for HPV detection, the viral genome is revealed by sample analysis of blood, mucous, or biopsies cervico-uterine from infected people.
2.3.1
Identification of human papillomavirus without genotyping
These techniques allow detecting the presence of all the HPV genotypes without specifying the infecting genotype present in the sample.
2.3.1.1 Signal amplification method: liquid phase in situ hybridization The principle of this technique is based on a step of hybridization in solution of a known nucleotide sequence by a complementary DNA or RNA probe: this is a liquid phase immunocapture (Eide & Debaque, 2012). This involves denaturing the target viral DNA extracted, then carrying out the hybridization in liquid phase by affixing two different known complementary RNA probe cocktails or “aggregates” on the denatured DNA. The two cocktail probes used in Table 2.1 correspond to 13 HR-HPV genotypes (HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59 and 68), or to 5 LRHPV genotypes (HPV 6, 11, 42,43 and 44) (Melo´n et al., 2013; Ollier & Giordanengo, 2008). Among the main tests used for signal amplification or so called “cocktail” or “aggregate” detection, there are the Amplicor polymerase chain reaction (PCR) kit (Roche Diagnostics), the Hybrid Capture II (HC2) test (Qiagen ex Digene), and the Hybrid Capture III (HC3) (Digene, United States) (Eide & Debaque, 2012; Melo´n et al., 2013). The product of the hybridization reaction will be visualized through a set of immunoenzymatic reactions as follows: the DNA/RNA hybrids formed are stable and are captured on the membrane of the microplate wells, by dint of polyclonal antiduplex DNA/RNA antibodies attached thereto. During the following stage of revelation, anti-duplex DNA/RNA antibodies coupled to alkaline phosphatase (ALP) react with the free part of the DNA/RNA duplexes. Then in the presence of a chemiluminescent substrate, they trigger a light signal detected by a luminometer. This means that the test result is positive: the presence of HPV at high oncogenic risk (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). This technique provides a semiquantitative measurement of DNA-HPV. It detects the presence or absence of HR-HPV but does not allow the identification of the genotypes of HPV. Although it is rapid, reproducible, and extensively applicable, it is sensitive just like PCR.
TABLE 2.1 HPV (human papillomavirus) DNA detection tests of known cocktails or aggregates. Type of test
Detection by a mixture of probes
Kit name
Amplicor HPV test
Hybrid capture II
Test Probe Set RUO
Abbott RealTime High Risk HPV
Cervista HPV HR
BD HPV-GT
Manufacturers
Roche Diagnostics
Qiagen
Qiagen
Abbott
Hologic
Becton Dickinson
Protocol: Amplification
PCR with PGM Y09/11 primers
—
—
RT-PCR amplification with GP5 1 /GP6 1 primers
Invader technology
PCR consensus Primers
Protocol: Hybridization
On microplate
On microplate with RNA probes
On microplate with RNA probes
On microplate
On microplate
On microplate
Protocol: Detection
Colorimetric
Chemiluminescence
Chemiluminescence
Fluorescence
Fluorescent probes Freight
Fluorescence
Target region
L1
Whole genome
Whole genome
L1
E6/E7
HPV detected
High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68
High risk: 16/18/31/ 33/35/39/45/51/52/ 56/58/59/68
High risk: 16/18/45
High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68
High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68
High risk: 16 /18 / 45 /31 /51 /52 /(33/ 58), (56/59/66) et 35/ 39/68
Certification
CE-IVD
FDA approved et CE-IVD
CE-IVD
CE-IVD
FDA approved et CE-IVD
FDA in progress CEIVD in progress
Automating
Cobas 4800
Rapid Capture System
Rapid Capture System
Abbott m2000
Yes
BD ViperTM LT
Many tests are used for signal amplification or so-called cocktail or aggregate detection, and to each his specialty and specificity according to the recommendations of the manufacturers. Source: From Eide, M. L. & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401409. https://doi.org/10.1016/j.annpat.2012.09.200.
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2.3.1.2 Polymerase chain reaction amplification technique Gene amplification or PCR techniques initially require a small amount of a sequence (DNA or RNA) characteristic of the HPV genome. This known DNA or RNA sequence undergoes an exponential amplification step in vitro which allows duplicating it in order to identify it. This method is sensitive, inexpensive, and rapid, and it relies on the validation of the specificity of the types of PCR primers. However, several genome amplification techniques using different pairs of primers have been developed to cope with the diversity of HPV genotypes.
2.3.1.2.1
Polymerase chain reaction consensus
This is a sensitive and specific technique, allowing the amplification of a genomic sequence of a single HPV genotype, using pairs of specific primers of types designed. This allows the detection of numerous amplified viral fragments of different sizes depending on the genotype, which are then identified using type-specific probes (Melo´n et al., 2013). Consensus PCR exclusively amplifies a highly conserved region of viral DNA, which targets the L1 gene fragment of the viral capsid. The protocol of this technique involves two successive PCR steps, with the use of two pairs of different primers on the same target. This improves the sensitivity of the diagnosis. The main consensus primers targeting the detection of the L1 region of HPV (Table 2.2) are MY09/MY11, GP5 1 /GP6 1 and SPF10, which respectively lead to amplicons of 450, 150, and 65 bp (Eide & Debaque, 2012; Kleter et al., 1999; Tadlaoui et al., 2020).
TABLE 2.2 The primers sequences MY09/MY11 & GP5 1 /GP6 (Tadlaoui et al., 2020). Primers
Sequences
MY09
50 CGTCCMARRGGAWACTGATC30
MY11
50 GCMCAGGGWCATAAYAATGG30
GP5 1
5TTTGTTACTGTGGTAGATACTAC30
GP6 1
50 GAAAAATAAACTGTAAATCATATTC30
Source: From Tadlaoui, K. A., Hassou, N., Bennani, B. & Ennaji, M. M. (2019). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. In Emerging and reemerging viral pathogens: Volume 1: Fundamental and basic virology aspects of human, animal and plant pathogens (pp. 539570). Elsevier. https://doi.org/10.1016/B978-0-12-819400-3.00024-7.
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Oncogenic Viruses Volume 2
2.3.2
Human papillomavirus genotyping
Until now, around 50 HPV genotypes, causing human genitourinary infections, have been identified by analyzing the DNA sequence (Ljubojevic & Skerlev, 2014), with more than 13 types of them classified as HR-HPVs and involved in CCs (Tadlaoui et al., 2020). However, the patient may be infected with only one type of HPV or may have multiple infections. In front of the genotypic diversity of HPV and the incidence of multiple infections, different methods of HPV genotyping have been developed, allowing the identification of the viral DNA of each type of HPV present in an analyzed biological sample (Melo´n et al., 2013; Ollier & Giordanengo, 2008). These genotyping techniques are based on a PCR amplification step followed by an in situ hybridization step with type-specific oligonucleotide probes (Table 2.3). Two constraints must be checked to choose the region to amplify: the ends of the targeted DNA sequence must be highly conserved in order to allow gender PCR, and then the internal fragment of the sequence must be divergent in order to permit genotyping by comparing with sequences of recognized genotypes (Ollier & Giordanengo, 2008). However, the quality of the sample and the handling and the good running of the amplification and revelation steps require validation by a control PCR (Ollier & Giordanengo, 2008). Genotyping can be done by different methods: sequencing, Luminex flow cytometry technology, or in situ hybridization, using oligonucleotide probes specific for HPV types (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). Then, the revelation of the hybrids is carried out by enzymatic reactions or using fluorophores (reverse hybridization). Nevertheless, while awaiting prodigious sequencing, automated methods are now the most promising, as they simplify the test procedure, increase the capacity, speed and quality of sample processing, reduce costs, minimize human handling errors, and can be developed in several laboratories (Melo´n et al., 2013). As already reported and despite its limitations, sequencing could be considered the gold standard for HPV genotyping, due to the possibility of identifying virtually all virus types without mistaken classifications through cross-reactions among similar types, which can occur using tests based on hybridization. Nevertheless, it was disadvantaged at identifying genotypes in samples with multiple infections, in which viral sequences overlap and it is not possible to distinguish the various types (Melo´n et al., 2013).
2.3.2.1 Genotyping by sequencing Sequencing is the reference method since the different HPV genotypes are defined by a complete analysis of the DNA sequence encoding the L1 region of the viral capsid of HPV.
TABLE 2.3 Main genotyping tests. Type of test
Genotyping
Kit name
Linear array genotyping test
INNO-LIPA genotyping extra
HPV Genotyping RH
Seeplex HPV4 ACE screening
Papillo check
Clart HPV 2 HPV
Genotyping LQ
Manufacturers
Roche diagnostics
Innogenetics
Qiagen
BioNoBisSeegene
Greiner Bio One GmbH
Genomica
Qiagen
Protocol: Amplification
PCR Consensus PGM Y primers (L1) fragment of 170 bp
PCR Consensus primers GP5 1 / GP6 1
PCR Consensus primers GP5 1 / GP6 1
PCR DPO (dual priming oligonucleotide)
PCR Consensus primers E1 fragment de 350 pb
PCR Consensus primers L1 fragment de 450 pb
PCR Consensus primers GP5 1 /6 1 fragment de 150 pb
Protocol: Hybridization
Reverse on strip
Reverse on strip
Reverse on strip
On plastic slide
On tube bottom
On polystyrene microbeads
Detection
Chromogenic
Chromogenic
Chromogenic
fluorescent
Chromogenic
fluorescence
Automated agarose or capillary gel electrophoresis
(Continued )
TABLE 2.3 (Continued) Type of test
Genotyping
Target region
L1
L1
L1
?
E1
L1
L1
HPV detected
Low risk: 6, 11, 40, 42, 53, 54, 55, 61, 62, 64, 67, 69, 70, 71, 72, 81, 84, IS39, CP6108 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68, 73, 82, 83
Low risk: 6, 11, 40, 43, 44, 54, 70, 69, 71, 74 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82
High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51,52, 53, 56, 58, 59, 66, 68, 73, 82
Low risk: 6, 11, 42, 43, 44 High risk: 16, 18, 31, 33, 35, 45, 51, 56, 58, 59, 66, 67, 70
Low risk: 6, 11, 40, 42, 43, 44 High risk: 16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73, 82
Low risk: 6, 11, 40, 42, 43, 44, 54, 61, 62, 70, 71, 72, 81, 83, 84, 85, 89 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82
High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82
Certification
CE-IVD
CE-IVD
CE-IVD
CE-IVD
CE-IVD
CE-IVD
CE-IVD
Automating
_
_
YES
YES
YES
_
YES Liquichip
Source: From Eide, M. L. & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401409. https://doi.org/10.1016/j.annpat.2012.09.200.
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Regularly, the genotype is produced from a small DNA sequence, that of amplicons: a variable region depending on the type, which will be aligned with reference DNA fragments of the different HPV genotypes (Ollier & Giordanengo, 2008). Although sequencing is the most efficient technique for genotyping viruses, it does not identify the viral types present in multiple infections, due to the overlap of the sample viral sequences, which does not allow the different types to be distinguished. On the other hand, this technique makes it possible to detect HPV variants (mainly for HPV 16 and 18), the distribution of which varies according to geographic areas (Choi et al., 2005; Melo´n et al., 2013; Ollier & Giordanengo, 2008).
2.3.2.2 DNA microarray genotyping The principle of microarray technology is to increase the number of hybridizations in a small space, and therefore ensure rapid genotyping (Albrecht et al., 2006; Hwang et al., 2003; Ollier & Giordanengo, 2008). It also allows detecting the different types of HPV present in multiple infections (Ollier & Giordanengo, 2008). It is a simple, sensitive, and rapid technique. The protocol of this technique is based on the use of fluorescent primers, and then the phenomenon of hybridization of the complementary DNA strands is carried out on DNA chips, the support of which is in the form of slides. These contain specific probes of the desired HPV genotypes deposited in the form of micro-drops. The chips involved in this analysis have a low density, with 5100 different probes attached to each slide (Ollier & Giordanengo, 2008). Only the hybridized target strands remain. The nonhybridized strands will be eliminated after washing, before reading the chip. Thus, since the PCR product is fluorescent, it is read directly on a fluorescence scanner that locates the targetprobe hybrid and deduces the desired genotype therefrom (Ollier & Giordanengo, 2008). 2.3.2.3 Genotyping using Luminex technology Luminex technology is a molecular biology technology founded on the principle of two-laser flow cytometry, which is based on the use of fluorescent microspheres coupled to specific oligonucleotide probes and a double analysis after excitation by two lasers (Moalic et al., 2004). Thus, this multianalytical system allows the detection of multiple reactions in one tube (Ollier & Giordanengo, 2008). After amplification of the fluorescent primers with HPV DNA, the latter is denatured and then hybridized on the microspheres each labeled with a fluorophore specific for an HPV genotype. And each type of microsphere is encoded in flow cytometry by a specific color code. Thus, during the analysis, carried out by flow cytometry with two lasers, the first detects the
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Oncogenic Viruses Volume 2
fluorophore indicating the presence of an HPV amplicon, while the second detects the color of the microsphere and therefore the HPV genotype (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). This technique can simultaneously analyze up to 100 types of microspheres and therefore analyze the different types of HR-HPV present in the sample (Oh et al., 2007). Furthermore, HPV Genotyping LQ test (Qiagen) is a marketed test for genotyping using Luminex technology, which allows the detection and genotyping of 18 HR-HPV (Eide & Debaque, 2012).
2.3.3
Human papillomavirus E6/E7 mRNA and protein detection
The involvement of the oncogenic proteins E6 and E7 in the carcinogenesis induced by HR-HPV is clearly demonstrated by numerous molecular studies of CC. These studies have shown that when the viral genome integrates within the genome of the host cell, the overexpression of the oncoproteins E6 and E7 is highly induced, consequently causing the malignant transformation of cervical cells and therefore of HPV-dependent cervical carcinomas (Tadlaoui et al., 2020). Therefore, the detection of E6/E7 messenger RNAs is a potential biomarker allowing the early detection of precancerous lesions before their evolution into an active cancerous pathology (high expression), and the differentiation between these ones and transient lesions (low expression) (Eide & Debaque, 2012).
2.4
Conclusion
HPV is responsible for cancer of the cervix and others. HPV infection in the genitourinary area is very common and is often asymptomatic, making early viral detection difficult. However, the detection of HPV, in particular HRHPV, remains an essential tool for the management of lesions of the uterine cervix. Currently, the rapid development of different molecular biology techniques allows for multiple means of HPV screening, typing, and quantification. Eventually, these techniques will be used in the follow-up of cervical lesions after treatment and in the early diagnosis of other precancerous lesions. Faced with the multiplicity of HPV detection and typing techniques, it is now necessary to develop method validation guidelines with appropriate reference materials and laboratory accreditation, in order to ensure the analytical value of HPV molecular detection tests.
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Acknowledgment I would like to extend my deep thanks and gratitude to Faculty of Sciences and Techniques of Mohammedia, University Hassan II of Casablanca, Morocco, my Professor My Mustapha ENNAJI, and my research team of virology, oncology, and biotechnologies for their support, encouragement, and contribution.
References Albrecht, V., Chevallier, A., Magnone, V., Barbry, P., Vandenbos, F., Bongain, A., Lefebvre, J. C., & Giordanengo, V. (2006). Easy and fast detection and genotyping of high-risk human papillomavirus by dedicated DNA microarrays. Journal of Virological Methods, 137(2), 236244. Available from https://doi.org/10.1016/j.jviromet.2006.06.023. Choi, Y. D., Jung, W. W., Nam, J. H., Choi, H. S., & Park, C. S. (2005). Detection of HPV genotypes in cervical lesions by the HPV DNA chip and sequencing. Gynecologic Oncology, 98 (3), 369375. Available from https://doi.org/10.1016/j.ygyno.2005.04.044. de Martel, C., Plummer, M., Vignat, J., & Franceschi, S. (2017). Worldwide burden of cancer attributable to HPV by site, country and HPV type. International Journal of Cancer, 141(4), 664670. Available from https://doi.org/10.1002/ijc.30716. De Villiers, E. M., Fauquet, C., Broker, T. R., Bernard, H. U., & Zur Hausen, H. (2004). Classification of papillomaviruses. Virology, 324(1), 1727. Available from https://doi.org/ 10.1016/j.virol.2004.03.033. de Villiers, E. M., Whitley, C., & Gunst, K. (2005). Identification of new papillomavirus types. Methods in Molecular Medicine, 119, 113. Doorbar, J. (2006). Molecular biology of human papillomavirus infection and cervical cancer. Clinical Science, 110(5), 525541. Available from https://doi.org/10.1042/CS20050369. Doorbar, J. (2013). The E4 protein; structure, function and patterns of expression. Virology, 445 (12), 8098. Available from https://doi.org/10.1016/j.virol.2013.07.008. Eide, M. L., & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401409. Available from https://doi.org/10.1016/j.annpat.2012.09.200. Georgieva, S., Iordanov, V., & Sergieva, S. (2009). Nature of cervical cancer and other HPV Associated cancers. Journal of B.U.ON, 14(3), 391398. Graham, S. V. (2010). Human papillomavirus: Gene expression, regulation and prospects for novel diagnostic methods and antiviral therapies. Future Microbiology, 5(10), 14931506. Available from https://doi.org/10.2217/fmb.10.107. Griffiths, W., Lewontin, G., Suzuki, & Miller. (2006). Introduction a` l’analyse ge´ne´tique. In La re´gulation ge´ne´tique du nombre de cellules: Les cellules normales et les cellules cance´reuses (Vol. 17, pp. 549550). Han, R., Cladel, N. M., Reed, C. A., & Christensen, N. D. (1998). Characterization of transformation function of cottontail rabbit papillomavirus E5 and E8 genes. Virology, 251(2), 253263. Available from https://doi.org/10.1006/viro.1998.9416. Hwang, T. S., Jeong, J. K., Park, M., Han, H. S., Choi, H. K., & Park, T. S. (2003). Detection and typing of HPV genotypes in various cervical lesions by HPV oligonucleotide microarray. Gynecologic Oncology, 90(1), 5156. Available from https://doi.org/10.1016/S00908258(03)00201-4.
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Kabsch, K., Mossadegh, N., Kohl, A., Komposch, G., Schenkel, J., Alonso, A., & Tomakidi, P. (2004). The HPV-16 E5 protein inhibits TRAIL- and FasL-mediated apoptosis in human keratinocyte raft cultures. Intervirology, 47(1), 4856. Available from https://doi.org/ 10.1159/000076642. Kalof, A. N., & Cooper, K. (2006). p16INK4a immunoexpression: Surrogate marker of high-risk HPV and high-grade cervical intraepithelial neoplasia. Advances in Anatomic Pathology, 13 (4), 190194. Available from https://doi.org/10.1097/00125480-200607000-00006. Kim, C. J., Um, S. J., Kim, T. Y., Kim, E. J., Park, T. C., Kim, S. J., Namkoong, S. E., & Park, J. S. (2000). Regulation of cell growth and HPV genes by exogenous estrogen in cervical cancer cells. International Journal of Gynecological Cancer, 10(2), 157164. Available from https://doi.org/10.1046/j.1525-1438.2000.00016.x. Kleter, B., Van Doorn, L. J., Schrauwen, L., Molijn, A., Sastrowijoto, S., Ter Schegget, J., Lindeman, J., Ter Harmsel, B., Burger, M., & Quint, W. (1999). Development and clinical evaluation of a highly sensitive PCR-reverse hybridization line probe assay for detection and identification of anogenital human papillomavirus. Journal of Clinical Microbiology, 37 (8), 25082517. Available from https://doi.org/10.1128/jcm.37.8.2508-2517.1999. Kroupis, C., & Vourlidis, N. (2011). Human papilloma virus (HPV) molecular diagnostics. Clinical Chemistry and Laboratory Medicine, 49(11), 17831799. Available from https:// doi.org/10.1515/CCLM.2011.685. Lambert, P. F., Spalholz, B. A., & Howley, P. M. (1987). A transcriptional repressor encoded by BPV-1 shares a common carboxy-terminal domain with the E2 transactivator. Cell, 50(1), 6978. Available from https://doi.org/10.1016/0092-8674(87)90663-5. Leversha, M. A., Fielding, P., Watson, S., Gosney, J. R., & Field, J. K. (2003). Expression of p53, pRB, and p16 in lung tumours: A validation study on tissue microarrays. Journal of Pathology, 200(5), 610619. Available from https://doi.org/10.1002/path.1374. Lizano, M., Berumen, J., & Garc´ıa-Carranc´a, A. (2009). HPV-related carcinogenesis: Basic concepts, viral types and variants. Archives of Medical Research, 40(6), 428434. Available from https://doi.org/10.1016/j.arcmed.2009.06.001. Ljubojevic, S., & Skerlev, M. (2014). HPV-associated diseases. Clinics in Dermatology, 32(2), 227234. Available from https://doi.org/10.1016/j.clindermatol.2013.08.007. Melo´n, S., Alvarez-Argu¨elles, M., & de On˜a, M. (2013). Molecular Diagnosis of Human Papillomavirus Infections. Human papillomavirus and related diseasesFrom bench to bedside a diagnostic and preventive perspective (pp. 126), IntechOpen. Mirabello, L., Clarke, M. A., Nelson, C. W., Dean, M., Wentzensen, N., Yeager, M., Cullen, M., Boland, J. F., Alemany, L., Banks, L., Bass, S., Buck, C., Burdett, L., Chaturvedi, A., Clifford, G., DiMaio, D., Doorbar, J., Hildesheim, A., Laimins, L., . . . Burk, R. D. (2018). The intersection of HPV epidemiology, genomics and mechanistic studies of HPV-mediated carcinogenesis. Viruses, 10(2), 80. Available from https://doi.org/10.3390/v10020080. Moalic, V., Mercier, B., & Ferec, C. (2004). Technologie LuminexTM: Principe, applications, et perspectives. Immuno-Analyse & Biologie Spe´cialise´e, 19(4), 181187. Available from https://doi.org/10.1016/j.immbio.2004.05.004. Monsonego, J. (2006). Prevention of cervical cancer: Challenges and perspectives of hpv prophylactic vaccines. Emerging issues on HPV infections: From science to practice (pp. 184205). S. Karger AG. Available from https://doi.org/10.1159/000092755. Oh, Y., Bae, S. M., Kim, Y. W., Choi, H. S., Nam, G. H., Han, S. J., Park, C. H., Cho, Y., Han, B. D., & Ahn, W. S. (2007). Polymerase chain reaction-based fluorescent Luminex assay to detect the presence of human papillomavirus types. Cancer Science, 98(4), 549554. Available from https://doi.org/10.1111/j.1349-7006.2007.00427.x.
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Ollier, L., & Giordanengo, V. (2008). Me´thodes de de´tection et d’identification des HPV. Revue Francophone Des Laboratoires, 38(405), 5155. Available from https://doi.org/10.1016/ s1773-035x(08)74278-3. Pauck, A., Lener, B., Hoell, M., Kaiser, A., Kaufmann, A. M., Zwerschke, W., & Jansen-Durr, P. (2014). Depletion of the cdk inhibitor p16INK4a differentially affects proliferation of established cervical carcinoma cells. Journal of Virology, 88(10), 52565262. Available from https://doi.org/10.1128/jvi.03817-13. Pim, D., Collins, M., & Banks, L. (1992). Human papillomavirus type 16 E5 gene stimulates the transforming activity of the epidermal growth factor receptor. Oncogene, 7(1), 2732. Serrano, M., Hannon, G. J., & Beach, D. (1993). A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4. Nature, 366(6456), 704707. Available from https://doi.org/10.1038/366704a0. Spano, J. P., Marcelin, A. G., & Carcelin, G. (2005). Cancer et infection a` papillomavirus humain. Bulletin du Cancer, 92(1), 5964. Tadlaoui, K. A., Hassou, N., Bennani, B., & Ennaji, M. M. (2020). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. In Emerging and Reemerging Viral Pathogens, (pp. 539570). Academic Press. Wang, Q., Griffin, H., Southern, S., Jackson, D., Martin, A., McIntosh, P., Davy, C., Masterson, P. J., Walker, P. A., Laskey, P., Omary, M. B., & Doorbar, J. (2004). Functional analysis of the human papillomavirus type 16 E1 5 E4 protein provides a mechanism for in vivo and in vitro keratin filament reorganization. Journal of Virology, 78(2), 821833. Available from https://doi.org/10.1128/JVI.78.2.821-833.2004. Wilson, V. G., West, M., Woytek, K., & Rangasamy, D. (2002). Papillomavirus E1 proteins: Form, function, and features. Virus Genes, 24(3), 275290. Available from https://doi.org/ 10.1023/A:1015336817836. Yajid, A. I., Zakariah, M. A., Zin, A. A. M., & Othman, N. H. (2017). Potential role of E4 protein in human papillomavirus screening: A review. Asian Pacific Journal of Cancer Prevention, 18(2), 315319. Available from https://doi.org/10.22034/APJCP.2017.18.2.315. Zheng, Z. M., & Baker, C. C. (2006). Papillomavirus genome structure, expression, and posttranscriptional regulation. Frontiers in Bioscience, 11(1), 22862302. Available from https://doi.org/10.2741/1971.
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Chapter 3
Risk of the development of cancers induced by the consumption of mussels accumulating metallic trace elements Hanaaˆ Bazir1, Najwa Hassou1, Mohammed Nabil Benchekroun1, Hlima Bessi1 and Moulay Mustapha Ennaji2 1
Laboratory of Virology, Microbiology, Quality and Biotechnology/Ecotoxicology and Biodiversity, Faculty of Sciences & TechniquesMohammedia, University Hassan II of Casablanca, Casablanca, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
3.1
Introduction
Water is an indispensable and essential element of life. Its presence covers more than 71% of the earth’s surface (China et al., 2003). Only 2.6% of this water is in the form of fresh water, of which less than 1% is accessible to the population, while the rest is in the form of ice (Kadouche, 2013). However, it is involved in all biological composition and activity. Water is being increasingly contaminated with different types of pollutants, generated by human and natural activities, such as heavy metals. The harmfulness of these pollutants is linked to their speciations. Their concentrations are generally very low, which explains their designation of “trace metallic elements” (TMEs), such as lead, cadmium, and mercury (Rahal, 2012). The long-term toxic effects of excessive ingestion of potentially toxic nonessential metals can be chronic or acute; they can be mutagenic (genetic mutation), carcinogenic, and teratogenic (developmental abnormalities), and also affect metabolism.
Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00015-7 Copyright © 2023 Elsevier Inc. All rights reserved.
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These elements are not biodegradable, so they can be transferred in the food chain, and accumulate and concentrate in living organisms, such as bivalve molluscs. Bivalve molluscs are the first cultivated marine organisms, recognized for a long time as a consumable protein source. These filter feeder species select their food from fine food particles, unlike most molluscs, which use their radula for food, which is why they are considered to be good indicators of metal pollution (Medhioub, 2011). In addition, they are a food to be monitored for two main reasons: (1) they are excellent sensors of pollutants, including metallic trace elements; and (2) the concentration is fixed in the organs of assimilation such as the equivalents of the liver and kidney, which are precisely what humans eat. Aerobic systems are continuously subjected to oxidative pressures, which sometimes lead, during a disruption of the antioxidant balance, to oxidative damage involving lipids, proteins, and DNA. It is mainly the free radicals resulting from these oxidative pressures that degrade the cellular components. Among the many substances responsible for the excessive production of oxidizing species are metals. Some are purely toxic to living beings (cadmium, lead, mercury, etc.), while others are essential for the body and for many biological functions, but they generate toxic effects on the body, beyond a certain concentration threshold (copper and zinc). In either case, the accumulation of these TMEs inside an organism can trigger a defense reaction. Among these transition metals are those that have the ability to interact directly with oxygen (copper) and those that have an indirect action (cadmium and zinc). Free radicals are chemical species (atoms or molecules) with an unpaired electron. These free radicals have important biochemical and cellular consequences. By generating abnormal biological molecules and causing the overexpression of certain genes, oxidative stress potentiates the appearance of multifactorial diseases (diabetes, Alzheimer’s, etc.) and is the main cause of many diseases often linked to aging (cancer, cataracts, etc.). In addition, cells have developed defense systems to metabolize oxidative species and thus limit the damage they cause. These antioxidant systems protect cellular constituents from radical attacks by interacting directly with these radicals or indirectly by producing peptides such as metallothioneins or glutathione. Repair systems help eliminate product damage. However, the massive production of oxidant species and the inhibition of the activities of major antioxidant enzymes in a cell can promote excessive cell death or tumor development. The objective of this chapter is to study the risk generated following the consumption of mussels contaminated with metallic trace elements, leading to the development of cancers in humans, in order to better control the risks of carcinogenesis associated with involuntary consumption of chemical contaminants.
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Trace metal elements
TMEs belong to different families of substances based on metals or metalloids, but in small quantities. Their main characteristic is prevalence in the environment because those substances are neither created nor degraded in the environment. However, these substances can change their physical or chemical form and valence; this is done under the influence of chemical and/or biological reactions. The TMEs comprise 80 constituent chemical elements found in the earth’s crust (Baize, 1998). They are said to be in traces when their concentration in the soil is less than 1 g/kg of dry matter (Gouzy & Ducos, 2008). Some ecosystems are naturally very rich in metallic elements, while others are less so. The trace elements are those that are toxic to humans and the environment but in low doses. They include arsenic (Ar), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Mg), nickel (Ni), lead (Pb), and zinc (Zn) (Varrault, 2011). Most of them have the dual property of both trace elements and toxic elements, for both the animal and plant kingdoms (Doelsch, 2004).
3.2.1 Origin and cycle of trace metal elements in the natural environment The TMEs that enter the aquatic environment are largely governed by atmospheric and continental inputs. TMEs of terrestrial origin are mainly deposited in particulate form, while those of anthropogenic origin are much more soluble (Gouzy & Ducos, 2008). However, continental inputs, those from rivers and streams, are estimated from injections of water in the sea. It is an important source of nutrients, chemicals, and TMEs. However, the knowledge about the contributions of submarine volcanoes to TMEs is still lacking (Oursel, 2013).
3.2.2
Properties of trace metal elements
Some metals are trace elements. For this reason, the periodic table can be classified according to their physical and chemical characteristics into essential and nonessential elements, with the exception of a small number denoting metalloids. A metal is a chemical element most often derived from an ore, with a special luster. It is a good conductor of heat and electricity, with a level of hardness and malleability. The elements combine with other elements to form alloys used by humans since time immemorial (Rouane, 2013). Heavy metals are generally called natural metallic elements characterized by a high density, more than 5 g/cm3. Heavy metals are present in all compartments of the environment but in small quantities. They are basic constituents of the environment, found in air, water, and soil. They accumulate in living organisms as well as in the trophic chain (Merzouki et al., 2009).
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3.2.2.1 The essential elements The essential elements are those that meet the criteria laid down by COTZIAS: G G
G
be present in living tissue at a relatively constant concentration; cause, by their removal from the body, related structural and physiological abnormalities in several species; prevent or cure these disorders through only the element (Burnol et al., 2006).
Some heavy metals are essential elements required in the human body. Their presence in the body is less than 1 mg/kg of body weight, due to which these essential elements are also said to be present in a trace state. This is why they are referred to as trace elements. They differ from other mineral elements by their presence in larger quantities (of the order of a few ten to a few hundred grams) in the body, which are called major mineral elements (Rahal, 2012). At high concentrations, metals like iron (Fe), copper (Cu), zinc (Zn), cobalt (Co), and manganese (Mn) are toxic to human body.
3.2.2.2 Nonessential elements Nonessential or toxic elements are micropollutants. They have no known beneficial role in the cell. Their toxicities develop by bioaccumulation along the food chain. It is impossible to detect the presence of TMEs in water due to their low concentration, which is why sediments and marine organisms are looked into, where these TMEs accumulate (Doelsch, 2004). 3.2.2.2.1
Lead
Lead is a metal that has been mined for 5000 years. It is one of the toxic metals found in great abundance in the earth’s crust. It is present in all terrestrial compartments. Its use is directly linked to metallurgy, industry, and printing. It is also present in paints and automotive fuels, which are mainly responsible for the prevalence of lead in the environment today (Poe¨y & Philibert, 2000). It is a bluish-gray chemical element with the symbol Pb and atomic number 82. It exists in four naturally occurring isotopes: 204Pb, 206Pb, 207 Pb, and 208Pb. Elemental lead has low electrical conductivity and its high mass contributes to its significant absorption capacity for X, γ, and electromagnetic radiation (Garnier, 2005b). It is one of the elements forming TMEs; it can be bio-amplified in biological systems, becoming a potential contaminant for the various trophic links. In the environment, the highest influx of lead in the ocean comes from the atmosphere. Lead appears to be less toxic than copper at an equal molar concentration, due to the formation of complexes with hydroxides or silicates in the medium.
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Sources of lead G Natural sources: Lead is present in the earth’s crust where its average concentration is between 10 and 20 mg/kg in all compartments of the biosphere. In air, lead emissions from wind-blown volcanic dust are recognized to be minor contributors. Other natural processes, such as soil degradation and erosion and forest fires, contribute significantly to the release of Pb. But, generally, these natural processes rarely lead to high concentrations of Pb in the environment (Garnier, 2005b). G Food sources: The contamination of food by lead is an outcome of atmospheric contamination. The techniques used for the preparation of the food, in particular for the cooking and the conservation, also contribute to lead generation in the atmosphere. The food products that are most important vectors of Pb include bread, vegetables, fruits, and sugary drinks. These foods form a major part of the ingested lead-contaminated foods, including crustaceans, molluscs, and offal (kidneys) (Labat & Lhermitte, 2007). Another source of lead is drinking water. The contamination is mainly linked to tap water pipes with physicochemical characteristics (low pH, high temperature) conducive to the dissolution of lead (Barkouch, 2007). G Anthropogenic sources: Lead is mainly used in electric batteries, as an additive in gasoline, in the steel industry, and in the pickling and metal treatment industries. It is also used in waste incineration, wood combustion, cement factories, and battery manufacturing industries (Sabouraud et al., 2009). 3.2.2.2.1.1 Behavior of lead in aquatic environments According to Batley and Florence (1976), approximately 66% of lead in seawater is found in the form of organic complexes or biologically methylated by bacteria. Most inorganic lead compounds are poorly soluble in water (e.g., PbS, PbCO3, PbSO4), halogenated lead compounds (chloride, bromide), or lead acetates being more soluble. 3.2.2.2.1.2 Lead toxicity Lead is a metal that affects all the metabolic functions of the human body. It causes damage to the hematopoietic, nervous, and reproductive systems, as well as the urinary tract (Landrigan et al., 1990). Pb poisoning usually results from oral ingestion and absorption of Pb through the intestinal wall. Lead can also be transmitted during pregnancy from the mother to the embryo or fetus, which may lead to premature abortion, fetal death, or psychomotor delays and teratogenic conditions in children (Graziano et al., 1990). 3.2.2.2.1.3 Carcinogenic effect of lead Lead is only weakly mutagenic, but in vitro it inhibits DNA repair and acts synergistically with other mutagens. Lead acetate administered by mouth, skin, or intraperitoneally causes kidney, brain (glioma), and lung cancers in rodents, and works
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synergistically with other carcinogens. Most cytogenetic studies on workers with exposure to lead have shown an increase in chromosomal aberrations or sister chromatid exchanges, besides studies with positive exposure response trends. There are eight studies conducted on the mortality or incidence of cancer in highly exposed workers; most are cohort studies of lead smelters or battery workers exposed to lead conducted decades ago (Steenland & Boffetta, 2000). 3.2.2.2.2
Cadmium
Cadmium is a metal that is not commonly found in its natural state and occurs as an impurity in various minerals, including zinc, lead, and copper (Rousselet, 2007). Its name originates from cadmia in Latin. It was discovered from calamine in 1817 by the German chemist Friedrich Stromeyer in Thebes, Greece (Andujar et al., 2010). Cadmium is a metal from group IIb of the family of transition metals. It has eight naturally occurring isotopes. In its pure state, it is a soft, ductile metal with a bluish-white color. This metal can sublimate at relatively low temperatures. The most common form of cadmium is the Cd21 ion. This cation can combine with many anions to form salts with different physical and chemical properties. Cadmium is not essential for the development of animal or plant organisms and does not participate in cell metabolism. 3.2.2.2.2.1 Properties of cadmium Cadmium is a nonessential toxic transition metal, an indicator of oxidative stress. The most stable state of cadmium found in nature is Cd21, which allows a high solubility of lipids, a high bioaccumulation, and, consequently, a high degree of toxicity, which results essentially from the similarity of the metabolism of Cd to that of Zn: Cd replaces Zn in many enzymatic reactions (Khalid & Brhimi, 2009). On the other hand, its physical and chemical properties, similar to those of calcium, allow it to cross biological barriers and accumulate in tissues (Rousselet, 2007). 3.2.2.2.2.2 Sources of cadmium Natural sources: Cadmium naturally occurs in most rocks and in the earth’s crust at an average concentration of 0.2 part per million (ppm), as well as in coal and petroleum. It is generally present in zinc or lead ores (Brignon & Malherbe, 2005). It is formed following an alteration and erosion of cadmiferous rocks and also constitutes a refining product for other metals (Zn, Cu, Pb) (Rousselet, 2007). Cadmium inputs to the marine environment, mainly via rivers and rains, are linked to the zinc industry, coal combustion, iron and steel industry, and the manufacture and use of phosphate fertilizers (Burnol et al., 2006).
G
Risk of the development of cancers Chapter | 3 G
G
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Anthropogenic sources: Cadmium is widely used in electronics: its high resistance to corrosion and its shiny appearance enable its wide use in the automotive, aircraft, ship, construction, and communications industries. Cd sulfides are used as colorants in various industries: plastics, ceramics, paints, and textiles. Cd is also a staple product in the battery industry, thanks to its perfect reversibility in electrochemical reactions over a wide temperature range, its low self-discharge rate, and its easy recovery from used batteries (Andujar et al., 2010). Food sources: Cadmium is a metal that is toxic to humans, and the main source of exposure to cadmium in the general nonsmoking population is from food (Filippini et al., 2020). Cadmium is present in a significant amount in certain foods, such as seafood, offal, certain cereals (rice, wheat, etc.), mushrooms, and vegetables and, to a lesser extent, in fish, fruits, and meat (Andujar et al., 2010). The average daily intake is around 310 μg in nonsmoker adults. Smoking contributes to a significant intake of cadmium (approximately 1 μg per cigarette).
3.2.2.2.2.3 Behavior of cadmium in the aquatic environment Cadmium is found in the aquatic environment in various forms: dissolved, colloidal, and particulate. The dissolved forms of this element are the free species of Cd21 and those formed by associations of Cd with mineral or organic compounds (Brignon & Malherbe, 2005) and those found in chemical forms: mineral or organic. A set of physicochemical properties of the medium (salinity, pH, etc.) govern the transformation of Cd in the environment (Burnol et al., 2006). It is bioaccumulative and listed as toxic by the National Research and Safety Institute (INRS) in its sulfide and oxide forms. It is a substance classified as “priority dangerous” by European Directive 2000/60/EC (Khalid & Brhimi, 2009). 3.2.2.2.2.4 Cadmium toxicity There are two main routes of cadmium entry into the body: the respiratory tract by inhalation and the digestive tract by consumption of contaminated food. Acute intoxication by the digestive route (ingestion of 3 mg) may lead to bloody vomiting, intense abdominal pain, diarrhea, and myalgia (Andujar et al., 2010). Chronic cadmium poisoning can cause osteomalacia and sometimes osteoporosis. The bone toxicity of cadmium is thought to be the result of renal phosphocalcic leakage and also the disturbance by cadmium of vitamin D3, leading to a decrease in the digestive absorption of calcium (Andujar et al., 2010). 3.2.2.2.2.5 Carcinogenic effect of cadmium In its 1993 report in Lyon, the international cancer research center IARC classified cadmium in group I as a definite carcinogen for humans and animals (IARC, 1997). In this context, three
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cancer sites due to cadmium have been mentioned in the literature: bronchopulmonary cancer, kidney cancer, and pancreatic cancer (Andujar et al., 2010). G
G
Inhalation exposure Cadmium has been classified in group 1 by the IARC. The associated cancers are those of the respiratory system. In the respiratory environment, the results of cohort studies have established a link between exposure to cadmium and the occurrence of lung cancer (Kazantzis et al., 1988). In 2006, a study carried out in the general population near industrial sites, with exposure to cadmium, confirmed an increased risk of lung cancer with high urinary cadmium concentrations (Nawrot et al., 2006). Oral exposure Oral exposure to cadmium can induce the development of other cancers. A meta-analysis carried out in 2013 suggests a positive association (RR 5 1,15; 95% CI: 1.081.23) between the consumption of cadmium from food and the risk of cancer in Western countries, in particular certain hormone-dependent cancers such as prostate, breast, and endometrial cancers (Cho et al., 2013).
In addition, a casecontrol study conducted in the United States in 2006, on a population of 246 women aged 2069 years with elevated urinary cadmium levels, presented a double risk of developing breast cancer (OR 5 2.29; CI 95% 1.34.2) compared with those with low urinary cadmium levels after adjustment for certain risk factors such as exposure to other metals, in particular arsenic (McElroy et al., 2006). Another prospective study on a population of 32,210 Swedish menopausal women also demonstrated an association between exposure to cadmium via diet and the incidence of the development of endometrial cancer ˚ kesson et al., 2008). In 2014, Jancic and his team established a link between (A the occurrence of thyroid cancer and chronic exposure to cadmium. 3.2.2.2.3 Mercury Mercury is found in all compartments of the environment (air, water, soil), where it can be present in different chemical forms. It has no known beneficial role in living things. According to the WHO, mercury is among the most toxic pollutants for humans and the environment. Several regulations have also emerged in order to limit its use and prevent its risks. Understanding the distribution of mercury on a global scale requires knowledge of its sources (natural and anthropogenic) and of the biogeochemical transformations (biogeochemical cycle) governing its speciation and its transfer into the environment (Sharma & Sohn, 2009). 3.2.2.2.3.1 Property of mercury Mercury is a ubiquitous contaminant, with its presence in the atmosphere, pedosphere, hydrosphere, and biosphere (Engstrom, 2007). Anthropogenic Hg emissions are mainly carried out via the atmosphere in the form of elemental mercury Hg0 gas, which is then transported over long distances. This elemental mercury Hg0 can then be
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oxidized to HgII, which can then be deposited in the pedosphere and hydrosphere in a dry or wet form via precipitation. In aquatic environments, anerobic bacteria convert a small portion of HgII into methylmercury (MeHg), which can then bioaccumulate in the food chain (up to a factor of 106). Mercury is a contaminant whose toxicity and bioavailability depend on the chemical speciation of its dissolved form Sharma and Sohn (2009). Various physicochemical and biological processes are involved in the complex biogeochemical cycle of mercury: oxidation, reduction, precipitation, solubilization, adsorption, desorption, and biotransformations via various organisms (bacteria, microorganisms, algae, crustaceans, fish, etc.), leading to the interconversion of mercury between its different chemical forms and its transfer between different environmental compartments. 3.2.2.2.3.2 Sources of mercury Natural sources Mercury is a natural constituent of the earth’s crust and is released into the environment through different routes, especially from volcanoes in the form of vapor (Hg0). Weathering of rocks is also a potential and primary source of mercury, which is the reason mercury is naturally present in soils and constitutes the geochemical background. After being deposited on the continents and the oceans, a good part of the natural mercury volatilizes again, then redistributes itself in another form, both in continental and oceanic environments: these are the secondary emissions of Hg. These re-emissions occur from soils (often by wind erosion), water bodies, vegetation, and forest fires (Carmouze et al., 2001). G Anthropogenic sources There are many sources and anthropogenic emissions of mercury. Almost 80% of these emissions come from fossil fuels, mining activities, mineral purification, and the incineration and treatment of solid waste. Direct additions to the soil in the form of fertilizers and fungicides are responsible for 15% of emissions, and about 5% is contributed by industrial and/or mining effluents. In addition, there is a wide variety of diffuse sources of mercury, including used batteries, thermometers, and various industrial wastes and materials (Carmouze et al., 2001). Also, soils treated by certain agricultural practices (forest fires and deforestation) are subject to wind erosion and lead to an increase in aerosols rich in mercury (Lacerda et al., 2004). Due to its high vapor pressure and relatively long residence time in the atmosphere, mercury is constantly redistributed by air through the general atmospheric circulation. As a result, its distribution remains relatively homogeneous on a planetary scale (Carmouze et al., 2001; Mason et al., 1994). G
3.2.2.2.3.3 Mercury toxicity Mercury is particularly toxic in all its forms and chemical states, but the most toxic form is MeHg. This element is
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responsible for many harmful effects on natural ecosystems and on all living organisms (Peralta-Videa et al., 2009). Mercury has been enlisted by WHO as one of the 10 chemicals of very high public health concern. There are different modes of exposure to mercury for humans, including inhalation of mercury vapors and ingestion of contaminated water or food and consumption of fishery products contaminated with mercury (Bravo et al., 2010; Ekino et al., 2007; Veiga et al., 1999). The toxicity of mercury in its gaseous form (Hg0) is first demonstrated in the respiratory tract, then dissolving in plasma, blood and hemoglobin. Via the blood, it reaches the kidneys, the brain, and nervous system. Pregnant women are also at risk on exposure to mercury and it easily travels through the placenta to reach the fetus. Even after birth the risks persist because breast milk can also be contaminated. 3.2.2.2.3.4 Behavior of mercury in aquatic environments The impact of mercury on the aquatic food chain has been studied from aquatic invertebrates to carnivorous fish at the end of the food chain. It is mostly accumulated in the muscle tissues of aquatic organisms, all along the trophic chain (Peralta-Videa et al., 2009). Methylmercury is accumulated through direct uptake of water by certain organisms low in the food chain, such as bivalve molluscs, and biomagnifies along the food chain. The significant accumulation of mercury in aquatic organisms has led to their use as bioindicators of contamination of the aquatic environment by mercury. 3.2.2.2.3.5 Carcinogenic effect of mercury Very little information is available regarding the carcinogenic potential of mercury and its compounds (WHO, 2003). Some benign and malignant tumors have been reported in the kidneys following exposure to mercuric chloride. No data are available on a possible carcinogenic effect of elemental mercury. A study (IARC, 1997) carried out in rats and mice exposed for 2 years to gavage with mercuric chloride at 1.9 mg/kg/day revealed the following: 2 2 2 2
Male mouse: some renal adenomas and adenocarcinomas. Female rats: some renal adenocarcinomas. Male rats: an increased incidence of malpigial papillomas of the anterior estoma. Both sexes: dose-dependent hyperplasia of the anterior estoma.
3.2.3
Transfer of trace metal elements in the trophic chain
The transfer of metals between individuals follow a process called “trophic transfers.” The pollutant, present in algae and microorganisms, is ingested by a herbivore, which is a prey to a carnivore, which, in turn, is a prey to a
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supercarnivore, animal, or human being. At the end of the food chain, the final consumer will have bioaccumulated the soluble forms of metals (Barkouch, 2007).
3.2.4
Effects of metal toxicity on human health
The toxicity of a metal can be influenced by a number of factors, including those related to the contaminant (or extrinsic factors) such as the nature of the metal, chemical speciation, organotropism, and bioavailability (Burnol et al., 2006). These metals can act by binding to various ligands containing thiol groups, which can lead to the blocking of the functional groups of important molecules such as enzymes and polynucleotides. Several intrinsic factors such as age, sex, nutritional status, exposure conditions, and genetic variability can influence the bioavailability of the metal and its tissue distribution (Andujar et al., 2010).
3.3
Bivalve molluscs
Molluscs are one of the major branches of the animal kingdom, with more than 100,000 known living species. Eight classes are currently distinguished: Caudofoveata, Solenogastres, Polyplacophores, Monoplacophores, Scaphopods, Gastropods, Cephalopods, and Marine Bivalves. The latter class of species have a soft body with different characteristics. Some bivalves have a shell that is calcareous in nature and is opaque white in color. In the snail, the shell is more developed; it tightens for protection and support for the whole body. In other species, the shell consists of two valves, which cover the entire body, while other species lack an outer shell entirely. Seafood products, in particular bivalve molluscs or seafood, considered as food products, are consumed by humans either raw or undercooked, which may cause food poisoning. These are foodborne illnesses caused by the ingestion of pathogenic microorganisms or toxic products (heavy metals) (Myrand et al., 2007). These animals filter water and concentrate microorganisms and toxins (China et al., 2003). Bivalves or (lamellibranchs) form a class of molluscs. This class includes in particular the Pectinidae, mussels, oysters, clams, and many other families of shellfish. Their laterally flattened body is covered with a calcareous shell (calcium carbonate) consisting of two valves, due to which they are named bivalves. These valves present different characteristics, which facilitates in the identification of these species. Its size is one of the most important characters linked to the bioaccumulation of metallic elements in bivalves (Merzouki et al., 2009), as well as the shape (round or elongated, two identical or different valves), the color (white, gray or others), and ornamentation (smooth or wrinkled shell, presence of thorns).
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The water, laden with nutrient particles and oxygen, enters through the inhaling siphon, and is pumped by the beating of the millions of small cilia of the gill-like gills. The food sorted by the gills is transported to the mouth and then digested. The excrements are evacuated through breathing out by the exhaling siphon; hence, the bivalve molluscs are considered as filter feeders. The foot, which alternately lengthens and shortens, acts like a hydrostatic skeleton. This action is also done by fixing the adhesion of the species to the substrate via the shell or by the byssus secreted by the foot. The byssus is made of protein filaments, which solidify like a glue to adhere the species to the rock. Mussels, in particular Mytilus galloprovincialis, are widely used as bioindicators of metal pollution, because it is a marine organism widely integrated in biomonitoring programs of aquatic environments because of its qualities as a sentinel species, given its wide geographical distribution, being sessile, tolerant to environmental variations, and excellent bio-accumulators of many xenobiotics ((Dumas et al., 2020). Xenobiotics are substances with toxic properties, like metals. The metal particles contained in water and associated with food penetrate the gills and the skin barrier, which leads to the bioaccumulation of trace elements in the body. This transfer from the environment to the organism depends on the concentrations present in these sources and the influence of several biotic and abiotic factors (Cheggourm, 1989).
3.3.1
Classification of lamellibranchs (bivalves)
Modern taxonomy divides bivalves into four subclasses of varying importance: G
G
G
G
Protobranchs (proto: first and branch: gill): These are the most primitive because their gills are simple filaments that are not involved in nutrition. Eulamellibranchs (eu: true, lamelli: lamellae, branch: gill): Their gills form real lamellae connected by tissues. Septibranchs (septum: septum, branch: gill): The septum separates the gills from the palleal cavity. This group brings together species that live at great depths. The filbranches (thread: filament, branch: gill): As the name suggests, their branchial filaments are linked together by stiff threads. The inside of their shell is usually covered with the mother-of-pearl.
Some families live associated with others (oysters, mussels, sea ham), while others are free and can swim by clicking their valves (comb, scallop).
3.3.1.1 Habitat The natural habitats of mussels are found in open coastal ecosystems, rocky shores, and muddy seas. They occupy different types of habitats, from shallow coastal waters to the estuary.
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3.3.1.2 Food Bivalve molluscs are suspensivores, capable of feeding on phytoplankton cells in suspension. They are made of a heterogeneous set of unicellular microalgae, mainly photosynthetic, living in suspension in water and subject to the movements of water masses. The cells can be solitary or grouped in colonies (Medhioub, 2011). The mode of nutrition of bivalves by filtration of sea water inevitably leads to their contamination by bacteria, viruses, toxins, and toxic chemicals present in their environment (Vidal, 2001). The degradation of the particles is possible thanks to the presence of the crystalline stylet, composed of mucoproteins, which projects through the stomach and rubs against the gastric shield. The particles are stirred by the rotations of the stylet, and undergo a start of lysis thanks to the action of enzymes (amylase) released by the stylet (Medhioub, 2011). 3.3.1.3 Metallic pollution bioindicators A bio-indicator is defined as a group of living species (community) that is capable of revealing, through its presence, the transformation of a given ecosystem. A bio-indicator must be available in abundance in the study area, easy to sample, inhabiting a sedentary site, accumulator of trace metals, and especially consumed by the local population. Bioindicators can thus play multiple roles. They are used to detect pollution. 3.3.2
The mytilidae as bioindicators
Various families of bivalves, such as Mytilidae, are recognized as good indicators of heavy metals and the best bio-accumulators of different heavy metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn). They are present in the marine environment mainly in three different forms: (1) dissolved form, present in filtered water; (2) organic form contained in the food ingested; and (3) inorganic, particulate form present in suspension in the water columns. The blue mussel Mytilus edulis is a bivalve mollusk, belonging to the Mytilidae family, widespread on the North Atlantic coasts (Marte & Pe´quignot, 2013). The blue mussel is characterized by an equivalent or in-equilateral shell, dark blue to black in color. It measures 312 cm in adults (Callier, 2008). The body is made of the visceral mass enveloped by two mantle lobes, white, beige or orange in color, with which it merges at the hepatopancreas (or digestive gland). The mantle lobes contain the gonads and delimit the palleal cavity, where the lamella-shaped gills are located. The muscular foot allows occasional locomotion. The anterior and posterior adductor muscles keep both valves
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closed. The mouth is surrounded by labial palps, which allow nutrient particles to be transported to the mouth. The gills provide respiration and nutrition (Callier, 2008). The water is sucked and pumped through the ciliature present on the branchial epithelium in the palleal cavity. It then passes through the gills, which filter it, and, finally, expelled through the exhaling orifice. Mussels are considered to be phytophagous and mainly absorb phytoplankton, but they also consume bacteria and zooplankton. This mode of nutrition by filtration allows an accumulation of many toxic chemical compounds from algae and pathogenic bacteria present in the water (Merzouki et al., 2009) in particular, if it is located in a polluted area. Even if these toxins do not affect mussels, their consumption proves dangerous and sometimes even fatal for humans and mammals generally (Callier, 2008).
3.3.3
Response of marine organisms to trace metal elements
3.3.3.1 Bioaccumulation It is defined by the bioconcentration factor (BCF—ratio between the concentration of the element in the tissues of the species at equilibrium and the free concentration of the element in water). Molluscs accumulate heavy metals mainly in two organs: the kidney and the hepatopancreas (Bouhaimi et al., 1997). The first phase of accumulation is based on phenomena of accessibility, interaction, and penetration through biological barriers that are in direct contact with the environment. The metals present in the aquatic environment are mainly found in hydrophilic and hydrated forms, which cannot cross biological membranes by simple diffusion and which involve protein transporters or transmembrane channels (Zegmout et al., 2011). There are four mechanisms by which a toxicant passes through a cell membrane: the most important is passive diffusion through the membrane, then filtration through membrane pores, active transport, and endocytosis (Fournier, 2005). Some species present a particular homeostatic mechanism, which is capable of maintaining the concentration of certain metals in their organism constant, while the external concentration increases within certain limits. So this phenomenon serves as a regulator for the species, in a zone of concentrations situated between the deficiency and the toxicity (Bouhaimi et al., 1997). Bioaccumulation is either due to direct transfer by water or due to desorption of metallic elements attached to the inert or living particles suspended in the water, which serve as their food. Thus, it constitutes a very useful tool for estimating the degree of pollution of the hydro system (Elbekkay & Melhaoui, 2011).
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Penetration of lead into cells is a result of transport as Pb21, although endocytosis in the gill epithelium has also been suggested for this metal. The absorption rate of lead is a direct function of its concentration in the marine environment. In the body, Pb21 ions compete with Ca21 ions. Bivalves initially accumulate cadmium mainly in hepatopancreas and in the kidney, and in lysosomes. This accumulation occurs preferentially in the digestive gland by supply to the mantle (Cooper, 2008). The transfer of TMEs from the medium to the organism depends on the concentrations present in these sources and the influence of several biotic and abiotic factors (Cheggourm, 1989). Bioaccumulation in the body depends on many factors, primarily the nature of the metallic element depending on whether it has a biological role or not and whether or not it is regulated by the body (Belbachir et al., 2014). Bioaccumulation also depends on biotic factors such as life stage, age, sex, and physiological state and several abiotic factors such as degree of pollution and season. In addition, most of these factors are interlinked and thus it is often difficult to distinguish them. Thus, during the life cycle, bioaccumulation can undergo significant variations (Zegmout et al., 2011). The phenomenon of bioaccumulation is maximal before reproduction and minimal after spawning when reserves have been depleted during gametogenesis (Bayed, 1998). The concentrations of the majority of metals increase markedly in winter and decrease in summer under the influence of numerous permanent discharges of urban, industrial, and agricultural origin (Bayed, 1998; Fournier, 2005), which reveals the impact of pollution of marine water by environmental inputs.
3.3.3.2 Sequestration and elimination 3.3.3.2.1 Sequestration Bivalve molluscs can either control the absorption of the metal or store it in a nontoxic form by complexing it with a protein called metallothionein, capable of sequestering metals. On the other hand, metallothioneins have no role in the sequestration and detoxification of Pb21 (Garnier, 2005a). 3.3.3.2.1.1 Metallothionein Definition: Metallothioneins (MTs) belong to the family of intracellular proteins, with molecular weight ,7000 Da. This molecular structure does not contain aromatic amino acids or histidine. It is very rich in cysteine (Cys) and has the ability to bind to metals, especially those that regulate the homeostasis of essential metals like copper and zinc (Letendre, 2009). They would ensure tolerance to heavy metals in certain populations of aquatic organisms, in a potentially contaminating environment (Hardivillier, 2005).
G
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They were isolated and characterized for the first time 57 years ago from horse kidneys. Potentially present in all living organisms, they fascinate many researchers because of their very peculiar chemical structure (Picard et al., 2010). G Structure of metallothioneins and metal sequestration properties The number and position of cysteines in TMs are highly conserved across species. Divalent metals are bonded to sulfur atoms in clusters of tetrahedral geometry. The affinity of metals for sulfur atoms varies among metals (Nzengue, 2008). The stability of the bond with copper is 100 times higher than that with cadmium, itself 1000 times stronger than that with zinc. Mercury and silver have an even greater affinity for TM than copper. G Role of metallothioneins The most important function of MTs is to regulate the intracellular concentrations of certain metals essential to the organism, such as copper and zinc, by sequestering them, in order to prevent their free circulation in the tissues and their binding to other vital proteins (Achard-Joris, 2005). The covalent bond of the divalent metals with the thiol groups of the MTs is dynamic, since the trapped metals can be released any time. TMs provide a protective role against metallic elements, by limiting their accessibility to other cell sites, thereby participating in cell detoxification (Nzengue, 2008). 3.3.3.2.2 Elimination Bivalve molluscs eliminate metals via several routes, including the urine, since the kidney has the capacity to excrete granules rich in metals. A major route of zinc excretion in mussels is renal leakage in granular form and through defecation, which removes metals from the digestive tract. As for the metals sequestered inside the hemocytes of molluscs, they are eliminated by migration from the intestinal tissues, through the epithelial barrier, toward the lumen of the digestive tract or toward the surrounding water by the mechanism of diapedesis.
3.4
Oxidative stress and cancer
The balance between the positive and negative effects of free radicals is particularly fragile. The production of these radicals is regulated by different organisms (Sies, 1991). Regulatory systems are made up of enzymes, proteins, small antioxidant molecules, and trace elements essential for the activity of enzymes. An imbalance in the antioxidant balance, in favor of the production of reactive oxygen species (ROS), constitutes oxidative stress. Oxidative stress will denature lipids, proteins, and DNA and cause pathologies (Curtin et al., 2002; Gutteridge & Halliwell, 1993).
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Numerous studies demonstrate the preponderant place of oxidative stress in the initiation and development of cancers. The existence of oxidative stress in patients can be shown both by the breakdown of antioxidant defenses and by the increase in the products of oxidative stress. Oxidation derivatives of DNA indicative of oxidative stress are found in the blood and tissues of cancer patients (Malins & Haimanot, 1991). Many aldehydes (such as malondialdehyde) are found in large quantities in the blood of children with cancer (Yazdanpanah et al., 1997). The increased lipid peroxidation is even observed in the precancerous stage in women with mammary or cervical dysplasia (Boyd & McGuire, 1991). The activity of the enzyme manganese superoxide dismutase (Mn-SOD), having the most effective antioxidant and anti-tumor activity (Behrend et al., 2003), is increased in the mucosa of patients with adenocarcinoma of the stomach, probably as a reaction to the excessive presence of ROS.
3.5
Conclusion
The collection of bivalve molluscs is a very popular activity that allows local residents to obtain a highly valued food and economic resource while keeping a traditional practice alive. However, the consumption of contaminated shellfish constitutes a major public health risk. The mode of nutrition of bivalves by filtration of sea water inevitably leads to their contamination by bacteria, viruses, toxins, and toxic chemicals present in their environment. Among the toxic pollutants of natural or anthropogenic origin, TMEs pose a serious environmental problem, in particular at the level of aquatic ecosystems and public health. In this chapter, we have tried to understand how bivalve molluscs, especially mussels, react to an environment polluted with TMEs, notably lead, cadmium, and mercury, and transfer them through their consumption to humans. Humans are at the top of the food chain, and their toxic TME levels (Cd, Pb, and Hg) ingested through consumption of contaminated food (bivalve molluscs) are determined through examination of their blood. It is one of the causes of oxidative stress in humans. Stress potentiates the appearance of multifactorial diseases (diabetes, Alzheimer’s, etc.) and is the main cause of many diseases often linked to aging (cancer, cataracts, etc.).
Acknowledgments The authors would like to thank the Minister of National Education, Professional Training, Higher Education and Scientific Research and members of the Virology, Oncology and Medical Biotechnology team at FSTM-Hassan II University of Casablanca, for all their efforts in supporting us during all work stages in this research.
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Poe¨y, J., & Philibert, C. (2000). Toxicite´ des me´taux. Revue Franc¸aise des Laboratoires, 2000 (323), 3543. Available from https://doi.org/10.1016/S0338-9898(00)80266-8. Rahal, W. (2012). Contribution a` l’e´tude de la bioaccumulation me´tallique dans les se´diments et diffe´rents maillons de la chaine trophique du littoral extreˆme ouest alge´rien (Doctoral dissertation). Rouane, H. O. (2013). Biosurveillance de la qualite´ des eaux coˆtie`res du littoral occidental Alge´rien, par le suivi des indices biologiques, de la biodisponibilite´ et la bioaccumulation des me´taux lourds (Zn, Cu, Pb et Cd) chez la moule Mytilus galloprovincialis et l’oursin Paracentrotus lividus (Doctoral dissertation). The`se doctorat, Universite´ d’Oran, faculte´ des sciences, Algerie. Rousselet, E. (2007). Re´ponses cellulaires vis-a`-vis de l’exposition au cadmium chez les animaux (Doctoral dissertation). Universite´ Joseph-Fourier-Grenoble I. Sabouraud, S., Coppe´re´, B., Rousseau, C., Testud, F., Pulce, C., Tholly, F., . . . Descotes, J. (2009). Intoxication environnementale par le plomb lie´e a` la consommation de boisson conserve´e dans une cruche artisanale en ce´ramique vernisse´e. La Revue de Me´decine Interne, 30(12), 10381043. Sharma, V. K., & Sohn, M. (2009). Aquatic arsenic: Toxicity, speciation, transformations, and remediation. Environment International, 35(4), 743759. Sies, H. (1991). Oxidative stress: From basic research to clinical application. The American Journal of Medicine, 91, 31S38S. Steenland, K., & Boffetta, P. (2000). Lead and cancer in humans: Where are we now? American Journal of Industrial Medicine, 38(3), 295299. Varrault, G. (2011). Les contaminants dans les milieux re´cepteurs sous forte pression urbaine (Doctoral dissertation). Universite´ Paris-Est. Veiga, M. M., Hinton, J., & Lilly, C. (1999). Mercure en Amazonie: un examen complet avec un accent particulier sur la bioaccumulation et les bio-indicateurs. Dans Proc. Forum NIMD (Vol. 99, pp. 1939). Vidal, M. L. (2001). Etude de marqueurs biochimiques de pollution chez le mollusque bivalve d’eau douce corbicula fluminea (Mu¨ller): Purification et caracte´risation des glutathion S-transfe´rases (Doctoral dissertation). Bordeaux 1. WHO, E.M., & Compounds, I.M. (2003). Human health aspects, concise international chemical assessment document 50. Geneva: World Health Organization. Yazdanpanah, M., Luo, X., Lau, R., Greenberg, M., Fisher, L. J., & Lehotay, D. C. (1997). Alde´hydes cytotoxiques comme marqueurs possibles du cancer de l’enfant. Free Radical Biology and Medicine, 23(6), 870878. Zegmout, M., El Addouli, J. A. M. A. L., Chahlaoui, A., Salima, D., & Chafi, A. (2011). Bioaccumulation de quelques me´taux lourds (Zn, Fe, Cu, Pb, Cd) chez la petite prairie au niveau de l’embouchure de la moulouya (Maroc Nord Oriental). ScienceLib, 3, 111212, ISSN 2111-4706 (2011).
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Chapter 4
Oncolytic virus cancer therapeutic options and integration of artificial intelligence into virus cancer research Vaishak Kaviarasan, Barath Ragunath and Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India
4.1
Introduction
Oncolytic virus (OV) treatment is viewed as a helpful method for the therapy of malignancy. An OV is a genetically changed or normally happening infection that can purposely duplicate in and kill cancer cells while making no mischief for typical tissues. This is in contrast to gene therapy, in which a virus is only utilized as a vehicle for transgene conveyance (Fukuhara et al., 2016). During the 1950s and 1960s, a few viruses were tried in both research center settings and on people. A vaccine strain of rabies virus was one of the first viruses to be used in a controlled clinical study to treat 30 patients with melanomatous, with eight of them showing tumor regression (Pack, 1950). Clinical trials of immune-oncology substances alone and in compositions are currently ongoing, but most of these blends of drug trials have shown to enhance the survival rate of the patients compared to other therapies (Antonia et al., 2014). Currently, two modified OVs are approved for marketing viz., (1) Oncorine used to treat head, neck, and esophagus cancer was approved in 2005 in China (Garber, 2006; Xia et al., 2004) and (2) T-Vec used to treat melanoma which was approved by FDA in the United States (October 2015), Europe, and Australia (Coffin, 2016; January & May, 20. A few OVs are under clinical trials such as G47 Δ, JX-594, CG0070, and Reolysin (Andtbacka et al., 2015). Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00001-7 Copyright © 2023 Elsevier Inc. All rights reserved.
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History
In 1804 Seishu Hanaoka directed the first surgical partial mastectomy using local sedation to treat breast cancer patients (Izuo, 2004). The medical procedure has been one of the staple therapies for restricted disease; complete tumor ejection and potential fixes are feasible if the malignant growth is identified early and has not spread fully. With Wilhelm Roentgen’s creation of X-beams in 1895 and Marie and Pierre Curie’s forward leap of radioactive radium and polonium in 1898, radiation treatment developed as a method for disease therapy (Connell & Hellman, 2009). S.W. Goldberg and Efim London utilized radium proficiently in 1903 to accomplish total reactions (CR) in two patients having basal cell carcinoma of the derma (Maron et al., 2006). Until around the last part of the 1940s, when enemies of metabolites (methotrexate) and alkylating specialists (nitrogen mustard) are being utilized as chemotherapeutic medications for disease, medical procedure, and radiation treatment surpassed the field of malignancy therapy (Lichtman, 2008). Notwithstanding the huge impact of mixed chemotherapy in leukemia and lymphoma, clinicians acknowledged by the 1950s that chemotherapy had limitations in accomplishing similar treatment results of a complete reduction in many modern strong tumors (DeVita & Chu, 2008). Subsequently, a coordinated exertion was made to comprehend the tumorigenesis through the examination and development of preclinical tumor models. During the 1970s, scientists created novel medications and medication mixes, such as the utilization of adjuvant chemotherapy after medical procedure to advance better supportability. Chemotherapy turned out to be more focused in the years that followed, taking a gander at explicit pathways like the enemy of angiogenesis, flagging pathways, or explicit changes (Kalyn, 2007). Oncolytic virotherapy is such an accommodating decision that uses replicable contamination for the therapy of malignancies (Russell et al., 2012). Despite the fact that around 3000 remarkable kinds of infections are present, few are sensible as oncolytic specialists. It ought to be nonpathogenic, have unimportant malignancy particular murdering action, or can be intended to impart constricting qualities or outfitting qualities (Maroun et al., 2017). From 1950s to1970s live infections were intentionally implanted into disease-influenced patients and promising results were observed (Kelly & Russell, 2007). Regardless, by using these viral particles poisonousness was noted, especially in cases of safe bargained patients with lymphoma or leukemia. Now, three OVs are economically available for malignant growth treatment. These fuse Rigvir asserted in Georgia, Armenia, and Latvia. Oncorine H101 was embraced in China, and Talimogenelaherparepvec (T-Vec) was supported in the United States (Southam & Moore, 1952). The principal clinical preliminary concerning the oncolytic infection HSV1716 was affirmed in Europe in 1996 (Rampling et al., 2000). Somewhere in the range of 1997 and 2003, strain HSV1716 was mixed into the tumors of patients with glioma, a dangerous mind tumor, with no confirmation of teratogenic or results and
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Cervical carcinoma treatment rabies virus
Genetically modified oncolytic virus concept
1912
1950s-1970s
1991
T-Vec approved for treatment of melanoma- USA
H101 approvedtreatment H&N cancer- China
Japan's first G47 trial for glioblastoma
G207 tested for malignant gliomaUSA
Replication competent viruses in clinical trials
1998
63
2005
2009
2015
FIGURE 4.1 Milestones of oncolytic viral therapy .
some drawn-out survivors (Papanastassiou et al., 2002). Description of milestones between the years 1912 to 2015 in oncolytic viral therapy is given in Fig. 4.1.
4.3
General properties of oncovirus
Oncovirus aids cell transformation and uncontrolled cell growth that causes malignant tumors (¸Sevik, 2012). The infections caused by oncoviruses are quite habitual, but all infections do not lead to cancers or tumor formations. Even if there is a virally infected tumor it will take up to a minimum of 15 and a maximum of 40 years to proliferate and cause complications. EpsteinBarr virus (EBV)-related lymphoproliferative disease is an exception in this scenario because it causes complications within a short period after infection (Zur Hausen, 2009). DNA oncoviruses encode the essential viral protein for viral replication, whereas RNA oncoviruses have altered models of the recipient genes but, they are not helpful in replication of the virus (Springs et al., 2001). Universally all oncoviruses aid carcinogenesis by inhibiting the two major tumor suppressor pathways, namely p53 and retinoblastoma (Rb) (Akram et al., 2017; Levine, 2009; Shackelford & Pagano, 2004), which are given in Fig. 4.2.
4.4
Oncolytic viral therapy: a new era of treatment
In this chapter, we will be focus on the therapeutic method using oncolytic viral therapy by altering the virus according to the required necessities.
4.4.1
Cancer immunoediting hypothesis
Almost every cancer can survive without being lysed by the immune system. Immunoediting is an extrinsic tumor suppressor mechanism that relates between the cancer cells and the immune system of the body. This process has three phases, namely elimination, equilibrium, escape (Schreiber et al., 2011), and the details are mentioned in Fig. 4.3.
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DNA & RNA Oncoviruses DNA-It produce essential viral proteins
Two major tumor suppressor pathways p53 & Retinoblastoma (Rb) Oncovirus
RNA-It has altered model of host gene
Complications occur 15- 40 years after being infected by the virus FIGURE 4.2 General properties of oncoviruses.
In the first phase (elimination), both innate and adaptive immunity act together to lyse the growing tumor before they became clinically engaged. However, in case some variants of cancer cells survive the first phase and then pass on to the second phase (equilibrium), the only function of adaptive immunity works to restrict the protuberance of tumor cell by immunological mechanisms. The second phase is almost an end-stage of the cancer immunoediting process, and tumor immunogenicity editing occurs in this phase. The third phase (escape) in which the tumor cells escape from the equilibrium phase is no longer recognized by the immune system or becomes insensitive to immune mechanisms, so tumor growth is no longer restricted by the immune system. Thus, the tumor cells develop to produce relevant disease that is clinically engaged (Vesely et al., 2011).
4.4.2
Pharmacokinetics of oncolytic viral therapy
According to the definition given in the dictionary, the movement of drugs inside a living organism is known as pharmacokinetics. It is derived from the Ancient Greek words Pharmakon meaning “drugs” and kinetics as “movement.” Cancer or tumor cells allow viruses to easily replicate and progress in the cell because cancer cells have reduced mechanisms (type I IFN pathway) by the host cell to respond to the viral activities (Platanias, 2005). IFNs (interferons) are extensively communicated cytokines that can restrict the
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Intrinsic tumor suppression
Environmental factors and genetic mutations
Immune System
Transformed cells
Escape
Equilibrium
Elimination
Innate & adaptive immunity Normal cell Carcinogenesis/ Tum origenesis
Extremely immunogenic transformed cell
Weak immunogenic & immunoevasion transformed cells
Extrinsic tumor suppression
FIGURE 4.3 Cancer immunoediting hypothesis and its phases. The cancer immunoediting hypothesis has three phases, viz. elimination, equilibrium, and escape. The figure highlights the information about relationship between cancer cells and immune system.
growth and antiviral effects and play a crucial role in immune surveillance for tumor cells. There are two broad divisions of related cytokines: type I IFN and type II IFN (Pestka et al., 1987; Pestka et al., 2004).
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The role of modified OVs is not to lyse the tumor cells, but also plays a key role in the activation of the immune system (antiviral and antitumor responses) to create a long immune memory (Marelli et al., 2018). The modified OVs are injected into the region of the tumor by direct or systemic methods. The benefit of OVs is that they do not infect the normal cells but only infect tumor cells. The OVs start to replicate once they infect the tumor cells, and create immunogenic cell death generally known as apoptosis, causing the lysis of tumor cells. The immune system turns the cold tumor into warm by recognizing the T-lymphocytes, macrophages, and dilation and curettage (DC). These cells produce cytokines into the microenvironment that helps to activate all the other immune cells and kill the tumor completely (Breitbach et al., 2016; Guo et al., 2017) (Fig. 4.4).
4.5
Applications of oncolytic viral therapy
Three viruses viz., RIGVIR, Oncorine, and T-VEC are under clinical trials and have shown promising therapeutic results against cancer. Furthermore, Modified OV’s
Cold tumor is turend to warm by immune system
Direct injection into tumor
Can replicate only in tumors
Oncolysis with production of more virions
Anti tumor & Antiviral response
Reduction of tumor & long immune memory
Normal cells
Infection to new tuomor cells by amplification
Tumor cells T-lymphocytes, Macrophages and Dentite cells
FIGURE 4.4 Pharmacokinetics of OVs. Mechanism of modified OVs targeting cancer cells and immune activation. OVs, Oncolytic virus.
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some of the OVs are undergoing pre-clinical testing; among them, vaccinia, adenovirus, and herpesvirus have shown promising outcomes in animal studies. The applications of various OVs are studied here for the diagnosis and treatment of cancers.
4.5.1
Diagnosis
As of now, a wide range of complex picture procurement for tumor discovery, especially CT and MRI, are basic in precisely situating and surveying a tumor nearby intrusion. Opportune screening of essential tumors and few metastases, yet stays a test, requiring the movement of sequencing procedures with improved accuracy and explicitness (Cao et al., 2020). Exceptionally explicit tumor imaging with OV has snatched a great deal of thought of late. OVs with unmistakable qualities can taint and replicate in tumor cells while additionally communicating qualities of interest, for example, the luciferase reporter 1 gene and the human Na 1 /I-symporter (hNIS) (Dingli et al., 2003). Also, we can identify transcriptomic items, for example, fluorescence in malignancy cells utilizing nonintrusive constant subatomic imaging in vivo (Weissleder, 2006). One of OV executions in tumor exact imaging was fluorescence imaging. green fluorescent protein (GFP) is a protein that could be utilized to perceive tumor practices like intensification, intrusion, and metastasis. It is separated from marine spineless creatures. In contrast with other imaging methods, mouse models effectively decided GFP after the mixture of adenovirus, vesicular stomatitis virus (VSV), vaccinia, and measles infection with the GFP quality (Rojas & Thorne, 2012). Recent developments indicate that malignancy cell tomography with the OV has more prominent unwavering quality and perseverance. Besides, the OV outlined huge benefits in atomic clinical imaging (Barton et al., 2008; Galanis et al., 2015). Atomic clinical hardware can follow the columnist qualities addressed by the OV in disease cells to pinpoint the specific area of tumors. hNIS quality, team kill (TK) quality, and human sort 2 somatostatin receptor quality are utilized as correspondent qualities (hSSTR2). Additionally, they all executed well in mice or clinical preliminaries (Abate-Daga et al., 2011; McCart et al., 2004). Optical subatomic imaging, single-photon outflow SPECT/CT, MRI, and PET are for the most part broadly known reenactments that help screen tumors with OVs. All the above could help in the evaluation of oncolytic virotherapy’s security and remedial intercessions viability, and the headway of a more exact demonstrative method for deciding tumor cause (Jacobs et al., 2001; Touchefeu et al., 2012). OVs have unmistakable benefits for exact imaging of the malignant tissues. The blend of imaging strategies OVs holds a great deal of guarantee for beginning phase tumor analysis (Table 4.1).
TABLE 4.1 The role of OVs (oncolytic virus) in cancer diagnosis. The role of OVs in each field and integration of OV with already existing therapies to make the diagnosis approach more advanced to treat cancer. S. no.
Imaging techniques
Cancer type
Viruses involved
Genes involved
Application
Reference
1.
Bioluminescence imaging
Breast cancer
HSV, HSV-1, HSV—Luc
Luciferase
It can track viral replication cycles
Galanis et al. (2015)
2.
Fluorescence imaging
Breast cancer
Vaccinia virus, Lister strain GLV 1h153
GFP
By infecting GLV-1h153, all positive surgical margins can be seen through FI
Gracia (2013)
3.
PET
Gastric cancer
Vaccinia virus, Lister strain, GLV-1h153
hNIS
Tumor imaging can be interpreted using 99mTc pertechnetate SPECT and 124I PET after the infestation of hNIS exhibiting GLV-1h153
Hacein-BeyAbina et al. (2003)
4.
CT
Prostate cancer
Adenovirus serotype, adenovirus serotype 5, adenovirus serotype 5yCD/mutTKSR3 9rephNIS
hNIS
Almost 78% of tumors can be detected
Guo et al. (2017)
5.
MRI
Prostate cancer
Vaccinia virus, Lister strain, GLV-1h68, 1h312, 1h460, 1h462
Tyrosinase, Tyrosinase (p1, p2) Melanin
After GLV-1 h 462 infections, MRI tumor signal enhancement can be detected. The doxycycline- inducible promoter-system can control melanin expression, reducing inhibition of viral replication caused by melanin overproduction
Hadjipanayis and DeLuca (2005)
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4.5.2
69
Tumor targeted cell delivery by oncolytic virotherapy
OV therapy is an antitumor treatment, yet the blood supply is a contentious setting wherein virions can be quelled by characteristic and versatile insusceptible frameworks, lessening viability (Evgin et al., 2015). Accordingly, discovering suitable transporters to convey the OV to the malignant tissues is pivotal for fruitful treatment. Transporter cell research has moved to incorporate the cells that do not just shield antibodies and enhancements from offtarget organs, yet additionally, de-focuses from askew organs, colonizes tumor destinations, and performs antitumor effector capacities. Antigenexplicit T cells aspartate aminotransferase (AST), mesenchymal immature microorganisms mesenchymal stem cells (MSCs), cytokine-instigated executioner cells cytokine induced killer (CIK), and blood outgrowth endothelial cells (BOECs) are the most regularly utilized cell transporters today (Na et al., 2019; Power et al., 2007; Ram´ırez et al., 2015). An ideal cell transporter should have the accompanying components: first, it should be defenseless against the irresistible specialist; second, it ought to be prepared to help the infection in finding tumor tissue while staying unseen by the insusceptible framework; lastly, it should shed offspring infection to attack far off tumor cells (Wei et al., 2007). The specific antitumor rule of AST is that affected by chemokines, AST enters the tumor site through an unclear surface connection molecule in relationship with the malignant cells. AST receptor then connects to the tumor surface, expresses antigen-significant of histocompatibility complexes, and secretes tumor destruction materials, which kills the malignant cells unequivocally (Ilett, 2020). In the particular event of CIK, it fathoms the tumor cells by annexing the NKG2D receptor to the tumor surface’s NKG2D ligand. Nonintrusive imaging modalities have uncovered that vaccinia infection tainted CIK can move vaccinia infection to tumor locales after intramuscular mixture and have an amazing antitumor impact in an immense number of clinical models (Sampath et al., 2013; Tang et al., 2013; Thorne et al., 2006). Moreover, this system brings about more proficient tumor freedom in different malignancy models, permitting CIK to be used as a phone transporter assaulting disease cells. In a mouse study, Manish R. Patel utilized blood outgrowth endothelial cells to accomplish VSV inspiring IFN to metastatic nonsmall cell lung cancer (NSCLC). The study found that these cells could productively move the OV and furthermore had higher clinical interpretation proficiency (Patel et al., 2020). The ultrasound targeted microbubble destruction (UTMD) methodology for the implantation of virions can help OV in effectively going through the hepatic framework while staying away from immunological freedom. Rupesh Dash used the UTMD to move the infection which is likened as therapeutics onto the tumor site in a mice prostate disease model and gained adequate outcomes from the investigation (Dash et al., 2011). The beginning of a nanocarrier has aroused the curiosity of numerous individuals. Metal,
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natural, polymeric, and lipid nanocarriers have noteworthy conveyance proficiency. They can improve OVs’ ability to uprise the restorative level at tumor destinations, tumor particularity, and assault just as immunological camouflage (Howard & Muthana, 2020).
4.5.3
Genetically modified oncolytic virus
To improve the helpful effect, hereditarily designed adjustments in OVs, like additions and cancellations in the hereditary data, can offer additional restorative particles to malignancy cells, effectively sidestepping the far-reaching protection from single-target anticancer medications (Cao et al., 2020). Right now, about 100 helpful exogenous qualities are being assessed, including apoptotic atoms, hostile to angiogenic particles, and little RNA particles that limit malignancy-related qualities. It is realized to a great extent that tumor affectability to this virotherapy is connected to the overarticulation of the PD-L1 in the disease and insusceptible cell (Zamarin et al., 2018). Recent discoveries indicate developments toward hereditary designing to make an OV that communicates PD-L1 inhibitor with GM-CSF. The designed OVs PD-L1 could keep PD-L1 from restricting malignancy and invulnerable cells (Emdad et al., 2018). Recent developments also indicate that OV can help in the mediation of malignancy neo-antigen explicit the response of T-cells, bringing about improved antitumor impacts, especially in influenced patients who are defenseless to PD-1/PD-L1 bar treatment (Wang et al., 2020). OVs furnished with the expert support of provocative cytokine interleukin 12 or Beclin-1 is likewise said to have unrivaled anti-tumor action compared to the parent OV (Deng et al., 2020; Nakao et al., 2020). Self-destruction quality treatment, additionally called viral-intervened protein hydrolytic drug antecedent treatment, is one more technique for tumor quality treatment virus directed enzyme prodrug therapy (VDEPT). The quality consolidation that encodes a delicate strand in the tumors permits the cells to have a significantly low harmfulness to drugs, bringing about tumor cell demise.
4.5.4
Integration of oncolytic viral therapy in radiotherapy
In different human malignancies, incorporating radiotherapy and oncolytic virotherapy complementarily affects disease treatment (Chen et al., 2001; Dilley et al., 2005). With oncolytic HSV, a synergistic impact among both radiotherapy and virotherapy was determined in one specific case (Adusumilli et al., 2007; Advani et al., 1998; Blank et al., 2002; Bradley et al., 1999; Dai et al., 2010). Radiation-instigated GADD34, p38-intervened viral advertised improvement, and oHSV-interceded DNA fix concealment are the basic cycles in this investigation (Hadjipanayis & DeLuca, 2005; Mezhir et al., 2005). Radiotherapy can crop up with oncolytic vaccinia infection to support proficiency. In one examination, the synergistic impact profoundly depended on
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the concealment of JNK signals brought by radiation (Kyula et al., 2014). Other than this, VACV-scAb-VEGF upgraded the explicitness of tumor cells to radiotherapy (Buckel et al., 2013). VACV-scAb-VEGF improved the anticancer effect in an examination using the mouse xenograft as a model (Young et al., 2013).
4.5.5
Integration of oncolytic viral therapy in chemotherapy
Although there were a few studies on the combination of immunotherapy with OV therapy, safely designated spot inhibitors focusing on PD-1 or/and CTLA-4 implantation with oncolytic virotherapy give modern approach for tumor treatment. Designated spot inhibitors work to pair with OV treatment to trigger or improved invulnerable reactions (Rajani et al., 2016). Such immunotherapy exhibit more noteworthy insurance, expanding the patient’s occupation significantly further (Zamarin et al., 2014). In addition, the mix of the vaccinia infection and paclitaxel had a synergistic impact (Liikanen et al., 2013). Paclitaxel works by permitting cells to get to the period of the cell cycle when the vaccinia infection is bound to influence cells (Huang et al., 2011). Sorafenib incorporated with oncolytic vaccinia infection showed brilliant anticancer outcomes in xenografts, while clinical preliminaries in patients exhibited amazing security and clinical reaction, and they have been supported for foundational use in malignant growth of organs like thyroid, kidney, and liver (Heo et al., 2011).
4.5.6 Integration of oncolytic viral therapy with immune inhibitor checkpoints Despite the fact that there have been few reports on the blend of invulnerable designated spot inhibitors and OV therapy, resistant designated spot inhibitors influencing PD-1 or/and CTLA-4 merged with OV therapy provide a novel malignant growth treatment methodology. Designated spot inhibitors are utilized related to OV treatment to start or improve the insusceptible reaction (Rajani et al., 2016; Zamarin et al., 2014). A few exploration have been directed, and the discoveries show that when incorporated with designated spot inhibitors, OVs had significantly more potential anticancer impacts (Engeland et al., 2014). Unattributed research has found that consolidating resistant designated spot inhibitors with oncolytic virotherapy does not build results. More resistant designated spot inhibitors are anticipated to be found to incorporate with OVs for improved treatment viability (Rojas et al., 2015).
4.6
Limitations
A huge amount of OV is under clinical progression across the globe. They have both advantages and disadvantages, since they pass on the parental attributes of wild-type infections (Andtbacka et al., 2015). A few clinical
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preliminaries have been performed using harmless human diseases like influenza contamination, coxsackievirus, measles, mumps, and adenovirus. Since malignant growth patients profoundly procure artful contaminations from such cold-delivering infections, which can have genuine ramifications, intricacies can happen during virotherapy (Chisholm et al., 2005; Cooksley et al., 2005; Hicks et al., 2003). For example, on account of the OV, HSV-1 like T-Vec and G47Δ is best managed intralesional, though intravenous conveyance isn’t proper (Varghese et al., 2006). One critical concern of oncolytic disease treatment has been that the feasibility may be decreased by the inclusion of orbiting antibodies (Gong et al., 2016). When infections that normally cause viremia are likely delicate to killing antibodies, the antitumor effect of the intravenous association in patients who have recently gotten care or inoculation might be confined. For instance, in a clinical preliminary utilizing MVNIS in patients with numerous myelomas, a troublesome impact of circling antibodies was grounded (Russell et al., 2014). Since all infections are possibly immunogenic, they can cause unfortunate results, especially in high portions. Adenoviruses directed i.v., for example, actuate transient liver exacerbation and poor quality spread intravascular coagulation at restoratively successful dosages of 1012 particles, which is an issue considering the great biosafety information amassed to date (Gomez-Manzano et al., 2004; Nemunaitis et al., 2001). The destruction of a patient with ornithine decarboxylase lacks who was treated with high-portion adenovirus in 1999 fills in as a calming sign of the perils of using infections in people. In France, three children with incredible X-connected immunodeficiency who were treated with retrovirally transduced T cells contacted leukemia due to viral combination and initiation of the LMO2 oncogene which is in like manner saw as another disaster in this strategy (Hacein-Bey-Abina et al., 2003). Prior insusceptibility can limit the feasibility of virotherapy; however, it can likewise prompt expanded harmfulness (Vlachaki et al., 2002). Moreover, high-portion infection treatment may bring about the consumption of cytotoxic T cells or antiviral interferons, trading off common tumor invulnerability, diminishing the adequacy of tumor inoculation, and making the most vulnerable to additional viral disease (a cycle known as resistant depletion) (Alsharifi et al., 2006; Krebs et al., 2005). Organization of adenovirus communicating TK into the CSF joined by i.v. ganciclovir caused viral meningitis in rodents and nonhuman primates in an investigation to target leptomeningeal metastases, and a clinical preliminary was ended after the intrathecal retrovirus-TK treatment caused huge harm in one patient (Driesse et al., 1998).
4.7 Integration of artificial intelligence or machine learning into cancer research Artificial Intelligence (AI) and Machine Learning (ML) are algorithms linked with human intelligence created using coding or programming, the
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more the data provided more the better result they provide. AI software approved by the Food and Drug Administration (FDA) screens breast cancer mammograms to progress images quickly. Integration of AI software with cancer research will improve perfection and help reduce the time taken for diagnosis that can create better health results. There is a paucity of medical experts to diagnose, and it takes a long time to analyze a patient’s history and give reports (Berwick & Gaines, 2018). The ML model understands the given set of information about the health orientation of the patients, but it requires lots of data to learn and perform its function (Bates et al., 2014). The algorithm also chooses patients who are eligible for clinical trials and can save many lives who are at high risks according to the data provided, but the data given should be of good quality (Ford & Norrie, 2016; Gracia, 2013; Schneeweiss, 2014).
4.8
Future concerns
Oncolytic viral therapy will be a new promising therapeutic option to treat various cancers. The other options are radiotherapy, chemotherapy, and drug delivery using nanoparticles. Although these methods are currently used to treat cancers each have their side effects that will be mentioned in Table 4.2.
TABLE 4.2 Comparison between OV (oncolytic virus) therapy and other therapeutic options. Comparison between OV therapy, radiotherapy, chemotherapy, and nanoparticles. S no
Treatment method
Results
1.
Oncolytic viral therapy
The most promising treatment option in the future, but it needs more time, advancements, and many are still in clinical trials. It targets only cancer cells and boosts immunity
2.
Radiotherapy
It will not only kill cancer cells but also affects the healthy cells surrounding the tumor. Common problems are hair loss on the radiotherapy given area because of radiation
3.
Chemotherapy
High dosage chemo drugs are introduced into the body of cancer patients through veins, which also affects the normal cells. Common problems are nerve damage and the body becomes sick and takes time for recovery
4.
Nanoparticles
Drug delivery using Nanoparticles is also a good option to treat cancer but nanoparticles are costlier and have various risk factors inside the human body like pH, compatibility, etc.
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Conclusion
To ensure that oncolytic viral therapy will be a promising therapeutic option, researchers have to make more efforts in concentrating on the advancements because some viruses cause specific issues when introduced into the body. The main aim should be not only to kill the tumor cells but also to increase the stability of the immune system against cancer. Integrating AI or ML into virus cancer research is essential because, considering the lack of medical expertise and the long-time taken for diagnosis, AI or ML can reduce the time taken for diagnosis and give better accuracy compared to human skills but it requires lots of data to process the results.
Acknowledgment The authors thank the Chettinad Academy of Research and Education for the constant support and encouragement for this study.
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Chapter 5
Oncoviruses: future prospects of molecular mechanisms and therapeutic strategies Iyshwarya Bhaskar Kalarani, Kamila Thasneem and Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India
5.1
Introduction
Cancer, a disease caused by uncontrolled cell growth, is associated with chemical carcinogens, such as tobacco, hormonal imbalance, or genetics. Cancer may be caused by different sources, including oncoviruses or cancercausing viruses. Oncovirus infection may have induced cancer in approximately 18.1 million cases worldwide as shown in Fig. 5.1 (Latest Global 1% 6% 21% America Europe Asia Africa 49%
23%
Oceania
FIGURE 5.1 Global cancer incidence. Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00012-1 Copyright © 2023 Elsevier Inc. All rights reserved.
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Cancer Data: Cancer Burden Rises to 18.1 Million New Cases & 9.6 Million Cancer Deaths in 2018. International Agency for Research on Cancer, 2018). Oncoviruses are often overlooked because current preventive measures against cancer focus on modifiable lifestyle risk factors, such as changes in lifestyle, physical activities and proper diet, inflammation, coinfection, immune suppression, or host mutations. The primary tumor causing virus is the Shope papilloma tumor virus (SPV), which was segregated from rabbit papilloma. It was discovered in 1933 and its significant analysis aimed at detecting oncovirus in humans that causes human cancers. Cancer was not thought to be associated with infections at that time. The discovery of oncovirus dates back to the early 20th century, when in 1908 Oluf Bang and Wilhelm Ellermann, the two researchers at the University of Copenhagen, demonstrated that leukemia could be induced in healthy chickens by treating them with a filterable extract containing the avian leukemia virus (Javier & Butel, 2008). The human oncovirus was discovered in 1964 when the EpsteinBarr virus was detected with the help of an electron microscope in Burkitt lymphoma (Epstein, Achong, & Barr, 1964). Studies have found monoclonal proliferation of EBV in tumors, which is a strong evidence in support of a carcinogenic character (Raab-Traub & Flynn, 1986). A turning point in Rous’ cancer research in the 20th century is the discovery of oncoviruses that caused cancers in humans. Epidemiological studies show that people with EBV are at higher risk for EBV-related cancers (De-The´ et al., 1978). About 12% people have at least one oncovirus gene, but only a few individuals suffer from cancer except for when the cause of cancer is an oncovirus, resulting in chronic inflammatory and environmental changes in the innate and adaptive immune response. Direct- and indirect-acting carcinogens are the two main classifications of chemical carcinogens. Direct-acting cancers are induced without the need of metabolism, for example, nitrosamines or ultraviolet light which is an agent that integrates directly with DNA. An indirect-acting carcinogen needs metabolic activation for inducing cancer, which includes heterocyclic aromatic amines and polycyclic aromatic hydrocarbons. Major oncoviruses discovered are EBV, hepatitis B virus (HBV), hepatitis C virus, mastadenovirus, aviadenovirus, rhadinovirus, varicellovirus, polyomavirus, adenoviruses, orthopoxvirus, leporipoxvirus, parvovirus, human immunodeficiency virus (HIV), human papillomavirus (HPV), human herpesvirus 8, and simplex virus (Fig. 5.2). About 12%15% of cancers in cancers worldwide are caused by the EBV (Center & Room, 2014). According to a 2018 study, EBV is associated with a wide range of malignancies, such as Burkitt lymphoma, and approximately 2.2 million new cases have been affected by EBV infection (Pei et al., 2020). It stimulates tumors using common signal and tumor suppressor pathways such as oncovirus p53 and retinoblastoma, inhibiting and controlling proliferation, differentiation, cell death, and the immune system (Akram et al., 2017).
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FIGURE 5.2 Discovery of oncovirus.
5.2
Mechanism of oncovirus
Oncogenic viruses are catagorized into DNA and RNA viruses. DNA contamination oncogenes, unlike RNA tumor infections, encode viral proteins needed for viral replication. RNA viruses have modified versions of standard infected cell genomes that are not necessary for cell proliferation. Oncogenesis causes cellular alteration, triggering an unregulated cell growth period, and improving malignant tumors. In juvenile leukemia, autoimmune diseases, and a number of healthy tumors, oncogenic defects can be seen. Virus-induced tumor changes in cells are the first step in the complicated oncogenesis process. Viral oncogenes are the genes located within the viral genome that alter host cell proliferation control, induce the synthesis of new proteins, and are responsible for transformation traits (Akram et al., 2017). Oncogenic viruses are classified as DNA or RNA tumor viruses based on their genetic material. There are two life bureaucracies in DNA tumor
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viruses. Viral replication induces cell cleavage and mortality in permissive cells. Viral DNA is predominantly incorporated into the extraordinary positions of their genomes in nonpermissive cells. It regulates amino acids like p53 and retinoblastoma by encoding specific sites and inhibits cellular increase. The interpretation of proteins that manipulate host cell DNA synthesis transforms the cell (Nikitin & Luftig, 2011). Rous sarcoma virus (RSV) was discovered to contain RNA in 1961. Oncogenic retroviruses are RNA tumor viruses; more than 30 oncogenes have been described in retroviruses. Retroviruses have three distinct characteristics (gag stands for group antigens (ag) and Pol stands for reverse transcriptase). Env is an acronym for envelope glycoproteins, which are used to combine underlying proteins, virion-associated chemical compounds, and envelope glycoproteins. Lentivirus-based complex retroviruses have an extra nonstructural gene (vonc). The viral sarcoma gene, for example, is the fourth gene in the RSV. RSV obtains this high-quality cell to taint cells. RNA tumor infections use a variety of oncogenic structures to enhance tumor growth. Some of them encode oncogenic proteins, which are similar to the cell proteins that regulate cellular growth. Cell proliferation is caused by the overproduction or alteration of these oncogenic materials. These RNA viruses have the potential to cause tumor growth. The RNA tumor infection encodes a protein that triggers cell feature outflow. As tolerant cells are infected with RNA tumor pathogens, the contaminated offspring is released from the cell surface through maturation, and perpetual hereditary transformations turn the corrupted cells into the disease. The virus is embedded in the chromosome of the cell, it is subject to manipulation by the cell’s regulator genes, and it may remain in the cells without causing any damage. Endogenous retroviruses are these types of retroviruses. When cells carrying a primary infection are exposed to mutagenic or cancerogenic factors, such as light, mutagens, or cancerogenic artificial materials, hormonal or immunological stimuli, etc., the infection is activated and cells begin to multiply. The interference of antiviral proteins with the host cell’s epigenetic equipment is a common key mechanism underlying virus-brought cell transformation (Doerfler, 2012). Epigenetic modulations are critical for oncovirus-mediated malignant transformation, allowing for different levels of healing intervention (Khoury et al., 2013). Viral oncoproteins use the mobile device to rewrite the epigenetic landscape. Healing opportunities for targeting each viral and host cellular factor that contributes to the epigenetics of transformation have been verified in light of such interactions. The majority of the work done to intervene with virus-added epigenetic reprogramming has focused on host cell proteins and epigenetic regulators (Yoo & Jones, 2006). Targeting the viral elements responsible for these types of changes has no’t been thoroughly investigated in many cases, even though viral proteins offer tremendous therapeutic potential due to their superior abilities and large versions when compared to host mobile proteins (Flanagan, 2007). The presence
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of such elements in essential biochemical processes in various healthy cells is a major issue with epigenetic elements focused on host cells. Based on this research, we can classify epigenetic events linked to the most active oncoviruses, as well as the improvement made in recovery due to interaction with their oncoproteins. Over the past 3 years, excellent studies on the epigenetics of malignant diseases have been published (Dawson et al., 2012). Epigenetic activities as therapeutic targets have sparked a flurry of interest as promising approaches to combating a variety of issues, especially cancers (Ramos & Lossos, 2011). Oncogenic virus epigenetics has also been the subject of both collective articles and reviews, in my opinion, in conjunction with pathways of epigenetic dysregulation in host cells, as shown in Fig. 5.3 (Weber et al., 2015; Zhang et al., 2013).
FIGURE 5.3 Oncoviral mechanism.
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5.3
Types and mechanism of oncoviruses
Oncogene activation necessitates genetic alterations in protooncogenes found in cells oncogenes are turned on. Mutation, gene amplification, and chromosome rearrangements are three genetic pathways that result in a change in protooncogene expression or structure. The oncoviruses are categorized as EBV, HBV, aviadenovirus, HIV, HPV, polyomavirus, herpes simplex virus, parvovirus, leporipoxvirus, and orthopoxvirus (Fig. 5.4).
5.3.1
EpsteinBarr virus
EBV is a common virus that mainly spreads through saliva and causes many infectious diseases. EBV (human herpesvirus 4) is a member of the herpes virus family. Symptoms of EBV infection include fever, rash, fatigue, and swollen lymph nodes in the neck. This virus can infect both children and adults equally. Sometimes in children the symptoms may not appear in the early stage but will be present in the later stage. Mostly EBV spreads through body fluids like saliva, blood, and semen. EBV is spread through some of the known ways, such as sharing drinks and food, eating in the same cups, and using the same toothbrush. EBV was identified using a blood test. There is no vaccine and treatment till now so the only way of protection is not to share eating utensils and toothbrushes with those who are EBV positive (Baumforth et al., 1999). EBV replicates, integrates in host cells and leads to tumor growth. EBV is associated with many cancers causing chronic
Hepatitis B Virus Human Papilloma Virus
Aviadeno Virus
Herpes Simplex Virus
Epstein Barr Virus Oncovirus
Parvo Virus
Orthopox Virus
Leporipox Virus
FIGURE 5.4 Types of oncoviruses.
Polyoma Virus
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inflammation and difficulties. It will integrate into the host cell and decrease the function of inflammation-related genes CDK15, PARK2, and TNFAIP3 (Xu et al., 2019). EBV has terminal repeats that are the actual reason for integration, recombination, and tumor growth in oncoviral cancer induction as illustrated in Fig. 5.5.
5.3.2
Hepatitis B virus
HBV causes a drastic infection that can damage the liver. Most commonly, this virus is transmitted from the mother to child during delivery and also through blood, body fluid, semen, sharing needles, razors, and other medical equipment that have been used by infected persons. Many people do not know that they are affected by this virus and they can spread the infection to others. Chronic hepatitis B is mostly seen in infants and acute in adults. Acute hepatitis B include symptoms such as fever, fatigue, dark urine, vomiting, abdominal pain, joint pain, nausea, and jaundice. People with chronic hepatitis B do not show any symptoms or illness. Blood tests are used to diagnose the infection. There is no treatment for acute and chronic infections. Vaccination can somehow prevent infection, therefore all infants should be vaccinated (Liang, 2009). HBV infection leads to inflammation; it will infect the host and start regeneration and alteration of the normal mechanism of the host. Genetic instability and viral protein accumulation can be seen in the host which later lead to the induction of cancer as shown in Fig. 5.6.
FIGURE 5.5 Epstein Barr virus mechanism of action.
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FIGURE 5.6 HBV mechanism of action. HBV, hepatitis B virus.
5.3.3
Aviadenovirus
Aviadenovirus cause a wide range of illnesses such as fever, bronchitis, pneumonia, pink eye, and diarrhea. An adenovirus infection can occur at any age. People with a history of cardiac or respiratory disease and weakened immune systems are at higher risk of adenovirus infection. These infections can spread through the air by coughing and sneezing, shaking hands with infected people, etc. To prevent this type of infection, we should not touch our mouth, nose, and eyes without washing hands and should regularly wash hands with soap. There is no proper treatment for this infection (Turnell, 2008).
5.3.4
Human immunodeficiency virus
HIV infects the immune system of the body and if not treated properly it can lead to AIDS. There is currently no effective cure for HIV infection. Once people get HIV, then it will remain lifelong. HIV came to humans from a chimpanzee in Africa. In early ages humans hunted infected chimpanzees for meat. Major symptoms of HIV infection are fever, sore throat, rash, muscle aches, etc. HIV infections have three major stages: acute HIV, chronic HIV, and AIDS. It is asexually transmitted disease (STD). An HIV positive mother can transmit the infection to her baby but it is not common. Using needles or syringes and the blood of infected people can also transmit HIV. Some of the rare transmission ways are oral sex, biting, open-mouth kissing, prechewed food, tattoos, etc. HIV can also transmit by body fluids like semen, vaginal fluid, blood, and saliva. The viral genome will be infected to the
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host cells through the capsid and it will prevent the normal function, stop reverse transcription, and regulate the nuclear pathway. Cyclophilin A (cyp A) is a viral protein that causes oncogenesis. HIV will infect the body and start to reduce control of oncogenesis virus function, reducing the immune response to cancer. The virus will attach to the cell and fuse, and the viral RNA will be reverse transcribed and cDNA produced. This cDNA combines with chromosomal DNA and begins to replicate as shown in Fig. 5.7.
5.3.5
Human papillomavirus
HPV spreads through direct skin contact. Sometimes the virus has no symptoms and it goes away on its own. The major symptoms of this virus are
FIGURE 5.7 Mechanism of human immunodeficiency virus.
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genital warts and warts in the throat. HPV infection is a common disease that leads to cervical cancers. Diagnosis of HPV in both men and women is different (Soto et al., 2017). Women should be screened with Pap tests every 3 years and HPV test every 5 years. But in men only screening tests are available. Genital warts can be treated using medications, burning by giving electric current, and freezing with liquid nitrogen. Sometimes HPV infection can go away and sometimes it can lead to cancer. Cancers caused by HPV are treated with chemotherapy, radiation therapy, and surgical method. More number of sexual partners, low immunity, unprotected sex, HPV positive sexual partners are the major causes of HPV infection. HPV can easily be prevented by using condoms and safe sex. Vaccines are given to children at the age of 11 to prevent this disease. HPV infection will inhibit E6 and E7 protein in the interferon pathway. Major histocompatibility complex (MHC) is not expressed on antigen presenting cell (APC) cell surface; later immune response will be altered and cancer is induced as shown in Fig. 5.8.
5.3.6
Polyomavirus
Polyomavirus are small DNA viruses, which are widespread in nature. In immunecompetent hosts, the viruses remain latent after primary infection. Polyomavirus consists of three proteins: VP1, VP2, and VP3. Normally polyomavirus does not infect humans but some of them have been associated with some disease conditions. Polyomavirus can infect people who have low immunity. There are 14 types of polyomaviruses that can infect humans. Polyomavirus infections are very common and usually do not show symptoms also. Polyomavirus can cause nephropathy, AIDS, etc. Normally antibody assay is used to detect the polyomavirus. In nephropathy, renal biopsy will be done to diagnose. When the polyomavirus enters the human body, it will capture the cell into S phase and then it will replicate in the host as illustrated in Fig. 5.9. To manage this, host immune-boosting is done (Ambalathingal et al., 2017).
5.3.7
Herpes simplex virus
Human herpesvirus 8, simplex virus, and varicella virus belong to the herpes virus family. Herpes virus is categorized into three types: alphaherpesviruses, herpes simplex virus, and varicella-zoster. There are more than 100 herpes viruses but only eight affect humans. Of these, the most common are herpesvirus 8 and simplex virus. After affecting the human host, the virus will be transcribed and the viral DNA will replicate. The most common diseases caused by the herpes virus are neonatal herpes, genital herpes, central nervous system infection, and chickenpox. To diagnose the herpes virus, isolate and culture the virus separately and check the desired gene present or not using the PCR method. Therapy and vaccines can be given to treat and
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HPV infection
E6&E7protein inhibits interferon pathway
Major Histocompatibility class 1 is not expressed on surface of antigen presenting cells
CD8 + CTL not activated
1. Viral protein 2. Antigen Presenting Cell 3. MHC antigen
HPV clearance
CD8+ CTL
Viral immune eluding HPV persistence
Induction of cancer
FIGURE 5.8 HPV mechanism of action. HPV, human papillomavirus.
prevent herpes virus infection (Roizman, 1990). Herpes virus glycoprotein attaches to receptors on the cell surface and starts releasing viral particles for replication in the cell as illustrated in Fig. 5.10 (Nolkemper et al., 2010).
5.3.8
Parvovirus
Parvovirus is very dangerous and can spread very fast. Mostly parvovirus spread by dog to dog contact. The infection has a mortality rate of more than 90% in dogs. Parvovirus that mostly infects humans is parvovirus B19. Parvovirus B19 can cause mild rash illness, anemia, joint pain, and fifth disease. Parvovirus commonly spreads through body fluids, such as saliva, mucus, blood, etc. When an infected person coughs or sneezes, it can infect the other person. Also, a pregnant woman who is affected by parvovirus can
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FIGURE 5.9 Mechanism of polyomavirus.
FIGURE 5.10 Mechanism of herpesvirus.
pass it to the baby. The blood test will confirm parvovirus infection. No vaccine or medicines are yet present to treat or prevent infection. The only way of prevention is not to get infected. Washing your hands regularly with soap, covering your mouth, and staying home when affected are the only ways to reduce infection spread. The uncoated virus penetrating into the cell through the plasma membrane replicates DNA followed by RNA transcription and protein translation, as shown in Fig. 5.11 (Heegaard & Brown, 2002).
5.3.9
Leporipoxvirus
Leporipoxvirus has a brick-shaped structure and rabbits and squirrels are the hosts. The major leporipoxvirus are myxoma, rabbit fibroma, and squirrel
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FIGURE 5.11 Mechanism of parvovirus.
fibroma. After the entry of the virus into the host, it can replicate in the cytoplasm which leads to cell death and the virus infection will be preserved till finding the other host. Using this virus ex vivo cancer therapy, the virus is attached to the cell losing its outer membrane, and the uncoated core releases viral DNA and DNA replication in the nucleus, which results in morphogenesis of immature particles released by exocytosis, as shown in Fig. 5.12 (Barrett & McFadden, 2007).
5.3.10 Orthopoxvirus Orthopoxviruses are enveloped in brick-shaped capsid. Mammals, humans, arthropods are its natural hosts. The virus envelope will attach to the plasma membrane and unleash the virus into the cytoplasm after the virus has reached the host. From the cytoplasm, the virus replicates and infection starts. Orthopoxviruses can cause rash illness, skin infections, smallpox, and cowpox. This infection is diagnosed by PCR and electron microscopy. The vaccine is available for treatment (Bennet et al., 2015). Major viruses causing cancers in humans are listed in Table 5.1.
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FIGURE 5.12 Mechanism of leporipoxvirus.
5.4
Genetics of virus
Viruses are simple organisms with no energy-generating equipment and minimal biological abilities. The simplest virus has only a few genomes, while the most important viruses have up to 200 genomes. Viruses, on the other hand, have multiple irregular functions of cells genetically. Viruses are difficult to mutate; however, the genomes of certain viruses may come together to form a new lineage; the activation of the viral gene may be controlled, and viral genetic material may be involved. The two processes by which viruses acquire genetic changes are mutation and recombination. Changes in the genetic material of a deadly disease may change the structure of viral proteins, resulting in the emergence of new viruses with altered virulence. Genetic changes are caused by one of three main mechanisms: the effects of biological mutations on nucleic acids, such as due to ultraviolet rays and Xrays; the normal action of the bases that make up nucleotides; and the inadequacy of the enzymes that reproduce nucleotides (Baron, 1996). Genes
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TABLE 5.1 Oncogenic viruses associated with human cancer. S. no
Viruses
Genome
Cancers in human
Infections
1
Adenovirus
dsDNA
Types are unspecified but the virus has transforming ability
Respiratory transmission via patient contact
2
HPV
dsDNA
Cervical cancer, head and neck carcinoma
Sexual transmission
3
HBV
dsDNA
Hepatocarcinoma
Transplacentally, during child delivery, via lactation, sexually, parentally, percutaneous
4
EBV
dsDNA
Burkitt lymphoma, Hodgkin lymphoma, post-transplantation lymphoma, nasopharyngeal carcinoma
Oropharyngeal secretions (saliva), transplacentally, via lactation, via blood, via organ transplantation
5
KSHV
dsDNA
Kaposi sarcoma, primary effusion lymphoma (PEL), multicentric Castleman disease (MCD), KSHV associated inflammatory syndrome (KICS)
Sexually, vertically, via drug injection, blood transfusion, solid organ transplantation, nonsexual horizontal transmission
6
HTLV1
ssRNAdsDNA
Adult T-cell leukemia (ATL)
Transplacentally, via lactation, sexually, via blood
7
HCV
ssRNA
Hepatocarcinoma
Injection equipment, blood and blood product transfusion, vertically, sexually
produce proteins that enhance cell division and survival. This first category also includes genes leading to tumor growth by inhibiting apoptosis. Genes can inhibit cell division actively or indirectly through their protein products. Protooncogenes are the unmutated versions of the first-class genes. Oncogenes are the corrupted or altered versions of the genes. It is insufficient in some normal cells and it is involved as the name implies in the gene in cell reaction to growth factors. HER2/neu gene amplification is expected to affect the tumor response and the tumor’s ability to mature and expand.
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The tumor may be more lethal with too many versions of the HER2/new gene, but it may also be more responsive to chemotherapy agents. Kinase signal systems control gene transcription and thus cell growth and differentiation (Schedin et al., 2018). RAS gene products play a prominent role. The RAS protein must bind to the cell to “turn on” a particular molecule. Membrane function is associated with the gene-2 proteins in cell-based lymphoma, which are a part of a complex mechanism that controls apoptosis (Vogelstein & Kinzler, 2002). Oncogenes associated with numerous cancer types are given in Table 5.2 (Gupta, 2014).
5.5
Types of treatment
Oncolytic virus therapy is a novel alternative experimental method for cancer treatment that has recently been approved. An oncolytic virus is a genetically modified or naturally occurring virus that can selectively replicate in cancer cells and destroy them while causing no damage to normal tissues. Oncologic treatments also resulted in more precise and complex regimens that aim to have the greatest influence on malignant cells, thus sparing nontumor tissues and minimizing side effects (Fukuhara et al., 2016). Various ways to treat cancer depend on the types and stages of cancer. The most effective treatment methods are radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, and stem cell transplant.
5.5.1
Immunotherapy
Our immune system works in complicated manner to combat infection. This process consists of cells, organs, and proteins. Cancer can normally get around a number of the immune device’s natural defenses, allowing cancer cells to maintain growth. There are numerous varieties of immunotherapy involving monoclonal antibodies and tumor-agnostic remedies, which include checkpoint inhibitors, oncolytic virus treatment, T cell therapy. Maximum cancer vaccines are illustrated in Fig. 5.13. A few immunosuppressive therapies can help stop or reduce the growth of most cancer cells in the immune system. Others help the immune system to damage most cancer cells or prevent cancers from spreading. Immunotherapy treatments can be used or mixed with most cancer treatments of one type or another. A certain new material that the immune system dislikes activates an alert, causing the immune system to respond by targeting it. Germs include molecules that contain beneficial proteins not naturally present in the human body. They are regarded as “distant sites” by the immune system, which destroys them. The immune system’s reaction is to kill something that contains a foreign material, such as germs or cancer cells (Bayer et al., 2019). Immunotherapy, also known as biological therapy or biological reaction reinforcement therapy, is a cancer treatment that makes use of the body’s immune system and
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TABLE 5.2 List of cancer associates with oncogenes and their function. S. no
Oncogene
Function
Cancer
1
ABL1
Promotes cell growth through tyrosine kinase activity
Chronic myelogenous leukemia
2
AFF4/ MLLT11
Fusion affects the MLLT11 transcription factor/methyltransferase. MLLT11 is also called HRX, ALL1, and HTRX1
Acute leukemia
3
AKT2
Encodes a protein-serine/ threonine kinase
Ovarian cancer
4
ALK
Encodes a receptor tyrosine kinase
Lymphomas
5
RUNX1 (AML1)
Encodes a transcription factor
Acute myeloid leukemia
6
AXL
Encodes a receptor tyrosine kinase
Hematopoietic cancers
7
MYC (cMYC)
Transcription factor that promotes cell proliferation and DNA synthesis
Leukemia; breast, stomach, lung, cervical, and colon carcinomas; neuroblastomas and glioblastomas
8
EGFR
Cell surface receptor that triggers cell growth through tyrosine kinase activity
Squamous cell carcinoma, glioblastomas, lung cancer
9
ERBB2
Cell surface receptor that triggers cell growth through tyrosine kinase activity; also known as HER2 or neu
Breast, salivary gland, and ovarian carcinomas
10
GNAS (GSP)
Membrane-associated G protein
Thyroid carcinoma
11
LCK
Tyrosine kinase
T-cell lymphoma
12
MYCL
Transcription factor
Lung carcinomas
13
MYB
Transcription factor
Colon carcinoma and leukemia
14
HRAS
G-protein. Signal transduction.
Bladder carcinoma
15
NTRK1
Receptor tyrosine kinase
Colon and thyroid carcinomas
16
TSC2
GTPase activator
Renal and brain tumors
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FIGURE 5.13 Immunotherapy in cancer treatment.
antibodies to destroy most cancer cells. Interferons, interleukins, monoclonal antibodies, colony-stimulating factors like cytokines, and vaccines are examples of biological therapies (Fig. 5.13). Researchers have discovered that the immune device may be able to decide the difference between healthful cells and cancer cells in the body and to do away with the cancer cells. Organic treatment options are designed to reinforce the immune device, either directly or circuitously (Tashiro & Brenner, 2017).
5.5.2
Chemotherapy
Chemotherapy is the use of medication to kill or prevent the growth of most cancer cells. Most chemotherapy treatments include a drug or a mixture of drugs administered intravenously or orally in the form of a tablet. Chemotherapy is not like surgical treatment or radiation remedy in that the cancer-stopping capsules flow inside the blood to parts of the frame wherein cancer might also moreover have spread and may kill or put off most cancer cells at websites first-rate distances from authentic cancer. As a result, chemotherapy is taken into consideration as systemic treatment and is illustrated in Fig. 5.14 (Shi et al., 2020).
5.5.3
Targeted therapy
It is designed to treat most cancer cells and reduce number of broken cells in comparison to healthy cells. Cancer remedies that “target” cancer cells can
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FIGURE 5.14 Chemotherapy treatment.
also offer the gain of decreased treatment-associated aspect effects and progressed consequences. Targeted therapy uses tablets to target unique genes and proteins that are involved in the survival and multiplication of cancer cells. This therapy targets tissue surroundings that help cancer growth and targets cells like blood vessel cells that promote cancer growth. Researchers first diagnosed the genetic changes assisting tumor development and then directed their therapy toward preventing specific mutations. Focused healing procedures may block or flip off alerts that inform most cancer cells to grow and divide, prevent the cells from residing longer than usual, and ultimately destroy cancer cells (Gore et al., 2013).
5.5.4
Radiation therapy
Radiation therapy, also known as radiotherapy, uses high-energy radiations to damage or destroy cancer cells by blocking them from developing and separating. Radiation therapy, like surgery, is a community procedure for removing detectable tumors. Radiation treatment has a low success rate in killing tumor cells that have already migrated to other areas of the body. Radiation therapy may be administered either directly or internally. Outside radiation, which comes from a tool outside the body, sends high-voltage rays straight to the tumor site online. The implantation of a small volume of radioactive material in or around most cancers is known as internal radiation or brachytherapy. Radiation may be used to treat or control maximum cancers, or to ease some of the symptoms and signs of most cancers as shown in Fig. 5.15. Now and then radiation is used with different types of other cancers treatment, which include chemotherapy and surgical remedy, and sometimes it is used on its own (Szatkowska & Krupa, 2020).
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FIGURE 5.15 Treatment in radiation therapy.
5.5.5
Hormonal therapy
Hormones are sincerely taking place materials inside the frame that stimulate the increase of hormone-sensitive tissues, collectively with the breast or prostate gland. Even as most cancers occur in breast or prostate tissue, their growth occurs because of the body’s very own hormones. Consequently, drugs that block hormone manufacturing or exchange the way hormones paintings, or the removal of organs that secrete hormones that encompass the endocrine glands, which consist of glands collectively with the thyroid, pancreas, ovaries, or testicles, are techniques of preventing most cancers. Hormone remedy, just like chemotherapy, is a systemic remedy because it can affect most cancer cells in unspecified time in the future within the frame (Fairchild et al., 2015). The estrogen receptor (ER) which regulates cell growth consists of six different functional domains including the ligandbinding domain for estrogen and the DNA-binding domain (Ya¸sar et al., 2017).
5.5.6
Surgery
Surgical treatment is used frequently in case of cancers, such as for diagnosing most cancers, figuring out the stage of cancer, getting rid of the primary tumor, and relieving signs. A biopsy is a rare type of surgical technique that can be used to aid in the diagnosis of cancer. It is a procedure in which a sample of tissue from a suspected cancerous area is taken and examined in a laboratory by a representative. It is effectively completed within the doctor’s workplace. A first-rate biopsy shows the presence of cancer, whereas a terrible biopsy may suggest that no cancer is present in the pattern and vice versa. In the surgical operation, cancer and a few tissues adjoining to the maximum cancers are commonly removed. In addition to promoting the development of different treatment methodologies for cancer, data collected from surgical operations prove beneficial in predicting the risk of most
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cancer recurrences guiding future research. Surgery, as used to cure cancer, is a method by which a skilled surgeon removes the majority of tumors from your body. Huo et al. (2019) studied typical techniques that may be used either during the surgical operation of most cancers or after surgical procedures.
5.6
Stem cell transplant therapy
Stem cells have self-renewal and differentiation capacity that can be used to repair human tissues after chemotherapy. Stem cell transplantation is the method that restores blood-forming stem cells because they grow into one-ofa-kind types of blood cells in most cancer patients who have had their cells destroyed via excessive doses of chemotherapy or radiation therapy. Find out approximately the styles of transplants, element consequences that might arise, and the manner stem cell transplants are implemented in most cancers treatment. White blood cells, which are components of the immune system and make the body resistant to infectious substances; red blood cells, which transport oxygen across the body; and platelets, which aid in blood clotting, are the three main types of blood cells. Stem cells can classified into embryonic stem cells (ESCs) obtained from the inner cell mass of blastocyst and adult stem cells (ASCs) found from any adult tissue. These may be further differentiated into hematopoietic, mesenchymal, and neural stem cells. Using stem cell therapy, cancer treatments have been developed, including hematopoietic stem cells transplantation surgery, mesenchymal stem cell infusion for post-cancer treatment, stem cells for therapeutic carriers, immune cell development, and vaccine production (Zhang et al., 2017).There are six steps involved in stem cell transplantation as shown in Fig. 5.16.
5.6.1
Oncotherapy
Oncotherapy is a method or a treatment by which we can cure cancer. It is a type of oncovirus gene therapy where the modified oncovirus will be sent to the targeted cancerous cells in the host body. Oncolytic virus will replicate in the host cancer cells and start to destroy the cancerous area; also, it won’t damage the normal cells or tissues. Oncotherapy method emerged in the early 1900s but after the discovery of chemotherapy and radiotherapy, viral therapy started to diminish slowly. In the 1990s, again oncolytic viral therapy returned with modified oncoviruses. This method is expected to be an emerging field for cancer treatment. The full method depends on the effectiveness of the oncovirus, as in how it kills cancer cells or how much faster it works. Oncotherapy can be carried out in two ways: the direct method or intratumoral method, where the modified oncovirus is transferred directly to the targeted area in the host, and the systemic or intravenous method, where through an intravenous injection the modified oncovirus is introduced in the
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FIGURE 5.16 Steps involved in stem cells transplantation.
host body. The systemic method was found very useful for metastatic cancers. Oncolytic virus has the potential to destroy all cancerous cells without affecting normal healthy cells through activating the immune system. Normally all cells will show defense when a viral particle enters a normal cell in the body. However, in oncotherapy method, the oncolytic viruses are modified with some kind of deletion or mutation because of which when we induce the oncovirus to the host if it enters a normal cell then the cell will turn on defensive mechanism thus would not be harmed, but if it enters the cancer cell then the cancer cell won’t react and it will be eliminated from the tissue. In another way, if the modified oncovirus enters the cancerous cells, those cells won’t show any defense and will let the virus grow and replicate and our oncovirus will start to kill or destroy the targeted cancerous area. We are selecting the virus by looking at the affinity they show to cells, this might ease out the whole process (Howard et al., 2013).
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Some viruses show more similarity to some cells and some show less. One of the most common examples is that neuronal cells preferred the herpes virus. Recent studies in this field of oncology have shown very promising results. It was very clear that oncoviruses or oncotherapy is a good way to kill cancer almost same as chemotherapy and radiation therapy. Very few studies have been conducted in this field, especially leukemia, which yielded promising results for customized immunotherapy or other oncologies. There are other studies also in radiation therapy and oncoviral therapy ex vivo and in vivo, which showed good results and the treatment is capable of killing tumor cells (Marelli et al., 2018). Oncotherapy started early in the 1900s but at that time it was not well established and did not attract much interest because of the existence of chemotherapy. At that time chemotherapy was most studied and focused area because of its easy methods. Chemotherapy became very friendly to all worldwide very fastly so that oncotherapy remain a little neglected but now again oncotherapy coming gaining interest. Although the oncotherapy field is now an upcoming area, it still has some barriers or difficulties. One of the major problems we are facing in oncotherapy is while we introduce the oncovirus to the host, it is turned off by the immune system of the host body. It was believed that if the oncovirus can cause cancer then it can also destroy cancer if modified properly. In the beginning of oncotherapy there were many side effects because of the overdosage of the virus or the viral toxic content in the body. But later modified oncoviruses were used for clinical trials and there was visible decrease in side effects and an increase in the effective killing of cancer cells. To improve the effect of oncotherapy first factor taken into account is enhanced delivery of oncovirus to the host or targeted area. Then a repeated dosage of the virus is practiced that leads to targeting and retargeting of the cancerous cells. This leads to modified oncolytic virus introduction and inactivation of the defensive mechanism of the host. The clinical and preclinical trials have shown that oncotherapy is one of the best methods other than chemotherapy and radiation therapy (Zhang et al., 2017).
5.7
Future of oncotherapy
Oncotherapy is the most attractive therapy in the cancer treatment field nowadays. Several treatment methods have been practiced in this field to destroy cancerous cells, but only oncotherapy the main method that showed positive results. Using oncolytic-virus-targeted treatment of cancerous cells is emerging in the modern era. The modified oncolytic virus can destroy cancer cells without harming healthy normal cells, and with a combination of chemotherapy and radiation therapy, the amount of oncotherapy that is most appropriate in antiviral and repetitive results is believed to have a bright future for
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cancer treatment in the field of cancer. After looking at the evolution and interest in cancer that many scientists have shown, it is very clear that the field of cancer prevention will be one of the brightest areas in future. Cancer may be treated easily and without any harm to other normal cells with the use of oncolytic viruses. Furthermore, we can learn about the mechanism by which the virus modifies and transitions to anticancer treatment and how it copes with the host immune system. Cancer treatment will be one of the most innovative in the field of oncology medicine in the future (Junaid et al., 2018).
5.8
Conclusion
In this chapter, we have discussed what is an oncovirus, its types, and the mechanisms that can be used in oncotherapy to destroy cancer cells without harming normal cells. This gives a proper insight about oncoviruses and their association with cancer therapy. In the early time when the oncoviruses were identified, all were very scared because of their cancer-causing properties. Later it was found that if an oncovirus can cause cancer then it can also kill cancer. Oncovirus therapy is a very advanced and emerging field for cancer treatment nowadays and is majorly concentrated on studying the interaction of oncolytic viruses and cancer environments. Oncolytic virotherapy is a treatment of cancer cells, with radiation therapy and chemotherapy, by modulation of the oncovirus for cancer treatment such that they target cancer and destroy the cancer-producing stem cells. In many clinical trials, the method has been found successful but the major problem which arises is reappearing or regrowth of tumor. Target-specific tailor-made oncotherapy will be one of the great ideas for developing cancer treatment. Direct induction of oncolytic viruses to a targeted cancer niche by an intrainjection method can increase the success rate of cancer treatment. Major types of advanced treatments using an oncolytic virus for cancer, their results, and obstacles have been discussed here. Now we have an understanding of oncotherapy and major problems of this field. We hope that the future of oncovirus therapy will be successful in the field of cancer therapy.
Acknowledgment The authors thank the Chettinad Academy of Research and Education for the constant support and encouragement during the study.
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Ambalathingal, G. R., Francis, R. S., Smyth, M. J., Smith, C., & Khanna, R. (2017). BK polyomavirus: Clinical aspects, immune regulation, and emerging therapies. Clinical Microbiology Reviews, 30(2), 503528. Available from https://doi.org/10.1128/ CMR.00074-16. Baron, S. (1996). Leptospiramedical microbiology. University of Texas Medical Branch at Galveston. Barrett, J. W., & McFadden, G. (2007). Genus leporipoxvirus (pp. 183201). Poxviruses. Baumforth, K. R. N., Young, L. S., Flavell, K. J., Constandinou, C., & Murray, P. G. (1999). The Epstein-Barr virus and its association with human cancers. Journal of Clinical Pathology Molecular Pathology, 52(6), 307322. Available from https://doi.org/10.1136/ mp.52.6.307. Bayer, V. R., Davis, E., & Gordan, R. A. (2019). Chemotherapy and immunotherapy guidelines and recommendations for practice (2019, pp. 149189). Oncology Nursing Society. Bennet, J. E., Blaser, M. J., & Dolin, R. (2015). Principles and practice of infectious diseases. Elsevier. Center, D., & Room, M. (2014). Fourth annual neuroscience. Behavior and Health Research Forum. Dawson, M. A., Kouzarides, T., & Huntly, B. J. P. (2012). Targeting epigenetic readers in cancer. New England Journal of Medicine, 367(7), 647657. Available from https://doi.org/ 10.1056/NEJMra1112635. De-The´, G., Geser, A., Day, N. E., Tukei, P. M., Williams, E. M., Beri, D. P., Smith, P. G., Dean, A. G., Bornkamm, G. W., Feorino, P., & Henle, W. (1978). Epidemiological evidence for causal relationship between Epstein-Barr virus and Burkitt’s lymphoma from Ugandan prospective study. Nature, 274(5673), 756761. Available from https://doi.org/10.1038/ 274756a0. Doerfler, W. (2012). Impact of foreign DNA integration on tumor biology and on evolution via epigenetic alterations. Epigenomics, 4(1), 4149. Available from https://doi.org/10.2217/ epi.11.111. Epstein, M. A., Achong, B. G., & Barr, Y. M. (1964). Virus particles in cultured lymphoblasts from burkitt’s lymphoma. The Lancet, 1, 702703. Available from https://doi.org/10.1016/ s0140-6736(64)91524-7. Fairchild, A., Tirumani, S. H., Rosenthal, M. H., Howard, S. A., Krajewski, K. M., Nishino, M., Shinagare, A. B., Jagannathan, J. P., & Ramaiya, N. H. (2015). Hormonal therapy in oncology: A primer for the radiologist. American Journal of Roentgenology, 204(6), W620W630. Available from https://doi.org/10.2214/AJR.14.13604. Flanagan, J. M. (2007). Host epigenetic modifications by oncogenic viruses. British Journal of Cancer, 96(2), 183188. Available from https://doi.org/10.1038/sj.bjc.6603516. Fukuhara, H., Ino, Y., & Todo, T. (2016). Oncolytic virus therapy: A new era of cancer treatment at dawn. Cancer Science, 107(10), 13731379. Available from https://doi.org/ 10.1111/cas.13027. Gore, L., DeGregori, J., & Porter, C. C. (2013). Targeting developmental pathways in children with cancer: What price success? The Lancet Oncology, 14(2), e70e78. Available from https://doi.org/10.1016/S1470-2045(12)70530-2. Gupta, S. P. (2014). Cancer-causing viruses and their inhibitors. Cancer-Causing Viruses and their Inhibitors (pp. 1482). CRC Press. Available from https://doi.org/10.1201/b16780. Heegaard, E. D., & Brown, K. E. (2002). Human parvovirus B19. Clinical Microbiology Reviews, 15(3), 485505. Available from https://doi.org/10.1128/CMR.15.3.485-505.2002.
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Chapter 6
Multi-omics methods and tools in dissecting the oncovirus behavior in human host Sheik S.S.J. Ahmed1, Ramakrishnan Veerabathiran2, Mookkandi Sudhan3, Harsh Panwar4 and Prabu Pramasivam5 1
Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 2Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 3Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chennai, Tamil Nadu, India, 4Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab, India, 5Department of Neurology, University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, United States
6.1
Introduction
Recent advancements in biotechnology and high-throughput techniques have generated an enormous amount of biological data. This huge data cannot be processed manually with ease, and hence we depend on computers to extract meaningful biological information (Raja et al., 2017). This led to the evolution of a new domain called bioinformatics which merges biology and information technologies.
6.1.1
Definition
Bioinformatics collects, organizes, and analyzes biological data to retrieve useful biological information on biomolecules, such as DNA, RNA, proteins, and metabolites. Bioinformatics integrates multiple domains such as computer science, engineering, and biostatistics to fill the vital gaps between molecules and pathophysiology (Ma & Dai, 2011). With advancements in omics research, bioinformatics witnessed tremendous growth over the last decade. In the current scenario, most research labs generate massive biological data as the outcome of omics-based experiments. These extensive data Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00007-8 Copyright © 2023 Elsevier Inc. All rights reserved.
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are stored in databases and shared across the world with the help of the internet. Bioinformatics plays a vital role in processing the omics data and helps to understand and address the multifaceted problems associated with biological research (Gomez-Cabrero et al., 2014). Omics is an emerging field of technology that mainly handles a large family of cellular molecules such as genes, proteins, and metabolites (Horgan & Kenny, 2011). This technology helps to understand the interrelationship between the cellular molecules that collectively participate in various biological and molecular functions in cells, tissue, or organs. In addition, molecular profiling using omics helps to differentiate the occurrence of molecular and cellular events between the cell, tissue, organs, or species. Currently, a huge amount of data generated using high-throughput omics techniques have been stored in biological databases (Shulaev, 2006). Furthermore, the data are processed through a bioinformatics approach to retrieve valuable and meaningful information. The omics-based techniques are primarily classified as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metallomics. Genomics deals with the coding and noncoding regions of a genome. Transcriptomics is the study of RNA, and proteomics deals with proteins, while metabolomics explores metabolites in a cell, tissue, and body fluids (e.g., serum, plasma, urine, and cerebrospinal fluids) (Haider & Pal, 2013). At the same time, the assessment of metal homeostasis in cells and tissue is referred to as metallomics (Fig. 6.1). Since
FIGURE 6.1 Various domains of omics. Omics that demonstrate functional behavior of genes, transcript, proteins, and metabolites.
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omics technology deals with a large family of cellular components; it is suggested to have a wide range of applications in the medicine, pharmaceutical, agriculture, and marine sectors. Currently, these omics-based approaches are primarily applied in medicine to understand pathophysiology, thus potentially helping in disease management (Noorbakhsh et al., 2015). In particular, the omics concept helps to determine the etiology, diagnosis, and prognosis of several diseases (Wheelock et al., 2013). This information is the vital resource for biomarkers and drug discovery processes that contribute to developing several domains within omics, including pharmacogenomics, lipidomics, glycomics, and metagenomics.
6.2 6.2.1
Types of omics Genomics
The entire DNA molecule present within a cell is referred to as the genome. The area of genomics is mainly concerned with genome sequencing and its analyses. The current focus of genomics is to perform whole-genome sequencing and genetic mapping, which helps to understand the function, mutation, and evolution of an organism (Krier et al., 2016; M. Perez-deCastro et al., 2012). Based on the potential application, genomics is further classified into functional genomics, structural genomics, and comparative genomics (Fig. 6.2). Functional genomics deals with the functions of genes; structural genomics deals with the genomic regions that contribute to the structural components of cells; whereas comparative genomics deals with the
FIGURE 6.2 Various domains of genomics. Genomics classifies the behavior of genes into functional structural and comparative genomics.
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structural and functional comparison between genes of the same or different organism (Tsai et al., 2016; Wilson et al., 2004). At present, several high-throughput methods are being used for genome analysis that helps in the diagnosis of genetic diseases such as cancer, sickle cell anemia, cystic fibrosis, and Huntington’s disease, which result from a certain abnormality in DNA sequences that encodes for specific proteins (Nishant et al., 2009; Reuter et al., 2015). Recent reports suggest that genomic studies have increased the rate of drug targets research on genes useful in the pharmaceutical field. In addition, genomics showed a potential contribution in the field of cancer research. Approximately 12% of all human cancers are caused by oncoviruses. These cancers are of very distinct types because of the complex nature of the virushost cell interaction, which leads to cancer development. In cancer, the genetic profile of oncoviruses was led to improve the dissecting of the behavior of human host cells. Applied research has led to the development of diagnostic, therapeutic, and even prophylactic approaches for these viral-mediated cancers. Oncogenic human papillomaviruses (HPV) and anogenital cancers serve as a classic example of this successful story, from the identification of cervical cancer epidemiology to the prophylactic vaccine’s development against the most oncogenic HPV. Oncoviruses are generally classified as direct or indirect carcinogens. Direct carcinogenic viruses possess viral oncogenes that can directly contribute toward neoplastic cellular transformation, whereas indirect carcinogens can result in oncogenic transformation mediated through chronic inflammation. The oncogenic potential of some viruses has been clearly established, and thus they are being targeted for cancer prevention and treatment. Successful vaccines are already available against hepatitis B virus (HBV) and HPV infection. Antiviral malignancy treatments and therapeutic vaccines are currently under investigation. Leads from few metagenomic projects have provided information on oncogenic DNA viruses (Table 6.1) including
TABLE 6.1 Genomics sequencing of oncovirus organisms. Organism
Kingdom
Group
Hepatitis B virus (HBV)
Pararnavirae
Hepadna
Hepatitis C virus (HCV)
Orthornavire
Flaviviridae
Human herpesvirus-8 (HHV-8)
Heunggongvirae
Herpusviridae
Human papillomavirus (HPV)
Shotokuvirae
Papillomaviridae
Merkel cell polyomavirus (MCPyV)
Shotokuvire
Papillomaviridae
Human T-cell lymphotropic virus-1 (HTLV-1)
Pararnavirae
Revtraviridae
EpsteinBarr virus (EPV)
Heunggongvirae
Herpusviridae
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EpsteinBarr virus (EBV), HBV, HPV, human herpesvirus-8 (HHV-8), Merkel cell polyomavirus (MCPyV), hepatitis C virus (HCV), and human Tcell lymphotropic virus-1 (HTLV-1) (Butt & Miggin, 2012). Epigenomics deals with the genome-wide characterization of reversible modifications of DNA or DNA-associated proteins, for example, DNA methylation or histone acetylation. Covalent modifications of DNA and histones regulate gene transcription and subsequently determine cellular fate. Both genetic and environmental factors can influence these modifications, which can be long-lasting and sometimes heritable. While the role of epigenetic modifications as mediators of transgenerational environmental effects remains controversial, their importance in biological processes and disease development is evident from many epigenome-wide association studies that have been reported. For example, differentially methylated regions of DNA can indicate the disease status of metabolic syndrome, cardiovascular disease, cancer, and many other such pathophysiologic states. Epigenetic signatures are often tissue-specific, and several large consortia are focusing on establishing comprehensive epigenomic maps in multiple human tissues. Thus, in addition to the insight gained from identifying epigenetic modifications correlating with different diseases, data generated by several studies have great potential to enhance our functional interpretation of genetic variants residing in those regions or of epigenetic markers associated with the disease. Particularly, epigenomic profiling has been performed to understand the behavior of cancer-causing oncoviruses. Genomics classifies the behavior of genes into functional, structural, and comparative genomics.
6.2.2
Transcriptomics
Transcriptomics deals with the complete set of transcriptomes (RNAs) encoded by a gene of a specific cell or organism. Understanding the transcriptome of an organism gives insight into the time-dependent activation of the gene across various environmental and experimental conditions. Human host transcriptomic behavior upon oncovirus infection has been potentially explored, which helped in understanding and management of virusassociated tumors by demonstrating viral integration patterns and hostpathogen interactions (Slatteryet al. 2012). Advances in transcriptome technology, such as DNA microarray and next-generation sequencing, make it possible to understand the gene regulation in a cell or tissue. These transcriptome-based techniques generate enormous data that have been stored in the databases for enabling biomedical researchers to understand disease pathology, biomarker detection, and drug discovery process (Alizon et al., 2019) (Table 6.2). The development of high-throughput DNA and RNA sequencing, in conjunction with new bioinformatics tools, allows for deep, whole-genome sequencing of the host and pathogen. Recent improvements
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TABLE 6.2 Transcriptome-based profiling of cancer oncogenic virus. S. No
Oncovirus
NCBI GEO DataSet
Title
1.
Hepatitis B virus (HBV)
GSE119072
Microarray upon Sp110 knockdown in HepG2.2.15 cells
2.
HBV
GSE160648
Gene expression profiles in epigenetically reprogrammed HepG2 cells
3.
HBV
GSE114783
Microarray gene expression from HBV infection to hepatocellular carcinoma (HCC)
4.
HBV
GSE132628
Molecular signature of HBV regulation by interferon gamma (IFN)-γ in primary human hepatocyte
5.
HBV
GSE96851
Role of humoral immunity against HBV core antigen in the pathogenesis of acute liver failure
6.
HBV
GSE109824
HBV deregulates cell cycle to promote viral replication and a premalignant phenotype
7.
HBV
GSE66699
Liver gene expression profiles of IFN therapy in chronic hepatitis B patients (Exon)
8.
HBV
GSE66698
Liver gene expression profiles of IFN therapy in chronic hepatitis B patients (mRNA)
9.
HBV
GSE84044
Characterization of gene expression profile in HBV-related liver fibrosis patients
10.
HBV
GSE64878
Global rewiring of p53 transcription regulation by the HBV X (HBx) protein
11.
HBV
GSE72068
Huh-7-Mock and Huh-7-HBV
12.
HBV
GSE72068
Gene expression response to HBV infection in primary human hepatocytes (PHH)
13.
HBV
GSE67764
β-Arrestin1 is involved in hepatocellular carcinogenesis via an inflammation-mediated Akt signal
14.
HBV
GSE55092
Viral expression and molecular profiling in liver tissue vs microdissected hepatocytes in HBVassociated HCC (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
15.
HBV
GSE44074
Gene expression profiling of hepatitis B- and hepatitis C-related HCC using graphical Gaussian modeling
16.
HBV
GSE41737
MicroRNA-27a regulates lipid metabolism and inhibits hepatitis C virus replication in human hepatoma cells
17.
HBV
GSE47197
Human liver infected by HBV: tumoral part vs nontumoral part of the liver
18.
HBV
GSE38941
Liver regeneration gene signature in HBV-associated acute liver failure identified by gene expression profiling
19.
HBV
GDS4387
HBV-associated acute liver failure (ALF) patients: liver explants
20.
HBV
GSE20140
Combining clinical, pathology, and gene expression data to predict recurrence of HCC
21
HBV
GSE19665
Aberrant DNA methylation in hepatitis B and C virus-related HCC
22
HBV
GSE22058
miR-122 as a regulator of mitochondrial metabolic gene network in HCC
23
HBV
GSE14668
B-Cell gene signature with massive intrahepatic production of antibodies to hepatitis B core antigen in HBVassociated acute liver failure
24
HBV
GSE10186
Integrative transcriptome analysis reveals common molecular subtypes of human HCC
25
HBV
GSE10143
Gene expression in fixed tissues and outcome in HCC
26
HBV
GSE10141
Gene expression in fixed tissues and outcome in hepatocellular carcinoma (Training Set, HCC)
27
HBV
GSE11064
AdHBx UV vs AdEasy UV vs HepG2 (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
28
HBV
GSE140114
Gene expression analysis of different passages and subclones of the Huh7 cell line
29
Hepatitis C virus (HCV)
GSE128726
Energy metabolism and cell motility defect in natural killer (NK) cells from patients with hepatocellular carcinoma
30
HCV
GSE117858
Genome-wide comparison of differentially expressed genes in hepatoma cell lines with and without treatment of dimethyl dicarboxylate biphenyl
31
HCV
GSE78737
Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC
32
HCV
GSE78736
Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC [cell line]
33
HCV
GSE69715
Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC [patient]
34
HCV
GSE110312
Expression data from HCV infected hepatoma carcinoma cell line Huh7-MAVSR cells
35
HCV
GSE64605
Transcriptome profiles of differentiated hepatoma cells infected with oncogenic HCV
36
HCV
GSE79340
Genome-wide transcriptomic analysis of hepatocyte-like cells upon ectopic miR-146a-5p expression
37
HCV
GSE71757
HCV upregulates B-cell receptor signaling: a novel mechanism for HCVassociated B-cell lymphoproliferative disorders
38
HCV
GSE68927
Comparison of Huh6 and Huh7 cells under IFN gamma treatment (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
39
HCV
GSE70781
HCV-induced expression profiling of hepatic and nonhepatic cancer cell lines
40
HCV
GSE62546
Quantitative mRNA expression comparison of HCV replicon (2a) on Huh7.5 cell lines
41
HCV
GSE60948
Expression data of genome-length HCV RNA-replicating OL8(3.5Y) cells and OL8(3.5Y)-derived ribavirin-resistant cells
42
HCV
GSE54101
Gene-expression profiles of paired biopsies and explanted liver from HCV-infected individuals
43
HCV
GSE54100
Expression profiles of 186-gene signature in US HCV-related liver cirrhosis
44
HCV
GSE54099
Expression profiles of 186-gene signature in Italian HCV-related liver cirrhosis
45
HCV
GSE51699
Endogenous intrahepatic IFNs and association with IFN-free HCV treatment outcome
46
HCV
GSE44074
Gene expression profiling of hepatitis B- and hepatitis C-related HCC using graphical Gaussian modeling
47
HCV
GSE41737
MicroRNA-27a regulates lipid metabolism and inhibits HCV replication in human hepatoma cells
48
HCV
GSE41804
Hepatic gene expression of HCV related HCC and noncancerous tissue with Il28B rs8099917 TT genotype and TG/GG genotype
49
HCV
GDS4887
IL-28B polymorphism effect on HCV-related HCC: resected live
50
HCV
GSE40812
Impaired TLR3-mediated immune responses from macrophages of patients chronically infected with HCV (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
51
HCV
GSE38226
MicroRNA profiling in TGFβ signaling and development of fibrosis
52
HCV
GSE38542
HIV/HCV coinfection activates a type 1 IFN response in monocytes that correlates with cognitive impairment
53
HCV
GSE32886
HCV-induced expression profiling of hepatic and nonhepatic cancer cell lines [mock-treated]
54
HCV
GSE19665
Aberrant DNA methylation in hepatitis B and C virus-related HCC
55
HCV
GSE17856
Gene expression in nontumoral liver tissue and recurrence-free survival in HCV-positive HCC
56
HCV
GSE14323
RMA expression data for liver samples from subjects with HCV, HCV-HCC, or normal liver
57
HCV
GSE3632
Post-hepatitis C and post-alcoholism HCCs: series 3
58
HCV
GDS2239
HCV core protein effect on hepatocyte cell line
59
HCV
GSE97318
Restoring PU.1 induces apoptosis and modulates viral transactivation via IFNstimulated genes in primary effusion lymphoma
60
Human herpesvirus (HHV)
GSE16547
Kaposi’s sarcoma-associated herpes virus (KSHV) manipulates notch signaling by upregulating Dll4 and JAG1 to alter cell cycle gene expression in lymphatic endothelial cells (LECs)
61
HHV
GSE6489
HHV-8 infection of pulmonary microvascular entdothelialcells
62
HHV
GDS3007
KSHV encodes an ortholog of miR-155
63
HHV
GDS1063
Primary effusion lymphomas and associated viral infections
64
HHV
GSE1880
Role of LANA in KSHV latent infection (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
65
HHV
GDS988
KSHV LANA overexpression in B lymphoid cells: time course
66
HHV
GSE69697
Altered global gene profile in KSHV 1 PEL cells exposure to dhC16-Cer
67
HHV
GSE138554
Characterization of organoid culture
68
Human papillomavirus (HPV)
GSE118776
E5 HPV type 16 oncoprotein modify ST3GAL3 and ST6GAL1 gene expression
69
HPV
GSE90930
Glycogene expression profiles based on microarray data of cervical carcinoma HeLa cells partially silenced in E6 and E7 HPV oncogenes
70
HPV
GSE75132
TMEM45A, SERPINB5 and p16INK4A transcript levels are predictive for development of high-grade cervical lesions
71
HPV
GSE73761
Changes in global gene expression profiles induced by HPV 16 E6 oncoprotein variants in cervical carcinoma C33-A cells
72
HPV
GSE65858
Gene expression patterns and TP53 mutations are associated with HPV RNA status, lymph node metastasis, and survival in head and neck cancer
73
HPV
GSE65166
Whole-genome analysis of cells permissive for late gene expression of HPV-16
74
HPV
GSE46842
Inhibitors of differentiation-1 promotes transformation of human papillomavirus type 16-immortalized cervical epithelial cells
75
HPV
GSE29570
The mtDNA amerindian haplogroup B2 enhances the risk for cervical cancer of HPV: deregulation of mitochondrial genes may be involved
76
HPV
GSE28266
Expression data from BJ-hTERT cells expressing vector, cyclin E, c-Myc or coexpression of both (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
77
HPV
GSE17892
HPV16 E7 expression effect on expression profile mediated by tumor necrosis factor
78
HPV
GSE8288
Identification of markers associated with deregulated hTERT during HPVmediated transformation
79
HPV
GSE11488
Molecular evidence and gene expression profiles implicating involvement of HPV in human retinoblastoma
80
HPV
GSE3292
Gene expression signature of HPV in head and neck squamous cell carcinoma
81
HPV
GDS1667
Head and neck squamous cell carcinoma tumors harboring HPV
82
HPV
GSE137328
MCPyV targets NDRG1 to promote cellular proliferation
83
Merkel cell polyomavirus (MCPyV)
GSE68503
Cellular transcriptional responses to MCPyV T antigens
84
MCPyV
GSE42334
Effect of cigarette smoke extract, cisplatin, nicotine and/or ionizing radiation on mRNA and microRNA expression in the NCI-H460 human lung large cell carcinoma cell line
85
MCPyV
GSE61589
Gene and microRNA expression in normal and tumoral esophageal squamous cell lines
86
MCPyV
GSE50434
Comparative transcriptional profiling of human Merkel cells and Merkel cell carcinoma
87
MCPyV
GSE44843
Involvement of miRNAs in the differentiation of human glioblastoma multiforme brain tumor stem-like cells
88
MCPyV
GSE33127
Comparison of microarray platforms for measuring differential microRNA expression (Continued )
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TABLE 6.2 (Continued) S. No
Oncovirus
NCBI GEO DataSet
Title
89
MCPyV
GSE119072
Microarray upon Sp110 knockdown in HepG2.2.15 cells
90
MCPyV
GSE39612
Distinct gene expression profiles of viral- and nonviral-associated Merkel cell carcinoma revealed by transcriptome analysis
91
MCPyV
GSE26177
Functional evidence that Drosha overexpression in cervical squamous cell carcinoma affects cell phenotype and microRNA profiles
92
Human T-cell lymphotropic virus1 (HTLV-1)
GSE16219
Identification of genes that are regulated by TAK1 in human cutaneous T cell lymphoma HuT-102 cells
93
EpsteinBarr virus (EPV)
GSE108137
Snail-dependent epithelial splicing regulatory protein 1 (ESRP1) silencing drives malignant transformation of human pulmonary epithelial cells
94
EPV
GSE41015
Dynamic transcriptome analysis reveals new prognosis biomarker in colorectal cancer
95
EPV
GSE39807
Gene and microRNA expression data from tumor-induced CD11b 1 myeloid-derived suppressor cells (MDSC)
96
EPV
GSE44843
Involvement of miRNAs in the differentiation of human glioblastoma multiforme brain tumor stem-like cells
97
EPV
GSE32688
Integrative survival-based molecular profiling of human pancreatic cancer
98
EPV
GSE19693
STAR RNA-binding protein, quaking, suppresses cancer via regulation of microRNA
in genomics and transcriptomics have made it possible to be precise. Hence, the transcriptomics profile of oncoviruses will enhance the knowledge of the host response at various critical time points that would be potentially useful for the development of novel anticancer drugs (Hasin et al., 2017).
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6.2.3
Oncogenic Viruses Volume 2
Proteomics
Proteomics is the study of all the proteins expressed in a cell, tissue, or organism. Protein is a complex molecule derived from the mRNA transcript synthesized in the cytoplasm of the cell for performing a molecular function. Understanding the cell proteome has become an important discipline of gene function, which provides insight into the mechanisms and biological processes. In general, proteomics provides information on protein structure, expression, and function that helps to understand the perturbations in molecular pathways of complex diseases, which improves the therapeutic properties of the protein, diagnostic markers, and protein replacement therapy (Slatteryet al. 2012). The current advancement in biotechnology made it possible to examine the expression, structure, and function of the protein using high-throughput technologies such as chromatography, mass spectroscopy, two-dimensional differential gel electrophoresis, protein microarray, X-ray diffraction, and nuclear magnetic resonance (NMR) spectroscopy (Zhang et al., 2014). These techniques contribute to the field of expression, structural, and functional proteomics (Fig. 6.3).
6.2.3.1 Types of proteomics 6.2.3.1.1 Protein expression proteomics It is a quantitative approach to estimate the level of proteins expressed in a cell as a function of stimulation, environmental conditions, or at the state of pathology. This proteomics analysis will be helpful in identifying the
FIGURE 6.3 Domains of proteomics. Proteomics classifies the behavior of proteins as functional, structural, and expression.
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signaling mechanisms under various biological conditions (e.g., time, drug, disease, or environmental toxins) (Graves & Haystead, 2002). Highthroughput techniques such as two-dimensional differential gel electrophoresis and protein microarray are currently used in expression proteomics which helps identify proteins responsible for various diseases, including heart diseases and cancers (Aslam et al., 2017). 6.2.3.1.2
Structural proteomics
The major goal of structural proteomics is to elucidate the three-dimensional (3D) structure of the protein encoded by the genome of an organism. Structural proteomics helps to understand the structure of proteins which provide clues about their biological and cellular functions (Manjasetty et al., 2012). Recent development in X-ray and NMR technologies has enabled the elucidation of the structure of the protein with high resolution in a limited time scale. Such an unprecedented rate of structure elucidation resulted in tremendous growth in the number of protein structures that were being deposited in databases such as Protein Data Bank (PDB). 6.2.3.1.3
Functional proteomics
An increase in the number of proteomic projects generates several protein sequences whose functions are largely unknown. Functional proteomics is an emerging field of proteomics that explores the biological functions of proteins. Investigating the protein functions will unravel the protein mechanisms and their interacting partners within the cell. In addition, the elucidation of protein interactions in the cell will provide the clue on signaling pathways which may be helpful for the treatment of complex diseases (Monti et al., 2007). In general, the protein of interest is expressed with a suitable tag with its specific partner (protein) to enable its separation from the cellular extract. Furthermore, each protein within the multi-protein complex is identified by subsequent mass spectrometric procedures. This helps to retrieve information on protein signaling and understanding the role of protein in various disease mechanisms and proteindrug interactions in a cell. Oncogenic viruses are responsible for around 15% of human cancers. Several recent studies document the prospects and challenges of viral proteomics in the study of oncogenic viruses (Table 6.3). These viruses have coevolved with their hosts and cause persistent infections. Each virus encodes oncoproteins that manipulate key cellular pathways to promote viral replication and evade host immune response. Viral proteomics can identify cellular pathways perturbed by a viral infection, identify cellular proteins involved in viral persistence and oncogenesis, and identify diagnostic and development of therapeutic targets. Conversely, a large number of oncolytic viruses have shown great potential for the treatment of certain types of cancer
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(Dzutsev et al., 2017; Guven-Maiorov et al., 2020; Kuzembayeva et al., 2012; Lupberger et al., 2019; Quiles et al., 2009; Seo et al., 2018) (Table 6.3).
6.2.3.2 Proteomic techniques Proteins are important regulators for biological activity, and their concentrations are influenced by mRNA as well as the host translational process. Proteins are the most reliable or appropriate data to characterize the biological system. Proteomics is the most important methodology for understanding cellular mechanisms, and is even more complicated than genomics. Change in gene expression levels could be measured using RNA or even by proteomics profiling to distinguish between two biological states of the cell. Here we suggest a few methodologies that are adopted for proteomic analysis. 6.2.3.2.1 Chromatography Chromatography-based methods, which include ion-exchange chromatography, size exclusion chromatography, and affinity chromatography, were used to get the purified proteins. The enzyme-linked immunosorbent assay (ELISA) and western blotting could be used to evaluate specific proteins. Both methods are capable of determining specific proteins. For complicated protein separation, two-dimensional gel electrophoresis (2-DE), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and twodimensional differential gel electrophoresis (2D-DIGE) are utilized. Proteins are separated into spots by 2-DE, with each spot representing a single protein. The protein may be enzymatically digested, and the peptide sequence could be determined based on mass using spectrometry. The mass of the degraded proteins was quantified by using a matrix-aided laser desorption ionization time-of-flight (MALDI-TOF). The MALDI-TOF-MS (mass spectroscopy) creates a massive amount of single charged ions, which have been used to determine the number of peptides. It generates protein data based on the masses after processing the data from the MS spectrum. 6.2.3.2.2
Mass spectroscopy
MS is a method for proteome analysis. In proteomic research, MS has become a gold standard technique to differentiate various proteins. By using the high-resolution mass spectrometry, qualitative data re obtained from the protein samples. The advanced high-resolution mass analyzers are mostly utilized to separate, identify, and splice by a mass filter, which will reduce the data complexity on the emitted primary ions. Moreover, MS methods were proven to be highly dependable for identifying and distinguishing proteins in various disease conditions with great accuracy, coverage, and reproducibility.
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TABLE 6.3 Cancer proteomic studies. S. No
Organism
Journal Name
Title of the article
Authors
1.
Hepatitis C virus
Gastroenterology
Combined analysis of metabolomes, proteomes, and transcriptomes of hepatitis C virusinfected cells and liver to identify pathways associated with disease development
Lupberger et al. (2019)
2.
EpsteinBarr virus
Plos One
Comparing proteomics and RISC immune precipitations to identify targets of EpsteinBarr viral miRNAs [RISC-IPseq]
Kuzembayeva et al. (2012)
3.
EpsteinBarr virus
Molecular Endocrinology
Mutational analysis of progesterone receptor delineates different sets of regulated genes
Quiles et al. (2009)
4.
EpsteinBarr virus
Journal of Molecular Biology
HMI-PRED: A web server for structural prediction of hostmicrobe interactions based on interface mimicry
GuvenMaiorov et al. (2020)
5.
Oncovirus
Annual Review of Immunology
Microbes and cancer
Dzutsev et al. (2017)
6.
Oncovirus
Stem Cells International
Stemness-attenuating miR-5033p as a paracrine factor to regulate growth of cancer stem cells
Seo et al. (2018)
6.2.3.2.3
X-ray Crystallography
X-ray crystallography (X-ray powder diffraction [XRD]) is an electromagnetic radiation technique, which absorbs and emits radiation in the frequency range of 0.0110 nm. XRD is mainly used to identify the arrangement of
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atoms within a crystal in diverse crystallized components. XRD technique reveals the three-dimensional structure and function of several biological substances such as proteins and nucleic acids. A purified protein crystal is subjected to an X-ray beam, and the X-ray is diffracted by the atoms in the protein crystals. An X-ray beam is diffracted in a different directions based on the arrangement of atoms into the crystal as well as the number of electrons in the atoms. Diffraction is generated from the angles and sensitivities of these diffracted beams. The electron density map is used to analyze the mean position of the atoms in a crystallized protein and it estimates the three-dimensional structure of proteins. 6.2.3.2.4 Nuclear magnetic resonance spectroscopy NMR spectroscopy works on the principle of electromagnetic radiation, which absorbs and emits radiation in the frequency range of 401000 MHz. It identifies molecules by atomic nuclear spin depending on their nature when the magnetic field is applied. Regularly in proteomics, 1H and 13C atoms were analyzed to get the precise structure of the protein. It identifies the major bonds in complex macromolecules, thus by having the structural information one can predict the function of the protein. Nowadays NMR spectroscopy has become a vital tool for elucidating structural proteomics studies. Both XRD- and NMR-based structural proteomics could provide complete structural datasets for predicting the essential biological activities of putative proteins discovered in many studies.
6.2.3.3 Computational process A massive amount of proteomics data are obtained with help of highthroughput technology. Bioinformatics systems are designed to manage massive amounts of data and their storage. Computational biology technologies have been proposed for 3D structural characterization, protein domain and finding motifs, proteinprotein interaction analysis, and MS data processing. The evolutionary connection between sequences and structures was determined by alignment techniques (Kuzembayeva et al., 2012; Lupberger et al., 2019). Proteome analysis, utilizing two or more different methods, offers an understanding of the characteristics of protein in the cell, which includes the cellular function in various diseases that gives the opportunity of therapeutic application. Computational proteomics is an inherent part of proteomics, the applications are increased gradually with the emergence of effective techniques that rely on sophisticated data processing. This current and innovative domain is offering unique techniques for managing vast and diverse proteome data, as well as the evolution of the discovery process. The application of bioinformatics for proteomics has grown substantially in the past several years. The establishment of innovative algorithms for the processing of large
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data sets with greater specificity and accuracy aids to detect and measure proteins to enhance high-throughput protein expression data. Proteomics-associated methods have been used in various scientific research, including the identification of several pre-disease diagnoses, drug target identification for candidates, exploring the mechanism of diseases, the changes in transcriptional regulation which react to variations signals, and the analysis of functional protein pathways in various diseases. Bioinformatics is a relatively complex field since it involves the study and identification of a genome’s total protein signatures. MS, LC-MS, and MALDI-TOF are commonly used methods and are the core of recent proteomics. Moreover, the use of proteomics resources, including programs for devices and databases, as well as the necessity of qualified staff, significantly raises the costs, limiting their widespread usage, particularly in the blooming countries. Also, the proteome is very transient due to complex regulatory mechanisms which regulate cellular behavior. Constant efforts have been made in various proteomics approaches to understand the antiviral properties and their application in medical sciences.
6.2.4
Metabolomics
Metabolomics is an emerging field in omics research that links both genomics and proteomics. Metabolomics deals with small molecules with a molecular weight is less than B800 Dalton which includes small peptides, oligonucleotides, carbohydrates, organic acids ketones, aldehydes, amines, amino acids, lipids, steroids, and alkaloids (Fig. 6.4). These molecules, mostly produced during the molecular and cellular processes, perform
FIGURE 6.4 Metabolomics classification. Metabolomics contributes toward the assessment of amino acids, nucleotides, peptides, and carbohydrates.
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Oncogenic Viruses Volume 2
TABLE 6.4 Metabolomic profiles of cancer. S. No.
Title of the study
Journal
Author
1
Downregulation of adipose triglyceride lipase by EB viral-encoded LMP2A links lipid accumulation to increased migration in nasopharyngeal carcinoma
Molecular Oncology
Zheng et al. (2020)
2
Cancer biology-causes and biomarkers of cancer
Current Research in Oncology
Bansode (2019)
3
Microbiome and human malignancies
Microbiome and Cancer
Saha and Robertson (2019)
4
Development and evaluation of biomarkers for early detection of cancer
Recent Advancements in Biomarkers and Early Detection of Gastrointestinal Cancers
Challa et al. (2020)
5
Oxidative stress in infection and consequent disease
Oxidative Medicine and Cellular Longevity
Ivanov et al. (2017)
6
The cancer microbiome: distinguishing direct and indirect effects requires a systemic view
Trends in Cancer
Xavier et al. (2020)
7
Single-cell analysis by ambient mass spectrometry
TrAC Trends in Analytical Chemistry
Yunyun et al. (2017)
8
High-sensitivity detection of micrometastases generated by GFP lentivirus-transduced organoids cultured from a patient-derived colon tumor
Jove Journal
Okazawa et al. (2018)
9
DNMT1 mediatesmetabolic reprogramming induced by EpsteinBarr virus latent membrane protein 1 and reversed by grifolin in nasopharyngeal carcinoma
Nature (cell death and disease)
Luo et al. (2018)
10
From super-enhancer noncoding RNA to immune checkpoint: frameworks to functions
Frontier in Oncology
Wu and Shen (2019)
(Continued )
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TABLE 6.4 (Continued) S. No.
Title of the study
Journal
Author
11
Mining inter-relationships in online scientific articles and its visualization: natural language processing for systems biology modeling
Journal of Online and Biomedical Engineering
Melethadathil et al. (2019)
12
Analysis of ligustrum lucidum AIT leaves using leaf spray mass spectrometry
Asian Journal of Pharmaceutical Analysis and Medicinal Chemistry
Zhang et al. (2016)
13
Molecular basis of cervical cancer: a review
European Journal of Pharmaceutical and Medical Research
Singh et al. (2020)
14
The shifting microbiome in surgical stress
Nutrition, Metabolism, and Surgery
Codner and Herron (2017)
15
The interplay between the genetic and immune landscapes of AML: mechanisms and implications for risk stratification and therapy
Frontier in Oncology
Mendez et al. (2019)
16
EDEM3 modulates plasma triglyceride level through its regulation of LRP1 expression
Cell Press
Xu et al. (2020)
several vital functions in the body. Studying cellular metabolomics helps to understand the biochemical and metabolic pathway of the cell at various cellular and environmental conditions. Recent developments in chromatography, NMR, and MS helped in the identification and quantification of metabolites with high sensitivity and specificity in cells, tissues, or biofluids (Ahmed et al., 2009). Thus, metabolomics is an integrative platform that deals with both genetic and proteomics factors, and which has an impact on biomarker discoveries, drugdisease mechanisms, and drug-induced toxicity. There is the potential application of metabolomic profiling in oncology, such as early detection and diagnosis of cancer, and use as a predictive and pharmacodynamic marker to study drug effects. Overall, the application of metabolomics mainly relies on the field of medicine, which helps to understand the real-time mechanism underlying complex biological functions (Bansode, 2019; Challa et al., 2020; Codner & Herron, 2017;
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Gain, C, 2020; Ivanov et al., 2017; Luo et al., 2018; Melethadathil et al., 2019; Mendez et al., 2019; Okazawa et al., 2018; Saha & Robertson, 2019; V´asquez & Chimoy, 2019; Wu & Shen, 2019; Xavier et al., 2020; Xu et al., 2020; Yang et al., 2017; Zheng et al., 2020) (Table 6.4).
6.2.4.1 Metallomics Metallomics, a discipline integrating sciences that address the biometals and metalloids, provides new opportunities for discoveries. As part of a systems biology approach, it draws attention to the importance of many chemical elements in biochemistry. Traditionally, biochemistry has treated life as organic chemistry, separating it from inorganic chemistry, considered a field reserved for investigating the inanimate world. However, inorganic chemistry is part of the chemistry of life, and metallomics contributes by highlighting the importance of a neglected fifth branch of building blocks in biochemistry. Metallomics adds chemical elements/metals to the four building blocks of biomolecules and the fields of their studies: carbohydrates (glycome), lipids (lipidome), proteins (proteome), and nucleotides (genome). The realization that nonessential elements are present in organisms in addition to essential elements represents a certain paradigm shift in our thinking, as it stipulates inquiries into the functional implications of virtually all the natural elements. There is urgent need to understand the role of metallomics for health and TABLE 6.5 Metallomic profile of cancer. S. No.
Organism
Title of the study
Journal
Author
1
Oncovirus
Profiling of immune related genes silenced in EBVpositive gastric carcinoma identified novel restriction factors of human gammaherpesviruses
Plos Pathogens
Fiches et al. (2020)
2
Oncovirus
Zinc metallochaperones as mutant p53 reactivators: a new paradigm in cancer therapeutics
MDPI
Kogan and Carpizo (2018)
3
Oncovirus
Cancer: a nanotechnological approaches
World Journal of Pharmaceutical Research
Kumar et al. (2016)
4
Oncovirus
Microbiome and human malignancies
Microbiome and Cancer
Saha and Robertson (2019)
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TABLE 6.6 List of genomic database and tools. S. No
Databases name
Description
Link
1.
EPD
Collection of eukaryotic promoters
http://epd.vital-t
2
OrthoDB
Hierarchical catalog of eukaryotic orthologs
http://www.orthodb.org/
3
SwissRegulon
Annotations of regulatory sites
http://swissregulon.unibas.ch
4
OMA
Orthology inference among complete genomes
http://omabrowser.org
5
arrayMap
Curated array data repository for cancer genomics
http://cega.ezlab.org/
6
CEGA
Conserved elements from genomic alignments
http://www.clipz.unibas.ch/
7
CLIPZ
Binding sites of RNAbinding proteins
http://www.clipz.unibas.ch
8
ElMMo
Mirna target predictions
http://gpsdb.expasy.org/
9
GPSDB
Gene and protein synonyms
http://gpsdb.expasy.org/
10
ImmunoDB
Insect immunerelated genes and gene families
http://cegg.unige.ch
11
miROrtho
Catalog of animal microRNA genes
http://cegg.unige.ch
12
OpenFlu
Influenza genetic and epidemiological data
http://openflu.vital-it.ch
13
Progenetix
Genomic copy number aberrations in cancer
http://www.progenetix.org/
14
EPD
Collection of eukaryotic promoters
http://epd.vital-it.ch/
15
ALF
Simulation of genome evolution
http://alfsim.org (Continued )
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TABLE 6.6 (Continued) S. No
Databases name
Description
Link
16
arrayMap
Curated array data repository for cancer genomics
http://www.arraymap.org/
17
Association Viewer
SNPs display in a genetic context
http://www.arraymap.org/
18
Scan
Identify natural selection
http://cmpg.unibe.ch
19
Boxshade
MSA pretty printer
http://embnet.vital-it.ch
20
BUSCO
Benchmarking universal single-copy orthologs
http://busco.ezlab.org/
21
CEGA
Conserved elements from genomic alignments
http://cega.ezlab.org/
22
ChIP-Seq
Chip-Seq data analysis tools
http://ccg.vital-it.ch
23
Decrease redundancy
Sequence redundancy reduction
http://web.expasy.org/ decrease_redundancy/
24
DIALIGN
Local multiple sequence alignment
https://bibiserv.cebitec.unibielefeld.de/dialign/
25
ElMMo
MiRNA target predictions
http://www.clipz.unibas.ch/ ElMMo3/
26
EMBOSS translation tools
Sequence translation tools
http://www.ebi.ac.uk/Tools/st/
27
ESTscan
Coding region detection
http://myhits.isb-sib.ch/cgi-bin/ estscan
28
FASTA
Search
http://www.ebi.ac.uk/Tools/sss/ fasta/
29
FastEpistasis
Test for epistasis effects
http://www.vital-it.ch/software/ FastEpistasis
30
Fastsimcoal
Coalescent simulation of genomic data
http://cmpg.unibe.ch/software/ fastsimcoal2/
31
FetchG
Tagger
https://sourceforge.net/projects/ tagger/ (Continued )
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TABLE 6.6 (Continued) S. No
Databases name
Description
Link
32
GENIO
Logo
http://www.biogenio.com/logo/
33
GMM
Copy number variation detection
https://www2.unil.ch/cbg/ index.php?title 5 GMM
34
Graphical Codon Usage Analyzer
Codon bias
http://gcua.schoedl.de/index. html
36
ISMARA
Genome-wide expression data modeling
https://ismara.unibas.ch/fcgi/ mara
37
IsotopIdent
Theoretical isotopic distribution
http://education.expasy.org/ student_projects/isotopident/ htdocs/
38
Kalign - EBI
Fast and accurate multiple sequence alignment
http://www.ebi.ac.uk/Tools/ msa/kalign/
39
LALIGN
Pairwise alignment
http://embnet.vitalit.ch/ software/LALIGN_form.html
40
MADAP
Clustering for genome annotation data
http://ccg.vital-it.ch/madap/
41
MAFFT-CBRC
Multiple sequence alignment
http://mafft.cbrc.jp/alignment/ server/index.html
42
MAFFT-EBI
Multiple sequence alignment
http://www.ebi.ac.uk/Tools/ msa/mafft/
43
MAMOT
HMM models
http://bcf.isb-sib.ch/mamot/
44
MaxAlign
Gap removal from alignments
http://www.cbs.dtu.dk/services/ MaxAlign/
45
miRmap
Search and predict miRNA targets with miRmap
http://mirmap.ezlab.org/
46
Multalin
Multiple sequence alignment
https://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl? page 5 npsa_multalin.html
47
MUSCLE
Multiple alignment server
http://www.ebi.ac.uk/Tools/ msa/muscle/
48
Newick Utilities
High-throughput phylogenetic tree processing
http://cegg.unige.ch/ newick_utils (Continued )
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TABLE 6.6 (Continued) S. No
Databases name
Description
Link
49
PACMAN
PACMAN: Pacific Biosciences methylation analyzer
http://bugfri.vital-it.ch/
50
Phylogibbs
Regulatory sites discovery
http://www.phylogibbs.unibas. ch/cgi-bin/phylogibbs.pl
51
Progenetix
Genomic copy number aberrations in cancer
http://www.progenetix.org/
52
QuasR
Quantify and annotate short reads in R
http://bioconductor.org/ packages/release/bioc/html/ QuasR.html
53
Reverse Transcription and Translation Tool
Transcription, translation, reverse transcription
http://www.attotron.com/ cybertory/analysis/trans.htm
54
Reverse Translate
Reverse translation
http://www.bioinformatics.org/ sms2/rev_trans.html
55
SAMBA
Systolic accelerator for molecular biological Appl
http://www.irisa.fr/SAMBA/
56
Sequence Similarity Maps (SSM)
Data mining of viral isolates
http://ssm.vital-it.ch/
57
Sequerome
BLAST similarity search and sequence profiling
http://sequerome.georgetown. edu/sequerome/
58
SHOPS
Genomic operon context analysis
http://bioinformatics. holstegelab.nl/shops/
59
ShoRAH
Tools for the analysis of NGS data
https://www.bsse.ethz.ch/cbg/ software.html
60
SIBsim4
Spliced sequence alignment
https://sourceforge.net/projects/ sibsim4/
61
SSA
Tools for the analysis of NGS data
http://ccg.vital-it.ch/ssa/
62
T-Coffee
Nucleic acid sequence motifs
http://tcoffee.vital-it.ch/apps/ tcoffee/index.html
63
T-Coffee - EBI
Sequence and structure multiple alignments
http://www.ebi.ac.uk/Tools/ msa/tcoffee/ (Continued )
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TABLE 6.6 (Continued) S. No
Databases name
Description
Link
64
T-Coffee - WUR
Multiple sequence alignment program
http://www.bioinformatics.nl/ tools/t_coffee.html
65
TagScan
Genome-wide sequence tag scanner
http://ccg.vital-it.ch/tagger/ tagscan.html
66
Translate
Nucleotide sequence translation
http://web.expasy.org/translate/
67
TriFLe
TRFLP species identification and simulation
http://cegg.unige.ch/trifle_docs
68
Tromer
Transcriptome analyzer
http://ccg.vital-it.ch/tromer/
69
WebLogo
Sequence logos
http://weblogo.berkeley.edu/ logo.cgi
70
Wise2
Genomic sequence to protein sequence comparison
http://www.ebi.ac.uk/Tools/psa/ genewise/
71
WU BLAST
Sequence similarity search in protein databases
http://www.ebi.ac.uk/Tools/sss/ wublast/
72
ZFN-Site
Zinc finger nuclease (off-) target sites search
http://ccg.vital-it.ch/tagger/ targetsearch.html
disease. The biological system consists of 30%40% metalloproteases (Fiches et al., 2020; Kogan & Carpizo, 2018; Kumar et al., 2016; Saha & Robertson, 2019; Yang et al., 2017) (Table 6.5).
6.2.5
Bioinformatics resources
In recent times, several omics experiments have been carried out that generate a vast amount of biological data. These data are classified into three major classes, i.e., sequence, expression, and structures that are archived in commercial or publically available databases for bioinformatics application. For instance, databases like NCBI (http://www.ncbi.nlm.nih.gov/), EMBL (http://www.embl.org/), and DDBJ (http://www.ddbj.nig.ac.jp/), contain biological information of DNA, RNA, and proteins; whereas the structural
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information of proteins is stored in PDB (http://www.rcsb.org/pdb/home/ home.do) and ExPASy (http://www.expasy.org/), which helps to retrieve useful information using bioinformatics tools (Milanowska et al., 2011). TABLE 6.7 List of transcriptomics database and tools. S. No
Database name
Description
Link
1
Bgee
Gene expression patterns comparison
http://bgee.org/
2
SwissRegulon
Annotations of regulatory sites
http://swissregulon.unibas.ch/sr/ swissregulon
3
CLIPZ
Binding sites of RNAbinding proteins
http://www.clipz.unibas.ch/
4
ElMMo
miRNA target predictions
http://www.clipz.unibas.ch/ ElMMo3/
5
ESTscan
Coding region detection
http://myhits.isb-sib.ch/cgi-bin/ estscan
6
ExpressionView
Exploring biclusters in gene expression data
https://www2.unil.ch/cbg/index. php?title 5 ExpressionView
7
ISA
Gene expression module discovery
https://www2.unil.ch/cbg/index. php?title 5 ISA
8
ISMARA
Genome-wide expression data modeling
https://ismara.unibas.ch/fcgi/ mara
9
Ping pong algorithm
Coherent patterns across paired datasets
https://www2.unil.ch/cbg/index. php?title 5 ISA
10
QuasR
Quantify and annotate short reads in R
http://bioconductor.org/ packages/release/bioc/html/ QuasR.html
11
SIBsim4
Spliced sequence alignment
https://sourceforge.net/projects/ sibsim4/
12
The Miner Suite
Tools for data analysis
https://discover.nci.nih.gov/
13
Translate
Nucleotide sequence translation
http://web.expasy.org/translate/
14
Tromer
Transcriptome analyzer
http://ccg.vital-it.ch/tromer/
15
GEO Datasets
Gene Expression Omnibus (GEO) repository
https://www.ncbi.nlm.nih.gov/ gds
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TABLE 6.8 List of proteomics database and tool. S. No
Databases name
Description
Link
1
UniProtKB
Functional information on proteins
http://www.uniprot.org/
2
UniProtKB Swiss-Prot
Protein sequence database
http://web.expasy.org/docs/ swiss-prot_guideline.html
3
STRING
Proteinprotein interactions
https://string-db.org/
4
SWISSMODEL Repository
Protein structure homology models
https://swissmodel.expasy. org/repository/
5
PROSITE
Protein domains and families
http://prosite.expasy.org/
6
ViralZone
Portal to viral UniProtKB entries
http://viralzone.expasy.org/
7
neXtProt
Human proteins
https://www.nextprot.org/
8
EMBnet services
Bioinformatics tools, databases, and courses
http://embnet.vital-it.ch/
9
ENZYME
Enzyme nomenclature
http://enzyme.expasy.org/
10
GlyTouCan
International glycan structure repository
https://glytoucan.org/
11
GPSDB
Gene and protein synonyms
http://gpsdb.expasy.org/
12
HAMAP
UniProtKB family classification and annotation
http://hamap.expasy.org/
13
MatrixDB
Proteinglycosaminoglycan interactions
http://matrixdb.ibcp.fr/
14
MetaNetX
Metabolic network repository and analysis
http://www.metanetx.org/
15
MIAPEGelDB
MIAPE document edition
http://miapegeldb.expasy. org/
16
PaxDb
Protein abundance database
http://pax-db.org/
17
Protein Spotlight
Informally written reviews on proteins
http://web.expasy.org/ spotlight/
18
SWISS2DPAGE
Proteins on 2-D and SDS PAGE maps
http://world-2dpage.expasy. org/swiss-2dpage/
19
SwissLipids
Knowledge resource for lipid biology
http://www.swisslipids.org/#/ (Continued )
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TABLE 6.8 (Continued) S. No
Databases name
Description
Link
20
UniPathway
Metabolic pathways for the UniProtKB
http://www.grenoble.prabi. fr/obiwarehouse/ unipathway
21
World2DPAGE Repository
Gel-based proteomics data
http://world-2dpage.expasy. org/repository/
22
SWISSMODEL Workspace
Structure homology modeling
https://swissmodel.expasy. org/
23
3of5
Find user-defined patterns in protein sequences
https://swissmodel.expasy. org/
24
AACompIdent
Protein identification by aa composition
https://www.expasy.org/ proteomics
25
ALF
Simulation of genome evolution
http://alfsim.org/#index
26
big-PI
Predict GPI modification sites
http://mendel.imp.ac.at/sat/ gpi/gpi_server.html
27
BLAST (UniProt)
BLAST search on the UniProt web site
http://imed.med.ucm.es/ Tools/blast2fasta.html
28
Blast2Fasta
BLAST to FASTA conversion
http://imed.med.ucm.es/ Tools/blast2fasta.html
29
Click2Drug
Directory of computational drug design tools
http://www.click2drug.org/
30
CSS-Palm
Prediction of palmitoylation sites in proteins
http://lipid.biocuckoo.org/
31
DictyOGlyc
GlcnacO-glycosylation sites in D. discoideum
http://www.cbs.dtu.dk/ services/DictyOGlyc/
32
Dotlet
Sequence similarity plots
http://myhits.isb-sib.ch/cgibin/dotlet
33
ELM
Eukaryotic linear motifs
http://elm.eu.org/
34
FUGUE
Sequence structure homology recognition
https://www.expasy.org/ proteomics
35
GlycoMod
Oligosaccharide structure prediction
http://web.expasy.org/ glycomod/
36
Mascot
Protein identification from mass spectrometry data
http://www.matrixscience. com/search_form_select. html (Continued )
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139
TABLE 6.8 (Continued) S. No
Databases name
Description
Link
37
NetOGlyc
Mammalian mucintype galnacO-glycosylation sites
http://www.cbs.dtu.dk/ services/NetOGlyc/
38
NQ-Flipper
Correction of unfavorable rotamers of Asn and Gln
https://flipper.services. came.sbg.ac.at/cgi-bin/ flipper.php
39
PLOGO
Sequence logos
http://rth.dk/resources/ plogo/
40
PROPSEARCH
Functional and characters
http://abcis.cbs.cnrs.fr/ propsearch/
41
QMEAN
Estimate quality of protein models
https://swissmodel.expasy. org/qmean/
42
REPRO
De novo repeat detection in protein sequences
http://www.ibi.vu.nl/ programs/reprowww/
43
SAPS
Statistical analysis of protein sequences
http://www.ebi.ac.uk/Tools/ seqstats/saps/
44
Swiss Mass Abacus
Calculator of peptideand glycopeptide masses
http://glycoproteome. expasy.org/swiss-massabacus/
6.2.6
Databases and tools
The advancement in omics research exponentially increases the amount of data that have been generated from various biological experiments. It becomes necessary to store, analyze, and share this information for various biological applications. Hence, the database management systems were used to collect, organize, and store the biological data to gain and share the knowledge worldwide using internet facilities. These biological databases were categorized as primary and secondary databases based on omics experiments. For instance, the nucleic acid-related information was available in databases like GenBank, EMBL Data Library, or the DNA Databank of Japan (Table 6.6). At the same time, the transcriptomic information is accessible in the GEO dataset and GEO profiles (Table 6.7).Similarly, protein information was obtainable from PROSITE and PDB (Table 6.8), and metabolomics information is available in metabolomics databases such as HMDB and PubChem (Table 6.9), and scientific literature. These databases help to
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TABLE 6.9 List of metabolomics database and tools. S. No
Database
Link
1
UCSD Marine Natural Products Database
http://libraries.ucsd.edu/blogs/blog/marinlit/
2
HMDB
http://www.h
3
BiGG
http://bigg.ucsd.edu/
4
MetaboLights database
http://www.ebi.ac.uk/metabolights/index
5
KEGG
http://www.genome.jp/kegg/
6
MetaCyc
https://metacyc.org/
7
HumanCyc
https://humancyc.org/
8
BioCyc
https://biocyc.org/
9
Reactome
http://reactome.org/
10
WikiPathways
http://www.wikipathways.org/index.php/ WikiPathways
11
PubChem
https://pubchem.ncbi.nlm.nih.gov/
12
ChEBI
http://www.chemspider.com/
13
ChemSpider
http://www.ebi.ac.uk/chebi/
14
KEGG Glycan
http://www.genome.jp/kegg/glycan/
15
IIMDB
http://metabolomics.pharm.uconn.edu/iimdb/
16
DrugBank
https://www.drugbank.ca/
17
Therapeutic Target Database
http://bidd.nus.edu.sg/group/cjttd/
18
PharmGKB
https://www.pharmgkb.org/
19
STITCH
http://stitch.embl.de/
20
BMRB
http://www.bmrb.wisc.edu/metabolomics/
21
MMCD
http://mmcd.nmrfam.wisc.edu/
22
MassBank
http://www.massbank.jp/
23
Golm Metabolome Database
http://gmd.mpimp-golm.mpg.de/
24
Metlin
https://metlin.scripps.edu/landing_page.php? pgcontent 5 mainPage
25
Fiehn GC-MS Database
http://fiehnlab.ucdavis.edu/19-projects/153metabolite-library
26
OMIM
https://www.ncbi.nlm.nih.gov/omim/
27
METAGENE
http://www.metagene.de/appl/index.html (Continued )
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141
TABLE 6.9 (Continued) S. No
Database
Link
28
OMMBID
http://ommbid.mhmedical.com/
29
Supernatural database
http://bioinformatics.charite
30
Marine compound database
http://www.progenebio.in/mcdb/index.html
31
NaPDoS
http://www.biokepler.org/use_cases/napdos
retrieved multiple information that helps to make decisions on scientific discoveries.
6.3
Conclusions
Genomics, transcriptomics, proteomics, and metabolomics-based approaches are now being used in biomedical research for the understanding of complex disease pathogenesis and identification of drug targets and markers for therapeutic and diagnostic development. Integration of multi-omics techniques and bioinformatics concepts of analyzing data has gained attention toward storing, systematic analyses, and interpretation of the data. Overall, omics and bioinformatics go hand in hand, where the extensive data generated in the omics tools by experimentation is successfully managed with the help of bioinformatics concepts by storing it in the biological database. Most of the biological databases are open access, which act as platforms for researchers all over the world. Hence, the integration of omics with bioinformatics provides an effective tool that provides novel and important insights about diverse cellular processes such as development, gene-expression dynamics, tissue heterogeneity, and disease pathogenesis.
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Chapter 7
Role of viral human oncogenesis: recent developments in molecular approaches ChandraLekha Saravanan1, Mahalakshmi Baskar1, Sheik S.S.J. Ahmed2 and Ramakrishnan Veerabathiran1 1
Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 2Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India
7.1
Introduction
Viruses that produce tumors in their natural hosts, experimental animals, or cell cultures are called oncogenic viruses. Oncovirus changes the growth properties of cells they infect—approximately 12% of all cancer caused by oncovirus. More than 80% of human cancer cases occur in developing countries (Donald, 2006). Tumors in animals are caused by many viruses, but only seven of them are associated with human oncogenic viruses. ˇ Oncoviruses are of two types, DNA and RNA oncovirus (Sevik, 2012). EpsteinBarr virus (EBV) was the first human oncogenic recognized herpesvirus (EBV) and identified in Burkitt lymphoma cells in 1964 (Epstein et al., 1964); since then, six human oncovirus have been identified: Kaposi’s sarcoma (KS), human T-cell lymphtropic virus, human papillomavirus (HPV), Merkel cell polyomavirus (MCV), and hepatitis B and hepatitis C viruses. EBV, also known as human herpesvirus 4, belongs to the herpes family. EBV is associated with four cancer types: Hodgkin’s lymphoma, nonHodgkin’s lymphoma linked with posttransplant, Burkitt’s tumor, and nasopharyngeal carcinoma (Epstein & Barr, 1964). Kaposi’s sarcoma-associated herpesvirus (KSHV) is also known as human herpesvirus-8 (HHV-8). Immunosuppressed individuals with HHV-8 cause KS, including patients
Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00006-6 Copyright © 2023 Elsevier Inc. All rights reserved.
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2008
2010 2000
1994
1990 1980 1970
1980
1989 1983
1964 1965
Epstein–Barr virus (EBV) hepatitis B virus (HBV) human T-lymphotropic virus-1 (HTLV-1) human genital papillomavirus (HPV) hepatitis C virus (HCV)
1960 1950
Kaposi sarcoma herpesvirus (KSHV/HHV8)
1940
Merkel cell polyomavirus (MCV)
FIGURE 7.1 Year of discovery of human oncovirus.
with HIV. Hepatitis B virus (HBV) is spread through contaminated blood or unsafe medical procedures. HBV is a single-stranded RNA virus that causes liver cancer (HCC). It is the third leading cause of death in the world. The first reported retrovirus was HTLV-1 that is transmitted through breast milk, sexually via semen, vaginal fluids, or blood transfusion. Historically, in 1908, Vilhelm and Bang Danish scientists from Copenhagen University published a paper on viral transmission of avian erythroblastosis (chicken leukemia). In 1911, Peyton Rous first described the association of viruses with malignancy. Edwin Shope, in 1932, isolated rabbit fibroma virus (Lunn et al., 2017). In1957, polyoma virus was discovered by Bernice Eddy and Sarah Stewart. The first recognized human oncovirus was identified from Burkitt lymphoma cells by Anthony Epstein, Yvonne Barr, and Bert Achong in 1964 (Pan et al., 2001). It is also called as human herpesvirus 4 but commonly called EBV, a herpesvirus. Harald zur Hausen proposed HPV as etiologic agent of cervical cancer in 1974, for which he received the Nobel Prize in 2008. The virus discovered based on the PCR technique in 1994 by Chang et al. is KSHV. The other virus discovered by digital transcriptome is MCV (Katze & Korth, 2015). Fig. 7.1 illustrates the year of discovery of oncovirus.
7.2
Prevalence of oncovirus
The IARC is the specialized cancer agency of WHO that estimated in 2002, 17.8% of human cancers were caused by infection, with 11.9% caused by oncovirus. Among 2658 samples collected from 38 various cancer types,16% were associated with oncovirus in 2020. The EBV is estimated to be positive in more than 90% of the world’s population (Gerber et al., 1996). Globally, they have estimated 5.5 billion were infected with this EBV. The incidence
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of EVB associated with Burkitt’s lymphoma is 510/100,000 children. EBV is present in all populations but causes Burkitt’s lymphoma in the African equatorial belts, accounting for about 74% of cancers in children (Santpere et al., 2014). HPV is transmitted through sexual contact and causes severe cervical cancer, affecting women 60%70% worldwide. The high-risk HPV types 16 and 18 are particularly responsible for cervical cancer. According to the US statistics, they have estimated 83% female and 74% males are associated with HPV (Krings et al., 2019). According to the global estimation, HPV showed a high rate of prevalence in these countries, namely SubSaharan Africa (24.0%), Eastern Europe (21.4%), and Latin America (16.1%). A large peak of prevalence in HPV is strongly identified in both genders connected with age. Researchers’ estimate that 2.3 billion people are infected by the hepatitis virus (Ferlay et al., 2018; Stelzle et al., 2021). HBV causes severe chronic and acute diseases. A liver disease associated with HBV includes complications of liver cirrhosis (HCC). Hepatitis C virus (HCV) infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan (Schweitzer et al., 2015). Genotype 1, genotype 2, genotype 3, genotype 4, genotype 5, and genotype 6 are the six main HCV genotypes. In overlapping areas of West and Central Africa, HCV genotypes 1 and 4 have been identified. A virus that affects our immune system and transmits through sexual intercourse, contact with blood, or transmission between mother to child is HIV. Global statistics estimate that about 34 million people were affected and tested positive. KSHV belongs to herpesvirus family. Transmission passes through sexual contact and nonsexual contact (saliva HHV-8, blood, tissue, or organ transplant), but people with healthy immune system will not get affected by this virus (Martin et al., 1998). Researchers have estimated moderate prevalence in countries around the Mediterranean .50% in sub-Saharan Africa and ,10% in Europe and Asia. In 2008, researchers discovered MCPyV at Pittsburgh University. MCPyV belongs to polyomaviruses and it is associated with about 80% of Merkel cell. It is estimated that at least 510 million people are affected worldwide. It is rare and causes high aggressive skin cancer and mortality rate in patients (Schowalter et al., 2010) (Fig. 7.2).
7.3
Classification of oncovirus
Oncovirus is divided into two types: DNA virus and RNA virus. Both oncogenic viruses cause cancer in humans. Research has demonstrated that these viruses do not directly cause cancer, their actions promote cancer development in humans (Hausen, 2009). Recently, they have recognized seven human oncoviruses that cause cancer. The known human oncoviruses are HPV, human T-cell lymphtropic viruses, EBV, human herpesvirus-8, KS, Burkitt’s lymphoma, MCPyV, and HBV and HCV viruses (Akram et al., 2017).
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1% 0.3%
Hepas B virus 3.1%
Hepas C virus
15%
Human papillomavirus Epstein-barr virus 1-2%
Human T-cell lymphotropic virus
5.2%
Kaposi's sarcoma-associated herpesvirus
FIGURE 7.2 Percentage of cancer caused in humans by oncovirus.
7.3.1
DNA tumor viruses
Oncogenic DNA viruses cause cancer in humans. They include p53 and retinoblastoma protein, two major tumor suppressor proteins in humans. Major human DNA oncovirus is classified into three categories: small DNA viruses (adenovirus, SV40, HPV), large DNA viruses (EBV, KSHV), and others (HBV). It triggers and inactivates cell lysis and cell death by creating tumors while DNA replicates (Levine, 2009). The family Herpesviridae belongs to large DNA viruses, including herpesvirus such as EBV, which causes cancer in human beings such as Burkitt’s lymphoma, infectious mononucleosis, nasopharyngeal carcinoma, and Hodgkin’s lymphoma (Shimamoto & Yamanishi, 1999). KSHV also belongs to herpesvirus family, which can be transmitted through sexual and nonsexual contact (saliva HHV-8, blood, tissue, or organ transplant), but people with healthy immune system will not get affected by this virus (Encyclopedia of Microbiology, 2019). The family Adenoviridae belongs to a small DNA virus which includes adenovirus. It causes illnesses like fever, coughs, respiratory illness, sore throats, diarrhea, and conjunctivitis (Thinley KalsangBhutia Adenovirus Infections, 2018). The family Papovaviridae is one of the double-stranded DNA viruses, which is small DNA oncovirus that includes papillomavirus and polyomaviruses. Papillomavirus, which causes cervical cancer in women, penile cancer, squamous cell, and carcinoma, is transmitted through sexual intercourse, contact with blood, or transmission between mother to child affecting our immune system. Polyomaviruses include MCP which is implicated in Merkel cell carcinoma, an aggressive skin cancer, and human polyomaviruses species (BKPyV) and (JCPyV) cause prostate and colorectal cancer. SV40 is a virus that affects both humans and animals
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(EH & Murphy, 2012). The family Hepadnaviridae includes HBV, which causes liver cancer in humans, leading to severe chronic and acute diseases (Dowd et al., 2003).
7.3.2
RNA tumor viruses
RNA tumor virus contains ribonucleic acid as their genome, which belongs to retrovirus family. RNA virus contains single-stranded RNA. All cancercausing viruses are said to be oncovirus. This oncovirus, which is associated with RNA, is also known as oncogenic RNA viruses or retroviruses. These oncogenic retroviruses are also known as RNA tumor viruses. In 1961, researchers found that Rous sarcoma virus (RSV) particles contain RNA. HTLV-1 is the first human retrovirus discovered in 1981. Retroviruses are divided into three groups: Oncovirinae, Lentivirinae, and Spumaviruses. RSV, HTLV-1, and HTLV-2 are examples of the first group virus. Human immunodeficiency virus (HIV) is an example of the second group that has the characteristic feature of a long period of latency. A different oncogenic mechanism is used in RNA tumor viruses. RNA has a high oncogenic efficiency than DNA. It contains a reverse transcriptase enzyme that produces DNA from the RNA template (Petropoulos, 1997; Silverthorn, 2015; Sverdlov, 2000; Wilson & Harrison, 1991). There are three basic gene pools in retrovirus, namely gag, pol, and env. The external envelope comes from the covering of the plasma cell. Textured proteins (high antigens) are encased in an env (envelope) type and embodied in glycosylated. One essential hereditary item is created; however, this includes the creation of more than one glycoprotein by a developed bacterium (decontaminated by the Golgi framework catalyst). The transmembrane protein, an essential protein, is connected to the endoplasmic reticulum and produced using ribosomes. Inside layer is an icosahedral capsid that contains proteins embedded by the gag quality (a particular antigen gathering). Gag-instigated proteins additionally incorporate genomic RNA. Likewise, there is one significant hereditary item. This is joined with virally stacked protease (from the pol type) (Dahlberg, 1988; Steinhauer & Holland, 1987; Uyen et al., 2017). Fig. 7.3 illustrates the classification of oncovirus. All the virus details are given in Table 7.1
7.4
Molecular tools used for oncovirus detection
The development of various molecular assays, such as multiplex-PCR, realtime PCR, PCR-ELISA, quantitative-PCR, etc., due to advancements in biotechnology and molecular biology helps identify and characterizepathogens such as oncovirus (Kabir, 2018). EBV, cancer-causing virus, includes the most popular PCR method. RNA in situ hybridization (RISH) method is also used to detect EBV. EBV can also be diagnosed by simple blood test and
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Oncovirus
DNA oncovirus
HBV Liver
HPV Skin
RNA oncovirus
CMV
EBV
HCV
HIV
HTLV-1
Skin
Lymph
Liver
T-cell
T-cell leukemia
FIGURE 7.3 Classification of oncovirus.
antibody test like IgG (Ebell et al., 2016; Morrison et al., 2015). HPV, cancer-causing virus, includes the main methods like PCR, RT-PCR, and FISH methods to detect HPV. They also used Pap test and DNA test to detect HPV (Dixit et al., 2011). HBV, cancer-causing virus, includes quantitative PCR and ELISA test to detect HBV. HBsAg and rapid diagnostic tests (RDTs) are used to detect the chronic HBV, which is used as a marker. HBV can also be detected by a blood test, liver ultrasound, and liver biopsy (Lok & Brain, 2007). HCV, cancer-causing virus, includes RNA PCR method, blood test like anti-HCV test, and quantitative test to detect HCV (Kumar et al., 2018). Immunofluorescence assay (IFA) was the first serological assay used for detecting KSHV antibodies. PCR assays are used to detect DNA sequences in KSHV, which is limited in diagnostic use. At present, a small number of proteins or viral peptides by EIA and ELISA are used to detect KSHV. Southern blotting with a labeled probe is also used to detect KSHV (Nascimento et al., 2007; Pan et al., 2001). PCR has become the best method for diagnosis in all stages of HIV. HIV RNA PCR can be used for monitoring the level of viremia. Screening tests used for HIV are ELISA, simple test, and rapid test. (Lakshmi et al., 2011; Parekh et al., 2018) Western blot test is a confirmatory test in which HIV proteins are separated based on mobility and molecular weight (Cassar & Gessain, 2017; Ruggieri et al., 2019). Clinical consequences of multiple chronic conditions’ viral status are not clearly understood. At present, there is no proper clinical assessment for detecting MCPyV. Immunohistochemistry and quantitative PCR are used for detecting MCPyV in tumor samples which is flawed (Brummer et al., 2016; Eid et al., 2017). Micro (mi) RNAs are 22-base pair nucleic acids that were originally identified in the worm Caenorhabditis elegans. They are tiny, noncoding, and very stable. They are genetic components that can move about and do work. Pancreatic insulin secretions, adipogenesis, oncogenesis, and viral
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TABLE 7.1 Major human oncovirus and its prevalence. Virus
Family
Associated cancer
Prevalence (worldwide estimation)
Genome
EBV
Herpesviridae
Burkitt’s lymphoma, Hodgkin’s lymphoma, nasopharyngeal carcinoma, gastric cancer
.90% of positive cases have been identified around the world’s populations
DNA
HPV
Papillomaviridae
Cancers of penis, cervix, skin, anus, vaginal, vulvar, and head and neck squamous cell carcinoma
According to the US statistics, they have estimated females .(83%) males ,(74%), so females are more affected. Cervical cancer is the fourth most common cancer among women globally, with an estimated 570,000 new cases in 2018
DNA
HBV
Hepadnaviridae
Hepatocellular carcinoma causes liver cancer
2.3 billion people are infected by the hepatitis virus. According to WHO, children ,5 years of age were chronically infected with HBV in 2019
DNA
HCV
Hepadnaviridae
Hepatocellular carcinoma causes liver cancer
Globally, 71 million people have been chronically infected by HCV
RNA
KSHV
Herpesviridae
Kaposi sarcoma, body cavity lymphoma
Prevalence of KSHV varies
DNA
(Continued )
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TABLE 7.1 (Continued) Virus
Family
Associated cancer
Prevalence (worldwide estimation)
Genome
HIV
Retroviridae
Kaposi sarcoma, non-Hodgkin lymphoma (NHL), and cervical cancer
Approximately 38 million people across the globe were infected with HIV in 2019. 36.2 million were adults, and 1.8 million were children ,15 years of age
RNA
HTLV-1
Retroviridae
Adult T-cell leukemia/ lymphoma
HTLV-1 infects approximately 1020 million people worldwide
RNA
MCPyV
Polyomaviridae
Merkel cell carcinoma
Global estimates at least 510 million people infected worldwide
DNA
illnesses are all influenced by miRNA genes. The first regulatory viral miRNA was miR-S1 in simian virus 40 (SV40), which promotes the recognition and destruction of infected cells by cytotoxic T cells. In contrast, the first trans-regulatory viral miRNA was miR-LAT in HSV-1 infection, which targets TGF-β and SMAD3, promoting cellular proliferation and preventing apoptosis. Pfeffer et al. were the first to characterize miRNA in the context of EBV infection, and 44 mature miRNAs derived from precursor miRNA were later identified. According to research, they are encoded in two areas: BART and BHRF1. During viral latency and lytic proliferation, they are expressed in a variety of cell lines and tumors. HIV-1 miRNA has been identified in the U3 region of the 3’-LTR of HIV-associated malignancies, where it suppresses cellular apoptosis, antagonizing transcription factor (AATF) gene production. Pichler et al. and Yeung et al. found that miRNA is either directly activated by tax or linked with HTLV-1-induced cell transformation during HTLV infection. As defined by Robin Holliday, epigenetic modifications in gene expression are heritable changes that are not produced by changes in the DNA sequence, including the methylation state of DNA and the posttranslational
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modification of histones. In mammals, the epigenetic process plays a significant role in carcinogenesis. DNA methylation is the most well-known epigenetic marker, and it, together with particular histone modification and specific miRNA, is thought to constitute a defining molecular landscape that is changed in cancer. By causing insertion mutations and chromosomal rearrangement, oncoviral genomes alter the host genome, predisposing infected cells to malignancy. In human malignancies, viral genes are also linked to an abnormal methylation profile in host-specific genes. Several researchers have sought to understand the epigenetic alterations in viruses. In the life cycle of EBV, epigenetic control of viral genes is a critical process. DNA methylation and histone modification govern the production of latent viral oncogenes, RNA, and miRNA, culminating in the total silence of the EBV gene. The transforming latency gene is subdued by methylation of the EBV DNA, which protects the host cell. HPV infection has also been linked to viral and host epigenetic modifications, such as DNA methylation and histone modification, both involved in pathogenesis and carcinogenesis. More than asymptomatic infections or dysplasia, viral DNA methylation is linked to cancer. For example, regardless of the stage of neoplastic development, the LCR and E6 sequences of HPV 16 and 18 were observed to be unmethylated, but the L1 region was highly methylated. According to methylation studies, the LCR of HPV16 was methylated in several primary cervical carcinomas, particularly at E2-binding sites (E2BS). E2BS has been found to block E2 binding, and methylation has been linked to E6 and E7 viral protein activation. HCV infection has also been linked to epigenetic alterations. HBV replication is linked to epigenetic markers such as H3 and H4 acetylation. Epigenetic changes have been seen in virtually all known oncoviruses.
7.5
Vaccines available for oncovirus
EBV is the first tumor virus that causes mononucleosis which is transmitted through saliva. Mostly children and young teenagers have been infected. It is contagious and can cause infection once again after months or years after treatment. The serious thing is no vaccine has been found, yet research for the vaccine is currently underway. So avoiding direct contact (sex, kissing, and sharing personal stuff) with the infected person is the best way to control the infection. Globally, .90% of positive cases have been identified around the world’s populations (Bu et al., 2019). HPV may cause severe cervical cancer; however, it is curable by the use of vaccine, which is entirely safe for consumption because some may cause side effects. This vaccine helps protect these high-rise type 16 and 18, mainly responsible for cervical cancer, and type 6 and 11 cause genital warts. Gardasil, Gardasil 9, and Cervarix are the vaccines used to prevent HPV. Gardasil 9 is licensed and only used in the United States, and the other vaccines are being used in many other countries, which majorly affects women 60%70% worldwide
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(Monie et al., 2008). HBV is spread through blood (contaminated) or unsafe medical procedures, which causes liver cancer in humans, leading to severe chronic and acute diseases. HBV vaccines are highly safe and effective but may cause some side effects to prevent the infection. Such vaccines provide greater protection to newborns, children, and adults recommended with three-stage dosage levels. The approved vaccines Engerix-B and Recombivax-HB are used to treat the infection at different stages of age (Alexander & Kowdley, 2006). HCV infects the liver, which tends to cause chronic hepatitis and acute hepatitis. No vaccine has been found to prevent HCV infection, but vaccines are currently available for other hepatitis viruses. HCV viral infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan (Petruzziello et al., 2016). KSHV has been subjected to little vaccine research compared to other human oncoviruses and herpesviruses. Perhaps because of a better general record for herpesvirus antiviral, there has been much more work to develop pharmacologic agents than anti-KSHV vaccines. KSHV may be considered a second-line vaccine. Efforts to avoid or cure HIV infection would result in a major reduction in direct efforts to prevent KSHV infection. The lack of animal samples of KSHV infection and/or KS development was reported as the second factor in vaccine research (Engels et al., 2008; Parkin, 2006; Sullivan et al., 2009; Westmoreland & Mansfield, 2008). Since there are no symptoms of early HTLV infection and cancer it causes, there is very little investment relative to the HTLV vaccine. Vaccine carriers to prevent the production of ATL and HAM/TSP may have a position in theory. There is a protein tax deduction, and there is some evidence that the multiepitope vaccine peptide vaccine is successful. The hidden period of ATL growth, on the other hand, is generally decades-long, and providence levels do not decrease. The vaccine’s primary use may be in the treatment of ATL. In certain cases, however, the absence of HIV type decreases education for these patients (Hino, 1997; Kannagi et al., 2005; Lynch & Kaumaya, 2006; Sundaram et al., 2004). Priority for the positive results from the RV144 study is that researchers determine the compatibility of protection against a key vaccine compound, that is, they must accurately determine how a highly potent compound is protected from HIV infection. The trial used the “first reinforcement” strategy for two HIV testing targets. The first was a synthetic vaccine using the canarypox virus, genetically engineered with antigenic HIV-coded protein codes. This vaccine was used as a “prime” and intended to stimulate the immune system (T cell response). The “boost” vaccine was made of an extra antigen-derived protein from HIV and intended to stimulate antibody production (B-cell responses) (Benson et al., 2009; Callahan, 2006; Harris et al., 2014; Malteˆz et al., 2011; Nitayaphan et al., 2004). Since the MCPyV compound is combined, the location varies from patient-to-patient, suggesting that the virus was present within the cell prior to or during
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oncogenic mutations. MCPyV genes in tissue do not join open-source T antigen (LT) tests, which are thought to release viral replication, compared to MCPyV for normal tissue. Essentially, the amino terminus of MCPyV T (LT) (aa1258) is expressed in MCPyV-positive tissues, which plays a crucial role in multiple chronic conditions oncogenesis, providing the ideal vaccine target (Liang, 2009; Zeng et al., 2012).
7.6
Statistical analysis of oncovirus
Hidden Markov models (HMMs) are well known for their ability to represent correlations between nearby symbols, domains, or events, and they have been widely applied in various disciplines, including voice recognition and digital communication. As a result, HMM can discover patterns common to a family of viruses but difficult to detect when compared to a single nucleotide. The HMM design was first used in computational biology about 20 years ago, although it has a limited use than BLAST-based alignments. The HMMER3 HMM technique has been used to find RNA viral sequences in plasma, investigate Ebola virus pathogenicity, and subtype human influenza virus. Using HMMER3 and the viral profile database vFamily, we sought to explore if we could expand the overall viral ecology in human biospecimens from various sources. The novel set of partial differential equations (sPDEs) that describe the HCV RNA cycle overcomes the limitations of multilinear diffusion and reaction coefficients by distinguishing between the functions of HCV RNA and nonstructural virus-encoded proteins (NSPs). The simulation platform UG4 was used to test the model using the finite volume technique (FV). This in silico model was tested on powerful and massively parallel supercomputers using fast multigrid solvers. Despite the more realistic approach and expansion of earlier models, most of the parameters are still heuristically determined. Although the simulations were limited to a portion of the intracellular ER, the new model is now flexible enough to allow for changes in experimental situations, and the interaction between in silico and in vitro/ in vivo research may even suggest new experiments for a better understanding of virus dynamics.
7.6.1
EpsteinBarr virus
Globally, .90% of positive cases have been identified around the world’s populations. It is very common, which may be identified in 9 of 10 people worldwide, but very few can cause cancer. The four cancer types associated with EBV are Hodgkin’s lymphoma, non-Hodgkin’s lymphoma linked with post-transplant, Burkitt’s lymphoma, and nasopharyngeal carcinoma. Globally, 5.5 billion people were infected by EBV. They estimated that seroprevalence is rapidly increasing in adolescence, both male and female. In the
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United Kingdom, seropositivity is high in females. According to statistics, more than 1% of human cancer is linked to EBV worldwide. In 2017, an estimated17 million people were infected, and 9 million people dead worldwide (Khan et al., 1990).
7.6.2
Hepatitis B virus
HBV causes liver cancer in humans and is the second leading cause of severe chronic and acute diseases. Each year 30 million people are infected worldwide. A total of 37 million HBV carriers are found in India, which infects a large population worldwide. WHO estimates that 257 million peoples were infected by HBV in 2015. In 2016, according to researchers, about 250 million people were infected by HBV. Only 10% of people were infected worldwide, and very few were treated. According to the US estimation, the prevalence rate has been controlled. It was about 5.7% during 19992002 and 4.3% during 20152018, which has been visibly decreased. About 2.3 billion people were infected by HBV worldwide. WHO estimates that children ,5 years of age were chronically infected with HBV in 2019 (Chen et al., 2007).
7.6.3
Human papillomavirus
HPV is a sexually transmitted virus that is associated with cervix, skin, anus, penis, vaginal, vulvar, head, and neck and causes severe cancer. It causes severe cervical cancer, which majorly affects women 60%70% worldwide. The high-risk types 16 and 18 are particularly responsible for cervical cancer. According to the US statistics, they have estimated females (83%) are more affected than males (74%) (Daneshvar et al., 2016). During 201114, 3.3% of adult females and 11.5% of adult males were infected by oral HPV, and 3.9% females and 45.2% males were infected by genital HPV. Females with cervix cancer and males with oropharynx cancer were estimated from 2012 to 2016. In 2017, approximately 70% of oropharynx cancer was caused by HPV (David et al., 2012). Globally, cervical cancer is the fourth most common cancer in women. In 2018, an estimated 570,000 cases were diagnosed with cervical cancer. Global estimation on HPV statistical prevalence was11.7%. Sub-Saharan Africa (24.0%), Eastern Europe (21.4%), and Latin America (16.1%) showed high rate in prevalence. A large peak of prevalence in HPV is strongly identified in both genders connected with age (Fourati et al., 2019).
7.6.4
Hepatitis C virus
The US estimated about 2.4 million people were infected by HCV in 201316. In the United States, 1.2 million people were infected with HIV in
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2018. In 2016, WHO estimated that approximately 399,000 people were infected with the primary liver cancer in HCV. Globally, 71 million people have been chronically infected by HCV in 2017, which may cause severe liver cancer and cirrhosis. HCV viral infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan. In 2018, according to CDC estimation, 3621 cases with acute HCV were reported. Later CDC analyzed that 50,300 cases were identified in the United States. They have about six major types of genotypes in HCV in which most commonly infected is genotype 1 with 44% cases, genotype 3 with 25% cases, and genotype 4 with 15% cases. Geographical distribution of HCV has been found worldwide (Cassler et al., 2016).
7.6.5
Kaposi sarcoma-associated herpesvirus
Chronic sarcoma is most prevalent in developed countries among Jews in Israel. From 1992 to 1995, serum was obtained from 1648 adults who had tested positive for HBV infection 20 years ago at blood donation; serra were also obtained from 2403 of their family members. Among family members, the seroprevalence of antibodies against KSHV was 9.9%; among the former blood donors who had tested positive for hepatitis B, it was 22%. The crude prevalence rate of KSHV among Jews in Israel is 9.9% (Davidovici et al., 2001). According to an international study based on cancer cases from about 23 studies in the United States, Europe, and Australia, the average incidence of KS in these countries decreased from 15.2 per 1000 years in 1992 to 4.9 per 1000 years between 1997 and 1999; this decrease was driven by a reduction in the number of cases of AIDS-related KS. A 2017 study, based on over 200,000 patients, reported raw KS incidence per 100,000 person-years in 42 cohorts from 57 countries, including North America (237 per 100,000 person-years), Latin America (244 per 100,000 person-years), Europe (180 per 100,000 person-years), Asia-Pacific (52 per 100,000 person-years), and South Africa (280 per 100,000 person-years). The risk of KS was nearly twice as high in men as in women (Cook-Mozaffari et al., 1998; Cottoni et al., 1996; Grulich et al., 1992; Lebbe´ et al., 2008).
7.6.6
Human immunodeficiency virus
This systematic review and global survey yielded 2203 datasets with 383,519 examples from 116 nations in 19902015. From 2010 to 2015, type C accounted for 46.6%of all HIV-1 contaminations worldwide. Type B has the highest pollution rate (12.1%), followed by type A (10.3%), CRF02 AG (7.7%), CRF01 AE (5.3%), subtypes G (4.6%), and subtype D (2.7%). Subtypes J, H, F, and K together accounted for just 0.9% of infections. Other CRFs represented 3.7%, making up a portion of all CRFs, 16.7% URFs make up 6.1%, representing 22.8% of the absolute HIV-1 disease worldwide
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(UNAIDS 2019; Global HIV Prevention Working Group. Behavior Change & HIV Prevention:(Re) Considerations for the 21st Century. Global HIV Prevention Working Group, 2008). The majority of HIV-positive people were living in low- and middleincome countries. In Japan and South Africa, there were a total of 20.7 million inhabitants (54%), 4.9 million (13%) and 5.8 million (15%) in Asia and the Pacific, and 2.2 million (6%) in Western, Central, and North Europe and North America by 2019. By 2019, 85% of HIV-positive pregnant ladies obtained ART to prevent HIV transmission to their toddlers during childbirth and defend their health (Iwanaga, 2020).
7.6.7
Human T-cell lymphotropic virus type 1
In Japan, there is evidence of two HTLV-I ancestral lists: the old cosmopolitan genotype, which represents about 25% of the HTLV-I present in Japan, and in found mainly in the southern islands among the inhabitants of Central Africa, especially the Pygmies. While the local subtypes vary from 2% to 8%, the HTLV-I quasi types present within a person appear to be very low, with a variation of ,0.5%. The prevalence of HTLV-1 was higher in women (80%), people between 31 and 50 years of age, heterosexual, unmarried, with low monthly income, with secondary education level or higher, occasional condom use, limited number of sexual partners, and no history of sexually transmitted diseases (Oliveira-Filho et al., 2019). The key pathways of HTLV-1 transmission in common areas include breast milk transmission from mother to infant and sexual transmission in adults; transmission from a male to a woman is almost four times high as transmission from a woman. However, in many countries, especially Japan, the spread of contaminated blood products has become a major issue. In many nations, including Japan, the United States, and Brazil, blood transfusions are also regularly screened for HTLV-1. Blood product monitoring and breastfeeding counseling were implemented following the onset of a decline in seroprevalence-based serum age of HTLV-1 in Japan (Martel-Jantin et al., 2014).
7.6.8
Merkel cell polyomavirus
According to phylogenetic research, there are five major MCPyV genotype species in these countries, [Europe/North America, Africa (Sub-Saharan), Oceania, South America, and Asia/Japan]. In 23 of the 26 cases, MCPyV DNA was used (88.5%). In enlarged components, four transformation patterns can be seen. MCPyV was common in Japanese patients with various chronic conditions. This research was conducted in the past. From January 1994 to December 2012, researchers examined the hospital’s patient index for all cases of various chronic conditions. Available skin samples from patients with various chronic conditions were analyzed using two PCR
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techniques (standard and real-time) to determine MCPyV DNA (Hattori et al., 2013; Neto et al., 2019). Analysis of regional or national cancer registration data for more than 100 people with multiple chronic conditions was detected in more than 19,000 cases from 2003 to 2007 and more than 12,000 cases in the 1990 to 2007 case analysis (Stang et al., unpublished data). In the early 1990s (199094), some cancer registrations showed multiple chronic conditions concentrations close to zero, suggesting inaccurate cases. Registrations for the US SEER White, Canada, Australia, Italy, and Netherlands have increased the number of multiple chronic conditions cases since the mid-1990s. Australia has the largest World Norm Population (males: 5.2 million per person per year; females: 2.2 million per person per year) from 2003 to 2007 (Abara et al., 2019a).
7.7
Oncovirus and cancer progression
Due to changes in the lifestyle and environment, they may lead changes in the growth property of cells, tumor growth, invasion, and metastasis which increases the risk of developing aggressive cancer and causes infection in oncovirus. It has been recently suggested that oncoviruses can contribute to the cancer progression (Mu¨ller-Coan et al., 2018). EBV, HBV, HPV, KSHV, and HCV are the most epidemiologically relevant human carcinogenesis oncoviruses, which plays a major role in cancer progression (Epstein et al., 1964). The three major categories in oncovirus and cancer progression are microenvironment, phenotypic modification, and genetic instability, which is illustrated in Fig. 7.4 (Banerjee et al., 2015; Mu¨ller-Coan et al., 2018).
7.7.1
Human papillomavirus on cancer progression
Herpesvirus belongs to large DNA viruses, including herpesvirus like EBV and KSHV which causes cancer in humans. These EBV and KSHV belong
FIGURE 7.4 Role of oncovirus in cancer.
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to gammaherpesvirus 4 (HHV4) and 8 (HHV8). A study found that tumorassociated macrophage with a high number expresses CCL18 associated NPC. EBV associates NPV by encoding LMP1, EBNAs, and miRNABARTs which play a major role in oncovirus cancer progression. Now research is going on how EVB affects other types of cancers (Elgui de Oliveira et al., 2016). KSHV, the herpesvirus with double strands, typically changes cell phenotype to increase the availability of growth factors and cytokine. KSHV associates with KS by allowing neoplastic spindle cells to become more angiogenic, proliferate, migratory, and invasive. KSHV uses cellular protein to interfere with the cellular pathway (Elgui de Oliveira et al., 2016).
7.7.2
Hepatitis B virus on cancer progression
HBV causes liver cancer in humans, leading to severe chronic and acute diseases. According to the research, an increase in tumorigenesis and invasion in HBV cancer progression associated with HBV-positive HCC tissues reduced the expression miRs 18a and 148a (Li et al., 2013). Research states that in HBV, the DNA in the host genome may contribute to tumor progression (Abara et al., 2019b).
7.7.3
Hepatitis C virus on cancer progression
HCV infects the liver, which tends to cause chronic hepatitis and acute hepatitis with high level of oxidative damage, causing oncogenesis. In HCV, the structural protein and nonstructural protein connect the cancer progression and liver carcinogenesis (Banerjee et al., 2010; Kasprzak & Adamek, 2008). Researchers have found several ways to contribute the cancer progression that can be direct or indirect. Such HCV can produce cell invasion, migration, and oncogenesis. HCV contributes to cancer progression to increase its tumor heterogeneity. The structural protein HCVc and nonstructural protein connect HCV infection and cause liver fibrosis, progression of HCC, and transformation of the cell (Verga-Ge´rard et al., 2013).
7.7.4
Human papillomavirus on cancer progression
HPV is genetically simple and has a limited contribution to cancer. The researchers found infected HPV malignant cells cause late progression of behavior response and are subjected to high immunological pressure, which can be seen in HPV-associated carcinomas. Due to the genomic alteration linked with HPV-associated carcinomas, the changes have been recorded in malignant behavior expression (Akagi et al., 2014; Vinokurova et al., 2008). Fig. 7.5 illustrates hallmark properties of cancer.
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Tissue activating invasion and metastasis
Resisting cell death
Evadiving growth suppressor
Change in growth property
Chronic inflammation
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Sustaining proliferation signalling
Genetic instability
FIGURE 7.5 Hallmarks of cancer.
7.8
Oncolytic virotherapy
Oncolytic virotherapy plays a promising role in oncology. Major steps in the field of cancer treatment and infectious diseases date back to the 20th century. Clinical trials involving the transport of body fluids containing viral particles to other cancer patients are performed, and the results are evaluated. Alice Moore was the first scientist who pioneered the task of testing for oncolytic viruses in animal cancer models. Southam and Moore have contributed significantly to this vast field of oncolytic bacteria by continuing to transfer this idea to paper and apply it. Directing 101 Egyptian parasites, they have attempted to study antimicrobial properties. These viruses have limited value due to the negative effects of neurotoxicity. This has prompted a continuous change in adenoviruses, paramyxoviruses, poxviruses, and so on. The main clinical preliminaries in oncolytic infection utilized as a treatment were acted in 1956 and 1972 with adenovirus, adenoidal, pharyngeal, conjunctival,and mumps infection separately (Hill, 1965; Hoster et al., 1949). Hereditary transformations have been known to us for a long time. This change is practically indistinguishable from the transformations brought about via carcinogenesis. This causes us to infer that some infections have a characteristic inclination, i.e., these oncotropic infections are normally utilized for oncolytic virotherapy. Newcastle sickness, reovirus, autonomous parvovirus, and stomatitis infection (VSV) are only a couple instances of viral contamination
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(Everts & Van Der Poel, 2005; Marchini et al., 2015; Moehler et al., 2001; Norman & Lee, 2000; Peeples et al., 1994; Stojdl et al., 2000). Oncolytic virotherapy has a brilliant future as late exploratory discoveries have brought trust in the examination of local area about the utilization of the infection as an enemy of tumor specialist that joins ordinary treatments, for example, chemotherapy and radiotherapy or substitution treatment later on. Additional reasoning work should be done in this handle; the new endorsement of tumor and oncolytic antibodies by the FDA and Chinese SFDA has significantly consolidated them into clinical practice. There is a long and developing rundown of new or improved sorts of oncolytic microorganisms. With the rise of new innovations and comprehension of OV and their part in disease improvement, recent concerns in the turn of events and full use of oncolytic infections can be addressed (Mhaske & Oncoviruses, 2015). Fig. 7.6 illustrates oncovirus effects in tumor cells. Tumor infected cell
Replication of viral cells
Release of new cells
Oncovirus
Normal cell
No viral replication
FIGURE 7.6 Oncovirus effect in normal and tumor cells.
Undamaged cell
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Conclusion
In many years of cancer research, the role of viruses in oncogenesis has been widely debated. We now agree that seven different viruses may play a major role in human cancer. A great deal of development has been made in the past 30 years. In humans, the cancer-causing virus is said to be oncovirus, which is about 12% of all cancer causing virus worldwide. Antibacterial vaccines offer rare cancer-preventing opportunities, which, more importantly, can be effective even under minimal resources. However, successful growth and distribution require a combination of several factors in the oncovirus vaccine. Current method of study tells that these cancer-causing viruses can be diagnosed and treated with less toxic antiviral compounds or diagnosed with simple blood tests. They might be easily prevented through vaccination, whereas HPV and HBV vaccines are widely available and have been used. Other nonvaccine viruses are in research to make them available. In the case of intermediate results, other tests and methods such as multiplex- PCR, real-time PCR, PCR-ELISA, quantitative-PCR, FISH, chemiluminescent immunoassay, and antibody test has been used to detect the presence of the oncogenic virus. The discovery and formation of oncoviruses have been at the forefront of biological research for decades. It provided important information based on cytology and carcinogenesis processes. In addition, these studies have created important interventions in public health that have the potential to prevent a huge number of human cancers. We hope that in the future, researchers and scientists will produce the latest exciting insights into cancer and effective ways to prevent it.
Acknowledgment The Authors thank Chettinad Academy of Research and Education for their constant support and encouragement.
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Martin, J. N., Ganem, D. E., Osmond, D. H., Page-Shafer, K. A., Macrae, D., & Kedes, D. H. (1998). Sexual transmission and the natural history of human herpesvirus 8 infection. New England Journal of Medicine, 338(14), 948954. Available from https://doi.org/10.1056/ NEJM199804023381403. Mhaske, S., & Oncoviruses. (2015). An overview of oncogenic and oncolytic viruses. Oncobiology and targets (Vol. 2). Medknow Publications and Media Pvt. Ltd. Moehler, M., Blechacz, B., Weiskopf, N., Zeidler, M., Stremmel, W., Rommelaere, J., Galle, P. R., & Cornelis, J. J. (2001). Effective infection, apoptotic cell killing and gene transfer of human hepatoma cells but not primary hepatocytes by parvovirus H1 and derived vectors. Cancer Gene Therapy, 8(3), 158167. Available from https://doi.org/10.1038/sj. cgt.7700288. Monie, A., Hung, C., Roden, R., & Wu, T. C. (2008). CervarixTM: A vaccine for the prevention of HPV 16, 18-associated cervical cancer. Biologics: Targets & Therapy, 2(1), 97105. Morrison, B. J., Labo, N., Miley, W. J., & Whitby, D. (2015). Serodiagnosis for tumor virusesz. Seminars in Oncology, 42(2), 191206. Available from https://doi.org/10.1053/j. seminoncol.2014.12.024. Mu¨ller-Coan, B. G., Caetano, B. F. R., Pagano, J. S., & Elgui de Oliveira, D. (2018). Cancer progression goes viral: The role of oncoviruses in aggressiveness of malignancies. Trends in Cancer, 4(7), 485498. Available from https://doi.org/10.1016/j.trecan.2018.04.006. Nascimento, M. C., De Souza, V. A., Sumita, L. M., Freire, W., Munoz, F., Kim, J., Pannuti, C. S., & Mayaud, P. (2007). Comparative study of Kaposi’s sarcoma-associated herpesvirus serological assays using clinically and serologically defined reference standards and latent class analysis. Journal of Clinical Microbiology, 45(3), 715720. Available from https:// doi.org/10.1128/JCM.01264-06. Neto, C. F., Oliveira, W. R. P., Costa, P. V. A., Cardoso, M. K., Barreto, P. G., Romano, C. M., & Urbano, P. R. (2019). The first observation of the association of Merkel cell polyomavirus and Merkel cell carcinoma in Brazil. International Journal of Dermatology, 58(6), 703706. Available from https://doi.org/10.1111/ijd.14325. Nitayaphan, S., Pitisuttithum, P., Karnasuta, C., Eamsila, C., De Souza, M., Morgan, P., Polonis, V., Benenson, M., VanCott, T., Ratto-Kim, S., Kim, J., Thapinta, D., Garner, R., Bussaratid, V., Singharaj, P., El Habib, R., Gurunathan, S., Heyward, W., Birx, D., . . . Brown, A. E. (2004). Safety and immunogenicity of an HIV subtype B and E prime-boost vaccine combination in HIV-negative Thai adults. Journal of Infectious Diseases, 190(4), 702706. Available from https://doi.org/10.1086/422258. Norman, K. L., & Lee, P. W. K. (2000). Reovirus as a novel oncolytic agent. Journal of Clinical Investigation, 105(8), 10351038. Available from https://doi.org/10.1172/JCI9871. Oliveira-Filho, A. B., Arau´jo, A. P. S., Souza, A. P. C., Gomes, C. M., Silva-Oliveira, G. C., Martins, L. C., Fischer, B., Machado, L. F. A., Vallinoto, A. C. R., Ishak, R., Lemos, J. A. R., & Kupek, E. (2019). Human T-lymphotropic virus 1 and 2 among people who used illicit drugs in the state of Par´a, northern Brazil. Scientific Reports, 9(1), 14750. Available from https://doi.org/10.1038/s41598-019-51383-7. Pan, L., Milligan, L., Michaeli, J., Cesarman, E., & Knowles, D. M. (2001). Polymerase chain reaction detection of kaposi’s sarcoma-associated herpesvirus-optimized protocols and their applicationto myeloma. Journal of Molecular Diagnostics, 3(1), 3238. Available from https://doi.org/10.1016/S1525-1578(10)60647-2. Parekh, B., Ou, C., Fonjungo, P., Kalou, M., Rottinghaus, E., Puren, A., Alexander, H., Cox, M., & Nkengasong, J. N. (2018). Diagnosis of human immunodeficiency virus infection. Clinical Microbiology Reviews, 32, e00064-18.
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Parkin, D. M. (2006). The global health burden of infection-associated cancers in the year 2002. International Journal of Cancer, 118(12), 30303044. Available from https://doi.org/ 10.1002/ijc.21731. Peeples, M. E., Reichard, K. W., Reyes, H. M., Phuangsab, A., & Walter, R. J. (1994). Complete regression of human fibrosarcoma xenografts after local newcastle disease virus therapy. Cancer Research, 54(23), 60176021. Petropoulos, C. (1997). Retroviral taxonomy, protein structures, sequences, and genetic maps (pp. 757805). Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press. Petruzziello, A., Marigliano, S., Loquercio, G., Cozzolino, A., & Cacciapuoti, C. (2016). Global epidemiology of hepatitis C virus infection: An up-date of the distribution and circulation of hepatitis C virus genotypes. World journal of gastroenterology, 22(34), 78247840. Available from https://doi.org/10.3748/wjg.v22.i34.7824. Ruggieri, M., Berini, C., Ducasa, N., Malkovsky, M., Fisch, P., & Biglione, M. (2019). Molecular detection of human T-lymphotropic virus type 1 infection among oncology patients in Germany: A retrospective view. PLoS One, 14(5), e0217560. Available from https://doi.org/10.1371/journal.pone.0217560. Santpere, G., Darre, F., Blanco, S., Alcami, A., Villoslada, P., Alba`, M. M., & Navarro, A. (2014). Genome-wide analysis of wild-type epstein-barr virus genomes derived from healthy individuals of the 1000 genomes project. Genome Biology and Evolution, 6(4), 846860. Available from https://doi.org/10.1093/gbe/evu054. Schowalter, R. M., Pastrana, D. V., Pumphrey, K. A., Moyer, A. L., & Buck, C. B. (2010). Merkel cell polyomavirus and two previously unknown polyomaviruses are chronically shed from human skin. Cell Host and Microbe, 7(6), 509515. Available from https://doi.org/ 10.1016/j.chom.2010.05.006. Schweitzer, A., Horn, J., Mikolajczyk, R. T., Krause, G., & Ott, J. J. (2015). Estimations of worldwide prevalence of chronic hepatitis B virus infection: A systematic review of data published between 1965 and 2013. The Lancet, 386(10003), 15461555. Available from https://doi.org/10.1016/S0140-6736(15)61412-X. ˇ Sevik, M. (2012). Oncogenic viruses and mechanisms of oncogenesis. Turkish Journal of Veterinary and Animal Sciences, 36(4), 323329. Available from https://doi.org/10.3906/ vet-1104-2. Shimamoto, T., & Yamanishi, K. (1999). Herpesviruses 6 and 7-Human (Herpesviridae). Encyclopedia of Virology (Second Edition). Elsevier. Silverthorn, D. U. (2015). Human physiology: An integrated approach (7th Edition). Austin: University of Texas. Steinhauer, D. A., & Holland, J. J. (1987). Rapid evolution of RNA viruses. Annual Review of Microbiology, 409431. Available from https://doi.org/10.1146/annurev. mi.41.100187.002205. Stelzle, D., Tanaka, L. F., Lee, K. K., Ibrahim Khalil, A., Baussano, I., Shah, A. S. V., McAllister, D. A., Gottlieb, S. L., Klug, S. J., Winkler, A. S., Bray, F., Baggaley, R., Clifford, G. M., Broutet, N., & Dalal, S. (2021). Estimates of the global burden of cervical cancer associated with HIV. The Lancet Global Health, 9(2), e161e169. Available from https://doi.org/10.1016/S2214-109X(20)30459-9. Stojdl, D. F., Lichty, B., Knowles, S., Marius, R., Atkins, H., Sonenberg, N., & Bell, J. C. (2000). Exploiting tumor-specific defects in the interferon pathway with a previously unknown oncolytic virus. Nature Medicine, 6(7), 821825. Available from https://doi.org/10.1038/77558. Sullivan, R. J., Pantanowitz, L., & Dezube, B. J. (2009). Targeted therapy for kaposi sarcoma. BioDrugs, 23(2), 6975. Available from https://doi.org/10.2165/00063030-200923020-00001.
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Sundaram, R., Lynch, M. P., Rawale, S., Dakappagari, N., Young, D., Walker, C. M., Lemonnier, F., Jacobson, S., & Kaumaya, P. T. P. (2004). Protective efficacy of multiepitope human leukocyte antigen-A 0201 restricted cytotoxic T-lymphocyte peptide construct against challenge with human T-cell lymphotropic virus type 1 Tax recombinant vaccinia virus. Journal of Acquired Immune Deficiency Syndromes, 37(3), 13291339. Available from https://doi.org/10.1097/00126334-200411010-00001. Sverdlov, E. D. (2000). Retroviruses and primate evolution. BioEssays, 22(2), 161171, https:// doi.org/10.1002/(SICI)1521-1878(200002)22:2 , 161::AID-BIES7 . 3.0.CO;2-X. Thinley KalsangBhutia Adenovirus Infections. Modified link of Web site: Merck Manuals Professional Version. (2018). Uyen, M., Christopher, H., & Stephen, T. (2017). Viral oncology: Molecular biology and pathogenesis. Journal of Clinical Medicine, 6, 111. Available from https://doi.org/10.3390/ jcm6120111. Verga-Ge´rard, A., Porcherot, M., Meyniel-Schicklin, L., Andre´, P., Lotteau, V., & Perrin-Cocon, L. (2013). Hepatitis C virus/human interactome identifies SMURF2 and the viral protease as critical elements for the control of TGF-β signaling. FASEB Journal, 27(10), 40274040. Available from https://doi.org/10.1096/fj.13-229187. Vinokurova, S., Wentzensen, N., Kraus, I., Klaes, R., Driesch, C., Melsheimer, P., Kisseljov, F., Du¨rst, M., Schneider, A., & Doeberitz, M. V. K. (2008). Type-dependent integration frequency of human papillomavirus genomes in cervical lesions. Cancer Research, 68(1), 307313. Available from https://doi.org/10.1158/0008-5472.CAN-07-2754. Westmoreland, S. V., & Mansfield, K. G. (2008). Comparative pathobiology of Kaposi sarcomaassociated herpesvirus and related primate rhadinoviruses. Comparative Medicine, 58(1), 3142. Wilson, J., & Harrison, T. R. (1991). Harrison’s principles of internal medicine. McGraw-Hill Professional. Zeng, Q., Gomez, B. P., Viscidi, R. P., Peng, S., He, L., Ma, B., Wu, T. C., & Hung, C. F. (2012). Development of a DNA vaccine targeting Merkel cell polyomavirus. Vaccine, 30(7), 13221329. https://doi.org/10.1016/j.vaccine.2011.12.072
Chapter 8
Strategies for the development of hepatitis B virus vaccines Fadoua El Battioui1, Fatima El Malki2 and Said Barrijal1 1
Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco, 2Institute of Nursing, Tangier, Morocco
8.1
Introduction
Hepatitis B virus (HBV) infection is a major worldwide health issue. It is considered a potentially fatal infection. Despite the effectiveness of currently available antiviral treatments against chronic hepatitis B (CHB), unfortunately, complete elimination of HBV from the liver is hard to achieve. Therefore almost 15%40% of chronic infections are complicated by cirrhosis and/or hepatocellular carcinoma (HCC) (Lai & Yuen, 2007; Lee, 1997). According to the World Health Organization (WHO), CHB affects approximately 260 million people worldwide (WHO1, 2020), with almost 890,000 deaths worldwide linked to its complications in 2015 (Berthier et al., 2020). Thus a safe and effective vaccine that offers 98%100% protection against hepatitis B is available. Prevention hinders the occurrence of complications, including the occurrence of disease, chronic and liver cancer, for which the development of therapeutic vaccines remains a perpetual challenge (WHO1, 2020).
8.2
Virus-like particle-based hepatitis B vaccines
Virus-like particles (VLPs) are nanoparticles that are assembled from viral structural proteins. Structurally, it is impossible to distinguish them from their corresponding viruses. The development of vaccines using VLP-based technology is the most effective method for preventing or treating certain infectious or chronic diseases like cancer. Due to their favorable size and their mimetic viral structure, the VLPs easily penetrate the lymphatic vessels and flow to the lymph Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00005-4 Copyright © 2023 Elsevier Inc. All rights reserved.
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FIGURE 8.1 A schematic diagram of virus-like particle (VLP) in triggering specific immune responses against tumor cells. Chimeric VLPs displaying tumor antigen (red oval) on their surface are administered into an animal model. Tumor antigen is taken up, processed, and presented by dendritic cells (DCs) to respective T cells. The presentation of tumor antigen by DCs converts naı¨ve cytotoxic and helper T cells into cytotoxic (Tc) and effector helper (Th) T cells, respectively. Tc cells (red triangles) kill tumor cells by releasing cytotoxic proteins, such as granzyme and perforin. Th1 cells support the activation of Tc cells by releasing interleukin-2 (IL-2) and interferon-gamma (IFNγ), whereas Th2 cells stimulate B cells to produce tumor antigen-specific antibodies, which are capable to bind and mark (red triangles) tumor cells for effective killing by natural killer (NK) cells and phagocytes. (Ong et al., 2017). https://doi.org/ 10.7717/peerj.4053.
nodes, thus inducing an efficient T-cell response by interaction with the antigen-presenting cells, especially in dendritic cells (DCs) (Fig. 8.1) (Cubas et al., 2009; Spohn & Bachmann, 2008), and inducing cytotoxic T-cell responses by cross-presentation (Chackerian, 2007; Inoue & Tanaka, 2020; Moron et al., 2003; Win et al., 2011). Genetic fusion and chemical conjugation are two classic approaches to displaying foreign epitopes on VLPs. However, gene fusion expression can induce poor VLP assembly and protein insolubility and requires different purification methods. Chemical conjugation is a postassembly way to decorate VLPs formed by coupling the epitope peptide with lysine, aspartic acid, or glutamic acid (Fig. 8.2) (Ji et al., 2020; Kushnir et al., 2012). Currently, the development of vaccines against HBV is based primarily on the VLP of the hepatitis B surface S antigen (HBsAg), which is the fundamental molecule for the entry of HBV into hepatocytes (Yan et al., 2012). The administration of a vaccine against HBV allows the generation of host immunity through the activation of B lymphocytes which release specific anti-HBs antibodies capable of neutralizing HBsAg (Chackerian, 2007).
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FIGURE 8.2 Illustration of virus-like particles (VLPs) production using different approaches. (A) Production of chimeric VLP using genetic insertion. The foreign antigen is fused in the coat protein by genetic engineering, and then chimeric VLP is expressed in a suitable host system. (B) Chimeric VLP is generated by the chemical conjugation of foreign peptides to the surface of the VLP. The VLP production can be carried out on a small scale for scientific research, while under the current GMP, it can be produced on a large scale for humans or veterinary. (Caldeira et al., 2020). https://doi.org/10.3390/v12050488.
FIGURE 8.3 Schematic representation of the overlap between the HBV polymerase and envelope open reading frames. The numbers indicate amino acid (aa) sites. Numbering is according to genotype D. The “a” determinant of HBsAg that is located between aa 124 and 149, and which includes the major antibody neutralization domain of HBV, is indicated. (Romano` et al., 2014). https://doi.org/10.4161/hv.34306.
Most of the vaccines currently licensed to prevent infection with HBV are made out of a region encompassing residues 124147 known as the “a” determinant, which is the most immunogenic part of HBsAg, it is shared by all genotypes and serotypes of the virus (Golsaz-Shirazi et al., 2020) (Fig. 8.3). This antigen is able to induce specific humoral immune responses to HBV. It has been used worldwide in various recombinant vaccines (Keating et al., 2003). Up to date, there are 12 VLP-based HBV vaccines on the market (Table 8.1), and the one most recently produced is the HEPLISAV-B vaccine, combined with a CpG adjuvant (Del Giudice et al., 2018).
TABLE 8.1 VLP (virus-like particle)-based HBV (hepatitis B virus) vaccines available on the market. Vaccine name
Company/ institution
Route of administration (adjuvant)
Expression system
VLP platform and vaccine antigen
VLP type
References
GenHevac B
Pasteur-Merieux Aventis
IM (aluminum hydroxide)
Mammalian (CHO cells)
HBsAg
Nonenvelopeda
Soulie´ et al. (1991)
Bio-Hep-B (Sci-B-Vac)
BTG (SciGen, FDS Pharma)
IM (aluminum hydroxide)
Mammalian (CHO cells)
HBsAg
Nonenvelopeda
SciGen (2012)
DTP-Hep B
P.T. Bio Farma
IM (aluminum hydroxide)
Yeast (Pichia pastoris)
HBsAg
Nonenvelopeda
WHO2 (2001), Bio Farma (2011)
Engerix-B
GSK
IM (aluminum hydroxide)
Yeast (Saccharomyces cerevisiae)
HBsAg
Nonenvelopeda
Harford et al. (1983), Hauser et al. (1987)
Enivac HB
Panacea Biotec
IM (aluminum hydroxide)
Yeast (Pichia pastoris)
HBsAg
Nonenvelopeda
Panacea (2012)
Euvax B
LG Life Sciences
IM (aluminum hydroxide)
Yeast (Saccharomyces cerevisiae)
HBsAg
Nonenvelopeda
Sanofi Pasteur (1998)
Gene Vac-B
Serum Inst. of India
IM (aluminum hydroxide)
Yeast (Hansenula polymorpha)
HBsAg
Nonenvelopeda
Serum Institute of India Ltd. Gene Vac-Bs (2012) http://www. seruminstitute.Com
Heberbiovac HB
CIGB-Heber Biotec
IM (aluminum hydroxide)
Yeast (Pichia pastoris)
HBsAg
Nonenvelopeda
D´ıaz Gonz´alez et al. (1997)
HepavaxGene
Crucell
IM (aluminum hydroxide)
Yeast (Hansenula polymorpha)
HBsAg
Nonenvelopeda
DiethelmLtd./Berna Biotech Korea Corp (2012)
Recombivax HB
Merck
IM (aluminum hydroxide)
Yeast (Saccharomyces cerevisiae)
HBsAg
Nonenvelopeda
Recombivax (2011), McAleer et al. (1984), Ellis and Gerety (1989)
Revac-B
Bharat Biotech International
IM (aluminum hydroxide)
Yeast (Pichia pastoris)
HBsAg
Nonenvelopeda
Bharat (2008)
Shanvac-B
Shantha Biotechnics Polish Academy of Sciences/TJU ASA
IM (Aluminum hydroxide) Oral (none) Oral (none)
Yeast (Pichia pastoris) Plant (Tg lettuce) Plant (Tg potato)
HBsAg
Nonenvelopeda
Shantha Biotechnics (2012)
Up to date, there are 12 VLP-based HBV vaccines on the market.
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These vaccines have a purely prophylactic role, contributing to a decrease in the incidence of HBV infection worldwide, but are unable to eliminate the preexisting infection (Caldeira et al., 2020).
8.3
Therapeutic vaccines
Therapeutic vaccines make it possible to destroy human cancer cells via a mechanism that is essentially based on the induction of cellular immunity with a direct antiviral effect in which CD 4 1 and CD 8 1 cells have main roles in the eradication of infected hepatocytes (Fig. 8.4) (Hu et al., 2018; Ji et al., 2020; Kosinska et al., 2017; Maini et al., 2000; Shouval et al., 2015). There are currently several types of therapeutic vaccines against HBV infection which are undergoing various stages of clinical trials (Table 8.2).
FIGURE 8.4 Immune therapeutic approaches to achieve a functional cure of hepatitis B virus (HBV). (A) A schematic representation of immune responses and viral load in chronic and resolved HBV infection. Chronic HBV infection is characterized by the loss or functional exhaustion of HBV-specific CD8 1 T cells and elevated HBV load in infected hepatocytes. Resolved HBV infection is characterized by the presence of functional HBV-specific T cells, antibodies blocking new infection, and a few hepatocytes harboring HBV. (B) A schematic representation of different immunotherapeutic strategies able to boost innate or adaptive immunity to suppress HBV replication. TLR, Toll-like receptor; RIG-I, retinoic acid-inducible gene-I; NKT, natural killer T; TCR, T cell receptor; APC, antigen-presenting cell; PD-1, programmed cell death-1; HBsAg, hepatitis B surface antigen. (Bertoletti et al., 2017) https://doi.org/ 10.1016/j.jcyt.2017.07.011.
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TABLE 8.2 Overview of significant gene vaccines in hepatitis B. Drug name
Class
Phase
Company
DV-601
Therapeutic vaccine: viral antigen complex based
Ib
Dynavax
Hi-8 HBV
Therapeutic vaccine: viral antigen complex based
II
Oxxon
ePA-44
Therapeutic vaccine: viral antigen complex based
II
Chongqing Jiachen Biotechnology Ltd
HB-110
Therapeutic vaccine: DNA based
I
Genexine
HBV-DNA plasmid pdpSC18 vaccine
Therapeutic vaccine: DNA based
I
PowderMed/Pfizer
GS-4774
Engineered to express a fusion protein containing HBsAg sequences
II
Gilead Sciences, Inc
TG1050
Therapeutic vaccine: Adenovirus based
I/Ia
Transgene
8.4
DNA-based vaccines
DNA-based vaccines constitute a promising approach in the field of development of therapeutic vaccination against HCC, and has several advantages over conventional vaccines: low manufacturing cost, high efficiency, and quick development (Karimkhanilouyi & Ghorbian, 2019). Indeed, the results of a study conducted in chimpanzees, for which a DNA-based HBV vaccine was administered, demonstrated that the vaccine induced a cellular immune response, as an absence of the virus was observed in the plasma for 6 months (Fig. 8.5) (Mancini-Bourgine & Michel, 2005). Yang et al. (2006) also found in their studies that essentially repeated vaccination with the basic hepatitis B DNA antigen (HBsAg), encoding the proteins preS1/preS2/S, polymerase (Pol), interleukin-12 (IL-12) adjuvant, and HBx, can elicit potent humoral and cell-mediated anti-HBV immunities (Chuai et al., 2018; Yang et al., 2006). The immunogenic effect of the vaccine was further enhanced by using a recombinant protein on DC cells targeted by a DNA vaccine (Wang et al., 2016).
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FIGURE 8.5 Mechanism of DNA vaccines for HBV. The vaccine used was a gene sequence that coded for medium and small HBV viral proteins in the pCMV plasmid. Patients were injected intramuscularly (i.m.). After immunization, transfected muscle cells may produce antigens that stimulate B cells of the immune system, which then produce antibodies. Transfected muscle cells could transfer the antigen to the antigen-presenting cells such as dendritic cells. The processing and display of MHC-antigen complexes arise. HBV, hepatitis B virus. Emil Behring (1854–1917); Paul Ehrlich (1854–1915); Elias Metchnikoff (1845–1916) (Mancini-Bourgine et al., 2004; Kaufmann, 2019).
8.5
mRNA-based vaccines
To induce antigen-specific humoral and cellular immune responses, with a superior safety profile compared to DNA vaccines, mRNA vaccines have been used primarily for therapeutic purposes against cancer or HIV (Vanham & Van Gulck, 2012). The process of developing therapeutic mRNA vaccines relies on ex vivo manipulation of DCs with mRNA and then their reinsertion into the patient. This technique is very promising and secure, but unfortunately, there are certain constraints inherent in the high cost of production and the complexity of the procedure (Vanham & Van Gulck, 2012).
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8.6
181
Proteins/peptides vaccines
During the last few years, several types of research are directed toward the development and the use of vaccines based on chimeric VLPs with epitopic peptides expressed or conjugated to prevent infectious diseases or to treat chronic diseases such as cancer, autoimmunity, allergy, and neurodegenerative diseases (Ji et al., 2020; Li, Fierer et al., 2014; Li, Joshi et al., 2014; Ong et al., 2017; Zeltins et al., 2017). Indeed, the orientation toward the design of this type of vaccine is justified by their advantageous characteristics compared to traditional vaccines, namely simple preparation, a good level of cost-effectiveness, storage stability, and harmlessness, and solubility (Gomes et al., 2019). However, peptide vaccines exhibit low immunogenicity due to the risk of enzymatic degradation. To deal with this problem, the researchers developed a strategy based on the use of multiple epitopes (Chaudhuri et al., 2020). Ding (2009) demonstrated that the use of the multiepitope approach of HBV x displayed to the core HBV protein elicited epitope-specific CD8 1 T cells and a more energetic response than a single peptide, proving that the VLP loaded by several epitopes increases the immunogenicity for each epitope by increasing the number of CTLs which leads to a more efficient tumor response (Caldeira et al., 2020; Ding et al., 2009). Recently, a new technology for the coupling of proteins by the spontaneous formation of an isopeptide bond between SpyCatcher (protein) and SpyTag (peptide) has emerged (Li, Fierer et al., 2014; Li, Joshi et al., 2014; Zakeri et al., 2012). Using this technique, SpyCatcher and SpyTag polypeptides react easily under a variety of mild conditions, do not require chemical coupling agents, and can maintain the natural structure of epitopes (Ji et al., 2020). The integration of this technology was done for the first time with HBc VLP and it has established a platform for rapid, convenient, universal, and safe cancer vaccines (Ji et al., 2020).
8.7
Cell-based vaccines
Therapeutic cell-based vaccines are primarily based on the principle of in vitro activation of intrinsic immune cells, including NK cells, or most commonly presenting dendritic cells by viral peptides or viral genes (Karimkhanilouyi & Ghorbian, 2019; Moron et al., 2003). Because HBVspecific T cells are depleted and present in limited numbers in patients with chronic HBV infection (Boni et al., 2007; Boudreau et al., 2011; Fisicaro et al., 2010), restoration of the peripheral and intrahepatic function of HBVspecific T cells can be done in vitro using therapies with anti-PD1/PDL-1 checkpoint inhibitors (Fig. 8.6). However, at present these therapies have
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FIGURE 8.6 Strategies to boost HBV-specific T cells. Restoration of peripheral and intrahepatic function of HBV-specific T cells can be done in vitro using therapies with anti-PD1/PDL-1 checkpoint inhibitors. HBV, hepatitis B virus. (Bertoletti et al., 2017). https://doi.org/10.1016/j. jcyt.2017.07.011.
only been used successfully in certain solid malignant tumors such as lung cancer, renal carcinoma, and melanoma, and no data are available up to date on chronic HBV (Bertoletti et al., 2017).
8.8
Nanovaccines
Currently, we are witnessing the emergence of new vaccine development techniques using the principles of nanotechnology, which allows efficient delivery to target tissues (Mei et al., 2020). Thanks to their ability to induce humoral and intercellular responses, nanovaccines are generally more convincing than ordinary vaccines. The nanovaccines are generally dedicated to particular objectives, for example, an assisted discharge and improvement of the delivery of an antigen. Therefore the determination of polymers is of extreme importance (Karthick Raja Namasivayam et al., 2020; Mei et al., 2020). Results from a study using a chitosan-PEG polymer-polymer made with a hepatitis B surface antigen nanocomposite demonstrated controlled discharge for several weeks with a high rate of immunogenicity (Karthick Raja Namasivayam et al., 2020). In terms of safety, despite the fact that the nanocomposite has demonstrated a high level of safety, additional investigations will be useful to abuse the standards of nanocomposite manufactured with the HBsAg plan in the field of biomedicine as a protected or biocompatible nanovaccine against hepatitis (Karthick Raja Namasivayam et al., 2020; Mei et al., 2020).
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8.9
183
Efficacy of therapeutic vaccines
Up to date, several studies have been carried out to evaluate the therapeutic efficacy of certain vaccines against HBV (Karimkhanilouyi & Ghorbian, 2019). In this context, the first clinical trial of the HBV vaccine was carried out in France by Mancini-Bourgine; the vaccine was a gene sequence that was encoded for the medium and small HBV viral proteins in plasmid pCMV (Mancini-Bourgine et al., 2006). The results of this clinical trial, carried out on 10 patients with chronic HBV infection, showed that the designed vaccine was safe and also effective in inducing an immune response against the viral infection. Administration of three doses of the vaccine 2 months apart generates a significant increase in the response rate of specific T lymphocytes that produce interferongamma. Quantitative PCR analysis also demonstrated a marked reduction in serum virus levels in five patients, with complete absence of viral infection in serum in one patient (Mancini-Bourgine et al., 2004). Another clinical trial to assess the therapeutic efficacy of a binary DNA vaccine in combination with a drug (lamivudine) was performed in patients with chronic HBV infection. In this study, two plasmids were used. One of the plasmids contained the MHBs-encoding antigen and the other was an adjuvant plasmid that contained the interleukin-2 fusion protein and the interferon gamma gene encoding. Vaccination was also performed at 0, 4, 12, and 24 weeks. The results of this trial demonstrated an increase in the number of T cells secreting a specific viral gamma-interferon after the administration of the vaccine alone and an evident decrease in the genotoxicity of the HBV virus in the serum of subjects who received the vaccine simultaneously with the lamivudine (Yang et al., 2012). However, therapeutic vaccines do not prevent the recurrence of chronic viral infection in most treated patients (Fontaine et al., 2014).
8.10 Harmlessness Most studies carried out, up to date, to assess the safety of therapeutic HBV vaccines do not show serious side effects in the subjects studied, except for some mild side effects that were not attributed to the vaccination (Karimkhanilouyi & Ghorbian, 2019).
8.11 Immunization coverage WHO has set the elimination of both hepatitis C virus (HCV) and HBV by 2030 as an ultimate goal (WHO1, 2020). To achieve this goal, the universal
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introduction of vaccination against hepatitis B for newborns remains the most reasonable strategy, since 90% of chronic HBV infections and 65% of mortality can be reduced through vaccination (Gomes et al., 2019). Thus WHO has included HB vaccination in the Expanded Program on Immunization and recommends that all infants should receive HB vaccine as soon as possible after birth, preferably within 24 hours (Locarnini et al., 2015). As a result, in 2019, the proportion of children under 5 years of age chronically infected with HBV decreased to just under 1%, compared to around 5% in the prevaccine era from 1980 until the beginning of 2000 (WHO1, 2020). However, coverage of the birth dose of the hepatitis B vaccine remains uneven. Global coverage is 43%, while coverage in the African Region is only 6% (WHO1, 2020). The full-series (three-dose) vaccination schedule induces protective antibody levels in more than 95% of infants, children, and young adults. With a protection life of up to at least 20 years, it probably lasts a lifetime. Thus the WHO does not recommend the booster vaccination for people who have completed the three-dose vaccination schedule (WHO1, 2020). In 2019, coverage of three doses of the vaccine reached 85% worldwide compared to around 30% in 2000 (WHO1, 2020).
8.12 Conclusion Hepatitis B remains a worrying health problem for several countries. Although vaccination has clearly been shown to be very effective in the prevention and control of the disease globally, the emergence of mutant resistant liver infections is of concern. Thus the development of new common vaccines for the prevention of all types of hepatitis is warranted. Global surveillance networks should be set up to monitor the epidemiological dynamics of hepatitis and the impact of vaccines on public health.
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Boni, C., Fisicaro, P., Valdatta, C., Amadei, B., Di Vincenzo, P., Giuberti, T., & Ferrari, C. (2007). Characterization of hepatitis B virus (HBV)-specific T-cell dysfunction in chronic HBV infection. Journal of Virology, 81(8), 42154225. Available from https://doi.org/ 10.1128/jvi.02844-06. Boudreau, J. E., Bonehill, A., Thielemans, K., & Wan, Y. (2011). Engineering dendritic cells to enhance cancer immunotherapy. Molecular Therapy, 19(5), 841853. Available from https://doi.org/10.1038/mt.2011.57. Caldeira, J. C., Perrine, M., Pericle, F., & Cavallo, F. (2020). Virus-like particles as an immunogenic platform for cancer vaccines. Viruses, 12(5), 488. Available from https://doi.org/ 10.3390/v12050488. Chackerian, B. (2007). Virus-like particles: Flexible platforms for vaccine development. Expert Review of Vaccines, 6(3), 381390. Available from https://doi.org/10.1586/14760584.6.3.381. Chaudhuri, D., Datta, J., Majumder, S., & Giri, K. (2020). In silico designing of peptide-based vaccine for hepatitis viruses using reverse vaccinology approach. Infection, Genetics, and Evolution, 84, 104388. Available from https://doi.org/10.1016/j.meegid.2020.104388. Chuai, X., Xie, B., Chen, H., Tan, X., Wang, W., Huang, B., Deng, Y., Li, W., & Tan, W. (2018). The immune response of rhesus macaques to novel vaccines comprising hepatitis B virus S, PreS1, and core antigens. Vaccine, 36(26), 37403746. Available from https://doi. org/10.1016/j.vaccine.2018.05.061. Cubas, R., Zhang, S., Kwon, S., Sevick-Muraca, E. M., Li, M., Chen, C., & Yao, Q. (2009). Virus-like particle (VLP) lymphatic trafficking and immune response generation after immunization by different routes. Journal of Immunotherapy, 32(2), 118128. Available from https://doi.org/10.1097/cji.0b013e31818f13c4. Del Giudice, G., Rappuoli, R., Didierlaurent, A. M., Gonz´alez., Lay, L., Gonz´alez, L. o´pezCh´avez, & Flaquet, P. (2018). Correlates of adjuvanticity: A review on adjuvants in licensed vaccines. Seminars in Immunology. Available from https://doi.org/10.1186/1742-4690-9-72. D´ıaz Gonz´alez, M., Rodr´ıguez Lay, L., Lo´pez-Ch´avez, A. U., Bravo Gonz´alez, J. R., & Pedroso Flaquet, P. (1997). Adverse reactions and immune response to heberbiovac-HB vaccine administered to infants simultaneously with DPT and VA-MENGOC-BC. Revista Cubana de Medicina Tropical, (49), 196203. DiethelmLtd./Berna Biotech Korea Corp. Hepavax-Genes (2012). Summary of product characteristics. Available from: ,http://drug.fda.moph.go.th/zone.pdf.. Ding, F. X., Wang, F., Lu, Y. M., Li, K., Wang, K. H., He, X. W., & Sun, S. H. (2009). Multiepitope peptide-loaded virus-like particles as a vaccine against hepatitis B virus-related hepatocellular carcinoma. Hepatology, 49(5), 14921502. Available from https://doi.org/ 10.1002/hep.22816. Ellis, R. W., & Gerety, R. J. (1989). Plasma-derived and yeast-derived hepatitis B vaccines. American Journal of Infection Control, 17, 181189. Fisicaro, p, Valdatta, C., Massari, M., Loggi, E., Biasini, E., Sacchelli, L., & Ferrari, C. (2010). Antiviral intrahepatic T-cell responses can be restored by blocking programmed death-1 pathway in chronic hepatitis B. Gastroenterology, 138(2), 682693. Available from https:// doi.org/10.1053/j.gastro.2009.09.052. Fontaine, H., Kahi, S., Chazallon, C., Bourgine, M., Varaut, A., Buffet, C., Godon, O., Meritet, J. , F., Saı¨di, Y., Michel, M. , L., Scott-Algara, D., Aboulker, J. , P., & Pol, S. (2014). AntiHBV DNA vaccination does not prevent relapse after discontinuation of analogues in the treatment of chronic hepatitis B: A randomised trial—ANRS HB02 VAC-ADN. Gut, 64, 139147. Available from https://doi.org/10.1136/gutjnl-2013-305707.
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Golsaz-Shirazi, F., Asadi-Asadabad, S., Sarvnaz, H., Mehdi Amiri, M., Hojjat-Farsangi, M., Chudy, M., Jeddi-Tehrani, M., & Shokri, F. (2020). Immunoreactivity pattern of monoclonal antibodies against hepatitis B vaccine with global Hepatitis B virus genotypes. Clinica Chimica Acta, 510, 203210. Available from https://doi.org/10.1016/j.cca. 2020.07.026. Gomes, C., Wong, R. J., & Gish, R. G. (2019). Global perspective on hepatitis B virus infections in the era of effective vaccines. Clinics in Liver Disease, 23(3), 383399. Available from https://doi.org/10.1016/j.cld.2019.04.001. Harford, N., Cabezon, T., Crabeel, M., Simoen, E., Rutgers, A., & DeWilde, M. (1983). Expression of hepatitis B surface antigen in yeast. Developments in Biological Standardization, 54, 125130. Hauser, P., Voet, P., Simoen, E., Thomas, H. C., Peˆtre, J., De Wilde, M., et al. (1987). Immunological properties of recombinant HBsAg produced in yeast. Postgraduate Medical Journal, 63(Suppl 2), 8391. Hu, Z., Ott, P. A., & Wu, C. J. (2018). Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nature Reviews Immunology, 18(3), 168182. Available from https://doi. org/10.1038/nri.2017.131. Inoue, T., & Tanaka, Y. (2020). Cross-protection of hepatitis b vaccination among different genotypes. Vaccines, 8(3), 121. Available from https://doi.org/10.3390/vaccines8030456. Ji, M., Zhu, J., Xie, X., Liu, D., Wang, B., Yu, Z., & Liu, R. (2020). A novel rapid modularized hepatitis B core virus-like particle-based platform for personalized cancer vaccine preparation via fixed-point coupling. Nanomedicine: Nanotechnology, Biology and Medicine, 28, 102223. Available from https://doi.org/10.1016/j.nano.2020.102223. Karimkhanilouyi, S., & Ghorbian, S. (2019). Nucleic acid vaccines for hepatitis B and C virus. Infection, Genetics, and Evolution, 75, 103968. Available from https://doi.org/10.1016/j. meegid.2019.103968. Karthick Raja Namasivayam, S., Nishanth, A. N., Arvind Bharani, R. S., Nivedh, K., Syed, N. H., & Rosario Samuel, R. (2020). Hepatitis B-surface antigen (HBsAg) vaccine fabricated chitosan-polyethylene glycol nanocomposite (HBsAg-CS-PEG- NC) preparation, immunogenicity, controlled release pattern, biocompatibility or non-target toxicity. International Journal of Biological Macromolecules, 144, 978994. Available from https:// doi.org/10.1016/j.ijbiomac.2019.09.175. Kaufmann, S. H. E. (2019). Immunology’s coming of age. Frontiers in Immunology, 10. https:// doi.org/10.3389/fimmu.2019.00684. Keating, G. M., Noble, S., Averhoff, F. M., Belloni, C., Duval, B., Goldwater, P. N., Hall, A. J., Honorati, M. C., Kallinowski, B., Leroux-Roels, G., & Poovorawan, Y. (2003). Recombinant hepatitis B vaccine (Engerix-Bs): A review of its immunogenicity and protective efficacy against hepatitis B. Drugs, 63(10), 10211051. Available from https://doi. org/10.2165/00003495-200363100-00006. Kosinska, A. D., Bauer, T., & Protzer, U. (2017). Therapeutic vaccination for chronic hepatitis B. Current Opinion in Virology, 23, 7581. Available from https://doi.org/10.1016/j. coviro.2017.03.011. Kushnir, N., Streatfield, S. J., & Yusibov, V. (2012). Virus-like particles as a highly efficient vaccine platform: Diversity of targets and production systems and advances in clinical development. Vaccine, 31(1), 5883. Available from https://doi.org/10.1016/j.vaccine.2012.10.083. Lai, C. L., & Yuen, M. F. (2007). The natural history and treatment of chronic hepatitis B: A critical evaluation of standard treatment criteria and end points. Annals of Internal Medicine, 147 (1), 5861. Available from https://doi.org/10.7326/0003-4819-147-1-200707030-00010.
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Lee, W. M. (1997). Hepatitis B virus infection. New England Journal of Medicine, 337(24), 17331745. Available from https://doi.org/10.1056/nejm199712113372406. Li, L., Fierer, J. O., Rapoport, T. A., & Howarth, M. (2014). Structural analysis and optimization of the covalent association between spycatcher and a peptide tag. Journal of Molecular Biology, 426(2), 309317. Available from https://doi.org/10.1016/j.jmb.2013.10.021. Li, W., Joshi, M., Singhania, S., Ramsey, K., & Murthy, A. (2014). Peptide vaccine: Progress and challenges. Vaccines, 2(3), 515536. Available from https://doi.org/10.3390/ vaccines2030515. Locarnini, s, Hatzakis, A., Chen, D. S., & Lok, A. (2015). Strategies to control hepatitis B: Public policy, epidemiology, vaccine and drugs. Journal of Hepatology, 62(1), 7686. Available from https://doi.org/10.1016/j.jhep.2015.01.018. Maini, M. K., Boni, C., Lee, C. K., Larrubia, J. R., Reignat, S., Ogg, G. S., & Bertoletti, A. (2000). The role of virus-specific Cd8 1 cells in liver damage and viral control during persistent hepatitis B virus infection. The Journal of Experimental Medicine, 191(8), 12691280. Available from https://doi.org/10.1084/jem.191.8.1269. Mancini-Bourgine, M., & Michel, M. L. (2005). Therapeutic vaccination against chronic hepatitis B virus infection. The´rapie, 60(3), 257265. Available from https://doi.org/10.2515/ therapie:2005033. Mancini-Bourgine, M., Fontaine, H., Bre´chot, C., Pol, S., & Michel, M.-L. (2006). Immunogenicity of a hepatitis B DNA vaccine administered to chronic HBV carriers. Vaccine, 24(21), 44824489. Available from https://doi.org/10.1016/j.vaccine.2005.08.013. Mancini-Bourgine, M., Fontaine, H., Scott-Algara, D., Pol, S., Bre´chot, C., & Michel, M.-L. (2004). Induction or expansion of T-cell responses by a hepatitis B DNA vaccine administered to chronic HBV carriers. Hepatology, 40(4), 874882. Available from https://doi.org/ 10.1002/hep.20408. McAleer, W. J., Buynak, E. B., Maigetter, R. Z., Wampler, D. E., Miller, W. J., & Hilleman, M. R. (1984). Human hepatitis B vaccine from recombinant yeast. Nature, 307(5947), 178180. Available from https://doi.org/10.1038/307178a0. Mei, J., Jie, Z., Xi-xiu, X., Dong-qun, L., Bin, W., Zhuo, Y., & Rui-tian, L. (2020). A novel rapid modularized hepatitis B core virus-like particle-based platform for personalized cancer vaccine preparation via fixed-point coupling. Nanomedicine: Nanotechnology, Biology and Medicine, 28, 102223. Available from https://doi.org/10.1016/j.nano.2020.102223. Moron, V. G., Rueda, P., Sedlik, C., & Leclerc, C. (2003). In vivo, dendritic cells can cross-present virus-like particles using an endosome-to-cytosol pathway. The Journal of Immunology, 171(5), 22422250. Available from https://doi.org/10.4049/jimmunol.171.5.2242. Ong, H. K., Tan, W. S., & Ho, K. L. (2017). Virus like particles as a platform for cancer vaccine development. PeerJ, 5, e4053. Available from https://doi.org/10.7717/peerj.4053. Panacea Biotech. Hepatitis B Virus Vaccine IP Recombinant (Genetically Engineered) (2012): Enivac HB. Available from: ,http://www.panaceabiotec.Com/product-pdf/vaccine1.pdf.. Recombivax HBs. Prescribing information. Merck; (July 2011). Available from: ,https://pdf. hres.ca/dpd_pm/00016542.PDF.. Romano`, L., Paladini, S., Galli, C., Raimondo, G., Pollicino, T., & Zanetti, A. R. (2014). Hepatitis B vaccination. Human Vaccines & Immunotherapeutics, 11(1), 5357. Available from https://doi.org/10.4161/hv.34306. Sanofi Pasteur Ltd. Euvax Bs (1998). Summary of product characteristics. Available from: ,http://drug.fda.moph.go.th/zonesearch/files/.pdf.. SciGen Ltd. Sci-B-VacTM (2012). Available from: ,http://www.scigenltd.com/productsscibvacs.htm..
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Serum Institute of India Ltd. Gene Vac-Bs (2012). Available from: ,http://www.seruminstitute.Com/content/products/productgenevac.htm.. Shantha Biotechnics Ltd. Shanvacs-B (2012). Available from: ,http://www.shanthabiotech. com/files/.pdf.. Shouval, D., Roggendorf, H., & Roggendorf, M. (2015). Enhanced immune response to hepatitis B vaccination through immunization with a Pre-S1/Pre-S2/S vaccine. Medical Microbiology and Immunology, 204(1), 5768. Available from https://doi.org/10.1007/s00430-014-0374-x. Soulie´, J. C., Devillier, P., Santarelli, J., Goudeau, A., Vermeulen, P., Guellier, M., & Huchet, J. (1991). Immunogenicity and safety in newborns of a new recombinant hepatitis B vaccine containing the S and pre-S2 antigens. Vaccine, 9(8), 545548. Available from https://doi. org/10.1016/0264-410x(91)90240-7. Spohn, G., & Bachmann, M. F. (2008). Exploiting viral properties for the rational design of modern vaccines. Expert Review of Vaccines, 7(1), 4354. Available from https://doi.org/ 10.1586/14760584.7.1.43. Vanham, G., & Van Gulck, E. (2012). Can immunotherapy be useful as a “functional cure” for infection with Human Immunodeficiency Virus-1? Retrovirology, 9(1), 72. Available from https://doi.org/10.1186/1742-4690-9-72. Wang, Y., Wu, S., Wang, Z. C., Zhu, X. M., Yin, X. T., Gao, K., & Yu, J. Y. (2016). Enhanced immunity and antiviral effects of an HBV DNA vaccine delivered by a DC-targeting protein. Journal of Viral Hepatitis, 23(10), 798804. Available from https://doi.org/10.1111/ jvh.12542. WHO1. (2020). Facts heets for Chronic Hepatitis B. ,https://www.wh/detail/hepatitis-bo.int/fr/ news-room/fact-sheets.. WHO2. (2001). Hepatitis B immunization: Introducing hepatitis B vaccine into national immunization services. Available online from ,http://whqlibdoc.who.int/hq/2001/WHOV&B01.28. pdf.. Win, S. J., Ward, V. K., Dunbar, P. R., Young, S. L., & Baird, M. A. (2011). Cross-presentation of epitopes on virus-like particles via the MHC I receptor recycling pathway. Immunology and Cell Biology, 89(6), 681688. Available from https://doi.org/10.1038/icb.2010.161. Yan, H., Zhong, G., Xu, G., He, W., Jing, Z., Gao, Z., Huang, Y., Qi, Y., Peng, B., Wang, H., Fu, L., Song, M., Chen, P., Gao, W., Ren, B., Sun, Y., Cai, T., Feng, X., Sui, J., & Li, W. (2012). Sodium taurocholate cotransporting polypeptide is a functional receptor for human hepatitis B and D virus. eLife, 2012(1), e00049. Available from https://doi.org/10.7554/ eLife.00049. Yang, F. Q., Yu, Y. Y., Wang, G. Q., Chen, J., Li, J. H., Li, Y. Q., Rao, G. R., Mo, G. Y., Luo, X. R., & Chen, G. M. (2012). A pilot randomized controlled trial of dual-plasmid HBV DNA vaccine mediated by in vivo electroporation in chronic hepatitis B patients under lamivudine chemotherapy. Journal of Viral Hepatitis, 19(8), 581593. Available from https:// doi.org/10.1111/j.1365-2893.2012.01589.x. Yang, S. H., Lee, C. G., Park, S. H., Im, S. J., Kim, Y. M., Son, J. M., & Soung, Y. C. (2006). Correlation of antiviral T-cell responses with suppression of viral rebound in chronic hepatitis B carriers: A proof-of-concept study. Gene Therapy, 13(14), 11101117. Available from https://doi.org/10.1038/sj.gt.3302751. Zakeri, B., Fierer, J. O., Celik, E., Chittock, E. C., Schwarz-Linek, U., Moy, V. T., & Howarth, M. (2012). Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proceedings of the National Academy of Sciences, 109(12), E690E697. Available from https://doi.org/10.1073/pnas.1115485109.
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Zeltins, A., West, J., Zabel, F., El Turabi, A., Balke, I., Haas, S., & Bachmann, M. F. (2017). Incorporation of tetanus-epitope into virus-like particles achieves vaccine responses even in older recipients in models of psoriasis, Alzheimer’s, and cat allergy. NPJ Vaccines, 2(1), 30. Available from https://doi.org/10.1038/s41541-017-0030-8.
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Chapter 9
MYC oncogenes as potential anticancer targets ˇ Radostina Alexandrova1 and Crtomir Podlipnik2 1
Department of Pathology, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, Sofia, Bulgaria, 2Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
9.1
Introduction
The viral MYC (v-MYC) oncogene was originally identified as a transforming factor in the genome of the acute avian leukemia retrovirus called myelocytomatosis virus MC29, which was isolated from a Rhode Island red chicken with spontaneous myelocytomatosis in 1961 (Ivanov, 1964). Later, the virus MC29 was accepted as the prototype of the group of myelocytomatosis viruses, including viruses such as MH2, CMII, and OK10. Similarly to other acute avian oncogenic retroviruses, MC29 is also a defective one: there are defects in all three structural genes (gag, pol, env, the pol gene is completely absent). Therefore, its replication can only take place in the presence of a replication-competent helper virus. The original MC29 strain contains helpers from subgroups A and B of the avian leukemia-sarcoma retroviruses. In place of the missing parts in the genome of the MC29 strain, there is a specific nucleotide sequence with a size of 1.5 kb and its product causes malignant transformation of the targeted cells. This sequence was originally denoted by the initials MAC or MCV, and later by MYC. In vitro, the virus induces transformation of fibroblasts, epithelial cells, and macrophages. In vivo transmission of the virus causes primarily myelocytomatosis and myelocytomas in chickens, but it is also responsible for a broad spectrum of leukemias and tumor growths, including endothelioma, mesothelioma sarcoma, and erythroblastosis as well as some epithelial tumors in the kidney, pancreas, and liver. Identical or similar sequences have been found in the genomes of other myelocytomatosis viruses (Lee & Reddy, 1999; Mladenov, 1974; Payne & Nair, 2012; Payne, 1992; Weiss, 1982). Cell homologs of v-MYC have been found in the genome of both avian species and mammals, including humans. In humans, MYC family consists of three Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00011-X Copyright © 2023 Elsevier Inc. All rights reserved.
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members: c-MYC (8q24), N-MYC (2p24), and L-MYC (1p34), recognized as “super transcription factors,” which control almost all cellular processes. v-MYC is thought to have originated through a specialized form of viral transduction in which a spliced form of c-MYC (from which introns are excised) has been incorporated into the retroviral genome. Duplication of the gene in the early stages of vertebrate evolution led to the emergence of the c-MYC line and another line, which later, by repeated duplication, gave rise to the N- and L-MYC genes. The general structure and organization of the c-, N- and L-MYC genes, as well as their transcripts, are similar (Alexandrova, 2005; Weiss, 1982). Studies in avian species and mammals have shown that in many respects, v-MYC has a significantly more pronounced effect than c-MYC due to various factors: (1) There is a high level of transcriptional activity due to the presence of strong activating elements in long terminal repeats (LTR) of the retrovirus. (2) Various mutations have been observed in v-MYC proteins, due to which they differ from their cellular counterparts (c-MYC). The V-MYC gene of MC29 contains five to seven (depending on the virus isolate) amino acid substitutions compared with the c-MYC. Replacement of threonine with methionine at position 61 (regulating the phosphorylation site) leads to significantly higher transforming activity of v-MYC than its cellular counterpart. The OK10 and MH2 viruses also contain key mutations at position 61. Most of the mutations occur at the N- and C-termini of the v-MYC regions required for cell transformation. (3) v-MYC is three times more stable than c-MYC. After the reverse mutation, in which methionine is replaced with threonine, the degradation periods of c-MYC and v-MYC are equalized—their half-life is about 2030 s (Alitalo et al., 1983; Gavine et al., 1999; Lee & Reddy, 1999; Watson et al., 1983).
9.2
Biological role of MYC genes
MYC genes are involved in a wide variety of key biological processes, including cell proliferation, growth, survival and apoptosis, (energy) metabolism, differentiation, angiogenesis, DNA repair, and immune response (Beltran, 2014; Duffy et al., 2021). MYC proteins are mainly known as transcription factors, but they also take part in DNA synthesis and protein translation and have been reported to regulate many different microRNAs (Chang et al., 2008; O’Donnell et al., 2005). c-MYC is expressed in virtually all proliferating cells during development and in dividing cells of adult tissues, but its expression is blocked in nondividing cells due to various mechanisms, including terminal differentiation (Beltran, 2014). N-MYC is expressed during embryogenesis in pre-B cells, kidney and brain, and at a significant level in adult tissues (Beltran, 2014; Yoshida, 2018). L-MYC is expressed in the developing brain and primitive neuronal cells as well as in neonatal and adult lung tissue (Beltran, 2014). Both c-MYC and N-MYC are proven to be
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essential for mammalian embryogenesis, whereas L-MYC is dispensable (Hatton et al., 1996; Hurlin, 2013).
9.3
MYC in normal tissues and cancer
MYC proteins preferentially regulate the same sets of genes and are drivers of critical cell processes such as proliferation, differentiation, metabolism, survival, and death. Their availability in excess and functioning may result in malignant cell transformation and cancer. During the evolution, normal cells adopt multiple systems to maintain MYC RNA and MYC protein levels such as cross-regulation, compensation between the three main MYC genes (c-, N-, and L-MYC), regulated degradation via the ubiquitinproteasome system as well as MYC-trigged apoptosis when in excess (Conacci-Sorrell et al., 2014; Malynn et al., 2000; Meyer & Penn, 2008). This tight control can be disrupted as a result of various mechanisms (Table 9.1) that convert an MYC gene into an oncogene. There is data-based evidence that dysregulation and/or overexpression of MYC is involved in more than 70% of human cancers (including breast, prostate, colon and cervical cancers, myeloid leukemia, lymphoma, etc.) and is often associated with aggressive behavior, drug resistance, and poor prognosis (Dang, 2012; Fallah et al., 2017; Kalkat et al., 2017; Nesbit et al., 1999; Srutova et al., 2018). Many of these tumors are oncogene-addicted (Weinstein, 2002), which means that deregulation of the MYC gene product is required to maintain their malignant phenotype and progression (Gabay et al., 2014; Kuzyk & Mai, 2014). MYC can be activated through gene amplification, chromosome translocation, retroviral promoter insertion, mutation, enhanced cell signaling, and altered protein stability (Table 9.1) (Duffy et al., 2021; Kuzyk & Mai, 2014; Lee & Reddy, 1999). Various single-nucleotide (Ghoussaini et al., 2008) polymorphisms (SNPs) near MYC have been reported to be associated with the increased risk (by 25%50%) of developing cancer (e.g., prostate, colon, breast, nasopharynx, and bladder) (Easton et al., 2007; Guo et al., 2019; Lancho & Herranz, 2018). MYC genes increase the total capacity for protein synthesis and are responsible for the energy (glucose) metabolism of cancer cells. Influencing activities of several microRNAs modulate cell growth, survival/ death, differentiation, and transformation (Chang et al., 2008; O’Donnell et al., 2005). The increased expression of MYC induces DNA damages (as a result of ROS-dependent and ROS-independent mechanisms), replication stress, and a number of alterations in a nuclear organization that can affect the position and function of telomeres (Kuzyk & Mai, 2014). Overexpressed MYC has also been found to play an important role in promoting escape from antitumor immune responses, which is one of the essential hallmarks of cancer (Casey et al., 2018; Vera de Jonge et al., 2020).
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TABLE 9.1 Mechanisms of MYC activation in cancer. Type of alteration
Mechanism
Gene amplification
One of the most frequent mechanisms for MYC activation (especially for c-MYC) found in 10%60% of carcinomas (such as ovarian, oesophagal, breast, and squamous lung cancers) and sarcomas, depending on the tumor type; can be associated with the molecular subtype of cancer; more typical for c-MYC, and less frequently observed in N-MYC (neuroblastoma, medulloblastoma, retinoblastoma, neuroendocrine/ small-cell prostate cancer, etc.) and L-MYC (some small cell lung cancers). MYC is the most frequently amplified gene among cancers (Beltran, 2014; Duffy et al., 2021; Yoshida, 2018)
Chromosomal translocation
Found mainly in hematological malignancies, such as Bcell lymphomas (for example, in 100% of Burkitt’s lymphomas) and less frequently in T-cell lymphomas and multiple myeloma; documented for the first time in Burkitt’s lymphoma. Three types of MYC translocations have been reported joining the long arm of chromosome 8 to the immunoglobulin (IG) heavy locus [t(8;14)(q24; q32)] to the kappa light chain locus [t(2;8)(p11;q24)] or to the lambda light chain locus [t(8;22)(q24;q11)]. These translocations place the MYC gene under the control of powerful IG gene enhancers, which stimulate constitutive MYC expression (Duffy et al., 2021; Kuzyk & Mai, 2014)
Retroviral promoter insertion
First identified in avian chronic leukemic retroviruses (ALVs) that do not have their own oncogenes. The tumorigenic effect occurs when these viruses integrate near the cellular c-MYC because the long terminal repeats of retroviruses contain powerful enhancers and significantly increase the expression of c-MYC
Mutation
It is rarely observed. In the case of both gene amplification and chromosome translocation, the MYC gene stays intact with no change in the protein. MYC mutations are found in a subset of Burkitt’s lymphoma (with the translocated allele undergoing secondary mutations occurring in the MYC transactivation domain with hotspots in the MYC box 1 motif) and in diffuse large B cell lymphomas (showing different impacts on MYC function and clinical outcomes). In contrast to MYC gene translocations and overexpression, most MYC gene mutations seem to have no role in driving lymphomagenesis (Duffy et al., 2021) (Continued )
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TABLE 9.1 (Continued) Type of alteration
Mechanism
Increased gene expression and/or stability
It takes place through activation of signaling pathways and other oncogenes (i.e., ERK, PI3K, β-catenin, RAS, SRC, NOTCH), inactivation of tumor suppressor genes (such as APC, p53), various posttranslational modifications (phosphorylation, acetylation, sumoylation), participation of long noncoding RNAs, E3 ubiquitin ligase recruitment, and proteasomal degradation
9.4
MYC signal transduction pathway
Nearly every major signal transduction pathway regulating cell proliferation or quiescence depends on the MYC promoter and regulates MYC transcription, either directly or indirectly. These signaling pathways are activated by various signaling molecules, including mitogens, growth factors, hormones, cytokines, oncogenes, and tumor suppressors. c-MYC plays a critical role in controlling cell proliferation, cell cycle progression, differentiation, and apoptosis. Global transcriptional analyses estimate that approximately 15% of the human genome genes are under the transcriptional regulation of c-MYC (Fernandez et al., 2003; Li et al., 2003). Experimental data have shown that c-MYC directly binds to the promoters of many genes with different cellular functions and regulates their expression. For example, c-MYC directly activates the transcription of CDK4 and CDC25A genes and represses P27 and P15 genes, which play a role in cell cycle regulation. Direct targets of cMYC are also genes involved in metabolism (such as CAD, LDHA and ODC1) (Bello-Fernandez et al., 1993; Fernandez et al., 2003; Shim et al., 1997), regulation of cell apoptosis (such as BAX, Mcl-1 and Bcl-2) (Mitchell et al., 2000), and proliferation (such as MINA53 and PTMA) (Gaubatz et al., 1994; Mu¨ller & Eilers, 2008; Tsuneoka et al., 2002). Besides, c-MYC directly binds and regulates the expression of other transcription factors such as E2F1 and CEBPα (Li et al., 1994; Sears et al., 1997), thus expanding the pool of downstream targets of c-MYC. Besides, studies on microRNAs (miRNAs), a class of endogenous small noncoding RNAs that play essential regulatory roles by targeting protein-coding genes (Bartel, 2004), have identified some miRNAs (such as the miR-1792 cluster, let-7) whose expression is transcriptionally regulated by c-MYC (Chang et al., 2008). On the other hand, c-MYC lies downstream of many critical signaling pathways in the cell (Fig. 9.1). Palomero et al. showed that NOTCH1 directly binds to the c-MYC promoter and regulates c-MYC
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FIGURE 9.1 Many critical signaling pathways regulate the expression of c-MYC, for example, Wnt/beta-catenin, Ras/MEK/MAPK, and the Ras/PI3K/AKT. c-MYC controls the expression of many genes involved in regulating cell cycle progression, cell apoptosis, proliferation, or metabolism as a transcription factor. Transcriptional regulation of miRNAs by c-MYC further increases the complexity of the c-MYC signaling network. The genes marked in red signify activation, and the genes marked in green signify repression.
mRNA and protein expression in T-ALL, thereby promoting leukemic cells’ growth (Palomero et al., 2006). It has also been shown that signaling through the Src/Rac pathway mediates c-MYC mRNA induction by PDGFR (Chiariello et al., 2001). Upon activation of the Wnt/β-catenin pathway, c-MYC transcription is also activated. The Ras/Raf/ERK and Ras/PI3K/ AKT/GSK-3β pathways regulate c-MYC protein stability (Sears et al., 2000). Considered together, the response of the c-MYC gene to a variety of signaling pathways and the emerging role of the c-MYC protein as a central transcriptional regulator suggest a critical role for c-MYC as an essential signaling node making this oncogene a promising target for cancer therapy. Fig. 9.1 shows that the major signal transduction pathway depends on the MYC promoter.
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Structure of MYC
The human MYC gene, 6 kb long, is located at locus 8q.24.21 on the eighth chromosome. The gene consists of three exons, of which exon 1 is noncoding; the other two exons encode two proteins. The MYC gene’s main product is a 62 kDa protein with a sequence of 439 amino acids and belongs to the class of transcription factor basic helixloophelix zipper (bHLHZip). This protein contains several highly conserved regions that are functionally important and equally organized among the three paralogs (c-MYC, N-MYC, and L-MYC) (ConacciSorrell et al., 2014). The N-terminal transactivation domain (NTD) contains the transcription activation domain (TAD) and two MYC boxes, MBI and MBII, which are highly conserved sequence elements involved in transcriptional regulation and protein stability. The central part of c-MYC contains a nuclear localization signal and two other conserved sequence elements, MBIII and MBIV. The C-terminal domain contains the bHLHZip motif, which remains partially unstructured until it dimerizes with another bHLHZip protein, MAX. It then forms an ordered alphahelix structure, which is subject to several posttranslational modifications and protein interactions that regulate the function of c-MYC. Proteins from the MYC family are unstable with half-lives of 2030 min in normal cells (Gregory & Hann, 2000); however, in many tumors, MYC’s stabilization contributes to its deregulation. Several ubiquitin ligases have been shown to control MYC stability. Fig. 9.2 represents conserved domains of the MYC protein and some of its binding partners. The C-region is essential for DNA binding by forming a basic helixloophelix leucine zipper (bHLHZ) domain complex with Max (Brownlie et al., 1997). Additional conserved sequence motifs known as MYC boxes (MB0-IV) serve as docking sites for proteinprotein interactions.
FIGURE 9.2 Domain structure of MYC proteins with conserved regions are shown as blue boxes; MB, MYC boxes; bHLHZ, basic helixloophelix leucine zipper; functional annotation of regions and proteins interacting at these sites; structures of protein complexes containing MYC (red). PDB entries are 1MV0 (BIN1), 5G1X (AURORA-A), 4Y7R (WDR5), 1EE4 (KPNA), and 1NKP (MAX).
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The MYC transactivation domain (TAD) spans the N-terminal conserved motifs MB0, MBI, and MBII.
9.6
The MYCMax interaction
Basic helixloophelix (bHLH) and leucine zipper (LZ) domains are often part of the domains of eukaryotic transcription factors. The combination of the two domains folding into multimeric helix bundles allows the formation of specific heterodimers. Modularity provides means to control function by differential expression of each member of the pair. It has been shown that cMYC forms homodimers in analogy to other bHLH- and LZ-containing transcription factors at very high concentrations of the bacterially overexpressed protein; on the other hand, c-MYC fails to form soluble dimers with an affinity for the specific DNA-binding site. Co-immunoprecipitation experiments of a GST-MYC bHLH-LZ with phage display library highlighted Max (MYC-associated factor X) as an interaction partner of MYC. The c-MYC binds to the partner protein Max via a polypeptide patch, which has a similar domain and can also be a homo-dimer. Both LZ are disordered before binding, but after binding, both chains become ordered and form a left-handed four-helix bundle (Nair & Burley, 2003). The resulting heterodimer (the functional species) binds DNA and modulates gene expression, transcriptional activation, and apoptosis (Amati et al., 1993; Ponzielli et al., 2005). Deregulation of c-MYC is often associated with aggressive tumors in various tissues (breast, lung, cervix, etc.) and regulation of genes controlled by RNA polymerase (Grandori et al., 2005). As noted in the previous section, a cMYC that belongs to the bHLHZip class of the transcriptional factor comprises three domains: N-terminal transactivation domain, central region, and a C-terminal domain. The C-terminal domain composed of 79 amino acids contains the bHLHZip motif, which plays an essential role in binding with its obligatory partner Max, also bHLZip protein. Both c-MYC and Max are rich in charged amino acids, which show a disorder-promoting propensity (Habchi et al., 2014; Kumar et al., 2017). The amino acid composition of the c-MYCMax complex based on PDB structure 1NKP is shown in Table 9.2.
TABLE 9.2 Amino acid composition of the c-MYCMax complex based on the PDB-ID: 1NKP. Leu
Lys
Arg
Glu
Asp
Total
cMYC
12 (13.6%)
10 (11.4%)
10 (11.4%)
11 (12.5%)
2 (2.3%)
88
Max
8 (9.6%)
7 (8.4%)
10 (12.0%)
4 (4.8%)
8 (9.6%)
83
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Upon binding c-MYC to Max, the C-terminal domain transforms disordered conformation to an ordered α-helical structure, which extends into a left-handed coiled coil formed by the two LZ motifs (Amati et al., 1993; Grandori et al., 2000). This stable four-helix formation binds to specific DNA sequences, for example, CACGTG E-box motifs, in promoters and enhancers of MYC-regulated genes. The dimerization is driven by the LZ and HLH motifs, while the basic regions are responsible for the interaction with DNA. The helixloophelix region of c-MYC targets several posttranslational modifications such as acetylation, phosphorylation, ubiquitination, and sumoylation (Mu¨ller & Eilers, 2008; Suna et al., 2018; Tu et al., 2015; Vervoorts et al., 2006). This region is also involved in proteinprotein interactions (PPIs) that mediate and regulate c-MYC functions. The first structure of an MYCMax heterodimer was determined by X-ray crystallography in 2003 (PDB ID: 1NKP) (Nair & Burley, 2003). This X-ray structure represents a c-MYCMax bHLHZip complex bound to DNA containing an E-box motif bound by an artificial disulfide bridge created by adding a cysteine residue C-terminus of the LZ of both c-MYC and Max proteins. The structure of the complex of DNA and an MYCMax heterodimer is shown in Fig. 9.3. Another interesting study related to c-MYC is the NMR study of the vMYC, both in free form and in complex with v-Max in the absence of DNA (Baminger et al., 2004). The extensive studies on the MYCMax complex structure were performed by Sammak et al. (2019). They performed a detailed structural analysis of the apo-form of the c-MYCMax complex, without an artificial linker, in solution both by nuclear magnetic resonance
FIGURE 9.3 The structure of the complex of DNA and an MYCMax heterodimer.
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FIGURE 9.4 The X-ray structures of the c-MYCMax apo complex (PDB-ID: 6K6K) and superposition of apo and DNA bound c-MYCMax (PDB-ID: 1NPK).
spectroscopy (NMR) and by X-ray crystallography. They reported crystal structures in three different crystal forms with resolutions between 1.35 and ˚ , showing an extended helical structure in the basic region. 2.2 A Determination of the α-helical NMR chemical shift analysis indicates that the basic regions of c-MYC and Max adopt a helical conformation; moreover, c-MYC exhibits an intrinsic helical propensity of the basic region even without an interaction with Max. The presence of a helical structure in the basic regions in the absence of DNA suggests that molecular recognition occurs by conformational selection rather than induced alignment. The X-ray structures of the c-MYCMax apo complex (PDB-ID: 6K6K) and superposition of apo and DNA bound c-MYCMax (PDB-ID: 1NPK) are shown in Fig. 9.4.
9.7
MYC as a potential target for antitumor therapy
MYC has been identified as a promising target for antitumor therapy (Chen et al., 2018; Dang, 2012; Duffy et al., 2021). Unfortunately, despite research and efforts over more than 40 years, we still do not have an MYC gene inhibitor for use in clinical practice. The challenges facing the development of such MYC gene inhibitors are as follows: 1. Functioning of MYC is required for both normal and malignant cells; hence, one of the most important questions is whether its suppression will negatively affect the implementation of physiological processes.
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FIGURE 9.5 Different strategies to combat MYC. Inhibitors of CDK7, CDK9, and BRD4 inhibit MYC transcription. Inhibition of the PI3K/AKT/mTOR pathway prevents MYC translation, while USP7, AURKA, and PLK1 inhibitors destabilize MYC at the posttranslational level. 10058-F4 and Omomyc act as disruptors of the MYCMax dimeric complex.
2. MYC proteins are localized in the cell nucleus and monoclonal antibodies cannot reach them. 3. These proteins possess an intrinsically disordered structure, and there are no drug-binding pockets into which compounds can bind with high affinity. They have no catalytic activity and cannot be blocked by traditional enzyme inhibitors. 4. There is no known natural ligand to guide us in the development of inhibitors. In recent years, several compounds that directly or indirectly inhibit MYC have been developed, and some of them have shown promising anticancer activity in preclinical models and even entered clinical trials (AllenPetersen & Sears, 2019; Duffy et al., 2021; Whitfield et al., 2017). To reduce the devastating effects of MYC, scientists have explored several strategies, some of which target MYC directly and some indirectly. Some of these strategies are shown in Fig. 9.5 and are described in more detail in this section.
9.8 Targeting the MYCMax interaction with small molecule inhibitors Currently, there are no small-molecule inhibitors approved for commercial use that would successfully inhibit the interaction between MYC and Max directly. However, some conventional small molecules have been identified
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that inhibit c-MYCMax dimerization or DNA binding. The IDP structure of c-MYC and Max monomers has hampered novel modulators’ discovery by traditional structure-based design, resulting in inhibitors being identified mainly through high-throughput screening of chemical libraries. In initial screens using small molecule libraries, compounds IIA6B17 (Lu et al., 2008) and NY2267 (Kiessling et al., 2006) were identified as capable of interfering with the c-MYCMax interaction. However, both compounds were later found to be nonselective and acted on c-Jun, probably due to similarities in the LZ components (Fletcher & Prochownik, 2015). This absence of specificity is a common problem with small molecule inhibitors of c-MYC function. Two compounds, 10058-F4 (Huang et al., 2006) and 10074-G5 (Yap et al., 2013), identified from a library of 10,000 compounds using a yeast twohybrid screen, have been shown to bind to c-MYC in the IDP state and prevent it from folding into the conformation required for dimerization with Max. Both ligands have a modest affinity to the c-MYC; molecule 10058-F4 binds to the interface of Helix 2 and the c-MYC LZ (KD 5 42 μM), whereas 10074-G5 binds to Helix 1 and the basic region (KD 5 20 μM). Thioxothiazolidinone 10058-F4 is effective in vitro against acute myeloid leukemia, pancreatic ductal adenocarcinoma (PDAC), and ovarian carcinoma, inducing apoptosis (Aksoz et al., 2018; Lin et al., 2007), but fails in vivo to reduce the growth of human prostate cancer xenografts in mice due to its low metabolic stability (Guo et al., 2009). Further optimization of the inhibitor 10074-G5 leads to the formation of the compound JY-3094, a much more potent inhibitor of c-MYCMax dimerization than the parent compound, an esterified prodrug form of JY-3094. The low metabolic stability and poor distribution of the mentioned inhibitors is the main reason for unsuccessful in vivo experiments despite promising in vitro potency. Mycro3 is an inhibitor of c-MYCMax dimerization based on the pyrazole-[1,5-a]pyrimidine scaffold, the result of optimizing hits from high-throughput screening. This inhibitor has more improved pharmacokinetic properties compared to other c-MYC inhibitors, and in vivo studies resulted in prolonged survival and reduced tumor size in a KRas-driven PDAC mouse model (Stellas et al., 2014). MYCMI-6 is another small molecule inhibitor that can selectively bind to the MYC family’s bHLHZip domain at low micromolar concentrations. The obstruction of c-MYCMax interaction by MYCMI-6 in cells leads to the suppression of tumor growth in several cell lines, especially those expressing high levels of c-MYC protein, without cytotoxicity to normal human cells. Moreover, MYCMI-6 promotes significant apoptosis and reduces tumor proliferation in a neuroblastoma xenograft model in vivo (Castell et al., 2018). MYCi361 and MYCi975 were discovered with the initial selection of hits from the library of 16 million compounds via a pharmacophore-based high-throughput virtual screen, followed by a secondary screen assessing c-MYC inhibitory activity in vitro, and later coupled with a rapid in vivo screen in mice bearing a c-MYC-dependent
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FIGURE 9.6 Inhibitors that target c-MYC directly or interaction between c-MYC and Max.
E-box luciferase reporter. These two inhibitors have significant antitumor activity in mice and good pharmacokinetic properties, such as high plasma concentration, longer half-life, and improved tumor penetration. Among the published inhibitors, KJ-Pyr-9 exhibited the highest reported binding affinity for c-MYC with a KD of 6.5 nM. KJ-Pyr-9 was isolated from a Kroehnke pyridine library in which fluorescence polarization (FP) was used to screen for c-MYCMax dimerization inhibitors. KJ-Pyr-9 has also shown anticancer activity in vivo by inhibiting tumor growth in a human triple-negative breast cancer xenograft model without acute toxicity. Small molecules like MYCMI-6, MYCi361/975, and KJ-Pyr-9 are promising prospects for further development as potential clinical candidates. Some of the important inhibitors that directly target c-MYC or c-MYCMax interaction are shown in Fig. 9.6.
9.9
Indirect targeting of the MYC
One of the possible strategies for treating MYC-driven cancers is the inhibition of crucial factors in MYC transcription, translation, stability, and activation. The first option is to target BRD4, CDK7, or CDK9, which are involved in MYC transcription. Then, we can prevent MYC translation by inhibition of the PI3K/AKT/mTOR pathway. The ubiquitinproteasome system tightly controls MYC stability; thus, one potential strategy to combat MYC is selective inhibition of the kinases (AURKA, PLK1) and deubiquitinases (USP7) that stabilize MYC. Disabling MYCMax complex formation,
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which we discussed in the previous section, is also an approach to inhibiting MYC signal transduction.
9.10 Targeting MYC transcription MYC is regulated by both transcription and protein stability. BRD4 transcriptional and epigenetic regulator activates MYC transcription. BRD4 directly destabilizes the protein MYC by phosphorylating it at a site, which leads to ubiquitination and degradation, thereby maintaining homeostatic levels of the protein MYC (Mertz et al., 2011; Zuber et al., 2011). The small-molecule inhibitor JQ1 of BRD4 showed potent anticancer effects in several cancers associated with c-MYC overexpression hematopoietic cancers and PDAC (Puissant et al., 2013). The structure of the JQ1 inhibitor and its interaction with BRD is shown in Fig. 9.7. Another strategy to combat MYC-driven cancers is inhibition of the kinases CDK7 and CDK9; these two cyclin-dependent kinases play critical roles in transcription initiation and elongation (Fisher, 2005). CDK7 is a part of the transcription factor IIH complex (TFIIH), and CDK9 is a kinase subunit of P-TEFb (Peng et al., 1998; Shiekhattar et al., 1995). Administration of specific inhibitors against CDK7 and/or CDK9 significantly reduces c-MYC overexpression. Consequently, it has an antitumor effect in cancers associated with c-MYC overexpression, such as T-cell acute lymphoblastic leukemia, mixed lineage leukemia, and neuroblast and small cell lung carcinoma (Chen et al., 2018). THZ1, a selective and potent covalent CDK7 inhibitor, has an IC50 of 3.2 nM. THZ1 also inhibits the closely related kinases CDK12 and CDK13. It downregulates the expression of MYC (Kwiatkowski et al., 2014). AZD4573 is a potent and highly selective CDK9 inhibitor (IC50 , 4 nM) that allows transient targeting for the treatment of hematologic malignancies (Huang et al., 2021). (1)-BAY-1251152 a potent
FIGURE 9.7 Structure of JQ1, small-molecule inhibitor of BRD4 (left), and complex between JQ1 and BRD4 (right).
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FIGURE 9.8 CDK inhibitors.
and selective CDK9 inhibitor with an IC50 of 3 nM has also shown antitumor activity (Diamond et al., 2018). CDK inhibitors mentioned in this paragraph are shown in Fig. 9.8.
9.11 Targeting of MYC expression Deregulation of the PI3K/AKT/mTOR pathway is typical of several cancers (Bjornsti & Houghton, 2004). mTOR serine/threonine kinase acts as the catalytic subunit of two distinct complexes called mTOR complexes 1 and 2 (mTORC1 and mTORC2), which play an essential role in protein synthesis. The mTORC1-dependent phosphorylation of 4EBP1 decreases its ability to downregulate the translation initiation factor eIF4E and, consequently, promote the translation of mRNAs, leading to the synthesis of MYC (Bjornsti & Houghton, 2004). Pharmacological inhibition of the PI3K/AKT/mTOR pathway significantly decreased MYC levels and showed remarkable therapeutic efficacy in MYC-driven cancers, including neuroblastoma, small cell lung cancer, breast cancer, and various hematopoietic cancers (Chen et al., 2018). Another target for inhibition of MYC expression are proteins from the CPEB family, which expression is frequently downregulated in human cancers (Fern´andez-Miranda & Me´ndez, 2012). The CPEB family proteins are sequence-specific RNA-binding proteins that control poly(A) tail elongation and polyadenylation-induced translation. It has been shown that the c-MYC mRNA contains CPEs that CPEB can recognize (Groisman et al., 2006). Mechanistically, CPEB recruits Caf1 deadenylase by interacting with Tob, an antiproliferative protein, and inhibits c-MYC expression by accelerating deadenylation and decay of its mRNA (Ogami et al., 2014).
9.12 Targeting MYC stability Several deubiquitinating enzymes are involved in the stabilization of MYC. USP28 has been shown to bind c-MYC and antagonize its E3 ligase activities in the nucleus by interacting with FBW7α, leading to MYC stabilization and tumor cell proliferation (Fan et al., 2013). Together with FBW7γ, USP36 deubiquitinates and stabilizes c-MYC in the nucleolus (Otto et al., 2009). USP7 binds directly to N-MYC and stabilizes it by deubiquitination
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FIGURE 9.9 Inhibitors that target MYC stability.
in neuroblastoma cells, and a small molecule inhibitor of USP7, P22077, markedly suppressed the growth of N-MYC-amplified neuroblastoma in a xenograft model (Fan et al., 2013). In principle, targeting these deubiquitinases causes MYC destabilization and tumor suppression. AURKA forms a complex with N-MYC, which protects N-MYC from FBW7-mediated proteasomal degradation (Otto et al., 2009). Two AURKA inhibitors, MLN8237 and MLN8054, disrupt the MYCAURKA complex, leading to N-MYC degradation and tumor regression in N-MYC-amplified neuroblastomas. MLN8237 also induced c-MYC degradation in P53-mutated human hepatocellular carcinoma cells. AURKA inhibitors may be potential cures for the treatment of MYC-dependent cancers (Brockmann et al., 2013). Inhibitors of PLK1, such as BI6727 or BI2356, preferentially induce substantial apoptosis of MYC-overexpressing tumor cells and synergistically potentiate the therapeutic efficacy of BCL-2 antagonists. These findings reveal a PLK1FBW7MYC signaling circuit underlying tumorigenesis and validate PLK1 inhibitors, alone or with BCL-2 antagonists, as potentially effective therapeutic MYC-overexpressing cancers (Xiao et al., 2016). Inhibitors that target MYC stability are shown in Fig. 9.9.
9.13 Synthetic lethality with MYC Synthetic lethality occurs when a combination of deficits in the expression of two or more genes results in cell death, whereas a deficit in only one of these genes does not. Overexpression of MYC, which occurs in many cancers, sensitizes cells to apoptosis by allowing the synthetic lethality gene to target cancer-relevant MYC overexpression to kill only cancer cells but spare normal cells. Synthetic lethality can be exploited to indirectly target the nontargetable MYC oncoprotein and prevent tumorigenesis in MYC-driven tumors (Thng et al., 2021).
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FIGURE 9.10 Compounds that interact with synthetic lethality targets of MYC.
Purvalanol B, an inhibitor of CDK1, selectively induced apoptosis in cells overexpressing MYC and significantly reduced tumor growth in MYCdependent lymphoma and hepatoblastoma mouse models. The selective induction of apoptosis after CDK1 inhibition is connected with upregulation of the pro-apoptotic molecule BIM and/or downregulation of the antiapoptotic molecule (Goga et al., 2007; Kang et al., 2014). The basic idea of using CHK1 (checkpoint kinase 1) inhibitors to treat cancer arose from the observation that tumor cells turn off DNA damage checkpoints during tumorigenesis or therapy, making them highly susceptible to additional genomic instability. MYC deregulation is sufficient to induce genome instability. MYC induces replication stresses and DNA damages through excessive replication-fork firing, making MYC-overexpressing tumors substantially more sensitive to CHK1 inhibition. CHK1 inhibition leads to cell death in MYC-overexpressing neuroblastomas, lymphomas, and breast and lung cancers (Ferrao et al., 2012; Murga et al., 2011). Glutamine metabolism is an essential energy source for the sustained growth and proliferation of some types of tumor cells (Xiang et al., 2015). Oncogenic MYC alters mitochondrial metabolism by increasing the surface expression of glutamine transporters involved in the transport of endogenous glutamine, which is also used as a bioenergetic substrate in cancer cells (Yuneva et al., 2007). Glutamine is converted into glutamate by GLS, an enzyme that is highly expressed in tumor cells. Inhibition of glutamine metabolism by GLS inhibitors selectively induces apoptosis in MYCoverexpressing tumor cells. The glutaminase inhibitor telagenastat (CB-839) is currently in use in clinical trials and can be an important new agent for the treatment of a broad spectrum of cancers associated with MYC deregulation (Duffy et al., 2021; Gross et al., 2014). Structures of two compounds that interact with synthetic lethality targets of MYC are shown in Fig. 9.10.
9.14 G-quadruplexes and expression of c-MYC This section discusses the mechanism for controlling MYC expression, which involves four-stranded tetrahelical DNA G-quadruplex (G4) structures overexpressed in gene promoters with small molecule inhibitors.
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As mentioned above, it is challenging to design small molecule inhibitors for the intrinsically disordered c-MYC protein because of the lack of a unique binding site and also because it has a very short half-life (Gregory & Hann, 2000). Instead of inhibiting the protein, we can also control the c-MYC protein expression by ligands that stabilize the G-quadruplex (G4) structures located at the beginning of the promoter region c-MYC gene. G-quadruplex, considered to repress transcription, is a unique four-stranded conformation of DNA formed from single-stranded guanine (G)-rich sequences, in which each guanine is hydrogen-bonded to the two adjacent guanines, forming a square planar shape known as the G-tetrad. Two or more such G-tetrads stack on top of one another to form the G-quadruplex’s four-stranded helical structure. The stability of a G4 largely depends on the monovalent cations, specifically K1 and Na1, where the positively charged cations occupy the stacked G-tetrads’ central channel and neutralize the electrostatic repulsion of guanine O6 oxygens (Dingley et al., 2005). The G4 structure of the c-MYC promoter, which adopts a parallel conformation of single-stranded DNA, consists of three G tetrads. The thermodynamically stable conformation has a significantly high melting temperature. The 30 and 50 regions of c-MYC form a capping structure at the corresponding terminal tetrads, providing a nice binding pocket for the small molecule ligands (Dai et al., 2011). Simonsson et al. first reported a parallel G-quadruplex structure in the proximal promoter region of the c-MYC proto-oncogene in 1998 (Simonsson et al., 1998). Siddiqui-Jain et al. subsequently showed that a small-molecule stabilization of G4 in the c-MYC promoter region could repress c-MYC transcription (Siddiqui-Jain et al., 2002). Interest in small-molecule development for promoter G-quadruplexes intensified when a synthetic ligand TMPyP4 successfully downregulated c-MYC proto-oncogene expression (Grand et al., 2002). The TMPyP4 ligand bound to G4-tetrade is shown in Fig. 9.11.
FIGURE 9.11 1H NMR solution of TmPyP4 in complex with c-MYC G4 revealed the stacking interaction of TmPyP4 on the top of the tetrad and electrostatic interaction of the positive charges of the ligand with the negatively charged phosphate backbone.
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The human genome contains more than 376,000 putative G4-forming sequences (PQS), and it is challenging to assign G4-related biological effects in human cells; apart from telomeric G4, many genes (50% or more) contain a G4-responsive motif in their promoters and are therefore potentially responsive to G4 ligands (Huppert & Balasubramanian, 2005). Targeting the G4-responsive motifs of the human genome by small molecules is nowadays one of the most accepted approaches to find anticancer agents, and numerous G4 ligands are listed in the database G4LDB (G4 ligands database; http:// www.g4ldb.org). Below, we describe some small molecules that inhibit the c-MYC protein expression by stabilizing the G-quadruplex at the beginning of its gene’s promoter region. (Structures are shown in Fig. 9.12). The earliest reported G4 interacting ligands are 2,6-diamido anthraquinones (Sun et al., 1997), TmPyP4 (Wheelhouse et al., 1998), and PIPER (Rangan et al., 2001), which have been used as a starting point for designing multiple closely related compounds. 2,6-Diamido anthraquinone has inspired the synthesis of acridines such as BRACO-19, the first reported telomerase inhibitor with an IC50 value of 115 nM (Gowan et al., 2002; Mitrasinovic,
FIGURE 9.12 Small molecules that inhibit the c-MYC protein expression by stabilizing the G-quadruplex.
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2020). Telomestatin, a macrocyclic natural product, is the benchmark compound for G4 recognition; it is a nanomolar in vitro inhibitor for G4s with high selectivity over duplex DNA ( . 70-fold) (Kim et al., 2003). Pyridostatin is a small synthetic molecule rationally designed as a G4 binder; it is composed of a flat electron-rich aromatic surface with the ability to form hydrogen bonds with G4; the molecules have shown an unprecedented stabilization of the human telomeric G4 (Rodriguez et al., 2008). Quarfloxin CX-3543 is a fluoroquinolone derivative and the first G-quadruplexinteracting agent to be tested in human clinical trials. It binds to Gquadruplex DNA and has been shown to interfere with rDNA G-quadruplexes’ interaction with nucleolin protein selectively, thereby inhibiting Pol I transcription and inducing apoptotic death in cancer cells (Drygin et al., 2009). For those interested in the field of targeting MYC with quadruplexes, more details on current inhibitors can be found in the recent review published in J. Med. Chem. (Chaudhuri et al., 2021).
9.15 Conclusions and perspective The targeting of MYC is an attractive opportunity for inhibiting a key player of cancer formation and progression and a powerful weapon to combat cancer. In recent years, considerable progress has been made in the development of MYC inhibitors, although this gene had been considered “undruggable” for decades; however, even after 40 years of intensive research, there is no MYC gene inhibitor available that can be used in clinical practice. The reason it is so difficult to develop a targeted drug for MYC is MYC is essential for the functioning of both normal and cancer cells, and the protein expressed by MYC is a very short-lived, unstructured protein that has no specific binding site in the nucleus, where the administration of drug is difficult; also, there is no known natural ligand to guide us in the development of inhibitors. This chapter describes some methods by which researchers have been able to circumvent the above problems and develop small molecules that indirectly inhibit c-MYC protein overexpression by inhibiting transcription and translation of the gene MYC and destabilizing the expressed protein. In recent years, significant progress has been made in the development of therapeutics targeting c-MYC expression, and we believe that further development will continue in the future, leading to the development of effective and safe target drugs for the gene MYC, which is overexpressed in more than 70% of cancers.
Acknowledgments This work is supported by the National Science Fund, Bulgarian Ministry of Education and Science—Grant No KL-06-KOCT/16 from 16.12.2020 and by the Slovenian Research Agency (ARRS) program and project grant P10201.
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Payne, L. N. (1992). Biology of avian retroviruses. The Retroviridae. Available from https://doi. org/10.1007/978-1-4615-3372-6_6. Payne, L. N., & Nair, V. (2012). The long view: 40 years of avian leukosis research. Avian Pathology, 41(1). Available from https://doi.org/10.1080/03079457.2011.646237. Peng, J., Marshall, N. F., & Price, D. H. (1998). Identification of a cyclin subunit required for the function of Drosophila P-TEFb. Journal of Biological Chemistry, 273(22). Available from https://doi.org/10.1074/jbc.273.22.13855. Ponzielli, R., Katz, S., Barsyte-Lovejoy, D., & Penn, L. Z. (2005). Cancer therapeutics: Targeting the dark side of Myc. European Journal of Cancer, 41(16). Available from https://doi.org/10.1016/j.ejca.2005.08.017. Puissant, A., Frumm, S. M., Alexe, G., Bassil, C. F., Qi, J., Chanthery, Y. H., Nekritz, E. A., Zeid, R., Gustafson, W. C., Greninger, P., Garnett, M. J., Mcdermott, U., Benes, C. H., Kung, A. L., Weiss, W. A., Bradner, J. E., & Stegmaier, K. (2013). Targeting MYCN in neuroblastoma by BET bromodomain inhibition. Cancer Discovery, 3(3). Available from https://doi.org/10.1158/2159-8290.CD-12-0418. Rangan, A., Fedoroff, O. Y., & Hurley, L. H. (2001). Induction of duplex to G-quadruplex transition in the c-myc promoter region by a small molecule. Journal of Biological Chemistry, 276(7). Available from https://doi.org/10.1074/jbc.M005962200. Rodriguez, R., Mu¨ller, S., Yeoman, J. A., Trentesaux, C., Riou, J. F., & Balasubramanian, S. (2008). A novel small molecule that alters shelterin integrity and triggers a DNA-damage response at telomeres. Journal of the American Chemical Society, 130(47). Available from https://doi.org/10.1021/ja805615w. Sammak, S., Hamdani, N., Gorrec, F., Allen, M. D., Freund, S. M. V., Bycroft, M., & Zinzalla, G. (2019). Crystal structures and nuclear magnetic resonance studies of the Apo form of the c-MYC:MAX bHLHZip complex reveal a helical basic region in the absence of DNA. Biochemistry, 58(29). Available from https://doi.org/10.1021/acs.biochem.9b00296. Sears, R., Ohtani, K., & Nevins, J. R. (1997). Identification of positively and negatively acting elements regulating expression of the E2F2 gene in response to cell growth signals. Molecular and Cellular Biology, 17(9). Available from https://doi.org/10.1128/ mcb.17.9.5227. Sears, R., Nuckolls, F., Haura, E., Taya, Y., Tamai, K., & Nevins, J. R. (2000). Multiple Rasdependent phosphorylation pathways regulate Myc protein stability. Genes and Development, 14(19). Available from https://doi.org/10.1101/gad.836800. Shiekhattar, R., Mermelstein, F., Fisher, R. P., Drapkin, R., Brian, D., Wessling, H. C., Morgan, D. O., & Reinberg, D. (1995). Cdk-activating kinase complex is a component of human transcription factor TFIIH. Nature, 374(6519). Available from https://doi.org/10.1038/ 374283a0. Shim, H., Dolde, C., Lewis, B. C., Wu, C. S., Dang, G., Jungmann, R. A., Dalla-Favera, R., & Dang, C. V. (1997). c-Myc transactivation of LDH-A: Implications for tumor metabolism and growth. Proceedings of the National Academy of Sciences of the United States of America, 94(13). Available from https://doi.org/10.1073/pnas.94.13.6658. Siddiqui-Jain, A., Grand, C. L., Bearss, D. J., & Hurley, L. H. (2002). Direct evidence for a Gquadruplex in a promoter region and its targeting with a small molecule to repress c-MYC transcription. Proceedings of the National Academy of Sciences of the United States of America, 99(18). Available from https://doi.org/10.1073/pnas.182256799. Simonsson, T., Pecinka, P., & Kubista, M. (1998). DNA tetraplex formation in the control region of c-myc. Nucleic Acids Research, 26(5). Available from https://doi.org/10.1093/nar/ 26.5.1167.
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Srutova, K., Curik, N., Burda, P., Savvulidi, F., Silvestri, G., Trotta, R., Klamova, H., Pecherkova, P., Sovova, Z., Koblihova, J., Stopka, T., Perrotti, D., & Polakova, K. M. (2018). BCR-ABL1 mediated miR-150 downregulation through MYC contributed to myeloid differentiation block and drug resistance in chronic myeloid leukemia. Haematologica, 103(12). Available from https://doi.org/10.3324/haematol.2018.193086. Stellas, D., Szabolcs, M., Koul, S., Li, Z., Polyzos, A., Anagnostopoulos, C., Cournia, Z., Tamvakopoulos, C., Klinakis, A., & Efstratiadis, A. (2014). Therapeutic effects of an antiMyc drug on mouse pancreatic cancer. Journal of the National Cancer Institute, 106(12). Available from https://doi.org/10.1093/jnci/dju320. Sun, D., Thompson, B., Cathers, B. E., Salazar, M., Kerwin, S. M., Trent, J. O., Jenkins, T. C., Neidle, S., & Hurley, L. H. (1997). Inhibition of human telomerase by a G-QuadruplexInteractive compound. Journal of Medicinal Chemistry, 40((14). Available from https://doi. org/10.1021/jm970199z. Suna, X. X., Chena, Y., Sua, Y., Wanga, X., Chauhana, K. M., Lianga, J., Daniela, C. J., Searsa, R. C., & Daia, M. S. (2018). SUMO protease SENP1 deSUMOylates and stabilizes c-Myc. Proceedings of the National Academy of Sciences of the United States of America, 115(43). Available from https://doi.org/10.1073/pnas.1802932115. Thng, D. K. H., Toh, T. B., & Chow, E. K. H. (2021). Capitalizing on synthetic lethality of MYC to treat cancer in the digital age. Trends in Pharmacological Sciences, 42((3). Available from https://doi.org/10.1016/j.tips.2020.11.014. Tsuneoka, M., Koda, Y., Soejima, M., Teye, K., & Kimura, H. (2002). A novel Myc target gene, mina53, that is involved in cell proliferation. Journal of Biological Chemistry, 277(38). Available from https://doi.org/10.1074/jbc.M204458200. Tu, W. B., Helander, S., Pilsta˚l, R., Hickman, K. A., Lourenco, C., Jurisica, I., Raught, B., Wallner, B., Sunnerhagen, M., & Penn, L. Z. (2015). Myc and its interactors take shape. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1849((5). Available from https://doi.org/10.1016/j.bbagrm.2014.06.002. Vera de Jonge, A., Mutis, T., Roemer, M. G. M., Scheijen, B., & Chamuleau, M. E. D. (2020). Impact of myc on anti-tumor immune responses in aggressive b cell non-hodgkin lymphomas: Consequences for cancer immunotherapy. Cancers, 12((10). Available from https://doi. org/10.3390/cancers12103052. Vervoorts, J., Lu¨scher-Firzlaff, J., & Lu¨scher, B. (2006). The ins and outs of MYC regulation by posttranslational mechanisms. Journal of Biological Chemistry, 281((46). Available from https://doi.org/10.1074/jbc.R600017200. Watson, D. K., Reddy, E. P., Duesberg, P. H., & Papas, T. S. (1983). Nucleotide sequence analysis of the chicken c-myc gene reveals homologous and unique coding regions by comparison with the transforming gene of avian myelocytomatosis virus MC29, Δgag-myc. Proceedings of the National Academy of Sciences of the United States of America, 80(8 I). Available from https://doi.org/10.1073/pnas.80.8.2146. Weinstein, I. B. (2002). Cancer: Addiction to oncogenes - The Achilles heal of cancer. Science, 297((5578). Available from https://doi.org/10.1126/science.1073096. Weiss, R. (1982). Experimental biology and assay of retroviruses. Molecular biology of tumor viruses. RNA tumor viruses (2nd ed., pp. 209260). Cold Spring Harbor Laboratory. Wheelhouse, R. T., Sun, D., Han, H., Han, F. X., & Hurley, L. H. (1998). Cationic porphyrins as telomerase inhibitors: The interaction of tetra- (N-methyl-4-pyridyl)porphine with quadruplex DNA [1]. Journal of the American Chemical Society, 120(13). Available from https:// doi.org/10.1021/ja973792e.
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Whitfield, J. R., Beaulieu, M. E., & Soucek, L. (2017). Strategies to inhibit Myc and their clinical applicability. Frontiers in Cell and Developmental Biology, 5. Available from https:// doi.org/10.3389/fcell.2017.00010. Xiang, Y., Stine, Z. E., Xia, J., Lu, Y., O’Connor, R. S., Altman, B. J., Hsieh, A. L., Gouw, A. M., Thomas, A. G., Gao, P., Sun, L., Song, L., Yan, B., Slusher, B. S., Zhuo, J., Ooi, L. L., Lee, C. G. L., Mancuso, A., McCallion, A. S., . . . Dang, C. V. (2015). Targeted inhibition of tumor-specific glutaminase diminishes cell-autonomous tumorigenesis. The Journal of Clinical Investigation, 125(6), 22932306. Available from https://doi.org/10.1172/ JCI75836. Xiao, D., Yue, M., Su, H., Ren, P., Jiang, J., Li, F., Hu, Y., Du, H., Liu, H., & Qing, G. (2016). Polo-like Kinase-1 regulates Myc stabilization and activates a feedforward circuit promoting tumor cell survival. Molecular Cell, 64(3). Available from https://doi.org/10.1016/j. molcel.2016.09.016. Yap, J. L., Wang, H., Hu, A., Chauhan, J., Jung, K. Y., Gharavi, R. B., Prochownik, E. V., & Fletcher, S. (2013). Pharmacophore identification of c-Myc inhibitor 10074-G5. Bioorganic and Medicinal Chemistry Letters, 23(1). Available from https://doi.org/10.1016/j. bmcl.2012.10.013. Yoshida, G. J. (2018). Emerging roles of Myc in stem cell biology and novel tumor therapies. Journal of Experimental and Clinical Cancer Research, 37(1). Available from https://doi. org/10.1186/s13046-018-0835-y. Yuneva, M., Zamboni, N., Oefner, P., Sachidanandam, R., & Lazebnik, Y. (2007). Deficiency in glutamine but not glucose induces MYC-dependent apoptosis in human cells. Journal of Cell Biology, 178(1). Available from https://doi.org/10.1083/jcb.200703099. Zuber, J., Shi, J., Wang, E., Rappaport, A. R., Herrmann, H., Sison, E. A., Magoon, D., Qi, J., Blatt, K., Wunderlich, M., Taylor, M. J., Johns, C., Chicas, A., Mulloy, J. C., Kogan, S. C., Brown, P., Valent, P., Bradner, J. E., Lowe, S. W., & Vakoc, C. R. (2011). RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature, 478(7370). Available from https://doi.org/10.1038/nature10334.
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Chapter 10
Current status of viral biomarkers for oncogenic viruses Kazim Yalcin Arga1,2 and Medi Kori1 1
Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey, Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey 2
10.1 Introduction In 1911, Peyton Rous introduced the idea that viruses might play a role in the etiology of malignancies, based on his observations about Rous sarcoma virus (RSV) (Rous, 1911). The reporting of RSV pointed to the idea that some viruses could cause malignancies, and the presence of oncogenes should be further investigated. In the 1930s, two tumor viruses were identified in mammals, suggesting that viruses might also be involved in human cancers. In light of this information, the first human tumor viruses were discovered in the 1960s and 1970s (White et al., 2014). Today, according to the International Agency for Research on Cancer (IARC), six oncogenic viruses are classified as Group 1 human carcinogens, meaning that there is sufficient evidence for these viruses to cause carcinogenicity in humans. Group 1 carcinogens include EpsteinBarr virus (EBV), hepatitis B virus (HBV), hepatitis C virus (HCV), human T-cell lymphotropic virus-1 (HTLV-1), human herpesvirus-8 (HHV-8), also known as Kaposi’s sarcoma herpesvirus (KSHV), and highly oncogenic human papillomaviruses (HPVs) (IARC, 2012) (Table 10.1). Although the different types of viruses cause different types of cancer, they share some common features, such as the following: (1) oncogenic viral infection is essential for the development of cancer but is not sufficient on its own; (2) viral cancers occur in persistent infections and emerge years to decades after acute infection; (3) the immune system can play a harmful or protective role, for example, immunosuppression increases in several viral cancers (Mesri et al., 2014).
Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00009-1 Copyright © 2023 Elsevier Inc. All rights reserved.
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TABLE 10.1 Human oncogenic viruses. Oncogenic virus
Discovery
Type
Oncogenic proteins
Infection prevalence
Associated cancer(s)
Cancer causing infection prevalence
EBV
1964
DNA (linear doublestranded)
EBNA-1 and LMP-1
Positive in approximately 90% of the world population
Gastric cancer, nasopharyngeal cancer, Burkitt’s lymphoma, and Hodgkin’s lymphoma
Causes 1.5% of all cancers in the world
HBV
1965
DNA (enveloped, partially doublestranded, circular)
HBx
Approximately 3.4% individuals worldwide have chronic HBV infection
Hepatocellular carcinoma
Induces 80% of the total virusassociated hepatocellular cancer cases
HCV
1989
RNA (enveloped, positive-sense single-stranded)
core and NS3
Approximately 0.6% individuals worldwide have chronic HCV infection
Hepatocellular carcinoma
Induces up to 20% of the total virusassociated hepatocellular cancer cases
HTLV-1
1979
RNA (enveloped, single-stranded)
tax and HBz
Approximately 0.13% people in the world have infection
Adult T-cell leukemia/ lymphoma (ATLL)
About 2%5% infected individuals develop ATLL
HHV-8
1994
DNA (enveloped, doublestranded)
LANA
90% of the adult population in subSaharan Africa have infection
Kaposi’s sarcoma
Kaposi’s sarcoma incidence in infected individuals is 0.001
HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, and -59
1977
DNA (nonenveloped, circular, doublestranded)
E6 and E7
Positive in approximately 12% of the world population
Cervical, anal, vaginal, penile, oropharyngeal, and vulvar cancers
About 5% of all cancers in the world
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It has been estimated that 13% of all human malignancies worldwide are due to infections (de Martel et al., 2020). Although just half a century has passed since the first discovery of oncoviruses, there is still a considerable gap to be filled to reduce much of the global human cancer burden. For example, today, specific treatment strategies for most virus-related cancers are inadequate. Therefore developments in detecting and treating these cancers caused by oncogenic viruses will transform and advance life science research and the life science industry. Biomarkers are the indicators of pharmacological response to normal biological processes, pathological processes, or therapeutic processes that can be objectively measured and tested. Biomarkers enable early detection of malignancy and guide the choice of targeted therapy based on the specific molecular features of cancer. According to their recognized applications, they are divided into subtypes such as diagnostic, prognostic, or predictive biomarkers (Califf, 2018). In this chapter, we aim to provide a global perspective on the biomarkers of the above-mentioned oncogenic viruses by clarifying the current state of knowledge regarding biomarkers that promote the search for detecting diseases and therapeutic strategies.
10.2 Epstein-Barr virus EBV is the first virus associated with human cancers (1964) and the first human virus to be sequenced (Baer et al., 1984). EBV infection is the most common infection in humans, positive in approximately 90% of the world’s population (Fugl & Andersen, 2019). EBV belongs to the gammaherpesvirus group and has a linear double-stranded DNA genome (Sugimoto et al., 2019). The EBV life cycle can be divided into two phases: (1) lytic phase (EBV replication) and (2) latent phase (EBV blocks most of its proteincoding genes). In the latent phase, the EBV genome encodes five nuclear proteins (the EBNAs), two latent membrane proteins (LMPs), two untranslated RNAs (EBERs), and nontranscribed BART. EBNA-1 and LMP-1 are the prominent oncoproteins that induce EBV-related cancer development. EBNA-1 is crucial for the amplification of the EBV genome, and it minimizes the activation of tumor suppressor p53 and cell death. LMP-1 is one of the LMPs that play an important role in EBV persistence and latency. It induces telomerase activity and activates important signaling pathways such as the JNK, NF-κB, and p38 pathways. In addition, the LMP-1 oncogene modulates migration, differentiation, and tumorigenesis processes (Kang et al., 2016).
10.2.1 Epstein-Barr virus-associated cancers EBV is responsible for developing approximately 1.5% of all cancers in the world (Farrell, 2019). EBV can infect B cells and epithelial cells and affect
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both B cells and malignancies of epithelial origin. It induces gastric cancer, nasopharyngeal cancer, Burkitt’s lymphoma, and Hodgkin’s lymphoma (Chesnokova & Hutt-Fletcher, 2014; Young et al., 2016). Gastric cancer is the third leading cause of cancer-related deaths, and 10% of gastric cancers have been associated with EBV infection (Ayee et al., 2020). One of the head and neck cancers, nasopharyngeal carcinoma, is an epithelial carcinoma and accounts for 7% of all cancers diagnosed worldwide in 2018 (Chen et al., 2019). LMP-1 was found in 65% of nasopharyngeal carcinomas, indicating an association between EBV infection and nasopharyngeal carcinoma (Ayee et al., 2020). The most common pediatric cancer worldwide, Burkitt’s lymphoma, is an aggressive B-cell lymphoma linked to EBV. Especially in the high-risk regions, such as Africa, more than 80% of Burkitt’s lymphoma cases have been linked to EBV (Ha¨mmerl et al., 2019). Hodgkin’s lymphoma is a B-cell lymphoma, and in 2020, a total of 8480 individuals were diagnosed with Hodgkin’s lymphoma in the United States (Siegel et al., 2020). The prevalence of EBV association with Hodgkin lymphoma varies by region. Around 30%40% of Hodgkin’s lymphoma cases in North America and Europe were reported to be EBV positive, while in other parts of the world such as Latin America, Asia, and Africa, EBV was found in almost 100% (Ayee et al., 2020).
10.2.2 Epstein Bar virus-associated cancer biomarkers The LMP-1 oncogene is active in almost all EBV-associated cancers and modulates migration, differentiation, and tumorigenesis processes (Kang et al., 2016). A study based on a literature-based meta-analysis concluded that LMP1 expression could be used as a prognostic biomarker in all EBVassociated cancers (Chen, Zhang et al., 2015). As part of The Cancer Genome Atlas (TCGA) project, researchers comprehensively analyzed the molecular and clinical characteristics of 295 primary gastric cancers and investigated EBV-associated gastric cancers (EBVaGCs) in this study. Accordingly, they found PIK3CA mutations in 80% of EBVaGC cases, ARID1A mutations in 55%, BCOR mutations in 23%, and overexpression of PD-L1/PD-L2, CDKN2A promoter hypermethylation in 100% (2014). Similarly, another group of researchers evaluated EBVaGC cases as a part of the TCGA project. In contrast to the previous work, they concluded that the expressions of JAK2, ERBB2, and CD44 were amplified, while WWOX, IMMP2L, PTPRD, FAM190A, PTEN, and MACROD2 were deleted in EBVaGCs (Gulley, 2015). These aberrations and features can be presented as markers for EBVaGC and should be further evaluated by clinical studies. For example, the expression of PD-L1 was investigated in gastric cancer patients, and in accordance with the above TCGA study, PD-L1 was found to be overexpressed in EBVaGCs. Therefore the study proposed PD-L1 as a prognostic biomarker for EBVaGC cases (Bo¨ger et al., 2016).
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Among the above aberrations, Kang et al. (2016) also assessed the major molecular alterations in 234 EBVaGC patients at the protein level and suggested that PTEN may serve as a prognostic biomarker. The researchers reported that patients with PTEN loss had a higher risk of lymph node metastasis and a worse 5-year survival rate than cases without PTEN inactivation (Kang et al., 2016). In another study investigating the clinical relevance and prognostic role of PIK3CA mutations in EBVaGC patients, Seo et al. (2019) conducted a study and found that exon 9 mutation of PIK3CA is a prognostic biomarker to estimate the survival of patients with EBVaGCs (Seo et al., 2019). Researchers investigated the potential role of p16 as part of EBVassociated nasopharyngeal carcinoma (EBVaNC) biomarkers. They found that upregulation of p16 was associated with improved progression-free survival. As a result of the study, the researchers identified p16 as a potential complementary marker for the prognosis of EBVaNCs (Jiang et al., 2016). In another study by Zuo et al. (2017), activation of CDH6 and RUNX2 were found to be novel mesenchymal markers in EBVaNC (Zuo et al., 2017). The long noncoding RNAs MALAT1, AFAP1-AS1, and AL359062 were found to have higher expression in EBVaNC than in EBV-negative cancers. Moreover, the expression of these long noncoding RNAs decreased after the individuals received therapy. Therefore this study suggested that MALAT1, AFAP1-AS1, and AL359062 serve as diagnostic and prognostic biomarkers for nasopharyngeal carcinomas (He et al., 2017). In EBV-associated Hodgkin’s lymphoma (EBVaHL), FoxO3a expression was generally downregulated, while ID1 was upregulated. As a result, researchers introduced FoxO3a and ID1 as potential cancer-initiating cell markers for EBVaHL (Jun-Ichiro et al., 2016). In another study, the viral genome of EBV was found to be present in the plasma of EBVaHL individuals, while its DNA was undetectable in patients receiving therapy. From this point of view, the researchers suggested that free plasma EBV DNA can be used as a noninvasive biomarker for EBVaHL patients. They also suggested that serial monitoring of free plasma EBV DNA can estimate the patient’s response to therapy (Gandhi et al., 2006). Moreover, the increased miR-127 expression is a key event in developing EBV-positive Burkitt’s lymphoma (Leucci et al., 2010), which may serve as a marker (Table 10.2).
10.3 Hepatitis B virus and hepatitis C virus In 1965, Baruch Blumberg discovered HBV using the serological screening method, and he was awarded the Nobel Prize in Medicine in 1976 based on his discovery (Chang et al., 2017). HBV belongs to the Hepadnaviridae family and consists of enveloped, partially double-stranded circular DNA. The HBV genome encodes seven proteins, and one of the encoding proteins, HBx, plays an important role in developing HBV-induced cancer. The HBx
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TABLE 10.2 EpsteinBarr (EBV)-associated cancer biomarkers. Cancer
Biomarker
Clinical application
References
All cancers
LMP1 expression activation
Prognostic biomarker
Chen, Zhang et al. (2015), Chen, Stephen et al. (2015)
Gastric cancer
PIK3CA, ARID1A, and BCOR mutations, overexpression of PD-L1/ PD-L2, CDKN2A promoter hypermethylation
Biomarker
The Cancer Genome Atlas Research Network (2014)
JAK2, ERBB2, and CD44 were amplified, and WWOX, IMMP2L, PTPRD, FAM190A, PTEN, and MACROD2 were deleted
Biomarker
Gulley (2015)
PD-L1 overexpressed
Prognostic biomarker
Bo¨ger et al. (2016)
PTEN deletion
Prognostic biomarker
Kang et al. (2016)
Exon 9 mutation of PIK3CA
Prognostic biomarker
Seo et al. (2019)
p16 upregulation
Prognostic biomarker
Jiang et al. (2016)
CDH6 and RUNX2 activation
Mesenchymal biomarkers
Zuo et al. (2017)
MALAT1, AFAP1-AS1, and AL359062 upregulation
Diagnostic and Prognostic biomarkers
He et al. (2017)
FoxO3a downregulation, and ID1 upregulation
Cell biomarker
Ikeda et al. (2016)
Presented free plasma EBV DNA
Noninvasive biomarker
Gandhi et al. (2006)
Increased miR-127 expression
Biomarker
Leucci et al. (2010)
Nasopharyngeal cancer
Hodgkin’s lymphoma
Burkitt’s lymphoma
protein supports cell cycle progression, deactivates negative growth regulators, and binds and inhibits p53 expression (Kew, 2011). It has been estimated that 55% of cases with HBx protein expression develop HBV-induced
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cancer (Madihi et al., 2020). One of the important roles of HBx is mediating the replication of HBV (Ringehan et al., 2017). Infection with HBV can lead to acute or chronic infections. HBV-induced cancer development has been quite common in chronically infected individuals. It has been estimated that 5%10% of EBV infections lead to chronic infection, and approximately 257 million people worldwide have chronic HBV infection. Diseases caused by chronic HBV infection could cause 20 million deaths worldwide between 2015 and 2030 (Yang et al., 2019). The discovery of HCV was based on cDNA library screening in 1989 (Chang et al., 2017), and in 2020, Charles Rice, Harvey Alter, and Michael Houghton were awarded the Nobel Prize in Physiology or Medicine for their discovery of the virus (Nobel Prize, 2020). HCV is a blood-borne and enveloped virus that belongs to the Flaviviridae family. HCV has a positive-sense single-stranded RNA genome. Therefore HCV cannot integrate directly into the host genome and its genome functions as a direct mRNA (Bukh, 2016). The genomic RNA of HCV contains three structural and seven nonstructural proteins. One structural protein, core, is essential for the formation of the viral capsid to protect the genomic RNA. It interacts with numerous transcription factors and regulates signaling pathways that play a role in cell proliferation and apoptosis, including TGF-β, VEGF, WNT, COX-2, and PPARα signaling pathways. Accordingly, core protein is a major oncoprotein that induces HCV-associated cancer development (Mahmoudvand et al., 2019). Another HCV oncoprotein involved in HCV-associated cancer development is a nonstructural protein called NS3. It has been reported that NS3 can bind p53 (Ishido & Hotta, 1998). Like HBV infection, HCV infection also leads to acute or chronic infection. Globally, an estimated 51 million people have chronic HCV infection, and of these 51 million, up to 20% are diagnosed with HCV-related diseases (Yang et al., 2019).
10.3.1 Hepatitis B virus- and hepatitis C virus-associated cancers HBV and HCV induce the development of hepatocellular carcinoma. Hepatocellular carcinoma is a primary malignancy of the liver that originates from hepatocytes and accounts for 75%85% of liver cancers (Ghouri et al., 2017). Liver cancer is the fifth most common cancer, causing 8.2% of deaths worldwide in 2018 (Bray et al., 2018). In 2020, approximately 42,810 individuals in the United States were diagnosed with liver cancer, of which30,160 died (Siegel et al., 2020). Hepatocellular carcinoma is the fourth leading cause of cancer death worldwide (Yang et al., 2019). HBV infection is thought to cause 80% of total virus-associated hepatocellular cancers, while HCV accounts for 20% of cases (Petruzziello, 2018). Current vaccination against HBV has the potential to reduce the prevalence of developing HBVinduced hepatocellular carcinoma, but there remains a need for
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complementary treatments in addition to vaccines. Besides, no vaccine is currently available for HCV infection (Ringehan et al., 2017).
10.3.2 Hepatitis B virus-associated cancer biomarkers Although the expression of heat shock proteins (HSPs) is frequently upregulated in hepatocellular cancer, their role in HBV-associated hepatocellular carcinoma (HBVaHCC) is unknown. Based on this viewpoint, researchers investigated the role of HSPs in HBVaHCC patients. They found that HSP expression is excessive in HBVaHCCs. Moreover, glucose-regulated proteins such as GRP78 and GRP94 might have crucial functions in the development of malignancy. Through this, they recommended GRP78, GRP94, and HSP90 as prognostic biomarkers for HBVaHCC patients (Lim et al., 2005). In addition to protein markers, potential biomarkers derived from serum miRNAs remain under investigation for HBVaHCC. Researchers analyzing the possible role of miRNAs in HBVaHCC patients found that serum miR18a was significantly higher in patients than in healthy controls. The researchers deduced from the study that serum miR-18a is a potential noninvasive biomarker for HBVaHCC screening (Li et al., 2012). Sixteen serum cytokine expressions from 105 HBVaHCC patients were measured and analyzed to determine whether the expressions were associated with various clinical factors. The analysis revealed that a deficiency in platelet count, serum albumin, and interleukin-6 (IL-6) expressions negatively affected disease-free survival. Given this information, researchers suggested that serum IL-6 deficiency is a prognostic risk factor for HBVaHCC recurrence (Cho et al., 2015). Liver transplantation is the best treatment option for patients with hepatocellular carcinoma. A study investigating the prognostic value of glypican 3 (GPC-3) in liver transplanted HBVaHCC patients found that patients with positive GPC-3 had a worse prognosis. The researchers concluded that GPC3 is a potential prognostic biomarker for liver-transplanted HBVaHCC patients (Cui et al., 2015). In a retrospective study, serum alpha-fetoprotein (AFP) was determined to have diagnostic and prognostic potential in patients with hepatocellular carcinoma depending on their pathogenic characteristics (i.e., hepatitis infection, cirrhosis). As a result of the study, researchers obtained that serum AFP levels were significantly overexpressed in HBVaHCC patients, and they concluded that serum AFP is a diagnostic and prognostic biomarker for HBVaHCC patients (Yao et al., 2016). A recent study sought to evaluate PTX3 expression by comparing: (1) HBV-infected patients with healthy donors and (2) HBVaHCC patients with chronic hepatitis or cirrhosis. After the analysis, the researchers found that PTX3 expression levels may be associated with HBVaHCC. They specified PTX3 as an independent risk factor
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TABLE 10.3 Hepatitis B virus (HBV)-associated cancer biomarkers. Cancer
Biomarker
Clinical application
Reference
Hepatocellular carcinoma
GRP78, GRP94, and HSP90 overexpression
Prognostic biomarkers
Lim et al. (2005)
Serum miR-18a upregulation
Noninvasive biomarker
Li et al. (2012)
Serum IL-6 deficiency
Prognostic biomarker
Cho et al. (2015)
Positive GPC-3 cause worse prognosis
Prognostic biomarker
Cui et al. (2015)
Serum alpha-fetoprotein (AFP) overexpression
Diagnostic and prognostic biomarker
Yao et al. (2016)
Low miR-150 and high miR-101 levels
Noninvasive biomarkers
Safaa et al. (2017)
miR-22 and miR-122 downregulation
Biomarker
Qiao et al. (2017)
PTX3 expression levels
Diagnostic biomarker
Deng et al. (2020)
involved in developing HBVaHCC. They identified PTX3 as a diagnostic marker for HBVHCC patients (Deng et al., 2020). Safaa et al. (2017) conducted a research study in a cohort of Egyptian patients to investigate miRNAs in HBVaHCC. The study concluded that both miR-150 (low expression) and miR-101 (high expression) could be used as noninvasive biomarkers for the early detection of HBVaHC (Safaa et al., 2017). Another study that investigated the expression levels of miR-22 and miR-122 and their correlation with the clinical characteristics of HBVaHCC patients found that the expression of both miRNAs decreased depending on lymph node metastasis, differentiation grade, and tumor size. Therefore researchers interpreted the results that miR-22 and miR-122 have the potential to serve as markers for HBVaHCC (Qiao et al., 2017) (Table 10.3).
10.3.3 Hepatitis C virus-associated cancer biomarkers In a study utilizing cDNA microarrays on 20 HCV-positive patients, several markers comprising 50 genes were highly overexpressed in cancerous samples (Smith et al., 2003). These markers include genes associated with cell proliferation such as STK15 and secreted or plasma proteins such as PGCP,
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PLA2G13, and PLA2G7. The researchers pointed out that the 50 marker genes can serve as diagnostic and prognostic markers and provide insights into the mechanism of HCV-associated hepatocellular carcinoma (HCVaHCC) (Smith et al., 2003). In another study, where patients were stratified according to their clinical characteristics (chronic hepatitis infection, liver cirrhosis, and hepatocellular carcinoma), 328 serum samples were collected from patients chronically infected with HCV, and the serum level KL-6 (one of the MUC1 antigens) was measured by enzyme immunoassay. Accordingly, the highest serum KL-6 expression was shown in patients with HCVaHCC, and a high KL-6 serum level was associated with multiple tumor nodules. Moreover, a high KL-6 serum level was associated with later cancer development in chronic hepatitis and liver cirrhosis. As a result of all these findings, the study concluded that serum KL-6 is a novel diagnostic and prognostic tumor marker in HCVaHCC (Kurosaki et al., 2005). To understand the clinical significance of oxidative stress markers in HCVaHCC patients, the levels of manganese superoxide dismutase (MnSOD) and thioredoxin (TRX) were measured from serum samples, and the correlation between the marker levels and patient outcome was investigated. The analysis results showed that both oxidative stress markers studied were significantly overexpressed in HCVaHCC patients. Besides, these patients had a worse prognosis. Taken together, the researchers suggested that serum MnSOD and TRX levels can predict patients’ prognosis and, thus have the potential to be clinical biomarkers (Tamai et al., 2011). Similar to HBVaHCC, AFP and GPC-3 markers also correlated with HCVaHCCs. Seven hundred and seven chronically infected HCV patients were included in the study to assess the value of noninvasive risk factors for the development of HCVaHCC. As a result of the study, the researchers indicated that in addition to elevated AFP levels ($20 ng/mL), preliminarily high AFP levels (620 ng/mL) also contribute to HCVaHCC progression. Thus researchers indicated that in addition to the fibrosis stage, the AFP level is also a reliable risk factor for HCVaHCC (Tateyama et al., 2011). Another study examined plasma GPC-3 levels by immunoassay and expression levels by immunohistochemical staining in 56 patients and 60 controls. After analysis, the researchers found that HCV infection strongly affects the secretion and expression of GPC-3 in cancer samples and suggested that GPC-3 may serve as a predictive biomarker for HCVaHCC (Shimizu et al., 2020). Serum levels of DKK-1, GP73, and MDK were examined in HCVinfected patient samples diagnosed with hepatocellular carcinoma or liver cirrhosis. In addition, two groups with chronic HCV infection or no history of liver disease were included in the study. As a result of the expression evaluation of the studied proteins individually or in combination, the researchers showed that the expressions of DKK-1, GP73, and MDK were higher in HCVaHCC patients than in other groups. According to the results of sensitivity (100%), accuracy (82.8%), and specificity (74.4%), the study
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found that GP73 and DKK-1 are the best dual markers for HCVaHCC (N Zekri et al., 2020). A study that analyzed the miRNA expression profiles in the serum of HCVaHCC patients found that miR-125a-5p is overexpressed in HCVaHCC patients at both early and advanced stages, while the applied treatment reduces the same miRNA expression. From this point of view, the researchers offered that miR-125a-5p is a noninvasive biomarker that allows early diagnosis of HCVaHCC (Oura et al., 2019). In addition to experimental methods, researchers implemented a bioinformatics approach based on transcriptome profiling and protein-protein interaction network reconstruction. They uncovered that altered HMMR, CCNB1, and KIF20A lead to worse clinical outcomes, and they are potential targets for the diagnosis and treatment of HCVaHCC (Liu et al., 2019) (Table 10.4).
TABLE 10.4 Hepatitis C virus-associated cancer biomarkers. Cancer
Biomarker
Clinical application
Reference
Hepatocellular carcinoma
Overexpression of 50 genes (cell proliferation-related genes and secreted or plasma proteins)
Diagnostics and prognostics biomarker
Smith et al. (2003)
High serum KL-6 expression
Diagnostic and prognostic biomarker
Kurosaki et al. (2005)
MnSOD and TRX overexpression
Clinical biomarkers
Tamai et al. (2011)
Elevated AFP levels
Biomarker
Tateyama et al. (2011)
miR-125a-5p overexpression
Noninvasive biomarker
Oura et al. (2019)
Altered HMMR, CCNB1, and KIF20A cause worse clinical outcome
Diagnostic biomarker
Liu et al. (2019)
GPC-3 upregulation
Predictive biomarker
Shimizu et al. (2020)
DKK-1 and GP73 upregulation
Biomarker
Zekri et al. (2020)
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10.4 Human T-cell lymphotropic virus-1 HTLV-1 was discovered in 1979 and first mentioned in an article published in 1980 (Poiesz et al., 1980). HTLV-1 belongs to the retrovirus family and is the first human retrovirus to possess oncogenic potential (Coffin, 2015). The retrovirus family includes RNA viruses that use retroviral reverse transcriptase to amplify their genomic RNA. The discovery of retroviral reverse transcriptase dates back to 1970, and its discovery paved the way for the discovery of human cancer retroviruses (Balvay et al., 2007). HTLV-1 is an enveloped RNA virus that encodes the structural gag polyprotein, an accessory gene called HBz, and two regulatory proteins, Tax and Rex. Among the HTLV-1-encoded proteins, tax and HBz have been reported as key factors in the development of HTLV-1-associated pathologies (Yamada et al., 2021). Tax has been reported to ruin the cell cycle by disabling cell cycle checkpoints, promote cellular proliferation by mediating interleukin expression, and activate various transcriptional pathways (i.e., canonical and noncanonical NF-κB pathways) (Currer et al., 2012). T cells are one of the primary host cells for HTLV and HBz. HTLV and HBz support T-cell proliferation, induce regulatory T cell differentiation, and inactivate cell death and senescence (Zhao, 2016). HTLV-1 causes lifelong infection in approximately 10 million people worldwide (Hirons et al., 2021), and the virus is thought to be transmitted between people, mostly through blood transfusions (Kannian & Green, 2010). HTLV1 infection is endemic in southwestern Japan, Africa, Caribbean Islands, and among Australian aborigines (Hoshino, 2012).
10.4.1 HTLV-1-associated cancers Human infection with retroviruses can cause a variety of pathologies, including sarcomas, breast carcinomas, and leukemias (Balvay et al., 2007). Adult T-cell leukemia/lymphoma (ATLL) is a neoplasm that occurs due toHTLV-1 infection. ATLL is an immensely aggressive, rare T-cell malignancy that is difficult to diagnose and prognosticate. Moreover, there is currently no fulfilling therapy for ATLL (Hermine et al., 2018). The neoplasm most commonly occurs after the age of 50 in individuals who were infected in childhood. ATLL is classified into the following subtypes: acute, lymphocytic, chronic, and smoldering, in terms of lymphocyte quantity, biochemical criteria, the intensity of symptoms, and solid organ involvement (Bangham, 2018). The acute ATLL subtype accounts for 55%60%, the lymphocytic ATLL type accounts for 20%25%, the chronic ATLL type accounts for 10%20%, and the smoldering ATLL type accounts for 5%10% of ATLL patients (El Hajj et al., 2020). Approximately 2%5% of individuals infected with HTLV-1 during their lifetime develop ATLL (Shah et al., 2020).
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10.4.2 HTLV-1-associated cancer biomarkers To uncover biomarkers for ATLL, a study used microarray to examine about 12,000 genes. After analysis, the researchers found that three genes, including TSLC1, CAV1, and PGDS, were significantly upregulated (more than 30-fold). Among these three genes, the researchers underlined TSLC1 expression and decided to confirm TSLC1 upregulation on ATLL cells and cell lines due to the role of TSLC1 in lung cancer as a tumor suppressor. After confirming the high ectopic expression of TSLC1 also in ATLL- and HTLV-1-infected cell lines, the researchers introduced TSLC1 as a new marker for acute-type ATLL (Sasaki et al., 2005). Nishioka et al. (2005) conducted a study to determine the expression levels of both the membrane type and soluble form of CD30 (also known as TNFRSF8) in ATLL patients and cell lines (MT-2, L540, and Karpas 299). The analysis results showed that the soluble form of CD30 expression was correlated with disease progression. Namely, patients with high CD30 expression had more aggressive ATLL. Moreover, despite the treatment regimen applied, the patients who died of ATLL had high soluble CD30 serum levels, while their soluble CD30 levels decreased compared to those whose treatment resulted in success. Therefore the study associated CD30 expression with ATLL cell proliferation and survival, and they suggested that soluble CD30 may be a useful marker for patient follow-up (Nishioka et al., 2005). Recently, another study suggested that soluble CD30 is a marker for ATLL. In this study, researchers compared the amount of soluble CD30 in ATLL patients with plasma from noncancerous HTLV-1-positive individuals and found that soluble CD30 expression was overexpressed in ATLL patients. Besides, researchers reported that the amount of soluble CD30 in noncancerous HTLV-1-positive individuals may help predict the risk of developing ATLL (Takemoto et al., 2016). To determine the expression patterns of soluble OX40 (also known as CD134, a member of the tumor necrosis factor receptor superfamily), the quantitative enzyme-linked immunosorbent assay was applied in another study to blood samples from ATLL patients, nonsymptomatic HTLV-1positive individuals, and healthy groups. The results of the analysis showed that the level of soluble OX40 was remarkably high in ATLL patients compared to other groups. As a result, researchers suggested that abnormally high soluble OX40 plasma levels serve as a diagnostic biomarker for ATLL (Tanaka et al., 2019). Another study intended to discover potential protein biomarkers for ATLL disease by analyzing a total of 85 plasma samples using a Slow Offrate Modified Aptamer (SOMAmer)-based high-throughput proteomic assay. In this study, the researchers compared proteomic profiles of (1) nonsymptomatic HTLV-1-positive individuals with ATLL patients, (2) acute, lymphatic, smoldering, and chronic ATLL types, and (3) pre-remission and proremission states of ATLL patients. The analysis revealed that soluble
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TABLE 10.5 Human T-cell lymphotropic virus-1 (HTLV-1) associated cancer biomarkers. Cancer
Biomarker
Clinical application
Reference
Adult T-cell leukemia/ lymphoma
TSLC1 upregulation
Biomarker
Sasaki et al. (2005)
High soluble CD30 expression
Biomarker
Nishioka et al. (2005)
High soluble CD30 expression
Biomarker
Takemoto et al. (2016)
Soluble OX40 upregulation
Diagnostic biomarker
Tanaka et al. (2019)
Soluble TNFR2 upregulation
Diagnostic biomarker
Guerrero et al. (2020)
Soluble CADM1 upregulation
Clinical biomarker
Nakahata et al. (2021)
TNFR2 expression is significantly high (10-fold) in acute ATLL patients compared with nonsymptomatic HTLV-1 carriers. TNFR2 levels also dropped in nonsymptomatic HTLV-1 carriers after the successful remission of the disease. Considering all these consequences, the researchers concluded that soluble TNFR2 is a diagnostic biomarker for acute ATLL (Guerrero et al., 2020). Soluble CADM1 levels were also measured using AlphaLISA technology by comparing HTLV-1 carriers, ATLL patients, and healthy groups with peripheral blood samples. The results of the analysis showed that soluble CADM1 was remarkably upregulated in ATLL patients. Besides, the researchers found that soluble CADM1 expression increased in response to the disease state. The aggressive ATLL patients have higher expression of soluble CADM1. In addition, soluble CADM1 expression varied depending on the treatment. Overall, the level of soluble CADM1 in plasma is reported as a clinical biomarker for ATLL patients (Nakahata et al., 2021) (Table 10.5).
10.5 Human Herpesvirus-8 Human Herpesvirus-8 (HHV-8), also known as KSHV, was discovered in 1994 by using representational difference analysis (a PCR-based method) and is the last human oncovirus discovered to date (Mesri et al., 2010). HHV-8 is an enveloped, large, double-stranded DNA virus that belongs to the gammaherpesvirus family like EBV. HHV-8 is the only rhadinovirus
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(second genus in the gammaherpesvirus family) discovered in humans (Edelman, 2005). HHV-8 remains in the host for life and exists in a latent form in infected cells. Although the HHV-8 genome encodes about 100 genes, several viral genes are expressed during the latent form (Juillard et al., 2016). Among them, latency-associated nuclear antigen (LANA) plays an important role in the development of HHV-8-associated diseases. LANA is critical for replication and persistence. It enhances the expression of genes regulated by various transcription factors, promotes the expression of latency genes, contributes to genomic reprogramming in infected cells, and stabilizes and activates the c-Myc oncogene (Liu et al., 2007). HHV-8 infection prevalence in the world is unstable and changes according to geographic region and ethnicity. The highest HHV-8 infection prevalence is found in subSaharan Africa, representing up to 90% of the adult population. It is also estimated that the incidence of infection is 20%30% in the Mediterranean region and less than 10% in Northern Europe, Asia, and the United States (Cesarman et al., 2019).
10.5.1 HHV-8-associated cancers HHV-8 is associated with an aggressive and lethal malignancy called Kaposi’s sarcoma. The incidence of Kaposi’s sarcoma in infected individuals is 1 in 100,000 (Facciola` et al., 2017). Although the relationship between HHV-8 and Kaposi’s sarcoma is not very old, the first cases of Kaposi’s sarcoma lesions were described in 1872 by a dermatologist named Moritz Kaposi (Antman & Chang, 2000). In 2018, approximately 42,000 new cases and 20,000 deaths were associated with Kaposi’s sarcoma worldwide (Bray et al., 2018). Kaposi’s sarcoma is a multifocal neoplasm that usually appears as dark purple lesions on the skin. The cancerous lesions contain more than one cell type, and the dominant cell is a spindle cell derived from the endothelial origin (Verma & Robertson, 2003). Kaposi’s sarcoma can occur in several forms, such as (1) classic Kaposi’s sarcoma (rare and seen in the elderly), (2) endemic Kaposi’s sarcoma (common and seen in children in the Saharan Africa subgroup), (3) AIDS-associated Kaposi’s sarcoma (seen in HIV-infected individuals), and (4) iatrogenic Kaposi’s sarcoma (immunosuppression- or transplantation-associated) (Dittmer & Damania, 2013). Currently, one of the most common causes of morbidity and mortality in HIV-positive patients is Kaposi’s sarcoma (Schneider & Dittmer, 2017), and individuals who have undergone solid organ transplantation are 200 times more likely to be diagnosed with Kaposi’s sarcoma than the general population (Cesarman et al., 2019).
10.5.2 HHV-8-associated cancer biomarkers The protein expression of endogenous cyclin D1 and retinoblastoma tumor suppressor protein (pRb) was investigated in Kaposi’s sarcoma and AIDS-
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positive tissues from Kaposi’s sarcoma patients by immunohistochemical analysis. The results of the analysis showed that the expression of endogenous cyclin D1 was barely perceptible in the early stages of the disease, while it increased in advanced stages. Therefore the researchers associated endogenous cyclin D1 expression with tumor progression. Besides, the researchers found pRb expression in all Kaposi’s sarcoma lesions (Horenstein et al., 1997). Another study examined the expression of IL-8 (a pro-inflammatory cytokine) by comparing HIV-positive Kaposi’s sarcoma cell lines (KS Y-1, KSC10, KSC29, KSC59, KSC13, and KS-38) with control cells such as human umbilical vein endothelial cells (HUVECs), human aortic smooth muscle cells (AoSM), or fibroblast cells. Following the analyses, the researchers pointed out that both IL-8 and its receptors are expressed in Kaposi’s sarcoma cell lines, and preventing IL-8 expression leads to Kaposi’s sarcoma cell growth inhibition. Moreover, serum levels of IL-8 were remarkably upregulated in Kaposi’s sarcoma cell lines compared with control groups. Given these results, the researchers posited IL-8 as a potential surrogate biomarker for Kaposi’s sarcoma (Masood et al., 2001). Although studies have claimed that a chemokine CXCL12 and its receptors, CXCR4 and CXCR7, may play a role in Kaposi’s sarcoma etiology, researchers have not used their expression correlation with disease or biomarker feasibility. In a study by Desnoyer et al. (2016), the expression of CXCL12/CXCR4-CXCR7, LANA, Ki67, and VEGF was measured in Kaposi’s sarcoma, HIV-infected Kaposi’s sarcoma, angioma patients, and healthy donors using immunohistochemistry and quantitative imaging. After analysis, researchers showed that the expression levels of CXCL12/CXCR4CXCR7 proteins were significantly higher in Kaposi’s sarcomaand HIVinfected Kaposi’s sarcomawhen compared with other samples. Moreover, CXCL12/CXCR4-CXCR7 expressions were associated with the severity of Kaposi’s sarcomalesions. This study recommended the evaluation of the CXCL12/CXCR4-CXCR7 trio as potential biomarkers for Kaposi’s sarcomapatients (Desnoyer et al., 2016). As previously mentioned, PD-L1 was overexpressed in EBVaGCs, and its expression was also overexpressed according to HHV-8 infection. Indeed, one study reported that HHV-8 infection on primary human monocytes caused overexpression of PD-L1 at either the transcriptional or protein level. Besides, researchers investigated the effect of viral dose on PDL-L1 expression and indicated that increased doses of HHV-8 cause an increase in PDL1 transcription and protein production (Host et al., 2017). To discover circulating miRNA biomarkers to predict the prognosis of Kaposi’s sarcoma patients, the researchers compared HIV-infected Kaposi’s sarcoma patients with nonsymptomatic HHV-8- or HIV-infected individuals in their study. To do this, the researchers took plasma samples from the patients before and after treatment (which was successful). Then, the researchers examined 377 miRNAs from these samples and found that miR-
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TABLE 10.6 Human herpesvirus-8 (HHV-8)-associated cancer biomarkers. Cancer
Biomarker
Clinical application
Reference
Kaposi’s sarcoma
Increased endogenous cyclin D1 expression
Biomarker
Horenstein et al. (1997)
Serum IL-8 levels upregulation
Surrogate biomarker
Masood et al. (2001)
High expression of CXCL12/ CXCR4-CXCR7 proteins
Biomarker
Desnoyer et al. (2016)
Overexpression of PD-L1
Marker
Host et al. (2017)
miR-375 upregulation
Prognostic biomarker
Piano et al. (2019)
375 levels were upregulated in HIV-infected Kaposi’s sarcoma patients, and its level decreased after treatment. As a result, the researchers reported that miR-375 levels serve as a prognostic biomarker in HIV-infected Kaposi’s sarcoma patients, especially in treatment-naive patients (Piano et al., 2019) (Table 10.6).
10.6 Human papillomavirus HPV was discovered in the early 1980s by the DNA hybridization method (Chang et al., 2017). HPV is a small, unsheathed, circular, double-stranded DNA that belongs to the Papillomaviridae family. More than a hundred HPV types have been characterized by whole genome sequencing. The identified HPV types differ in their oncogenic potential. HPVs that cause benign lesions are defined as low-risk types, while HPVs that cause malignant tumors are classified as high-risk types (Graham, 2010). IARC has defined HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, and -59 as high-risk, meaning that they are carcinogenic to humans (Group 1) (IARC, 2012). Over 12 high-risk HPV types, HPV-16 and -18 are the most prevalent worldwide. Most HPVs encode eight major proteins and have different roles in the viral life cycle. Among these, the E6 and E7 oncoproteins come to the fore due to their important contributions to carcinogenesis, particularly through their interactions with various cellular targets (Pappa et al., 2018). The most important function of the E6 oncoprotein is to inhibit apoptosis and cause cell immortalization by binding p53. Besides p53 binding, E6 can increase telomerase activity and initiate genomic instability. E7 can bind pRB. pRB and its signaling pathway play a critical role in monitoring cell proliferation
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and apoptosis by coordinating the family of E2F transcription factors, and it is known that inactive pRB can promote cell proliferation. E7 can inhibit the function of pRB; thus pRB cannot suppress aberrant cell proliferation in cancer cells (Boulet et al., 2007; Kori & Arga, 2020). HPV is the most common sexually transmitted viral infection worldwide, according to WHO (Ferris et al., 2020).
10.6.1 Human papillomaviruses-associated cancers Approximately 5% of all cancers worldwide are caused by oncogenic HPV infection (Gilbert et al., 2019). Highly oncogenic HPV infection is found in almost all cases of cervical cancer (Ferris et al., 2020). Cervical cancer was the fourth leading cause of death worldwide in 2018 (311,000 deaths) and the fourth most commonly diagnosed cancer (570,000 cases) (Bray et al., 2018). Besides, it caused 13,800 new cases and 4290 deaths in the United States in 2020 (Siegel et al., 2020). HPV-16 and -18 are responsible for up to 70% of cervical cancer cases (WHO, 2019). The scientist who had discovered the relationship between HPV-16, -18, and cervical cancer won the Nobel Prize in Physiology and Medicine in 2008 (Bogolyubova, 2019). HPV is positive in about 88% of anal cancer, 78% of vaginal cancer, 51% of penile cancer, 30%70% of oropharyngeal cancer, and less than 25% of vulvar cancer (Ferris et al., 2020). Anal cancer accounts for about 2% of cancers affecting the gastrointestinal system. Approximately 48,000 new anal cancers are detected around the world each year (Silva Dalla Libera et al., 2019). Vaginal and vulvar cancers are rare gynecological cancers. Vaginal cancer led to 17,600 new cases and 8062 mortality in 2018 in the world (Bray et al., 2018). Vulvar cancer encompassed 6120 new cases and 1350 deaths in the United States in 2020 (Siegel et al., 2020). Oropharyngeal cancer is one type of head and neck cancer, and it consists of tonsil and base of the tongue region malignancies. HPV-16 genome is encountered in about 90% of HPVassociated oropharyngeal cancer cases (Pan et al., 2018). In 2018, 92,887 individuals were diagnosed with oropharyngeal cancer, whichled to 51,005 deaths in the world (Bray et al., 2018).
10.6.2 Human papillomaviruses-associated cancer biomarkers Syndecans have notable roles in a variety of solid and blood malignancies. From this point of view, researchers have studied the expression of syndecan-1 (SDC1) in cervical cancer for the first time. The SDC1 expression profile was measured on 121 cervical cancer tissues by immunohistochemistry. The relationship of SDC1 expression with clinical parameters of the patients was also specified. Researchers stated that SDC1 is expressed in 83.5% of the patients; also, there is a correlation between SDC1 intense expression and patient’s survival (Kim et al., 2011).
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In another study, the expression patterns of the SUMO conjugating enzyme, UBC9, in cervical biopsies were examined. Researchers performed immunohistochemistry analysis in 130 patients’ cervical biopsies in a lesiondependent manner. Following the results, researchers demonstrated that UBC9 is significantly overexpressed in cervical lesions, and its expression increases in patients with high-grade cancer. Researchers suggested that UBC9 can serve as a diagnostic or monitoring biomarker for cervical cancer (Mattoscio et al., 2015). The MCM-2 upregulation in cervical cancer has been shown in former studies via DNA microarray and transcriptional profiling. Based on this aspect, a study conducted by Zheng (2015) attempts to indicate HPV type and MCM-2 association in the diagnosis of cervical cancer. Similar to previous studies, MCM-2 was found to be upregulated. Besides, it was revealed that MCM-2 upregulation positively correlates with high oncogenic subtypes of HPV in cancerous cells. In the light of this information, a researcher stated that instead of genotyping HPV, MCM-2 detection in cancerous cells would be much better. Therefore the study suggested MCM-2 as a screening biomarker that can support diagnosis (Zheng, 2015). It is known that GSK3β involves in various cancer developments. Also, one of the target proteins of GSK3β is cyclin D1. In this respect, the researchers conducted a study to evaluate both GSK3β and cyclin D1 expression in different types and grades of cervical cancer to determine the proteins’ correlation with the disease. Thereby, immunohistochemical analyses were performed for cancerous and control groups, and following the analyses, it was found that GSK3β expression is upregulated according to grade, whereas cyclin D1 decreases. Also, GSK3β expression varied from squamous cell carcinoma to adenocarcinoma (Park et al., 2016). Although the two interleukins, IL-1α and IL-6, were associated with the prognosis of many types of malignancies, the association of these interleukins with cervical cancer has not been reported yet. To this end, researchers performed an immunohistochemistry study to determine the two interleukins’ expression and the expression association with patients’ clinical parameters with respect to controls. According to the analyses, researchers indicated that IL-1α and IL-6 significantly increase in cervical cancer tissues compared with controls. Moreover, the two interleukins’ expression remarkably correlated with tumor size, FIGO histology grade tumor differentiation, and worse prognosis. Consequently, the study recommended IL-1α and IL-6 as prognostic markers, which can specify possible therapeutic targets for cervical cancer (Song et al., 2016). In order to assign stem cell markers in cervical cancer, researchers first performed RT-qPCR and investigated the mRNA expressions of ALDH1A1, OCT4, NANOG, SOX2, and Twist1. As a result of the analysis, researchers found notable upregulation in ALDH1A1 and OCT4 compared with cervical squamous cell carcinoma and controls. For this response, the researchers
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investigated the protein expression of ALDH1A1 and OCT4 by immunohistochemical staining and ELISA and redemonstrated the overexpression of the two proteins. In consequence of the study, researchers considered ALDH1A1 and OCT4 as potential biomarkers that can be used for the early detection of cervical carcinoma (Tulake et al., 2018). A meta-analysis was performed on five cervical cancer-associated gene expression datasets to reveal biomarker candidates and potential therapeutic targets. For this purpose, the gene expression data were analyzed, and obtained differentially expressed genes were integrated with genome-scale biomolecular networks such as proteinprotein interaction, metabolic, and posttranscriptional regulatory networks. This study notified already-known cervical cancer biomarkers as well as novel biomarker candidates (i.e., EPHA4, EPHA5, NCOA3, NR2C1, E2F4, ETS1, KAT2B, PARP1, CDK1, miR-1925p, miR-193b-3p, and miR-2155p) (Kori & Arga, 2018). Also, the researchers identified genomic biomarker candidates for cervical cancer by performing differential coexpression network analysis for the same five transcriptome datasets. Researchers identified coexpressed modules and performed KaplanMeier survival and principle component analyses to identify diagnostic and prognostic capabilities of the modules. As a result, seven distinct coexpressed gene modules were identified, which represent potential biomarker candidates for diagnosis and prognosis for cervical cancer (Kori et al., 2019). Another study conducted by Roslind et al. (2020) assessed the association of high YKL-40 serum levels with a worse prognosis in cervical carcinoma. For these, researchers included 116 cervical cancer patients, 152 cervical intraepithelial neoplasia patients, and control groups and measured their YKL40 serum concentration by ELISA. Following the analysis, researchers showed that YKL-40 serum concentrations are high when they are compared with the other two groups. Also, with univariate cox analysis, researchers determined that YKL-40 is correlated with recurrence-free survival and overall survival. Analysis results demonstrated serum YKL-40 as a biomarker related to overall survival in cervical cancer patients (Roslind et al., 2020). Researchers conducted a study that aims to determine the marker potential of ANXA2 by applying both bioinformatics and experimental analysis. Researchers first pointed out that ANXA2 is upregulated in cervical cancer tissues when compared with controls by bioinformatics analysis. Moreover, researchers performed KaplanMeier survival analysis and found that expression of ANXA2 correlates with cervical cancer prognosis. In order to validate these results experimentally, researchers did immunohistochemical staining on 121 cervical carcinoma tissues and 29 normal tissues. While the researchers detected ANXA2 expression in the cytoplasm and cell membrane of malignant tissues, ANXA2 expression is found only in cell membranes in controls. Taken together with these results, researchers provide that ANXA2 is a potential marker for the diagnosis of human cervical cancer (Wang et al., 2021).
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The study about oropharyngeal squamous cell carcinoma and HPV relation pointed out that IGSF4 methylation was an independent biomarker of HPV-positive individuals (Chen, Stephen et al., 2015). Based on the reporting of MCM2 importance in cervical cancer, another study investigated the biomarker potential of MCM2 and TOP2A in anal cancer. Accordingly, the
TABLE 10.7 Human papillomavirus-associated cancer biomarkers. Cancer
Biomarker
Clinical application
Reference
Cervical cancer
SDC1 expression
Biomarker
Kim et al. (2011)
UBC9 over-expression
Diagnostic or monitoring biomarker
Mattoscio et al. (2015)
MCM-2 upregulation
Screening biomarker
Zheng (2015)
GSK3β upregulation and cyclin D1 downregulation
Biomarker
Park et al. (2016)
IL-1α and IL-6 upregulation
Prognostic biomarker
Song et al. (2016)
Upregulation in ALDH1A1 and OCT4
Biomarker
Tulake et al. (2018)
EPHA4, EPHA5, NCOA3, NR2C1, E2F4, ETS1, KAT2B, PARP1, CDK1, miR1925p, miR-193b-3p, and miR-2155p
Biomarker candidates
Kori and Arga (2018)
Seven distinct coexpressed gene modules
Biomarker candidates
Kori et al. (2019)
High YKL-40 serum concentrations
Biomarker
Roslind et al. (2020)
ANXA2 upregulation
diagnostic Biomarker
Wang et al. (2021)
Oropharyngeal cancer
IGSF4 methylation
Biomarker
Chen, Zhang et al. (2015), Chen, Stephen et al. (2015)
Anal cancer
MCM2 and TOP2A expression
Prognostic biomarker
ScapulatempoNeto et al. (2017)
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expressions of MCM2 and TOP2A were screened immunohistochemically in 75 anal carcinoma lesions. The analyses’ results indicated that MCM2 and TOP2A are constantly and notably expressed in anal carcinoma lesions. The study concluded that both proteins could be used to utilize the prognosis of anal cancer (Scapulatempo-Neto et al., 2017) (Table 10.7).
10.7 Conclusions Oncogenic viruses, present in about one in ten people, are also one of the apparent causes of cancer-related deaths worldwide. Therefore supporting existing screening or treatment approaches with new strategies will help reduce some of the global human cancer burdens. Biomarkers that enable early diagnosis, prognosis, and appropriate or complementary therapies will help reduce the incidence of disease, including viral cancers. Today, a new virus called COVID-19 has entered the scene, causing an ongoing global pandemic. Studies that investigated the link between COVID-19 and cancer indicated that cancer patients are more affected by COVID-19 than healthy people, and especially in lung cancer, the viral symptoms are more virulent. However, there are still too many unknowns about the virus. For example, will people infected with COVID-19 have an increased risk of cancer in the future? Possibly. Although there are still many questions, there is growing interest in COVID-19 in virology and viral cancers. We believe that in the foreseeable future, efforts to prevent COVID19 (such as mRNA vaccine technology) will also lead to significant advances in the treatment of viral cancers. Considering all these aspects, we have reviewed the six human oncogenic viruses in this book chapter and highlighted the biomarkers recommended for them. In this book chapter, we believe that we have provided information to set new goals for the treatment, prognosis, and diagnosis of oncogenic viruses and guide the scientific community to achieve this goal.
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Nobel Prize. (2020). Available from: ,http://www.nobelprize.org.. Oura, K., Fujita, K., Morishita, A., Iwama, H., Nakahara, M. A. I., Tadokoro, T., Sakamoto, T., Nomura, T., Yoneyama, H., Mimura, S., Tani, J., Kobara, H., Okano, K., Suzuki, Y., & Masaki, T. (2019). Serum microRNA-125a-5p as a potential biomarker of HCV-associated hepatocellular carcinoma. Oncology Letters, 18(1), 882890. Available from https://doi.org/ 10.3892/ol.2019.10385. Pan, C., Issaeva, N., & Yarbrough, W. G. (2018). HPV-driven oropharyngeal cancer: Current knowledge of molecular biology and mechanisms of carcinogenesis. Cancers of the Head & Neck, 3, 12. Available from https://doi.org/10.1186/s41199-018-0039-3. Pappa, K. I., Kontostathi, G., Lygirou, V., Zoidakis, J., & Anagnou, N. P. (2018). Novel structural approaches concerning HPV proteins: Insight into targeted therapies for cervical cancer (Review). Oncology Reports, 39(4), 15471554. Available from https://doi.org/10.3892/or.2018.6257. Park, H., Lee, M., Kim, W. D., Hong, Y. S., & Lee, H. (2016). Glycogen synthase kinase 3β and cyclin D1 expression in cervical carcinogenesis. Obstetrics & Gynecology Science, 59, 470478. Available from https://doi.org/10.5468/ogs.2016.59.6.470. Petruzziello, A. (2018). Epidemiology of hepatitis B virus (HBV) and hepatitis C virus (HCV) related hepatocellular carcinoma. The Open Virology Journal, 12, 2632. Available from https://doi.org/10.2174/1874357901812010026. Piano, M. A., Gianesello, L., Grassi, A., Del Bianco, P., Mattiolo, A., Cattelan, A. M., Sasset, L., Zanovello, P., & Calabro`, M. L. (2019). Circulating miRNA-375 as a potential novel biomarker for active Kaposi’s sarcoma in AIDS patients. Journal of Cellular and Molecular Medicine, 23(2), 14861494. Available from https://doi.org/10.1111/jcmm.14054. Poiesz, B. J., Ruscetti, F. W., Gazdar, A. F., Bunn, P. A., Minna, J. D., & Gallo, R. C. (1980). Detection and isolation of type C retrovirus particles from fresh and cultured lymphocytes of a patient with cutaneous T-cell lymphoma. Proceedings of the National Academy of Sciences of the United States of America, 77(12), 74157419. Available from https://doi. org/10.1073/pnas.77.12.7415. Qiao, D. D., Yang, J., Lei, X. F., Mi, G. L., Li, S. L., Li, K., Xu, C. Q., & Yang, H. L. (2017). Expression of microRNA-122 and microRNA-22 in HBV-related liver cancer and the correlation with clinical features. European Review for Medical and Pharmacological Sciences, 21(4), 742747. Ringehan, M., McKeating, J. A., & Ulrike, P. (2017). Viral hepatitis and liver cancer. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1732), 20160274. Available from https://doi.org/10.1098/rstb.2016.0274. Roslind, A., Palle, C., Johansen, J. S., Christensen, I. J., Nielsen, H. J., & Mosgaard, B. J. (2020). Prognostic utility of serum YKL-40 in patients with cervical cancer. Scandinavian Journal of Clinical and Laboratory Investigation, 80(8), 687693. Available from https:// doi.org/10.1080/00365513.2020.1846209. Rous, P. (1911). A sarcoma of the fowl transmissible by an agent separable from the tumor cells. Journal of Experimental Medicine, 13(4), 397411. Available from https://doi.org/10.1084/ jem.13.4.397. Safaa, M., Marwa, M., & Noha, E. (2017). Microrna-150 and microrna-101 as diagnostic markers for hepatitis B virus related hepatocellular carcinoma in Egypt. Journal of Liver: Disease & Transplantation, 6, 156. Available from https://doi.org/10.4172/23259612.1000156. Sasaki, H., Nishikata, I., Shiraga, T., Akamatsu, E., Fukami, T., Hidaka, T., Kubuki, Y., Okayama, A., Hamada, K., Okabe, H., Murakami, Y., Tsubouchi, H., & Morishita, K. (2005). Overexpression of a cell adhesion molecule, TSLC1, as a possible molecular marker
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for acute-type adult T-cell leukemia. Blood, 105(3), 12041213. Available from https://doi. org/10.1182/blood-2004-03-1222. Scapulatempo-Neto, C., Veo, C., Fregnani, J. H. T. G., Lorenzi, A., Mafra, A., Melani, A. G. F., Loaiza, E. A. A., Rosa, L. A. R., DE Oliveira, C. M., Levi, J. E., & Longatto-Filho, A. (2017). Characterization of topoisomerase II α and minichromosome maintenance protein 2 expression in anal carcinoma. Oncology Letters, 13(3), 18911898. Available from https:// doi.org/10.3892/ol.2017.5650. Schneider, J. W., & Dittmer, D. P. (2017). Diagnosis and treatment of Kaposi Sarcoma. American Journal of Clinical Dermatology, 18(4), 529539. Available from https://doi.org/ 10.1007/s40257-017-0270-4. Seo, A. N., Kang, B. W., Bae, H. I., Kwon, O. K., Park, K. B., Lee, S. S., Chung, H. Y., Yu, W., Jeon, S. W., Kang, H., & Kim, J. G. (2019). Exon 9 mutation of PIK3CA associated with poor survival in patients with Epstein-Barr virus-associated gastric cancer. Anticancer Research, 39(4), 21452154. Available from https://doi.org/10.21873/anticanres.13328. Shah, U. A., Shah, N., Qiao, B., Acuna-Villaorduna, A., Pradhan, K., Adrianzen Herrera, D., Sica, R. A., Shastri, A., Mantzaris, I., Derman, O., Kornblum, N., Braunschweig, I., Ye, B. H., Verma, A., & Janakiram, M. (2020). Epidemiology and survival trend of adult T-cell leukemia/lymphoma in the United States. Cancer, 126(3), 567574. Available from https:// doi.org/10.1002/cncr.32556. Shimizu, Y., Mizuno, S., Fujinami, N., Suzuki, T., Saito, K., Konishi, M., Takahashi, S., Gotohda, N., Tada, T., Toyoda, H., Kumada, T., Miura, M., Suto, K., Yamaji, T., Matsuda, T., Endo, I., & Nakatsura, T. (2020). Plasma and tumoral glypican-3 levels are correlated in patients with hepatitis C virus-related hepatocellular carcinoma. Cancer Science, 111(2), 334342. Available from https://doi.org/10.1111/cas.14251. Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA Cancer Journal for Clinicians, 70(1), 730. Available from https://doi.org/10.3322/caac.21590. Silva Dalla Libera, L., Almeida de Carvalho, K. P., Enocencio Porto Ramos, J., Oliveira Cabral, L. A., de Cassia Goncalves de Alencar, R., Villa, L. L., Alves, R. R. F., Rabelo Santos, S. H., Aparecida dos Santos Carneiro, M., & Saddi, V. A. (2019). Human papillomavirus and anal cancer: Prevalence, genotype distribution, and prognosis aspects from midwestern region of Brazil. Journal of Oncology, 2019, 110. Available from https://doi.org/10.1155/ 2019/6018269. Smith, M. W., Yue, Z. N., Geiss, G. K., Sadovnikova, N. Y., Carter, V. S., Boix, L., Lazaro, C. A., Rosenberg, G. B., Bumgarner, R. E., Fausto, N., Bruix, J., & Katze, M. G. (2003). Identification of novel tumor markers in hepatitis C virus-associated hepatocellular carcinoma. Cancer Research, 63(4), 859864. Song, Z., Lin, Y., Ye, X., Feng, C., Lu, Y., Yang, G., & Dong, C. (2016). Expression of IL-1α and IL-6 is associated with progression and prognosis of human cervical cancer. Medical Science Monitor, 22, 44754481. Available from https://doi.org/10.12659/MSM.898569. Sugimoto, A., Yamashita, Y., Kanda, T., Murata, T., & Tsurumi, T. (2019). Epstein-Barr virus genome packaging factors accumulate in BMRF1-cores within viral replication compartments. PLoS One, 14(9), e0222519. Available from https://doi.org/10.1371/journal. pone.0222519. Takemoto, S., Iwanaga, M., Sagara, Y., & Watanabe, T. (2016). Plasma soluble CD30 as a possible marker of adult T-cell leukemia in HTLV-1 carriers: A nested case-control study. Asian Pacific Journal of Cancer Prevention, 16(18), 82538258. Available from https://doi. org/10.7314/APJCP.2015.16.18.8253.
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Tamai, T., Uto, H., Takami, Y., Oda, K., Saishoji, A., Hashiguchi, M., Kumagai, K., Kure, T., Mawatari, S., Moriuchi, A., Oketani, M., Ido, A., & Tsubouchi, H. (2011). Serum manganese superoxide dismutase and thioredoxin are potential prognostic markers for hepatitis C virus-related hepatocellular carcinoma. World Journal of Gastroenterology, 17(44), 48904898. Available from https://doi.org/10.3748/wjg.v17.i44.4890. Tanaka, Y., Takahashi, Y., Tanaka, R., Miyagi, T., Saito, M., & Fukushima, T. (2019). Association of high levels of plasma OX40 with acute adult T-cell leukemia. International Journal of Hematology, 109(3), 319327. Available from https://doi.org/10.1007/s12185018-02580-z. Tateyama, M., Yatsuhashi, H., Taura, N., Motoyoshi, Y., Nagaoka, S., Yanagi, K., Abiru, S., Yano, K., Komori, A., Migita, K., Nakamura, M., Nagahama, H., Sasaki, Y., Miyakawa, Y., & Ishibashi, H. (2011). Alpha-fetoprotein above normal levels as a risk factor for the development of hepatocellular carcinoma in patients infected with hepatitis C virus. Journal of Gastroenterology, 46(1), 92100. Available from https://doi.org/10.1007/s00535-010-0293-6. Tulake, W., Yuemaier, R., Sheng, L., Ru, M., Lidifu, D., & Abudula, A. (2018). Upregulation of stem cell markers ALDH1A1 and OCT4 as potential biomarkers for the early detection of cervical carcinoma. Oncology Letters, 16(5), 55255534. Available from https://doi.org/ 10.3892/ol.2018.9381. Verma, S. C., & Robertson, E. S. (2003). Molecular biology and pathogenesis of Kaposi sarcoma-associated herpesvirus. FEMS Microbiology Letters, 222(2), 155163. Available from https://doi.org/10.1016/S0378-1097(03)00261-1. Wang, Z., Jiang, C., Pang, L., Jia, W., Wang, C., Gao, X., Zhang, X., Dang, H., & Ren, Y. (2021). ANXA2 is a potential marker for the diagnosis of human cervical cancer. Biomarkers in Medicine, 15(1), 5767. Available from https://doi.org/10.2217/bmm-2020-0629. White, M. K., Pagano, J. S., & Khalili, K. (2014). Viruses and human cancers: A long road of discovery of molecular paradigms. Clinical Microbiology Reviews, 27(3), 463481. Available from https://doi.org/10.1128/CMR.00124-13. WHO. (2019). Available from: ,https://www.who.int/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer.. Yamada, K., Miyoshi, H., Yoshida, N., Shimono, J., Sato, K., Nakashima, K., Takeuchi, M., Arakawa, F., Asano, N., Yanagida, E., Seto, M., & Ohshima, K. (2021). Human T-cell lymphotropic virus HBZ and tax mRNA expression are associated with specific clinicopathological features in adult T-cell leukemia/lymphoma. Modern Pathology, 34(2), 314326. Available from https://doi.org/10.1038/s41379-020-00654-0. Yang, J. D., Hainaut, P., Gores, G. J., Amadou, A., Plymoth, A., & Roberts, L. R. (2019). A global view of hepatocellular carcinoma: Trends, risk, prevention and management. Nature Reviews Gastroenterology and Hepatology, 16(10), 589604. Available from https://doi. org/10.1038/s41575-019-0186-y. Yao, M., Zhao, J., & Lu, F. (2016). Alpha-fetoprotein still is a valuable diagnostic and prognosis predicting biomarker in hepatitis B virus infection-related hepatocellular carcinoma. Oncotarget, 7(4), 37023708. Available from https://doi.org/10.18632/oncotarget.6913. Young, L. S., Yap, L. F., & Murray, P. G. (2016). Epstein-Barr virus: More than 50 years old and still providing surprises. Nature Reviews Cancer, 16(12), 789802. Available from https://doi.org/10.1038/nrc.2016.92. Zekri, A.-R. N., Kassas, M. E. L., Salam, E. S. T. A. E., Hassan, R. M., Mohanad, M., Gabr, R. M., Lotfy, M. M., Abdel-zaher, R. A. T., Bahnassy, A. A., & Ahmed, O. S. (2020). The possible role of Dickkopf-1, Golgi protein-73 and Midkine as predictors of
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hepatocarcinogenesis: A review and an Egyptian study. Scientific Reports, 10(1), 6. Available from https://doi.org/10.1038/s41598-020-62051-6. Zhao, T. (2016). The role of HBZ in HTLV-1-induced oncogenesis. Viruses, 8(2), 34. Available from https://doi.org/10.3390/v8020034. Zheng, J. (2015). Diagnostic value of MCM2 immunocytochemical staining in cervical lesions and its relationship with HPV infection. International Journal of Clinical and Experimental Pathology, 8(1), 875880. Available from http://www.ijcep.com/files/ijcep0003901.pdf. Zuo, L. L., Zhang, J., Liu, L. Z., Zhou, Q., Du, S. J., Xin, S. Y., Ning, Z. P., Yang, J., Yu, H. B., Yue, W. X., Wang, J., Zhu, F. X., Li, G. Y., & Lu, J. H. (2017). Cadherin 6 is activated by Epstein-Barr virus LMP1 to mediate EMT and metastasis as an interplay node of multiple pathways in nasopharyngeal carcinoma. Oncogenesis, 6(12), 402. Available from https://doi.org/10.1038/s41389-017-0005-7.
Chapter 11
Bioinformatics serving oncoviral studies Virupaksha Ajit Bastikar1, Pramodkumar Pyarelal Gupta2, Alpana Bastikar3, Santosh Subhash Chhajed4 and Santosh Ajabrao Bothe5 1
Amity Institute of Biotechnology, Amity University Mumbai, Maharashtra, India, 2School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, Maharashtra, India, 3Department of Computer-Aided Drug Design, Navin Saxena Research and Technology Pvt. Ltd, Gandhidham, Gujarat, India, 4Department of Pharmaceutical Chemistry, METs Institute of Pharmacy, Nashik, Maharashtra, India, 5Saraswati College, Gaulkhed Road, Shegaon, Dist Buldhana, Maharashtra, India
Oncovirus cancer is a group of more than 100 different diseases. It can develop almost anywhere in the body (Cancer.net, 2020; What Is Cancer?, 2020a, 2020b). Initiation of cancer largely due to genetic changes interferes with the normal process, leading to uncontrolled cell growth. This huge cell mass transforms into a tumor, which can be benign or cancerous and can be malignant, which can grow and spread to other parts of the body. Whereas some types of cancers are nontumorous like leukemia, cancers like lymphoma and myeloma (What Is Cancer?, 2020a, 2020b) are tumorous. Apart from genetic mutations and other environmental factors such as chemical changes and change in lifestyle, approximately 12% of human cancers occur due to viruses and they are known as oncoviruses. A huge group of people harbors at least one of these oncoviruses, but few go on to develop into cancer. The path from oncovirus infection to the onset of cancer in humans involves a complex process. The viral factors and host interaction create a favorable microenvironment for oncogenesis. At present, seven human oncoviruses are known: EpsteinBarr virus (EBV), human papillomavirus (HPV), Hepatitis B and C viruses (HBV and HCV), human T-cell lymphotropic virus-1 (HTLV-1), human herpesvirus-8 (HHV-8), and Merkel cell polyomavirus (MCPyV) (Mui et al., 2017). As the first human oncovirus, EBV was first detected in Burkitt lymphoma cells by electron microscopy in 1964 (Epstein et al., 1964). Since then work in oncovirology has been carried out on a large scale to understand the hostvirus pathogenesis. Globally, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00002-9 Copyright © 2023 Elsevier Inc. All rights reserved.
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around 20% of all cancers are caused by infectious agents and 12% are caused due to the oncoviruses, and of these, approximately 80% of viral cancers occur in the developing nations (Bouvard et al., 2009; Boyle & Levin, 2008; de Martel et al., 2012; Mui et al., 2017; zur Hausen, 2009). With the help of the existing biochemical techniques such as genomics and proteomics, the identification of virus and their role in the cancer pathogenesis could be investigated, but still knowledge about their microenvironment and interactions is lacking. Current computational techniques support the deep mining of information, and knowledge modeling with preexisting data helps in understanding the sequence-to-sequence mapping followed by structure elucidation, which brings more curated data into the picture. Bioinformatics, cheminformatics, and amalgamation of artificial intelligence play an important role in research in the field of oncovirology. The large datasets obtained from multiple biochemical experiments contain gigabytes of data related to the sequence, structure, and interactions of oncoviruses. Techniques such as computational genomics, computational proteomics, interactomics, sequence analysis, structure modeling, systems biology, protein modeling, and computer-aided drug design have significantly contributed to making research in this field cost-effective. Genomics and proteomics of oncoviruses and bioinformatics are significantly contributing to our understanding of the onset of disease and the mutation or pathogenic interactions underlying these diseases. A series of investigations into disease outbreaks and pathogenesis, genomic variation in hosts and pathogens, and host immune circumvention mechanisms in the identification of potential diagnostic markers and vaccine targets have been carried out. Currently, big data generated from high-throughput genomic experimental technologies applied on animal models and pathogens can be combined with the host genomics and patient health record systems to understand the trend and guide the treatment selection as well as potential drug and vaccine interactions (Bah et al., 2018). Merkel cells are mainly found in the hair follicles, certain mucosal tissues, and are also present in areas of the skin giving the sensation of touch (Spurgeon & Lambert, 2013). Merkel cell carcinoma (MCC) is an extremely contentious neuroendocrine carcinoma of the skin induced by either the integration of Merkel cell polyomavirus (MCPyV) and expression of viral T antigens or by ultraviolet-induced damage to the tumor genome from excessive sunlight exposure. Using the advancements in the area of genome science and sequencing techniques, the authors Starrett et al. have carried out a detailed study with a cohort of 71 MCC patients using deep sequencing with OncoPanel, a clinically designed next-generation sequencing assay targeting over 400 cancer-associated genes. PCR and IHC methods were implemented to validate the accuracy and sensitivity of the study and developed a hybrid capture bait set against the compete MCPyV genome, and indigenous software was design to detect the integration sites and structure. Recurrent somatic alterations common across MCC and alterations specific to each class of tumor were reported,
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associated with differences in overall survival (Starrett et al., 2020). Marc Wilkins in 1996 first ever used the term “Proteomics,” which means “PROTein complement of a genOME” (Wilkins et al., 1996). The proteome is a well-known concept now and can be defined as the global protein content of a cell represented in terms of localization, interactions, posttranslational modifications, and turnover, at a particular time. Proteomics consists of numerous applications of technologies for the identification and quantification of comprehensive proteins present in a cell, tissue, or whole organism. Proteomics-based technologies are employed in a wide range of research settings such as the identification and mining of diagnostic markers, vaccine production, understanding pathogenicity mechanisms, alteration of expression patterns in response to different signals, and interpretation of functional protein pathways in different diseases (Aslam et al., 2017). Virion proteomics is a powerful, dedicated, and unbiased methodology to achieve insights into the process of virus assembly and the host factors, including their intra- and interinteractions, important for understanding the infection and onset of disease. In hepatitis C virus (HCV), the infectious virions are assembled alongside endogenous lipoproteins and give rise to a chimeric lipoviral particle. These host-derived apolipoproteins coat the exterior of circulating particles, leading to the formation of a veiled pathogen, but the protein composition of HCV still remains unidentified. Lussignol et al. using MS-based analysis revealed and identified the novel viral and cellular proteins specifically associated with HCV virions and discovered an important interaction between the capsid protein and the nucleoporin Nup98, which is required for virion biogenesis (Lussignol et al., 2016).
11.1 Biological database With the advancement of tools and technologies for the investigation of biological organisms (including oncoviruses), there has been an exponential growth in the raw data generated by these technologies. Biological databases hosted by NCBI, EMBL, ExPASy, RCSB, and others are fueled by various research groups and organizations carrying out the research work on oncovirus. Some of the well-established databases include NCBI and EMBL (nucleotide, gene, and genome), Uniprot, NextProt, and PIR (protein sequence database), RCSB-Protein Data Bank (protein structural database), and PubChem, DrugBank, and ZINC (small chemical structure compounds and drugs). HPV strains cause 70% of cervical cancers and 90% of genital warts. The two types of HPV, i.e., HPV type 16 and 18, account for approximately 70% of the total cases (Braaten & Laufer, 2008; “Human papillomavirus (HPV) and cervical cancer,” 2020). With more biological data available, it will be easier for biologists and chemists to come up with dedicated solutions to diseases. As of March 2021, there had been a total of 9 reviewed and 8282 unreviewed protein sequences added to the UniProtKB
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and UniProt TrEMBL databases (Apweiler, 2004). KEGG Pathway database consists of information related to EpsteinBarr virus infection—Homo sapiens (human), pathway entry for hsa05169, pathways for Huan T-cell leukemia virus1 infection, hepatitis B and C—Homo sapiens (human), Kaposi sarcomaassociated herpesvirus infection—Homo sapiens (human), and human papillomavirus infection—Homo sapiens (human) (Ogata et al., 1999).
11.2 Sequence analysis Sequence analysis is the process of analyzing the biological data, DNA, RNA, or a protein sequence by comparing them pairwise or in multiples. The sequence analysis methods help us to understand the similarity and identity among the biological sequences and correlate their features, structures, functions, or evolution. The methodologies include using either one sequence as a target and comparing it with multifold high biological sequences present in the databases or identifying relationships among two or more biological sequences. Various tools such as BLAST and FASTA are widely used to compare an individual oncovirus biological sequence with other sequences from the database, the process also known as homology search. Tang et al. described the de novo genome assembly of viruses and missing strains, and novel virus strains were matched using the BLAST tool of NCBI with high sensitivity (Tang et al., 2013). Gene prediction and high-end sequencing techniques can provide us with the complete genome sequence of a virus within days. The next step toward understanding the details of a virus life cycle is to identify the whole viral genes and proteins. This information can provide critical insights on many occasions. For instance, for a team working on an antiviral drug design, promising drug targets would be those viral proteins that have identical major strains and are significantly different from the proteins in the host, e.g., humans (Mills, 2003). Numerous gene prediction online servers are available such as GenMark, Glimmer, FGENESH, and GenScan. Increasing population and deteriorating health condition is a major problem worldwide. In the process of disease diagnosis and therapy design, we often come across a situation where functional characterization of a protein is needed. Currently, few protein structures have been solved using experimental techniques, and a large dataset is not yet solved due to various experimental challenges. Computational modeling in structure biology enables scientists overcome such difficult biological challenges. Predicting the 3D structure of a protein from its amino acid sequence is one of the most challenging aspects of structural bioinformatics. Various techniques like homology modeling, fold recognition, and threading and ab-initio / de-novo based modeling are available to solve the protein 3D structure (Deng et al., 2018). Homology modeling, fold recognition, and threading are templated-based structure prediction methods, whereas ab-initio-based methods build the
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structure based on the first principles of physics, which does not rely on any previously solved structure (Deng et al., 2018). SwissModel, Phyre2, ITasser, and Robetta are well-known online protein structure prediction servers. Aarthy et al. modeled the E7 oncoprotein of human papillomavirus using the homology-based method and studied its conformation using molecular dynamics simulation (Aarthy et al., 2018). Implementing the immunoinformatics approach, Purnama and Kharisma carried out epitope mapping of capsid protein L1 from human papillomavirus, and the work supported in the development of a cervical cancer vaccine (Purnama & Kharisma, 2018).
11.3 Molecular dynamics simulations Molecular dynamics (MD) is an investigative approach in the identification of the location of atom, position, and its dynamic behavior within a given environment. The MD-based analysis also provides insights into the thermodynamic properties of the molecules. The MD study is widely used in the determination of protein and other biological structures and in the refinement of experimentally solved structures such as a 3D structure solved using X-ray crystallography and an NMR method (Polanski, 2009). Multifold protein sequences lead to the generation of a huge amount of data in comparison to structure, and the difference is huge, and structural bioinformatics help model the protein 3D structure using a computational modeling method to fulfil the biological research objectives. MD-based analysis helps refine and understand the protein conformational space for the approximate activity. The wide applications of MD not only explore the 3D structure of biological macromolecules but also play a wide role in drug design and discovery projects. A common strategy while determining conformational space is to perform the simulation at a very high temperature as this enhances the ability of the system to overcome energy barriers. Structures are then selected at regular intervals from the trajectory for subsequent energy minimization (Leach, 2007). MD can be carried out by using numerous programs or packages built for project-specific objectives (Han et al., 2019). AMBER (Assisted Model Building with Energy Refinement) was created for refining NMR structures. The name AMBER refers to both forcefield and MD program packages. The most widely used AMBER forcefield versions are AMBER94, AMBER99SB, and AMBER03, in addition to other AMBER forcefields such as AMBER-96, AMBER-GS, AMBER99φ, and AMBER99SB (Han et al., 2019; Sorin, 2021). CHARMM (Chemistry at HARvard Macromolecular Mechanics) refers to both program packages and forcefield, developed by Martin Karplus at Harvard University, USA. CHARMM molecular simulation program package has a broad application for many-particle systems, with energy function. There are a variety of enhanced sampling methods and multiscale techniques, including QM/MM, MM/CG, and a range of implicit solvent systems. CHARMM primarily targets biological systems, including peptides, proteins, prosthetic groups, small
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molecule ligands, nucleic acids, lipids, and carbohydrates in solution, crystals, and membrane environments. CHARMM also finds broad applications for inorganic materials with applications in material design (“CHARMM,” 2021; Han et al., 2019). GROMACS (GROningen Machine for Chemical Simulations) is a molecular dynamics package and is designed for MD simulations of proteins, nucleic acids, and lipids. Unlike AMBER and CHARMM, GROMACS does not have its own forcefield, and uses the forcefield of AMBER, CHARMM, GROMOS, and OPLS to run the MD simulations for the selected input (Han et al., 2019; Pronk et al., 2013). NAMD (NANnoscale Molecular Dynamics) is developed by the Theoretical and Computational Biophysics Group at the University of Illinois, Urbana-Champaign, USA, It acts as an interface and is compatible with other MD programs such as CHARMM (Han et al., 2019; Phillips et al., 2005). Desmond is developed by the D. E, Shaw Research group, which has its own novel algorithm to achieve high computing performance. Desmond also has the ability to import the AMBER, CHARMM, and OPLS forcefields to run MD simulations (Bowers et al., 2006; Han et al., 2019). One of the major fatal cancers in women is cervical cancer (CC) with a high rate of mortality worldwide. Typically, human papilloma virus (HPV) is one of the prime factors for cervical cancer. HPV 16 and HPV 18 were identified to be more virulent for cancer among others. E6 oncoprotein from HPV 16 of sequence length 158 aa and HPV 18 of sequence length 159 aa of Indian origin (NCBI-id: AMS04044, APO85550) share a sequence identity up to 52% (Fig. 11.1). Ramakrishnan et al. have explored the structural association between E6 oncoprotein from HPV 16 and that from HPV 18 in the cause of cancer. The protein 3D structure was modeled using the I-Tasser server, and MD simulation is carried out using the GROMACS package with the help of the AMBER03 forcefield for 50 ns to understand the dynamics. Due to the high degree of conformational fluctuation between the E6 oncoprotein of HPV-16 and that of PV-18, it is important to differentiate both the structures at various residual positions. However, the radius of gyration depicted the continuous fluctuation between 35 and 50 ns in the E6
FIGURE 11.1 Comparison between HPV 16 sequence length of 158 aa and HPV 18 sequence length of 159 aa from Indian origin (NCBI-id: AMS04044, APO85550).
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oncoprotein of HPV18 as compared to in the E6 oncoprotein of HPV18HPV16. This fluctuation shows the high compactness of E6 oncoproteins of HPV16 as compared to HPV18 (Akram Husain et al., 2018). The EpsteinBarr virus (EBV) is associated with a variety of cancers, and chronic active EBV infection is a severe systematic disease associated with a high rate of mortality and morbidity. Chung and Ishida performed the interaction and MD simulation based study of EBV-expressed latent membrane protein 1 (LMP1) with cellular signaling intermediate tumor necrosis factor receptor (TNFR)-associated factor 3 (TRAF3) using standard classical MD protocols. In a comparative analysis, the authors performed the MD simulation of TRAF3 with CD40, and a TNFR mimicked by LMP1 to effect EBV infection is also calculated under similar conditions. The MD simulation is carried out using the AMBER 9 suite. The outcome from both the MD of TRAF3 with CD40 and LMP1 revealed a stable conformation, which was not revealed in earlier X-ray structures. The stability and detailed interaction data from both the MD studies revealed a binding pattern, which might help in further new drug design or vaccine development (Chung & Ishida, 2011).
11.4 Computer-aided drug discovery Computer-based drug design (CADD) uses a computational approach in the design and development of compounds suitable for the desired activity as per the requirement. The advent of new computational techniques supported the drug discovery projects and drastically reduced the timeline, helping in the early detection of error and preventing late failures. Structure-based drug design (SBDD) and Ligand-based drug design (LBDD) are widely used approaches of CADD. In SBDD, the modeling starts with the presence of the 3D structure of target protein, and molecular docking and structure-based virtual screening are the major tools of SBDD. In case the 3D structure of the target protein could not be determined, we generally apply the LBDD approach and modeling, and QSAR and QSPR are the major tools for the LBDD. Numerous academic freeware and commercial expensive tools are available in the market that facilitate CADD. Singh et al. carried out a work involving the CADD-SBDD and LBDD approaches to identify the compounds analogous to (-)-Epigallocatechin-3-gallate (EGCG), a green tea component that inhibits the development of cervical cancer through apoptosis generation, cell cycle arrest, and regulation of gene expression. A shape-based screening was employed in Maestro 10.4 and the given template structure was screened against the ZINC and NCI databases. Further, the SBDD-based molecular docking was employed using Induced fit and GLIDE XP in Schrodinger. Post-docking, binding free energy calculation was carried out using the Prime MMGBSA method. The selective compounds with acceptable ADME properties, high binding affinity, and similar pharmacophoric interactions were studied for their dynamic behavior using MD simulations in GROMACS 5.16, and post-
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dynamics, binding free energy calculations were done using MMPBSA. Finally, molecules analogous to EGCG from the natural small molecule library were identified, and then a docking strategy was applied on the identified small molecules, which showed that the compounds ZINC49069570 and ZINC49115270 possess better docking score and binding energy in comparison with the EGCG and other identified molecules. The mean RMSD of the compound ZINC49069570 exhibits higher stability when compared to the apoprotein and the other selective molecules, including EGCG. A principal component analysis has been performed, which depicts that the compound ZINC49069570 has collective motions when compared with the EGCG. The compound ZINC49069570 possesses a minimal conformational phase space depicting that the compound can be a better inhibitor of the protein. The 100 ns MD simulation of ZINC49069570 and protein E7 complex exhibited a stable post dynamics binding free energy. Insights gained from the study could assist in the identification of novel inhibitors analogous to EGCG, which could shed light on the conformation of the protein, which, in turn, inhibits the oncogenic property of the protein E7 (Aarthy et al., 2020). Fig. 11.2 and Table 11.1 enlist few tools used in CADD. In a similar study, Talele et al. implemented LBBD methods, i.e., comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), to develop a QSAR model using 67 benzimidazole derivatives HCV NS5B polymerase inhibitors. The LBDD-based 3D QSAR model was initially performed based on the lowest energy conformations employing the atom fit alignment method. Secondly, the SBDD, receptorbased 3D QSAR model, was generated from the predicted binding conformations obtained by docking all NS5B inhibitors at the allosteric binding site of NS5B (PDB ID: 2DXS). The outcomes of the ligand-based model were superior as compared to the receptor-based model. Based on the ligand-based model, CoMFA r2cv values 5 0.630 and CoMSIA r2cv values 5 0.668. Based on the receptor-based model, CoMFA r2cv values 5 0.536 and for CoMSIA r2cv values 5 0.561. r2 values for ligand-based CoMFA and CoMSIA models were 0.734 and 0.800, whereas the r2 values for the receptor-based CoMFA and CoMSIA models were 0.538 and 0.639.
FIGURE 11.2 Screening of EGCG structural analogs. EGCG, Epigallocatechin-3-gallate.
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TABLE 11.1 Tools used in CADD (computer-based drug design). Sr no
Tool
Application
Freeware/commercial
1
BioVia Discovery studio
CADD package
Commercial
2
Schrodinger
CADD package
Commercial
3
Sybyl
CADD package
Commercial
4
MOE
CADD package
Commercial
5
VLife MDS
CADD package
Commercial
6
FlexX
CADD package
Commercial
7
Dock
Molecular docking
Free ware (Academic)
8
AutoDock Packages
Molecular docking
Free ware
9
Chimera
Visualization
Free ware
10
Pymol
Visualization
Free ware
11.5 Systems biology approach Systems biology is an integrative approach that connects the bits and pieces to accommodate the whole information in modeling, biochemical reactions, metabolic pathway, cells, tissue, organ, or a complete organism (Wanjek, 2011). It widely covers the paradigm of genomic, transcriptomic, proteomic, interactomics, molecular network modeling, pharmacokinetic, pharmacodynamic, and agent-based modeling in the design and development of the conceptual framework of metabolic pathways and analysis to view the diagrammatic representation of the healthy versus diseased models. Successful modeling of disease, the infection spread, and implementation of concepts are greatly assisted by the data standards (Gupta, 2018). Numerous approaches have been devised to model the metabolomics data in a mechanistic model such as kinetic or constraint-based models. These computational tools and developed models allow the researchers to explore new avenues in metabolism regulation. Considering the problem from the mathematical point of view, we can perform data integration in two main ways: variable constraints and parameter fitting. In variable constraints, the upper and lower limits of a metabolic reaction flux are fixed and the changes in the phenotype are calculated. In parameter fitting, we estimate or find the kinetic parameters such as the turnover rate or the MichaelisMenten constant to predict or describe another metabolic state (Volkova et al., 2020). Tables 11.2 and 11.3 list the systems biology databases and tools. Despite significant advancements in cancerology, surgery, chemotherapy, or radiotherapy remains ineffective under certain conditions. Oncolytic virotherapy
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TABLE 11.2 Systems biology databases. Sr no
Name
Link
1
BioModel Database
https://www.ebi.ac.uk/biomodels/
2
BRENDA Database
https://www.brenda-enzymes.org/
3
IntAct
https://www.ebi.ac.uk/intact/
4
KEGG Pathway Database
https://www.genome.jp/kegg/pathway.html
5
MetaCyc
https://metacyc.org/
TABLE 11.3 System biology tools. Sr no
Name
Link
1
BioTapestry
http://www.biotapestry.org/
2
Cell Designer
http://www.celldesigner.org/
3
Copasi
http://copasi.org/
4
Cytoscape
https://cytoscape.org/
5
Systems Biology Workbench (SBW)
http://sbw.sourceforge.net/
6
Tinker Cell
http://www.tinkercell.com/
7
Simbiology Package— MATLABs
https://www.mathworks.com/products/ simbiology.html
opens a new perspective in the management of cancer disease. Laaroussi et al. have carried out an extensive mathematical modeling “Analysis of Multiple Delays Model for Treatment of Cancer with Oncolytic Virotherapy.” All simulations were carried out using the following parameters: growth rate constant (r), maximal tumor size (K), infection rate (βi), cell-to-cell fusion rate constant (ρ), infected cells death rate (δ), burst size of a virus (b), elimination rate of free virus particles (γ), and time to complete lytic cycle variables (τi). The authors concluded that if (reproductive ratio) R0 , 1, the virotherapy fails as the population of tumor cells increases and the population of infected tumor cells decreases, and if R0 . 1, the virotherapy success and treatment will reach the equilibrium point (El Alami laaroussi et al., 2019).
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11.6 Artificial intelligence approaches Artificial intelligence (AI) helps mimic human cognitive functions. Exponential growth in data in the healthcare sector has opened new avenues of learning and evaluation of data via a self-learning algorithm. The AI techniques can be applied to various types of healthcare data (structured and nonstructured). Before deploying AI in the biological and healthcare systems, the systems are needed to be trained through the data generated from the clinical and biological activities such as high-throughput experiments of genomics, proteomics, interactomics, metabolomics, drug interactions, images, screening, diagnosis, and treatment (Jiang et al., 2017). Hepatitis B or hepatitis C is one of the most common diseases worldwide and the cause of liver cancer. Diagnosis and treatment of hepatitis B and C are guided by liver biopsies (Keltch et al., 2014). The most accepted type of liver biopsy or hepatectomy is percutaneous liver biopsy, which involves the insertion of a thin needle through the abdomen into the liver and removing a small piece of tissue by a surgeon. The tissue is examined by a pathologist to determine the fibrosis stage in the range F0 (no damage) to F4 (cirrhosis) (Keltch et al., 2014; “Liver Resection,” 2021). As AI and data mining tools are widely accepted in clinical decision support systems (CDSS), the commonly applied techniques are Bayesian classifiers, decision trees, genetic algorithm, fuzzy logic, neural networks, support vector machines, and hybrid systems. Keltch et al. have explored a dataset of 424 hepatitis B and hepatitis C patients from publicly available datasets, considering the demographic and standard serum marker data (albumin, alkaline phosphate, cholinesterase, biluribin direct, gamma glutamyl transpeptidase, and gamma globulin) to predict the fibrosis stage and then compare these predictions with known biopsy results. The authors have implemented the Neuro3 NN method in an AI-CDSS based system and compared it with four other methods based on AI, Neural Network (NN), Decision Tree (DT), Naı¨ve Bayesian (NB), and Logistics Regression (LR), using the Weka 6.2 tool, to evaluate the best outcome. The comparative analysis resulted in a good prediction ranging from the F0 to F4 level of disease, i.e., insignificant fibrosis (F0 and F1) and significant fibrosis (F2, F3, and F4), to evaluate the accuracy of models in order to distinguish diseased cases from normal cases (Keltch et al., 2014).
11.7 Conclusion Overcoming the challenges facing the identification and diagnosis of oncoviruses and disease conditions is a global need. Numerous scientist groups and laboratories worldwide are involved in the design and development of new techniques, algorithms, and tools. With the amalgamation of high-throughput experimental outcomes, bioinformatics, and AI, accurate detection of these oncoviruses, oncovirus components, and their infection might be possible.
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References Aarthy, M., Kumar, D., Giri, R., & Singh, S. K. (2018). E7 oncoprotein of human papillomavirus: Structural dynamics and inhibitor screening study. Gene, 658, 159177. Available from https://doi.org/10.1016/j.gene.2018.03.026. Aarthy, M., Panwar, U., & Singh, S. K. (2020). Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7. Scientific Reports, 10(1), 8661. Available from https://doi.org/10.1038/s41598-020-65446-7. Akram Husain, R. S., Rajakeerthana, R., Sreevalsan, A., Prema Jayaprasad, P., Ahmed, S. S. S. J., & Ramakrishnan, V. (2018). Prevalence of human papilloma virus with risk of cervical cancer among south Indian women: A genotypic study with meta-analysis and molecular dynamics of HPV E6 oncoprotein. Infection, Genetics and Evolution, 62, 130140. Available from https://doi.org/10.1016/j.meegid.2018.04.029. Apweiler, R. (2004). UniProt: The Universal Protein knowledgebase. Nucleic Acids Research, 32(90001), 115D119D. Available from https://doi.org/10.1093/nar/gkh131. Aslam, B., Basit, M., Nisar, M. A., Khurshid, M., & Rasool, M. H. (2017). Proteomics: Technologies and their applications. Journal of Chromatographic Science, 55(2), 182196. Available from https://doi.org/10.1093/chromsci/bmw167. Bah, S. Y., Morang’a, C. M., Kengne-Ouafo, J. A., AmengaEtego, L., & Awandare, G. A. (2018). Highlights on the application of genomics and bioinformatics in the fight against infectious diseases: Challenges and opportunities in Africa. Frontiers in Genetics, 9, 575. Available from https://doi.org/10.3389/fgene.2018.00575. Bouvard, V., Baan, R., Straif, K., Grosse, Y., Secretan, B., Ghissassi, F. El, & Cogliano, V. (2009). A review of human carcinogens—Part B: Biological agents. The Lancet Oncology, 10(4), 321322. Available from https://doi.org/10.1016/S1470-2045(09)70096-8. Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., . . . Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. In ACM/IEEE SC 2006 Conference (SC’06) (pp. 4343). IEEE. https://doi.org/ 10.1109/SC.2006.54 Boyle, P., & Levin, B. (Eds.), (2008). World Cancer Report 2008. IARC Press. Available from https://www.cabdirect.org/cabdirect/abstract/20103010665. Braaten, K. P., & Laufer, M. R. (2008). Human papillomavirus (HPV), HPV-related disease, and the HPV vaccine. Reviews in Obstetrics & Gynecology, 1(1), 210. Available from http:// www.ncbi.nlm.nih.gov/pubmed/18701931. Cancer.net. (2020). Retrieved March 2, 2021, from https://www.cancer.net/cancer-types CHARMM. (2021). Retrieved February 22, 2021, from https://www.charmm.org/ Chung, W. C., & Ishida, T. (2011). An MD simulation of the decoy action of EpsteinBarr virus LMP1 protein mimicking the CD40 interaction with TRAF3. Theoretical Chemistry Accounts, 130(23), 401410. Available from https://doi.org/10.1007/s00214-011-1006-9. de Martel, C., Ferlay, J., Franceschi, S., Vignat, J., Bray, F., Forman, D., & Plummer, M. (2012). Global burden of cancers attributable to infections in 2008: A review and synthetic analysis. The Lancet Oncology, 13(6), 607615. Available from https://doi.org/10.1016/ S1470-2045(12)70137-7. Deng, H., Jia, Y., & Zhang, Y. (2018). Protein structure prediction. International Journal of Modern Physics B, 32(18), 1840009. Available from https://doi.org/10.1142/S021797921840009X. El Alami laaroussi, A., Hia, M. El, Rachik, M., & Ghazzali, R. (2019). Analysis of a multiple delays model for treatment of cancer with oncolytic virotherapy. Computational and Mathematical Methods in Medicine, 2019, 112. Available from https://doi.org/10.1155/2019/1732815.
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Epstein, M., Achong, B., & Barr, Y. (1964). Virus particles in cultured lymphoblasts from Burkitt’s lymphoma. The Lancet, 283(7335), 702703. Available from https://doi.org/ 10.1016/S0140-6736(64)91524-7. Gupta, P. P. (2018). Biological systems and pathway modeling approaches. Austin J Biotechnol Bioeng., 5(3), 1099. Han, X., Shin, W.-H., Christoffer, C. W., Terashi, G., Monroe, L., & Kihara, D. (2019). Study of the variability of the native protein structure. Encyclopedia of bioinformatics and computational biology (pp. 606619). Elsevier. Available from https://doi.org/10.1016/B978-012809633-8.201489. Human papillomavirus (HPV) and cervical cancer. (2020). Retrieved December 5, 2020, from https:// www.who.int/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230243. Available from https://doi.org/10.1136/svn-2017-000101. Keltch, B., Lin, Y., & Bayrak, C. (2014). Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients. Journal of Medical Systems, 38(8), 60. Available from https:// doi.org/10.1007/s10916-014-0060-y. Leach, A. R. (2007). Ligand-based approaches: Core molecular modeling. Comprehensive medicinal chemistry II (pp. 87118). Elsevier. Available from https://doi.org/10.1016/ B008-045044-X/00246-7. Liver Resection. (2021). Retrieved April 21, 2021, from https://surgery.ucsf.edu/conditionsprocedures/liver-resection.aspx#:B:text 5 A liver resection is the,a deceased donor (cadaver). Lussignol, M., Kopp, M., Molloy, K., Vizcay-Barrena, G., Fleck, R. A., Dorner, M., & Catanese, M. T. (2016). Proteomics of HCV virions reveals an essential role for the nucleoporin Nup98 in virus morphogenesis. Proceedings of the National Academy of Sciences, 113 (9), 24842489. Available from https://doi.org/10.1073/pnas.1518934113. Mills, R. (2003). Improving gene annotation of complete viral genomes. Nucleic Acids Research, 31(23), 70417055. Available from https://doi.org/10.1093/nar/gkg878. Mui, U., Haley, C., & Tyring, S. (2017). Viral oncology: Molecular biology and pathogenesis. Journal of Clinical Medicine, 6(12), 111. Available from https://doi.org/10.3390/ jcm6120111. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 27(1), 2934. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26 (16), 17811802. Available from https://doi.org/10.1002/jcc.20289. Polanski, J. (2009). Chemoinformatics. Comprehensive chemometrics (pp. 459506). Elsevier. Available from https://doi.org/10.1016/B978-044452701-1.00006-5. Pronk, S., P´all, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., & Lindahl, E. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845854. Available from https://doi.org/10.1093/bioinformatics/btt055. Purnama, E. R., & Kharisma, V. D. (2018). Epitope mapping of capsid protein L1 from human papillomavirus to development cervical cancer vaccine through computational study. Journal of Physics: Conference Series, 1108, 012096. Available from https://doi.org/ 10.1088/1742-6596/1108/1/012096. Sorin, E. J. (2021). FFAMBER. Retrieved January 5, 2021, from http://ffamber.cnsm.csulb.edu/ ffamber.php
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Spurgeon, M. E., & Lambert, P. F. (2013). Merkel cell polyomavirus: A newly discovered human virus with oncogenic potential. Virology, 435(1), 118130. Available from https:// doi.org/10.1016/j.virol.2012.09.029. Starrett, G. J., Thakuria, M., Chen, T., Marcelus, C., Cheng, J., Nomburg, J., & DeCaprio, J. A. (2020). Clinical and molecular characterization of virus-positive and virus-negative Merkel cell carcinoma. Genome Medicine, 12(1), 30. Available from https://doi.org/10.1186/s13073020-00727-4. Tang, K.-W., Alaei-Mahabadi, B., Samuelsson, T., Lindh, M., & Larsson, E. (2013). The landscape of viral expression and host gene fusion and adaptation in human cancer. Nature Communications, 4(1), 2513. Available from https://doi.org/10.1038/ncomms3513. Volkova, S., Matos, M. R. A., Mattanovich, M., & Mar´ın de Mas, I. (2020). Metabolic modelling as a framework for metabolomics data integration and analysis. Metabolites, 10(8), 303. Available from https://doi.org/10.3390/metabo10080303. Wanjek, C. (2011). Systems biology as defined by NIH an intellectual resource for integrative biology. The NIH Catalyst, 19(6), 120. Available from https://irp.nih.gov/sites/default/ files/catalyst/catalyst_v19i6.pdf. What Is Cancer? (2020a). Retrieved December 15, 2020, from https://www.cancer.net/navigating-cancer-care/cancer-basics/what-cancer What Is Cancer? (2020b). Retrieved December 3, 2020, from https://www.cancer.org/cancer/cancer-basics/what-is-cancer.html Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 1950. Available from https://doi.org/10.1080/ 02648725.1996.10647923. zur Hausen, H. (2009). The search for infectious causes of human cancers: Where and why. Virology, 392(1), 110. Available from https://doi.org/10.1016/j.virol.2009.06.001.
Chapter 12
QSAR approach for combating cancer cells ˇ Said Byadi1, Aziz Aboulmouhajir1 and Crtomir Podlipnik2 1
Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa, 2 Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
12.1 Introduction A molecule’s structural features determine its behavior in physical, chemical, biological, or environmental processes and are thus essential for understanding and modeling compound action. The quantitative structureactivity relationship (QSAR) methodology attempts to create a quantitative connection between a compound’s molecular structure and its biological activity. Similarly, the quantitative structureproperty relationship aims to model the relationship between a chemical structure and a wide range of physical or chemical properties (Kubinyi, Folkers, et al., 1998). This chapter introduces readers to the QSAR approach to modeling, especially the anticancer activity of the small molecules. Although the amount of experimental data is constantly increasing, the number of newly synthesized or in silico engineered compounds increases much faster. Moreover, compounds for which no experimental evidence is available can be built in silico, thanks to virtual library screening. Indeed, their biological activities can also be modeled using a reliable model, correlating biological processes and structural features, as expressed (Muratov et al., 2020) in Eq. (12.1). Biological activity 5 fðdescriptorsÞ
ð12:1Þ
Hansch and Fujita (1964) should be credited with popularizing physicochemical properties and statistical methods in structureactivity relationship (SAR) studies. Hansch’s initial study involved the linear combination of many properties using multiple linear regression (MLR) to produce a quantitative model. Typically, the modeled properties were equilibrium constants, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00014-5 Copyright © 2023 Elsevier Inc. All rights reserved.
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K, in log units. Since log K is linearly related to free energy, this method is also known as the linear free energy relationship. The first QSAR models were developed based on the observation that partition coefficients (e.g., log P) are associated with biological results. In some instances, the interaction did not seem to be linear. Nonlinear models such as the parabolic and bilinear models can be used in this case to obtain a better fit between the model and the experimental data (Kubinyi, 1976a, 1976b). The FreeWilson method, which was established in the early days of QSAR, is another technique in which biological behavior is associated with the inclusion of unique structural features in molecules. The reader is guided to the literature (Free & Wilson, 1964) for additional detail. Typically, a QSAR model is developed using a series of previously characterized compounds, thus constituting a training set. To correlate the measured molecular descriptors, which reflect the structural features that determine the property or behavior of a group of molecules, with biological activities, mathematical models are created. There are two types of models to build: classification models that categorize an entity (often referred to as SAR) and regression or correlation models that simulate an activity quantitatively (QSAR). The last method is known as modeling or, in chemometrics, calibration. Several scientists are using the QSAR approach as a powerful chemoinformatic tool for anticancer activity modeling, allowing them to discover new anticancer drugs, for chemotherapy (Alam & Khan, 2017) or immunotherapy (Qianhong, 2010). The generation of QSAR models is based on the following steps (Roy, Kar & Das, 2015), schematized in the flowchart presented in Fig. 12.1.
FIGURE 12.1 QSAR steps in anticancer activity modeling. QSAR, quantitative structureactivity relationship.
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Chemical database collection with biological activities. Structures drawing and database construction. Molecular descriptors calculation. Multivariate analysis. Model generation and correlation. Model examination and validation. Applicability domain. Model application to predict the activities of new compounds. This is the subject of the following section.
12.2 Handling and curation of chemical and biological data The primary goal of any QSAR research is to discover a relationship between biological data and compound molecular structures. The molecular data of all compounds used in the research are needed to set up a QSAR sample. In the last decade, several campaigns have been launched to gather chemical and biological evidence for the general public. These databases are precious for validating novel QSAR models and methods. ChEMBL (Gaulton et al., 2012) and PubChem (Kim et al., 2016) are the two major publicly accessible databases. ChEMBL includes chemical structural data as well as bioactivity data for thousands of drug targets. The latest edition (ChEMBL22) incorporates over 14.4 million bioassay data for about 2.0 million compounds and 11,200 targets. The PubChem database (Chen et al., 2005), administered by the National Institutes of Health in the United States, is another accessible repository for chemical and biological data. PubChem contains bioactivity reports from 1.1 million high-throughput screening systems with millions of values and about 94 million compounds compiled from over 70 depositing organizations. Furthermore, the accuracy of the input data in a dataset is a necessary prerequisite of every modeling analysis. Thus, data curation is vital for any QSAR research, particularly in recent years when the availability of compound databases in the public domain has increased drastically. Several recent studies (Tetko et al., 2008) have clearly shown that the nature of chemical descriptors has a much more significant impact on the prediction efficiency of QSAR models than the design of model optimization techniques. These findings demonstrate that having false constructs in a dataset has a significant impact on model efficiency. Indeed, models built with inaccurate data would produce incorrect models that are unstable for prediction. Therefore, it is critical to validate the consistency of primary data wherever possible before developing any model. On the other hand, biological evidence is often articulated in terms that cannot be explicitly used for QSAR modeling. Since QSAR is based on the relationship between free energy and equilibrium constants, the data for a QSAR model must be associated with biological signal-free energy
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variations. The logarithmic transformation converts asymmetrically distributed biological data to a nearly regular distribution. Thus, log [C] or log 1/ [C] are often used to express concentrations resulting from biological experiments. The chemical structures should be cleaned and standardized (duplicates removed, salts stripped, neutral formed, and canonical tautomer standardized) to enable rigorous model development (Fourches et al., 2010).
12.3 Structures drawing and database building Creating chemical structures by standard drawing software programs is highly time-consuming and virtually impossible. We now have drawing applications with built-in models, bonds limited to set lengths and angles, and other features that make drawing easier. Molecule editors can manipulate chemical structure representations in either a two-dimensional (2D) space or a three-dimensional (3D) space, using 2D or 3D computer graphics. 2D production is used to construct diagrams or to scan chemical databases. The drawing software applications run on the Windows, Mac operating systems, and Linux in some cases. Windows itself handles most functions (printing, screen and printer fonts, resolution, or some other device-wide parameters), and its setup is the deciding factor. Another significant factor is the program’s usability. The best-known software applications for chemical drawing publication content on Microsoft’s Windows platform are presented further (Kaushik, 2014): G G G G
G G G G
Accelrys (Symyx) Draw 4.1 Academic Edition. ChemBioDraw Ultra 17.0.0.206 (ChemDraw). DrawIt-KnowIt All Academic Edition 2020.1. ACD/ChemSketch 2020.2.9 Free and Commercial (Advanced Chemistry Development). Chemistry 4-D Draw 9.0.0 Pro (ChemInnovation Software). ChemDoodle 9.0.0 (iChemLabs LLC). Mestrenova NMR predict desktop 14.1.2 (Mesterlab Research). MarvinSketch 6.3.0 (ChemAxon Ltd).
12.4 Molecular descriptors In the mathematical model, molecular descriptors reflect structure properties. More than 5000 molecular descriptors have been established (Todeschini & Viviana, 2009), and they are classified depending on their nature (physicochemical, quantum, topological, etc.) (Todeschini & Viviana, 2009; Todeschini & Consonni, 2010) dimensionality, or structural representation.
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In addition to theoretical descriptors presented further, experimental descriptors such as log P are essential. The 0-D descriptors are derived solely from the counting of atoms and bonds (e.g., number of nitrogen atoms) and their composition (e.g., bond orders hybridization states). We may also apply the total or average of atomic properties such as weight, length, electronegativity, and others; these descriptors are simple to compute, but they do not distinguish enough when used alone (Toropov & Benfenati, 2007). The general formula of the molecular structure can be thought of as a one-dimensional (1D) description of compounds and provides 1D descriptors, as it defines the composition and structural features of a molecule in a way that can be conveniently measured and treated. Where a compound contains a fragment or a fundamental feature, we refer to fingerprint descriptors. If a particular bit of the string is set to 1 (true), the rest of the string is set to 0 (false). Each bit in this sequence represents a distinct fragment. A hash-coding algorithm assigns a particular collection of bits along with the fingerprint to each pattern of a molecule (Leszczynski et al., 2012). Topological indices are single-valued descriptors that can be determined from a molecule’s 2D graph representation. In contrast, fingerprints are usually encoded as binary bit strings, the configurations of which yield different effects. Biological behavior is often the product of a small molecule’s shape and electrostatic complementarity with a protein target structure. 3D descriptors are typically associated with a structure or a conformation, which is more informative than topological descriptors. Geometric descriptors, on the other hand, have several disadvantages, such as they are very hard to interpret. It is commonly unclear which conformation of a molecule is bioactive. As a result, a multiconformation method with statistical ratios for model construction must be used to derive the bioactive conformation for all compounds under analysis (Chandrasekaran et al., 2018).
12.5 Multivariate analysis A chemical database provides knowledge about different characteristics and properties of molecules, and a wide variety of methods for obtaining useful information from databases is available. Pattern detection, machine learning, data mining, and chemometrics are some of the names assigned to these techniques (Pirhadi et al., 2015). The learning process typically starts with discovering a database, which is then split into two subsets: a testing array used to produce the model (training set) and a test set used to validate the outcome. The used methods, which can be linear or nonlinear, employ the training set and attempt to learn from it. In other words, a mechanism that maps molecular descriptors into biological behavior must be discovered (Luco & Ferretti, 1997). The model’s
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ability to forecast the outcomes of test sets is used to approximate learning efficiency.
12.6 Multiple linear regression analysis MLR is the primary approach for correlating binding affinities with molecular descriptors. It is the first mathematical technique used in classical QSAR studies to generate model coefficients. It provides a model to a set of features using a straight line. Via least-squares fitting, the expected values are correlated to the empirical values, and the model that better reproduces the experimental values is used to predict the bioactivities of new compounds against the same limit (Kubinyi, 1976a, 1976b; Kubinyi, Folkers, et al., 1998). ˆ and χ i In MLR, the relationship between the dependent variable Y the independent variable demonstrates the explanatory variables equation: ˆ Y
5
b0
1
b1χ1
1
b2χ2
1
::: 1
bnχn
1
ei ;
ð12:2Þ
where b0 and bi are fixed parameters (y-intercept and grade, respectively), and ei are errors. The sum of squares of the variations between the observable and predicted values for each measurement in the unit can be minimized P 2using the least-squares criterion to estimate the equations. That is, ei will be minimized.
12.7 Principal component regression Many implementations of principal component regression (PCR) and similar procedures for estimating a dependent variable from many strongly clustered predictors have been made in QSAR. Xiao Li et al. related toxicity to physical and chemical descriptors QSAR by the PCR method (Kubinyi, 1976b). There are descriptions of the application of PCR in various fields of QSAR (Adisyahputra et al., 2014). In PCR, the solution vector is regressed on the principal components (PCs) rather than the original variables (Hemmateenejad et al., 2012). In general, a PCA is performed on X, with only a subset of all PCs kept as necessary. A multiple regression analysis of the response variable versus the reduced PCs collection is constructed using traditional least-squares estimation. Thus, the main advantage of PCR over multiple regression is that the number of variables is reduced to a few uncorrelated variables.
12.8 Partial least squares Wold et al. created partial least square (PLS), which is a generalization of MLR (Lorber et al., 1987; Wold et al., 2001), and it straddles the line of
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MLR and PCR, and both are rare cases of continuum regression. PCR factors catch the greatest amount of variation in the predictor (x) variables, while MLR seeks an individual feature that better compares predictor (x) variables with expected (Y) variables. The principle behind PLS regression is that both the independent matrix X and the dependent matrix Y can be generalized into a low-dimensional factor space and that the rates of the two blocks have a linear relationship. PLS seeks variables that include variation but still achieve correlation. Furthermore, the matrix Y’s columns are linear combinations of the fundamental variables. The following is the relation in matrix form: Y
5
XW;
ð12:3Þ
where W(m k) is the matrix of coefficients representing linear combinations and Y is the new matrix whose columns form “artificial variables,” obtained by the linear combination of the fundamental variables; after this transformation, MLR is applied to table Y in place of X.
12.9 Kernel partial least squares Kernel partial least squares (KPLS) has been used for nonlinear multivariate consistency estimation (Chandrasekaran et al., 2018), and process monitoring (Zhang & Hu, 2011) over the last decade. KPLS (Rosipal & Trejo, 2001) is a new nonlinear PLS technique used to solve nonlinear problems. Through the use of a kernel function, the original input data are nonlinearly translated into linear ones in high-dimensional space. As a result, KPLS preserves the benefits of PLS and has a strong nonlinear mapping capability. In contrast to other nonlinear PLS methods, such as spline PLS (Wold, 1992), quadratic PLS (Abdel-Rahman & Lim, 2009), and neural network PLS (Xing et al., 2014), KPLS requires only linear algebra in high-dimensional space, making it as simple to perform as linear PLS. Furthermore, by using various types of kernel functions, KPLS can accommodate a wide variety of nonlinearities.
12.10 Artificial neural network As a modeling system, the artificial neural network (ANN) has become a familiar and effective chemoinformatic tool (Habibi-Yangjeh et al., 2006; Su & Zhou, 2006). Unlike traditional statistical approaches, ANN-based techniques do not need prior knowledge of the numerical form of the relationship connecting the variables (Aoyama et al., 1990), making the ANN ideal for extrapolating the complex and unstable relationships between the biological occurrence and the 2D or 3D configuration of the compounds. ANN is one of the most effective approaches for determining the relationship between nonlinear descriptors. ANN is made up of nodes or neurons and the connections (weights) that link them. The neurons in an ANN are arranged in layers
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(input, covered, and output), with monodirectional connections. The neurons in the neighboring layers are fully related, but there are no connections between the neurons in the same layer. The architecture calculates a numerical output value for a given mathematical input vector. A formal neuron adds up incoming signals compounded by relation weights, subtracts a threshold value, and uses a transfer function to measure an output signal. A hidden layer of neurons has the following sigmoid transport functions: tf 5
1 ð1 1 e2 inputt Þ
ð12:4Þ
where tf is the transport function that restricts the neuron’s output signal to values between 0 and 1.
12.11 Other methods Generative topographic mapping (GTM) (Jaworska et al., 2005) is a nonlinear mapping approach used successfully in various data processing domains. Each point in the low-dimensional (typically 2D) latent space is mapped onto the manifold embedded in the initial descriptor space in GTM (Sidorov et al., 2017). Ferreira invented gene expression programming (GEP) in 1999 based on genetic algorithms and genetic programming (GP). GEP employs the same kind of diagram representation as GP, but the entities evolved by GEP (translation trees) are gene expression. Instead of considering the whole dataset, Local Lazy Regression (LLR) extracts a prediction by locally interpolating the nearby examples of the query that are considered significant based on a distance metric.
12.12 Classification-based QSAR approaches The term “K-means” refers to a kind of partitioning (k-means) and is a vector quantization method derived from signal processing that aims to divide observations into k clusters, with each observation belonging to the cluster with the closest mean (cluster centers or cluster centroid). James MacQueen used K-means partitioning (k-means) for the first time in 1967 (Macqueen, 1967)—a nonhierarchical sorting method that can be used when the number of relations present in the subjects or cases is recognized. Overall, the kmeans process yields precisely k distinct clusters. The following sentences highlight the algorithm’s benefits: 1. The k-means algorithm is standard because it is easy to understand and apply. 2. It has intellectual simplicity and quickness. 3. It is applicable to vast volumes of data by picking a good notion of distance.
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It should be remembered that the data were separated into a “training package” for model generation and an “external evaluation collection” for external validation (Burden et al., 2000).
12.13 QSAR model generation Using statistical methods, firstly, we remove the duplicates and clean the data; then, we divided our database into a training set (70% of the compounds) and a validation set (30% of the compounds) using k-means partitioning as the classification method (R´acz et al., 2015). We select the most relevant descriptors that carry the maximum amount of information to avoid redundancy and collinearity between the descriptors. We construct the predictive models that connect structure-based descriptors with experimentally measured affinity using the statistical method whatever are linear or nonlinear. Then, we estimate the correlation coefficient (R2) and standard deviation (SD) by modeling the training (internal validation).
12.14 Model examination and validation Evaluate the predictiveness of the model by evaluating the validation coefficient and root mean square error (RMSE) base based on the prediction set (external validation). There are two main kinds of QSAR model validation: internal and external validation.
12.15 Internal validation Internal methods of validating a model include least-squares fit (R2), crossvalidation (Q2), modified R2 (R2 adj), root-mean-squared error (RMSE), and bootstrapping (Bajorath, 2004). 2 32 P P P N XY 2ð XÞð YÞ 6 7 R2 5 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð12:5Þ P 2 P 2 P 2 P 2 5 ð½N X 2ð XÞ ½N Y 2ð YÞ Þ Least-squares fitting is the most widely used internal model validation process. This validation approach is close to linear regression in that the fitting coefficient is R2 adj for evaluating predicted and observational behaviors. The RMSE approach can be used to assess the fit of QSAR models. This approach is used to assess whether or not the model preserves the predictive efficiency shown in R2. The RMSE method calculates the difference between the mean of empirical values and estimated biological activities: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n X ðy^ 2ym Þ2 RMSE 5 & i ; ð12:6Þ n21 i51
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ˆ denote the experimental and expected behavior for various where y and Y compounds in the training sample, ym denotes the mean of the experimental operations, and n denotes the number of compounds in the dataset. Even though the model has a good R2 value (0.7), large RMSE values (around 1) represent the model’s poor ability to predict events correctly. The RMSE values for a good statistical model should be lower than 0.4 (Byadi et al., 2020). However, better R2 and RMSE values are not indicative of model validity. As a result, alternative parameters must be produced to demonstrate the predictiveness of models. Cross-validation (Q2), a common approach for internal validation of a QSAR model, replicates the regression on subsets of the database several times.
12.16 External validation Internal evaluation is suitable for judging the consistency and goodness of the model. The only drawback of this approach is the model’s lack of predictability when extended to an entirely new dataset. We use external validation to ensure the predictability and applicability of the generated QSAR model to predict untested molecules. The external validation estimates the predictive ability of a QSAR model to analyze the expected and experimental activities of an external test set of compounds not used in model production (Guha & Jurs, 2005; Zefirov & Palyulin, 2001). The predictive potential is also dependent on the determination coefficient Q2 test between the observed and predicted activities of the test set; the higher the Q2 measure ( . 0.5), the better the model’s efficiency. Golbraikh and Tropsha suggested R2 as a correlation coefficient between expected and actual events (Kubinyi, Hamprecht, et al., 1998).
12.17 Applicability domain The applicability domain (AD) of a QSAR has been defined (Netzeva et al., 2005) as the response and the chemical structure space in which the model makes predictions with given reliability. Netzeva et al. (2005) and Jaworska et al. (2005) have reviewed methods for defining an AD and made several recommendations for their determination and use. They suggested that the concept of an AD is the task of the model maker rather than the model consumer and that the starting point should be the publishing of all training set compounds, including all structures and descriptors. A QSAR model cannot be called a general model since it is based on a small number of compounds that do not cover the entire chemical space. There are many approaches for evaluating a QSAR model’s applicability domain; the leverage approach is the most widely used. The one mentioned above is based on the dependent variable’s uniform residuals variance, with the distance between the descriptor values and their means being referred to
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as leverage (Roy, Kar & Ambure, 2015). Whether a compound’s residual and leverage surpass the h 5 3 p/n threshold (where p is the number of descriptors plus one and n is the number of observations included in the training set), the compound is assumed to be beyond the applicability scope of the elaborated model (Netzeva et al., 2005). The hi-value is estimated for each compound in the initial space of the independent variables (xi) using the following rule: L5X
iT
ðX T XÞ21
Xi
½i 5 1; 2; 3::k:
ð12:7Þ
The Williams map, a scatter plot of the uniform cross-validated residuals versus leverages (or hat values) hi, was used to approximate and visualize the final model’s applicability area: hi 5 mi ðMt MÞ21 mi t :
ð12:8Þ
Another validation method is to use the model to estimate the affinity from the decoy and the active datasets, and use binary metrics to generate the receiver operating characteristic curves (ROC curves) (Byadi et al., 2020). Gaspar et al. took a different approach, applying a likelihood-dependent applicability domain to GTM models and RF, kNN, M5P, and PLS models based on the original or GTM descriptors (Gaspar et al., 2015).
12.18 Model application for the prediction of compounds activity After building the QSAR models using different statistical methods and validating them with statistical parameters, we also assess their predictability. Usually, the developed and validated QSAR models were used to examine chemical structure sets, such as the Pubchem, PubMed, or Zinc database. After screening the library, we found active compounds against the same target; we evaluated the activities of these compounds by an anchoring approach stimulating the interaction of the compounds with the site binding of the protein; finally, we examined the stability of the complex formed by molecular dynamics.
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Alam, S., & Khan, F. (2017). 3D-QSAR studies on Maslinic acid analogs for anticancer activity against breast cancer cell line MCF-7. Scientific Reports, 7(1), 113. Available from https:// doi.org/10.1038/s41598-017-06131-0. Aoyama, T., Suzuki, Y., & Ichkawa, H. (1990). Neural networks applied to quantitative structure-activity relationship analysis. American Chemical Society, 1, 25832590. Available from https://doi.org/10.1021/jm00171a037. Bajorath, J. (2004). Chemoinformatics concepts, methods, and tools for drug discovery (275). American Standards Institute, 524524. Burden, F. R., Ford, M. G., Whitley, D. C., & Winkler, D. A. (2000). Use of automatic relevance determination in QSAR studies using Bayesian neural networks. Journal of Chemical Information and Computer Sciences, 40(6), 14231430. Available from https://doi.org/ 10.1021/ci000450a. ˇ & Aboulmouhajir, A. (2020). Fingerprint-based 2DByadi, S., Hachim, E., Sadik, K., Podlipnik, C., QSAR models for predicting Bcl-2 inhibitors affinity. Letters in Drug Design & Discovery, 17, 12061215. Available from https://doi.org/10.2174/1570180817999200414155403. Chandrasekaran, B., Tekade, R. K., & Design, F. (2018). Computer-aided prediction of pharmacokinetic (ADMET) properties. Dosage form design parameters. Academic Press. Chen, J., Swamidass, S. J., Dou, Y., Bruand, J., & Baldi, P. (2005). ChemDB: A public database of small molecules and related chemoinformatics resources. Bioinformatics (Oxford, England), 21(22), 41334139. Available from https://doi.org/10.1093/bioinformatics/bti683. Fourches, D., Muratov, E., & Tropsha, A. (2010). Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling, 50(7), 11891204. Available from https://doi.org/ 10.1021/ci100176x. Free, S. M., & Wilson, J. W. (1964). A mathematical contribution to structure-activity studies. Journal of Medicinal Chemistry, 7(4), 395399. Available from https://doi.org/10.1021/ jm00334a001. Gaspar, H. A., Baskin, I. I., Marcou, G., Horvath, D., & Varnek, A. (2015). GTM-based QSAR models and their applicability domains. Molecular Informatics, 34(67), 348356. Available from https://doi.org/10.1002/minf.201400153. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(D1), 11001107. Available from https://doi.org/10.1093/nar/gkr777. Guha, R., & Jurs, P. C. (2005). Interpreting computational neural network QSAR models: A measure of descriptor importance. Journal of Chemical Information and Modeling, 45(3), 800806. Available from https://doi.org/10.1021/ci050022a. Habibi-Yangjeh, A., Danandeh-Jenagharad, M., & Nooshyar, M. (2006). Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR. Journal of Molecular Modeling, 12(3), 338347. Available from https://doi.org/10.1007/s00894005-0050-6. Hansch, C., & Fujita, T. (1964). ρ-σ-π analysis. A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 16161626. Available from https://doi.org/10.1021/ja01062a035. Hemmateenejad, B., Miri, R., & Elyasi, M. (2012). A segmented principal component analysisregression approach to QSAR study of peptides. Journal of Theoretical Biology, 305, 3744. Available from https://doi.org/10.1016/j.jtbi.2012.03.028.
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Jaworska, J., Nikolova-Jeliazkova, N., & Aldenberg, T. (2005). QSAR applicability domain estimation by projection of the training set in descriptor space: A review. ATLA Alternatives to Laboratory Animals, 33(5), 445459. Available from https://doi.org/10.1177/ 026119290503300508. Kaushik, M. (2014). A review of innovative chemical drawing and spectra prediction computer software. Mediterranean Journal of Chemistry, 3(1), 759766. Available from https://doi. org/10.13171/mjc.3.1.2014.04.04.16. Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., & Bryant, S. H. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44(D1), D1202D1213. Available from https://doi.org/10.1093/nar/gkv951. Kubinyi, H. (1976a). Quantitative structure-activity relationships. IV. Non-linear dependence of biological activity on hydrophobic character: A new model. Arzneimittel-Forschung, 26(11), 19911997. Available from http://europepmc.org/abstract/MED/1037231. Kubinyi, Hugo (1976b). Quantitative structure-activity relationships. 2. A mixed approach, based on hansch and free-Wilson analysis. Journal of Medicinal Chemistry, 19(5), 587587. Kubinyi, Hugo, Folkers, G., & Martin, Y. C. (1998). 3D QSAR in drug design. Kluwer Academic. Available from https://doi.org/10.1007/0-306-46857-3. Kubinyi, Hugo, Hamprecht, F. A., & Mietzner, T. (1998). Three-dimensional quantitative similarityactivity relationships (3D QSiAR) from SEAL similarity matrices. Journal of Medicinal Chemistry, 41(14), 25532564. Available from https://doi.org/10.1021/jm970732a. Leszczynski, J., Kaczmarek-kedziera, A., Puzyn, T., Reis, M. G. P. H., & Shukla, M. K. (2012). Handbook of computational chemistry (p. 2365) Springer. Available from https://doi.org/ 10.1007/978-3-319-27282-5. Lorber, A., Wangen, L. E., & Kowalski, B. R. (1987). A theoretical foundation for the PLS algorithm. Journal of Chemometrics, 1(1), 1931. Available from https://doi.org/10.1002/ cem.1180010105. Luco, J. M., & Ferretti, F. H. (1997). QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. Journal of Chemical Information and Computer Sciences, 37(2), 392401. Available from https://doi.org/ 10.1021/ci960487o. Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Mathematics, 233, 281297. Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). QSAR without borders. Chemical Society Reviews, 49(11), 35253564. Available from https://doi.org/10.1039/d0cs00098a. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., Van De Sandt, J. J. M., Tong, W., & Yang, C. (2005). Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA Alternatives to Laboratory Animals, 33(2), 155173, https://doi.org/DOI 10.1016/j.ympev.2010.04.030. Pirhadi, S., Shiri, F., & Ghasemi, J. B. (2015). Multivariate statistical analysis methods in QSAR. RSC Advances, 5(127), 104635104665. Available from https://doi.org/10.1039/ c5ra10729f.
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Qianhong, Z. (2010). QSAR for anticancer activity by using mathematical descriptors (Issue July). http://purl.umn.edu/93639 Rosipal, R., & Trejo, L. J. (2001). Kernel partial least squares regression in reproducing Kernel Hilbert space. Journal of Machine Learning Research, 2, 97123. Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 2229. Available from https://doi.org/10.1016/j.chemolab.2015.04.013. Roy, K., Kar, S., & Das, R. N. (2015). A primer on QSAR/QSPR modeling. Springer International Publishing. Available from https://doi.org/10.1007/978-3-319-17281-1. R´acz, A., Bajusz, D., & He´berger, K. (2015). Consistency of QSAR models : Correct split of training and test sets, ranking of models and performance parameters. SAR and QSAR in Environmental Research, 26(7), 2125. Available from https://doi.org/10.1080/ 1062936X.2015.1084647. Sidorov, P., Viira, B., Davioud-Charvet, E., Maran, U., Marcou, G., Horvath, D., & Varnek, A. (2017). QSAR modeling and chemical space analysis of antimalarial compounds. Journal of Computer-Aided Molecular Design, 31(5), 441451. Available from https://doi.org/10.1007/ s10822-017-0019-4. Su, Q., & Zhou, L. (2006). QSAR modeling of AT1 receptor antagonists using ANN. Journal of Molecular Modeling, 12(6), 869875. Available from https://doi.org/10.1007/s00894-006-0105-3. ¨ berg, T., Todeschini, Tetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., O R., Fourches, D., & Varnek, A. (2008). Critical assessment of QSAR models of environmental toxicity against tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling, 48(9), 17331746. Available from https://doi.org/10.1021/ci800151m. Todeschini, R., & Consonni, V. (2010). Molecular descriptors for chemoinformatics (2, pp. 1252). Deutsche Nationalbibliografi. Available from https://doi.org/10.1002/9783527628766. Todeschini, R., & Viviana, C. (2009). Molecular Descriptors for Volumes I & II. Wiley, 12651265. Toropov, A. A., & Benfenati, E. (2007). SMILES as an alternative to the graph in QSAR modelling of bee toxicity. Computational Biology and Chemistry, 31(1), 5760. Available from https://doi.org/10.1016/j.compbiolchem.2007.01.003. Wold, S. (1992). Nonlinear partial least squares modelling II. Spline inner relation. Chemometrics and Intelligent Laboratory Systems, 14, 7184. Wold, S., Sjo¨stro¨m, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109130. Available from https:// doi.org/10.1016/S0169-7439(01)00155-1. Xing, J., Luo, R., Guo, H., Li, Y., Fu, H., Yang, T., & Zhou, Y. (2014). Chemometrics and intelligent laboratory systems radial basis function network-based transformation for nonlinear partial least-squares as optimized by particle swarm optimization : Application to QSAR studies. Chemometrics and Intelligent Laboratory Systems, 130, 3744. Available from https://doi.org/10.1016/j.chemolab.2013.10.006. Zefirov, N. S., & Palyulin, V. A. (2001). QSAR for boiling points of “small” sulfides. are the “high-quality structure-property-activity regressions” the real high quality QSAR models? Journal of Chemical Information and Computer Sciences, 41(4), 10221027. Available from https://doi.org/10.1021/ci0001637. Zhang, Y., & Hu, Z. (2011). Multivariate process monitoring and analysis based on multi-scale KPLS. Chemical Engineering Research and Design, 89(12), 26672678. Available from https://doi.org/10.1016/j.cherd.2011.05.005.
Chapter 13
Human papillomaviruses and their carcinogens effect Elamrani Elhassani Salma and Bahia Bennani Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco
13.1 Introduction Papillomaviruses represent a large family of viruses that infect mucosal and cutaneous epithelia and which are implicated in several cancers. The virus particles are nonenveloped icosahedrons (T 5 7) with a diameter of 5055 nm. The encapsidated genome is circular double-stranded DNA (Day & Schelhaas, 2014; Louie et al., 2008). The large group of human papillomaviruses (HPVs) including more than 220 genotypes is classified into five genera (α,β, γ, μ and ν) (Chiantore et al., 2020). From identified genotypes, those with mucosal tropism were grouped in the genus Alphapapillomavirus, and those with cutaneous tropism were included in the three genera: Alphapapillomavirus, Betapapillomavirus, and Gammapapillomavirus (De Villiers et al., 2004; Tadlaoui et al., 2019). The HPV classification is based on the nucleotide sequence of the capsid protein L1 gene. The mucosal types are also classified according to their carcinogenic potential (Molet et al., 2019). However, the molecular biology of HPV is very complex. Three oncogenes (E5, E6, and E7) modulate the transformation process, two regulatory proteins (E1 and E2) modulate transcription and replication, and two structural proteins (L1 and L2) constitute the viral capsid (De Villiers et al., 2004). The E6 and E7 products interfere with the p53 and pRB functions, respectively, and deregulate the cell cycle (Furumoto & Irahara, 2002).
13.2 Epidemiology of human papillomaviruse HPV is one of the most common causes of sexually transmitted diseases worldwide. An estimated 6.2 million people are infected every year (Larkin, 2006). HPV 16 and 18 are the most common high-risk (HR)-HPV types Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00008-X Copyright © 2023 Elsevier Inc. All rights reserved.
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found worldwide, which are currently preventable by vaccination (Aubin et al., 2007). According to the World Health Organization (WHO), the highest prevalence of HPV was observed among young people (,25 years old), with an adjusted value of 24.0%, while a second peak of prevalence was observed among middle-aged groups (4550 years) (WHO, 2017; Tadlaoui et al., 2019). In human cancer etiology, a major discovery has been the recognition that cervical cancer is a rare consequence of infection by some mucosatropic types of HPV (Jenkins & Xavier Bosch, 2019). HPV infection and its association with cervical cancer were first recognized by Meisels & Fortin (1976), who demonstrated that HPV DNA was present in approximately 70%80% of cervical carcinomas (Furumoto & Irahara, 2002). In fact, HPV DNA is associated with several different anogenital cancers other than cervicals like the vulva, vagina, anus, and penis. It has also been identified in head and neck cancers, in the oral cavity, the oropharynx, and the larynx in both sexes. In men, 80%85% of anal cancers and approximately 50% of penile cancers are associated with HPV infection. In women, HPV DNA is prevalent in 36%40% of vulvar cancer cases and close to about 90% of vaginal cancers (Giuliano et al., 2008). Mainly HPV types 16 and 18 are identified as causative agents of at least 90% of cervical cancer and are also etiologically linked to more than 50% of other anogenital cancers (Finzer et al., 2002). The etiologic role of HPV in nonmelanoma skin cancer is also discussed. Its role was first demonstrated in patients with the rare hereditary disease epidermodysplasia verruciformis (EV). Those patients are often found to have flat warts and macular lesions. In 2014, a study on the involvement of HPVs in skin carcinogenesis proved the immortalization and transformation properties of some HPVs, and this was related to the E6 and E7 proteins (viral oncoproteins) that can support the promotion and the progression of the epidermal tumor in the presence of UVR (Aubin, 2014). Different types belonging to β-HPV species 2 are detected in cutaneous squamous cell carcinoma (CSCs) with a higher frequency than in healthy skin and other skin cancers. In fact, study showed that 12.0% of healthy skin samples were positive for HPV DNA, compared with 22.5% of actinic keratoses (AKs), 17.5% of basal cell carcinomas, and 25.6% of CSCs (Forslund et al., 2007).
13.3 Human papillomaviruse classification As mentioned above, the HPVs classification is based on the nucleotide sequence of the capsid protein L1 gene. The mucosal types belong to the Alphapapillomavirus genus and were also classified into three categories according to their carcinogenic potential: low risk (LR), intermediate risk, and HR (Table 13.1) (De Villiers et al., 2004). Thus, genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, and 82 were classified as HR
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TABLE 13.1 Classification of HPVs (human papillomaviruses) according to their carcinogenic potential. Classification
Types
High risk
16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, 82
Intermediate risk
26, 53, 66
Low risk
6, 11, 13, 40, 42, 43, 44, 54, 61, 70, 72, 81, 89
HPVs not shown in the table are considered as indeterminate risk (Aubin et al., 2007; World Health Organization, 2017).
HPVs, the types 26, 53 and 66 as probably HR HPVs and the types 6, 11, 13, 40, 42, 43, 44, 54, 61, 70, 72, 81, and 89 were classified as LR HPVs. Other types were considered indeterminate risks (Table 13.1) (Molet et al., 2019). Cutaneous HPVs belong to different genera, of which the Betapapillomavirus genus contains five distinct species (ß1: HPV 5, 8, 12, 14, 19, 20, 21, 24, 25, 36, 47, and 93; ß2: HPV 9, 15, 17, 22, 23, 37, 38, and 80; ß3: HPV 49, 75, and 76; ß4: HPV 92; and ß5: HPV 96), the genus Gammapapillomavirus contains seven HPV types (HPV 4, 48, 50, 60, 65, 88, and 95), the genus Mu-papillomavirus contains two HPV types (HPV 1 and 63), and the genus Nu-papillomavirus contains type 41 (Andersson et al., 2008). Even if most of Alpha-papillomavirus are mucosal types, some types are cutaneous as α2 (HPV 3, 10, 28, 29, 77, 78, and 94), α4 (HPV 2, 27, and 57), and α8 (HPV 7, 40, 43, and 91) species (De Villiers et al., 2004). Several studies have shown that the different types of cutaneous HPV, mainly belonging to beta and gamma genera, are widely present on the surface of the skin in the general population. In fact, some sequencing studies in Sweden reported that the β-HPV genus is the most frequently detected genus in keratinocytes of infected skin (Bzhalava et al., 2015). The HPV types found in macular lesions are called EV-HPV types and include HPV types 5, 8, 9, 12, 14, 15, 17, and 1925 (Pfister, 1992). HPV types 5 and 8 are associated with EV, and this infection can progress to CSCs (Louie et al., 2008). Other β-HPV species 2 are also detected in CSCs with a higher frequency than in healthy skin and other skin cancers (12.0% of healthy skin samples were positive for HPV DNA, compared with 22.5% of AKs, 17.5% of basal cell carcinomas, and 25.6% of CSCs) (Forslund et al., 2007).
13.4 Human papillomavirus transmission HPVs are responsible for skin and mucosal epithelial infections and their transmission can be direct or indirect since they retain their infectious capacity in the external environment (Doorbar et al., 2015).
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13.4.1 Vertical transmission HPV vertical transmission can occur from the father or mother to the child. Thus, the virus can be transmitted to the embryo, fetus, or baby during pregnancy or childbirth (perinatal and intrauterine transmission). It can also occur at the time of fertilization via infected oocyte or spermatozoon (Sabeena et al., 2017; Syrja¨nen, 2010).
13.4.2 Horizontal transmission The most frequent mode of HPV horizontal transmission is sexual activity. There is currently strong epidemiological evidence of the sexual activity role, with or without penetration, in the HPV transmission (Liu et al., 2016). Horizontal transmission is also possible through indirect contact with soiled objects (clothes and underwear, etc.) or contaminated contact surfaces (toilets, bath, pool floor, etc.) (Tadlaoui et al., 2019). Nevertheless, the virus can also be transmitted, but rarely, between sexual partners through the hands (Liu et al., 2016).
13.5 Structure, genomic organization, and viral proteins All papillomavirus genomes had a common structure and genomic organization. They are nonenveloped, icosahedron capsid particles with a doublestranded circular DNA of approximately 8 kbp of size (de Sanjose´ et al., 2018). This small genome can be divided into three major regions: early, late, and a long control region (LCR or noncoding region [NCR]) which regulates the expression of the open reading frames (ORFs) (Fig. 13.1) (Zheng & Baker, 2006). The three regions are separated by two polyadenylation sites: early and late ones (Tadlaoui et al., 2019).
FIGURE 13.1 HPV 16 structure and viral proteins. HPV, human papillomaviruse. From de Sanjose´, S., Brotons, M. & Pavo´n, M. A. (2018). The natural history of human papillomavirus infection. Best Practice and Research: Clinical Obstetrics and Gynaecology, 47, 213. https:// doi.org/10.1016/j.bpobgyn.2017.08.015.
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13.5.1 The long control region The LCR region is a segment of about 850 bp (10% of the HPV genome), located between L1 and E6/E7. This region has no protein-coding function but bears the origin of replication as well as multiple transcription factors binding sites that are important in the regulation of RNA polymerase IIinitiated transcription from viral early as well as late promoters (Zheng & Baker, 2006).
13.5.2 The early region The early region contains ORFs encoding proteins that are necessary for viral DNA replication and oncogenesis, namely ORFs E1, E2, E4, E5, E6, and E7 (Table 13.2) (Dillner, 1990). The E6 and the E7 ORFs play a key role in cellular neoplastic transformation processes. The E6 protein binds p53 and promotes its degradation, whereas the E7 protein binds and inactivates pRb. These viral oncoproteins determine cell cycle entry and inhibition of p53-mediated apoptosis (Suh et al., 2014).
13.5.2.1 E1 The E1 gene encodes E1 viral protein of 73-kDa. It is a phosphoprotein that composed of a long amino acid (Aa) sequence varying from 593 to 681 depending on the HPV type (Chiang et al., 1992). This protein binds to a specific DNA sequence called E1BS (E1 binding sites) in the viral origin of replication and assembles into hexameric complexes involving a second viral protein called E2 (Humans et al., 2007). TABLE 13.2 Main functions of the proteins of HPV. Protein
Function
E1
Assists episomal replication DNA helicase
E2
Transcription factor
E4
Assists packaging of virus
E5
Prevents cell differentiation
E6
Prevents cell differentiation promotes p53 degradation
E7
Prevents cell-growth arrest/differentiation-inhibits inhibitors of E2F transcription factor
L1
Major capsid protein (structural)
L2
Minor capsid protein assists packaging of DNA
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13.5.2.2 E2 The E2 gene encodes an E2 protein of 4045 kDa divided into three functional domains. The C-terminal domain that recognizes and bind 12-bp palindromic DNA sequences, defined as E2-BSs; the middle region called “the hinge region” that regulate the stability of some E2 proteins and determine their nuclear localization in others; and the A-terminal domain essential to regulate the transcription and the viral DNA replication through the interaction with E1 protein (Kim et al., 2000). Moreover, E2 interacts with L2 and leads to the amplification of viral DNA to facilitate the production of new viral progeny (Cobo, 2012). 13.5.2.3 E4 The E4 gene encodes 17 kDa E4 protein located in the E region, overlapping with E2. It is a fusion product with a 5-amino acid sequence from the Nterminus of E1 expressed as E1^E4. E4 is the first protein expressed primarily at later stages followed by late proteins L1 and L2 and is the most abundant viral protein in the virus life cycle (Humans et al., 2007). E4 plays a role in facilitating and supporting viral genome amplification, regulation of late gene expression, control of virus maturation, and the meditation of virus release (Cobo, 2012). 13.5.2.4 E5 The E5 encodes 710 kDa E5 protein primarily localized in the intracellular endoplasmic reticulum and Golgi apparatus membranes. This protein plays a role in carcinogenesis that results in stimulating cells proliferation by interacting with the E7 protein and the formation of activating complexes with growth factor receptors (Esteˆva˜o et al., 2019). It also plays a vital role in cell signaling modulation by its association with the vacuolar proton ATPase (Straight et al., 1995). E5 plays a role in decreasing cell death and then promoting the accumulation of cells with abnormal DNA genetic mutations and consequently promoting the malignancy process (Oh et al., 2010). This protein plays role in virus protection against cellular immunity (Suprynowicz et al., 2008; Tadlaoui et al., 2019). Furthermore, E5 inhibits gap junctions by decreasing connexin 43 expression. This phenomenon is often found in transformed cells (Mougin et al., 2008). 13.5.2.5 E6 The E6 gene encodes 1618 kDa E6 protein. It is one of the three wellestablished oncoproteins that are associated with the malignant progression of HPV-infected cells (Howie et al., 2009). Its main function is the ability to
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bind and degrade the tumor suppressor protein p53 through the protein ligase E6 associated protein (Cobo, 2012). The binding of E6 protein to p53 leads to its rapid degradation, and the eclipse in the G1 phase, DNA repair mechanisms, and apoptosis are done (Alp Avcı, 2012). E6 protein binds to other several cellular proteins such as the proteins involved in cell polarity and motility, tumor suppressors and inducers of apoptosis, as well as the replication proteins and DNA repair factors. It also induces the expression and activity of telomerase and further cell immortalization (Cobo, 2012).
13.5.2.6 E7 The E7 gene encodes 15 kDa protein E7. It is an accessory protein that is not encoded by all papillomaviruses. It plays a central role in the human papillomavirus life cycle, reprogramming the cellular environment to be conducive to viral replication (Roman & Munger, 2013). E7 protein interacts with pRb and mitotically interactive cellular proteins such as cyclin-E, causing stimulation of cellular DNA synthesis and cell proliferation (Alp Avcı, 2012). In addition to binding pRb, this protein can bind to p107 and p130. The interaction with these pocket proteins underlies the ability of E7 to immortalize cells and to abrogate normal responses of DNA damage (Humans et al., 2007). E6 and E7 proteins inhibit p53 and pRb proteins functions and cause uncontrolled proliferation and immortalization of the cells (Perrard, 2019). 13.5.2.7 E3 and E8 E3 and E8 proteins were recently identified in the early gene region and found only in a few papillomavirus types (HPV 1, 11, 16, 31, 33). A fusion protein, E8^E2C, may play a role in the control of viral copy number as well as in the stable maintenance of HPV episomes (Alp Avcı, 2012). 13.5.3 The late region: L1 and L2 The late region of HPV consists of about 2500 base pairs that codes for the viral capsid proteins: L1 and L2 (Table 13.2) (Beutner & Tyring, 1997). L1 codes for the major structural protein of papillomavirus and has a weight of about 5560 kDa. It is highly immunogenic, presents conformational virus-neutralizing epitopes, and could be used to detect HPV antibodies in the sera of patients with high specificity (Cobo, 2012; Humans et al., 2007). L1 has the ability to self-assemble into virus-like particles (VLPs) which is widely used in HPV prophylaxis vaccine production (Rautava & Syrja¨nen, 2012).
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L2 is the minor capsid protein of papillomavirus with a size of 70 kDa. It contributes to the interaction of the virion with the cell surface (Cobo, 2012; Humans et al., 2007).
13.6 Human papillomavirus replication cycle Papillomaviruses and their natural host tissue are perfectly adapted to each other. They exploit the cellular machinery for their own purposes (Mun˜oz et al., 2006). HPV penetrates cutaneous or mucosal epithelium through microlesions and infects the basal layer, where they bind to and enter into cells. For infection maintenance, the virus has to infect an epithelial stem cell (Doorbar, 2005). The HPV genome is replicated in an episomal form in the nucleus of epithelial cells. The replication cycle is completed on two times. First, the viral genome is replicated into a copy number and carried on for different periods of time at this low copy number within the initially infected competent cells (Doorbar, 2005). Second, after the basal cells are pushed to the suprabasal compartment, they lose their ability to divide and initiate the terminal differentiation program. In this compartment, papillomaviruses replicate and take advantage of the disintegration of epithelial cells that occurs following their natural renewal in the superficial layers to be released into the environment (Mun˜oz et al., 2006). The mechanisms of HPV entry are not fully explained. It has been shown that some HPV types use heparan sulfate for attachment to the cell surface and an integrin would be necessary for the virus entry (Giroglou et al., 2001). Recently, it has been reported that LN5 (Laminine 5), uniquely epithelial laminin secreted as a heterotrimeric complex by migrating and basal keratinocytes, may function in vitro as an extracellular transreceptor for infection (Culp et al., 2006). The successful transfer of virus particles from LN5 to membrane-associated receptors on adjacent cells appears to be mediated by the expression of α6 integrin, which binds LN5 (Culp et al., 2006). The caveola-mediated endocytosis pathway is reported to be part of the entry process (Bousarghin et al., 2003). Nonenveloped DNA viruses, HPV-16 and HPV-58, are usually internalized by clathrin-coated vesicles. The virions are then transported to the nucleus via the microtubules and actin microfilaments of the cytoskeleton and decapsidation occurs just before the viral DNA enters the nucleus to begin replication (Coursaget et al., 2007). The viral genome replication in stem cells is under the control of early proteins E1 and E2. This is known as the establishment phase, which allows the production of 50100 copies of viral DNA per cell. This phase takes place during the S phase of the cell cycle and is called nonproductive because there is no production of virions. In maintenance phase, viral genomes occurs and consists to maintain a constant number of HPV genomes during cell divisions (Lim et al., 1998). Then, the rolling circle replication of the viral DNA occurs (Flores & Lambert, 1997). After that, the L1 and L2
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proteins are expressed in the most superficial layers of the epithelium, and the genome is encapsulated leading to the production of new infectious virions. The virions are released into the environment with the desquamating cells and the mucosa becomes very infecting that increases the risk of HPV transmission (Monsonego, 2007).
13.7 Infection evolution Most HPV infections are nonpersistent and are known as acute. According to Virgin et al. (2009), acute infection is defined as a nonequilibrium process that results in either clearance of the infection, host death, or chronic infection. However, the importance of viral persistence in HPV is related to its ability to induce some cancers. This has led most researchers to focus on chronic infections and to relatively neglect acute ones because cancers usually occur after several years of infection (Alizon et al., 2017). In fact, most infections progress to viral clearance which results in spontaneous healing. When the virus remains latent in the cells, the genome remains either in an extrachromosomal form (episomal) or integrated into the cellular genome (Denis et al., 2008). Thus, detection of HPV in many specimens may result not only from recent acquisition or re-infection, but also from recurrent detection of latent infection, self-inoculation from other epithelial sites (e.g., anus), or transient deposition of viral nucleic acid (Baay et al., 2011; Gravitt & Winer, 2017). In fact, HPV infection is related to various cutaneous and mucosal lesions, benign or malignant, affecting both children and adults. Cutaneous warts, vulvar warts, and mosaic plantar warts are some of the lesions related to HPV infection. Vulvar and mosaic plantar warts are mainly due to different HPV genotypes notably (HPV2, 27, and 57 (genus α), HPV4 (genus γ), and HPV1 (genus μ)). These warts can be resorbed on 24 months or persist for several additional months (Zaidane, 2017). Persistent infection with beta-HPV types seems to play a potential role in the initiation of nonmelanoma skin cancers. This association is still widely discussed (McBride & Mu¨nger, 2018). Infections with mucous HPV-HR types can also be cleared or persistent. In this late case, the virus can cause deregulation of the cell cycle, which promotes the proliferation of infected cells and their immortalization. This can lead to precancerous lesions and cancers of the cervical, vulva, vagina, penis, and many other regions (Lepiller et al., 2021). The progression from simple infection to cancer is a multistep process passing through precancerous lesions notably “low-grade squamous intraepithelial lesion” (LSIL) and “high-grade squamous intraepithelial lesion” (HSIL) successively. Sometimes, direct progression from infection to HSIL is noted (Denis et al., 2008). Usually, the time required for the development of severe dysplasia is one to several decades, but this period is sometimes shortened to 12 years (Hantz et al., 2005). The prevalence of HR genital HPV infections is age-dependent with two peaks of high rates, one at 25 years of age and the other at 50 years
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of age. In contrast, the incidence of LR types decreases with age (Denis et al., 2008; Mun˜oz et al., 2004).
13.8 Molecular mechanisms of HPV-induced carcinogenesis Carcinogenesis is a complex and multistep process requiring the acquisition of several genetic and/or epigenetic alterations (Lehoux et al., 2009). In fact, the inability to develop effective cell-mediated immunity to clear or control infection results in persistent infection. This infection is associated with cancer progression and can lead to several modifications such as excessive cell proliferation, deficient DNA repair, and accumulation of genetic damage in the infected cell (Doorbar et al., 2012). The oncoprotein E6, in association with its associated protein E6-AP protein, activates telomerase which allows the maintenance of telomere length during successive divisions and represents a primordial step in immortalization which leads to persistent infection and cell transformation (Mougin et al., 2008). This association leads to rapid degradation of tumor suppressor p53 via the ubiquitin-proteasome pathway (Fig. 13.2), which causes inhibition of the
HPV E6
P53
HPV
RB
P16
P53 RB HPV E7
P21 RB
CykD1 CDK4/6
E2F CykE
CDK2 P RB
P P
S G2
E2F G1
M
FIGURE 13.2 Mechanism of action of the human papillomavirus (HPV) on cell cycle regulation. From Suh, Y., Amelio, I., Guerrero Urbano, T. & Tavassoli, M. (2014). Clinical update on cancer: Molecular oncology of head and neck cancer. Cell Death & Disease, 5, e1018e1018. https://doi.org/10.1038/cddis.2013.548.
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proapoptotic functions of p53 and bypass of the p53-mediated checkpoints (Scheffner et al., 1990; Suh et al., 2014). The E7 oncoprotein enters into competition with the E2F transcription factor for binding to the pRb tumor suppressor, displacing E2F. E2F activates genes implicated in cell cycle progression through the G1 to S phases, including cyclin A, E, and DNA polymerase, causing inactivation of checkpoints and regulatory pathways, and ultimately promoting cellular proliferation and transformation (Fig. 13.2) (Dyson et al., 1989; Suh et al., 2014). Retinoblastoma protein (pRb) is a negative regulator of the cyclin-dependent kinase inhibitor p16, and thus inactivation of pRb results in p16 upregulation. This can be identified by immunohistochemistry in HPV-associated tumor samples and represents a biologically significant surrogate marker for HPV oncoprotein expression (Suh et al., 2014; Weinberger et al., 2006). To progress from G1 to S cell cycle phase, cells have to pass the G1 restriction point that is under the control of the retinoblastoma protein (pRb). pRb binds and represses E2F transcriptional factors. Mitogenic signaling through CyclinD1/CDK4 or CyclinD1/CDK6 phosphorylates pRb, promoting E2F release. CyclinE/CDK2 completes pRb phosphorylation, allowing S-phase entry. HPV affects the cell cycle by using the two viral oncoproteins: E6 and E7 (Lehoux et al., 2009; Suh et al., 2014).
13.9 Mechanisms of cell transformation HPV, as mentioned before, targets stem cells of the squamous epithelium. The complete life cycle of the virus involves three phases, with a sequential expression of viral genes leading to viral DNA replication and the production of highly infectious virions. Viral DNA integration occurs and leads to the overexpression of two viral oncoproteins: E6 and E7. These proteins, in combination with E5, promote the immortalization and transformation of infected cells (Pre´tet et al., 2007). Two scenarios explain the progression from immortalization to malignant cells. The first one focuses on that alteration in host cell DNA might interact with viral oncoproteins to allow progression from immortalization to transformation (Hausen, 2000). The second hypothesis is that a distinct signaling cascade within or between cells stops the progression of immortalized cells to malignant tissue. Either oncogene transcription or viral oncoprotein expression may be controlled by the retinoic acid receptor or by cytokines such as transforming growth factor, interferon, or tumor necrosis factor α (Zur Hausen, 2002). These data suggest that cytokine-mediated intercellular control has an important role in suppressing malignant transformation, even in immortalized cells. Progression to malignant cells requires a genetic change in the pathways controlling intracellular or intercellular signaling. The chromosomal
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instability found in HPV infection could lead to such genetic changes (Hausen, 2000; Zur Hausen, 2002).
13.10 Conclusion HPV is one of the few viruses definitively linked to human malignancies and especially to carcinoma. It has revealed much about the pathways leading to cellular transformation. Further elucidation of the viral and cellular events that lead to cancer will help to find ways to treat HPV infections or prevent malignant transformation of benign growths. Otherwise, HPV cancers can be prevented if diagnosis and treatment are done at early stages.
Acknowledgments Our gratitude goes to the Team of Human Pathologies, Biomedicine, and Environment laboratory for providing their support and encouragement.
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Finzer, P., Aguilar-Lemarroy, A., & Ro¨sl, F. (2002). The role of human papillomavirus oncoproteins E6 and E7 in apoptosis. Cancer Letters, 188(12), 1524. Available from https://doi. org/10.1016/S0304-3835(02)00431-7. Flores, E. R., & Lambert, P. F. (1997). Evidence for a switch in the mode of human papillomavirus type 16 DNA replication during the viral life cycle. Journal of Virology, 71(10), 71677179. Available from https://doi.org/10.1128/jvi.71.10.7167-7179.1997. Forslund, O., Iftner, T., Andersson, K., Lindelo¨f, B., Hradil, E., Nordin, P., Stenquist, B., Kirnbauer, R., Dillner, J., & De Villiers, E. M. (2007). Cutaneous human papillomaviruses found in sunexposed skin: Beta-papillomavirus species 2 predominates in squamous cell carcinoma. Journal of Infectious Diseases, 196(6), 876883. Available from https://doi.org/10.1086/521031. Furumoto, H., & Irahara, M. (2002). Human papilloma virus (HPV) and cervical cancer. Journal of Medical Investigation, 49(34), 124133. Giroglou, T., Florin, L., Scha¨fer, F., Streeck, R. E., & Sapp, M. (2001). Human papillomavirus infection requires cell surface heparan sulfate. Journal of Virology, 75(3), 15651570. Available from https://doi.org/10.1128/JVI.75.3.1565-1570.2001. Giuliano, A. R., Tortolero-Luna, G., Ferrer, E., Burchell, A. N., de Sanjose, S., Kjaer, S. K., Mun˜oz, N., Schiffman, M., & Bosch, F. X. (2008). Epidemiology of human papillomavirus infection in men, cancers other than cervical and benign conditions. Vaccine, 26(10), K17K28. Available from https://doi.org/10.1016/j.vaccine.2008.06.021. Gravitt, P. E., & Winer, R. L. (2017). Natural history of HPV infection across the lifespan: Role of viral latency. Viruses, 9(10), 267. Available from https://doi.org/10.3390/v9100267. Hantz, S., Alain, S., & Denis, F. (2005). Vaccins anti-papillomavirus et pre´vention du cancer du col de l’ute´rus: Avance´es et perspectives. Presse Medicale, 34(10), 745753. Available from https://doi.org/10.1016/s0755-4982(05)84030-8. Hausen, H. z. (2000). Papillomaviruses causing cancer: Evasion from host-cell control in early events in carcinogenesis. Journal of the National Cancer Institute, 92(9), 690698. Available from https://doi.org/10.1093/jnci/92.9.690. Howie, H. L., Katzenellenbogen, R. A., & Galloway, D. A. (2009). Papillomavirus E6 proteins. Virology, 384(2), 324334. Available from https://doi.org/10.1016/j.virol.2008.11.017. Humans, I. W. G., of., & to. (2007). Human papillomaviruses. World Health Organization. Jenkins, D., & Xavier Bosch, F. (2019). Human papillomavirus: Proving and using a viral cause for cancer. Human papillomavirus: Proving and using a viral cause for cancer (pp. 1414). Elsevier. Available from https://doi.org/10.1016/C2016-0-02398-7. Kim, S. S., Tam, J. K., Wang, A. F., & Hegde, R. S. (2000). The structural basis of DNA target discrimination by papillomavirus E2 proteins. Journal of Biological Chemistry, 275(40), 3124531254. Available from https://doi.org/10.1074/jbc.M004541200. Larkin, M. (2006). Buffett gift will benefit global health and disease research. The Lancet Infectious Diseases, 6, 468. Available from https://doi.org/10.1016/s1473-3099(06)70534-5. Lehoux, M., D’Abramo, C. M., & Archambault, J. (2009). Molecular mechanisms of human papillomavirus-induced carcinogenesis. Public Health Genomics, 12(56), 268280. Available from https://doi.org/10.1159/000214918. Lepiller, Q., Puget, L., Debernardi, A., & Pre´tet, J. L. (2021). Infections a` papillomavirus humains et le´sions associe´es. Journal de Pe´diatrie et de Pue´riculture, 34(3), 122129. Available from https://doi.org/10.1016/j.jpp.2021.02.003. Lim, D. A., Gossen, M., Lehman, C. W., & Botchan, M. R. (1998). Competition for DNA binding sites between the short and long forms of E2 dimers underlies repression in bovine papillomavirus type 1 DNA replication control. Journal of Virology, 72(3), 19311940. Available from https://doi.org/10.1128/jvi.72.3.1931-1940.1998.
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Liu, Z., Rashid, T., & Nyitray, A. G. (2016). Penises not required: A systematic review of the potential for human papillomavirus horizontal transmission that is non-sexual or does not include penile penetration. Sexual Health, 13(1), 1021. Available from https://doi.org/10.1071/SH15089. Louie, K., Didelot, M.-N., Damay, A., Nagot, N., Mayaud, P., & Segondy, M. (2008). Papillomavirus humains (HPV) et cancers associe´s: Aspects e´pide´miologiques. Revue Francophone Des Laboratoires, 405, 2734. Available from https://doi.org/10.1016/s1773035x(08)74275-8. McBride, A. A., & Mu¨nger, K. (2018). Expert views on HPV infection. Viruses, 10(2), 94. Available from https://doi.org/10.3390/v10020094. Meisels, A., & Fortin, R. (1976). Condylomatous lesions of the cervix and vagina. I. Cytologic patterns. Acta Cytologica, 20(6), 505509. Molet, L., Girlich, D., Bonnin, R. A., Proust, A., Bouligand, J., Bachelerie, F., Hantz, S., & Deback, C. (2019). Identification by high-throughput sequencing of HPV variants and quasispecies that are untypeable by linear reverse blotting assay in cervical specimens. Papillomavirus Research, 8, 100169. Available from https://doi.org/10.1016/j.pvr.2019.100169. Monsonego, J. (2007). Pre´vention du cancer du col ute´rin (I): apport du de´pistage, re´cents progre`s et perspectives. Presse Medicale, 36(1), 92111. Available from https://doi.org/ 10.1016/j.lpm.2006.10.023. Mougin, C., Nicolier, M., & Decrion-Barthod, A. Z. (2008). HPV et cancers: Me´canismes de l’oncogene`se. Revue Francophone des Laboratoires, 38(405), 3542. Available from https://doi.org/10.1016/s1773-035x(08)74276-x. Mun˜oz, N., Castellsague´, X., de Gonz´alez, A. B., & Gissmann, L. (2006). Chapter 1: HPV in the etiology of human cancer. Vaccine, 24(3), S1S10. Available from https://doi.org/10.1016/j. vaccine.2006.05.115. Mun˜oz, N., Me´ndez, F., Posso, H., Molano, M., Van Den Brule, A. J. C., Ronderos, M., Meijer, C., & Mun˜oz, A. (2004). Incidence, duration, and determinants of cervical human papillomavirus infection in a cohort of Colombian women with normal cytological results. Journal of Infectious Diseases, 190(12), 20772087. Available from https://doi.org/10.1086/425907. Oh, J. M., Kim, S. H., Cho, E. A., Song, Y. S., Kim, W. H., & Juhnn, Y. S. (2010). Human papillomavirus type 16 E5 protein inhibits hydrogen peroxide-induced apoptosis by stimulating ubiquitin-proteasome-mediated degradation of Bax in human cervical cancer cells. Carcinogenesis, 31(3), 402410. Available from https://doi.org/10.1093/carcin/bgp318. Perrard, J. (2019). Re´gulation de l’expression des oncoge`nes du papillomavirus humain de type 16: E´tude dans des ligne´es cellulaires de cancers du col de l’ute´rus traite´es avec un agent de´me´thylant. (Ph.D. thesis), Universite´ Bourgogne Franche-Comte´. Pfister, H. (1992). Human papillomaviruses and skin cancer. Seminars in Cancer Biology, 3(5), 263271. Pre´tet, J.-L., Charlot, J.-F., & Mougin, C. (2007). Aspects virologiques et carcinologiques des papillomavirus humains HPV. Bulletin de l’Acade´mie Nationale de Me´decine, 191(3), 611623. Available from https://doi.org/10.1016/s0001-4079(19)33048-1. Rautava, J., & Syrja¨nen, S. (2012). Biology of human papillomavirus infections in head and neck carcinogenesis. Head and Neck Pathology, 6(1), 315. Available from https://doi.org/ 10.1007/s12105-012-0367-2. Roman, A., & Munger, K. (2013). The papillomavirus E7 proteins. Virology, 445(12), 138168. Available from https://doi.org/10.1016/j.virol.2013.04.013. Sabeena, S., Bhat, P., Kamath, V., & Arunkumar, G. (2017). Possible non-sexual modes of transmission of human papilloma virus. Journal of Obstetrics and Gynaecology Research, 43(3), 429435. Available from https://doi.org/10.1111/jog.13248.
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Scheffner, M., Werness, B. A., Huibregtse, J. M., Levine, A. J., & Howley, P. M. (1990). The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Cell, 63(6), 11291136. Available from https://doi.org/10.1016/0092-8674(90)90409-8. Straight, S. W., Herman, B., & McCance, D. J. (1995). The E5 oncoprotein of human papillomavirus type 16 inhibits the acidification of endosomes in human keratinocytes. Journal of Virology, 69(5), 31853192. Available from https://doi.org/10.1128/jvi.69.5.3185-3192.1995. Suh, Y., Amelio, I., Guerrero Urbano, T., & Tavassoli, M. (2014). Clinical update on cancer: Molecular oncology of head and neck cancer. Cell Death & Disease, 5, e1018. Available from https://doi.org/10.1038/cddis.2013.548. Suprynowicz, F. A., Disbrow, G. L., Krawczyk, E., Simic, V., Lantzky, K., & Schlegel, R. (2008). HPV-16 E5 oncoprotein upregulates lipid raft components caveolin-1 and ganglioside GM1 at the plasma membrane of cervical cells. Oncogene, 27(8), 10711078. Available from https://doi.org/10.1038/sj.onc.1210725. Syrja¨nen, S. (2010). Current concepts on human papillomavirus infections in children. APMIS: Acta Pathologica, Microbiologica, et Immunologica Scandinavica, 118(67), 494509. Available from https://doi.org/10.1111/j.1600-0463.2010.02620.x. Tadlaoui, K. A., Hassou, N., Bennani, B., & Ennaji, M. M. (2019). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. Emerging and reemerging viral pathogens: Volume 1: Fundamental and basic virology aspects of human, animal and plant pathogens (pp. 539570). Elsevier. Available from https://doi.org/10.1016/B978-0-12819400-3.00024-7. Virgin, H. W., Wherry, E. J., & Ahmed, R. (2009). Redefining chronic viral infection. Cell, 138 (1), 3050. Available from https://doi.org/10.1016/j.cell.2009.06.036. Weinberger, P. M., Yu, Z., Haffty, B. G., Kowalski, D., Harigopal, M., Brandsma, J., Sasaki, C., Joe, J., Camp, R. L., Rimm, D. L., & Psyrri, A. (2006). Molecular classification identifies a subset of human papillomavirusAssociated oropharyngeal cancers with favorable prognosis. Journal of Clinical Oncology, 24(5), 736747. Available from https://doi.org/10.1200/ JCO.2004.00.3335. WHO. (2017). Human papillomavirus vaccines: WHO position paper, May 2017. Releve Epidemiologique Hebdomadaire, 92(19), 241268. Zaidane, I. (2017). Les formes cliniques et diagnostic diffe´rentiel des verrues cutane´es et anoge´nitales chez l’enfant. (Medical thesis), Universite´ Mohammed V de Rabat. Zheng, Z. M., & Baker, C. C. (2006). Papillomavirus genome structure, expression, and posttranscriptional regulation. Frontiers in Bioscience, 11(1), 22862302. Available from https:// doi.org/10.2741/1971. Zur Hausen, H. (2002). Papillomaviruses and cancer: From basic studies to clinical application. Nature Reviews. Cancer, 2(5), 342350. Available from https://doi.org/10.1038/nrc798.
Chapter 14
Progress in the development of vaccines against human papillomavirus Fadoua El Battioui1, Fatima El Malki2, Hassan Ghazal3 and Said Barrijal1 1
Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco, 2Institute of Nursing, Tangier, Morocco, 3National Center for Scientific and Technical Research (CNRST), Rabat, Morocco
14.1 Introduction Human papillomavirus (HPV) is the most common viral infection that affects the reproductive system of both women and men (WHO, 2017). Up to now, more than 200 types of HPV have been identified (Cheng et al., 2020). Most HPV infections may vanish spontaneously thanks to the natural defense of the immune system. However, in some cases of immunosuppression, the infection may persist and give rise to precancerous lesions and cancers of the cervix, vagina, vulva, anus, penis, head, and neck (Bergman et al., 2019). According to the World Health Organization (WHO), HPV constituted approximately 4.5% of all kinds of cancer worldwide in 2012, 8.6% of women’s cancer, and 0.8% of male’s cancer (De Martel et al., 2017). Therefore the efforts have been directed toward reinforcing the preventive measures through vaccination as the best means to hinder the spread of this virus. In 2006, the prophylactic HPV vaccine was licensed in more than 100 countries (Markowitz et al., 2012; Zhou et al., 2020). Vaccine plays an essential role in reducing the incidence of HPV-related disease, especially cancer of the cervix. However, the introduction of HPV vaccination into national immunization programs remains low and varies from one state to another. Only 82 countries have adopted this vaccine in 2017, with a wide gap in coverage rates ranged between 8% and 98% (Brotherton & Bloem, 2018). Adopting a vaccination strategy with the most important impact and an optimal vaccine for the strategic objective of HPV eradication are presently the main issues. Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00004-2 Copyright © 2023 Elsevier Inc. All rights reserved.
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FIGURE 14.1 A schematic of HPV (human papillomavirus virion). A double-stranded DNA genome (red) is surrounded by the capsid, which is composed of two proteins: the major capsid protein (L1, shown in light green) and the minor capsid protein (L2, shown in brown color). The L1 protein forms pentamers, and L2 protein is inserted on vertices of the pentamers. Seventytwo copies of the pentamers and about1272 copies of L2 protein assemble to form a virion (Yadav et al., 2019). https://doi.org/10.3390/v12010018.
14.2 Virus-like particle vaccination strategy The virus-like particles (VLPs) are proteins devoid of the viral genome and do not produce any infectious effect or oncogenes, but are similar to the natural virus particles. They have first been highlighted by Romanowski et al. in 1990. VLPs can be produced in bacteria, yeast, or insect cells (Kirnbauer et al., 1992). These particles have become an essential means for the development and manufacture of vaccines, either for prophylactic or therapeutic purposes, for human beings as well as animals. However, most of the available manuscripts dealt with the development of prophylactic vaccines for humans. Currently, anti-HPV vaccines based on VLP have assembled from the main papillomavirus capsid L1 protein (Fig. 14.1) (Zhai et al., 2019). Vaccination with VLPs can induce different types of specific antibodies which permits the annihilation of the virus by preventing its absorption by the target cell (Wang et al., 2019). The second-generation VLPs, such as L2-VLPs (Fig. 14.1), are drawing a lot of attention for their wider genotypic coverage (Huber et al., 2017; Schellenbacher et al., 2009).
14.3 Vaccines prophylactic against human papillomavirus 14.3.1 Types of vaccines The main role of prophylactic vaccines is the development of humoral immunity against the late proteins L1 or L2 of HPV (Wang et al., 2019). The prophylactic HPV vaccine is the first that has been clinically proven to prevent cervical cancer and other types of cancers (Zhou et al., 2020). Three types of prophylactic HPV vaccines are currently available: Cervix (GlaxoSmithKline Biologicals, Belgium), Gardasil (Merck & Co., United
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States), and Gardasil 9 (Merck & Co., United States). These three vaccines have different immune components (Bogani et al., 2018). Cervix is a bivalent vaccine against HPV (2vHPV); it consists of two VLPs, including HPV 16 and 18, and protects against these two types of HPV which are responsible for nearly 70% of cervical malignancies (Khan et al., 2005). Gardasil is an HPV quadrivalent vaccine (4vHPV) containing the VLPs of HPV types 16, 18, 6, and 11. It protects from these four types of HPV, which are responsible for 90% of genital warts (Winer et al., 2008). Gardasil 9 is a nonavalent vaccine against HPV (9vHPV) 6/11/16/18/31/ 33/45/52/58, and protects from all these nine types of HPV. The administration of three doses of the bivalent or quadrivalent HPV vaccine is enough to protect a woman against precancer of the cervix caused by the HPV types included in the vaccine (Zhou et al., 2020). Similar protection from cervical precancerous lesions, vaginal and vulvar, is ensured by nonavalent vaccines (Bergman et al., 2019). In addition, vaccine-mediated protection against HPV is not type-specific, and cross-protection exists after vaccination against a closely related type of the same species (Cameron et al., 2016; Kavanagh et al., 2017; Zhou et al., 2020). To ensure a better protection against HPV, the vaccine should normally be administered before any exposure to the virus, that is before the beginning of a sexual intercourse. Thus all national HPV vaccination programs target preadolescent girls. Some countries have opted for vaccinating girls only, considering that the immunization of women against HPV offers indirect protection to men. Other countries, however, have expanded their program to males, given the fact that vaccinating females only do not protect homosexuals against anal cancer and warts anogenital (Drolet et al., 2019).
14.4 Immunization procedures and doses To ensure easy access and broad adherence to the HPV vaccine, simplified vaccination schedules with fewer doses are required. Several studies have been carried out to determine if less than three doses in 6 months would not be less effective than three doses for girls aged 1518 years (Bergman et al., 2019). Schiller (2018) in his review, explained the important role of antibodies synthesized after administration of an HPV L1 VLP vaccine as the main mediators of protection against HPV, he also noted that the immunogenicity after two doses of vaccination against HPV was as effective as with three doses. Therefore two doses are not less effective than three doses for the prevention of HPV-related illnesses for girls aged 1518 years (Fig. 14.2) (Zhou et al., 2020). It has also been reported that the vaccine has similar efficacy, even when given in a single dose. This is probably due to the structural characteristics of the VLPs, which permits efficient production of long-lived plasma cells with reduced dosage regimens (Schiller & Lowy, 2018).
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FIGURE 14.2 Timeline of trials and licensure/registration of the HPV vaccines. 4vHPV, quadrivalent HPV vaccine; 2vHPV, bivalent HPV vaccine; 9vHPV, nonvalent HPV vaccine; FDA, The US Food and Drug Administration; HPV, human papillomavirus; EU, European Union; VLP, virus-like particle.1.1 HPV and HPV infection. From (Zhou et al., 2020). https://doi.org/ 10.3389/fimmu.2020.01434.
However, it has been shown that the immune response differs depending on age. In fact, preadolescents and adolescents (aged 915) produce stronger antibody responses, using HPV type protein virus vaccines, than older teens and adult (Block et al., 2006; Sankaranarayanan et al., 2018). For this reason, WHO has advocated two doses of 2vHPV and 4vHPV vaccines for people under 15 years old and three doses for women over 15 years old (Zhou et al., 2020). To date, several tests have tempted to demonstrate the efficacity of a single dose of vaccine. Accordingly, a large case-control study in India was carried out among women who received a single dose of 4 HPV vaccines. After a 7year follow-up, the results demonstrated durable protection of the vaccine against HPV16 and related disease HPV18 (Sankaranarayanan et al., 2018). Single-dose vaccination is more affordable and beneficial for wider implementation in low-income countries. However, data on the efficacity of a single dose of vaccine do not exceed 7 years (Zhou et al., 2020).
14.5 Efficacy and safety of human papillomavirus vaccines 14.5.1 Efficacy According to the results of a systematic meta-analysis carried out recently on nearly 60 million people from 14 high-income countries, vaccines against HPV significantly reduced the prevalence of parameters related to HPV (genital HPV infections, anogenital wart diagnoses, or histologically confirmed CIN2 1 ) among girls, women, and boys (Cheng et al., 2020; Drolet et al., 2019). The most current E-type HPV (HPV 16 and 18) significantly decreased by 83%, and HPV 31%, 33%, and 45% decreased by 54%, among girls aged
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1319 years. The prevalence of anogenital warts decreased by 67% and CIN2 1 by 51% (Cheng et al., 2020; Drolet et al., 2019). The main results in relation to the degree of immunogenicity of each type of vaccine are summarized in Table 14.1 and Fig. 14.3. Overall, all three vaccines demonstrated sufficiently high and lasting antibody response levels to protect against infection and subsequent disease (Phillips et al., 2018).
TABLE 14.1 Comparison of the three prophylactic vaccines against HPV (human papillomavirus) available in the market. HPV vaccines
Cervarix
Gardasil
Gardasil 9
Time of FDA Approval
2009
2006
2014
Antigen
L1 VLP of HPV 16 and 18
L1 VLP of HPV 6, 11, 16, and 18
L1 VLP of HPV 6, 11, 16, 18, 31, 33, 45, 52, and 58
Expression system
Baculovirus— insect cell
Yeast
Yeast
Adjuvant
50 μg MPL absorbed on 500 μg aluminum hydroxide (AS04)
225 μg aluminum hydroxyphosphate sulfate
500 μg aluminum hydroxyphosphate sulfate
Indications
Females: cervical precancer and cancer Males: not approved for use in males
Females: cervical precancer and cancer; genital warts Males: anal precancer and cancer; genital warts
Females: cervical precancer and cancer; genital warts Males: anal precancer and cancer; genital warts
Cervical cancer Protection rate
70%
7075%
90%
Immunogenicity
High anti-HPV16 and 18 immunogenicity, which can prevent the impact of the infection for at least 10 years 1 high protection ( . 90%) against HPV-related precancerous anomalies and lesions, including cervical
Significant decrease in HPV infections at the level of the anus, vulva, and penis as well as in the oral cavity 1 a high prevention rate .90%, (injection before exposure to HPV) against CIN 2 or worse (CIN 2 1 ), CIN 3 1 , and
High immunogenicity following vaccination up to 6 years 1 inhibition of about 90% and 80%85% of vulvar and vaginal diseases, respectively 1 effective transfer of antibodies across the placenta, which potentially (Continued )
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TABLE 14.1 (Continued) HPV vaccines
Cervarix
Gardasil
Gardasil 9
intraepithelial neoplasia 2 (CIN2), CIN3, and adenocarcinoma in situ (AIS) 1 93% decrease in the prevalence of HPV16 and 18 oral infections
vulvar / vaginal intraepithelial neoplasia of grade 2 or worse (VIN/ VaIN 2 1 )
protects the infant from HPV 6 and 11 infections
Cross protection
Long-term crossprotection against the HPV 31 and 45 at
High efficacy of protection against the HPV 31, 33, 45, 52, and 58 (46%, 29%, 7%, 18%, and 6%, respectively) at
Low crossprotection efficacy at
Vaccination schedule
0, 1, and 6 months
0, 2, and 6 months
0, 2, and 6 months
FIGURE 14.3 HPV VLP types in VLP vaccines. VLPs in the 2vHPV, 4vHPV, and 9vHPV are shown with the proportion of neoplastic disease attributed to each group. HPV, human papillomavirus; VLP, virus-like particle (Zhou et al., 2020).
14.5.2 Safety and security The vaccine safety is the most shared concern of all stakeholders in the implementation of public vaccination.
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TABLE 14.2 AEs (Adverse effects) of three vaccines against HPV (human papillomavirus). Type of vaccine
Cervarix
Gardasil
Gardasil9
AEs
Reactions at injection site as pain and swelling 1 systemic symptoms, such as fever, nausea, vomiting, dizziness, diarrhea, and myalgia 1 headache and fatigue
Reactions at injection site as pain and swelling 1 small fiber neuropathy and dysautonomia 1 syndrome chronic regional pain
General symptoms, but no increased risk of systemic symptoms
References
Gonc¸alves et al. (2014), Cheng et al., 2020, Paavonen et al. (2007), Van Klooster et al. (2011)
Mart´ınez-Lav´ın (2015), Ozawa et al. (2017), Gonc¸alves et al. (2014), Cheng et al. (2020)
Gonc¸alves et al. (2014), Cheng et al. (2020)
No Permission Required.
The HPV vaccine is considered to be one of the most studied vaccines up to now, given the active monitoring of safety signals since its preapproval (Angelo et al., 2014). The results of several studies have shown that the three HPV vaccines are excellently safe and tolerant within different age groups (Phillips et al., 2018; Cheng et al., 2020). Most side effects are reactions caused by the vaccination process and not by the vaccine itself (Centers for Disease Control Prevention, 2008; Zhu FC et al., 2019; Zhou et al., 2020). The most-reported undesirable effects are listed in Table 14.2. In 2014, the safety of the 9vHPV vaccine was evaluated in seven studies before being licensed by FDA. These prelicensing studies indicated that the 9vHPV vaccine has similar safety to that of the 4vHPV vaccine. In addition, approximately 92% of reports about the 4vHPV vaccine were classified as non serious (Wang et al., 2019). Numerous experimental data show that the vaccine does not cause any serious or unexpected adverse effects. Therefore we should restore public confidence in the safety of HPV vaccines by expanding the scope of vaccination and creating awareness about its benefits and safety (Cheng et al., 2020).
14.6 L2-based human papillomavirus prophylactic vaccines Vaccination with L2 using adjuvants such as aluminum provides long-lasting immunity among animal models, and passive transfer studies have shown
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that low titers of neutralizing antibodies (nAb) but sufficient for protection (Kalnin et al., 2017; Tumban et al., 2013). The L2-based vaccines have the potential to provide protection against various HPV at a low cost of vaccination due to their simple form (Huber et al., 2017; Wang et al., 2015).
14.7 Human papillomavirus vaccine coverage In general, national HPV programs cover about 30% of the global target population, with low “full-dose” coverage in many regions (Bruni et al., 2016). In fact, up to December 13, 2017, 65 countries worldwide have adopted the immunization schedules against HPV in two doses for girls. However, only a few countries with high income (namely Australia, Switzerland, and the United States) recommended HPV vaccination for boys (Bergman et al., 2019). An estimated 118 million women have received a dose of HPV vaccine worldwide (Bruni et al., 2016; Wang et al., 2019). Otherwise, there is a great disparity in vaccine coverage against HPV between regions of the world (Fig. 14.4). The HPV vaccine coverage is significantly higher in high-income countries, where about 32% of females aged 1020 years received full-dose vaccination in 2014 (De Martel et al., 2017). However, most low- and middle-income countries (LMIC) remain unprotected, as only about 1% of adolescent girls have received a full cycle of HPV vaccines (Bruni et al., 2016). The HPV vaccination gap also exists between urban and rural areas for LMIC countries. Several factors maintain this situation which requires more effort for the remedy (Wang et al., 2019).
FIGURE 14.4 Worldwide HPV vaccination rates. HPV, human papillomavirus. From (Zhou et al., 2020).
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14.8 Factors influencing vaccination coverage There are many determinants that influence vaccination coverage: 1. Geographic areas: Studies have shown that Gardasil 9 protects against a cervix cancers associated with HPV types with an efficiency of 92% in Africa and North America, 91% in Europe, 90% in Latin America and the Caribbean, 88% in Asia and 87% in Australia (Ogembo et al., 2015; Zhai & Tumban, 2016). 2. Ethnic disparities: A meta-analysis has assumed that ethnic minorities in the United States are more likely to start but less likely to follow the full series of HPV vaccination (Spencer et al., 2019). 3. Vaccines’ high price: The expensive price of most of these vaccines hinders meeting the needs of low-income populations (Wang et al., 2019). 4. Cold chain: The transport and storage condition for most of the vaccines also limit the large-scale deployment in developing countries. This can be solved by lyophilized formulations or thermostable capsomer preparations (Hassett et al., 2015). 5. Women’s knowledge and educational interventions: Educational programs prevent the transmission of HPV, increase the HPV vaccine acceptability, and help to improve HPV vaccination coverage (Dixon et al., 2018; Larasati et al., 2018). 6. Interventions of communication training: Training on communication strategies and tools significantly improved the launching of the series of HPV vaccines (10%) (Dempsey et al., 2018).
14.9 Therapeutic vaccines Unlike the neutralizing role of prophylactic HPV vaccines, therapeutic vaccines aim at eliminating preexisting damage caused by HPV, by stimulating cell-mediated immunity that kills infected cells (Hancock et al., 2018). In fact, Bellone et al. (2009) demonstrated that CD8 1 T and specific CD4 1 lymphocytes L1 gene have therapeutic effects among patients with cervical cancer (Bellone et al., 2009; Karimi et al., 2020). Most currently available or under clinical testing HPV therapeutic vaccines are vaccines that target proteins E6 and E7, as these are essential to the induction and the growth of cancerous cells (Karimi et al., 2020). Various mechanisms have been adopted for the development of these vaccines: those which are based on vectors, acid/protein/peptide, and others based on cells, and combined therapies (Maciag et al., 2009; Alvarez et al., 2016; Cheng et al., 2020). The advantages, disadvantages, efficacy, and safety of all types of therapeutic vaccines used against HPV are summarized in Table 14.3.
TABLE 14.3 List of therapeutic vaccines used against HPV (human papillomavirus). Vaccine type
Advantages
Disadvantages
Bacterial vector vaccines
Highly immunogenic 1 introducing innate and adaptive immune response to pathogens 1 reducing purification difficulties of the target antigen 1 lowcost manufacturing and a simple inoculation
Generation of neutralizing antibodies restricts the efficacy of repeated therapy 1 potential immunodominance to the vectors 1 potentially toxic / harmful to patients
Viral vector vaccines
Highly rates of nAbs against adenovirus type 5 (Ad5) 1 inducing a solid innate and adaptative immune response associated with pro-inflammatory cytokines 1 wide range of selection for available vectors 1 can be engineered to include and express multiple genes such as costimulatory molecules/cytokines
Potentially toxic / harmful to patients 1 potential immunodominance to the vectors 1 generation of neutralizing antibodies restrict the efficacy of repeated therapy
Vaccinia virus
High transgenic insertion potential 1 easy manufacturing 1 administration through various routes (intranasal, intravaginal, intra rectal, intradermal) 1 thermostability
Uncertain efficacy
DNA vaccines
Easy fabrication and purification 1 high stability 1 reliable efficacy 1 ability to induce both humoral and cell-mediated immune responses 1 long-term protection through repeated vaccination 1 capacity to include and express multiple genes such as costimulatory molecules / cytokines 1 multiple delivery methods available
Lack of intrinsic ability to self-amplify and spread to surrounding cells 1 potential risk of chromosomal integration 1 low immunogenicity
References
Hancock et al. (2018), Rosales et al. (2014)
RNA-based vaccines
Inducing innate immune responses 1 ensuring safety without risk of chromosomal 1 integration or cell transformation 1 replicating rapidly 1 producing high expression in the cytoplasm 1 combining with polymer or liposome-based encapsulation approaches
Poor stabilit y 1 inability to spread intercellularly 1 preparation/production is labor intensive 1 difficult to prepare in large quantity
Peptide- based vaccines
Great stability and security 1 ease of production and stockage Can be modified for better MHC binding
Low immunogenicity 1 MHC specific that need to match the patient’s human leukocyte antigen (HLA) haplotypes
Protein vaccines
Stable, safe, easy to produce 1 no limitation of MHC restriction
Low immunogenicity 1 inducing more humoral responses than cell-mediated responses
Coleman et al. (2016)
Cellular vaccines (Dendritic cell vaccines)
Highly immunogenic 1 serve as natural adjuvants 1 multiple methods of antigen loading 1 inducing both potent innate and adaptive immune responses
Difficult for large-scale production 1 lack of a consensus on the optimal methods of DC preparation 1 different culture techniques may result in inconsistent vaccine quality 1 limited life span due to the T-cell-mediated apoptosis
Santin et al. (2005)
No Permission Required.
Lundstrom (2018, 2016)
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14.9.1 Bacterial vector vaccines Therapeutic vaccines using bacterial vector to deliver antigens constitutes a promising approach. This is due to their potential to generate the cellular and humoral immune responses against pathogen agents while protecting the host. Recently, several bacterial vectors have been tested in clinical essays, such as Listeria monocytogenes (Lm), Lactobacillus casei, Lactobacillus lactis, and Salmonella (Wang et al., 2019).
14.9.2 Viral vector vaccines Thanks to their strong ability to express antigens and their natural potential to transduce their genetic information in the host, viral vectors are widely used to design therapeutic vaccines. Recently, several viral vectors have been tested, such as adenovirus (Ad), adeno-associated virus, alphavirus and vaccinia virus. Adenovirus constitutes a promising vaccine platform used for vaccine vectors and gene therapy (Wang et al., 2019). 1. The results of studies carried out by Khan et al. (2017) have demonstrated a high therapeutic efficacy of the TC-1 vaccine administered in the mouse model against carcinoma related to HPV16 and HPV18. The concerned vaccine was designed from an Ad26 replication deficient and Ad35-based vector vaccines made up of fusion proteins of HPV E2, E6 (Khan et al., 2017). 2. Data from the study carried out by C ¸ uburu et al. (2017) to test the immunogenicity of TC-1 vaccine via heterologous methods combining intramuscular and intravaginal immunization in mice, and found this combination to be very promising to induce considerable responses of CD8 1 T lymphocytes (C ¸ uburu et al., 2017).
14.9.3 Vaccinia virus It is a double-stranded DNA virus, which belongs to the Poxviridae family. The poxvirus has a stable and large genome that can express large amounts of foreign antigen. Modified vaccinia virus Ankara (MVA) is licensed as a third-generation smallpox vaccine and serves as an effective vector platform for the development of new vaccines (Wang et al., 2019).
Efficacy and safety A phase III study has been carried out by Rosales et al. (2014) to evaluate the therapeutic efficacy of a viral vector vaccine (MVA) targeting HPV16
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E2 (MVA-E2). The study included 1176 women and 180 men with intraepithelial lesions associated to HPV infection. The vaccine was injected directly into the uterus, urethra, vulva, or anus. The results obtained demonstrate that all male patients and 89.3% of female patients experienced complete lesion removal and no apparent adverse events were observed (Rosales et al., 2014).
14.9.4 DNA vaccines DNA vaccines are a valuable form of immunotherapy which permits the activation of CTLs and induce a long-term immune response (Karimi et al., 2020; Saade & Petrovsky, 2012). This type of vaccine has become a safe alternative to live and inactivated vaccines for human and animal infections (Delany et al., 2014). In a clinical trial of a DNA vaccine GX-188E co-expressed HPV-16 and -18 antigens E6 and E7 and the Fms-liketyrosine kinase-3 ligand (Flt3L), which could activate dendritic cells (DCs), performed in patients with HPV16/18 1 infection.The results of this test showed that the vaccine was able to elicit E6/E7 IFN-γ-secreting T cell responses in nine patients with HPV16/18 1 CIN 3 , seven of whom showed complete regression of lesions within 36 weeks. No serious side effects associated with the vaccine were identified in female patients (Kim et al., 2014). The outcomes of another randomized, double-blind, placebo-controlled phase IIb study, to test the efficacy of a VGX-3100 vaccine among women with CIN2/3, demonstrated a histopathological regression in 49.5% of vaccines versus 30.6% of placebo recipients (Trimble et al., 2015). Nowadays, the VGX-3100 is the first therapeutic DNA vaccine which has been shown to be effective against CIN2/3 (Wang et al., 2019). DNA vaccines are an interesting approach to vaccination, but their potency in clinical essays has been insufficient to generate effective immunity, which may be related to DNA degradation via nucleases, poor delivery to antigen presenting cells (APC), and insufficient uptake of DNA plasmids by cells upon injection (Karimi et al., 2020).
14.9.5 RNA-based vaccines Due to its very high potential of autonomous replication, the administration of an RNA virus as a viral vector for vaccines could elicit strong immune responses and generate protection against infectious agents and tumor cells. Recently, several RNA viruses are used as vectors for the expression of antigens, such as retrovirus, flavivirus, alphavirus, rhabdovirus, lentivirus, measles virus, Newcastle disease, and picornavirus (Lundstrom, 2019). Most of the studies conducted so far have shown that viral RNA vectors against diseases associated with HPV can generate a high-level expression of recombinant heterologous antigens.
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14.9.6 Peptide-based vaccines Peptide-based vaccines can be divided into synthetic long peptides and specific epitope peptides (short). Short peptides are specific to the HLA type of each patient and can only be administered after the establishment of a preliminary HLA typing (Vici et al., 2016). In 2011, Solares et al., evaluated the efficacy of a therapeutic vaccine against HPV in seven women with a high grade HPV16 CIN. This vaccine was based on short peptides (CIGB-228) adjuvanted with a proteoliposome. The results reported showed that the vaccine induced a T lymphocyte response associated with IFN- γ and that five patients had a regression of the lesions and a clearance of HPV (Solares et al., 2011). After studying the immunogenicity of a synthetic long-peptide vaccine among 20 women with high-grade vulvar intraepithelial-related HPV16 neoplasia (VIN), the same results were demonstrated by Kenter et al, where 47% of patients had presented a response of T lymphocytes CD4 1 and CD8 1 associated with IFN- γ, to 12 months of follow-up, and maintained at 24 months (Kenter et al., 2009). The Phase I clinical study of PepCan also found encouraging results. This test was conducted among 24 patients with CIN2/3 administering them by a vaccine composed of four synthetic peptides HPV16 E6 and a new adjuvant Candin. The study showed that 45% of patients had histologic regression of lesions and a high immune response with a significant decrease in viral load (Coleman et al., 2016). Another study was carried out in 2013 to test the efficacy of a vaccine composed of 13 long peptides overlapping HPV16 E6 and HPV16 E7, in combination with the adjuvant Montanide ISA-51. This essay was done on 20 women with advanced cervical cancer. Unfortunately, the results were disappointing, as the participants experienced no tumor regressions or prevention of disease progression, and only nine women developed an HPV16specific T-cell response associated with IFN-γ., TNFα, IL-5, and/or IL-10.
14.9.7 Protein vaccines Compared with peptide-based vaccines, protein-based vaccines can avoid the limitation of MHC restriction due to numerous CD4 1 and CD8 1 T epitopes (Coleman et al., 2016). The results of several clinical trials testing a vaccine based on a fusion protein HPV16 E6E7L2 (TA- CIN) have shown strong immunogenicity (De Jong et al., 2002; Davidson et al., 2004). Indeed, the study conducted by Daayana et al. (2010), administering a vaccine TA-CIN after an immunomodulator (imiquimod) to treat 19 women affected by a VIN grades 2 and 3, has shown that 63% of patients developed
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complete histological regression of VIN and increased local infiltration of CD4 1 and CD8 1 T cells (Daayana et al., 2010).
14.9.8 Cellular vaccines (Dendritic cell-based vaccines) Dendritic cells (DCs) are recognized to be the most potent APCs in vivo, due to their strong ability to mediate and induce both the humoral immune and the adaptive immune response (Wang et al., 2019). Thus DCs constitute a promising approach to therapeutic vaccine design, since they can be loaded with specific peptide or antigens. They can serve as natural adjuvants to increase the capacity of the specific antigen immunotherapy against cancer (Santin et al., 2005). A clinical test carried out by Ramos et al. (2013) in phase I/II to treat 12 patients with metastatic epithelial cancers associated with HPV 16 E6 has proven remarkable regression of metastatic epithelial cancer.
14.10 Conclusion HPV vaccination has played a primary role in reducing the incidence of HPV-related cancers worldwide, given that prophylactic HPV vaccines can prevent approximately 95% of vaccinated individuals from the persistent infections and precancerous lesion. However, there are still obstacles facing their generalization, especially in low-income countries, where the burden of cervical cancer is very high. The high manufacturing cost remains the primary challenge to improve immunization coverage and reach the most vulnerable populations. To do this, affordable vaccine manufacturing processes and sustainable delivery must be developed. The second-generation HPV L2-based vaccines are currently under clinical tests, and may solve this problem of cost. Unlike prophylactic HPV vaccines, progress in the development of therapeutic strategies has so far been very slow; thus intensified efforts must be deployed to develop therapeutic HPV vaccines to ensure a simple and effective treatment for sick people.
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Karimi, H., Soleimanjahi, H., Abdoli, A., & Banijamali, R. S. (2020). Combination therapy using human papillomavirus L1/E6/E7 genes and archaeosome: A nanovaccine confer immuneadjuvanting effects to fight cervical cancer. Scientific Reports, 10(1), 115. Available from https://doi.org/10.1038/s41598-020-62448-3. Kavanagh, K., Pollock, K. G., Cuschieri, K., Palmer, T., Cameron, R. L., Watt, C., & Robertson, C. (2017). Changes in the prevalence of human papillomavirus following a national bivalent human papillomavirus vaccination programme in Scotland: A 7-year cross-sectional study. The Lancet Infectious Diseases, 17(12), 12931302. Available from https://doi.org/10.1016/ s1473-3099(17)30468-1. Kenter, G. G., Welters, M. J. P., Valentijn, A. R. P. M., Lowik, M. J. G., Berends-van de Vloon, r Meer, D. M. A, A. P. G., & Melief, C. J. M. (2009). Vaccination against HPV-16 oncoproteins for vulvar intraepithelial neoplasia. New England Journal of Medicine, 361(19), 18381847. Available from https://doi.org/10.1056/nejmoa0810097. Khan, M. J., Castle, P. E., Lorincz, A. T., Wacholder, S., Sherman, M., Scott, D. R., & Schiffman, M. (2005). The elevated 10-year risk of cervical precancer and cancer in women with human papillomavirus (HPV) Type 16 or 18 and the possible utility of type-specific HPV testing in clinical practice. JNCI: Journal of the National Cancer Institute, 97(14), 10721079. Available from https://doi.org/10.1093/jnci/dji187. Khan, S., Oosterhuis, K., Wunderlich, K., Bunnik, E. M., Bhaggoe, M., Boedhoe, S., & Custers, J. (2017). Development of a replication-deficient adenoviral vector-based vaccine candidate for the interception of HPV16- and HPV18-induced infections and disease. International Journal of Cancer, 141(2), 393404. Available from https://doi.org/10.1002/ijc.30679. Kim, T. J., Jin, H.-T., Hur, S.-Y., Yang, H. G., Seo, Y. B., Hong, S. R., & Sung, Y. C. (2014). Clearance of persistent HPV infection and cervical lesion by therapeutic DNA vaccine in CIN3 patients. Nature Communications, 5(1), 5317. Available from https://doi.org/10.1038/ncomms6317. Kirnbauer, R., Booy, F., Cheng, N., Lowy, D. R., & Schiller, J. T. (1992). Papillomavirus L1 major capsid protein self-assembles into virus-like particles that are highly immunogenic. Proceedings of the National Academy of Sciences, 89(24), 1218012184. Available from https://doi.org/10.1073/pnas.89.24.12180. Larasati, L., Afiyanti, Y., Rahmah, H., & Milanti, A. (2018). Women’s knowledge, beliefs, and behaviors toward the prevention of human papillomavirus transmission. Enfermer´ıa Cl´ınica, 28, 191194. Available from https://doi.org/10.1016/s1130-8621(18)30065-2. Lundstrom, K. (2016). Replicon RNA viral vectors as vaccines. Vaccines, 4(4), 39. Available from https://doi.org/10.3390/vaccines4040039. Lundstrom, K. (2018). Self-replicating RNA Viruses for RNA therapeutics. Molecules, 23(12), 3310. Available from https://doi.org/10.3390/molecules23123310. Lundstrom, K. (2019). RNA viruses as tools in gene therapy and vaccine development. Genes (Basel), 10, 189. Available from https://doi.org/10.3390/genes10030189. Maciag, P. C., Radulovic, S., & Rothman, J. (2009). The first clinical use of a live-attenuated Listeria monocytogenes vaccine: A Phase I safety study of Lm-LLO-E7 in patients with advanced carcinoma of the cervix. Vaccine, 27(30), 39753983. Available from https://doi. org/10.1016/j.vaccine.2009.04.041. Markowitz, L. E., Tsu, V., Deeks, S. L., Cubie, H., Wang, S. A., Vicari, A. S., & Brotherton, J. M. L. (2012). Human papillomavirus vaccine introduction The first five years. Vaccine, 30, F139F148. Available from https://doi.org/10.1016/j.vaccine.2012.05.039. Mart´ınez-Lav´ın, M. (2015). Hypothesis: Human papillomavirus vaccination syndrome small fiber neuropathy and dysautonomia could be its underlying pathogenesis. Clinical Rheumatology, 34(7), 11651169. Available from https://doi.org/10.1007/s10067-015-2969-z.
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A. J., Sylvester, A., Khan, K. E., Broderick, R. J., Juba, T. A., Herring, J., . . . Weiner, M. L. (2015). Bagarazzi, safety, efficacy, and immunogenicity of VGX-3100, a therapeutic synthetic DNA vaccine targeting human papillomavirus 16 and 18 E6 and E7 proteins for cervical intraepithelial neoplasia 2/3: A randomised, double-blind, placebo-controlled phase 2btrial. Lancet (London, England), 386. Tumban, J., Peabody, D. S., & Peabody, B. (2013). Chackerian, A universal virus-like particlebased vaccine for human papillomavirus: Longevity of protection and role of endogenous and exogenous adjuvants. Vaccine, 31, 46474654. Van Klooster TM, Kemmeren JM, van der Maas NA, de Melker HE. (2011). Reported adverse events in girls aged 1316 years after vaccination with the human papillomavirus (HPV)16/18 vaccine in the Netherlands. Vaccine. 29:46014607. Available from https://doi.org/ 10.1016/j.vaccine.2011.04.050. review of vaccines, 15 (2016) 13271336. Vici, P., Pizzuti, L., Mariani, L., Zampa, G., Santini, D., Di Lauro, L., & Venuti, A. (2016). Targeting immune response with therapeutic vaccines in premalignant lesions and cervical cancer: Hope or reality from clinical studies. Expert Review of Vaccines, 15(10), 13271336. Available from https://doi.org/10.1080/14760584.2016.1176533. Wang, D., Li, Z., Xiao, J., Wang, J., Zhang, L., Liu, Y., & Xia, N. (2015). Identification of broad-genotype HPV L2 neutralization site for pan-HPV vaccine development by a crossneutralizing antibody. PLoS One, 10(4), e0123944. Available from https://doi.org/10.1371/ journal.pone.0123944. WHO.(2017). Human papillomavirus vaccines: WHO position paper, May 2017. Weekly Epidemiological Record 2017; 92:241-268. Available from: https://www.who.int/wer/2017/ wer9219/en/ Wang, R., Pan, W., Jin, L., Huang, W., Li, Y., Wu, D., Gao, C., Ma, D., & Liao, S. (2019). Human papillomavirus vaccine against cervical cancer: Opportunity and challenge. Cancer Letters, 471, 88102. Available from https://doi.org/10.1016/j.canlet.2019.11.039. Winer, R. L., Feng, Q., Hughes, J. P., O’Reilly, S., Kiviat, N. B., & Koutsky, L. A. (2008). Risk of female human papillomavirus acquisition associated with first male sex partner. The Journal of Infectious Diseases, 197(2), 279282. Available from https://doi.org/10.1086/ 524875. Yadav, R., Zhai, L., & Tumban, E. (2019). Virus-like particle-based L2 vaccines against HPVs: Where are we today? Viruses, 12(1), 18. Available from https://doi.org/10.3390/v12010018. Zhai, L., & Tumban, E. (2016). Gardasil-9: A global survey of projected efficacy. Antiviral Research, 130, 101109. Available from https://doi.org/10.1016/j.antiviral.2016.03.016. Zhai, L., Yadav, R., Kunda, N. K., Anderson, D., Bruckner, E., Miller, E. K., Basu, R., Muttil, P., & Tumban, E. (2019). Oral immunization with bacteriophage MS2-L2 VLPs protects against oral and genital infection with multiple HPV types associated with head & neck cancers and cervical cancer. Antiviral Research, 166(February), 5665. Available from https://doi.org/10.1016/j.antiviral.2019.03.012. Zhou, X., Sun, L., Yao, X., Li, G., Wang, Y., & Lin, Y. (2020). Progress in vaccination of prophylactic human papillomavirus vaccine. Frontiers in Immunology, 11, 1434. Available from https://doi.org/10.3389/fimmu.2020.01434. Zhu, F. C., Hu, S. Y., Hong, Y., Hu, Y. M., Zhang, X., Zhang, Y. J., et al. (2019). Efficacy, immunogenicity and safety of the AS04-HPV-16/18 vaccine in Chinese women aged 1825 years: End-of-study results from a phase II/III, randomised, controlled trial. Cancer Medicine, 8, 61956211. Available from https://doi.org/10.1002/cam4.2399.
Chapter 15
Development and characterization of an electrochemical sensor using molecularly imprinted polymer based on a gold screen-printed electrode for the detection of creatinine and glucose in human urine and saliva Benachir Bouchikhi1, Alassane Diouf2, Moulay Mustapha Ennaji3 and Nezha El Bari2 1
Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Moulay Ismaı¨l University, Meknes, Morocco, 2Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco, 3Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco
15.1 Introduction Due to its high sensitivity, specificity, efficiency, and cost-effectiveness, electrochemical techniques are now widely used for the detection and evaluation of biological substances in body fluids. However, invasive sampling of these body species is becoming increasingly controversial due to the suffering that patient’s experience from skin irritation and the risks of injection. Like blood, urine and saliva provide a lot of metabolic information, and can therefore reflect the state of health of the human body. They seem to be more practical for a truly noninvasive diagnosis of diseases. As an example, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00016-9 Copyright © 2023 Elsevier Inc. All rights reserved.
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a number of diseases have been recognized and successfully diagnosed from the analysis of urine and saliva. These fluids can therefore be used to detect and monitor certain diseases such as kidney disease. On the one hand, before discussing the use of urine and saliva as diagnostic tools in medicine, the term biomarker should be defined. It is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (Biomarkers Definitions Working Group et al., 2001). Indeed, biomarkers could be used for risk prediction or screening, monitoring, and diagnosis (Mayeux, 2004). Recently, the use of biomarkers in biomedicine has become an indispensable tool due to their ability to serve as a quantitative and individualized reflection of an individual’s health status. On the other hand, a procedure is defined as noninvasive technique when no break in the skin is created and there is no contact with the mucosa, or a break in the internal body cavity beyond a natural or artificial body orifice (Mosby, 2009). It can therefore be said that the collection of urine and saliva is noninvasive. Therefore, the aim of this study is first to present a brief state of the art of urine and saliva analysis. Secondly, their use as a means of noninvasive diagnosis of diseases through biochemical substances will be discussed. Finally, the development of noninvasive electrochemical sensors for the detection of creatinine and glucose will be discussed.
15.2 Urine and saliva as noninvasive sources of biomarkers Body fluids carry information (biomarkers) about the physical health of the human body. They are widely used for diagnostic and therapeutic purposes. It is well established that these biomarkers can provide useful information about the metabolic state of an organism. These disease-related compounds may be part of the cascade of reactions that occur in the body’s response to damage. These sources can be used for invasive or noninvasive detection for disease diagnosis. On-site analysis of biomarker levels in liquid media facilitates the assessment of disease diagnosis. In general, compounds in the body are first released into the circulatory system and then passed into biological fluids such as blood, breath, urine, blood, and saliva. However, the collection of blood or tissue is not practical, as it requires specific instruments and trained operators, and it is also uncomfortable for many patients. Therefore, the search for analytes for disease detection can be done through biofluids such as urine and saliva. These fluids also carry a great deal of vital physical and chemical information about the human body. In addition, they are more convenient because of their safe, painless collection, which is more acceptable to children, the elderly, pregnant women, mentally and physically handicapped patients, and high-risk patients. Most
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importantly, the feasibility of the sample offers the potential for rapid monitoring of human disease. The kidneys concentrate the analytes in the urine before they are excreted from the body, which is an advantage over other biofluids. Urine testing is an established method of detecting many diseases noninvasively.
15.3 Biomarkers in the bloodstream can infiltrate the acini and eventually be secreted into the saliva Salivary amylase, a form of amylase found in human saliva, catalyzes the hydrolysis of starch into maltose and sometimes glucose in the mouth. The main diagnostic advantage of saliva is simple, safe, and noninvasive sample collection with a reduced risk of infection compared to invasive methods. Saliva, as a noninvasive and safe source, could therefore replace blood in the diagnosis and prognosis of diseases. Human saliva is 99% water and the rest is made up of organic molecules such as salivary amylase, mucopolysaccharide, mucin, and lysozymes, and some inorganic materials including sodium salts, potassium, calcium, chlorate, bicarbonate, and phosphate, as well as organic compounds such as uric acid (2,6,8-trihydroxypurine, UA), lactate, hormones, polypeptides, and proteins, such as immunoglobulins, enzymes, and mucins (Goll & Mookerjee, 1978). Other constituents that may represent potential biomarkers, including neopterin, nicotine, nitrates, nitrites, and glutathione, can also be found in saliva. Recent research suggests that salivary fluid may have the potential for noninvasive, continuous, and instantaneous monitoring of various diseases. With an abundance of compounds in urine and saliva, these are promising biofluids for the continuous and real-time monitoring of various diseases.
15.3.1 Recognition of particular compounds as an indicator of diseases Several compounds can reflect pathologies in the human body. To name but a few, with great ease of collection, salivary immunoglobulins (sIgA), in particular immunoglobulin A (Salimetrics), are the most studied markers of the mucosal immune system. It is a very important biomarker as selective sIgA deficiency has been observed in people with a high incidence of infection (Levinsky, 1984) or low saliva flow (Fox et al., 1985). Biomarkers such as salivary C-reactive protein and neopterin also provide information about inflammation (Ouellet-Morin et al., 2011; Punyadeera et al., 2011; Ozmeric¸ et al., 2002). Similarly, cardiac troponin T and creatine kinase-MB have also been used in biomedical work on cardiovascular stress (Mirzaii-Dizgah & Riahi, 2013) and muscle injuries (Mirzaii-Dizgah et al., 2012), respectively. Specific interleukins can be detected in saliva (Ives et al., 2011; Cullen et al., 2015).
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Myoglobin is muscle specific and its detection in urine is diagnostically relevant and indicates trauma-induced muscle damage (Uberoi et al., 1991). 3-Methylhistidine is a commonly used marker of muscle damage that directly represents myofibrillar breakdown (Bird et al., 2006). Urine and saliva were used to assess “stress” through cortisol (Gouarne et al., 2005; Salimetrics), testosterone (Testoste´rone Salimetrics; Blazevich & Giorgi, 2001), epitestosterone (Robinson et al., 2006), tetrahydrobiopterin (BH4) (Lindsay et al., 2016), and cannabinoids (PharmaDrugs, 2022).
15.4 Current electrochemical sensor devices Electrochemical sensors have been considered the most sensitive devices for creatinine monitoring. Studies on nonenzymatic electrochemical creatinine sensors are limited compared to those using enzymatic sensors. Although not prevalent, nonenzymatic sensors have good storage stability and robust repeatability, unlike their enzymatic counterparts. They are easily implemented and exhibit metrologically relevant properties with minimal interference. With these nonenzymatic sensors, creatinine interacts with nanoparticles on an electrode (Chen & Lin, 2012) or as a composite (Viswanath et al., 2017). Creatinine detection has also been achieved using molecularly imprinted polymer (MIP) technology. In fact, over the past two decades, a variety of MIP-based creatinine sensors have been identified (Sreenivasan & Sivakumar, 1997; Subrahmanyam et al., 2001). Zhang et al. (2002) exploited the Langmuir model for creatinine detection. Other MIP-based creatinine sensors have been reported. For the creatinine detection, some authors have synthesized poly(ethylene-co-vinyl alcohol) (EVAL) as a functional monomer (Lee et al., 2008), methacrylic acid (Miura et al., 2013), acrylamido methylpropane sulfonic acid (Reddy & Gobi, 2013), poly(melamine-cochloranil) (Patel et al., 2008), b-cyclodextrin (b-CD) (Hsieh et al., 2006; Tsai & Syu, 2005), and 4-vinylpyridine (Hsieh et al., 2006). Among the sensors proposed in the literature, some use gold electrodes as support (Khadro et al., 2010; Panasyuk-Delaney et al., 2002). This type of electrodes has been exploited by Delaney et al. who coated them with a monolayer of hexadecane thiol (Panasyuk-Delaney et al., 2002). In other reported works, hanging mercury drop electrodes were used (Lakshmi et al., 2006). Glucose is not commonly measured in blood in routine practice, although it is an important molecule and plays a key role in the diagnostic work-up of diabetes. Blood glucose concentration could be monitored by saliva (Satish et al., 2014). Given its easy accessibility and a large number of biomarkers, saliva is a biofluid of choice for monitoring and screening general health and certain diseases (Viswanath et al., 2017). Some analytical devices have been identified in recent years (Mannoor et al., 2012; Kim et al., 2015). To correlate blood and salivary glucose levels, several authors have developed glucose
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detection devices (Dhanya & Hegde, 2016; Kadashetti et al., 2015). This demonstrates the possibility of using saliva as a noninvasive biofluid for diabetes diagnosis. One example is the work of Mitsubayashi et al. who developed a device for quantifying salivary glucose (Arakawa et al., 2016). Garcia-Carmona et al. have even reported a device for analyzing biofluids in newborns, including their saliva (Garcia-Carmona et al., 2019).
15.5 Applications of gas sensors in oncology or virology as tools for the detection of biomarkers Nanotechnology represents a promising means for detection, staging, and treatment of cancer (Fortina & Kricka, 2007; Bajaj & Miranda, 2009). The combination of nanotechnology with the need for medicine to find a noninvasive method to detect and prevent cancer is a rapidly evolving field. Current screening techniques, such as colonoscopy, are invasive, traumatic, and unpleasant for already debilitated patients (Hompes & Cunningham, 2011). It is known that volatile organic compound (VOC) emissions are linked to tumor growth, which is accompanied by gene and/or protein changes that may lead to these volatile emissions (Horvath & Lazar, 2009; Buzewski & Rudnicka, 2012). VOCs can be considered as biomarkers for different types of cancers, and their analysis is a new frontier in medical diagnostics because it is noninvasive and potentially inexpensive (Amann & Spanel, 2007; Mazzone, 2008). Tumor VOCs can be detected directly from the headspace of cancer cells or through an exhaled breath. A medical study has been performed by Malagu` et al. (2014) that aimed to use chemoresistive gas sensors for the detection of VOCs as indicators of colorectal cancer biomarkers. An array of 12 metal-oxide semiconductors combining SnO2 and TiO2 materials working at their best temperatures have been used to discriminate gases of oncological interest. The findings reveal that the most relevant VOC biomarkers of colorectal cancer are benzene compounds and the molecule, 1-iodo-nonane (C9H19I).
15.6 Experimental 15.6.1 Chemicals and reagents Creatinine (98%) was purchased from Riedel-de Haen (Seelze, Germany); polyvinyl carboxylic chloride (PVC-COOH), glucose, ethanol, 0.01 M phosphate-buffered saline (PBS), sodium hydroxide (NaOH) (96%), picric acid (99%), 1,4-dioxane (99.8%), and urea (99.5%) were purchased from Sigma Aldrich, France. N-(3-dimethylaminopropyl)-N0 -ethyl carbodiimide hydrochloride (EDC), ammonium persulphate (APS), N, N0 -methylene-bisacrylamide (NNMBA), acrylamide (AAM), N, N, N0 , N0 -Tetramethyl ethylenediamine (TEMED), N-hydroxysuccinimide (NHS), potassium ferricyanide
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K3[Fe(CN)6], and potassium ferrocyanide K4[Fe(CN)6] were all obtained from Fluka. The PBS used as electrolyte was prepared using a solution of monobasic potassium phosphate (KH2PO4) and dibasic sodium phosphate (Na2HPO4). To 800 mL of distilled water, 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4, and 0.24 g KH2PO4 were added. The solution was then made up to 1 L with distilled water. The resulting pH was 7. The pH was adjusted with NaOH and HCl. Here, the focus will be on faradic processes using a redox couple in an equimolar proportion of the reduced and oxidized forms to simplify the analysis. Thus ferri/ferrocyanide was chosen because, when present in the electrolyte solution, they are not adsorbed on the electrode surface and involve only one electron in the electrochemical reactions. Indeed, for the preparation of the redox probe, 164 mg of potassium ferricyanide K3[Fe(CN)6] (with a molar mass of 329.24 g/mol) is dissolved in 100 mL of PBS buffer at pH 7.4. Then, 211 mg potassium ferrocyanide K4[Fe(CN)6] (molar mass 422.41 g/mol) is dissolved in 100 mL PBS. The two solutions are mixed to obtain 5 mM ferri/ferrocyanide used as a redox probe for electrochemical measurements. Then, 2-amino-2-hydroxymethyl-1,3-propanediol (TRIS 0.5 M) was obtained from Panreac Quimica. In this work, the buffer solutions used were TRIS and PBS. To prepare the TRIS solution and ammonium persulphate, 60 g and 13 g were dissolved in 100 mL of PBS, respectively. From a standard solution of the analyte [1 mg in 1 mL PBS (pH 7.4)], less concentrated solutions were prepared by precise dilutions of the previous solution using the same PBS buffer. To ensure complete dissolution of creatinine in PBS, a vortex and hot plate shaker were used. All solutions in the adopted range were stored at 4 C.
15.6.2 Polymer synthesis Acrylamide, as a functional monomer, was used because of its stability and availability in large quantities. Furthermore, it polymerizes under mild conditions and has uncharged groups that can establish hydrogen bonds and dipoledipole interactions with creatinine. Indeed, the polymerizations take place in a liquid medium consisting of a mixture of acrylamide and the cross-linker bis-acrylamide (N, N0 methylene-bis-acrylamide) that react together. The acrylamide polymerizes in the presence of free radicals typically provided by ammonium persulphate. Then, in the presence of TEMED as a catalyst, the added ammonium persulphate is converted to a sulfate ion free radical (SO4-) which initiates the polymerization of acrylamide. Furthermore, in its geometric structure, acrylamide has a double bond between two carbons, which is a prime target for the sulfate radical. This results in a radical acrylamide molecule. This reacts with an identical
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FIGURE 15.1 Polymerization reaction between the functional monomer and the analyte (creatinine).
acrylamide molecule to form straight-chain acrylamide (polyacrylamide) polymers. To obtain a stable cross-linked polymer matrix, it becomes necessary to use a cross-linking agent that will link these chains. Bis-acrylamide, equivalent to 2 methyl-linked acrylamide monomers, with two free radical attack sites (two double bonds between carbon atoms), is a cross-linking agent of choice (Kanai et al., 2013). In the absence of bis-acrylamide, acrylamide would polymerize into long strands (linear polymer). Bis-acrylamide contains two double bonds, which allow the compound to act as a crosslinking agent between the acrylamide chains. A very interesting mesh formation is obtained that should capture functional species. Thus the functional groups of the polymer will covalently bind the amine groups of the creatinine molecules that are added. It could also link glucose molecules through hydrogen bonding. Fig. 15.1 illustrates the polymerization reaction with creatinine.
15.6.3 Electrochemical sensors fabrication steps 15.6.3.1 Creatinine molecularly imprinted polymer sensor For creatinine detection, a sensor based on MIP on gold screen-printed electrodes (Au-SPE) was developed. Before any modification, the working electrode (Au-SPE) was rinsed three times with ethanol. A 5-μL volume of an 8.8 mg/mL solution of polyvinyl carboxylic acid (PVC-COOH) dissolved in 1.4 dioxanes was deposited as a homogeneous layer on the electrode. To activate the -COOH groups, 5 μL of an aqueous solution containing 4 mg of N-(3-dimethylaminopropyl)-N0 -ethyl carbodiimide hydrochloride (EDC) and 1 mg of NHS prepared in 1 mL of distilled water was incubated on the
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modified electrode. A rinse with distilled water followed to remove unreacted carbodiimide species. Next, a 3.5 μL volume of the previously prepared creatinine standard solution (at 1 mg/mL in PBS buffer solution at pH 7.4) was deposited on the surface of the Au-SPE/PVC-COO- and incubated for 4 h at 4 C. The Au-SPE/PVC-COO-/Cre electrode was then rinsed twice with PBS buffer solution at pH 7.4 to remove the creatinine molecules that failed to bind. The next step consisted of a TRIS solution for 30 min to block the -COO2 groups that did not bind with creatinine molecules. This was followed by a rinsing step with distilled water. The pre-polymerization solution was then prepared by adding 13 mg of ammonium persulphate to a solution already containing 11 mg of AAM as the functional monomer and 71 mg of NNMBA as the cross-linking agent in 2 mL of PBS to create a mold around the template. To accelerate the polymerization, 20 μL of a solution of TEMED was also added to the pre-cure solution. Finally, a volume of 50 μL of the pre-polymerization solution was deposited on Au-SPE/PVC-COO2/ Cre overnight at 4 C. The analyte was then removed from the polymer layer by incubating the Au-SPE/PVC-COO2Cre/polymer in distilled water at room temperature for 15 min and with ethanol for 10 min. The new electrode obtained is called Au-SPE/MIP. It was ready for use after being rinsed with distilled water. For comparison, the nonimprinted polymer-modified electrode (Au-SPE/ NIP) was prepared in the same way but without adding creatinine. The MIP sensor was used to detect creatinine in urine samples. The Jaffe´ method was used as a validation technique.
15.6.3.2 Glucose molecularly imprinted polymer sensor On the other hand, a MIP approach was adopted to develop a nonenzymatic electrochemical sensor to quantify salivary glucose. Here, the same procedure for preparing the pre-polymerization solution as the previous sensor is adopted except for the addition of glucose. AAM was used as the functional monomer and NNMBA as the cross-linking agent. In contrast, an electropolymerization method is used to immobilize the MIP by cyclic voltammetry (CV) in a potential range of 20.35 to 0.8 V at a scan rate of 0.1 V/s for 10 cycles. During electropolymerization, a membrane (analyte 1 polymer) gradually formed on the working electrode. The glucose molecules migrated through the polymer and became trapped there. This ensures the formation of a poly-AAM/glucose membrane. After this step, the glucose molecules are extracted from the membrane to create memory sites of similar size, shape, and functionality to glucose. For comparison, a NIP sensor was prepared in the same way but without the addition of glucose. The electrodes modified with a MIP and a NIP were then dried and stored at room temperature (25 C) until analysis. In a practical application, this MIP sensor was used to determine the concentration of
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salivary glucose in relation to the blood glucose level determined by a glucose meter.
15.6.4 Physicochemical characterization The morphology of the electrodes was studied using characterization techniques, such as Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS). To highlight the immobilization process of MIP to glucose, the morphology and chemical composition of the electrode surface at different stages of immobilization (Au-SPE, Au-SPE/MIP, and that of the extraction step) were observed by SEM-EDS (EDS, FEI QUANTA 250). In addition, AFM characterization was performed using a device (NANOVEA, United States), having a maximum resolution of 110 μm in a scan area of (76 μm/76 μm) at 1 line/ 2 s. The electrodes were dried and held at a temperature of 4 C overnight before analysis. SEM images were used to study how the materials change the geometric surface of the Au-SPE at low magnification ( 3 2500) and an accelerating voltage of 15 kV. For this purpose, by the FTIR technique, the spectrum of the surface sample was obtained based on the sensitivity of chemical functions to specific wavelengths between 4000 and 400/cm. Therefore, the apparatus allowed, via the detection of the characteristic vibrations of chemical bonds, to carry out a topography of the electrode’s surfaces. The AFM study was carried out in contact mode where the tip pressed against the surface of the substrate. Thus the tip swept and rubbed the electrode surface following its relief. The deformation of the lever, which was illuminated by a laser, was measured by a photodetector and recorded on a computer, which could reconstruct a 3D image of the surface. For SEM analysis, an electron gun and an electronic column produced a fine electronic probe on the working electrode, a microscope stage to move it in three directions, and detectors for capturing and analyzing the emitted radiation. The interaction between the electron probe and the electrode generated secondary, low-energy electrons that were accelerated to a secondary electron detector that amplified the signal. At each point of impact corresponded an electrical signal. The intensity of this electrical signal depended both on the nature of the electrode at the point of impact that determined the secondary electron yield and on its topography. By scanning the beam on the electrode, a map of the scanned area was obtained. As known, each atom has a unique number of electrons that reside in normal conditions in specific positions. In an SEM, when X-rays are generated, we have a two-step process. In the first step, the electron beam hits the electrode and transfers a part of its energy to the existing atoms on the electrode.
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This energy is used by the electrons of atoms to transit from an energy level to a higher or to be removed from the atoms. If such a transition occurs, the electron leaves behind a “hole.” In the second stage of the process, the positively charged holes attract electrons from higher energy levels. When an electron from such higher energy fills the hole of the lower, the energy difference of that transition is released as an X-ray. This X-ray has characteristic energy of the energy difference between these two levels. It depends on the atomic number, which is a unique property of each chemical element. In this way, X-rays are a “fingerprint” of each element and were used to identify the chemical elements that exist on the electrode surface of concern. Absorbance measurements were performed using an ultravioletvisible (UVVis) 220 spectrophotometer. The absorbances of the urine samples were measured with this in the range of 200700 nm using quartz cuvettes (1 cm wide). The wavelength was calibrated at 520 nm.
15.6.5 Electrochemical measurements The different electrochemical processes are characterized by the electrochemical cell described in Fig. 15.2. To study the electrochemical behavior of the electrodes, electrical characterization techniques, such as CV, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) were used. CV and DPV measurements were performed using PBS as the supporting electrolyte containing 5 mM of [Fe(CN)6]3-/4- solution as the electroactive probe. For the
FIGURE 15.2 Photograph of the electrochemical measurement system via the screen-printed sensors.
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CV measurements, a potential range of 20.4 V to 0.6 V was used with a scan rate of 30 mV/s. For the DPV, a potential window of 20.1 V to 0.2 V was used with a sweep rate of 10 mV/s. The same potentiostat was used to perform analyses using EIS in a frequency range between 0.1 Hz and 50 kHz with 10 mV as the open circuit AC voltage amplitude. The impedance of the electrode is determined by applying a low amplitude (peak-to-peak) sinusoidal potential and measuring the resulting sinusoidal current. The EIS data can be represented by Bode and Nyquist diagrams (Lakshmi et al., 2006). The results of the electrochemical impedance measurements were analyzed based on the best-fit equivalent circuit analysis, using the implemented EIS spectrum analysis software.
15.7 Results and discussion 15.7.1 Morphological characterization of the fabricated sensor Fig. 15.3 shows the results of the SEM-EDS analyses of the unmodified electrode surface, after its modification with glucose MIP, and after elution of the glucose molecules. The structure of the unmodified Au-SPE surface is shown in Fig. 15.3A, that of the electrode modified with MIP is shown in Fig. 15.3B, and the structure of the electrode after the glucose extraction step is shown in Fig. 15.3C. In the latter two cases, the electrode surfaces were found to be clearly modified compared to the first. The SEM image (Fig. 15.3C) of the polymer-modified electrode (Au-SPE/MIP) shows a uniformly formed mesh structure over the entire surface compared to the image of the unmodified Au-SPE, which showed a plate-like structure. In the case of the electrode obtained after the extraction step, a plate-like structure was observed but the surface was more porous. SEM studies clearly revealed that the surface of the electrode without glucose (extraction phase) had a relatively more porous structure with smaller particles that could promote effective interaction with the target molecules. Simultaneously, the chemical composition of the electrodes was studied by the energy-dispersive X-ray spectroscopy (EDS) technique using the same apparatus. This provided information on the chemical composition of a point or area of interest on the gold screen-printed working electrode. For the results after analysis, the Kratio represents the ratio of the characteristic intensities measured on the sample and the control. In practice, there are “matrix effects” arising from the nature of the interactions of electrons and X-rays with the material, which modify the measured intensities and depend on the unknown composition of the sample. Therefore, the intensities measured from the sample must be corrected using the coefficients of (Eq. 15.1). To do this, the peaks in the region of interest are deconvoluted and a conventional ZAF-type matrix effects correction procedure is applied to the net
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FIGURE 15.3 Scanning electron microscopy (SEM) images for: (A) Au-SPE, (B) Au-SPE/ MIP after electropolymerization of molecularly imprinted polymer (MIP). (C) Au-SPE/MIP after glucose extraction, and spectra obtained using energy-dispersive X-ray spectroscopy (EDS) for: (D) Au-SPE, (E) Au-SPE/MIP after electropolymerization of MIP, (F) Au-SPE/MIP after glucose extraction.
intensities (I) to calculate the elemental concentrations. Z is an atomic number correction that accounts for differences in backscattered electron yields between the pure element and the electrode (pure elements with a higher atomic number will produce fewer X-rays because some of the electrons in
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the beam leave the electrode before losing all their energy); A is the absorption correction which compensates for X-rays generated in the electrode but which cannot escape when absorbed in the electrode (low-energy X-rays tend to be strongly absorbed); and F is the fluorescence correction which corrects for the generation of X-rays by other higher energy X-rays. The concentration of the ith element on the sample is calculated using the following equation (Goldstein et al., 2017): Weight ð%Þ 5
Ci 5 Z:A:F Ccont
Ii Icont
5 Z:A:F Kratio ;
ð15:1Þ
where CCconti is the weight, which corresponds to the relative concentraIi tion of element i in the sample compared to the control Icont , which corresponds to the ratio K, and could be defined as the ratio of the characteristic intensities measured on the sample compared to the control. ZAF is the correction factor that permits to determine the element i. Indeed, Table 15.1 shows that the area of interest of the Au-SPE was composed of 81.16% by weight gold (Au), 11.05% by weight oxygen (O), and 7.79% by weight aluminum (Al). The high presence of gold demonstrates that the Au-SPE has not been modified. Aluminum and oxygen are the constituent atoms of the electrode support which is made of ceramic. Table 15.2 shows that after being modified with MIP, the layer on the surface of interest of the electrode contained carbon (C), oxygen (O), sodium (Na), and sulfur (S) in percentage (18.32% of the weight of C, 23.76% of the weight of O, 5.94% of the weight of Na, and 4.01% of the weight of S). The appearance of Na, S, C, and O in the chemical composition proved that the pre-polymerization solution (polymer 1 glucose) dissolved in PBS was effectively immobilized on the electrode surface. Table 15.3 shows that after the extraction of the glucose molecules from the MIP layer, the layer on the electrode surface of interest consisted of 9.11% by weight C and 6.45% by weight O. The presence of C and O at a lower percentage compared to the MIP result proves a successful elution of glucose molecules from the Au-SPE/MIP surface. In all these cases, the images obtained by SEM and the spectra given by EDS showed a homogeneous distribution of the elements on the electrode surface, demonstrating its successful modification. Indeed, the topography of the electrode surface after each step of the MIP sensor development was analyzed using the AFM technique. The root mean square (RMS) value of roughness which is related to the physicochemical properties of the surfaces could be acquired from the images. Fig. 15.4 shows the AFM topographies of the electrode surface after each modification step. Fig. 15.4A shows the image of the unmodified Au-SPE with a RMS (Rq) roughness of 724 nm, while Fig. 15.4B shows the surface of the gold electrode after its modification with the MIP, exhibiting a higher Rq value (1090 nm) due to the immobilization of the polymer-glucose membrane. In
TABLE 15.1 Chemical composition of Au-SPE found using energy dispersive X-ray. Elements
Weight (%)
% Atomic
Net intensity
Error (%)
Kratio
Z
A
F
O (K)
11.05
49.65
128.15
11.41
0.0426
1.5495
0.2488
1.0000
Al (K)
7.79
20.74
211.04
7.02
0.0675
1.3816
0.6273
1.0001
Au (M)
81.16
29.61
769.82
3.11
0.7487
0.8941
1.0319
0.9996
TABLE 15.2 Chemical composition of Au-SPE/MIP after electro-polymerization of molecularly imprinted polymer (MIP) found using energy dispersive X-ray spectroscopy. Elements
Weight (%)
% Atomic
Net intensity
Error (%)
Kratio
Z
A
F
C (K)
18.32
36.61
163.53
11.06
0.0714
1.1398
0.2464
1.0000
O (K)
23.76
35.64
386.80
9.55
0.1106
1.0883
0.3085
1.0000
Na (K)
5.94
6.19
135.73
8.83
0.0428
0.9859
0.5283
1.0001
S (K)
4.01
2.99
114.68
7.42
0.0443
0.9635
0.8293
0.9979
TABLE 15.3 Chemical composition of Au-SPE/MIP after glucose extraction found using energy dispersive X-ray spectroscopy. Elements
Weight (%)
% Atomic
Net intensity
Kratio
Z
A
F
C (K)
9.11
48.98
259.72
Error (%) 9.70
0.1022
1.3521
0.3275
1.0000
O (K)
6.45
26.05
201.78
11.29
0.0519
1.2952
0.2452
1.0000
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FIGURE 15.4 Images obtained using atomic force microscopy (AFM) for (A) Au-SPE, (B) Au-SPE/MIP after electropolymerization of molecularly imprinted polymer (MIP), (C) Au-SPE/ MIP after glucose extraction.
this case, homogeneous surface topography was noticed. After elution of the glucose molecules, the surface topography changed (Fig. 15.4C) and a decrease in roughness was observed (Rq 5 0.87 μm). This indicates not only the successful immobilization of the MIP on the electrode surface but also the satisfactory extraction of the template. In summary, the morphological and chemical composition analysis results obtained by SEM-EDS and AFM are in good agreement and indicate the good modification of the electrode surface for each experimental step demonstrating the feasibility of the MIP sensor.
15.7.2 Voltammetric array and electrochemical impedance spectroscopy responses 15.7.2.1 Creatinine molecularly imprinted polymer sensor The electrochemical characterization results of the different preparation steps of the creatinine MIP sensor using the two electrochemical systems (CV and EIS) are shown in Fig. 15.5. Using CV (Fig. 15.5A), the signal corresponding to the creatinine deposit admits a larger oxidation current peak than that of PVC-COOH. This is probably caused by the movement of H1 to form NH2 (tautomerism) in an aqueous solution. This generates cations that attract the [Fe(CN)6]3-/4- anions causing easy electron transfer between the electrode and the probe. After
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FIGURE 15.5 Study of the responses according to the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the: (A) cyclic voltammograms, (B) Nyquist diagrams of the redox probe (5 mM [Fe(CN)6]3-/4-), observed after PVC-COOH deposition, after creatinine deposition, after polymer deposition, and after creatinine extraction.
polymer deposition, a decrease in the peak oxidation current was observed due to the formation of a compact layer on the electrode that prevents charge transfer. But after extraction, the amplitude of the signal becomes larger than the previous one. This is explained by the formation of memory sites that reduce the compactness of the membrane on the electrode and facilitate the movement of electrons from the redox probe [Fe(CN)6]3-/4-. The diagrams obtained by the EIS technique are presented in Fig. 15.5B in which a significant variation of the charge transfer resistances after the different steps of the development of the sensor based on a MIP is noticed. These variations follow the same trend as those observed by the CV technique. This reveals that the two techniques show consistent results.
15.7.2.2 Glucose molecularly imprinted polymer sensor For the glucose MIP sensor, the behavior of the electrode, after each step of the sensor assembly, was studied using a PBS electrolyte solution containing 5 mM [Fe(CN)6]3-/4-. The results obtained for each step of the glucose sensor preparation are presented in Fig. 15.6. Indeed, for the characterization using CV, a specific voltammogram of the couple [Fe(CN)6]3-/4- is observed for the unmodified Au-SPE. The Au-SPE modified with MIP displays a signal that is different in shape and amplitude from the previous voltammogram. This demonstrates that the electrode surface has been successfully modified. It is called AuSPE/MIP. After the characterization of the NIP sensor, a slightly higher current amplitude is observed. This can be explained by the absence of glucose molecules in the layer deposited on the electrode. Here, in comparison with MIP, the redox probe ([Fe(CN)6]3-/4-) was able to access the electrode surface more easily for charge transfer. This confirms that the glucose molecules, in the case of the MIP test, were indeed trapped in the polymeric matrix preventing the [Fe(CN)6]3-/4- from accessing the electrode.
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FIGURE 15.6 Study of the responses as a function of the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the cyclic voltammograms.
FIGURE 15.7 Study of the responses as a function of the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the Nyquist diagrams of the redox probe (5 mM [Fe(CN)6]3-/4-), observed for Au-SPE, after electropolymerization of the MIP, and after electropolymerization of the nonimprinted polymer (NIP).
Useful information on changes at the electrode interface was also provided by the EIS to characterize the sensor fabrication procedures. The Nyquist representation generally consists of a semicircle part corresponding to the electron transfer and a linear part related to the diffusion process. Thus Fig. 15.7 shows the Nyquist diagrams of the electrodes, given by EIS at each preparation stage. The faradic impedance measurements were in good agreement with the results given by CV as the diameters of the semicircles for the Nyquist diagrams correlated with the variations in the oxidation current values after the different stages of sensor development.
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FIGURE 15.8 Current variation curves of the sensor based on molecularly imprinted polymer (MIP) in the presence of creatinine as a function of (A) polymerization time, (B) extraction time, and (C) incubation time.
To improve the performance of the MIP sensor, some parameters were optimized (Fig. 15.8). The prepolymerization solution was first deposited on the Au-SPE/PVC-COO2/Cre with an incubation time of one night. In the second step, this time was optimized using 3, 4, 5 h polymerization and 6 h as incubation times. Fig. 15.8A shows the evolution of the current as a function of the incubation time which illustrates that an optimal signal is obtained for a duration of 5 h. After this incubation time, no higher current peaks are recorded. Subsequently, an elution step was performed using ethanol and distilled water. This deposition (rinsing) initially lasted 45 min. This time was then revised to 15, 20, 25, 30, and 35 min. The optimal extraction time was obtained for a duration of 25 min during which the signal remained constant (Fig. 15.8B). Finally, a study concerning the optimization of the incubation time of a concentration of creatinine on the surface of the MIP sensor was carried out revealing that the optimal incubation time remains close to 30 min (Fig. 15.8C).
15.7.3 Repeatability, reproducibility, selectivity, and stability of the sensor 15.7.3.1 Creatinine molecularly imprinted polymer sensor The signals shown in Fig. 15.9 represent the results found when detecting creatinine in synthetic media. It can be seen that the signal varies as the concentration of the analyte increases. This demonstrates the retention capacity of the proposed MIP sensor. Urea and glucose were used to test the selectivity of the printed polymer sensor to creatinine (Horvath & Lazar, 2009; Buzewski & Rudnicka, 2012). Fig. 15.10 shows the differences observed for these three analytes using the same MIP sensor. Concerning the analysis of interferents, the signals obtained by using DPV show small variations in terms of current amplitude while the Nyquist diagrams show a very negligible variation of the charge transfer resistance (Rct). This certainly means that the surface of the fabricated MIP sensor is not really sensitive in the presence of urea and glucose.
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FIGURE 15.9 Responses of the molecularly imprinted polymer (MIP)-based sensor to increasing concentrations of creatinine as: (A) Pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.
FIGURE 15.10 Comparison of the calibration curves of the nonimprinted polymer (NIP) sensor vs the molecularly imprinted polymer (MIP) sensor exposed to creatinine, and interfering molecules (urea and glucose).
All these observations suggest a clear conclusion that the recognition sites of the MIP sensor are specific to creatinine.
15.7.3.2 Glucose molecularly imprinted polymer sensor Once the retention capacity of the glucose sensor has been established (Fig. 15.11), its selectivity was investigated. Selective recognition of target molecules is of great importance for an MIP sensor, allowing it to be distinguished from interfering species with nearly similar structures. In this work, the selectivity test for the glucose MIP sensor was performed using two interfering analytes coexisting in physiological saliva: lactose and sucrose (Amann & Spanel, 2007). The responses of the developed MIP sensor to glucose were first compared to those obtained in the presence of the same
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FIGURE 15.11 Responses of the molecularly imprinted polymer (MIP) sensor to increasing concentrations of glucose as: (A) pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.
FIGURE 15.12 Comparison of the calibration curves of the molecularly imprinted polymer (MIP) sensor versus the non-imprinted polymer (NIP) sensor exposed to glucose and interfering molecules (sucrose and lactose).
concentrations of the above-mentioned interfering species and then compared to the cross-reactivity of the interferents using a mixing method. Fig. 15.12 shows a comparison of the calibration curves of the imprinted and NIP-based sensors. It can be seen from the figure that the MIP-based sensor did not show significant responses to either analog, illustrated by small changes in their current amplitudes and an almost negligible change in the charge transfer resistance of the Nyquist plots. Indeed, the slopes of the analogs were significantly lower than those of glucose, indicating that the proposed MIP sensor exhibited remarkable selectivity toward glucose. In conclusion, it can be stated that the interfering species used did not cause any obvious interference and that the memory sites of the MIP are specific to glucose.
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In addition, the reproducibility, repeatability, and stability of the MIP sensor were also studied. For this purpose, three MIP sensors were prepared in a similar way and used for the detection of the same glucose concentrations to test the reproducibility. A relative standard deviation (RSD) of 3.4% was obtained, which confirms the reproducibility of the sensor. The repeatability of the sensor was also investigated by making several measurements of the same glucose concentration and the sensor responses were studied, giving a satisfactory RSD of 4%. Generally, stability is of great importance for the development of MIP sensors. In this study, it was investigated by monitoring the current response of the sensor to a glucose concentration at regular time intervals for a period of 3 months. After this time, the MIP sensor retained 85% of its initial response. This means that the proposed sensor has acceptable stability.
15.7.4 Real samples detection 15.7.4.1 Creatinine detection in human urine The analytical applicability of the present detection device was evaluated with a creatinine assay in human urine samples from a group of healthy individuals with different creatinine levels. Given the larger clinical range of creatinine in the urine sample (5001500 μg/mL) (Mazzone, 2008) and the linear range of determination of the present sensor (0.1 ng/mL1 μg/mL), it could therefore be used to determine the creatinine level in urine samples. Indeed, after the development stage of the MIP creatinine sensor, its biological application was then explored. For this purpose, different biological urine samples were collected in polystyrene bottles, put in the freezer, diluted in PBS, and shaken before analysis. A classification study of the different urine samples was previously performed using the Jaffe´ UV-Vis method (Table 15.4). Three types of samples were used: for each creatinine level (low ,10 mM, medium between 10 and 22 mM, and high .22 mM), two urine samples were tested using the MIP sensor developed to measure creatinine levels. Fig. 15.13 shows an example of a result obtained using DPV and EIS; the measurement was carried out on a urine sample of a volunteer (P122) with an elevated creatinine level taking into account the value obtained by UV-Vis. For each sample, the quantification test is performed repeatedly to take the mean value of the creatinine concentration. For the values obtained by EIS, an optimal adjustment was necessary using the EIS spectrum analysis software to determine the charge transfer resistance (Rct). The Rct values obtained were used to determine the creatinine level. For the DPV and EIS techniques, creatinine concentrations in human urine samples were determined by the calibration curves in Fig. 15.10. The creatinine concentrations in the urine samples obtained by both techniques are in agreement with those
TABLE 15.4 Results of the molecularly imprinted polymer (MIP) sensor for reproducibility of urine creatinine measurements.
Code Spectrophotometer (10 μg/mL) 3
High creatinine levels
Medium creatinine levels
Low creatinine levels
P88
P128
P99
P122
P77
P118
2.703
2.975
1.074
1.662
0.780
0.599
2.748
3.054
1.142
1.685
0.791
0.554
2.760
3.099
1.346
1.787
0.803
0.576
RSD (%) (n 5 3)
0.890
1.680
9.730
3.180
1.190
3.190
DPV (10 μg/mL)
2.668
2.975
0.974
1.705
0.565
0.498
2.670
2.990
1.030
1.800
0.650
0.510
2.720
2.980
1.200
1.740
0.700
0.580
0.890
0.250
8.900
2.200
8.730
6.830
98.700
100.800
99.600
105.200
82.000
88.400
2.670
2.850
1.090
1.300
1.010
0.600
2.700
2.910
1.730
1.900
0.590
0.540
2.730
2.880
1.100
1.870
1.020
0.570
0.900
1.040
22.900
16.330
22.900
4.290
99.800
96.800
121.900
102.200
111.500
3
RSD (%) (n 5 3) Recovery (%) EIS (10 μg/mL) 3
RSD (%) (n 5 3) Recovery (%)
95.10
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FIGURE 15.13 Responses of the molecularly imprinted polymer (MIP)-based sensor exposed to a urine sample (P122) as: (A) pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.
FIGURE 15.14 PLS-R prediction models of urine sample creatinine content given by the molecularly imprinted polymer (MIP)-based sensor: (A) DPV and (B) EIS as a function of concentration given by the Jaffe´ method. DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy.
obtained by the Jaffe´ method via the spectrophotometer. This method presented for the assessment of creatinine in human urine describes meaning RSD of 5% for the DPV and 11% for the EIS techniques with recoveries of 96% and 105%, respectively. This revealed satisfactory accuracy and precision of measurement. A statistical technique named partial least squares regression (PLS-R) was used to correlate the two measurements. Fig. 15.14 describes the results of the PLS-R model for the prediction of creatinine concentrations in human urine. In this study, the PLS-R model was constructed using all the data without separating them in the training matrix and the validation matrix. A latent variable and a leave-one-out cross-validation technique were used to build this model. Fig. 15.14 shows the results of DPV and EIS, respectively, as a function of creatinine concentrations determined by the Jaffe´ method. Good linearity of the calibration plots was obtained in both cases with a correlation coefficient of 0.99. From these data, the ability of the MIP sensor to assess urine creatinine content seems to be the most remarkable result.
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15.7.4.2 Glucose detection in human saliva Knowing that the clinical glucose range in the saliva is 0.5216.1 μg/mL, the MIP sensor could be applied to saliva samples after its development stage. For this purpose, saliva samples from six healthy students were exposed to the MIP sensor and the results were compared with their corresponding blood glucose levels given by the glucometer as a reference method. The saliva samples were collected under fasting conditions using the following procedure: Rinse the mouth with water so that the measurements are not influenced by food. Release the saliva into the tube and keep it at 4 C. Immediately after collection, the saliva samples were heated to 100 C (Malagu` et al., 2014) to remove any toxins that might influence the measurements. Centrifugation was then adopted at 1500 rpm for 15 min to remove any residual particles. Each saliva sample was split into three aliquots and used directly to avoid degradation and the consequent change in their properties; after analysis, they were stored at 4 C in a refrigerator. During the biological detection step, 5 μL of the saliva sample was deposited on the working electrode of the MIP sensor. A 30 min incubation was followed to ensure good retention of the glucose molecules. Measurements were performed three times to take the average value of the glucose concentration. As a result, fasting blood glucose values ranged from 940 to 1470 μg/mL and salivary glucose values from 2.2 to 8.6 μg/mL. Fig. 15.15 shows first the relationship for predicting the blood glucose concentration using PLS-R, knowing the salivary glucose concentration delivered by the developed sensor. Then, the relationship between the blood and salivary glucose concentration is plotted. Indeed, the results delivered by the MIP sensor were thus exploited to build a prediction model using the PLS-R technique. This model could be used as a relationship between the developed MIP sensor and the glucometer. This could be of interest for the diagnosis of diabetes mellitus. Fig. 15.15A shows the regression model obtained for the determination of the glucose
FIGURE 15.15 PLS-R prediction models of the glucose content of saliva samples given by: (A) electrochemical sensor versus blood glucose concentration from a glucometer and (B) calibration curve between blood and saliva glucose concentration.
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level. One latent variable and a leave-one-out cross-validation technique were used to construct this model. In this case, the PLS-R model was constructed using all the data without separating them in the training matrix and the validation matrix. A regression coefficient of R 5 0.99 was obtained. In addition, a direct calibration plot was established (Fig. 15.15B). According to this plot, an acceptable correlation was obtained with a regression coefficient of 0.96.
15.8 Conclusion Biosensors are generally classified according to the nature of the receptors and transducers that compose them. Due to their significant advantages, biomimetic electrochemical receptors have recently become very popular in the biomedical field. This technology has been exploited to develop biomimetic receptor-based sensors to quantify target analytes, namely creatinine and glucose, which play an important role in certain kidney diseases. As a first step, a biomimetic electrochemical sensor based on MIP was developed for the detection of creatinine (Cre), using Au-SPE. The first layer of polyvinyl carboxylic chloride (PVC-COOH) was first immobilized on the Au-SPE surface. Creatinine molecules were attached to the Au-SPE/PVCCOOH surface after activation by carbo-di-imide species. Then, a polymer composed of acrylamide cross-linked with N, N0 -methylene bis-acrylamide was formed around the Cre molecules to form the Au-SPE/MIP. Extraction of the Cre molecules from the formed layer left specific binding sites, thus forming the MIP sensor capable of selective recognition of creatinine at different concentrations. To confirm the sensitivity of the MIP sensor, the same procedure was adopted without the addition of creatinine to form the nonimprinted sensor (Au-SPE/NIP). Their analytical properties were studied using three electrochemical techniques: CV, DPV, and EIS. UV-Vis spectrophotometry was used as a validation method using the Jaffe´ method. The results obtained prove the selectivity of the MIP sensor to creatinine compared to urea and glucose. In a linear detection range of 0.1 ng/mL to 1 μg/mL, a detection limit of 0.016 and 0.081 ng/mL was found by using EIS and DPV, respectively. As a practical application, this MIP sensor was successfully tested on urine samples of volunteers with different creatinine levels. To correlate the two analytical instruments (MIP sensor and UV-Vis spectrophotometer), a quantitative pattern recognition method called PLS-R was used with satisfactory results. This study provided a promising strategy to fabricate MIP-based sensing devices with highly selective recognition capability, simplicity of operation, small-scale integration, and low cost. In the second phase, a study was conducted on the development of a MIP-based electrochemical sensor for the quantification of salivary glucose. Indeed, the MIP approach was used to develop a nonenzymatic electrochemical sensor for the determination of salivary glucose. Electropolymerization
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was used as a method of immobilizing the MIP (acrylamide/bis-acrylamide 1 glucose) on an Au-SPE. CV, DPV, and EIS were used for the electrochemical measurements using PBS as a supporting electrolyte containing ferri/ferrocyanide. Morphological characterizations of the developed MIP sensor were performed using AFM and SEM-EDS. Under optimal conditions, the MIP sensor could efficiently detect glucose while avoiding interference from structurally similar substances such as lactose and sucrose. In the working range of 0.550 μg/mL, detection and quantification limits of 0.59 and 1.9 μg/mL, respectively, were found. Furthermore, using PLS-R, the determination of glucose in human saliva was correlated with that of fingerprick blood with satisfactory results (R2 5 0.99). As a result, this work demonstrated a cheap, simple, and effective detection device for nonenzymatic glucose detection, which makes it a promising tool for the future evolution of noninvasive, accurate, and reliable diabetes diagnosis.
Acknowledgments Authors gratefully acknowledge Moulay Ismaı¨l University of Meknes for financial support of the project “Research support”. This work has been funded in part by CANLEISH under H2020-MSCA-RISE-2020 project, grant agreement number: 101007653: Non-invasive volatiles test for canine leishmaniasis diagnosis.
Declaration of competing interest We declare that we have no personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
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Bird, S. P., Tarpenning, K. M., & Marino, F. E. (2006). Liquid carbohydrate/essential amino acid ingestion during a short-term bout of resistance exercise suppresses myofibrillar protein degradation. Metabolism: Clinical and Experimental, 55, 570577. Blazevich, A. J., & Giorgi, A. (2001). Effect of testosterone administration and weight training on muscle architecture. Medicine & Science in Sports & Exercise, 33, 16881693. Buzewski, S., & Rudnicka, J. (2012). Analytical and unconventional methods of cancer detection using odor. Trends in Analytical Chemistry, 38, 112. Chen, C. H., & Lin, M. S. (2012). A novel structural specific creatinine sensing scheme for the determination of the urine creatinine. Biosensors and Bioelectronics, 31, 9094. Cullen, T., Thomas, A. W., Webb, R., & Hughes, M. G. (2015). The relationship between interleukin-6 in saliva, venous and capillary plasma, at rest and in response to exercise. Cytokine, 71, 397400. Dhanya, M., & Hegde, S. (2016). Salivary glucose as a diagnostic tool in Type II diabetes mellitus: A case-control study. Nigerian Journal of Clinical Practice, 19, 486490. Fortina, P., & Kricka, L. (2007). Application of nanoparticles to diagnostics and therapeutics in colorectal cancer. Trends in Biotechnology, 25, 145152. Fox, P. C., van der Ven, P. F., Sonies, B. C., Weiffenbach, J. M., & Baum, B. J. (1985). Xerostomia: Evaluation of a symptom with increasing significance. Journal of the American Dental Association, 110, 519525. Garcia-Carmona, L., Martin, A., Sempionatto, J. R., Moreto, J. R., Gonzalez, M. C., Wang, J., & Escarpa, A. (2019). Pacifier biosensor: Toward noninvasive saliva biomarker monitoring. Analytical Chemistry, 91, 1388313891. Goldstein, J. I., Newbury, D. E., Michael, J. R., Ritchie, N. W., Scott, J. H. J., & Joy, D. C. (2017). Scanning electron microscopy and X-ray microanalysis. Springer. Goll, R. D., & Mookerjee, B. K. (1978). Correlation of biochemical parameters in serum and saliva in chronic azotemic patients and patients on chronic hemodialysis. Journal of Dialysis, 2, 399414. Gouarne, C., Groussard, C., Gratas-Delamarche, A., Delamarche, P., & Duclos, M. (2005). Overnight urinary cortisol and cortisone add new insights into adaptation to training. Medicine & Science in Sports & Exercise, 37, 11571167. Hompes, R., & Cunningham, C. (2011). Colorectal cancer: Management. Medicine, 29, 254258. Horvath, L., & Lazar, Z. (2009). Exhaled biomarkers in lung cancer. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology, 34, 261275. Hsieh, R. Y., Tsai, H. A., & Syu, M. J. (2006). Designing a molecularly imprinted polymer as an artificial receptor for the specific recognition of creatinine in serums. Biomaterials, 27, 20832089. Ives, S. J., Blegen, M., Coughlin, M. A., Redmond, J., Matthews, T., & Paolone, V. (2011). Salivary estradiol, interleukin-6 production, and the relationship to substrate metabolism during exercise in females. European Journal of Applied Physiology, 111, 16491658. Kadashetti, V., Baad, R., Malik, N., Shivakumar, K. M., Vibhute, N., Belgaumi, U., Gugawad, S., & Pramod, R. C. (2015). Glucose level estimation in diabetes mellitus by saliva: A bloodless revolution. Romanian Journal of Internal Medicine, 53, 248252. Kanai, T., Sanskriti, C., Vislawath, P., & Samui, A. B. (2013). Acrylamide based molecularly imprinted polymer for detection of m-nitrophenol. Journal of Nanoscience and Nanotechnology, 13, 30543061.
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Khadro, B., Sanglar, C., Bonhomme, A., Errachid, A., & Jaffrezic-Renault, N. (2010). Molecularly imprinted polymers (MIP) based electrochemical sensor for detection of urea and creatinine. Procedia Engineering, 5, 371374. Kim, J., Imani, S., de Araujo, W. R., Warchall, J., Valde´s-Ram´ırez, G., Paixa˜o, T. R. L. C., Mercier, P. P., & Wang, J. (2015). Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics. Biosensors and Bioelectronics, 74, 10611068. Lakshmi, D., Prasad, B. B., & Sharma, P. S. (2006). Creatinine sensor based on a molecularly imprinted polymer-modified hanging mercury drop electrode. Talanta, 70, 272280. Lee, M. H., Tsai, T. C., Thomas, J. L., & Lin, H. Y. (2008). Recognition of creatinine by poly (ethylene-co-vinylalcohol) molecular imprinting membrane. Desalination, 234, 126133. Levinsky, R. J. (1984). Primary and secondary immunodeficiency disorders. Archives of Disease in Childhood, 59, 393. Lindsay, A., Healy, J., Mills, W., Lewis, J., Gill, N., Draper, N., & Gieseg, S. P. (2016). Impactinduced muscle damage and urinary pterins in professional rugby: 7, 8-dihydroneopterin oxidation by myoglobin. Scandinavian Journal of Medicine & Science in Sports, 26, 329337. Malagu`, C., Fabbri, B., Gherardi, S., Giberti, A., Guidi, V., Landini, N., & Zonta, G. (2014). Chemoresistive gas sensors for the detection of colorectal cancer biomarkers. Sensors, 14, 1898218992. Mannoor, M. S., Tao, H., Clayton, J. D., Sengupta, A., Kaplan, D. L., Naik, R. R., Verma, N., Omenetto, F. G., & McAlpine, M. C. (2012). Graphene-based wireless bacteria detection on tooth enamel. Nature Communications, 3, 19. Mayeux, R. (2004). Biomarkers: Potential uses and limitations. NeuroRx: The Journal of the American Society for Experimental NeuroTherapeutics, 1, 182188. Mazzone, P. J. (2008). Analysis of volatile organic compounds in the exhaled breath for the diagnosis of lung cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, 3, 774780. Mirzaii-Dizgah, I., Hejazi, S. F., Riahi, E., & Salehi, M. M. (2012). Saliva-based creatine kinase MB measurement as a potential point-of-care testing for detection of myocardial infarction. Clinical Oral Investigations, 16, 775779. Mirzaii-Dizgah, I., & Riahi, E. (2013). Salivary high-sensitivity cardiac troponin T levels in patients with acute myocardial infarction. Oral Diseases, 19, 180184. Miura, C., Funaya, N., Matsunaga, H., & Haginaka, J. (2013). Monodisperse, molecularly imprinted polymers for creatinine by modified precipitation polymerization and their applications to creatinine assays for human serum and urine. Journal of Pharmaceutical and Biomedical Analysis, 85, 288294. Mosby. (2009). Mosby’s medical dictionary (8th ed). Elsevier Inc. Ouellet-Morin, I., Danese, A., Williams, B., & Arseneault, L. (2011). Validation of a highsensitivity assay for C-reactive protein in human saliva. Brain, Behavior, and Immunity, 25, 640646. Ozmeric¸, N., Baydar, T., Bodur, A., Engin, A. B., Uraz, A., Eren, K., & Sahin, G. (2002). Level of neopterin, a marker of immune cell activation in gingival crevicular fluid, saliva, and urine in patients with aggressive periodontitis. Journal of Periodontology, 73, 720725. Panasyuk-Delaney, T., Mirsky, V. M., & Wolfbeis, O. S. (2002). Capacitive creatinine sensor based on a photografted molecularly imprinted polymer. Electroanalysis, 14, 221224. Patel, A. K., Sharma, P. S., & Prasad, B. B. (2008). Development of a creatinine sensor based on a molecularly imprinted polymer-modified sol-gel film on graphite electrode. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis, 20, 21022112.
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PharmaDrugs, NarcoChecks saliva test. , https://www.pharmadrugtest.com/saliva-tests/38-thcsaliva-test-cannabis.html . . Punyadeera, C., Dimeski, G., Kostner, K., Beyerlein, P., & Cooper-White, J. (2011). One-step homogeneous C-reactive protein assay for saliva. Journal of Immunological Methods, 373, 1925. Reddy, K. K., & Gobi, K. V. (2013). Artificial molecular recognition material-based biosensor for creatinine by electrochemical impedance analysis. Sensors and Actuators B: Chemical, 183, 356363. Robinson, N., Castella, V., Saudan, C., Sottas, P. E., Schweizer, C., Dimo-Simonin, N., Mangin, P., & Saugy, M. (2006). Elevated and similar urinary testosterone/epitestosterone ratio in all samples of a competition testing: Suspicion of a manipulation. Forensic Science International, 163, 148151. Salimetrics. Salivary cortisol. ,https://salimetrics.com/wp-content/uploads/2018/03/salivary-cortisol-elisa-kit.pdf.. Salimetrics. Salivary secretory IgA. ,https://salimetrics.com/wp-content/uploads/2018/03/secretory-iga-saliva-elisa-kit.pdf.. Satish, B. N. V. S., Srikala, P., Maharudrappa, B., Awanti, S. M., Kumar, P., & Hugar, D. (2014). Saliva: A tool in assessing glucose levels in diabetes mellitus. Journal of International Oral Health: JIOH, 6, 114. Sreenivasan, K., & Sivakumar, R. (1997). Interaction of molecularly imprinted polymers with creatinine. Journal of Applied Polymer Science, 66, 25392542. Subrahmanyam, S., Piletsky, S. A., Piletska, E. V., Chen, B., Karim, K., & Turner, A. P. (2001). ‘Bite-and-Switch’ approach using computationally designed molecularly imprinted polymers for sensing of creatinine. Biosensors and Bioelectronics, 16, 631637. Testoste´rone Salimetrics. Salivary testosterone. ,https://salimetrics.com/wp-content/uploads/ 2018/03/testosterone-saliva-elisa-kit.pdf.. Tsai, H. A., & Syu, M. J. (2005). Synthesis of creatinine-imprinted poly (β-cyclodextrin) for the specific binding of creatinine. Biomaterials, 26, 27592766. Uberoi, H. S., Dugal, J. S., Kasthuri, A. S., Kolhe, V. S., Kumar, A. K., & Cruz, S. A. (1991). Acute renal failure in severe exertional rhabdomyolysis. Journal of the Association of Physicians of India, 39, 677679. Viswanath, B., Choi, C. S., Lee, K., & Kim, S. (2017). Recent trends in the development of diagnostic tools for diabetes mellitus using patient saliva. TrAC Trends in Analytical Chemistry, 89, 6067. Viswanath, K. B., Devasenathipathy, R., Wang, S. F., & Vasantha, V. S. (2017). A new route for the enzymeless trace level detection of creatinine based on reduced graphene oxide/silver nanocomposite biosensor. Electroanalysis, 29, 559565. Zhang, L., Cheng, G., & Fu, C. (2002). Molecular selectivity of tyrosine-imprinted polymers prepared by seed swelling and suspension polymerization. Polymer International, 51, 687692.
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Chapter 16
Detection of triclosan and sodium lauryl sulfate in environmental samples and cosmetic product by electrochemical sensor based on biomimetic recognition combined with electronic nose Nezha EL Bari1, Soukaina Motia1, Moulay Mustapha Ennaji2 and Benachir Bouchikhi3 1
Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco, 3Biosensors and Nanotechnology Group, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco
16.1 Introduction A safe and clean environment free of toxic threats, safe food and water are increasingly important concerns in our society. Wastewater is frequently considered a nuisance to be disposed of rather than a resource (Guz & Guz, 2018). Nevertheless, it is an important source of valuable substances: water, energy, nutrients, organic matter, and other by-products. It is an important part of the water cycle and needs to be managed throughout the water cycle: from abstraction, treatment, distribution, collection, and post-use treatment of freshwater to its reuse and subsequent return to the environment, where it replenishes the source for further water abstraction. Because of their high toxicity, nondegradability, and adverse effects on living beings, plants, microorganisms, and ecosystems, the analysis and control of Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00003-0 Copyright © 2023 Elsevier Inc. All rights reserved.
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cosmetic residues are of considerable importance for the protection of the environment and even more so for human health (Jing et al., 2020). These products are among the most challenging contaminants to detect and remove from water. Surfactants and preservatives are one of the leading cosmetic products found in wastewater. However, these cosmetic products contain chemical substances that are potentially toxic to humans (such as skin irritation, hormones modification (hyperthyroid), testosterone levels change, causing a reduction of sperm production, found in breast milk, urine, and blood) and the environment (Wallen-Russell & Wallen-Russell, 2017). Cleaning agents (anionic compounds, etc.), antibacterial preservatives (sodium lauryl sulfate [SLS], triclosan [TCS], etc.), foaming agents, emulsifiers, and additives (perfumes, etc.) can thus be considered as pollutants. To meet these legitimate demands, complex scientific research is carried out, and its effectiveness is closely linked to the quality of the analysis tools made available to it. Nowadays, there are various conventional methods for the determination of cosmetic products in environmental media, such as liquid chromatographymass spectroscopy (Chu & Metcalfe, 2007), ion-selective electrodes (Kov´acs et al., 2001), and gas chromatographyion trap mass spectrometry (Chau et al., 2008; Wu et al., 2007). These techniques are reliable and accurate, but they require heavy, expensive, highly sophisticated equipment, skilled labor, and sometimes lengthy sample preparation. In this respect, it is desirable to develop new reliable, sensitive, selective, and inexpensive methods for detecting and determining heavy metals. Electrochemical techniques using modified electrodes as sensors have proven to be a promising alternative to conventional methods for the qualitative and quantitative analysis of heavy metals and various organic molecules. Indeed, electrochemical detection systems offer many advantages such as simple instrumentation, high sensitivity and selectivity, ease of use, possible miniaturization of the instrumentation, minimal sample pretreatment, short analysis time, portability, and the possibility to perform analyses in the field. Neglected for a long time, the electrochemical approach has seen a resurgence of interest in the last 15 years with the appearance of new electrochemical sensors (Ghosh et al., 2019; Raziq et al., 2021). These are often simple and compact devices transforming the biochemical and/or chemical signal into an easily exploitable electrical signal. They generally consist of a selective part (sensitive layer) and a transducer system that transforms the physicochemical changes induced by the recognition in the sensitive layer into an electrical signal. They also have an operating environment that allows the electrical processing of the signals. Electrochemistry offers interesting prospects for the miniaturization and industrialization of simple, reliable, and robust sensors at low cost (Qian et al., 2021). In order to achieve levels of sensitivity comparable to those obtained by spectroscopic techniques, a great deal of research is being conducted. Metallic films are
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associated with electrodes to design electrochemical sensors that can determine residues of cosmetic products at trace levels and obtain remarkable sensitivities. Accordingly, in this contribution, the objective of this chapter is to present a brief overview of the state of the art of micropollutant analysis in wastewater samples. On the other hand, the development of electrochemical sensors to detect TCS and SLS will be discussed.
16.1.1 Wastewater as sources of micropollutants The rising global consumption of chemicals has triggered an increase in chemical pollution of groundwater and surface, with largely unanticipated effects on human health and aquatic life. Many of these “micropollutants” have an urban background and are used daily in homes, workplaces, or the urban environment. Most of them end up in sewers (Dittmer et al., 2020). This is the case for personal care products (PCPs), pharmaceuticals, and their metabolites excreted in urine and feces, and several household chemicals such as food or plastic additives or flame retardants in textiles. Among the main emerging products, PCPs involve ingredients found in shampoos, washing lotions, skincare products, dental care products, sunscreen agents, cosmetics, perfumes, hairdressing products, etc. Because of their vast consumption and type of use (often dermal application), they enter municipal wastewater mainly through washing in the shower or bath. Domestic wastewater is contaminated with nondomestic pollutants such as the mentioned products. If they are not eliminated in wastewater treatment plants (WWTPs), they can impact the fauna and flora of the receiving waters (Nguyen et al., 2021). These substances are referred to as emerging contaminants (EC). This raises a global environmental concern regarding water quality, a potentially serious threat to human health, wildlife, aquaculture life, and ecosystems. Due to their mild estrogenic effect and ubiquitous occurrence in human tissue, they may be candidates’ substances for human health concerns. In addition, they are pollutants of concern because of their endocrine disruption and their association with numerous human health problems (reproductive, developmental, and neurodevelopmental impairment) (Weatherly & Gosse, 2017). They are responsible for adverse effects on human health, such as cancer, reproductive, and developmental effects. Consequently, the wastewater is a promising environment for continuous and in situ monitoring of the so-called micropollutant.
16.1.2 Current electrochemical sensors for environmental residues Electrochemical sensors have been documented to fulfill some of these characteristics due to their high sensitivity, fast response, small equipment size, ease of installation, simplicity of sample preparation, and suitability for in
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situ analysis (Bounegru & Apetrei, 2020). There are several reports on the electrochemical detection of TCS and SLS. Although the performance and sensitivity of these methods are quite good, they are not selective, and in most cases, their preparation or real-life application is very difficult. Among several strategies, the combination of molecularly imprinted polymers (MIPs) as sensing elements in electrochemical analysis has proven to be one of the most promising techniques in recent years (Sobiech et al., 2021). On the one part, the detection of TCS was achieved using MIP technology. Indeed, Wu et al. (2017) assessed the graphene/palladium nanoparticles hybrids for TCS detection. However, in this paper, they employed graphene oxide nanosheets decorated by Fe3O4@Au nanostructure stabilized on polypyrrole for TCS sensing (Saljooqi et al., 2020). Huang et al. reported the TCS detection with carbon nanospheres as electrocatalysts (Huang et al., 2021). Thus, this work reports TCS detection by using a molecularly imprinted photoelectrochemical sensor based on g-C3N4-AuNPs (Feng et al., 2018). Finally, a study by Kong et al. employed MIP functionalized magnetic Fe3O4 for the highly selective extraction of TCS (Kong et al., 2020). Other MIP-based TCS sensors have been reported. On the other part, SLS detection has also been carried out using MIP. Among others, the work of Hao et al. used a modified eosin Y/polyethyleneimine electrode (Hao, Lei, Li, et al., 2014). In this paper, they employed a piezoelectric quartz crystal sensor (Albano & Sevilla, 2007). Devi et al. employed a polyvinyl chloride (PVC) matrix membrane sensor for TCS detection (Devi & Chattopadhyaya, 2013). However, the electrochemical detection of SLS has been used little.
16.1.3 Potential sensors for applications in the fields of virology and oncology On the one hand, cancer is one of the leading causes of death globally. On the other hand, the direct detection of virus particles can provide more information on the stage of infection. Both diseases have high morbidity and mortality rates. Rapid, accurate, and sensitive monitoring of the corresponding biomarkers is essential for successful treatment. They are key indicators of disease growth (Jørgensen, 2021). Nevertheless, conventional laboratory methods (Niu et al., 2021; Yang et al., 2021) are labor-intensive, time-consuming, and relatively insensitive. The samples must be transported to diagnostic laboratories for testing. These conditions increase costs and response times while reducing the quality of patient care. Because of these many limitations, scientists are now focusing on developing biosensor systems and electrochemical sensors for efficient, rapid, and noninvasive detection of virus and cancer biomarkers. Viral and oncological electrochemical biosensors offer attractive alternatives and promise to provide inexpensive, miniaturized, sensitive, rapid, realtime detection, and portable platforms that offer patients and medical staff
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quick and easy results. The major influence of nanotechnology on the development of biosensors is that they will revolutionize the diagnosis and therapy of diseases (Zhang & Lyu, 1948). Several bio/electrochemical sensors have been developed and tested for cancer detection, in particular, the lung (Hussain et al., 2021), the breast (Novodchuk et al., 2021), and the prostate (Altunko¨k et al., 2021). A biomimetic sensor was applied to detect miRNA related to liver cancer (Zhang et al., 2020). In addition, some of these sensors were developed for the virus, including SARS-CoV-2 (Abid et al., 2021; Kumar et al., 2022), influenza (Tram et al., 2021), and hepatitis A (Wang et al., 2021). Accordingly, accurate and early diagnosis in oncological and virological applications will help to reduce the mortality rate. There is no doubt that this decade will make extensive use of biosensor and electrochemical sensorbased systems due to their distinctiveness mentioned above.
16.1.4 Electronic nose technology Recently, electronic nose (e-nose) technology has also been promoted as a very exciting approach for environmental analysis (Park et al., 2019). This approach has demonstrated simplicity, speed, and promise in minimizing analysis time and costs. MOS multigas sensor systems and e-noses for air quality monitoring represent a promising alternative for detecting gaseous pollutants in indoor air. Since the 1980s, following the evolution of microtechnologies and analytical microsystems, e-nose systems have experienced a strong development due to their numerous advantages: compactness, ease of use, reliability, and low manufacturing cost compared to the different types of devices available on the gas sensor market (Suriya et al., 2021). However, for monitoring these two molecules, we rarely find articles that report their detection using this type of device. In cancer research, e-nose tools are commonly used to search for a typical “breath print” that can discriminate cancer patients from healthy controls. It allows the study of differences in exhaled breath composition between categories of cancer subjects according to disease severity. It is used in the diagnosis of cancers, including lung (Behera et al., 2019), prostate (Waltman et al., 2020), and bladder (Bassi et al., 2021).
16.2 Experimental 16.2.1 Chemicals and reagents TCS, carboxylic PVC (PVC-COOH), N-hydroxysuccinimide (NHS), 1,4 dioxane (99.8%), potassium ferrocyanide ([Fe(CN)6]42), potassium ferricyanide ([Fe(CN)6]32), 2,4,6-trichlorophenol, catechol, phosphate-buffered
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saline (PBS), SLS, 2-aminothiophenol (2-ATP), ethanol, Tween 80, and urea were ordered from Sigma-Aldrich. N-(3-dimethylaminopropyl)-N0 ethyl-carbodiimide hydrochloride (EDC), N,N0 -methylene-bisacrylamide (NNMBA), N,N,N0 ,N0 -tetramethyl ethylene diamine (TEMED), and acrylamide (AAM) were purchased from Fluka. Ammonium persulfate (APS) was from Scharlau Chemie S.A., methanol, (hydroxymethyl) aminomethane (TRIS 0.5 M), and acetic acid were obtained from Reactifs RAL, Panreac Quimica S.A., and VWR Chemicals, respectively. Ethylenediaminetetraacetic acid (EDTA) was from Fluka. For the sensors calibration objective, the following procedure is adopted: From a stock standard solution of TCS (1 mg/mL in methanol), working solutions were prepared by dilutions with the accuracy of the previous solution with PBS (pH 5 7.4). All these solutions of the adopted range (0.11 ng/mL) were stocked at 4 C until analysis. A hotplate apparatus and vortex were employed to achieve the complete solutions dissolution. In addition, the stock solution of SLS was also prepared (1 mg/mL in water). The SLS working solutions were prepared by a dilution with water. Distilled water (DW) was employed for preparing the solutions used in this study and for electrode washing. To perform the electrochemical characterization, a standard stock solution of 5 mM [Fe(CN)6]32/42 was prepared with PBS (pH 5 7.4) (Motia et al., 2021a; Nasraoui et al., 2021). This later was selected because when present in the electrolyte solution, it is not adsorbed on the surface of the electrode and implies only one electron in the electrochemical reactions. To start a test, the 1 3 8 sensor array was inserted into the potentiostat device slot. For each sensor development step, 30 μL of the electrolyte [Fe (CN)6]32/42 is dropped on the electrode for characterization to investigate the electrochemical changes (Motia et al., 2021b). This droplet was allowed to remain on the surface of the electrodes for a few seconds to allow the sample to react properly with the preloaded reagents, and finally the characterization was initiated.
16.2.2 Polymer synthesis The development of the MIP sensor consisted of functionalizing the gold screen-printed electrode (Au-SPE) with a PVC-COOH coating. The PVCCOOH polymer was selected because TCS molecules are enabled to cooperate with it by intermolecular forces under mild conditions. The reaction mechanism is mostly controlled by the creation of intermolecular hydrogen bonds between the -COOH groups of the PVC-COOH shell and the polar groups of TCS. As a result, the -COOH groups were activated by the chemistry of carbodiimides and succinimides (NHS and EDC). In the second step, a volume of the TCS stock solution was dropped to the modified electrode. The resulting Au-SPE/PVC-COOH/TCS assembly
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was carefully washed with PBS buffer to remove the unreacted TCS molecules. A TRIS solution was employed to sequester the unreacted -COOH on the electrode. Next, a polymerization step was started by preparing a mixture of AAM as a functional monomer (Sullivan et al., 2021) and a cross-linker, which was used to achieve a highly cross-linked matrix. In effect, the polymerization of the AAM was commenced. The polymerization reaction is exemplified in Fig. 16.1. In the SLS-MIP sensor, 2-aminothiophenol (2-ATP) (Menon et al., 2018) was employed to prepare the electrochemical MIP sensors. It can interact with target analytes through hydrogen bonds and be electropolymerized.
16.2.3 Electrochemical sensors fabrication steps 16.2.3.1 TCS-MIP sensor For the preparation of the TCS-MIP sensor, the following steps were adopted. The working area of the bare Au-SPE was cleaned by washing it with ethanol (99.95%) and DW. The PVC-COOH was prepared in 1,4-dioxane at 40 C because intermolecular interactions increase its melting point. So, a volume of 5 μL of a PVC-COOH solution (8.8 mg/mL in 1,4-dioxane) was dropped on the working electrode (Au-SPE) and incubated for 90 min at room temperature. Then, to activate the groups (COOH) of the resulting Au-SPE/PVC-COOH, 5 μL
FIGURE 16.1 Schematic representation of the reaction during polymerization.
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of a solution containing (4 mg/mL) of EDC and (1 mg/mL) of NHS was incubated for 90 min. The EDC and NHS molecules, which did not react, were eliminated by rinsing thoroughly with DW. Next, a volume of 5 μL of the TCS solution (1 mg/mL in methanol) was dropped on the electrode for 3 h at 4 C, enable TCS molecules binding to the activated groups (COOH) of the PVC-COOH film. Afterward, the unbounded templates were removed by rinsing with PBS buffer (pH 5 7.4). A TRIS solution (pH 5 9) of 5 μL was then incubated for 30 min on the Au-SPE/PVC-COOH/TCS electrode to block no specific sites significantly. Several washes with DW followed this step. Thus, a polymer solution was prepared by mixing 11 mg/mL of AAM and 71 mg/mL of NNMBA in PBS. Then, the polymerization initiator (APS 5 13 mg/mL in PBS) was added to the mixture. The polymerization was accelerated by adding the catalyst (TEMED solution 5 5%). After that, a volume of 5 μL of the obtained solution was dropped and incubated on the working electrode for 5 h at 4 C. Finally, the TCS imprinted sites were obtained after extracting the TCS template from the polymer. For this aim, 5 μL of a solution containing a mixture of methanol and acetic acid (7:3, (v/v)) was incubated on the electrode surface for 15 min. As the control step, a nonimprinted polymer electrode (Au-SPE/NIP) was elaborated in the same manner but without the TCS. The elaborated MIP and NIP sensors were stored at 4 C until use. Furthermore, the sensors were incubated with the selected concentrations of TCS solutions (0.1 pg/mL to 1 ng/mL) for 30 min. The MIP sensor was employed to detect TCS in wastewater samples for the real detection step. Then, the spectrophotometer was used as a validation method.
16.2.3.2 SLS-MIP sensor For the aim of elaborating the SLS-MIP sensor, the following steps were adopted. Before commencing any functionalization, the working electrode was cleaned by rinsing with DW and ethanol. A film of 2-ATP was first formed after deposit onto the bare Au-SPE 10 μL of a solution containing (5.4 mg/ mL of ethanol) for incubation of 12 h at 4 C. The electrodes were then rinsed with ethanol and DW to eliminate the loosely and nonbounded 2-ATP molecules. Through an electrochemical polymerization technique, both MIP and NIP films were elaborated on the Au-SPE by using cyclic voltammetry (CV). In order to obtain an imprinted layer, the electropolymerization of MIP was performed by employing 30 μL of a solution comprising 2-ATP (0.1 mg/mL) and SLS (1 mg/mL) in a supporting electrolyte (10 mM [Fe(CN)6]32/42 in PBS pH 7.2). The electropolymerization phase was performed in a potential range between 20.35 V and 0.80 V with a scan rate of 100 mV/s for 10
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cycles. In addition, to facilitate the binding and recognition of the SLS analytes, an extraction step of the template from the MIP layer was done by dipping the correct prepared electrode in DW solution for 20 min at room temperature. Similarly, a nonimprinted film (Au-SPE/NIP) was prepared in the same way but without adding the SLS template to the solution of the electropolymerization process. This was performed to validate that the remarked impact during the MIP detection step was only due to the imprinting characteristics. As a practical application, the developed SLS-MIP sensor was employed to determine the SLS concentrations in cosmetic product samples. The spectrophotometer is used as a reference method.
16.2.4 Surface morphotogical analysis As a reference method, a UVvis spectrophotometer was employed in this study by using the device ANACHEM instrument in the range 200700 nm employing quartz cuvettes. The wavelengths were calibrated at 452 and 654 nm for TCS and SLS to measure the absorbances of the wastewater and cosmetic samples. The morphological and composition structure characterization were fulfilled by employing the techniques of Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM) to examine all the elaboration MIP sensor steps.
16.2.5 Electrochemical measurements An electrochemical transducer (potentiostat) interfaced with a computer was employed to perform the electrochemical measurements (Fig. 16.2). To examine the electrochemical attitude of the electrodes, CV, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) were employed as electrochemical techniques. For the CV experimentation, the potential was accessed over a range of 0.4 to 0.6 V with a scan rate of 30 mV/s. The DPV characterizations were fulfilled over a potential range of 0.2 to 0.4 V at a scan rate of 10 mV/s and a pulse amplitude of 50 mV. For the EIS experimentations, an alternating current was applied with a frequency range of 0.150 kHz. Nyquist plots were registered at a polarization potential of 0.01 V. Based on these curves, the electrode’s charge-transfer resistance (Rct) was defined after a correct fitting with the simple Randles equivalent circuit using the implemented EIS spectrum analyzer software. To obtain meaningful information on the modifications of the Au-SPE surface, the impedance data were properly fitted by utilizing the suitable Randles equivalent circuit (Choi et al., 2020).
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FIGURE 16.2 Photograph of the electrochemical measurement setup.
16.2.6 E-nose setup and measurement In this study, the experimental device used for the analysis of the same wastewater samples is the electronic nose (e-nose) which can be divided into three main parts (Amari et al., 2009) (Fig. 16.3): G G
G
The sampling system of the odorous substance to be analyzed. A box designed to house the measuring cell, which contains the matrix of sensors sensitive to odorant compounds, temperature and humidity sensors, a microcontroller, an LCD, and an air pump. A laptop computer equipped with software for data preprocessing, classification, and identifying odors.
This e-nose system was equipped with five metal oxide Taguchi gas sensors: TGS 815 (CH4), TGS 821 (H2), TGS 822 (alcohols, xylene, and toluene), TGS 824 (NH3), TGS 825 (H2S), and TGS 842 (methane) (Haddi et al., 2011) obtained from Figaro Engineering, Inc. (Osaka, Japan). It equally contains a temperature sensor (LM335Z) and a relative humidity sensor (HIH400001) from National Semiconductor (Santa Clara, CA, USA) for constantly monitoring the inner sensor chamber temperature and relative humidity. The TGS 8xx sensor requires two voltage inputs to operate a 5 V heater voltage (VH) to stabilize the sensing circuit inside the sensor and a 10 V circuit voltage (VC) for measuring the sensor output. This system qualitatively investigated the five wastewater samples spiked with different TCS concentrations (0.1, 1, 10, 100, and 1000 pg/mL). Before starting the measurements, a 20 mL sample of water was placed in a 50 mL Erlenmeyer flask and kept at a temperature of 32 6 0.5 C. Then the effluent transfer takes place: the collected volatile compounds are transferred to the sensor matrix. Pure nitrogen gas with 60 mL/min flow rate is used as carrier
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FIGURE 16.3 Electronic nose system designed for triclosan analysis.
gas from the headspace to the measuring cell. The operational system comprises mainly of three parts: the sensor chamber, the sampling system, and the data acquisition system.
16.3 Results and discussion 16.3.1 Morphological characterization of the fabricated sensors 16.3.1.1 TCS-MIP sensor The AFM technique was applied to monitor the morphological features of the Au-SPE, MIP surface before and after the removal of TCS. The AFM-3D morphological image of bare Au-SPE, presented in Fig. 16.4, discloses a rugged surface structure with a root mean square (Rq) roughness of 0.69 μm, achieved in scan areas of 10 3 10 μm 2. It should be mentioned here that this surface is formed by small grain structures. After the polymer was assembled on the bare Au-SPE surface, the Rq of the coating decreased to 0.21 μm (Fig. 16.4), unveiling a more homogeneous surface with a surface morphology distinctly different from that of the bare Au-SPE. This type of grain superstructure is typically encountered in polymeric layers. After extraction, the AFM-3D image of the MIP sensor revealed a different surface morphology, but it maintained a homogeneous shape (Fig. 16.4), even though the roughness had risen to 0.84 μm. These morphological variations of both surfaces confirm the formation of free imprinted sites, and hence the successful elimination of the TCS molecules. 16.3.1.2 SLS-MIP sensor To examine the morphology of the composition structure of the MIP sensor during its elaboration process, two techniques were employed, namely FTIR.
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FIGURE 16.4 AFM-3D morphology of (A) Bare Au-SPE, (B) and Au-SPE after polymerization, and (C) extraction of TCS. AFM, Atomic force microscopy; Au-SPE, gold screen-printed electrode; TCS, triclosan.
The characterization was achieved for modified Au-SPE after the electropolymerization and after the extraction of SLS molecules. As for the method of FTIR, the Au-SPE electrodes were positioned in an FTIR spectrophotometer sample holder (HATR, ABB, MB3000). Infrared spectra (64 scans) were recorded at room temperature in the wavenumber range of 5004000/cm and a resolution of 4/cm. Simultaneously, Fig. 16.5 compiles the FTIR spectra of the MIP sensor elaboration. In this, the presence of SLS molecules is demonstrated by sulfate vibration bands S 5 O (993 and 1121/cm) and CH bands (3032 and 2920/cm). The presence and absence on the spectra (Fig. 16.5) of the sulfate functions belonging to SLS validate the legend of the SLS analytes and their removal from the MIP matrix. By employing the FTIR method, the obtained outcomes successfully validate SLS bounding and then the complete removal from the MIP mesh. In a nutshell, the findings of the morphology and chemical composition analyses obtained by FTIR and AFM are in a good concordance and suggest the correct modification of the electrode surface for each experimental step, thereby supporting the applicability of the MIP sensor.
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FIGURE 16.5 FTIR spectra of (A) Au-SPE after polymerization and (B) extraction step. FTIR, Fourier transform infrared spectroscopy; Au-SPE, gold screen-printed electrode.
16.3.2 Electrochemical characterization of the sensors’ fabrication stages 16.3.2.1 Electrochemical characterization: TCS-MIP sensor The outcomes of the electrochemical characterization of the different elaboration steps of the TCS-MIP sensor employing the two electrochemical techniques (CV and EIS) are presented in Fig. 16.6.
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FIGURE 16.6 Electrochemical characterization of the modification steps of the Au-SPE using 5 mM of [Fe(CN)6]32/42 in PBS (pH 5 7.4) by (A) CV and (B) EIS techniques. Au-SPE, Gold screen-printed electrode; EIS, electrochemical impedance spectroscopy.
The CV technique is commonly used to highlight changes in the surface of the gold electrodes. The CV voltammograms registered during the progressive manufacture of the MIP sensor are illustrated in the graph below. The deposition of PVC-COOH provided a barrier for electron transfer to occur at the electrode interface. This widening in peak separation after the PVC-COOH layer formation is caused by the repulsive interaction of anionic probe ([Fe(CN) 6 ] 32/42) and poly-anions (COO) at the surface interface. This finding provides evidence of the formation of a PVC-COOH film at the Au-SPE surface. After the deposition of TCS, the redox current peak becomes larger than that obtained with PVC-COOH. It is well documented that TCS analytes are electrochemically active (Fujioka et al., 2014). This can be attributed to a decrease in repulsive interactions on the resulting electrode. It is more likely that the mechanism is regulated by an increase in the concentration of TCS at the electrode membrane, improving the global conductivity of the electrode. Furthermore, after the polymer deposition, a dense polymer film coated the surface of the gold Au-SPE electrode. This MIP formation had a tendency to impede the charge transfer from the redox probe to the electrode posting a lower signal in terms of magnitude. The EIS technique can also be useful as an additional confirmatory tool for the chemical changes occurring on the surface of the Au-SPE. The obtained findings from the CV technique corroborate those registered by the EIS technique, which also supports the corresponding variations in the semicircle diameters (Rct).
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16.3.2.2 Electrochemical characterization: SLS-MIP sensor In the case of the SLS-MIP sensor, the performance of the electrode after each step of the sensor preparation was evaluated by using a PBS electrolyte solution containing 5 mM [Fe(CN)6]32/42. The following and corresponding results are provided in Fig. 16.7. CV was applied for characterization in a [Fe(CN)6]32/45 mM solution containing PBS. The obtained data are displayed in Fig. 16.7. As can be seen, the CV response of the MIP electrode is higher than that of the NIP, suggesting the SLS was effectively scavenged on the 2-ATP mesh, stopping the [Fe(CN)6]32/42 from diffusing through the polymer toward the Au-SPE surface. In fact, the lower current of the NIP electrode is likely caused by the absence of SLS molecules in the polymer matrix. Impedance spectroscopy was further employed to characterize the modified MIP and NIP electrodes. This figure depicts the corresponding Nyquist plots. The impedance measurements are in good accordance with the CV results as the diameter of the semicircles for the Nyquist plots is found to be in close correlation with the variations in peak current. The Nyquist representation is composed of a semicircle part related to the electron transfer and a small linear part linked to the diffusion process. To summarize, the impedance measurements were in close accordance with the findings of CV, as the diameters of the semicircles in the Nyquist diagrams correlated with the variations in the oxidation current levels after the different steps in the development of the sensor. In order to assess the suitability of the sensor for the detection of TCS in wastewater samples, the parameters influencing its behavior, namely polymerization, elution, and incubation times, were further optimized. After each
FIGURE 16.7 Electrochemical characterization of the MIP and NIP sensors using 5 mM of [Fe (CN)6]32/42 in PBS (pH 5 7.4) by (A) CV and (B) EIS. MIP, Molecularly imprinted polymer; NIP, nonimprinted; EIS, electrochemical impedance spectroscopy.
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deposition, a redox probe, typically [Fe(CN)6]32/42, was employed to investigate the charge transfer processes onto the electrode. Optimization of the polymerization sequence was achieved using different ratios and polymer incubation times (412 h). In order to check the density of the polymer, different concentration ratios of the complex (AAM 1 NNMBA) were tested for the best ratio by measuring the sensor responses. The concentrations used are quoted in Table 16.1. According to the outputs, the binding efficiency of the three MIPs for different ratios was distinguished. The best response was attained with MIP2, as attested by a current (15.3 μA) almost two times higher than the other MIPs. These findings support the conclusion that the concentrations of AAM and NNMBA are critical for binding the molecules, thus enhancing the effectiveness of the binding. Therefore, the ratio used in MIP2 was selected in this work. As the polymer solution was dropped onto the Au-SPE, a chemical reaction was produced on the electrode, quantified by [Fe(CN)6]32/42. The current signals generated by CV are displayed in Fig. 16.8. The highest [Fe (CN)6]32/42 current peak, corresponding to polymer deposition, was obtained between 5 and 6 h of polymerization. Above these values, a reduction in the response is observed. Therefore, the experimental polymerization time was selected to be 5 h. The evolution of the DPV responses for various elution times is illustrated in the graph. The [Fe(CN)6]32/42 current peak corresponding to the TCS deposit enhanced further with increasing elution times, achieving a maximum at 15 min, and staying almost unchanged after this time. This indicates that the elution of TCS was finished within 15 min. Therefore, the optimal elution time assumed was 15 min. The incubation time is also of great significance for the sensitivity of the MIP sensors. Fig. 16.8 depicts the peak current of the sensor responses to different TCS incubation times (1060 min). The findings indicate that the MIP sensor response to TCS rises with incubation times from 10 to 30 min, and is not significantly changed from 30 to 60 min. Thus, 30 min was considered to be the optimized incubation time.
TABLE 16.1 Composition and reaction conditions of prepared polymers. MIP
Functional monomer (AAM) (mg)
Cross-linker (NNMBA) (mg)
Current peak (μA)
MIP1
11
14
5.1 6 0.4
MIP2
11
71
15.3 6 1.4
MIP3
11
22
9.04 6 0.14
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FIGURE 16.8 The impact of analytical parameters on the MIP sensor response regarding the TCS detection. MIP, Molecularly imprinted polymer; TCS, triclosan.
16.3.3 Reproducibility, selectivity, and stability of the sensor 16.3.3.1 Analytical parameters: TCS-MIP sensor After preparation and throughout each stage of the detection process, the sensor was stored securely at 4 C in a refrigerator and all measurements were conducted at room temperature (25 C). During the MIP test, DPV and EIS techniques were applied to check small changes on the sensor surface. The obtained data is depicted in Fig. 16.9. Different concentrations of TCS were used, ranging from 0.1 pg/mL to 1 ng/mL; the cavities within the MIP matrix were then glued with TCS molecules. As TCS concentrations were raised, the current peaks in the voltammograms became higher by the DPV technique. With regard to the EIS technique, the Nyquist plots, reported in Fig. 16.9, proved that the Rct values, obtained from the diameter of the semicircle, diminished with elevating TCS concentrations. This implies that the electron transfer resistance at the electrode was linked to the TCS concentrations. In effect, the diameter of the semicircle decreased after that, evidencing the trapping of TCS molecules by the Au-SPE/PVC-COOH/MIP surface. The rise in electron transfer was likely caused by the increase in TCS molecules at the working electrode, involving a more conductive membrane. The findings from the DPV technique were congruent with those obtained from EIS technique. An expected increase in peak current (DPV) and a concomitant decrease in Rct (EIS) can be regarded as proportional to the increase in TCS concentrations.
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FIGURE 16.9 MIP sensor responses at different TCS concentrations using 5 mM of [Fe (CN)6]32/42 in PBS (pH 5 7.4) by (A) DPV and (B) EIS. MIP, Molecularly imprinted polymer; TCS, triclosan; EIS, electrochemical impedance spectroscopy.
It is well documented that the selectivity characteristic is an identified significant factor for the implementation of molecularly imprinted sensors in real sample analysis. To this end, MIPs need to have a specific selectivity toward the target analytes. Therefore, the specificity of the sensor developed for TCS was tested and evaluated against some of the potentially interfering molecules, namely 2,4,6-trichlorophenol and catechol (Liu et al., 2009). As depicted in Fig. 16.10, the sensor did not present significant responses to these analogs, exhibiting only limited changes in their current amplitude responses. In fact, the slopes of the interfering were found significantly smaller than those of TCS, stating that the proposed MIP sensor exhibited notable selectivity to TCS.
16.3.3.2 Analytical parameters: SLS-MIP sensor In order to verify the absorption process for varying concentrations of SLS, DPV and EIS techniques were used. Fig. 16.11 illustrates the DPV responses of the MIP sensor for the detection of SLS over a concentration range of 0.11 ng/mL. Throughout the procedure, [Fe(CN)6]32/42 was mediated between the printed electrode and the substrate solutions. As can be observed, the redox current peaks diminish with the increase in SLS concentrations, which can be assigned to the gradual heavy film spanning the Au-SPE surface, confirming the attachment of SLS molecules that impedes the electron transport of the redox probe [Fe(CN)6]32/42. Regarding Fig. 16.11, Nyquist plots according to the different SLS concentrations are plotted.
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FIGURE 16.10 Responses of the MIP sensor toward interfering species. MIP, Molecularly imprinted polymer.
FIGURE 16.11 (A) Differential pulse voltammograms and (B) Nyquist plots in the synthetic detection by MIP sensor. MIP, Molecularly imprinted polymer.
In order to examine the selectivity feature of the MIP electrochemical sensor for the quantification of SLS analyte, the interference of some analogs with similar molecular structures, namely Tween 80, urea, and EDTA, was
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considered (Hao, Lei, Bing, et al., 2014). As depicted in Fig. 16.12, the monitored current responses of the MIP sensor with respect to SLS were much greater than those of the other analogs, pointing to more efficient adsorption and binding capacity for the template molecules. These findings signify that the engineered MIP sensor could effectively prevent interference, which renders it more relevant for the specific detection of SLS. In summary, it can be concluded that the interfering analytes employed did not interfere and that the MIP sensor was selective to SLS. In parallel, the reproducibility and stability of the MIP sensor were also examined. The reproducibility is achieved by using six electrodes manufactured under the same conditions employed to determine the SLS contents. The DPV technique results indicate a relative standard deviation (RSD) value of 4.9%, which connotes the correct reproducibility of the sensor. In addition, the operational stability of the designed sensor was verified to check the capability of the MIP sensor to sustain its initial response. The sensor was stored at 4 C in a refrigerator in PBS solution (pH 5 7.2) throughout the stability test. As such, the sensor’s response to SLS is verified for 3 months at 4 C and it retained 92.56% of the initial response with no noticeable worsening in its analytical behavior. This signifies that the proposed sensor has reasonable stability.
16.3.4 Practical application 16.3.4.1 TCS detection by MIP in wastewater In the real-world detection step, 30 μL of the wastewater sample was applied to the working electrode of the MIP sensor. A 30 min incubation ensued to
FIGURE 16.12 MIP sensor outcomes for interfering species. MIP, Molecularly imprinted polymer.
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ensure good retention of the TCS molecules. Measurements were performed three times to take the average value of the TCS concentration. The designed MIP sensor was used to determine the TCS in five wastewater samples collected in the city of Meknes and its surroundings (Morocco): Ain Karma, Bab Bouamair, Kasba, Toulal, and the Quinta do Conde WWTP (Portugal). The TCS levels of these samples were determined by means of both techniques (DPV and EIS). The obtained outcomes are outlined in Table 16.2. Based on the DPV and EIS techniques, the TCS content measurements of these samples yielded good results. These findings indicate a satisfying accuracy and precision of the suggested MIP sensor at an acceptable RSD of less than 12%. An RSD of the equivalent order of magnitude was also obtained using spectrophotometric measurements. Consequently, the TCS contents in environmental water samples obtained by the MIP sensor developed by DPV and EIS measurements were compared and found to agree with those obtained by spectrophotometry as a reference method.
16.3.4.2 TCS detection by e-nose in wastewater Wastewater samples enriched with TCS were used for qualitative analysis using the e-nose system. Fig. 16.13 shows typical responses generated by the TGS 842 sensor in the presence of different wastewater samples (enriched and nonenriched). It can be seen that the sensor response changes characteristically depending on the sample. This may be due to the difference between the presented VOCs. Radar plots of the wastewater enriched with TCS expressed as the normalized values deduced from the six gas sensors are depicted in Fig. 16.14. We observed that the shape of the radial plots varies with the change in TCS in the sample. The PLS model was created using all the data without separating them into training or validation matrices (Saeed et al., 2021). The correlation is achieved with R 5 0.98 between the concentrations predicted by the e-nose and the actual concentrations by the MIP sensor, as depicted in Fig. 16.15. This enables us to assert that the developed e-nose is indeed an accurate predictor of TCS concentrations in wastewater samples. Based on these findings, the capability of the MIP sensor to evaluate the TCS content of the wastewater is the most outstanding achievement. 16.3.4.3 SLS detection by MIP in cosmetic products The freshly designed MIP sensor was evaluated to detect SLS in different cosmetics samples to prove its suitability for practical applications. The obtained outcomes are presented in Table 16.3. The RSD values achieved are good percentages between 0.03% and 7.38%. For a
TABLE 16.2 TCS (triclosan) determination in wastewater samples. Wastewater samples
MIP sensor
Spectrophotometer
DPV (pg/ mL)
RSD (%) (n 5 3)
EIS (pg/ mL)
RSD (%) (n 5 3)
Spectro (pg/ mL)
RSD (%) (n 5 3)
Ain Karma/Morocco
1.76 6 0.42
3
1.6 6 1.2
8
2.88 6 0.33
5
Bab Bouamair/Morocco
4.9 6 0.3
4
4.8 6 1.2
3
5.2 6 0.3
3
Kasba/Morocco
13.9 6 2.0
7
13.6 6 1.0
7
12.9 6 0.2
3
Toulal/Morocco
4.6 6 1.1
2
4.6 6 0.3
5
5.3 6 1.1
5
Quinta do Conde WWTP/ Portugal
4.8 6 0.2
7
2.2 6 0.2
12
3.4 6 0.3
2
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FIGURE 16.13 Electrical conductance changes in the presence of wastewater samples using the TGS 842 sensor.
FIGURE 16.14 Radar plots of the e-nose responses toward the six sensors (Gs as a feature).
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FIGURE 16.15 PLS prediction model of TCS content in wastewater samples using MIP sensor versus e-nose. MIP, Molecularly imprinted polymer; TCS, triclosan.
TABLE 16.3 Determination of SLS (sodium lauryl sulfate) in cosmetic products samples. Cosmetic product samples
MIP sensor
Spectrophotometer
DPV (103 ng/ mL)
RSD (%) (n 5 3)
EIS (103 ng/ mL)
RSD (%) (n 5 3)
Spectro (103 ng/ mL)
RSD (%) (n 5 3)
Shampoo 1
5.25
0.10
5.21
0.09
5.04
0.30
Shampoo 2
0.86
0.95
0.86
0.55
0.83
0.60
Toothpaste
77.55
0.03
77.53
0.09
73.26
3.85
Shower gel
2.65
5.38
2.31
7.38
2.54
8.20
confident assessment of SLS in cosmetic product samples, the obtained results of the MIP sensor are also checked against the results of the spectrophotometric method. As demonstrated in this table, the results
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FIGURE 16.16 PLS prediction model of SLS content in cosmetic samples using MIP sensor versus spectrophotometer. PLS, Partial least squares; SLS, sodium lauryl sulfate; MIP, molecularly imprinted polymer.
registered by both methods are in good accordance. Considering these findings, the global results mainly prove that the proposed approach for SLS determinations based on the electrochemical MIP sensor is precise, responsive, coherent, and trustworthy. A well-recognized statistical method, referred to as PLS, was applied to scale this study to predict a correlation between the designed MIP sensor and the spectrophotometer. The ultimate objective was to examine whether the MIP sensor could predict SLS levels in cosmetic samples. To construct suitable models, a latent variable and a leave-one-out cross-validation technique were employed. Fig. 16.16 displays the PLS-predicted SLS contents vs. actual SLS values for cosmetic samples. Calibration curves with respect to correct linearity were achieved with satisfying regression coefficients of 0.99. Because of the good correlation between the MIP sensor technique and the spectrophotometer, the designed MIP sensor could be applied as a rapid and alternative method to determine SLS content in cosmetic samples.
16.4 Conclusion The development of materials with biomimetic properties has been one of the key challenges for many researchers over the past decades. The evolution of molecular imprinting technology has given a considerable boost. Indeed,
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since their invention, imprinted materials have been used as elements capable of mimicking the ability of biological systems to recognize specific ligands. Because of their high sensitivity, selectivity, and stability, printed materials have been successfully used in various fields. This technology has been harnessed to develop receptor-based biomimetic sensors to quantify the target analytes, notably TCS and SLS. In the first part of this research study, the development of an electrochemical sensor based on a MIP, assembled on an Au-SPE, dedicated to detecting TCS in environmental water sources, is reported. To realize this objective, an acrylamide/ bisacrylamide solution was polymerized after bonding the TCS to the PVCCOOH layer on the Au-SPE. The fabrication of the sensing device and its retention capabilities were characterized by CV, DPV, EIS, AFM, and FTIR. Negligible responses were obtained during the unprinted polymer (NIP) test as a control experiment. The sensor efficiently detected TCS by avoiding interference from structurally similar substances such as 2,4,6-trichlorophenol and catechol. Under optimal conditions, the sensor responses were found to be logarithmic in the concentration range of 0.11000 pg/mL. Indeed, compared to the reported work, this sensor has a lower limit of detection and limit of quantification of 0.23 and 0.78 pg/mL, respectively. The developed sensor was effectively applied to wastewater samples to detect TCS and showed satisfactory performance. Simultaneously, the wastewater samples were analyzed by the e-nose device in conjunction with the developed MIP sensor and showed good response and correlation with (R 5 0.98). The second part of this study presents a new MIP sensor for the electrochemical detection of SLS. The MIP-based Au-SPE was prepared by electrochemical polymerization of 2-ATP in the presence of SLS as the target molecule. As a control, a NIP sensor was also constructed in an identical manner omitting the SLS template. In addition, the selectivity of the proposed MIP sensor was investigated using analogs such as ethylene diamine tetraacetic acid (EDTA), Tween 80, and urea, revealing a satisfactory selectivity toward SLS. The electrochemical characterization of the developed sensor was performed using CV, DPV, and EIS techniques. The morphology of the MIP sensor was studied by AFM and FTIR. Some experimental parameters, such as the number of cycles for electropolymerization, incubation time of 2-ATP, and incubation time of elution and SLS, were optimized to improve the sensor’s performance. Under optimal conditions, the electrochemical sensor has a logarithmic working range of 0.11000 pg/mL, and a detection limit of 0.18 pg/mL. In addition, compared to the reported work, the sensor has remarkable properties, such as higher sensitivity and selectivity, good reproducibility, wider logarithmic range, lower detection limit, and long-term stability. As a real-world application, the developed MIP sensor has been well used to determine SLS content in environmental waters and cosmetic samples. A UVVis spectrophotometer was used as a reference method. A PLS-R technique was used to investigate the correlation between the spectrophotometer and the MIP sensor technology with a satisfactory regression coefficient (R 5 0.99).
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Acknowledgments The authors acknowledge the University Moulay Ismail of Meknes, as well as all the national and international donors who made it possible to finance this work, notably UMI through the project “Appui a` la recherche” and the European Commission for Framework Program for Research and Innovation through the project “TROPSENSE.”
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supplementary data and supplementary material related to this article can be found in the online version.
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Liu, Y., Song, Q. J., & Wang, L. (2009). Development and characterization of an Amperometric sensor for triclosan detection based on electropolymerized molecularly imprinted polymer. Microchemical Journal, 91, 222226. Menon, S., Jesny, S., & Kumar, K. G. (2018). A voltammetric sensor for acetaminophen based on electropolymerized-molecularly imprinted poly (o-aminophenol) modified gold electrode. Talanta, 179, 668675. Motia, S., Bouchikhi, B., & El Bari, N. (2021a). An electrochemical sensor based on molecularly imprinted polymer conjointly with a voltammetric electronic tongue for quantitative diphenyl phosphate detection in urine samples from cosmetic product users. Sensors and Actuators B: Chemical, 332, 129449. Motia, S., Bouchikhi, B., & El Bari, N. (2021b). An electrochemical molecularly imprinted sensor based on chitosan capped with gold nanoparticles and its application for highly sensitive butylated hydroxyanisole analysis in foodstuff products. Talanta, 223, 121689. Nasraoui, S., Al-Hamry, A., Teixeira, P. R., Ameur, S., Paterno, L. G., Ali, M. B., & Kanoun, O. (2021). Electrochemical sensor for nitrite detection in water samples using flexible laserinduced graphene electrodes functionalized by CNT decorated by Au nanoparticles. Journal of Electroanalytical Chemistry, 880, 114893. Nguyen, H. T., Yoon, Y., Ngo, H. H., & Jang, A. (2021). The application of microalgae in removing organic micropollutants in wastewater. Critical Reviews in Environmental Science and Technology, 51, 11871220. Niu, C., Dong, L., Gao, Y., Zhang, Y., Wang, X., & Wang, J. (2021). Quantitative analysis of RNA by HPLC and evaluation of RT-dPCR for coronavirus RNA quantification. Talanta, 228, 122227. Novodchuk, I., Bajcsy, M., & Yavuz, M. (2021). Graphene-based field effect transistor biosensors for breast cancer detection: A review on biosensing strategies. Carbon, 172, 431453. Park, S. Y., Kim, Y., Kim, T., Eom, T. H., Kim, S. Y., & Jang, H. W. (2019). Chemoresistive materials for electronic nose: Progress, perspectives, and challenges. InfoMat, 1, 289316. Qian, L., Durairaj, S., Prins, S., & Chen, A. (2021). Nanomaterial-based electrochemical sensors and biosensors for the detection of pharmaceutical compounds. Biosensors and Bioelectronics, 175, 112836. ¨ pik, A., & Syritski, V. (2021). Development Raziq, A., Kidakova, A., Boroznjak, R., Reut, J., O of a portable MIP-based electrochemical sensor for detection of SARS-CoV-2 antigen. Biosensors and Bioelectronics, 178, 113029. Saeed, F., Khan, M. A., Sharif, M., Mittal, M., Goyal, L. M., & Roy, S. (2021). Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification. Applied Soft Computing, 103, 107164. Saljooqi, A., Shamspur, T., & Mostafavi, A. (2020). A sensitive electrochemical sensor based on graphene oxide nanosheets decorated by Fe3O4@ Au nanostructure stabilized on polypyrrole for efficient triclosan sensing. Electroanalysis, 32, 12971303. Sobiech, M., Luli´nski, P., Wieczorek, P. P., & Mar´c, M. (2021). Quantum and carbon dots conjugated molecularly imprinted polymers as advanced nanomaterials for selective recognition of analytes in environmental, food and biomedical applications. TrAC Trends in Analytical Chemistry, 142, 116306. Sullivan, M. V., Dennison, S. R., Hayes, J. M., & Reddy, S. M. (2021). Evaluation of acrylamide-based molecularly imprinted polymer thin-sheets for specific protein capture-a myoglobin model. Biomedical Physics & Engineering Express, 7, 045025. Suriya, P., Naveenkumar, K., Sangeetha, S. P., Divahar, R., & Vijaykumar, S. (2021). A Study on detection Of E-coli bacteria in drinking water using e-nose system. IOP Conference Series: Materials Science and Engineering, 1119, 012009.
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Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.
A Acridines, 209210 Acrylamide (AAM), 321323, 353354 Acrylamido methylpropane sulfonic acid, 320 Acute avian leukemia retrovirus, 191192 Acute myeloid leukemia, 201203 Adenoviruses, 82 Adult stem cells (ASCs), 101 Adult T-cell leukemia/lymphoma (ATLL), 233 Aerobic systems, 40 Age, 7 “Aggregate” detection, 27 AIDS-associated Kaposi’s sarcoma, 236 Alcohol, 8 Aldehydes, 55 Alkaline phosphatase (ALP), 27 Alpha-fetoprotein (AFP), 229 Aluminum, 329 2-amino-2-hydroxymethyl-1,3-propanediol, 322 2-aminothiophenol (2-ATP), 353355 Ammonium persulfate (APS), 321322, 353354 Amplicor polymerase chain reaction, 27 Anal cancer, 239 Antagonizing transcription factor (AATF), 152154 Anti-HPV vaccines, 298 Antigen-explicit T cells (AST), 6970 Applicability domain (AD), 276277 Arsenic (Ar), 41 Artificial Intelligence (AI), 7273 approaches, 263 Artificial neural network (ANN), 273274 Assisted Model Building with Energy Refinement (AMBER), 257259 AMBER-GS, 257259 AMBER03, 257259 AMBER94, 257259
AMBER-96, 257259 AMBER99SB, 257259 AMBER99φ, 257259 Atomic force microscopy (AFM), 325, 357 Au-SPE/MIP, 323324 AURKA, 206 Aviadenovirus, 82, 88
B Bacterial vector vaccines, 308 Basic helixloophelix (bHLH), 198 Basic helixloophelix zipper (bHLHZip), 197198 Bio-indicator, 51 Bio/electrochemical sensors, 353 Bioaccumulation, 5253 Bioconcentration factor (BCF), 52 Biofluids, 318319 Bioinformatics, 109110 approach, 232 resources, 135138 Biological behavior, 271 Biological database, 255256 Biomarkers, 221224 in bloodstream, 319320 gas sensors in oncology or virology as tools for detection of, 321 urine and saliva as noninvasive sources of, 318319 Biomimetic sensor, 353 Biosensors, 352353 Biotechnology, 151152 Bis-acrylamide, 322323 Bivalve molluscs, 40, 4954 lamellibranchs, 5051 Mytilidae as bioindicators, 5152 BLAST tool, 256 Bloodstream, biomarkers in, 319320 Blue mussel (Mytilus edulis), 51 Breast cancer, 18
379
380
Index
Breast cancer (Continued) anatomy, 23 endogenous hormonal factors, 5 early age of first menstruation, 5 late menopause, 5 EpsteinBarr virus, 910 exogenous hormonal factors, 6 food needs and sources, 1213 generality, 2 genetic, family, demographic, and health factors, 68 human papilloma viruses, 9 lifestyle and nutrition factors, 8 mechanism of action, 1415 mouse mammary tumor virus like, 89 reproductive factors, 6 risk factors, 58 storage sites, 13 symptoms, 34 types, 4, 4t viral etiology, 8 vitamin D, 1012 and breast cancer prevention, 1516 new therapy for breast cancers prevention, 14 receptors, 1314 Breastfeeding, 6 Breath print, 353 Burkitt’s lymphoma, 224225
C c-MYC, 191192, 195196 G-quadruplexes and expression of, 207210 Cadmium (Cd), 41, 4446 behavior in aquatic environments, 45 carcinogenic effect, 4546 properties, 44 sources, 4445 toxicity, 45 Cancer, 5455, 8182, 352 biomarkers Epstein Bar virus-associated, 225226 hepatitis B virus-associated, 229230 hepatitis C virus-associated, 230232, 232t HHV-8-associated, 236238 HTLV-1-associated, 234235 human papillomaviruses-associated, 239243 cancer-causing viruses, 8182, 151 Epstein-Barr virus-associated, 224225
hepatitis B virus-and hepatitis C virusassociated, 228229 HHV-8-associated, 236 HTLV-1-associated, 233 human papillomaviruses-associated, 239 immunoediting hypothesis, 6364 MYC in normal tissues and, 193194 mechanisms of MYC activation in cancer, 194t Cannabinoids, 320 Carbon (C), 329 Carboxylic PVC (PVC-COOH), 353354 Carcinogenesis, 25, 290 Catechol, 353354 CD40, 259 Cell cell-based vaccines, 181182 homologs of v-MYC, 191192 transformation mechanisms, 291292 Cellular vaccines, 311 Cervarix, 155156 Cervical cancers (CCs), 23, 239, 257259 Cervix, 299 Checkpoint kinase 1 (CHK1), 207 ChEMBL, 269 Chemical conjugation, 174 Chemical database, 271 Chemistry at HARvard Macromolecular Mechanics (CHARMM), 257259 Chemotherapy, 6263, 98 integration of oncolytic viral therapy in, 71 Chromatography, 124 Chromium (Cr), 41 Chronic hepatitis, 230231 Chronic hepatitis B (CHB), 173 Chronic sarcoma, 159 Circuit voltage (VC), 358 Classic Kaposi’s sarcoma, 236 Classification-based QSAR approaches, 274275 Cobalt (Co), 42 “Cocktail” detection, 27 Comparative genomics, 111112 Comparative molecular field analysis (CoMFA), 260 Comparative molecular similarity indices analysis (CoMSIA), 260 Computational process, 126127 Computer-aided drug discovery, 259260 Computer-based drug design (CADD), 259260 Copper (Cu), 41
Index Core protein, 228 Cosmetic products, MIP in, 369373 Cosmetic residues, 349350 Curation of chemical, 269270 Cutaneous squamous cell carcinoma (CSCs), 282 Cyclic voltammetry (CV), 324325, 356357 CV technique, 362 Cyclin-dependent kinase inhibitor 2A (CDKN2A), 26 Cyclin-dependent kinases, 204205 Cyclophilin A (cyp A), 8889 Cysteine (Cys), 5354 Cytokine-instigated executioner cells (CIK), 6970
D Database building, 270 Dendritic cells (DCs), 173174, 309 Deoxyribonucleic acid (DNA), 23 hybridization method, 238239 methylation, 154155 microarray genotyping, 33 oncovirus, 147148 tumor viruses, 8385, 150151 vaccines, 179, 309 Deubiquitinating enzymes, 205206 Diagnostic biomarkers, 221224 2,6-diamido anthraquinones, 209210 Differential pulse voltammetry (DPV), 326327, 357, 365 1,4dioxane, 353354 Distilled water (DW), 354 Domestic wastewater, 351 Drugs, 6465
E E1 gene, 285 E2 gene, 286 E2-binding sites (E2BS), 154155 E3 gene, 287 E4 gene, 286 E5 gene, 286 E6 gene, 286287 E7 gene, 287 E8 gene, 287 Early control region (E control region), 25 Early region, 285287 EBV-associated gastric cancers (EBVaGCs), 225
381
EBV-associated Hodgkin’s lymphoma (EBVaHL), 226 EBV-associated nasopharyngeal carcinoma biomarkers (EBVaNC biomarkers), 226 Electrochemical detection systems, 350 Electrochemical impedance spectroscopy (EIS), 326327, 357 creatinine molecularly imprinted polymer sensor, 333334 responses, 333336 Electrochemical sensor devices, 320321 for environmental residues, 351352 experimental, 353359 chemicals and reagents, 353354 E-nose setup and measurement, 358359 electrochemical measurements, 357 polymer synthesis, 354355 surface morphological analysis, 357 fabrication steps, 323325, 355357 creatinine molecularly imprinted polymer sensor, 323324 SLS-MIP sensor, 356357 TCS-MIP sensor, 355356 practical application, 368373 SLS detection by MIP in cosmetic products, 369373 TCS detection by e-nose in wastewater, 369 TCS detection by MIP in wastewater, 368369 results, 359373 characterization of sensors fabrication stages, 361364 morphological characterization of fabricated sensors, 359360 reproducibility, selectivity, and stability of sensor, 365368 Electrodes, 320 Electronic nose technology (e-nose technology), 353 setup and measurement, 358359 in wastewater, 369 Electrons, 325326 Elimination, 54 Embryonic stem cells (ESCs), 101 Emerging contaminants (EC), 351 Endemic Kaposi’s sarcoma, 236 Energy-dispersive X-ray spectroscopy (EDS), 327329 Envelope glycoproteins (Env), 8385
382
Index
Environmental analysis, 353 Environmental residues, electrochemical sensors for, 351352 Enzyme-linked immunosorbent assay (ELISA), 124 Epidermal growth factor receptor (EGF receptor), 25 Epidermodysplasia verruciformis (EV), 282 Epigallocatechin-3-gallate (EGCG), 259260 Epigenomics, 113 Epitestosterone, 320 EpsteinBarr virus (EBV), 810, 63, 82, 8687, 112113, 147148, 221, 224226, 253254, 259 EBV-associated cancer biomarkers, 227t EBV-positive Burkitt’s lymphoma, 226 Epstein Bar virus-associated cancer biomarkers, 225226 Epstein-Barr virus-associated cancers, 224225 infection, 224 life cycle, 224 statistical analysis of oncovirus, 157158 Essential elements, 42 Estrogen receptor (ER), 100 Ethanol, 353354 Ethylenediaminetetraacetic acid (EDTA), 353354 Eulamellibranchs, 50 External validation, 276
F Fabricated sensors morphological characterization of, 327333, 359360 SLS-MIP sensor, 359360 TCS-MIP sensor, 359 FastA tool, 256 Fibroblast cells, 236237 Filbranches, 50 Finite volume technique (FV technique), 157 Fluorescence polarization (FP), 201203 Fold recognition, 256257 Food and Drug Administration (FDA), 7273 Food needs and sources, 1213 Fourier transform infrared spectroscopy (FTIR), 357 Free radicals, 40 Functional genomics, 111112 Functional proteomics, 123124
G G-quadruplexes and expression of c-MYC, 207210 G-tetrad, 208 G4 ligands database (G4LDB), 209 Gardasil, 155156, 299 Gardasil 9, 155156, 299 Gas chromatography, 350 Gas sensors in oncology or virology as tools for detection of biomarkers, 321 Gastric cancer, 224225 Gene expression programming (GEP), 274 Generative topographic mapping (GTM), 274 Genes, 63 prediction, 256 Genetic fusion, 174 Genetic mutations, 6 Genetic programming (GP), 274 Genetically modified oncolytic virus, 70 Genetics of virus, 9496 Genome-scale biomolecular networks, 241 Genomics, 110113 of oncoviruses and bioinformatics, 254255 sequencing of oncovirus organisms, 112t Genotype, 148149 Genotyping by sequencing, 3033 using Luminex technology, 3334 Glucose, 320 glucose-regulated proteins, 229 molecularly imprinted polymer sensor, 324325, 334336 sensor, 337338 Glutamine metabolism, 207 Glypican 3 (GPC-3), 229 Gold screen-printed electrodes (Au-SPE), 323324, 354 GROningen Machine for Chemical Simulations (GROMACS), 257259 Group 1 carcinogens, 221
H Hash-coding algorithm, 271 HBV-associated hepatocellular carcinoma (HBVaHCC), 229 HCV-associated hepatocellular carcinoma (HCVaHCC), 230231 Heat shock proteins (HSPs), 229 Helixloophelix leucine zipper (bHLHZ), 197198 Hepatitis B surface S antigen (HBsAg), 174
Index Hepatitis B virus (HBV), 82, 87, 112113, 147148, 173, 221, 226232, 253254 on cancer progression, 162 cell-based vaccines, 181182 DNA-based vaccines, 179 efficacy of therapeutic vaccines, 183 harmlessness, 183 HBV-induced cancer, 226228 hepatitis B virus-associated cancer biomarkers, 229230, 230t and hepatitis C virus-associated cancers, 228229 immunization coverage, 183184 mRNA-based vaccines, 180 nanovaccines, 182 proteins/peptides vaccines, 181 statistical analysis of oncovirus, 158 therapeutic vaccines, 178 virus-like particle-based hepatitis B vaccines, 173178 Hepatitis C virus (HCV), 82, 112113, 147149, 183184, 221, 226232, 253255 on cancer progression, 162 and hepatitis B virus-associated cancers, 228229 hepatitis C virus-associated cancer biomarkers, 230232, 232t statistical analysis of oncovirus, 158159 Hepatocellular carcinoma (HCC), 173, 228229 HER2/new gene, 9496 Herpes simplex virus, 9091 Hi-value, 277 Hidden Markov models (HMMs), 157 HMMER3 HMM technique, 157 High-end sequencing techniques, 256 High-grade squamous intraepithelial lesion (HSIL), 289290 High-risk oncogenic HPV (HR-HPV), 23 High-throughput methods, 112113 Hodgkin’s lymphoma, 224225 Homology modeling, 256257 Hormonal therapy, 100 Hormone replacement therapy (HRT), 6 HSV1716, 6263 Human aortic smooth muscle cells (AoSM), 236237 Human genome, 209 Human herpesvirus 4. See EpsteinBarr virus (EBV)
383
Human herpesvirus-8 (HHV-8), 82, 112113, 147148, 221, 235238, 253254 HHV-8-associated cancer biomarkers, 236238, 238t HHV-8-associated cancers, 236 Human immunodeficiency virus (HIV), 82, 8889, 151 statistical analysis of oncovirus, 159160 Human MYC gene, 197198 Human Na 1 /I-symporter (hNIS), 67 Human oncoviruses, 149 Human papillomavirus (HPV), 9, 23, 82, 8990, 112113, 147148, 238243, 253254, 257259, 281, 297 bacterial vector vaccines, 308 on cancer progression, 161162 cell transformation mechanisms, 291292 cellular vaccines, 311 classification, 282283 diagnosis of human papillomavirus viral genome, 2634 identification of human papillomavirus without genotyping, 2729 DNA vaccines, 309 E6/E7 mRNA and protein detection, 34 efficacy, 300301 epidemiology, 281282 etiopathogenesis, 2426 factors influencing vaccination coverage, 305 genome structure, 2425 genotyping, 3034 HPV 16, 257259, 281282 HPV 18, 257259, 281282 human papillomaviruses-associated cancers, 239 biomarkers, 239243, 242t immunization procedures and doses, 299300 infection evolution, 289290 L2-based human papillomavirus prophylactic vaccines, 303304 mechanism of human papillomavirus infection in cervix and carcinogenesis, 2526 molecular mechanisms of HPV-induced carcinogenesis, 290291 peptide-based vaccines, 310 protein vaccines, 310311 replication cycle, 288289 RNA-based vaccines, 309 safety and security, 302303
384
Index
Human papillomavirus (HPV) (Continued) statistical analysis of oncovirus, 158 structure, genomic organization, and viral proteins, 284288 therapeutic vaccines, 305311 transmission, 283284 horizontal transmission, 284 vertical transmission, 284 vaccine coverage, 304 vaccines prophylactic against, 298299 vaccinia virus, 308 viral vector vaccines, 308 Human T-cell lymphotropic virus-1 (HTLV1), 112113, 221, 233235, 253254 HTLV-1-associated cancer biomarkers, 234235, 235t HTLV-1-associated cancers, 233 statistical analysis of oncovirus, 160 Human T-cell lymphtropic virus, 147148 Human umbilical vein endothelial cells (HUVECs), 236237 Human urine, creatinine detection in, 339341 Hybrid Capture II (HC2), 27 Hybrid Capture III (HC3), 27 25-hydroxyvitamin D3 (25(OH) D3), 1112
I I-Tasser, 256257 Iatrogenic Kaposi’s sarcoma, 236 Immune inhibitor checkpoints, integration of oncolytic viral therapy with, 71 Immunization procedures and doses, 299300 Immunoediting, 63 Immunofluorescence assay (IFA), 152 Immunotherapy, 9698 Infectious disease, 181 Interferons (IFNs), 6465 Interleukin-6 (IL-6), 229 Interleukins, 240 Internal validation, 275276 International Agency for Research on Cancer (IARC), 221 Ion trap mass spectrometry, 350 Ion-selective electrodes, 350 Ionizing radiation, 7 Iron (Fe), 42
K K-means, 274275 Kaposi’s sarcoma (KS), 147148, 236 lesions, 236
Kaposi’s sarcoma herpesvirus (KSHV). See Human Herpesvirus-8 (HHV-8) Kaposi’s sarcoma-associated herpesvirus (KSHV), 147148 statistical analysis of oncovirus, 159 Kernel partial least squares (KPLS), 273 KSHV. See Human Herpesvirus-8 (HHV-8)
L L-MYC, 191192 L1 protein, 287 L2 protein, 287 L2-based human papillomavirus prophylactic vaccines, 303304 Lamellibranchs, 5051 food, 51 habitat, 50 metallic pollution bioindicators, 51 Laminine 5 (LN5), 288 Langmuir model, 320 Late region (L region), 25, 287288 Latency-associated nuclear antigen (LANA), 235236 Latent membrane proteins (LMPs), 224 LMP1, 225, 259 Lead (Pb), 4144 behavior in aquatic environments, 43 carcinogenic effect, 4344 sources, 43 toxicity, 43 Learning process, 271272 Least-squares fitting, 275276 Leporipoxvirus, 82, 9293 Leucine zipper (LZ), 198 Ligand-based drug design (LBDD), 259260 Liquid chromatography, 350 Liquid phase in situ hybridization, 2728 Liver cancer, 228229 Liver cirrhosis, 230231 Liver transplantation, 229 Local Lazy Regression (LLR), 274 Long control region (LCR), 24, 284285 Long terminal repeats (LTR), 191192 Low-and middle-income countries (LMIC), 304 Low-grade squamous intraepithelial lesion (LSIL), 289290 Luminex technology, genotyping using, 3334
M Machine Learning (ML), 7273
Index MALDI-TOF-MS, 124 Mammography density, 8 Manganese (Mn), 42 Manganese superoxide dismutase (MnSOD), 231 Mass spectroscopy (MS), 124, 350 Mastadenovirus, 82 Mercury (Mg), 41, 4648 behavior in aquatic environments, 48 carcinogenic effect, 48 property, 4647 sources, 47 toxicity, 4748 Merkel cell carcinoma (MCC), 254255 Merkel cell polyomavirus (MCPyV), 147148, 253255 statistical analysis of oncovirus, 160161 Merkel cells, 254255 Mesenchymal immature microorganisms (MSCs), 6970 Metabolic regulatory networks, 241 Metabolomics, 127135 metallomics, 130135 Metallic films, 350351 Metallomics, 130135 Metallothioneins (MTs), 5354 Methacrylic acid, 320 Methylmercury (MeHg), 4647 Micro RNAs (mi RNAs), 152154 Micropollutants, wastewater as sources of, 351 MicroRNAs (miRNAs), 195196 Mitogen-activated protein (MAP), 25 Mixed lineage leukemia, 204205 Modified vaccinia virus Ankara (MVA), 308 Molecular biology, 151152 Molecular descriptors, 270271 Molecular dynamics simulations (MD simulations), 257259 Molecular mechanisms of HPV-induced carcinogenesis, 290291 Molecularly imprinted polymer electrochemical measurements, 326327 experimental, 321327 chemicals and reagents, 319320 physicochemical characterization, 325326 real samples detection, 339343 creatinine detection in human urine, 339341 glucose detection in human saliva, 342343 results, 327343
385
morphological characterization of fabricated sensor, 327333 Molecularly imprinted polymer technology (MIP technology), 320 Molecularly imprinted polymers (MIPs), 351352 in cosmetic products, 369373 in wastewater, 368369 Molluscs, 49 Mouse mammary tumor virus like (MMTVlike), 89 mRNA-based vaccines, 180 mTOR serine/threonine kinase, 205 Multigas sensor systems (MOS), 353 Multiple linear regression analysis (MLR analysis), 267268, 272 Multivariate analysis, 271272 Mussels, 50 MYC biological role of MYC genes, 192193 boxes, 197198 expression, 205 gene inhibitors, 200201 MYC-driven cancers, 203204 MYCMax interaction, 198200 amino acid composition of c-MYCMax complex, 198t with small molecule inhibitors, 201203 oncogenes biological role of MYC genes, 192193 G-quadruplexes and expression of cMYC, 207210 indirect targeting of MYC, 203204 MYC as potential target for antitumor therapy, 200201 MYC in normal tissues and cancer, 193194 MYC signal transduction pathway, 195196 MYCMax interaction, 198200 structure of MYC, 197198 synthetic lethality with MYC, 206207 targeting MYC stability, 205206 targeting MYC transcription, 204205 targeting MYCMax interaction with small molecule inhibitors, 201203 targeting of MYC expression, 205 proteins, 192193 signal transduction pathway, 195196 transcription, 204205 MYC transactivation domain (MYC TAD), 197198
386
Index
Myelocytomatosis virus MC29, 191192 viruses, 191192 Myoglobin, 320 Mytilidae as bioindicators, 5152
N
N,N,N0 ,N0 -tetramethyl ethylenediamine (TEMED), 321322, 353354 N,N0 -methylene-bis-acrylamide (NNMBA), 321322 N,N0 -methylene-bisacrylamide (NNMBA), 353354 N-(3-dimethylaminopropyl)-EDC, 321324 N-(3-dimethylaminopropyl)-N0 ethylcarbodiimidehydrochloride (EDC), 321324, 353354 N-hydroxysuccinimide (NHS), 321322, 353354 N-MYC, 191192 N-terminal transactivation domain (NTD), 197198 NANnoscale Molecular Dynamics (NAMD), 257259 Nanotechnology, 352353 Nanovaccines, 182 Neoplasm, 233 Neopterin, 319 Neuroblast, 204205 Nickel (Ni), 41 Noncoding region (NCR), 284 Nonenzymatic electrochemical creatinine sensors, 320 Nonessential elements, 4248 Nonstructural protein, 228 Nuclear magnetic resonance spectroscopy (NMR), 122, 126, 199200
O Omics, 110141 bioinformatics resources, 135138 databases and tools, 139141 domains, 110f genomics, 111113 metabolomics, 127135 proteomics, 122127 transcriptomics, 113121 Oncogenic HPV, 24 Oncogenic viruses, 147148 Epstein-Barr virus, 224226 hepatitis B virus and hepatitis C virus, 226232 human herpesvirus-8, 235238
human oncogenic viruses, 222t human papillomavirus, 238243 human T-cell lymphotropic virus-1, 233235 Oncology, 103 applications in fields of, 352353 as tools for detection of biomarkers, 321 Oncolytic virotherapy, 6266, 163164 applications, 6671 diagnosis, 6768 genetically modified oncolytic virus, 70 integration of oncolytic viral therapy in chemotherapy, 71 integration of oncolytic viral therapy in radiotherapy, 7071 integration of oncolytic viral therapy with immune inhibitor checkpoints, 71 tumor targeted cell delivery by oncolytic virotherapy, 6970 cancer immunoediting hypothesis, 6364 future concerns, 73 integration of artificial intelligence or machine learning into cancer research, 7273 limitations, 7172 pharmacokinetics, 6466 Oncolytic virus (OV), 61 history, 6263 Oncoproteins, 285 Oncorine H101, 6263 Oncotherapy, 101103 future, 103104 Oncovirology, 253254 Oncovirus, 8182, 147148 cancer, 253254 and cancer progression, 161162 classification, 149151 general properties of, 63 genetics of virus, 9496 mechanism, 8385 molecular tools used for oncovirus detection, 151155 prevalence, 148149 statistical analysis of oncovirus, 157161 stem cell transplant therapy, 101103 types and mechanism, 8693 types of treatment, 96101 vaccines available for, 155157 Open reading frames (ORFs), 24, 284 Oral contraceptives, 6 Oropharyngeal cancer, 239 Oropharyngeal squamous cell carcinoma, 242243
Index Orthopoxvirus, 82, 93 Ovarian carcinoma, 201203 Overweight, 8 Oxidative stress, 5455 Oxygen (O), 329
P p53, 63 p53 and retinoblastoma protein (pRb), 25 Pancreatic ductal adenocarcinoma (PDAC), 201203 Papillomaviruses, 281 Parity and early age at first motherhood, 6 Partial least square (PLS), 272273, 369 Partial least squares regression (PLS-R), 341 Parvovirus, 82, 9192 B19, 9192 Peptide-based vaccines, 310 Personal care products (PCPs), 351 Pharmacokinetics of oncolytic viral therapy, 6466 Phosphate-buffered saline (PBS), 321322, 353354 Phyre2, 256257 PI3K/AKT/mTOR pathway, 205 Poly(ethylene-co-vinyl alcohol) (EVAL), 320 Polymer solution, 364 synthesis, 322323, 354355 Polymerase chain reaction (PCR), 27 amplification technique, 29 consensus, 29 Polyomavirus, 82, 90, 148 Polyvinyl carboxylic chloride (PVC-COOH), 321324 Polyvinyl chloride (PVC), 352 Post transcriptional regulatory networks, 241 Potassium ferricyanide ([Fe(CN)6]32), 321322, 353354 Potassium ferrocyanide ([Fe(CN)6]42), 321322, 353354 Predictive biomarkers, 221224 Principal component regression (PCR), 272 Principal components (PCs), 272 Prognostic biomarkers, 221224 Prophylactic HPV vaccine, 298 Prophylactic vaccine, 298299 Proteinprotein interactions (PPIs), 199 Proteins, 124 expression proteomics, 122123 proteinprotein interaction, 241 proteins/peptides vaccines, 181 vaccines, 310311
387
Proteomics, 122127 of oncoviruses and bioinformatics, 254255 techniques, 124126 Protobranchs, 50 PubChem, 269 Purvalanol B, 207 Putative G4-forming sequences (PQS), 209 Pyridostatin, 209210
Q Quantitative structureactivity relationship methodology (QSAR methodology), 267 applicability domain, 276277 artificial neural network, 273274 classification-based QSAR approaches, 274275 external validation, 276 generation, 275 handling and curation of chemical and biological data, 269270 internal validation, 275276 kernel partial least squares, 273 model application for prediction of compounds activity, 277 model examination and validation, 275 molecular descriptors, 270271 multiple linear regression analysis, 272 multivariate analysis, 271272 partial least square, 272273 principal component regression, 272 structures drawing and database building, 270 Quarfloxin CX-3543, 209210
R Radiation therapy, 99 Radiotherapy, integration of oncolytic viral therapy in, 7071 Rapid diagnostic tests (RDTs), 151152 Reactive oxygen species (ROS), 54 Relative standard deviation (RSD), 339, 368 Retinoblastoma (Rb), 63 Retinoblastoma tumor suppressor protein (pRb), 236237 Retinoid receptor X (RXR), 1415 Retroviruses, 8385, 233 Reverse hybridization, 30 Rhadinovirus, 82 RNA oncovirus, 147148 RNA-based vaccines, 309
388
Index
RNA (Continued) tumor viruses, 8385, 151 Robetta, 256257 Root mean square (RMS), 329333 Root mean square error (RMSE), 275276 Rous sarcoma virus (RSV), 8385, 151, 221
S Safety, 302303 Saliva glucose detection in, 342343 infiltrate acini and eventually be secreted into, 319320 as noninvasive sources of biomarkers, 318319 Salivary amylase, 319 Salivary C-reactive protein, 319 Salivary immunoglobulins (sIgA), 319 Scanning electron microscopy coupled with energy-dispersive spectroscopy (SEMEDS), 325 Screening techniques, 321 Sensor electrochemical characterization of sensors fabrication stages, 361364 reproducibility, selectivity, and stability of, 365368 SLS-MIP sensor, 366368 TCS-MIP sensor, 365366 SLS-MIP sensor, 363364 composition and reaction conditions of polymers, 364t TCS-MIP sensor, 361362 Septibranchs, 50 Sequence analysis, 256257 Sequencing, genotyping by, 3033 Sequestration, 5354 Serological screening method, 226228 Sexually transmitted disease (STD), 8889 Shope papilloma tumor virus (SPV), 8182 Signal amplification method, 2728 Simian virus 40 (SV40), 152154 Simplex virus, 82 Single-dose vaccination, 300 Single-nucleotide polymorphisms (SNP), 193 Slow Off-rate Modified Aptamer (SOMAmer), 234235 Small cell lung carcinoma, 204205 Small molecule inhibitors, targeting MYCMax interaction with, 201203 Smoking, 8 Sodium (Na), 329
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), 124 Sodium hydroxide, 321322 Sodium lauryl sulfate (SLS), 350 detection by MIP in cosmetic products, 369373, 372t SLS-MIP sensor, 356357 Soluble OX40, 234 Statistical analysis of oncovirus, 157161 Statistical method, 373 Stem cell transplant therapy, 101103 Storage sites, 13 Structural genes, 191192 Structural genomics, 111112 Structural proteomics, 123 Structure-based drug design (SBDD), 259260 Structureactivity relationship studies (SAR studies), 267268 Sulfur (S), 329 Surface morphological analysis, 357 Surgery, 100101 SwissModel, 256257 Syndecan-1 (SDC1), 239 Syndecans, 239 Synthetic lethality with MYC, 206207 Systems biology approach, 261262
T T-cell acute lymphoblastic leukemia, 204205 Talimogenelaherparepvec (T-Vec), 6263 Targeted antitumor therapy, MYC as potential, 200201 Targeted therapy, 9899 Telagenastat, 207 Telomestatin, 209210 Terminal differentiation mechanisms, 192193 Testosterone, 320 Tetrahydrobiopterin, 320 The Cancer Genome Atlas (TCGA), 225 Therapeutic vaccines, 178, 305311 efficacy of, 183 Thioredoxin (TRX), 231 Threading, 256257 Three-dimensional space (3D space), 270 3D descriptors, 271 Tolerable upper intake (TMA), 12 Topological indices, 271 Trace metal(lic) elements (TMEs), 39, 4149 effects of metal toxicity on human health, 49
Index
389
origin and cycle, 41 oxidative stress and cancer, 5455 properties, 4148 response of marine organisms to, 5254 transfer of trace metal elements in trophic chain, 4849 Transcription activation domain (TAD), 197198 Transcription factor IIH complex (TFIIH), 204205 Transcriptomics, 113121 Transition metals, 40 2,4,6-trichlorophenol, 353354 Triclosan (TCS), 350 detection by MIP in wastewater, 368369, 370t TCS detection by e-nose in wastewater, 369 TCS-MIP sensor, 355356 Tumor necrosis factor receptor (TNFR), 259 Tumor necrosis factor receptor-associated factor 3 (TRAF3), 259 Tumor targeted cell delivery by oncolytic virotherapy, 6970 Two-dimensional differential gel electrophoresis (2D-DIGE), 124 Two-dimensional gel electrophoresis (2-DE), 124 Two-dimensional space (2D space), 270
Viral MYC oncogene (v-MYC oncogene), 191192 Viral proteins, 284288 Viral vector vaccines, 308 Virology, 243 applications in fields of, 352353 as tools for detection of biomarkers, 321 Virus-like particles (VLPs), 173, 298 vaccination strategy, 298 virus-like particle-based hepatitis B vaccines, 173178 Vitamin D, 1, 1012 absorption, 1 biosynthesis, 1112 and breast cancer prevention, 1516 D2 and D3, 10 deficiency, 1 generality, 1011 new therapy for breast cancers prevention, 14 relationship between vitamin D and breast cancer, 14 Vitamin D receptor (VDR), 11, 1314 Vitamin D response element (VDRE), 15 Volatile organic compound (VOC), 321 Voltammetric array, 333336 creatinine molecularly imprinted polymer sensor, 333334 Vulvar cancer, 239
U
Wastewater, 349 e-nose in, 369 MIP in, 368369 as sources of micropollutants, 351 Wastewater treatment plants (WWTPs), 351 Water cycle, 349 Western blotting, 124 Williams map, 277 World Health Organization (WHO), 173, 282, 297
Ubiquitin ligases, 197198 Ubiquitinproteasome system, 193, 203204 Ultravioletvisible (UVVis), 326 Upstream regulatory region (URR). See Long control region (LCR) Uric acid (UA), 319 Urine as noninvasive sources of biomarkers, 318319 testing, 319
V Vaccination coverage, 305 Vaccines, 297 available for oncovirus, 155157 Vaccinia virus, 308 Vaginal cancer, 239 Varicellovirus, 82 4-vinylpyridine, 320 Viral cancers, 221 Viral etiology, 8
W
X X-ray powder diffraction (XRD), 125126 X-rays, 327329 crystallography, 125126, 199200 Xenobiotics, 50
Z 0-D descriptors, 271 Zinc (Zn), 41 ZINC49069570, 259260 ZINC49115270, 259260