Cancer Research: An Interdisciplinary Approach 3031324579, 9783031324574

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Interdisciplinary Cancer Research  1

Nima Rezaei  Editor

Cancer Research: An Interdisciplinary Approach

Interdisciplinary Cancer Research Volume 1 Series Editor Nima Rezaei , Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran Editorial Board Members Atif A. Ahmed, University of Missouri–Kansas City, Kansas City, MO, USA Rodrigo Aguiar, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil Maria R. Ambrosio, University of Siena, Siena, Italy Mehmet Artac, Department of Medical Oncology, Necmettin Erbakan University, Konya, Türkiye Tanya N. Augustine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa Rolf Bambauer, Institute for Blood Purification, Homburg, Germany Ajaz Ahmad Bhat, Division of Translational Medicine, Sidra Medical and Research Center, Doha, Qatar Luca Bertolaccini, European Institute of Oncology, Milan, Italy Chiara Bianchini, University Hospital of Ferrara, Ferrara, Italy Milena Cavic, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia Sakti Chakrabarti, Medical College of Wisconsin, Milwaukee, WI, USA William C. S. Cho, Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong Anna M. Czarnecka, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland Cátia Domingues, University of Coimbra, Coimbra, Portugal A. Emre Eşkazan, Istanbul University-Cerrahpaşa, Istanbul, Türkiye Jawad Fares, Northwestern University, Chicago, IL, USA Carlos E. Fonseca Alves, São Paulo State University, São Paulo, São Paulo, Brazil Pascaline Fru, University of the Witwatersrand, Johannesburg, South Africa Jessica Da Gama Duarte, Olivia Newton-John Cancer Research Institute, Heidelberg, Australia Mónica C. García, Universidad Nacional de Córdoba, Córdoba, Argentina Melissa A. H. Gener, Children’s Mercy Hospital, Kansas City, MO, USA José Antonio Estrada Guadarrama, Universidad Autónoma del Estado de México, Toluca, Mexico Kristian M. Hargadon, Gilmer Hall, Hargadon Laboratory, Hampden–Sydney College, Hampden Sydney, VA, USA

Paul Holvoet, Catholic University of Leuven, Leuven, Belgium Vladimir Jurisic, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia Yearul Kabir, University of Dhaka, Dhaka, Bangladesh Theodora Katsila, National Hellenic Research Foundation, Athens, Greece Jorg Kleeff, Martin-Luther-University Halle-Wittenberg, Halle, Germany Chao Liang, Hong Kong Baptist University, Hong Kong, Hong Kong Mei Lan Tan, Universiti Sains Malaysia, Pulau Pinang, Malaysia Weijie Li, Children’s Mercy Hospital, Kansas City, MO, USA Sonia Prado López, Institute of Solid State Electronics, Technische Universität Wien, Vienna, Austria Muzafar A. Macha, Islamic University of Science and Technology, Awantipora, India Natalia Malara, Magna Graecia University, Catanzaro, Italy Adile Orhan, University of Copenhagen, Copenhagen, Denmark Heriberto Prado-Garcia, National Institute of Respiratory Diseases “Ismael Cosío Villegas”, Mexico City, Distrito Federal, Mexico Judith Pérez-Velázquez, Helmholtz Zentrum München, Munich, Germany Wafaa M. Rashed, Children’s Cancer Hospital, Cairo, Egypt Francesca Sanguedolce, University of Foggia, Foggia, Italy Rosalinda Sorrentino, University of Salerno, Fisciano, Salerno, Italy Irina Zh. Shubina, N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russia Heloisa Sobreiro Selistre de Araujo, Universidade Federal de São Carlos, São Carlos, São Paulo, Brazil Ana Isabel Torres-Suárez, Universidad Complutense de Madrid, Madrid, Spain Jakub Włodarczyk, Medical University of Lodz, Lodz, Poland Joe Poh Sheng Yeong, Singapore General Hospital, Singapore, Singapore Marta A. Toscano, Hospital de Endocrinología y Metabolismo Dr. Arturo Oñativia, Salta, Argentina Tak-Wah Wong, National Cheng Kung University Medical Center, Tainan, Taiwan Jun Yin, Central China Normal University, Wuhan, China Bin Yu, Zhengzhou University, Zhengzhou, China

The “Interdisciplinary Cancer Research” series publishes comprehensive volumes on different cancers and presents the most updated and peer-reviewed articles on human cancers. Over the past decade, increased cancer research has greatly improved our understanding of the nature of cancerous cells which has led to the development of more effective therapeutic strategies to treat cancers. This translational series is of special value to researchers and practitioners working on cell biology, immunology, hematology, biochemistry, genetics, oncology and related fields.

Nima Rezaei Editor

Cancer Research: An Interdisciplinary Approach

Editor Nima Rezaei Cancer Immunology Project (CIP) Universal Scientific Education and Research Network (USERN) Stockholm, Sweden

ISSN 2731-4561 ISSN 2731-457X (electronic) Interdisciplinary Cancer Research ISBN 978-3-031-32457-4 ISBN 978-3-031-32458-1 (eBook) https://doi.org/10.1007/978-3-031-32458-1 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Cancer is still a major public health concern and one of the leading causes of death worldwide. Cancer is a complex problem that remains to be fully resolved, while different factors in association with cancer could further complicate the condition. As there should not be a simple solution for a complex problem, interdisciplinary approach might suggest solutions to many unanswered questions and challenges about cancer. The rapid flow of interdisciplinary research in cancer during recent years has increased our understanding of nature of cancer. Such interdisciplinary approach could be helpful not only in the diagnosis, but also more for effective therapeutic strategies. The “Interdisciplinary Cancer Research” series publishes comprehensive volumes on different cancers. It plans to present the most updated and peer-reviewed interdisciplinary chapters on cancers. The first volume of the book, entitled Cancer Research: An Interdisciplinary Approach, starts with an introduction on interdisciplinary approach in cancer research. Tumor microenvironment (TME) and spatial transcriptomics approaches to understanding TME are discussed in the Chaps. 2 and 3. The role of mesenchymal stem/stromal cells, cancer-associated fibroblasts, tumoroids, and myokines is the subject of Chaps. 4, 5, 6 and 7. Then, epigenetic modifications in cancer, a few examples on tumor antigen and carcinogens, as well as epidrugs are explained in Chaps. 9, 10, 11 and 12. After discussion on oncologic emergencies in Chap. 13, a few associated conditions such as inborn errors of immunity (immunodeficiencies), allergy, and psychological disease are the main discussion of Chaps. 14, 15, 16, 17, 18 and 19. In the last chapter, attention to childhood cancer survivors is emphasized. This interdisciplinary book series is of special value to researchers and practitioners working on cell biology, immunology, hematology, biochemistry, genetics, oncology, and related fields. This is the main concept of Cancer Immunology Project (CIP) and Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), which are two active interest groups of the Universal Scientific Education and Research Network (USERN).

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I hope that this interdisciplinary book will be comprehensible, cogent, and of special value for researchers and clinicians who wish to extend their knowledge on cancer. Stockholm, Sweden

Nima Rezaei

Contents

Interdisciplinary Approaches in Cancer Research . . . . . . . . . . . . . . . . . Niloufar Yazdanpanah and Nima Rezaei

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Role of Immune Cells in the Tumor Microenvironment . . . . . . . . . . . . . B. Handan Özdemir

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Spatial Transcriptomic Approaches for Understanding the Tumor Microenvironment (TME) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Habib Sadeghi Rad, Yavar Shiravand, Payar Radfar, Rahul Ladwa, Majid Ebrahimi Warkiani, Ken O’Byrne, and Arutha Kulasinghe Role of Mesenchymal Stem/Stromal Cells in Cancer Development . . . . . Marta E. Castro-Manrreza and Ignacio Martínez

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Cancer-Associated Fibroblasts and Their Role in Cancer Progression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Lukáš Lacina, Pavol Szabo, Ivo Klepáček, Michal Kolář, and Karel Smetana, Jr. The Role of Tumoroids in Cancer Research . . . . . . . . . . . . . . . . . . . . . . 135 Mahsa Yousefpour Marzbali and Nima Rezaei Myokine Expression in Cancer Cachexia . . . . . . . . . . . . . . . . . . . . . . . . 157 Emilia Manole, Laura C. Ceafalan, Gisela F. Gaina, Oana A. Mosoia, and Mihail E. Hinescu Epigenetics in Cancer Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Richard A. Stein and Abhi N. Deverakonda Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and Epigenetic Regulation . . . . . . . . . . . . . . . . . . . . . . . 241 Bakiye Goker Bagca and Cigir Biray Avci Telomerase Reverse Transcriptase in Humans: From Biology to Cancer Immunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Magalie Dosset, Andrea Castro, Su Xian, Hannah Carter, and Maurizio Zanetti ix

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Molecular Mechanisms of Metal-Induced Carcinogenesis . . . . . . . . . . . . 295 Ehsan Ghaedi, Shadi A. Esfahani, Mahsa Keshavarz-Fathi, and Nima Rezaei Epi-Drugs Targeting RNA Dynamics in Cancer . . . . . . . . . . . . . . . . . . . 361 Guglielmo Bove, Ida Lettiero, Giulia Sgueglia, Nunzio Del Gaudio, Lucia Altucci, and Carmela Dell’Aversana Oncologic Emergencies: Pathophysiology, Diagnosis, and Initial Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Ardavan M. Khoshnood Malignancies in Inborn Errors of Immunity . . . . . . . . . . . . . . . . . . . . . . 417 Yesim Yilmaz Demirdag and Sudhir Gupta Hematopoietic Stem Cell Transplantation in Patients with Inborn Errors of Immunity and Malignancy . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Andrew R. Gennery and Mary A. Slatter Personalized Immuno-Oncology with Immunodeficiency Mouse Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Jui-Ling Wang, Wen-Hui Ma, Tak-Wah Wong, and Chun-Keung Yu Allergy and Cancer: New Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Parnian Jamshidi, Narjes Mosavari, Donya Najafi, Mohammad Amin Siri, Noosha Samieefar, and Nima Rezaei Depression and Cancer: The Inflammatory Bridge . . . . . . . . . . . . . . . . . 529 Fernanda Leite and Ângela Leite Impact of Cancer-Related Sarcopenia on Systemic Immune Status . . . . 567 Shuang Liu and Masaki Mogi Surveillance of Subclinical Cardiovascular Complications in Childhood Cancer Survivors: Exercise as a Diagnostic and Therapeutic Modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 Takeshi Tsuda and Joanne Quillen Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609

About the Editor

Nima Rezaei Professor Nima Rezaei gained his medical degree (MD) from Tehran University of Medical Sciences and subsequently obtained an MSc in Molecular and Genetic Medicine and a PhD in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also spent a short-term fellowship of Pediatric Clinical Immunology and Bone Marrow Transplantation in the Newcastle General Hospital. Professor Rezaei is now the Full Professor of Immunology and Vice Dean of Research and Technologies, School of Medicine, Tehran University of Medical Sciences, and the Co-founder and Head of the Research Center for Immunodeficiencies. He is also the Founder of Universal Scientific Education and Research Network (USERN). Prof. Rezaei has already been the Director of more than 100 research projects and has designed and participated in several international collaborative projects. Prof. Rezaei is the editor, editorial assistant, or editorial board member of more than 40 international journals. He has edited more than 50 international books, has presented more than 500 lectures/posters in congresses/meetings, and has published more than 1200 scientific papers in the international journals.

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Interdisciplinary Approaches in Cancer Research Niloufar Yazdanpanah and Nima Rezaei

Abstract

Cancer is a major public health concern globally, and about ten million death worldwide is attributed to cancer in 2020. Cancer, itself, is a complex problem that remains to be fully resolved, while different factors in association with cancer could further complicate the condition. Reviewing advances in cancer research through the history, interdisciplinary works could suggest solutions to many unanswered questions and challenges about cancer. During the last 35 years, 11 Nobel Prize winners in medicine and physiology had a non-medical background in chemistry, physics, and engineering, which highlights the significant role of interdisciplinary studies in medical science research, including cancer research.

N. Yazdanpanah Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran School of Medicine, Tehran University of Medical Sciences, Tehran, Iran Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran N. Rezaei (*) School of Medicine, Tehran University of Medical Sciences, Tehran, Iran Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_19 Published online: 21 October 2022

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Keywords

Cancer · Diagnosis · Interdisciplinary · Multidisciplinary · Transdisciplinary · Treatment

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Introduction

Cancer is a major public health concern globally, corresponding for about ten million death worldwide in 2020 (about one in six deaths is recorded for cancer) (World Health Organization 2022). Despite advances in cancer either in practice or research, in 2020, cancer diagnosis and treatment was negatively affected by the COVID-19 pandemic. Due to the emergence of the drastically spreading virus, some healthcare centers faced closure, and many had to only admit patients affected by COVID-19 to control the condition. Therefore, diagnosis and treatment for many suspected and/or confirmed cancer cases were held up, which could give rise to a short-term temporary decrease in cancer incident rate probably followed by a considerable rise in cancer cases with advance stages and in the mortality rate (Siegel et al. 2021). Cancer influences almost all aspects of life; being involved with a chronic disease and taking different treatments expose patients to a variety of adverse effects and complications, while the chronic nature of the disease – in most cases – besides the uncertainty about the disease outcome acts as a powerful stressor and predispose patients to psychological problems and mental complications. In a bigger scale, cancer influences families, healthcare system, and societies. Patients who need to go on long intermittent sick leaves sometimes face job dismissal and unemployment, which in turn impact the economic status of patients and families. In a larger scale, the economic status of societies is affected due to the cancer-related medical care expenses. In addition, cancer is associated with different medical conditions, including autoimmune diseases, inborn errors of immunity (also known as primary immunodeficiency diseases), and infections. In conditions like spread of a newly emerged infection, different patterns are possible in how chronic diseases, including cancer, react to the epidemic. For instance, in the COVID-19 pandemic, questions raised about whether COVID-19 could result in cancer progression or appearance of cancer in undiagnosed patient. This effect was attributed to the activation of pro-inflammatory and pro-tumor mediators and signaling pathways. Putting together, cancer itself is a complex problem that remains to be fully resolved, while different factors in association with cancer could further complicate the condition. Interdisciplinary approaches and collaboration of scientists from different fields have been put forward as a potential tool to combat cancer and its associated complications. During the last 35 years, 11 Nobel Prize laureates in medicine and physiology had a non-medical background in chemistry, physics, and engineering, which highlights the significant role of interdisciplinary studies in medical science research, including cancer research (Smye and Frangi 2021).

Interdisciplinarity in Cancer Research

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What Is Interdisciplinarity?

With the unprecedented pace of development in science, it is hard to presume disciplines to remain isolated without any connections to others. Interdisciplinarity could be defined as the integration of different disciplines into one activity with a single main target (AI-Saleem 2018); it is about thinking and doing beyond the boundaries and borders between different fields of science. As a term, “interdisciplinarity” was born in the twentieth century; nevertheless, in practice, there are footprints of interdisciplinary works in ancient societies, including ancient Greeks, Egyptians, and Mesopotamians, many millennia ago (AI-Saleem 2018). However, various interpretations of this term were dominant in different ages. In addition, in some periods of the history, interdisciplinarity was known as an important factor in science development, whereas in some periods, it was relatively neglected (AI-Saleem 2018). “Multidisciplinary,” “interdisciplinary,” and “transdisciplinary” are progressively being used in the literature and in scientific communications, although are not clearly defined and in some cases are mistakenly used interchangeably. To describe the three terms each in a single distinguishable word, multidisciplinary and interdisciplinary could convey the words additive and interactive, respectively, while holistic has been suggested for transdisciplinary. Thus, each of the three terms implies a specific concept which cannot be used interchangeably. In addition, another term for elaborating the interaction between disciplines is “multiple disciplinary.” It is applied to situations with unrevealed or undetermined involvement of different disciplines (Flinterman et al. 2001; Rawson 1994; Whitfield and Reid 2004; Med 2006; Klein 2008). “Multidisciplinary” could be depicted as two separate circles without any overlaps. In this model of teamwork, individuals from different disciplines work either parallel or sequentially, but independently, on different facets of a single project. Of note, each member (discipline) has its own specific duty and methodology and is pursuing a distinct goal; however, they are supposed to have interrelated roles with the aim of learning about each other, indicating a collaborative relationship. Multidisciplinary is defined as an integration of juxtapositioned disciplines, in which there is an external coherence among participants (disciplines). In this model, the boundaries of the involved disciplines are preserved, and the result of the whole system is only the summation of the individual sections (Rawson 1994; Med 2006). “Interdisciplinary” could be pictured as two circles having a partial overlap. In this model, participants from different disciplines work jointly on a shared project. Contrary to multidisciplinary, in interdisciplinary approach there is a main common target for all participants from different disciplines. Participants leave some parts of their initial discipline’s roles, while maintaining the fundamental of that discipline. Individuals working in this system not only learn about each other, but also learn from each other, which is indicative of an interactive collaboration. Interdisciplinary includes the integration, synthesis, and bidirectional interactions of disciplines. The whole group in the interdisciplinary model comes together with an internal coherent factor. In this model, boundaries of different disciplines are faded to various extents.

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Participants’ common roles and methodologies in this model could result in creation of novel perspectives or even new disciplines. Moreover, the total result is more than simply sum of what are obtained by individual sections (Whitfield and Reid 2004; Med 2006; Klein 2008). “Transdisciplinary” is to go across and work beyond the disciplines. In transdisciplinary projects, the aim is to solve a problem by means of applying on discipline’s concepts and viewpoints to another discipline. In other words, participants from different disciplines work with a common conceptual framework. Transdisciplinary projects include scientists from different disciplines that are related to the targeted problem in different aspects, non-scientists, and also stakeholders even if they could be non-academic individuals. Transdisciplinary is proposed as a potent tool to tackle community-based problems and challenges raised from the society. For instance, to create a transdisciplinary group to work on how to eradicate drug addiction, which is an important concern in many communities, the group probably includes scientists from different disciplines such as medicine, pharmacology, sociology, psychology, etc. as well as policy making authorities and even a number of tobacco company owners and drug addicts (involving stakeholders). In transdisciplinary, participants have shared goals and shared skills. Of note, role release, role expansion, and role alteration are common in transdisciplinary groups. In this model, boundaries of disciplines are transcended. Besides integration of disciplines, the results in transdisciplinary projects are derived from amalgamation, incorporation, unification, and assimilation of various views and approaches from different disciplines (Flinterman et al. 2001; Med 2006; Klein 2008).

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Interdisciplinary Approach in Identifying Cancer Etiopathogenesis

Interdisciplinary works with the aim of identifying disease ethiopathogenesis and human biology have been recorded in the history since many decades ago. For instance, discovery of the DNA structure as the result of studies by scientists in chemistry (Rosalind Franklin), physics (Francis Crick and Maurice Wilkins), and biology (James Watson) is considered as a remarkable interdisciplinary work, which is known as a turning point in biomedical research. Progress in microscopy that has greatly improved biology research was originated from interdisciplinary collaborations between physicists, material scientists, and biologists. Advanced mathematical modeling is a promising tool in modeling and predicting cancer behavior; nevertheless, the first application of mathematical modeling in cancer research is attributed to Armitage and Doll who put forward the multistage theory of carcinogenesis by interpreting cancer-induced death statistics in 1954 (Armitage and Doll 2004). In later years, avascular tumor growth and angiogenesis was modeled using mathematical tools (in 1972 and 1985, respectively) (Greenspan 1972; Balding and McElwain 1985). These are counted as significant steps by interdisciplinary works between biologists and mathematicians in search for responsible mechanisms for cancer formation and progression. Physics has made important

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steps toward predicting cancer behavior as well. Max Delbrück and Salvador Luria are recognized with the random mutation-derived phage resistance hypothesis which later became the basis for a model predicting the possibility of a tumor to become resistant to chemotherapy (Luria and Delbrück 1943). Environmental sciences have contributed great advances in understanding cancer ethiopathogenesis; for instance, identification of the role of some viral, bacterial, and parasitic infections in initiating cancer besides the carcinogenicity of the over-administration of some pesticides has been revealed through the collaboration of scientists from environmental, biological, and medical sciences (Plummer et al. 2016). Furthermore, the effect of air pollution, environmental pollutants, and chemical compounds found in the environment (particularly those who are responsible for occupational-exposure-induced cancer, e.g., asbestos, welding fumes, diesel engine exhaust, silica dust, and UV radiation) is being studied in interdisciplinary teams (Bassil et al. 2007; Vineis et al. 2007; Narayanan et al. 2010). Cancer, a multifactorial disease affecting the body in different aspects, has different etiological contributors that present different various abnormalities in the levels of DNA and RNA, protein expression and structure, metabolites and signaling pathways, and medical imaging. To systematically study cancer in abovementioned biological levels, scientists from different disciplines have collaborated to develop proper tools to progress cancer research; genomics, transcriptomics, proteomics, and metabolomics (Horgan and Kenny 2011), which together are known as biological omics, have made advance cancer research possible. After the introduction of Human Genome Project, the “omics” era began, which revolutionized cancer research and led to a paradigm shift in the concept of cancer research from a single-parameter model to a multiparameter systematic model (Tran et al. 2012). Omics technologies are powerful tools both in cancer basic research and clinical usage. Genomics and proteomics have led to a better understanding of cancer genetic factors involved either in susceptibility to cancer or in cancer progression. These attempts have revealed details and unknown contributors to cancer etiopathogenesis that could be translated to be used for clinical purposes. Incorporation of information from multi-omics could efficiently resolve remained unknown aspects in the intricate etiopathogenesis of cancer by demonstrating in details what really happens for a normal cell during transformation to a malignant cell and why it happens (Hu et al. 2013). Moreover, why the malignant transformation initiates and how the malignancy propagates in the body could be monitored by multi-omics. Results from these studies could be used to find new treatment targets, discover molecular resistance mechanisms, reduce treatment adverse effects, and discover novel biomarkers to diagnose the disease at early stages. Emergence of omics era is attributed to interdisciplinary and multidisciplinary works between scientists from biological sciences, medical sciences, chemical sciences, and formal sciences. The benefits of multi-omics in cancer research have been studied in the context of cancer pathogenesis and signaling pathways responsible for response to treatment and disease progression. Hu et al. performed an integrative genomic and transcriptomic data analysis in association with long-term clinical outcome evaluating the alteration of gene expression according to the number of somatic

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gene copy aberrations elaborated a new event indicating response to the treatment, which led to the development of a new molecular classification tool for breast cancer (Hu et al. 2013). The other example of advances in understanding cancer pathogenesis is the study of analyzing transcriptomics and proteomics data from glioblastoma patients that discloses a significant hyperactivity and enrichment of gonadotropinreleasing hormone (GnRH) signaling pathway (Jayaram et al. 2016); it was not revealed from analyzing single-omics datasets, which highlights the remarkable role of multi-omics studies in increasing our understanding of the intricate pathogenesis of cancer. Another multi-omics study contributed to advances in breast cancer research is the integrated analysis of genomics and proteomics data, which underpinned that phosphoinositide 3 kinase (PI3K) pathway defects are common in hormone receptor-positive breast cancer (Stemke-Hale et al. 2008). Translation of this finding to clinical studies might help in choosing proper targeted therapies for hormone receptor-positive breast cancer patients. Despite advances in omics studies, translation of some findings from multi-omics studies to the healthcare and clinical usage remained challenging. Some of the multiomics benefits are short-term, including novel biomarkers for diagnosis and followup and new targets for treatment. On the other side, some of the benefits are longterm that needs more time and research to become discernable in practice, including cancer early diagnosis and improvement in the overall survival of cancer patients.

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Interdisciplinary Approach in Cancer Diagnosis

The origin of the term “cancer” roots in the ancient Greece, when Hippocrates introduced the terms carcinos and carcinoma for non-ulcer-forming and ulcer-forming tumors, respectively. However, the initial evidence of cancer could be pursued to many years before, in ancient Egypt. The oldest available written document about cancer, although the term “cancer” was not invented, dates back to 3000 BC; in Edwin Smith Papyrus, there are eight cases recorded of tumors/ulcers in the breast region that were removed by ancient physicians. In this document, the author had mentioned that “there is no treatment.” Later on, owing to the advancement in technology and progresses in knowledge, treatment of cancer became possible following developing diagnostic tools to detect cancer before non-treatable stages. Some sources recognize 1851, when the malignant cells were found in the sputum for the first time, as the start of the history of cancer diagnosis. The first tools to directly look for any sign of tumors, gastroscope, and cystoscope were invented in 1881 and 1894, respectively. Later in 1896, X-ray was used to explore tumors in the body; this was the jumping up point in using imaging for cancer diagnosis. Attempt for identifying biomarkers for cancer started in the 1940s. Introduction of Pap smear (1930), mammography (1951), and fecal occult blood test (1967) and detection of prostate-specific antigen (PSA) and CA-125 (1980 and 1983, respectively), which each is an important step toward cancer diagnosis, yielded from interdisciplinary works.

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Scientists have been looking for a diagnostic tool to be capable of detecting tumors before growing to a size that is visible by unaided eye; biomarkers have made it possible to some extent. Biomarkers could be of different types, including oncofetal protein, fragments of cellular protein structures, membrane antigens, enzymes, tumor cell secretions, antibodies against tumors, and tumoral circulating naked nucleic acids. Alpha-fetoprotein (AFP) was the first discovered tumor marker (in 1956) (Bergstrand and Czar 1956). Later in 1965, carcinoembryonic antigen (CEA) was recognized (Gold and Freedman 1965), which is currently validated and known as a serum marker for epithelial malignancies. Nevertheless, the longest history for tumor markers is recorded for tissue polypeptide antigen (TPA), which was reported in 1953 as a protein complex derived from a mix of tumor cells; however, it failed to enter the routine clinical assessment for cancer patients (Weber et al. 1984). After several decades, TPA was introduced as a complex of some cytokeratines and useful in following up different malignancies (Weber et al. 1984). There are several serological detection tools for specific tumor biomarkers in the body. Furthermore, introduction of biosensors has brought great promises for cancer diagnosis. Biosensors function through the detection and measurement of a biological factor (such as proteins, nucleic acids, etc.) and then converting it to electrical signals to be detected, analyzed, and translated to clinical information. Biosensors are categorized into six groups according to the method that interactions between analytic and the detection element are translated into analyzable information, also known as signal transduction method; these categories are optical, magnetic, mass, electrochemical, biomechanical, and thermal (Bellah 2017). Biosensors provide information about whether the target marker is present in patient’s sample and, if present, how much elevated or reduced it is. So, it is possible to decide whether the patient has cancer and is it benign or malignant. In addition, the size of the tumor, its extent or pattern of progression and/or metastasis, and response to treatment could be predicted. Biosensors are capable of detecting multiple biomarkers simultaneously, which can reduce the time of diagnosis and decrease expenses of the diagnosis process, leading the physician to a firmer diagnosis by providing a profile of multiple biomarkers. Despite the advantages of biosensors in cancer diagnosis, metastatic cases remain challenging. It is reported that about 60% of cancer cases are diagnosed at the metastatic stage, which reduce the response to treatment and the survival rate. Nanotechnology can promote biosensor’s technology to tackle drawbacks and limitations of cancer diagnostic tools. Application of nanomaterials in making tiny biosensors leads to optimized cancer marker detection, robust signal enhancement, lower expenses, as well as high-throughput detection. Moreover, besides the detection of biomarkers, consolidation of biosensors and nanotechnology is beneficial in developing cancer imaging devices, designing drug delivery tools to boost the response to treatment while reducing the adverse effects, determining patient’s prognosis, as well as early detection of the disease. To draw a conclusion, application of biosensors and nanotechnology in cancer diagnosis, treatment, and prognosis reflects benefits of interdisciplinary works, which in this case includes physics, chemistry, biology, pharmacology, and medicine for addressing the challenges in complex issues such as cancer.

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With the introduction of omics, diagnosis of cancer has faced improvements due to the introduction of novel biomarkers and development of non-invasive diagnosis tools. For instance, integrative analysis of tissue transcriptomics and urine metabolomics in breast cancer patients has led to the recognition of novel urinary biomarkers that are reported to be more reliable than previously reported biomarkers by single-omics studies (Nam et al. 2009). Application of whole genome sequencing (WGS) in the circulation of cancer patients is a non-invasive diagnosis method capable of determining the genomic profile in details and distinguishing the main drivers of malignancy initiation. WGS can detect aberrations in chromosomal copy numbers, single nucleotide polymorphisms (SNPs), rearrangements, DNA hypomethylation locations, and tumoral heterogeneity (Chan et al. 2013a, b); this method could be a promising alternative to tissue biopsy, particularly in patients with unknown primary site of cancer and in patients with rare cancers (Munoz and Kurzrock 2012; Kou et al. 2016). Even though some potential biomarkers introduced by omics have shown a more desirable sensitivity and specificity in comparison with already FDA-approved biomarkers (such as AFP-L3 for hepatocellular carcinoma (Li et al. 2001) and sarcosine in prostate cancer (Sreekumar et al. 2009)), many of these biomarkers remained to be approved for clinical usage. Meanwhile, these biomarkers are required to pass the validation process in the follow-up studies. In the sixteenth and seventeenth centuries, dissecting corpses to determine the reason of death became more popular among physicians, which is attributed to be the basis of the concept of biopsy as a method to detect the life-threatening factor before death happens. In the next decades, with advances in technology, the procedure became easier to perform, more tolerable for patients, and associated with less errors; hence, biopsy became a common diagnostic tool in many cancer types. Imaging for medical purposes began with the discovery of X-ray by Wilhelm Conrad Roentgen in 1895–1896 (Scatliff and Morris 2014). The initial core concept of ultrasound technology is attributed to Jacques and Pierre Curie in 1877 when they utilized piezoelectricity to convert kinetic and mechanical energy to electrical energy, which is an important part of ultrasound transducer. In 1958, ultrasound was used to monitor the fetus during pregnancy, by Ian Donald (Newman and Rozycki 1998). Donald had been thinking whether ultrasound could distinguish different body tissues after he saw that in the boiler fabrication industry ultrasound was used to explore cracks in the welds. To some extents, this is considered as an achievement of interdisciplinary works. Computed tomography (CT) scan is another example indicating the potentials of interdisciplinary works in the development of technology and promotion of science. The concept of tomography rooted in 1930 when radiologist Alessandro Vallebona put forward a method for anatomically picturing one body slice on a radiologic film. Later in 1972, mathematicians/ physicists Godfrey Hounsfield and Allan Cormack developed CT scan, for which they were awarded the Nobel Prize in physiology and medicine in 1979 (Bhattacharyya 2016). The primary concept of magnetic resonance imaging was proposed by Raymond Vahan Damadian in 1969, which was based on application of radiofrequencies and magnetism to produce images at the atomic level from cells and tissues that vary in the amount of water (hydrogen) they contain (Ai et al. 2012). The

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initial concepts leading to the invention of the positron emission tomography (PET) scan roots in 1950s, which is also a good example of the importance of interdisciplinary works for science development (Jones and Townsend 2017). Molecular imaging techniques have enabled remote non-invasive measurement and detection of molecular processes in the living body (Higgins and Pomper 2011). Advances in interdisciplinary studies with the contribution of scientists from biological, chemical, and physical sciences have elaborated more details in cancer pathophysiology such as protein-protein interactions, transduction and signaling pathways and involved genetic, molecular, and cellular factors, which led to creation and progress of molecular imaging. While conventional imaging maintains as an integral component in cancer diagnosis, molecular imaging provides more details of the disease that could optimize the diagnosis and treatment processes; for instance, results from molecular imaging studies lead to early cancer diagnosis, a better monitoring of the response to therapy, and recognition of patient-specific abnormalities in cellular components, signaling, and metabolic interactions, which all could lead to a more efficient disease management (Higgins and Pomper 2011). Artificial intelligence (AI) is defined as the application of mathematical algorithms to simulate human cognitive functions to tackle complicated challenges such as complex biological abnormalities including cancer. Machine learning (ML) is known as a subset of AI, in which scientists work on neural networkbased algorithms to enable the machine to perform the learning and problem-solving processes as humans do. Deep learning (DL) is a branch of ML and aims to provide the machine with the human’s brains different abilities, including data process, enabling the machine to identify and distinguish images/objects, process language, and develop drug delivery, personalized medicine, and diagnostic tools. Advances in AI in recent decades have made it a game changer in many field; medical sciences and healthcare system are not exceptions. AI has emerged as a powerful promising tool for optimizing diagnosis, treatment, and prediction of prognosis of diseases. Due to the AI’s prominent image analysis power, medical imaging services, radiology, and pathology are the field considerably affected by European Society of Radiology (ESR) (2019). AI-based machines trained with a huge source of images (radiological images or pathological slides) to function as human brain are potent tools in disease diagnosis due to their ability to promptly review, evaluate, compare, distinguish differences, and report the result. Although radiologist is still keeping their role in image interpretation because it is not acceptable to rely 100% on machines, AI can facilitate the diagnosis process while increasing its efficiency. For instance, Esteva et al. developed an AI system using deep convolutional neural network (DCNN) model training it with 129,450 slides of pathological samples; the system was able to distinguish some skin malignancies, including keratinocyte carcinoma and malignant melanoma, with a reported acceptable accuracy in comparison to dermatologists (Esteva et al. 2017). Moreover, advances in AI have brought promises for cancer treatment, either via precision and personalized medicine to choose the best tailored treatment for each individual patient or via empowering the drug design, discovery, and repurposing industries by revealing molecular interactions and advanced modeling. AI can predict the disease response

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to treatment, therefore increasing the treatment efficacy, reducing adverse effects, and decreasing the duration of the treatment while leading to a better outcome. Nextgeneration sequencing (NGS) could be a proper tool to provide high-throughput datasets for developing precision medicine. Furthermore, AI is beneficial in surgical treatments for cancer by determining the precise location of the tumor and the best safe surgical excision border. Artificial intelligence, itself, is yielded from interdisciplinary works and could make further interdisciplinary works possible to target unsolved complicated questions in cancer research. Nevertheless, moral and ethical concerns are inseparable topics when talking about application of AI. Aiming to properly address the raised moral and ethical concerns about the application of AI in some medical settings, the role of interdisciplinary working groups is emphasized; interdisciplinary collaboration of professionals in medicine, physics, mathematics, engineering, psychology, ethics, anthropology, and sociology could brought promises to address these challenges.

5

Interdisciplinary Approach in Cancer Treatment

Besides identification of new cancer treatment targets, multi-omics technologies can recognize pitfalls in already approved cancer treatments. For instance, an integrative analysis of proteomics and phosphoproteomics was performed on hepatocellular carcinoma patient who has not responded to sorafenib. The analysis results demonstrated that although sorafenib effectively inhibited its main targeted kinase in the Raf-Erk-Rsk pathway, the downstream targets of Rsk-2, including filamin-A, were not affected (Dazert et al. 2016). The observations put forward the hypothesis that another pathway could be active, which induce treatment failure. Komurov et al. performed an integrated analysis of transcriptomics and proteomics in association with clinical outcome in patients with HER2-positive breast cancer who had become resistant to lapatinib (Komurov et al. 2012). The study uncovered that HER2 signaling was remained inhibited, while the intensity of this inhibition was reducible by glucose metabolism upregulation and activity of the endoplasmic reticulum stress pathways (Komurov et al. 2012). Putting together, multi-omics are potent tools in finding the pitfalls and challenges in currently available treatments to increase the efficacy of the treatment period and improve the treatment outcome. In addition, genome profiling provides scientists with different molecular subtypes that makes new patient’s classification possible, and it is the basis for developing personalized medicine. Multidisciplinary model in cancer care was recognized as the preferable model by both patients and the healthcare staff (Silbermann et al. 2013). However, in recent years, multidisciplinary model has been transformed to interdisciplinary model (Tremblay et al. 2017). Interdisciplinary teams in cancer care consist of experts in different medical and non-medical fields who work together to propose the best treatment and care plan for each individual case (Tremblay et al. 2017). Due to the complex nature of cancer and its effect on different parts of the body, it is not possible to combat cancer with a single discipline. To choose the best fitted treatment for each

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individual patient, physicians with different specialties need to discuss together to evaluate the effect of the disease in different systems in the body as well as the side effects of the administered treatment to preserve different body systems from druginduced damages. On the other side, nutrition and rehabilitative care have important roles in cancer; recommending a proper diet and a suitable rehabilitation program might help in shortening the treatment period and achieving better outcomes (Raman et al. 2013; Stout et al. 2021). Besides physiological effects, cancer remarkably influences mental health of patients. As an inevitable factor in human life, stress affects human in different aspects. Stress is known to induce an inflammatory state in the body that predisposes tissues to malignancies (Sin et al. 2015). In addition, cancer, itself, is a major stressor in patient’s life considerably affecting their mental health, predisposing patients to depression and other psychological complications (Chida et al. 2008). Hence, presence of psychologists and psychiatrists in interdisciplinary cancer care teams is crucial. To draw a conclusion upon this perspective, interdisciplinarity has a great potential in cancer treatment and patient care to increase the efficacy of the treatment, decrease the length of treatment, reduce adverse effects, and result in more desirable outcomes compared to single discipline-based treatment. Moreover, application of music therapy and art therapy (including dance/movement therapies, drawing-based and painting-based art therapy) in alleviating patient’s pain and treatment-induced adverse effects has become an interesting area of research (Geue et al. 2010; Wood et al. 2011; Li et al. 2020a, b). In addition, music therapy and art therapy have shown to be helpful in improving the mental wellness of cancer patients, which in turn could lead to better treatment outcomes and shorter treatment period (Bar-Sela et al. 2007, Bradt et al. 2015).

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Cancer Associated with Other Medical Conditions: Interdisciplinarity as the Solution?

Besides the intrinsic complexity of cancer, in some cases, cancer is associated with specific conditions that further complicate the disease management. For instance, there are well-recognized associations between cancer and inborn errors of immunity (IEI), also known as primary immunodeficiency diseases, autoimmune diseases, aging, and some specific infections. The immune system combats cancerous cells via the cancer immunesurviellance property before administrating any external anti-cancer therapy (Schumacher and Schreiber 2015). Considering the impaired immune function in patients with IEI, propensity to progress to some types of cancer could be rationalized. Mayor et al. studied the incidence of cancer in patients with IEI and observed that these patients had a 1.42-fold higher relative risk of cancer in comparison with age-matched control subjects (Mayor et al. 2018). In addition, they reported no significant increase in the lung, colorectal, breast, and prostate cancer as the most common cancer types in the control group among the IEI population; nevertheless, a significant increase in the incidence rate of lymphoma was reported in both men and women with IEI (ten-fold and 8.43-fold, respectively) (Mayor et al. 2018). Similar

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observations are reported in previous studies (Kersey et al. 1974; Kersey et al. 1988; Mellemkjaer et al. 2002; Vajdic et al. 2010; Gathmann et al. 2014). On the other side of the immune-mediated diseases spectrum, cancer is reported in patients with autoimmune diseases, either as a complication of the disease itself or as an adverse effect of immunosuppressive therapy. Cancer formation in patients with autoimmune diseases is attributed to the dysregulated immune system, including uncontrolled chronic inflammation and impaired number of T regulatory cells (Multhoff et al. 2012; Ohue and Nishikawa 2019). Cancer treatment in cases with a preexisting medical condition, particularly immune-mediated diseases, is complicated and requires interdisciplinary works to provide the best fitted care for such patients. Besides being link with some specific infections, including Epstein–Barr virus (with nasopharyngeal cancer), Helicobacter pylori (with gastric cancer), and Schistosoma haematobium (bladder cancer), cancer has presented different behaviors in wide infection spreads and epidemics. For instance, during the recent COVID-19 pandemic, cancer is assumed as a probable sequelae of long COVID-19 (Saini and Aneja 2021). This is supported by evidence on the ability of the virus to promote pro-inflammatory and pro-tumor pathways and inhibit anti-tumor responses. Immune responses against COVID-19 are mediated via pro-inflammatory cytokines such as IL-1, IL-6, IL-8, and TNF-α, while these are also involved in tumorigenesis pathways (Del Valle et al. 2020). Meanwhile, it is observed that COVID-19 is capable of activating oncogenic pathways such as JAK-STAT, MAPK, and NF-kB besides depleting T-cell repertoire (Li et al. 2020a, b). Moreover, COVID-19 mediates a state of chronic inflammation followed by hypoxia, which along with depletion of angiotensin-converting enzyme 2 by the virus could lead to oxidative stress. Contribution of chronic inflammation and oxidative stress could provoke malignant transformation (Chaudhary et al. 2015; Muz et al. 2015; Abassi et al. 2020). Furthermore, immense tissue injury induced by the virus, itself, is a driver of carcinogenesis (Turgeon et al. 2018; Renu et al. 2020). To dive deeper, studies have revealed that the non-structural protein 3 (Nsp3) and Nsp15 of SARS-CoV contribute to the degradation of tumor suppressor proteins, P53, and retinoblastoma (pRb), respectively (Bhardwaj et al. 2012; Ma-Lauer et al. 2016). Impaired function of tumor suppressor proteins is followed by genomic instability and abnormal cellular growth, accelerating tumorigenesis. Nevertheless, cancer is the result of an accumulation of mutagenic factors and hardly ever happens as a result of a single factor. Hence, in association with other predisposing factors, COVID-19 could predispose the patient to cancer and accelerate the tumorigenesis process. On the other side, due to lockdown condition and mandatory closure of some of the care services during the pandemic, many patients lost to follow-up their treatment, and many suspected cases failed to start their diagnosis process. This might considerably affect the rate of cancer diagnosis in advanced stages and probably the cancer-related mortality rate in the next decades. To draw a conclusion upon this discussion, not only medical care and treatment for cancer patients could be more efficient in form of interdisciplinary groups, but also managing cancer and cancer patients in specific conditions such as global epidemics requires more extensive interdisciplinary works between professionals in medical, biological,

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environmental, and social sciences (including economics, policy-making experts, psychologists, and sociologists).

7

Conclusion

During the last centuries, different disciplines have progressed in discovering their specific field, while the fortunes of the concept of interdisciplinary have waxed and waned. Nevertheless, with the identification of complex problems and incapability of single disciplines to properly resolve complex problems, interdisciplinary works have become an attractive area of research. Advances in cancer research have uncovered many unknown aspects of cancer and its associated complications, which makes cancer to be recognized as a complex problem. Therefore, interdisciplinary collaborations are required to address the challenges and remaining questions in cancer research and to develop cancer diagnosis and treatment. During the history, even without a clear definition for interdisciplinarity, many advances in our understanding of cancer ethipathogenesis, diagnosis, and treatment are the result of interdisciplinary works. Advances in cancer diagnostic tools such as imaging techniques, discovery of biomarkers and biosensors, multi-omics analysis technology, and application of AI and progresses in cancer treatment such as introduction of novel immunotherapeutic agents, application of AI for developing personalized medicine, and advanced surgical technologies have considerably improved cancer treatment outcomes and prognosis. However, many challenges remained to be tackled; interdisciplinary works could be a promising tool in finding solution to the remaining challenges and unanswered questions in cancer research. Acknowledgments None. Compliance with Ethical Standards The authors declare that there is no conflict of interest.

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Role of Immune Cells in the Tumor Microenvironment B. Handan Özdemir

Abstract

The tumor microenvironment (TME) encloses a repertoire of immune cells besides cancer cells. The role of immune cells in the TME plays an essential role in tumorigenesis, and it has gained more and more attention. The action of immune cells in tumor development and progression is nevertheless complex and bivalent depending on the nature of TME. These tumor-associated immune cells may generate tumor-antagonizing or tumor-promoting functions. It is crucial to understand and identify the interrelation between immune cells and tumor cells, which are closely related to the development and progression of the tumor. Even though the primary task of immune cells with antitumor properties is to destroy cancer cells during tumor development, tumor cells sometimes manage to escape the surveillance of immune cells. In addition, cancer cells can inhibit the cytotoxic functions of antitumor features of immune cells by using various mechanisms. Various immunotherapy treatments have been developed and applied to patients based on immune eradication mechanisms. Contrary to traditional chemotherapy, immunotherapy destroys cancer cells by harnessing the immune cells within or outside the TME. Immune checkpoint treatments and the application of adoptive immune cells, which have been used frequently recently, have shown successful antitumor effects in many different types of cancer, and such administrations have started a new era in cancer treatment. This chapter summarizes the features and functions of immune cells within TME and their participation in cancer immunotherapy.

B. H. Özdemir (✉) Pathology Department, School of Medicine, Baskent University, Ankara, Turkey e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2023_143 Published online: 16 February 2023

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Keywords

Cancer immunotherapy · Dendritic cells · Immune cells · Tumor microenvironment · Tumor-infiltrating lymphocytes

1

Introduction

The concept of a tumor has evolved over the years into a complex organized organ rather than a simple cluster of abnormally proliferating cells. The tumor consists of highly variable cells with very different functions, and this structure is called the tumor microenvironment (TME). The TME continuously evolves with the tumor and is a highly complex and functional structure. The various cells that compose the TME stimulate critical molecular, cellular, and physical changes in host tissues. Although the composition of the TME varies between tumor types, the crucial shared features include tumor parenchyma cells, fibroblasts, stromal cells, immune cells, extracellular matrix (ECM), blood vessels, and lymph vessels as well as tumor-infiltrating immune cells, chemokines, and cytokines. TME does not only act as a silent bystander in both tumor development and progression. On the contrary, it also acts as an active promoter in the proliferation of malignant cells and tumor progression (Anderson and Simon 2020). All ımmune cells within the TME are crucial because of their unique internal interactions and essential roles in tumor biology. Immune cells that have a fundamental role in tumor biology in TME include innate immune cells, adaptive immune cells, extracellular immune factors, and cell surface molecules (Gajewski et al. 2013; Binnewies et al. 2018; Greten and Grivennikov 2019). Several sequential changes must occur in the TME to sustain tumor development and growth. During tumor development, a dynamic and imminent interrelationship evolves between tumor cells and components of the TME to support cancer cell survival, local invasion, and metastatic spread. The induction of angiogenesis through the growth factors, immune cells, and many other soluble factors within the TME can overcome the hypoxic and acidic microenvironment within the tumor. This induced angiogenesis can also provide the nutrient supply necessary for tumor cell proliferation in addition to the elimination of metabolic wastes. The tumor induces an inflammatory response in the host, even in the pre-cancerous or benign lesions. Additionally, the tumor becomes infiltrated with diverse innate and adaptive immune cells that can stimulate both tumorigenic and anti-tumorigenic effects (Fig. 1). Thus, both the host immune system and the tumor itself have a critical role in tumor growth and survival. Indeed, the developing tumor has an active role in this interplay. Tumor cells take advantage of two particularly crucial host responses to increase their lifespan and growth. The first is based on using immune cells (immune selection) to destroy sensitive tumor cells and gradually replace them with those resistant to immune response. The other critical feature is to use the host as a participant in creating the microenvironment suitable for tumor

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Fig. 1 Immune cells in the tumor microenvironment. Immune TME has variable acts in tumor development. Various innate and adaptive immune cells infiltrate TME, which exerts tumorigenic and anti-tumorigenic features. Infiltrated immune cells in the TME have the ability both to suppress and to induce tumor development depending on the type of tumor. (a) The infiltrated immune cells in the TME initially abolish tumor cells through NK cell and CD8+ cytotoxic T-cell-killing mechanisms. The tumor cell-killing process is actualized with the induction of APCs and generation of inflammatory cytokines, such as IFN-γ and IL-2, that initiate the regional immune response. (b) Tumor cells can easily escape immune surveillance due to the progressive accumulation of various mutations in the tumor cell and various modifications occurring in the context of the TME. Tumor-associated macrophages (TAMs), regulatory T (Treg) cells, and diverse immune mediators, like IL-2, IL-4, IL-6, IL-10, TNF-α, VEGF, TGF-β, and MMPs, manage the configuration of the suitable microenvironment for the development of tumor

progression (immune evasion). To this end, tumor-infiltrating lymphocytes, macrophages, and dendritic cells (DC), together with fibroblasts and extracellular matrix forming a scaffold to support tumor growth, contribute to developing an inflammatory environment that nourishes the tumor and supports its invasion. In recent years, the immune cell components of TME have been studied extensively for their critical role in tumor development and control. Tumor-infiltrating immune cells such as tumor-associated macrophages (TAMs), cytotoxic lymphocytes, and myeloid-derived suppressor cells (MDSCs) are important determinants of cancer outcomes. Numerous reports have shown that increased intensity of MDSCs and TAM can induce tumor progression and invasion through various mechanisms (Ostrand-Rosenberg and Sinha 2009; Mantovani et al. 2006). Contrarily, the infiltration of cytotoxic lymphocytes within the TME is associated with a good prognosis in several cancers (Chew et al. 2012; Pag’es et al. 2010). Other components of the TME, such as extracellular matrix (ECM), collagen density, soluble factors, cytokines, and chemokines, may also change the regional equilibrium of tumorigenic and anti-tumorigenic immune responses (Wilson and Balkwill 2002; Balkwill 2004; Kuczek et al. 2019). Disruption of the ECM surrounding a tumor is crucial to invasive cancer development. Tumor-specific ECM, which is often more collagen-rich with increased stiffness, has significantly influenced tumor progression, invasion, and metastasis.

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Additionally, this altered ECM was found to affect the modulation and activation of some immune cells (Kuczek et al. 2019). Collagen type I, one of the significant components of the tumor ECM, has been shown to negatively influence the prognosis of breast cancer, gastric cancer, and oral cancer (Conklin et al. 2011; Ohno et al. 2002; Li et al. 2013). In vitro studies have also shown that an increased collagen density and stiff ECM can stimulate a process in epithelial cells that resembles malignant transformation (Paszek et al. 2005; Levental et al. 2009; Provenzano et al. 2008). Moreover, other mesenchymal cells, like fibroblasts and mesenchymal stem cells, have been shown to respond to the mechanical properties of the surrounding ECM. This process was named cellular mechanosensing (Puig et al. 2015; Engler et al. 2006). The ECM can also alter the immunosuppressive TME, thereby contributing to cancer’s evasion of immune demolition (Pickup et al. 2014). T cells have mechanosensing abilities, and mechanical force highly affects TCRs. The presentation of antigens bound to a stiff surface has been demonstrated to impair TCR-mediated T-cell activation (O’Connor et al. 2012; Feng et al. 2018). Moreover, altered tumor-associated ECM can also trigger the deposition of other ECM components like tenascin C, versican, SPARC, and osteopontin, which were demonstrated to possess immunosuppressive features (McMahon et al. 2016; Jachetti et al. 2015; Sangaletti et al. 2017). Kuczek et al. recently showed that T-cell proliferation significantly declined in a high-density ECM compared to a low-density ECM (Kuczek et al. 2019). They found a decline in the number of infiltrating CD8+ T cells in breast tumors with high collagen density, indicating that collagen density has a crucial role in altering T-cell density in human breast cancer. In addition, using 3D culture of T cells demonstrated that a high-density ECM leads to the downregulation of cytotoxic activity markers and the upregulation of regulatory T-cell markers (Kuczek et al. 2019). These transcriptional changes will impair the capability of tumor-infiltrating T cells to kill autologous cancer cells. Furthermore, various studies demonstrated that collagen-rich tumor-associated ECM could also limit the migration of T cells into the tumor islets, thereby restricting their contact with tumor cells (Salmon et al. 2012; Hartmann et al. 2014). All these studies displayed that collagen within the TME could be an essential regulator of anti-cancer immunity.

2

The Importance of T Cells in the TME

T cells (CD3+ TCR+) are the most significant component of the mononuclear tumor infiltrates in all human tumors (Fig. 2). Upon maturation in the thymus, naïve T cells bear TCR that identifies a specific antigen. Various T-cell populations have been known to influence tumorigenesis and tumor progression within the TME. It has been documented that CD4(+) T-helper (Th) lymphocytes and CD8(+) cytotoxic T lymphocytes (CTLs) operate jointly in a variety of tumor types. At the same time, they exhibit different dynamic trends in other tumors (Huang et al. 2015).

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Fig. 2 The role of the T-cell subsets in the TME. (a) The figure represents the chief T-cell subsets in the configuration of TME during tumor development or regression. (b) The table pointed out the specific role of each T-cell subset in tumor immunity. DC dendritic cells, NK natural killer cells, M1 type 1 macrophages, M2 type 2 macrophages, TH1 T-helper 1, TH2 T-helper 2, ECM extracellular matrix

Among all subtypes of T cells, cytotoxic CD8+ T lymphocytes are known to be the primary antitumor effector cells. A CD8+ T-cell infiltrate in the TME is often accepted as a sign of a promising prognostic marker in various cancers (Anderson and Simon 2020; Binnewies et al. 2018; Pag’es et al. 2010). CD8+ T lymphocytes detect tumor antigens expressed on cancer cells and differentiate cancerous cells from untransformed healthy cells, thereby targeting tumor cells for destruction. In addition to killing tumor cells, cytotoxic T cells suppress angiogenesis and inhibit tumor progression by secreting interferon-gamma (IFN-γ). Melanoma-associated antigen (MAGE)-1 is the first human tumor antigen recognized by cytotoxic CD8(+) T lymphocytes (van der Bruggen et al. 1991). The isolation of this tumor-specific cytotoxic CD8(+) T lymphocytes from patients’ tumors or circulating blood revealed the fact of CD8(+) T-cell-mediated antitumor immunity (Slingluff et al. 1994; Mackensen et al. 1993; Mami-Chouaib et al. 2002; Ito et al. 2005). The finding of tumor-associated antigen-specific CD8(+) T lymphocytes in spontaneously regressing tumors further supported the significance of tumor-specific cytotoxic CD8(+) T-cell responses (Slingluff et al. 1994). Cytotoxic CD8(+) T cells eliminate tumor cells through their T-cell receptor (TCR), which recognizes the tumor-associated antigens presented by major histocompatibility complex class I (MHC I). During the destruction process of tumor antigens, target cells are connected by activated cytotoxic T lymphocytes (CTLs) that release cytotoxic granules such as granulysin, perforin, and granzymes (Fig. 1a), leading to target cell death. CTLs would recognize tumor-specific antigens, finalize specific cytolysis, and, therefore, highly affect the outcome of the disease. In this context, effector CTLs may be able to lyse the tumor cell without needing the assistance or co-stimulation of CD4+ Th cells, as in the case of viral antigen recognition.

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Numerous studies documented a favorable outcome related to the high density of CD8(+) CTLs in the TME in various cancers such as melanoma and lung, bladder, ovary, gallbladder, and head and neck carcinomas (van der Bruggen et al. 1991; Slingluff et al. 1994; Mackensen et al. 1993; Mami-Chouaib et al. 2002; Ito et al. 2005; Al-Shibli et al. 2008; Dieu-Nosjean et al. 2008; Shibuya et al. 2002; Fluxá et al. 2018; Baras et al. 2016; Sato et al. 2005; Lieber et al. 2018). Mami-Chouaib et al. isolated specific CTL clones from a patient with lung cancer who had long-term survival (Mami-Chouaib et al. 2002). The analysis of CTL clones showed that these CTLs that were able to identify the tumor-specific antigen were of a CD3(+), CD8(+), CD4(-), and CD28(-) phenotype (Mami-Chouaib et al. 2002). In addition, in the bladder and ovarian cancers, the ratios of CD8(+) CTL to Treg in the TME have also been documented to be associated with a favorable clinical response to neoadjuvant chemotherapy (NAC) (Baras et al. 2016; Sato et al. 2005). Liber et al. showed in ovarian cancer patients that CD8(+) effector memory T-cell (TEM) density in their ascites and the expression of the chemokine CXCL9, which acts as a chemoattractant for CD8+ TEM, were associated with extended relapse-free survival (Lieber et al. 2018). In this study, T-cell dysfunction was also associated with a decreased expression level of critical signaling molecules within the TME (Lieber et al. 2018). Consequently, Liber et al. suggested that CD8(+) TEM density in ascites, CXCL9 level, and signal transduction protein expression could be used to predict the response to immunotherapies. Galon et al. reported that the immune status of patients, including CD8(+) CTLs, can be a better predictor of a patient’s outcome than the histopathological features currently used to grade and stage the tumors in different human cohorts (Galon et al. 2006). Even though cancers in the breast are not widely considered immunogenic, infiltrating CD8(+) CTLs in the stroma of TME have also been shown to exhibit antitumor immunity and a favorable outcome in some subgroups of patients (Mahmoud et al. 2011; Liu et al. 2017). Several studies showed that the immune environment influences the outcome of breast cancer, particularly in aggressive subgroups such as the non-luminal type (Mahmoud et al. 2011; Liu et al. 2017). Nevertheless, although a tumor-specific CD8 T-cell response does occur, they rarely confer protective immunity because tumor cells can frequently escape immune surveillance by reducing T cell’s effector and memory functions (Vesely et al. 2011; Chen and Mellman 2013). Confirming this, CTLs were found to be associated with neither overall survival nor disease-free survival in one study (Gao et al. 2007). Furthermore, Nakano et al. reported a dual role for CD8+ T cells in patients with renal cell carcinoma. Indeed, the existence of the CD8(+) T cells in renal cell carcinoma per se was associated with shorter survival. At the same time, proliferating CD8(+) T cells in the TME provided a favorable outcome (Nakano et al. 2001). Nowadays, various immunotherapies such as dendritic cell (DC) cancer vaccines, adaptive cell transfer of tumor-reactive T cells, and immune checkpoint blockade are used to increase CD8(+) T-cell-mediated antitumor immunity. Most of these treatments primarily increase the number and function of tumor-infiltrating lymphocytes (TILs) by inhibiting negative regulatory pathways found in the TME.

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Despite recent advances in immunotherapy, only a small group of patients benefit from these immunotherapies developed to harness the power of T cells. In fact, a complete cure is rarely achieved, and disease relapse is often observed in many patients who have responded to immunotherapies such as checkpoint or CAR T-cell therapies (Vonderheide 2018). Direct presentation of tumor antigens onto their MHCIs by tumor cells plays a vital role in the effector function of CD8(+) T cells. In contrast, cross-presentation by professional antigen-presenting cells, particularly DCs, is essential for naïve primary CD8(+) T cells and to sustain cytotoxic immune responses (Arina et al. 2002). Hence, increasing struggles have been made to fix and enhance insufficient T-cell priming by DCs to further enhance the effectiveness of immunotherapies due to the crucial role of DCs in priming and directing cytotoxic T cells to target tumor cells (Vonderheide 2018; Arina et al. 2002; Saxena and Bhardwaj 2018). The talent of DCs to cross-present tumor-associated antigens on the MHCI molecule to induce the priming of CD8(+) T cells forms the basis of the “cancer-immunity cycle” proposed by Chen and Mellman (Vesely et al. 2011). Therefore, a detailed understanding of the interaction of CD8(+) T cells and DCs is crucial in increasing the efficacy of existing cancer immunotherapies.

3

Role of Dendritic Cells in the Modulation of Antitumor T-Cell Responses

Dendritic cells (DCs) play a crucial central role in the interrelationship of innate and adaptive immune responses. DCs are the most critical and potent professional antigen-presenting cells (APCs). They promote all adaptive immune responses and activate naïve antigen-specific CD4 and CD8 T cells via processing and presenting various antigens, such as tumor antigens (Ma et al. 2013). Morphologically, DCs appear as multi-arm star-shaped structures ready to capture and process antigenic material. Compared to macrophages, endocytic compartments of DCs are less acidic, which prolongs the presence of antigenic molecules for MHC class I and class II presentations. DCs express high antigenpresenting molecules when activated by PAMPs, DAMPs, or indirectly induced inflammatory mediators, such as TNFα, IL-1β, IL-6, or prostaglandin E2 (PGE2). DCs originated in the bone marrow from macrophage/DC progenitors (MDP) that generate common DC progenitors (CDP). Subsequently, CDP was differentiated into two major DC subsets, classical/conventional DCs (cDCs) and plasmacytoid DCs (pDCs) (Onai et al. 2013; Murphy et al. 2016; Anderson et al. 2017). Murine cDCs consisted of two subtypes named cDC1s and cDC2s. In humans, gene expression and functional analyses suggest CD141+ (also known as BDCA3) DCs resemble the cDC1s. In contrast, the more abundant CD1c+ (BDCA1) DCs are similar to the cDC2s, and CD303+ (BDCA2) human pDCs mirror their murine pDC counterparts (Guilliams et al. 2014, 2016). These subtypes of cDCs were differentiated according to their transcriptional factor dependency, functional status, and phenotypes (Murphy et al. 2016;

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Anderson et al. 2017). cDC1 cells comprise lymphoid tissue CD8α+ cDC1s and migratory CD103(+) cDC1s (Gutierrez-Martinez et al. 2015). Regarding their development, cDC1 cells depend on interferon regulatory factor 8 (IRF8) and basic leucine zipper ATF-like transcription factor 3 (BATF3). They specialize in presenting internalized exogenous antigens onto MHCI to prime CD8 T cells by cross-presentation (Hildner et al. 2008). Concerning tumor immunology, the BATF3-dependent CD103+ or CD8α + cDC1s are considered the most critical subset given their tendency to secrete IL-12p70 but not IL-10 and induce CD4+ T-helper type I and CD8+ cytotoxic T lymphocytes from naïve precursors (Murphy et al. 2016; Anderson et al. 2017; Guilliams et al. 2014, 2016; Gutierrez-Martinez et al. 2015; Hildner et al. 2008). cDC2s rely on interferon regulatory factor 4 (IRF4) for their development. They comprise a heterogeneous population that efficiently presents internalized antigens on MHCII to activate CD4 T cells (Mildner and Jung 2014; Merad et al. 2013; Gardner and Ruffell 2016). IRF8 and BATF3-dependent cDC1s have also been shown to control the procurement and homeostasis of intraepithelial lymphocytes (IELs). They are a critical tissue-constrained subtype of γδ T cells that are considered as a part of a short-acting immunological defense different from classical αβ T cells (Hildner et al. 2008; Mildner and Jung 2014; Merad et al. 2013; Gardner and Ruffell 2016). pDCs are a multifunctional population known for their specialized ability to produce and secrete many type I interferons (IFNs) (Reizis et al. 2011; Swiecki and Colonna 2015; Mitchell et al. 2018). pDCs also express a high level of IRF8, similar to cDC1s, but require the E2-2 transcription factor for their development (Cisse et al. 2008). E2-2 is a member of the E family of basic helix-loop-helix transcription factors, and it is encoded by TCF4 (Kee 2009). In both mice and humans, E2-2 is required to differentiate pDCs from CDPs (Cisse et al. 2008). Induced deletion of E2-2 in mature pDCs results in the acquisition of cDC-like properties, such as dendritic morphology, MHCII, and CD8α expression, and the ability to induce the proliferation of allogeneic CD4 T cells (Ghosh et al. 2010). Deletion of E2-2 in pDCs also induces the expression of ID2, which is required for cDC1 development. Murine pDCs express Siglec-H, B220, Ly6c, PDCA1 (CD317), and intermediate level of CD11c, and human pDCs express HLA-DR, CD123, BDCA2 (CD303), and BDCA4 (CD304) but not CD11c (Swiecki and Colonna 2015; Veglia and Gabrilovich 2017). Initially reported as IFN-producing cells (IPCs), pDCs have been extensively studied for their function in sensing viral RNA and DNA by Toll-like receptor (TLR)-7 and TLR-9 (Colonna et al. 2004; Gilliet et al. 2008). pDCs have also been shown to play a critical role in immune tolerance, in addition to their function in producing IFNs. In autoimmune diseases, aberrant activation of pDCs has been implicated in the pathogenesis of psoriasis, systemic lupus erythematosus (SLE), and IFN-related autoimmune disorders (Swiecki and Colonna 2015; Lande et al. 2007; Li et al. 2017). Immunological tolerance to self and nonself antigens is provided centrally in the thymus, and it is controlled mainly by medullary thymic epithelial cells (mTECs) and thymic BATF3-dependent CD8α+ cDC1s (Murphy et al. 2016;

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Anderson et al. 2017; Guilliams et al. 2014, 2016; Gutierrez-Martinez et al. 2015; Hildner et al. 2008). However, tolerance occurs peripherally during continuous and dynamic interplay with harmless antigens devoid of PAMPs or DAMPs. In peripheral tolerance, DCs that provide tolerance have been demonstrated to depend on the major inhibitory receptors expressed on T cells, such as PD-1 and CTLA-4 (Vonderheide 2018). APC activation is required to initiate the adaptive T-cell response against tumor antigens. The innate signaling pathways involved in this activation step were first traced in transcriptome profiling of human tumors. It has been reported that type I IFN gene signature correlates significantly with T-cell infiltration (Harlin et al. 2009; Wolf et al. 2014). The activation of innate immune cells promotes inflammation, disrupts tissue homeostasis, and is reasonably related to tumor development. This critical pathway is controlled by myeloid-derived IL-6. IL-6 activates the transcription factors signal transducer and activator of transcription 3 (STAT3) and nuclear factor-κB (NF-κB), as demonstrated in several models of carcinoma (Diamond et al. 2011; Fuertes et al. 2011; Hildner et al. 2008; Schiavoni et al. 2002; Elinav et al. 2013; Yu et al. 2009). During the presentation of antigens to lymphocytes, STAT3 and NF-κB were shown to be activated within DCs. Hence, depending on the circumstances, DCs can be either pro-tumorigenic or anti-tumorigenic. For instance, in a mouse model of ovarian cancer progression in a p53/K-ras inducible mouse, it has been shown that infiltrating DCs prevents tumor growth until they progressively change their phenotype. Subsequently, the change in their phenotypes reasonably progresses the tumors aggressively despite remaining immunogenic. Accordingly, the attenuation of DCs has opposite consequences for cancer progression when initiated early or late in tumor development (Scarlett et al. 2012). Recently, it has been demonstrated that the mature DC-Lamp+ DCs in tertiary lymphoid structures are a decisive, independent prognostic factor for survival. According to the pTNM staging system, higher degrees of DC-Lamp+ DCs are noted in early- versus late-stage non-small cell lung cancer (NSCLC) patients (Goc et al. 2014; Remark et al. 2015). Thence, the accumulation of mature DCs during the progression of the tumor could be prevented by escape mechanisms. A selective pressure implemented by the adaptive immune system on the evolving tumor may determine the probability of time-dependent and preferred editing and the presentation of non-immunogenic cancer cell clones. Thus, it has been suggested that immunogenic early-stage tumors are infiltrated by mature DCs and thereby associated with antitumor CD4+ and CD8+ T cells. On the contrary, progressive late-stage tumors may present an increased number of immunosuppressive cell subsets, like tumor-associated macrophages (TAMs), regulatory CD4+ T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAFs) (Madar et al. 2013). It is believed that these changes are enhanced by both the stimulation of chronic inflammation and persistent innate immune activation induced by crucial primary factors such as IL-6, transcription factor β-catenin, and the lipid mediator PGE2 (Kalinski 2012; Spranger et al. 2015).

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Interferon-alpha/beta receptor alpha chain (IFNAR) and STAT1-related genes are reported to involve in IFN signaling. IFNAR1 constitutes one of the two chains of the receptor required for interferon alpha and beta. Binding and activating the receptor stimulates Janus protein kinases, which phosphorylate several proteins, including STAT1 and STAT2. Mice without IFNAR and STAT1-related genes showed that they could not control immunogenic tumors (Diamond et al. 2011; Fuertes et al. 2011). The required APC cells stimulated by type I IFN signals were mapped to a rare population of CD8α-positive classical dendritic cells (cDC1). The development of cDC1s is dependent on the transcription factors BATF3 and IRF8, and they are well-known for their ability to cross-present antigens (Hildner et al. 2008; Schiavoni et al. 2002). cDC1s are also crucial for the development of procurement of resident memory T cells in the lung and skin. BATF3-deficient mice restricted the generation of resident memory T cells in the skin after the immunization of intradermal vaccinia virus (VACV) (Iborra et al. 2016). Considering the essential role played by cDC1s in the priming of CD8(+) T cells against tumor antigens, it is likely that cDC1s also have critical roles in forming resident memory T cells against tumors. Findings and evidence from multiple studies have shown that non-T-cellinfiltrating tumors benefit from pathways that elicit tissue-damaging inflammation localized in the tumor tissue. Further, this causes the release of simultaneous damage-associated molecular patterns (DAMPs) and tumor-associated antigens (Pfirschke et al. 2016). It has been documented that DAMPs could stimulate type I IFN production in a sterile tumor without pathogen transmission. Numerous studies specified several DAMPs released by stressed and dying tumor cells, leading to productive T-cell priming. Angiogenesis is induced when the tumor needs an additional oxygen supply. This process may stimulate the release of danger signals and alert DCs to activate the adaptive immune system (Fuchs and Matzinger 1996). High-mobility group protein B1 (HMGB1) binding to Toll-like receptor 4 (TLR-4) and extracellular ATP binding to the P2X7 purinergic receptor triggering the NLRP3 inflammasome were both reported to induce DC maturation and subsequent activation of antitumor T cells (Apetoh et al. 2007; Ghiringhelli et al. 2009). Based on these findings, varied chemotherapy combinations were examined in a mouse model of non-T-cell-infiltrating lung adenocarcinoma (Pfirschke et al. 2016). Interestingly, chemotherapy combinations that stimulate the release of HMGB1 trigger the selective upregulation of TLR-4 and the activation of its adaptor molecule MyD88 in CD11c+ CD103+ DCs. Subsequently, it drives the influx of CD8-positive T cells that control the tumor and provide it susceptible to checkpoint therapy (Pfirschke et al. 2016). Unlike inducing DC maturation, extracellular ATP can also affect resident memory T-cell maintenance. Resident memory T cells have been shown to express P2RX7, which, when triggered, induces cell death (Stark et al. 2018). However, corrosion of resident memory T cells can be controlled explicitly by their ability to adjust the local ATP concentrations via the ectoenzyme CD39 pathway. Recent reports demonstrated that the cytosolic DNA sensor stimulator of interferon genes (STING) complex and associated adaptor molecules (MyD88) is needed

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for the spontaneous priming of tumor-specific CD8-positive T cells. Production of STING-dependent type I IFNs was induced when the DCs infiltrated the TME to detect and uptake tumor-derived DNAs (Sen et al. 2019). Endothelial cells of the tumor vasculature were also shown to generate type I IFNs in response to STING activation (Demaria et al. 2015). STING signaling activates DCs and induces the upregulation of adhesion molecules on endothelial cells of the tumor-associated vasculature. This process is the critical step for T-cell extravasation into the TME (Woo et al. 2014). It is not yet clarified how tumor-derived DNAs reach the cytosol to activate the STING pathway. Consistent with this mechanism, however, immunogenic tumors in mice lacking STING cannot be rejected and continue to grow progressively. In addition, spontaneous priming of CD8+ T cells against tumor antigens is virtually eliminated (Woo et al. 2014; Deng et al. 2014). Thus, chemotherapy-induced and spontaneous DC activation was shown to utilize different activation pathways. Furthermore, the processes by which DCs act in activating tumor-specific adaptive immune responses are complex and reflect contextdependent mechanisms. It is well-known that PD-1/PD-L1 blockade is a promising therapy in cancer patients. Thereby several studies aim to augment the response to checkpoint blockade therapy. T-cell-inflamed TME is crucial for positive responses to checkpoint blockade. The response to anti-PD-L1 treatment will abolish with the vanishing of the lymphocyte infiltration in the TME. Therefore, the infiltration of adaptive immune cells without the direct involvement of DCs due to the direct manipulation of tumor cells is another critical point that must be highlighted. For example, epigenetic reprogramming in human ovarian cancer cells is reversed by DZNep and 5-aza-2′ deoxycytidine. By targeting histone modifications and DNA methylations, DZNep and 5-aza-2′ deoxycytidine have been shown to stimulate Th1-type chemokines like CXCL10 to change the cell content of TME from poor T-cell infiltration to rich T-cell infiltration (Peng et al. 2015; Tang et al. 2016). Facilitating the migration of naïve T cells into de novo-generated tertiary lymphoid structures is the alternative way of reprogramming the TME. This reprogramming has been shown in the study of Tang et al. (2016). They targeted non-T-cell-inflamed tumor tissues and created T-cell-inflamed TME to overcome tumor resistance to checkpoint blockade by stimulating lymphoid neogenesis using an antibody-guided LIGHT. LIGHT has been shown to induce antitumor immunity in human and mouse tumor models by augmenting lymphocyte infiltration. This treatment supported lymphotoxin β-receptor signaling and reversed tumor resistance to PD-L1 blockade (Tang et al. 2016). In light of these data, it should be kept in mind that LIGHT alone or in combination with other immunotherapies may be a very effective strategy for cancer treatment. Besides CD103+ cDC1s, other immunosuppressive myeloid populations have been demonstrated in TME, including MDSCs and TAMs, also known as type II macrophages. Broz et al. studied several myeloid populations in cancer (Broz et al. 2014). The authors found two types of macrophages and two types of DCs using CD24 and F4/80 to distinguish DCs and macrophages. Notably, the scarce BATF3dependent CD103+ CD11c+ cDC1s fully have the potential to activate CD8+ T cells

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compared to the profuse CD11b+ cDC2s, which displayed functions resembling macrophages. Significantly, only the CD103+ cDC1s have been shown to associate strongly with clinical outcomes among multiple cancers. Accordingly, it has been suggested that, through prompting local PAMP/DAMP and fostering FLT3L-related expansion from hematopoietic precursors, tissueresiding CD103+ DCs are essential mediators driving type I interferon- and CD8+ T-cell-dependent immunological control of other than non-inflamed, progressively growing cancers. Subcapsular sinus CD169+ macrophages have been shown to localize in tumor-draining lymph nodes (TDLNs), in contrast to immunosuppressive TAMs, which are more abundant in tumor stroma. In patients with colorectal cancer, subcapsular sinus CD169+ macrophages were found to be correlated positively with more prolonged survival (Ohnishi et al. 2013). Recently, it has been suggested that in the lymph node cortex, these macrophages restrict tumor-derived extracellular vesicles from reaching B cells (Pucci et al. 2016). Previously, it has been demonstrated how B-cell-derived autoantibodies activate FcRγ on stromal macrophages, leading to chronic inflammation and de novo carcinogenesis (De Visser et al. 2005; Andreu et al. 2010). Consequently, subsets of macrophages and DCs may hinder the development of tumors despite the bulk of these myeloid populations being associated with immune evasion. In conclusion, during the presence of DAMPs, DCs serve the central role by priming CD8+ T cells promoting tolerance during homeostasis and controlling cancer or autoimmunity. However, chronic inflammation and tumor-promoting pathways can impair or change DC’s capability to prime CD8+ T cells, thereby promoting the exclusion of T cells from the TME. Alternatively, they can drive the expansion of Tregs and functionally exhaust CD8+ T cells expressing PD-1 and other checkpoints. The demonstration of intratumoral CD103+ DCs in numerous murine models of cancer provides evidence that boosting CD103+ DCs may profoundly improve current immunotherapy modalities in oncology.

4

The Immune Landscape of Tumor-Infiltrating T and B Cells in Cancer

Many tumor samples have been used to examine the genomic and transcriptomic data of TME. Evaluating immune infiltration cells in the TME of these tumor samples ensured the categorization of molecular subtypes of several tumors, such as melanoma and pancreatic and ovarian cancer (Bailey et al. 2016; The Cancer Genome Atlas Network 2015; Cancer Genome Atlas Research Network 2011; Iglesia et al. 2016). The immune response to tumors primarily relies on the tumorinfiltrating lymphocytes (TILs), mainly composed of T and B cells. TILs significantly affect the survival and treatment responses of patients with cancer. Various immunotherapies that target TILs have been accepted as a promising treatment for several types of cancer. Specifying the TME using gene expression signatures, B-cell receptor (BCR), and T-cell receptor (TCR) repertoire can provide valuable information in many cancer types or have predictive value.

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Thorsson et al. performed an extensive immunogenomic analysis of over 10,000 tumors comprising 33 divergent tumor subtypes using data from The Cancer Genome Atlas (Thorsson et al. 2018). They identified six immune subtypes that include numerous cancer types and are hypothesized to define immune response patterns influencing prognosis. The abundance of TILs shapes and specifies the six distinct subtypes: Wound Healing, IFN-γ Dominant, Inflammatory, Lymphocyte Depleted, Immunologically Quiet, and TGF-β Dominant. These subtypes are characterized by various macrophage or lymphocyte signatures, Th1:Th2 cell ratio, aneuploidy, overall cell proliferation, neoantigen load, the extent of intratumoral heterogeneity, and expression of intratumoral immunomodulatory genes and prognosis (Thorsson et al. 2018). The six resulting clusters of immune subtypes, C1–C6, were classified by a distinct distribution of scores over the five signatures. They demonstrated diverse immune signatures based on the dominant sample characteristics of their tumor samples. Immune subtypes included anatomical location and tumor type, while individual tumors varied significantly in the proportions of immune subtypes (Thorsson et al. 2018). C1 (Wound Healing) has been shown to demonstrate a higher proliferation rate, an increased expression of angiogenic genes, and a Th2-cell bias to the adaptive immune infiltrate. Lung squamous cell carcinoma, colorectal carcinoma, breast carcinoma, and head and neck squamous cell carcinoma were found to be rich in C1. C2 (IFN-γ Dominant) group demonstrated the highest M1/M2 macrophage polarization, a significant CD8 signal, and the most remarkable TCR diversity. C2 showed the highest proliferation signature. C2 was comprised of highly mutated BRCA, gastric, ovarian, and head and neck squamous cell carcinoma and cervical tumors. C3 (Inflammatory) group showed elevated Th17 and Th1 genes, low to moderate tumor cell proliferation, lower degrees of aneuploidy, and general somatic copy number alterations than the other subtypes. C3 was significant, mostly in prostate, kidney, and pancreatic cancers and papillary thyroid carcinomas. C4 (Lymphocyte Depleted) was significant in adrenocortical carcinoma, paraganglioma, pheochromocytoma, hepatocellular carcinoma, and gliomas. They displayed a marked macrophage signature, with Th1 suppressed and a high M2 response. C5 (Immunologically Quiet) exhibited the lowest lymphocyte and highest macrophage responses, dominated by M2 macrophages. C4 was significant, mainly in lower-grade gliomas. IDH mutations were significant in C5 over C4, suggesting an association of IDH mutations with favorable immune composition. C6 (TGF-β Dominant) demonstrated the highest TGF-β signature and a higher lymphocytic infiltrate with an even distribution of type I and type II T cells. It was not dominant in any cancer subtype, and C6 was a small group of mixed tumors.

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Thorsson et al. suggested that these six categories represented features of the TME that essentially cut across traditional tumor classifications to generate categories associated with prognosis, genetic, and immune modulatory alterations that may shape the specific types of immune environments (Thorsson et al. 2018). It is already known that TME plays an essential role in prognosis and response to therapy. Therefore, the definition of the immune subtype of cancer may play a critical role in predicting disease outcomes instead of relying solely on features specific to individual tumor types. They suggested that classifying immune response patterns provided a resource for understanding tumor-immune interactions, with implications for identifying ways to advance immunotherapy research. Thorsson et al. (2018) demonstrated that immunogenomic features were predictive of outcome, with progression-free interval (PFI) and overall survival (OS) differing between immune subtypes within and among tumor types. Category C3 had the most favorable prognosis, while C2 and C1 had the less favorable outcomes despite having a prominent immune component. On the other hand, subtypes C4 and C6 had the worst outcome. Functional orientation of the TME and immune subtypes was studied using the concordance index (CI), and the context-dependent prognostic impact was found among tumors. The higher lymphocyte signature is associated with improved outcomes in C1 and C2. However, C2 showed a less favorable survival despite having the highest lymphocytic infiltrate, a CD8 T-cell-associated signature, and the highest M1 and IFN-γ content. Although the C2 subgroup showed a robust antitumor immune response, the patient outcome was less favorable than the C3 subgroup. This discrepancy was explained by the fact that the tumor types within C2 were more aggressive than those within C3 subgroup. C2 showed the highest intratumoral heterogeneity and proliferation signature, while C3 was the lowest in both these categories. Based on this, it is suggested that the immune response alone cannot control the rapid growth of tumors in the C2 group. The C3 subgroup displayed the most distinct Th17 signature, implying that augmented Th17 expression is generally associated with improved OS in cancer. Th1 was associated with worse OS among most immune subtypes, and the Th2 signature had mixed effects. Categories C4 and C5 displayed composite signatures reflecting low lymphocytic, macrophage-dominated infiltrate with high M2 macrophage content, consistent with an immunosuppressed TME for which a poor outcome would be expected. In summary, six stable and reproducible immune subtypes were found to encompass nearly all human malignancies. These subtypes were associated with prognosis, genetic, and immune modulatory alterations that may shape the specific types of immune environments. Lymphocyte expression signature, cytokines generated by Th1 and Th17 cells, a high number of unique TCR clonotypes, and M1 macrophages are significantly associated with improved OS. Conversely, wound healing, TGF-β, and macrophage regulation are associated with worse OS. Among all tumor types, the C3 subgroup has been shown to correlate with better OS in six tumor types and C4 with poor OS in three cancer types (Thorsson et al. 2018). With our increasing understanding that the tumor immune environment plays a critical role in prognosis and response to therapy, the definition of the immune subtype of a tumor may play a

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critical role in predicting disease outcomes instead of relying solely on features specific to individual cancer types. The findings of Thorsson et al. were also supported by a recent study (Zheng et al. 2022). Zheng et al. evaluated the role of TIL-T and TIL-B cells in heterogeneous human malignancies. They demonstrated that TIL-T and TIL-B cells showed divergent prognostic consequences among heterogeneous tumor types. Distinct distributions of TIL-T and TIL-B cells were noted in various tumors and TME subtypes. The high LCK protein levels (T-cell marker) were correlated with poor prognosis in the tumor types of mesothelioma, pheochromocytoma, paraganglioma, breast carcinoma, prostate carcinoma, thyroid carcinoma, and urothelial bladder carcinoma. Nevertheless, higher LCK protein levels were associated with a favorable prognosis in other tumor subtypes such as ovarian serous cystadenocarcinoma, rectum adenocarcinoma, sarcoma, stomach adenocarcinoma, pancreas adenocarcinoma, testicular germ cell tumor, head and neck squamous cell carcinoma, lung squamous cell carcinoma, and endometrial carcinoma (Zheng et al. 2022). The high CD20 protein levels (B-cell marker) showed poor outcomes in the tumor types of adrenocortical carcinoma, stomach adenocarcinoma, diffuse large B-cell lymphoma, and prostate adenocarcinoma. On the other hand, high CD20 protein levels showed favorable associations with other cancer types such as low-grade glioma, melanoma, head and neck squamous cell carcinoma, testicular germ cell tumor, ovarian serous cyst adenocarcinoma, hepatocellular carcinoma, renal papillary cell carcinoma, uterine carcinosarcoma, sarcoma, lung squamous cell carcinoma, pancreatic adenocarcinoma, urothelial carcinoma, and thyroid carcinoma (Zheng et al. 2022). These findings underlined that TIL-B and TIL-T cells, measured by the CD20 and LCK protein levels, demonstrated various prognostic trends among different subgroups. They suggested that TILs play a complex role in the heterogeneous context of tumor subtypes. Hence, the differing effects of TIL-B and TIL-T cells might not be driven by the diverse clinical, therapeutic, and TME subtypes. Furthermore, they evaluated the cellular condition of the TME communication network involving the well-recognized chemokine receptors of TIL-T and TIL-B cells, suggesting the functional interrelationship with TME. They demonstrated that these chemokine receptors, expressed by TIL-T and TIL-B cells, were significantly associated with the intensity of TIL-T or TIL-B cell infiltrations in almost all the tumor types, indicating these chemokine receptors as universal targets for up- and downregulating the TIL-T and TIL-B cells (Zheng et al. 2022). It has been reported that TIL-T cells express immune checkpoint genes, such as programmed cell death protein 1 (PDCD1, PD-1) and the cytotoxic T lymphocyteassociated protein 4 (CTLA-4), which causes TIL-T to be ineffective against tumors (Zou et al. 2016; Wei et al. 2017). Nowadays, PD-1, CTLA-4, and TIGIT inhibitors enhance the T-cell response in cancer patients. Zheng et al. demonstrated that PD-1 and CTLA-4 were solely expressed in a sub-cluster of T cells in the peripheral blood, but in lung cancers, most TIL-T cells expressed PD-1 and CTLA-4 (Zheng et al. 2022).

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PD-L1 expression and immune cell content have also been reported to be varied by gender and germline variation (Thorsson et al. 2018). PD-L1 expression was more significant in women than men in head and neck squamous cell carcinoma, clear renal cell carcinoma, lung adenocarcinoma, thyroid carcinoma, and papillary renal cell carcinoma. On the other hand, mesothelioma demonstrated the opposite trend. It is known that the expression of PD-L1 is lower in people of African descent. The interconnection was consistent across many types of cancer. It was significant in breast, head, and neck squamous cell, thyroid, and colorectal carcinoma (Thorsson et al. 2018). Lymphocyte components tended to be lower in individuals of Asian ancestry, particularly in the bladder and endometrial carcinoma. The importance of these demographic and genetic factors on PD-L1 remains unclear. However, it is crucial to provide hypotheses for the effectiveness of checkpoint inhibitor therapy based on genetic ancestry.

5

The Pivotal Role of T-Helper Cells in Tumor Immunity

The CD4-positive T-helper (Th)-cell response is becoming more important in cancer immunotherapy. Th cells are critical in the immune response by activating antigenspecific effector cells and recruiting cells of the innate immune system, such as mast cells and macrophages. Th1 and Th2 are the two predominant T-helper subtypes. Th1 cells protect individuals against intracellular pathogens, including viruses, bacteria, and some intracellular parasites, mainly via macrophage activation. Various chemokine receptors are expressed on the surface of divergent Th1 cells that defend against these diverse pathogens. Th1 cells specifically generate TNF-alpha and IFN-γ, which are crucial in regulating and activating cytotoxic T cells (CTL). Th1 cells are also demonstrated to mediate delayed hypersensitivity and inflammation through secreting specific cytokines such as IL-2, IFN-γ, and TNF-β. Additionally, Th1 cells activate APC as well as induce the production of antibodies that can increase the recruitment of infected cells or tumor cells into APC. Th1 delivery relies on the local production of IL-12, and IL-4 promotes Th2 development without IL-12. Even though IL-18 and IFN type 1 (IFN-α and β) can provide the differentiation of Th1 cells, IL-12 has a critical role in the differentiation of Th1. IFN-γ is another cytokine that can promote the differentiation of Th1 cells. IFN-γ can trigger the first wave of T-bet expression independent of IL-12 by cooperating with TCR signaling. IL-12 signaling is activated in the next step, triggering the second wave of T-bet expression and enabling more Th1 differentiation. Activated macrophages and DCs are the primary origins of IL-12 and IL-18 (Sallusto 2016; Nakanishi 2018). IFN-α and IFN-β are also crucial in the differentiation of Th1 cells. Both IFN-α and IFN-β are produced by virally infected cells, while IFN-γ is released by natural killer cells which is also the major cytokine of Th1 cells. IFN-γ can also affect DCs and macrophages, enabling Th1 cells to differentiate. Mutually, IL-12 secreted from Th1 cells induces T-bet expression; as a result, T-bet provides the transcription of IFN-γ.

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The activation of CD4-positive T cells and their differentiation to Th1 cells are dependent on the activation of macrophages. Activation of macrophages results from phagocytosis of microorganisms and processing these into peptides for presentation to T cells. This process is known as cell-mediated immunity. Activated Th1 cells increase the antigen digestion and antigen presentation abilities of macrophages by secreting IFN-γ. Subsequently, these activated macrophages produce IL-12, which will increase the strength of Th1 cells (Ito et al. 2005; Nakanishi 2018). Regulating the Th1-cell response against a tumor antigen may lead to effective immune-based treatments. Numerous reports demonstrated that CD4-positive T-cell subsets influence cancer patients’ disease-free and overall survival (Ito et al. 2005; Fridman et al. 2012; Tay et al. 2021). Th1 cells kill tumor cells directly by releasing cytokines, activating death receptors on the tumor cell’s surface (Figs. 1 and 2). Some studies suggest that these cells can mediate cell death by the direct contact of the Th cell with the tumor cells. T-helper cells directly interact with tumor cells via class II MHC molecules (Topalian et al. 1994; Perez et al. 2002). The direct interaction of tumor cells and T-helper cells was first demonstrated in melanomas (Topalian et al. 1994). T-helper cells in melanoma directly recognized the tumor cells expressing class II MHC molecules through antigen presentation. The direct contact of the tumor cells and T-helper cells via class II MHC can influence the tumor in several states, such as by elaborating toxic secretions and upregulating death-inducing receptors. CD4-positive T-helper cells have been shown to stimulate tumor cell apoptosis through numerous interrelated mechanisms. T-helper cells can induce tumor cell apoptosis via the Fas/FasL pathway (Schattner et al. 1996). For instance, activated CD4-positive Th cells directly upregulate Fas expression on the cell surface of Burkitt’s lymphoma B cells by ligating CD40 at the B-cell surface. As a result, after the upregulation of Fas by lymphoma cells, CD4-positive Th cells stimulated tumor cell apoptosis directly by FasL ligation. Moreover, CD4-positive Th cells use other apoptosis mechanisms in different tumor types. Thomas and Hersey reported that Th cells induce the death of melanoma and T-cell lymphoma cells through the mechanism that involves TNF-related apoptosis-inducing ligand (TRAIL) (Thomas and Hersey 1998). Th1 cells may also kill T-lymphoma cells using a granzyme-perforin-dependent pathway (Echchakir et al. 2000). Consequently, these reports have demonstrated that CD4-positive Th cells have direct cytolytic effects via multiple pathways. The predominant CD4-positive T-cell subset could change within the tumor, depending on the stage of the disease. While Th1 cells are dominant in the early stages of breast cancers, Th17 and Treg cells are predominant in advanced-stage breast cancers. Th1 cells are generally recognized as highly effective immune response cells capable of eliminating tumor cells (Sallusto 2016; Nakanishi 2018; Fridman et al. 2012). The increased number of Th1 cells in the TME was reported to have a favorable prognosis in various tumors, such as melanoma and brain, colorectal, breast, ovarian, lung, and laryngeal carcinomas (Ito et al. 2005; Sallusto 2016; Nakanishi 2018; Fridman et al. 2012; Hoepner et al. 2013; Galon et al. 2006; Zhang et al. 2003; Sasaki et al. 2009; Xu et al. 2016). It has been demonstrated

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that tumor-specific Th1 cells were mainly responsible for the recruitment and the augmentation of CD8-positive T-cell functions in brain cancer (Hoepner et al. 2013). In addition, the co-transfer of CD4-positive Th1 cells with CD8-positive T cells has been shown to boost the antitumor response in brain tumor-bearing mice. Xu et al. have reported that patients who showed a high rate of Th1 cytokines, especially at the primary stages of laryngeal carcinoma, also demonstrated significantly increased antitumor immune response (Xu et al. 2016). Th1 cells have also been shown to predict the favorable prognosis in colorectal, lung, and melanoma cancer patients through the interferon signaling pathway (Slattery et al. 2011; Karachaliou et al. 2018). Enhancement of IFN-γ due to the activated and increased amount of Th1 cells was associated with a more prolonged disease-free survival. Consequently, Th1-cell-related immune responses are primarily efficient in the antitumor immune response and predict favorable prognosis in various cancers. On the other hand, in some conditions, such as chronic inflammation, Th1 immune cells stimulated by inflammation can induce tumor development. Tumor development in colorectal carcinoma and chronic myeloid leukemia in some patients is an example of this situation; in these patients, IL-1β cytokine secreted by Th1 cells has been shown to be associated with tumor progression and shorter survival (Konishi et al. 2005; Wetzler et al. 1994).

5.1

T-Helper-2 Cells

T-helper-2 (Th2) cells have a significant role in humoral immunity and play a primary role in infectious diseases and allergies. Additionally, Th2 cells are characterized by their production of IL-4, IL-5, IL-9, IL-10, and IL-13. The presence of IL-4 is critical in Th2-cell differentiation during T-cell activation (Sallusto 2016; Paul and Zhu 2010). IL-4 is produced by eosinophils, basophils, mast cells, NKT cells, and previously differentiated Th2 cells, promoting the phosphorylation of STAT6 and the induction of GATA3. During the induction of Th2 cells, various cytokines such as IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) play central roles in inducing the Th2 cells in addition to IL-4. GATA3 has a significant role in Th2 proliferation. It is the primary transcription factor of Th2 cells, leading to the differentiation of Th2 cells while inhibiting Th1 differentiation (Nakayama et al. 2017). GATA3 is also known to inhibit IFN-γ secretion, and it additionally plays a leading role in creating Th2 cells by stimulating IL-5 and IL-13 production (Paul and Zhu 2010). The expression of important cytokines such as IL-4, IL-5, IL-13, and IL-9 produced by Th2 is controlled by GATA3. Cytokine conditions at the site of antigen deposition or in the local lymph node are particularly critical in the Th differentiation stage. The release of IL-4 and IL-5 by Th2 cells can attract and activate the function of eosinophils and mast cells. Th2-generated cytokines also promote the isotype switching to immunoglobulin E (IgE) in activated B lymphocytes. Thereby, increased levels of IgE in Th2 reactions, combined with antigen exposure and FceR1 receptor expression by eosinophils or mast cells, result in the triggering and

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release of inflammatory factors such as histamine, leukotrienes, prostaglandins, and platelet-activating factor. This eosinophil-based process is known as immediate-type hypersensitivity (ITH) and is characterized by bronchial constriction, vascular dilation, and leakage (Sallusto 2016; Paul and Zhu 2010; Nakayama et al. 2017). Th2 cells activate M2 macrophages in contrast to Th1 cells, which activate the M1 macrophages to produce IL-12 and establish cell-mediated immune responses. M2 macrophages are significant in the generation of anti-inflammatory cytokines. They also have a prominent role in the tissue repair process by stimulating the production of growth and angiogenic factors. Th1 cells are the most significant CD4-positive T-helper population, critically correlated with favorable outcomes across a broad spectrum of malignant tumors. These promising outcomes in the prognosis of numerous tumors can be attributed to the potent IFN-γ production by Th1 cells and hence their pleiotropic downstream effects, including enhancement of CTL maturation and function, antitumor myeloid cell programming, and inhibition of angiogenesis. The act of Th2 cells in cancer is partly more nuanced and context-dependent in contrast to the consistent antitumor effects of Th1 cells and their cytokines in the TME. Th2 cells are reported to perform antitumor immunity by recruiting specific populations of innate immune cells to TMEs. IL-4 and IL-13 are the most critical cytokines generated from Th2 cells. Both are structurally similar pleiotropic cytokines that regulate the immune microenvironment and immunity, not only under normal physiological conditions but also in cancer. Although Th2 cells predominantly produced these immunoregulatory effector cytokines, macrophages, natural killer T cells (NKT), innate NK cells, dendritic cells, eosinophils, mast cells, and basophils also secreted IL-4 and IL-13. In cancer, IL-4 can also be produced by tumor cells themselves. IL-4 and IL-13 mediate common immunological responses like the inhibition of inflammatory cytokine production and upregulation of class II MHC and CD23 on monocytes. Although both cytokines induce the IgG and IgM synthesis in B cells, they do not affect resting or activated T cells (Nakayama et al. 2017; Mattes et al. 2003; Tepper et al. 1992). The inhibition of the growth of murine tumors such as adenocarcinomas, melanomas, and sarcomas was demonstrated to be dependent on IL-4-mediated eosinophil recruitment, which leads to significant tumor cell cytotoxicity (Mattes et al. 2003; Tepper et al. 1992). In these murine tumor models, treatment of tumor cells with IL-4 and the transfection of the IL-4 gene in tumor cell lines have shown that IL-4 has a robust antitumor effect on numerous types of malignant tumors, including breast, kidney, thyroid, and lung carcinomas (Tepper et al. 1989, 1992). Murine IL-4 exhibits potent antitumor activity in the area where tumor cells are present. Tepper et al. demonstrated that IL-4 transfected breast tumor cell lines elicited a significant antitumor response after being injected into mice. The antitumor action of IL-4 was also shown in nu/nu mice, which are devoid of T cells, and bg/bg mice, which are devoid of NK cells. In light of these findings, it has been suggested that IL-4-mediated tumor cytotoxicity has a lymphoid-independent nature and that T cells and NK cells were not necessary for the antitumor effect of IL-4 (Tepper et al. 1989).

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Histopathological findings of IL-4-producing tumor cells revealed that the majority of the inflammatory cell infiltrate associated with tumor cell death is conspicuously composed of eosinophils and macrophages, with only a few lymphocytes. To clearly show which exact cell is responsible for the antitumor effect of IL-4, antibodies that specifically block granulocytes at the site of inflammation were injected in vivo (Tepper et al. 1992). As a result of these studies, it has been shown that the leukocytes responsible for IL-4-mediated tumor cytotoxicity are primarily eosinophils. The complete eradication of tumor cells requires high concentrations of IL-4 locally because the action of IL-4 is dose-dependent. The antitumor effect of IL-4 is abolished due to in vivo administration of the anti-IL-4 antibody, giving us further evidence of the impact of IL-4 on tumor cytotoxicity. Additionally, IL-4 stimulates the antiproliferative action of other cytokines and growth factors, such as TNF-α (Nagai and Toi 2000). The increased cytotoxic effect of TNF-α via IL-4 was demonstrated in breast carcinoma cells (MDA-MB-330) in a dose-dependent manner. Similarly, IL-4-induced TNF-α-associated tumor cytotoxicity was shown in human epidermoid carcinoma and lymphoma cells (Klara and Bharat 1991). A combination of IL-4 treatment with various growth inhibitors like tamoxifen and TGF-β1 has shown additional efficacy in growth inhibition in breast carcinoma cell lines. IL-4 inhibits approximately 90% of the 17/β estradiol-induced proliferation of MCF-7 WT cells without alteration in estrogen receptor expression. In the absence of estrogens, the growth inhibition of the breast tumor cells by IL-4 was found to decrease (Nagai and Toi 2000). Numerous studies conclude that the antitumor action initiated by localized IL-4 secretion may be primarily due to the abrupt influx of host effector cells capable of mediating tumor cytotoxicity directly or indirectly through other tumoricidal cytokines (Tepper et al. 1989). IL-4 also shows another antitumor action that can induce apoptosis in numerous cancer cell lines. IL-4 and insulin-like growth factor-1(IGF-1) are known to share a common signaling pathway through the insulin receptor substrate-1 (IRS-1) molecule. The most critical point that must be underlined is that IL-4 cannot affect IGF-1-stimulated proliferation. This finding means that apoptosis will be inhibited in MCF-7 and MDA-MB-231 cell lines. IGF-1 reverses the action of IL-4, which inhibits tumor growth. This suggests that IL-4 inhibits tumor growth by inducing apoptosis (Gooch et al. 1998). Apoptosis has been shown to be induced in cultured breast cancer cells by IL-4 secretion (Nagai and Toi 2000; Gooch et al. 1998). Findings pointed out that the induction of apoptosis during the inhibition of the growth of cancer cells is based on the delicate balance between IGF-1 and IL-4. As an example, hormone-resistant breast cancer cells are likely to enhance IGF-1 production. Little is known yet about IL-4 expression in hormone-resistant tumors, but it should be noted that the IL-4 effect will be significantly reduced in IGF-1-dominated tumors. Furthermore, it has also been demonstrated that IL-4 plays a crucial role in regulating 3-beta-hydroxysteroid dehydrogenases and 17-beta-hydroxysteroid dehydrogenases. This finding indicates that IL-4 is vital not only for Th2-type immune reactions but also for tumor cell growth itself in breast carcinoma.

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The majority of normal epithelial cells do not express IL-4 receptors (IL-4R), whereas the receptors for two Th2-related cytokines, IL-4 and IL-13, are demonstrated to be upregulated and activated in various tumors such as breast, ovarian, colon, bladder, and pancreatic carcinomas (Murata et al. 2018; Todaro et al. 2008; Fujisawa et al. 2012; Barderas et al. 2012; Prokopchuk et al. 2005). The IL-13 gene shows a similar structural and functional activity to the IL-4 gene and has a 30% identity in the amino acid sequence to the IL-4 protein (Minty et al. 1993). IL-4 and IL-13 are the critical regulatory cytokines in the TME, and these cytokines both activate TAMs and myeloid-derived suppressor cells that regulate the protumoral action (Wang and Joyce 2010). The significant regression and elimination of resident tumor cells by localized IL-4-mediated cytotoxicity justify the use of IL-4-related drugs in treating malignant tumors (Tepper et al. 1992). Furthermore, IL-4R expression on cancer cells is so vigorous that IL-4R has been used as a targeting molecule for anti-cancer toxins fused to IL-4. Currently, in clinical trials for various cancers, IL-4-PE38KDEL cytotoxin is an example of IL-4-related cancer therapy (https://clinicaltrials.gov). This molecule was under investigation for numerous tumors, such as metastatic breast cancer, non-small cell lung cancer, and kidney cancer. These studies provide a rationale for therapeutically targeting IL-4R and IL-13R through various approaches. Conversely, T-helper cells expressing IL-4, IL-13, and IL-10 also boost lung metastasis in the MMTV-PyMT murine model of breast adenocarcinoma through stimulating pro-tumorigenic process in TAMs (Wang and Joyce 2010). In this context, the decline of either T-helper cells or IL-4 significantly reduced the density of circulating malignant cells and metastatic outgrowth. T-helper cell depletion or neutralization of either IL-4 or IL-13 also improved therapeutic responses to radiation therapy and paclitaxel chemotherapy in a syngeneic orthotopic model of murine breast carcinoma.

5.2

T-Helper-17 Cells

T-helper-17 (Th17) cells were recently described in 2005 as a distinct T-helper cell lineage independent from Th1 and Th2 subsets (Harrington et al. 2005; Park et al. 2005). Th17 cells enclose various CD4-positive T cells that produce IL-17 (also known as IL-17A), IL-17 F, IL-21, IL-22, and granulocyte-macrophage colonystimulating factor (GM-CSF) (Korn et al. 2009). Multiple cytokines such as TGF-β, IL-6, IL-1b, and IL-21 are needed to induce the differentiation of naïve CD4-positive T cells to Th17 cells (Korn et al. 2009). The RAR-related orphan receptor gamma (ROR-γt) or its homolog ROR-c in humans is reported as the main transcription factor for the differentiation of Th17 cells (Ivanov et al. 2006; Annunziato et al. 2007). In humans, the collaboration of IL-1, IL-6, and IL-23 cytokines induces the development of divergent Th17-cell subsets, and IL-23 provides to keep the pathogenic phenotype and survival of Th17 cells (Langrish et al. 2005). These subsets of Th17 cells express RAR-related orphan

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receptor gamma (ROR-γt) and transcription factor T-bet together and display various pathogenic acts (Patel and Kuchroo 2015). In addition to ROR-γt, the Th17 cells are also controlled by other primary transcription factors such as RORα, aryl hydrocarbon receptor (AHR), and interferon regulatory factor 4 (IRF4). Transcription factors and various cytokines generated by Th17 cells have been demonstrated to show both pathogenic and beneficial actions. Direct environmental effects on Th17 differentiation and function further complicate these controversial findings. The survival and the activity of Th17 cells have been shown to be influenced by some environmental factors, such as ultraviolet light and toxins. Th17 cells have taken action in numerous extracellular fungal and bacterial infections. They promote inflammation by recruiting neutrophils to the site of infection. Additionally, they generate antimicrobial peptides and maintain barrier immunity by providing epithelial cell integrity. Th17 cells can also exacerbate autoimmunity by augmenting the levels of some cytokines, such as IL-22, IL-17A, and IL-17F. Pathogenic Th17 cells were demonstrated to participate in numerous autoimmune diseases, such as inflammatory bowel disease, multiple sclerosis, and psoriasis (Hou and Bishu 2020; Moser et al. 2020; Park et al. 2022). Both environmental (toxins and UV exposure) and localized (cytokines) factors can affect the differentiation of Th17 cells and their resulting autoimmune manifestations (Veldhoen et al. 2008). Due to changes in the microenvironment, Th17 cells can differentiate into different subsets of lineages. The vital roles of Th17 cells in inflammation and tumor immunity have been reported. The relationship between inflammation and cancer is a crucial and intertwined process. The complex and contradictory ways in which Th17 cells adapt to this relationship are beginning to become apparent. Accompanied by the findings of many recent studies, it has been determined that inflammation in tumor tissue controls many immune cells, such as Th17 cells. This contributes to the dissipation of antitumor immunity and survival of tumor cells, worsening tumor growth and metastasis. However, in some cases, inflammation in the presence of Th17 cells has been observed to protectively initiate and maintain antitumor immunity. In this context, it’s essential to identify the presence of Th17 cells in diversified diseases because of the dual action of Th17 cells in both autoimmune disease and cancer, Th17 cells have a high level of plasticity compared to other stable T-cell lineages, such as Th1 and Th2 cells. Th17 cells chiefly demonstrated to transdifferentiate into Th1 or regulatory T (Treg) cells. Moreover, they also can be transdifferentiated into Th2 cells, type 1 regulatory T (TR1) cells, or follicular helper T (TFH) cells that authorize them to have divergent and opposite functions. Consequently, they can display qualitatively distinctive responses due to diverse microenvironments (Guery and Hugues 2015). Th17 cells transdifferentiate into Th1 cells in the absence or downregulation of TGF-β, IL-12, and IL-23 cytokines in the microenvironment. In comparison, sufficient levels of TGF-β preserve the phenotype of Th17 cells. In the normal state, Th17 cells are abundant in the mucosal tissues and the gut. At the same time, approximately only 0.1% of Th17 cells reside in the peripheral blood of healthy individuals (Zou and Restifo 2010). Contrarily, in cancer patients, the

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number of Th17 cells is at high levels, especially in the cancerous tissue compared to non-cancerous tissues (Zhang et al. 2009). The number of Th17 cells has been shown to increase in various cancer types, such as melanoma and breast, colon, ovarian, prostate, renal, and liver tumors (Zou and Restifo 2010). It has been suggested that these Th17 cells, which increase in cancerous tissue, may develop due to factors produced by cancerous cells (Kryczek et al. 2009). But the exact reason for the recruitment of a significant amount of Th17 cells in the tumor microenvironment is still unknown, whether they are induced, expanded, recruited, or converted from Tregs in tumoral tissues. The reason leading to this result may be due to the combination of all these factors. The degree of the intratumoral Th17-cell infiltration shows a significant association with the prognosis of the cancer course, either bad or good. Numerous studies concerning Th17-cell infiltration in tumor tissues constitute confusion about the impact of Th17 cells on prognosis in cancer patients (Kryczek et al. 2009). IL-17A, which is secreted by Th17 cells, has been demonstrated to impair immune surveillance and, as a result, promote tumor growth (He et al. 2011; Wang and Joyce 2010). In contrast, Th17 cells have been shown to directly destroy much more melanoma tumor cells in mice than Th1 cells. The antitumor features of Th17 cells take place via the induction of the recruitment of numerous immune cells, such as cytotoxic T lymphocytes, NK cells, and neutrophils, into the tumor microenvironment. On the other hand, Th17 cells promote tumor growth by inducing M2 macrophage differentiation and boosting Treg infiltration in the tumor microenvironment. In addition, they contribute the tumor growth by increasing the proliferation of tumor cells, angiogenesis, and metastasis (Numasaki et al. 2003; Liu et al. 2011). Angiogenesis is a must for the generation and progression of solid tumors and their metastasis. IL-17A, a well-known cytokine of Th17 cells, stimulates neoangiogenesis in the tumor microenvironment through the direct production of VEGF and angiogenin-2 (Numasaki et al. 2003). Regardless of the neoangiogenesis effect, VEGF also inhibits immunological activity by arresting the development of DCs, altering the differentiation of various hematopoietic lineages, influencing T-cell maturation, and thereby inducing tumor growth (Gabrilovich et al. 1998). Numerous cancers with a significant degree of Th17-cell infiltration have been shown to develop neoangiogenesis, such as liver (Zhang et al. 2009), colon (Liu et al. 2011), gastric (Iida et al. 2011), and pancreatic carcinomas. Cytokines other than IL-17A, such as IL-17F, IL-21, and IL-22, secreted by Th17 cells exert anti-angiogenic features in divergent tumor types. Conditions that prompt Th17 cells to secrete one or more of these cytokines regulate angiogenesis and tumor growth. Although IL-22 has anti-angiogenic features, the increased amount of IL-22 in patients with lung or pancreatic cancers has been shown to associate with poor prognosis and survival (Kobold et al. 2013; Xu et al. 2014). On the other hand, IL-22 is related to diminished tumor growth in breast cancer models (Weber et al. 2006). Th17-cell infiltration in the cancerous tissue was associated with favorable survival in prostate, lung, and ovarian carcinoma patients (Kryczek et al. 2009;

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Sfanos et al. 2008). However, the overall and disease-free survival were unfavorable in colon, liver, and pancreas carcinoma cases (Zhang et al. 2009; Tosolini et al. 2011; He et al. 2011). Other studies also reported significant differences concerning the impact of Th17 cells on the overall and disease-free survival of various human cancers (Punt et al. 2015; Wilke et al. 2011). These contradictious findings have also been shown in experimental animal tumor models. Although it is difficult to explain these controversial results, which are reported in both human and animal studies, the plasticity of Th17 cells can be suggested as the most likely cause. Th17 cells have the ability to differentiate into Th1-like cells that generate IFN-γ and express T-bet, which is necessary to eradicate tumor cells. They can also transdifferentiate into FOXP3- expressing Treg and display a significant immunosuppressive role (Narendra et al. 2013). It is still unclear how Th17 cells are regulated and behave in the TME, and it is difficult to understand how Th17 cells are induced and act. Various factors, such as tumor type and stage, significantly affect the tumor microenvironment, thereby influencing the Th17-cell plasticity. As a result, Th17 cells will acquire either antitumoral features or immunosuppressive properties that give rise to eradication or tolerance to the tumor, respectively (Guéry and Hugues 2015).

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Spatial Transcriptomic Approaches for Understanding the Tumor Microenvironment (TME) Habib Sadeghi Rad, Yavar Shiravand, Payar Radfar, Rahul Ladwa, Majid Ebrahimi Warkiani, Ken O’Byrne, and Arutha Kulasinghe

Abstract

The tumor microenvironment (TME) is a heterogeneous milieu of cellular and molecular factors that play a crucial role in tumor evolution and disease progression. These factors are important in all aspects of tumorigenesis as they reveal how cell types within the TME interact with one another. Characterizing the TME therefore paves the way for deeper insights into the tumor biology and addresses several unanswered questions in tumor progression and drug resistance. The emerging cellular and molecular profiling technologies with spatial phenotyping capabilities are rapidly changing our understanding of the TME architecture. These approaches allow for high-plex transcriptomic and proteomic phenotyping while also providing valuable spatial information on cell types within the TME. Here, we discuss tissue biomarkers associated with therapy response and describe cutting-edge technologies giving us new insights into cancer biology.

H. S. Rad · A. Kulasinghe (✉) Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, QLD, Australia e-mail: [email protected] Y. Shiravand Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy P. Radfar · M. E. Warkiani School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, Australia R. Ladwa · K. O’Byrne Princess Alexandra Hospital, Brisbane, QLD, Australia # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_111 Published online: 7 February 2023

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Keywords

Immunotherapy · Multiplex immunohistochemistry · Spatial profiling · Tumor microenvironment

1

Introduction

In 1863, Rudolf Virchow proposed the link between cancer and inflammation, and later, Steven Paget proposed the theory of seed and soil in 1889 (Jin and Jin 2020). These events were initial steps toward forming the first concept of tumor microenvironment (TME) which is a complex and dynamic milieu, composed of immune and non-immune cells (Sadeghi Rad et al. 2021). The TME is characterized with cancer hallmarks such as hypoxic conditions, endogenous H2O2, as well as altered expression of extracellular matrix (ECM) proteins (Kondo et al. 2017). Furthermore, anaerobic glycolysis is considered as the main cause of acidic environments, playing a significant role in tumor progression and invasion through enhancing the expression of different mediators such as interleukin-8 (IL-8) and vascular endothelial growth factor A (VEGFA) (Kondo et al. 2017). Two cut-off points were defined based on the location and population of cytotoxic T cells (CTLs) to categorize tumors into immune flamed (with over 500 intraepithelial CTLs/mm2), immune desert (with less than 50 stromal CTLs/mm2), and immune excluded (tumors without abovementioned characteristics) (Echarti et al. 2019). It was well known that infiltrating immune cells such as macrophages, existing in inflamed TME, could express programmed death-ligand 1 (PD-L1), thereby rendering this type of TME more sensitive to PD-1/PD-L1 blockade (Ahmed et al. 2020). Understanding the underlying interactions between the components of TME is of great importance as these interactions could affect the nature of TME, and conversely, the TME can influence the growth and progression of tumors (Ansell and Vonderheide 2013). Immune cells are essential components of the TME, which can be adaptive and innate immune cells, which can either promote or suppress tumor development (Truffi et al. 2020).

1.1

Cellular Components of TME

T cells: three different groups of T cells including cytotoxic T cells, T helper (Th), and T regulatory (Treg) are found within TME (Maimela et al. 2019). Cytotoxic T cells can recognize abnormal and altered tumor antigens existing on cancerous cells in order to target and destroy them (Maimela et al. 2019; Stachtea et al. 2021). These cells are able to inhibit angiogenesis via the secretion of interferon gamma (IFN-γ) (Anderson and Simon 2020). T helper cells secrete IFN-γ and IL-2 in order to support cytotoxic T cells (Najafi and Mirshafiey 2019; Jorgovanovic et al. 2020). However, Tregs are able to either promote tumor growth through suppressing antitumor response or secrete various growth factors to enhance the survival of

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tumor cells (Li et al. 2020a, b, c). B cells: these cells have different functions in the context of the TME, in that they either act as antigen presenting cells or recognize tumor-specific antigens in order to produce antibodies; however, these cells can demonstrate pro-tumorigenic activities through the activation of Tregs, and myeloidderived suppression cells (MDSC), as well as generation of pro-tumorigenic mediators (Largeot et al. 2019). Natural killer cells (NK cells): NK cells belong to the family of innate lymphoid cells (ILCs), targeting and eliminating tumor cells through the release of cytotoxic granules and secretion of immunomodulatory cytokines (Bald et al. 2020). In the blood, these cells play a part in killing tumors and inhibiting metastasis; however, in the context of TME, their roles are less efficient (Souza-Fonseca-Guimaraes et al. 2019). It was reported that intertumoral NK cells within the lung TME are enriched with Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4), an inhibitory receptor, regulating T cells after TCR stimulation through binding to CD80 and CD86, thereby reducing the ability of APCs for the activation of T cells via CD28 (Russick et al. 2020). Although there is no evidence of expressing CTLA-4 on human NK cells, a subgroup of intertumoral NK cells in a mouse modal of lung cancer, expressing CTLA-4, has been reported (Russick et al. 2020). Macrophage: macrophages are a pivotal part of innate immune system, showing different functions ranging from antigen presentation and phagocytosis to tissue repair and wound healing (Vitale et al. 2019). Generally, macrophages are divided into two different groups: M1 macrophages, demonstrating inflammatory and antitumor activities, and M2 macrophages which play a part in wound healing as well as immunosuppressive functions (Oshi et al. 2020). Although both M1 and M2 phenotypes are found within TME, tumors tend to promote M2 phenotype through IL-4 secretion and hypoxia in order to enhance tumor development (Oshi et al. 2020; Ye et al. 2021). Dendritic cells (DCs): DCs play an important role in presenting antigen to B and T cells, leading to stimulating these cells potentially (Kohli and Pillarisetty 2020). Phenotypically and functionally, three different populations of DCs including conventional DCs (cDC1/2), plasmacytoid DCs (pDCs), and monocyte-derived DCs (moDCs) have been reported (Verneau et al. 2020). cDC1s existing within TME support T cell function through secreting IL-12; moreover, these cells also play a key role in recruiting and activating CD8+ T cell-mediated antitumor (Poropatich et al. 2020). pDCs are the major source of interferon type I, playing a pivotal role in antiviral response; however, their role in the context of TME is not clearly understood (Poropatich et al. 2020). It was recently reported that pDCs presenting in the TME of head and neck squamous cell carcinoma express high levels of OX40. In the mouse OX40+ pDCs-rich tumor model, pDCs depletion enhanced tumor progression, suggesting an antitumoral activity of pDCs (Peng et al. 2021). moDCs respond to inflammation in specific tissues; however, it was reported that monocytes could differentiate into pDCs in the acidic conditions of TME (Peng et al. 2021). Stromal cells: stromal cells within TME can influence various processes such as angiogenesis, metastasis, proliferation, and invasion through the secretion of different mediators (Denton et al. 2018). Based on different tumor types, the stroma consists of heterogeneous cell populations including fibroblasts, cancer-associated fibroblasts (CAFs), adipocytes, and stellate cells

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(Denton et al. 2018). Fibroblast are one of the main components of the stroma and plays a role in normal tissue homeostasis (Alkasalias et al. 2018; Davidson et al. 2021). These cells are of great importance in recruiting immune cells through mechanisms such as TLRs or secretion of inflammatory mediators (Alkasalias et al. 2018). In the physiological conditions, fibroblasts tend to be in a quiescent phenotype; however, following stimuli, these cells became activated, thereby rendering them sensitive to epigenetic modification, leading to switching from normal fibroblasts toward CAFs (Cortez et al. 2014; Davidson et al. 2021). CAFs also known as tumor-associated fibroblasts are not found in blood circulation of healthy individuals but were reported in patients with prostate, colon, and breast cancer (Mishra et al. 2008). Some markers expressed on CAFs such as fibroblastspecific protein-1 (FSP-1), alpha-smooth muscle actin (α-SMA), and plateletderived growth factor receptor-α (PDGFR-α) are expressed by monocytes, pericytes, and normal fibroblasts, respectively (Denton et al. 2018; Ping et al. 2021). Fibroblast activation protein-α (FAP-α) is considered as a promising marker in order to purify these cells (Denton et al. 2018). Adipocytes, also known as fat cells, specialize in storing excess energy as fat in order to regulate energy balance (Cohen and Kajimura 2021). In the context of the TME, however, these cells promote tumor growth (Iyengar et al. 2003). Adipocytes are able to secrete various matrix metalloproteases (MMPs) such as MMP1-7-10-11-14, thereby playing an important role in modification of ECM (Nieman et al. 2011). Stellate cells are star-shaped cells that originated from mesenchymal stromal cells, found in the pancreas and liver, and support wound healing through secreting growth factors (Takeuchi et al. 2021). Nevertheless, it was reported that these cells play an important role in tumor formation and progression (Roife et al. 2020). Hepatic stellate cells (HSCs) within TME of hepatocellular carcinoma (HCC) produce TGF-β to modify the ECM and secrete proangiogenic mediators such as MMP-2 and vascular endothelial growth factor A (VEGF-A), leading to proliferating normal and neoplastic liver cells (Roife et al. 2020).

1.2

Noncellular Components of TME

Extracellular Matrix (ECM): CAFs within the TME are mainly responsible for secreting ECM, which is composed of various macromolecules such as collagen, glycoproteins (laminin and fibronectin), and proteoglycans (Brassart-Pasco et al. 2020). It was reported that high infiltration of fibroblasts and collagen deposition lead to desmoplasia which is associated with poor prognosis in patients with solid tumors (Bulle and Lim 2020). Metalloproteases can break down ECM in order to produce fragments of macromolecules that demonstrate either anti- or pro-tumorigenic functions in different cancers (Brassart-Pasco et al. 2020). It was reported that collagen IV-derived fragments such as tetrastatin, canstatin, and tumstatin could bind to integrins (α3β1, α5β1, or αVβ3), thereby decreasing invasiveness and proliferative characteristics of tumor cells in different cancer models (Lambert et al. 2018; Brassart-Pasco et al. 2020). Exosomes: exosomes are

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microvesicles containing proteins, lipids, and genetic materials (Li and Nabet 2019). It seems that hypoxic conditions not only stimulate tumor cells to generate exosome but also facilitate stroma cell transition toward CAFs (Li and Nabet 2019). Within TME, exosomes promote angiogenesis, tumor growth, invasion, and inflammation though facilitating tumor and stromal cell interactions (Li and Nabet 2019).

1.3

Primary and Metastatic Site Comparison

Metastasis is a complex process which is defined as the movement of cancer cell from primary sites to other sites in various tissues and organs; therefore, in order for metastasis to occur, steps including (1) increase of cell mobility for invasion, (2) cancer cells intravasating into the blood circulation or lymphatic vessels, (3) transit and survival of circulating tumor cells (CTCs), (4) homing these cells at secondary sites, (5) and finally colonization are required (Cacho-Díaz et al. 2020). There are clear differences between the immune microenvironment of primary and metastatic sites, meaning that the tumor-infiltrated lymphocyte (TIL) counts as well as PD-L1 expression in primary sites were higher than metastatic sites, for example, in breast cancer (Szekely et al. 2018). Increased generation of immunosuppressive molecules such as adenosine, M2 macrophage phenotype, and overexpression of inhibitory receptors were observed in metastatic TME compared to primary sites (Szekely et al. 2018).

2

Immunotherapy and Predictive Biomarkers

Immunotherapy uses immune system components such as tumor-targeting antibodies, adoptive cell transfer (ACT), and immune checkpoint inhibitors (ICIs) in order to kill tumor cells (Rizvi et al. 2015; Sadeghi Rad et al. 2021). In recent years, immunotherapy, ICIs in particular, has attracted much attention in the field of cancer therapy; however, not all patients benefit from this treatment, highlighting the need for predictive and prognostic biomarkers and the growing need to understand the TME (Paucek et al. 2019). PD-L1 expression, tumor mutational burden (TMB), microsatellite instability (MSI), presence/absence of tertiary lymphoid structures (TLSs), interferon gamma (IFN-γ), microbiome, adrenergic neurons, hypoxia, and cellular and molecular characterization of TME are a number of biomarkers used to determine response to immunotherapy (Gopalakrishnan et al. 2018; Mandal et al. 2019; Xiao et al. 2019; Fumet et al. 2020; Swanton 2020).

2.1

PD-L1

Programmed death-ligand 1 (PD-L1) also known as CD274 and B7-H1 with the molecular weight of 40 kDa, is expressed on tumor cells and APCs, whereas programmed cell death protein 1 (PD-1), is detected on tumor infiltrating

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lymphocytes (TILs) such as CD4+ TILs, CD8+ TILs, Treg cells, and tumorassociated macrophages (TAMs) (Oh et al. 2020; Grossman et al. 2021). PD-L1 is mainly expressed by T and B cells, DCs, macrophages, and bone marrow-derived mast cells (BMMCs), while the expression of PD-L2 is mainly restricted to activated macrophages and DCs (Mattox et al. 2017). In physiological conditions, PD-1/PDL1 pathway plays a part in immune tolerance and also regulates excessive tissue inflammation as well as autoimmunity; however, this pathway could be overactivated, thereby complicating tumor immunity, virus infection, and autoimmune diseases (Mansfield et al. 2016; Doroshow et al. 2021). To this end, PD-1/PDL1 blockade therapies such as anti-PD-1 antibodies (Nivolumab, Pembrolizumab, Cemiplimab), anti-PD-L1 antibodies (Atezolizumab, Avelumab, Durvalumab) have been applied to block PD-1/PD-L1 interaction, thereby promoting immune response against tumors (Herbst et al. 2014; Tumeh et al. 2014). The tumor proportion score (TPS), using PD-L1 expression, and was applied by the KEYNOTE-040 and CheckMate 141 trials, showed that PD-L1 expression by tumor cells was related to better clinical outcomes as well as overall survival (OS) in patients receiving antiPD-1 antibody (Ferris et al. 2016; Cohen et al. 2019). Moreover, it was reported that OS in patients with increased PD-L1 expression (>50%) was improved compared to those with lower PD-L1 expression, suggesting the role of PD-L1 expression as an indicator of response to ICIs (Cohen et al. 2019). Schoenfeld and colleagues evaluated the clinical and molecular features related to PD-L1 expression and ICIs response in patients with lung adenocarcinoma, in whom PD-L1 IHC testing and targeted NGS Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) were conducted on the same tissue (Schoenfeld et al. 2020). They reported that the varied distribution of PD-L1 expression among metastatic sites, enriched for lymph nodes and negative for bones, can influence the prediction of PD-L1 for ICIs response. In addition, high expression of PD-L1 was associated with mutations in MET, TP53, and KRAS, whereas mutations in EGFR and STK11 were negatively related to PD-L1 expression. In another study, molecular characteristics of response to ICIs were evaluated using targeted next-generation sequencing (MSK-IMPACT) in patients with nonsmall cell lung carcinoma (NSCLC) (Rizvi et al. 2018). They measured TMB in patients with durable clinical benefit (DCB) and no durable benefit (NDB) to compare the quantification of TMB using NGS and whole-exome sequencing (WES), showing that the enhanced TMB can improve benefit response to ICIs. Furthermore, TMB and PD-L1 expression had the same predictive capacity; however, these were considered independent variables.

2.2

TMB

Tumor mutation burden (TMB) is a definition used for the nonsynonymous mutations found in the DNA of cancer cells. However, there remain a number of challenges in determining the optimal TMB cut-off/threshold (Yao et al. 2020). NGS of tumor tissues have revealed a wide range of mutations (Linnemann et al. 2015).

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It is thought that a higher TMB elicits higher neo-antigen load, therefore eliciting an immunological response. In general, patients with high TMB (with 10 mut/Mb or greater), tend to have better outcomes to immunotherapy (Linnemann et al. 2015; Fusco et al. 2021). It was shown that tumor missense mutational burden (TEMMB) could increase CD8+ T cell infiltration, leading to a better response to immunotherapy; however, TEMMB are able to express neo-epitopes, and among this group, a small number may be recognized by T cells, therefore, being associated with T cell activation (Schumacher and Schreiber 2015). A direct correlation between high TMB and response to PD-1/PD-L1 inhibitors was demonstrated in patients with NSCLC (Yu et al. 2021). In patients with head and neck squamous cell carcinoma (HNSCC) receiving anti-PD-1/PD-L1 antibodies, a higher TMB was observed among responders compared to nonresponders; moreover, a correlation between total mutational load (TML) and immunotherapy outcomes was reported in these patients (Cristescu et al. 2018; Hanna et al. 2018). Furthermore, Rizvi and colleagues evaluated 1662 patients with different cancer types using MSK-IMPACT in order to study the link between TMB and clinical outcomes, and they reported that a higher TMB (top 20%) in each histology was related to better OS in these patients. In addition, researchers have defined different TMB cut-offs for various tumor types such as breast cancer (5.9 mut/Mb), NSCLC (13.8 mut/Mb), HNSCC (10.3 mut/Mb), melanoma (30.7 mut/Mb), and colorectal cancer (52.2 mut/Mb) (Samstein et al. 2019; Shao et al. 2020). It is clear that TMB refers to all nonsynonymous somatic mutations in the DNA of tumor cells; however, some mutations such as EGFR and STK11 are not associated with clinical benefits in patients receiving ICIs, or these mutations may be related to primary resistance to ICI therapy; therefore, Li and his colleagues developed the tumor mutation score (TMS) as a number of genes with nonsynonymous somatic mutations in candidate genes (Freitas et al. 2021). The MSK-IMPACT cohort reported novel TMS (TMS55) as the number of genes with nonsynonymous mutations in the 55 favorable prognostic genes, which can be considered as a better biomarker rather than TMB in predicting survival for pan-cancer patients after immunotherapy (Li et al. 2020a, b, c).

2.3

MSI

Microsatellites (MS) also known as simple sequence repeats (SSRs) or short tandem repeats (STRs), composing of one to six nucleotide repeated sequences (Hause et al. 2016; Li et al. 2020a, b, c). MS is usually generated during replication slippage due to lack or defect in mismatch repair (MMR) system in tumor cells, which can play an important role in tumor development (Li et al. 2020a, b, c). A study analyzed the link between microsatellite instability (MSI) status and immunotherapy outcomes in patients with colorectal cancer and reported that patients with microsatellite instability-high (MSI-H) tumors responded to ICIs compared to those with microsatellite stable tumors or microsatellite instability-low tumors (Lin et al. 2020). Therefore, the US Food and Drug Administration (FDA) has recently approved MSI-H as genetic test in order to choose patients for ICI-mediated immunotherapy

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regardless of cancer type (Zhang et al. 2020; Shimozaki et al. 2021). In silico analysis of tumor immunity in different cancer types demonstrated that immunological differences between MSI-H and non-MSI-H patients with colorectal cancer were distinguishable but for stomach adenocarcinoma were decreased and for patients with endometrial carcinoma of the uterine corpus completely disappeared; therefore, the number of the tumor-infiltrating immune cells (TIICs) compared to MSI status is highly associated with the clinical outcomes regardless of cancer types (Zhang et al. 2020).

2.4

TLSs

Within the TME, chronic inflammation leads to the secretion of cytokines and chemokines, which in turn recruit immune cells such as B and T cells; therefore, these cells form an ectopic lymphoid organ which is known as tertiary lymphoid structures (TLSs) (Sautès-Fridman et al. 2019; Chen et al. 2020). Structurally, TLS consist of germinal centers, mainly B cells, which are surrounded by T cells (SautèsFridman et al. 2019; Chen et al. 2020). Forming TLSs in TME or surrounding tumors could enhance immune responses toward cancerous cells (Kang et al. 2021). A high number of plasma cells are related to increased number of TLSs and B cells in HNSCC as well as breast cancer (Lechner et al. 2019; Seow et al. 2020). Moreover, these plasma cells are linked to increased number of TILs and overexpression of cytotoxicity-associated genes in ovarian cancer (Kang et al. 2021). In addition, it was observed that CD8+ TILs can be considered as prognostic markers only along with plasma cells, CD4+ TILs, and CD20+ TILs, proposing that these four groups of lymphocytes work together in order to promote antitumor responses (Kroeger et al. 2016). Furthermore, T cells from TLSs demonstrated a highly increased IFN-γ release in MC38 tumors, suggesting the important role of TLSs in activating effector T cells (Dieu-Nosjean et al. 2016). On the other hand, it was found that TLSs forming during hepatitis in the inflamed liver could serve as a niche for homing cancer stem cells (CSCs), thereby being associated with decreased survival and enhanced risk of late recurrence in hepatocellular carcinoma (Kang et al. 2021).

2.5

IFN-g

Interferons are a group of proteins produced by various cells in response to the virus infection (Mojic et al. 2017). Type I interferons (IFN-α and IFN-β) not only directly stimulate antiviral responses in surrounding or infected cells, but they are also critical in activating NK cells, thereby enhancing antiviral responses of the innate immune system (Lee and Ashkar 2018). However, type II interferon also known as IFN-γ functions differently (Lee and Ashkar 2018). Within TME, different cells such as T and B cells, NK cells, and Treg cells are able to produce IFN-γ; however, it was reported that all nucleated cells express IFN-γ receptor (IFNGR1), highlighting their ability to response to IFN-γ (Gocher et al. 2021). IFN-γ enhances the expression of various mediators such as CXCL9-10-11 and their receptors in order to recruit

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immune cells in the TME; moreover, IFN-γ also plays an important role in restricting tumor progression through recruiting CTLs and increasing their cytotoxicity function (Bhat et al. 2017). Nevertheless, IFN-γ could have some pro-tumorigenic functions so as to induce T cell apoptosis, tumor cell angiogenesis, and immune response suppression through indoleamine 2,3 dioxygenase 1 (IDO1), inducible nitric oxide synthases (iNOS), and first apoptosis signal receptor ligand (FASL), respectively (Bhat et al. 2017; Gocher et al. 2021). Taken together, IFN-γ could function as a double-edged sword in the context of TME, meaning that it has both anti- and pro-tumorigenic functions.

2.6

Microbiome

Microbiota is a general term, referring to the group of microbes which live on or in various sites of human body; therefore, these microbes encode a collection of microbial genes which is known as microbiome (Kovács et al. 2020). Human malignancies have been categorized into four different classes based on microbiome composition and microbial interactions (Kovács et al. 2020). Class A is usually associated with immune system involvement such as lymphoma and related disorders and class B such as carcinoma and sarcoma, requiring direct interactions with parenchymal cells; class C refers to local effects on epithelial tissues; and class D demonstrates the results of microbial community (Plottel and Blaser 2011; Kovács et al. 2020). Microbiome can promote tumorigenesis through different mechanisms such as stimulating immune response, inducing inflammation, and unbalancing cell death and proliferation (Kovács et al. 2020). However, it was shown that the gut microbiome may potentiate immune response to directly kill tumor cells through activating T cell-mediated response (Vétizou et al. 2015; Kovács et al. 2020). Furthermore, in patients receiving anti-CTLA-4 antibodies, the microbial composition was found to have changed from Bacteroidales and Burkholderiales toward Bacteroides and Clostridiales, demonstrating the potential role of Bacteroides fragilis in promoting Th1 responses and activating DCs through inducing IL-12 and, thus, increasing the effectiveness of CTLA-4 therapy (Vétizou et al. 2015). According to a study, patients with advanced melanoma responding to anti-PD-L1 therapy had increased levels of Enterococcus faecium, Collinsella aerofaciens, and Bifdobacterium longum (Matson et al. 2018).

2.7

Adrenergic Neurons

Nerves such as neurons and neuroglia have been reported to regulate tumor growth and progression in different cancer types including head and neck, breast, colon and rectal, prostate, and pancreatic cancers (Wang et al. 2021a, b). Nerve and tumor cell interactions lead to producing neuroactive molecules by nerves in order to improve tumor growth, angiogenesis, inflammation, and invasion through influencing tumor cells, macrophages, and lymphocytes (Wang et al. 2020, 2021a, b). Tumor cells, in turn, are able to induce reprogramming and regeneration of nerves via secreting cytokines;

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moreover, these cells can destroy nerves, thereby inducing the cancer-associated pain (Wang et al. 2021a, b). It was demonstrated that adrenergic stimulation and physiological stress may affect the genomic integrity and also increase somatic DNA mutations, leading to promoting induction and progression of tumor cells (Mravec et al. 2020). β-Adrenergic signaling can influence some components of TME such as immune cells, endocrine, and microbiota (Mravec et al. 2020). It was reported that β-adrenergic blockers could suppress oral carcinogenesis and decrease tumor invasion (Cecilio et al. 2020).

2.8

Hypoxia

Tumor cells proliferate rapidly, leading to decreasing oxygen supply for cancer cells as well as tumor stromal cells (Emami Nejad et al. 2021). To overcome this situation, tumor cells not only switch their metabolism toward glycolytic metabolism to acidify TME, but also, they can induce the expression of VEGF to enhance angiogenesis and vascularization (Moreira-Soares et al. 2018; Petrova et al. 2018). Furthermore, tumor cells can consume glycolytic metabolites such as lactate to promote tumor progression (Emami Nejad et al. 2021; Martínez-Reyes and Chandel 2021). In addition, tumor and stromal cells within the hypoxic TME produce some signaling mediators in order to induce the transformation of fibroblasts to CAFs, which in turn are able to produce an ECM, thereby supporting cell migration (Petrova et al. 2018; Emami Nejad et al. 2021). Moreover, overexpression of hypoxia-inducible factors (HIFs) such as HIF-α and HIF1β could induce transcriptional expression of integrins α5 and α6 genes (ITGA5 and ITGA6), forming a specific ECM receptor (Petrova et al. 2018). As a consequence of cell-ECM interaction, cells change their signaling pathways as well as motility (Petrova et al. 2018). HIF can also transcriptionally induce P4HA1/2, PLOD1/2, LOX, and LOXL2/4 genes, engaging in collagen posttranslational modification (Petrova et al. 2018). In patients with HNSCC, hypoxic TME is considered as a negative prognostic biomarker due to the fact that hypoxia decreases the production of reactive oxygen species (ROSs), leading to lowering radiation-mediated DNA damage, thereby rendering tumor cells less sensitive to radiotherapy (Petrova et al. 2018). In vitro observations showed that Nimorazole, a hypoxic radiosensitizer, could make both HPV+ and HPV- HNSCC cells sensitive to radiation; however, in vivo studies confirmed its effectiveness in favor of HPV- tumors (Topalian et al. 2012; Herbst et al. 2014; Mohebbi et al. 2018; Petrova et al. 2018).

3

Spatial Phenotyping of the Tumor Microenvironment

Bulk gene expression provides a total expression profile of the tumor sample without considering individual cellular behavior and characteristics (Merritt et al. 2019). To accomplish this, single-cell technologies paved the way for the investigation of various cell types at the single-cell level and resolution, resulting in the exploration

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of cellular heterogeneity (Merritt et al. 2019, 2020). However, when it comes to the position and location of different cell types within the TME, single-cell sequencing cannot provide information on the cell-cell interactions, particularly those between TME cell types and tumor cells (Nagasawa et al. 2021). Learning about the spatial distribution of cell types and their gene/protein expression can lead to tissue characterization and biomarker discovery, which can provide insights for treatment responses (Garber 2017; Yu et al. 2017). With the growing importance for understanding the TME, the role of spatial phenotyping becomes increasingly important as it aids in the investigation of underlying mechanisms in cancer (Fig. 1) (Berglund et al. 2018; Thrane et al. 2018). Furthermore, spatial transcriptomics and proteomics contribute to a better understanding of the biological mechanisms underlying treatment response and resistance in cancer immunotherapy (Fig. 2) (Kalita-de Croft et al. 2021; Rao et al. 2021). Several technologies have been developed to discover spatially resolved RNA and protein profiles within tumor cells and their surrounding microenvironment (Table 1). Spatial profiling technologies could capture the transcriptome, proteome, and epigenome information ranging from cellular to subcellular resolution (Rao et al. 2021). Generating tissue atlases, particularly tumor atlases, would be feasible, leading to the identification of the dynamic molecular, cellular, and morphological features of human cancers (Nagasawa et al. 2021; Rad et al. 2021). Taken together, spatial profiling of gene and protein expression allows researchers to gain a better understanding of the cellular and molecular characteristics of a growing tumor and its surrounding stroma.

3.1

Spatial Profiling Technologies for mRNA Expression

3.1.1 VIZGEN MERFISH Multiplex error-robust fluorescence in situ hybridization (MERFISH) is a platform for hybridizing in situ target mRNA sequences in individual cells using fluorescent probes (Hu et al. 2021). MERFISH employs four sets of probes with total sequence coverage up to 192 bp for a single transcript (Wang et al. 2021a, b). In order to achieve multiplexing, the platform utilizes successive rounds of hybridization and imaging combined with barcoding, followed by image decoding using error-robust barcoding associated with each transcript (Wang et al. 2021a, b). MERFISH enables tissue-level spatial transcriptomics, localizing transcripts with subcellular resolution, a new perspective on intracellular organization, and measuring the copy number of RNA species (Kalita-de Croft et al. 2021). Therefore, counting, mapping, and imaging of mRNA species, including low-expression genes, would be possible at tens of thousands of cells at the same time (Kalita-de Croft et al. 2021). MERFISH is also capable of providing new insights into biological processes as well as classifying and discovering cell types and states (Lu et al. 2021). Overall, MERFISH allows for not only multiplexing but also single-cell resolution and spatial context of mRNA molecules (Liu et al. 2020; Hara et al. 2021; Park et al. 2021).

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Fig. 1 Overview of key spatial transcriptomic approaches. With the advancements of technology, spatial transcriptomic field has been overwhelmed with wide ranges of techniques. Among different methods, In-situ Capture, In-situ Sequencing, In-situ Hybdridization and In-silico are the key approaches used for spatially mapping a tissue. This figure illustrates the typical steps taken for each of the methods. Inspired from Ref (Rao et al. 2021) and created with BioRender.com

Fig. 2 Data analysis of spatially resolved maps. Schematic illustration of potential pathways for analysis of information achieved through spatial transcriptomics. This could be primarily categorized among three key groups of characterization, clustering, and profiling of data. Inspired from Ref (Rao et al. 2021) and created with BioRender.com

Omics Transcriptome

Transcriptome

Transcriptome Proteome

Technology VIZGEN MERFISH

10× Visium

NanoString SMI

In situ hybridization

Next-generation sequencing (barcoded mRNA capture spots)

Methodology In situ hybridization

Single cell Subcellular

10 cells/ feature Anatomical features of 100 μm/ 55 μm

Resolution Subcellular

FFPE Fresh frozen

FFPE Fresh frozen

Sample type Fresh frozen

Table 1 Applications and characteristics of spatial profiling technologies

1000

Transcriptome

Plexing 100–1000

Targeted

Whole transcriptome

Sampling Targeted

High sample throughput Gene expression can be obtained while maintaining spatial information End-to-end workflow Whole tissue analysis 1000-plex RNA expression 3D mapping with subcellular resolution Single-cell resolution with spatial context

Advantages Spatially resolved RNA expression Highly multiplexed High sensitivity Single-cell resolution Whole tissue analysis Detects and corrects errors

(continued)

Limited profiling area

Disadvantages Sample destructive Limited to 1001 unique mRNAs Requires predesigned set of probes Low experimental throughput No FFPE validation Sample destructive Barcoded regions contain multiple cells No single-cell level resolution

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Omics Transcriptome

Transcriptome Proteome Genome

Proteome

Technology Resolve Biosciences Molecular Cartography

FISSEQ ReadCoor

Fluidigm IMC

Table 1 (continued)

Metal-based

In situ sequencing

Methodology In situ hybridization

Subcellular

Subcellular

Resolution Cellular Subcellular

FFPE Fresh frozen

FFPE Fresh frozen

Sample type FFPE Fresh frozen

37

200–400

Plexing 100

Targeted

Targeted

Sampling Targeted

Advantages High detection sensitivity/ specificity Nondestructive High resolution Detects the rarest transcripts Nondestructive Many genes detected Unbiased sequencing Multi-omics detection Eliminates sample autofluorescence 2D distribution maps for each mass measured End-to-end solution Noncyclic 4-log dynamic range Objective quantification

Low throughput Sample preparation The availability of antibodies (high cost) Data processing and analysis Long image processing

Low experimental throughput

Disadvantages Limited to 100 genes per sample

62 H. S. Rad et al.

Proteome

Transcriptome Proteome

Akoya CODEX

NanoString DSP

DNA-barcoding based

DNA-barcoding based

Metal-based

20–200 cells/ROI Custom down to 10 μm

Single cell

Subcellular

FFPE Fresh frozen

FFPE Fresh frozen

FFPE Fresh frozen

Targeted

Targeted

>40

18,000 for whole transcriptome atlas 1800 for cancer transcriptome atlas 96 for protein

Targeted

>50 High throughput Single staining and imaging step High sensitivity Nondestructive Dynamic resolution Low signal spillover No autofluorescence Nondestructive Eliminates sample autofluorescence One imaging system High-plex Cost-effective Uses common microscopes Nondestructive High level of automation End-to-end workflow Detects both RNA and protein High-plex Whole slide can be timeconsuming/ costly Requires special reagents and equipment No single-cell resolution Requires manual choice of regions No image reconstruction

Timeconsuming/ costly Requires specific slides Data analysis Long image processing

Data from (Kalina et al. 2019; Asp et al. 2020; Bingham et al. 2020; Nerurkar et al. 2020; Sadeghi Rad et al. 2020; Bassiouni et al. 2021; Gajdzis et al. 2021; Kalita-de Croft et al. 2021; Lewis et al. 2021; Maniatis et al. 2021; McGinnis et al. 2021; Nagasawa et al. 2021; Rad et al. 2021; Wang et al. 2021a, b)

Proteome

Ion Paths MIBI

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3.1.2 10× Genomics Visium 10× Genomics Visium is a technology for mapping and quantifying mRNA gene expression with high spatial resolution (Sadeghi Rad et al. 2020). Visium has been developed based on the next-generation sequencing (NGS) method, specifically singlecell RNA-sequencing (SC-RNA-seq) (Sadeghi Rad et al. 2020). The platform can be applied on both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) samples (He et al. 2020). Using spatial barcoding technology for library preparation, 10x Genomics Visium provides a precise location of transcripts within complex tissue samples (He et al. 2020). In order for Visium to measure gene expression in freshfrozen samples, mRNA is released, binds to spatial capture oligos, converts to cDNA, and generates barcoded libraries (Andersson et al. 2020; Ji et al. 2020). Moreover, to generate spatial gene expression on FFPE samples, the technology allows the tissue to be permeabilized, allowing ligated probe pairs to bind to the slides’ capture probes. When the probe pairs are extended, libraries are generated with spatial barcodes (Andersson et al. 2020; Ji et al. 2020). Now, in order to capture spatial information, those barcoded libraries from either fresh-frozen or FFPE samples are mapped back to a specific spot on the Capture Area, imaged with a high-resolution microscope, and then provide spatial visualization of any mRNA expression (Maynard et al. 2021). Visium employs poly (A) capture methods and RNA-templated ligation of gene target probe pairs to obtaining transcript expression on fresh-frozen and FFPE samples, respectively (Maynard et al. 2021). Visium offers some advantages, such as discovering novel biomarkers; investigating the relationship between cell phenotype, function, and location in the tumor microenvironment; and providing insights into tissue morphology, resulting in a more comprehensive view of tumor and TME heterogeneity (Boyd et al. 2020; FawknerCorbett et al. 2021; Janosevic et al. 2021). 3.1.3 NanoString Technologies CosMx Spatial Molecular Imager The NanoString CosMx Spatial Molecular Imager (SMI) performs multiplex profiling of single-cell protein and RNA expression as well as high-resolution spatial imaging (Rad et al. 2021). Using cyclic in situ hybridization, the SMI is capable of quantifying and imaging target transcriptome/proteome within morphologically intact tissue sections (He et al. 2021). Unique probes and antibodies labeled with a specific barcode system are employed to hybridize with mRNA and protein targets (Rad et al. 2021). Then, cyclic rounds of fluorescently labeled reporter probe imaging are performed to read out the barcodes. Finally, gene and protein expression are quantified by counting the number of imaged spots representing individual mRNA and protein species (He et al. 2021). The distinguishing feature of the SMI is that the platform uses standard pathology sample preparation, including standard in situ hybridization processing steps and standard glass slides (Rad et al. 2021). Furthermore, the SMI does not employ cDNA synthesis or amplification, giving rise to unbiased quantification and high detection efficiency (He et al. 2021). The NanoString SMI allows for spatially resolved single-cell biomarker quantification, as well as 3D and subcellular mapping, profiling, and imaging of mRNA and protein expression (Rad et al. 2021). Moreover, using this technology, a better understanding of different cell states, cellular activities, and cell-cell interactions in the TME could be achieved (He et al. 2021).

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3.1.4 Resolve Bioscience Molecular Cartography The molecular cartography is a technology based on in situ hybridization imaging that allows for the detection of hundreds of mRNA species in a single run (D’Gama et al. 2021). Using the platform, multiplexed and single-molecule detection of transcripts with spatially resolved subcellular resolution could be plausible. Following the cryosectioning of tissue samples, gene-specific probes are hybridized with target mRNAs and colored with fluorescent molecules using a proprietary unique combinatorial coding process (D’Gama et al. 2021). Thereafter, the samples are imaged and the probes are decolored. The cycle of colorization, imaging, and decolorization is repeated several times. These characteristics allow for high detection sensitivity and specificity while preserving tissue integrity (D’Gama et al. 2021). Therefore, the resolve bioscience molecular cartography platform is capable of detecting individual transcripts and rare signals in intact tissues in a subcellular spatial manner. Together, this technology is useful for understanding complex biological functions, producing deep context datasets, illuminating molecular interactions, and delivering a high-resolution view of transcriptomic activity (D’Gama et al. 2021). 3.1.5 FISSEQ ReadCoor ReadCoor is a spatially resolved multi-omics sequencing platform powered by fluorescent in situ sequencing (FISSEQ) (Bingham et al. 2020; Turczyk et al. 2020). ReadCoor employs both NGS and high-resolution imaging to sequence thousands of DNA, RNA, protein, and small molecules at the same time with spatial information. The platform also allows for 3D imaging of tissue within individual cells with subcellular resolution (Turczyk et al. 2020). Furthermore, ReadCoor is compatible with FFPE and fixed/fresh-frozen samples (Bingham et al. 2020). For this workflow, NGS libraries are created, nucleic acids are immobilized using targeted or de novo chemistries, and protein and small molecules are detected using oligo-conjugated scaffolds (Rauch and Dickinson 2018; Turczyk et al. 2020). ReadCoor is an automated multiplexed spatial sequencing technology that enables insights into the dimensional context of molecular interactions within the tumor and the TME by incorporating spatial processes underlying tumor initiation and progression, providing a multi-omics view of all biological molecules, and offering morphologic context to DNA and RNA localization (Turczyk et al. 2020). The platform contributes to precious information on pathology, immune-oncology, and drug discovery (Rauch and Dickinson 2018).

3.2

Spatial Profiling Technologies for Protein Expression

3.2.1 Fluidigm Imaging Mass Cytometry (IMC) Imaging mass cytometry (IMC) is a technology for profiling multiplex peptide and protein expression with spatial context (Chang et al. 2017). IMC makes use of affinity reagents in conjunction with mass spectrometry (Chang et al. 2017). Using metal-conjugated antibodies, the technology is capable of measuring up to 37 marker

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expressions with subcellular resolution. Tissue sections are first labeled with antibodies that are attached to metal isotopes (Baharlou et al. 2019). The metal isotopes are then ionized and quantified through a time-of-flight (TOF) analyzer. Finally, each spot is measured for isotope abundance to generate spatially resolved images. IMC could be applied on both fresh-frozen and FFPE tissues (Baharlou et al. 2019). Using this approach, a comprehensive characterization of the TME is possible, including cellular phenotypes and functional states (Ali et al. 2020). Furthermore, protein expression in different cell compartments can be visualized (Aoki et al. 2020). One of the unique characteristics of IMC is that it does not produce signal fading and autofluorescence, which are common drawbacks of traditional immunofluorescence (IF) techniques (Giesen et al. 2014). Additionally, by eliminating the need for serial sections, IMC could save valuable tumor tissue samples (Giesen et al. 2014).

3.2.2 Ionpath MIBI™ Technology Multiplexed ion beam imaging (MIBI) technology utilizes affinity reagents coupled with secondary ion mass spectrometry (SIMS) (Rost et al. 2017). SIMS involves rastering a primary ion beam across tissue samples, releasing reporter ions, and then recording the data using TOF detection (Rost et al. 2017; Chan et al. 2021). To generate spatial data, tissue samples must go through four steps, including preparation, staining, detection, and imaging (Ptacek et al. 2020a, b). After preparation of the slides, metal-tagged antibodies are used to stain the tissue samples (Chan et al. 2021). The staining protocol is the same as that used in standard IHC procedures. Following this, SIMS is employed to perform low and high-resolution scanning for marker detection (Ptacek et al. 2020a, b). Finally, using MIBItracker, TIFF images are visualized and analyzed. MIBIScope employs a nondestructive imaging method, which results in the preservation of tissue samples (Ptacek et al. 2020a, b). Moreover, the platform is compatible with fresh-frozen and FFPE samples (Ptacek et al. 2020a, b). Taken together, MIBIScope not only provide confocal imaging resolution but can also visualize more than 40 markers in a single staining and imaging step (Chan et al. 2021). As a result, this platform is a high throughput technology for cell classification and marker detection, as well as the only multiplex imaging assay validated for clinical trials by the National Cancer Institute (NCI) (Rad et al. 2021). 3.2.3 Akoya Biosciences CODEX CO-detection by antibody indexing (CODEX) is a multiplex fluorescence microscopy-based instrument that generates spatially resolved proteomics data using oligonucleotide-conjugated antibodies (Goltsev et al. 2018). By utilizing a fluorophore microscope, CODEX enables generating high-quality images of tissue samples (Schuerch et al. 2018). In addition, the technology performs a single staining step followed by numerous imaging and antibody-conjugated barcode removal cycles to preserve tissue integrity and speed up the experiment workflow (Schürch et al. 2020). CODEX can also analyze a variety of samples, including FFPE and fresh frozen. Having stained tissue slides, fluorophore-conjugated

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oligonucleotides are used to visualize CODEX antibodies (Schuerch et al. 2018). All antibodies would be quantified as part of the workflow through automated imaging, probe stripping, washing, and re-rendering processes (Rahman et al. 2020). CODEX presents a high level of multiplexed biomarker detection (40 biomarkers at the same time), with single-cell resolution and spatial context (Schürch et al. 2020). Furthermore, the technology allows for the study of the characterization and interactions of multiple cell types within the TME (Rahman et al. 2020). While the initial applications of CODEX were developed for profiling coverslips, this has now been superseded by the PhenoCycler-Fusion system which provides whole slide compatibility.

3.2.4 NanoString GeoMxTM DSP The NanoString GeoMxTM digital spatial profiler (DSP) was developed to quantify molecular and cellular biomarkers in a spatial manner (Monkman et al. 2020). The DSP technology can profile up to 18,000 RNA species and ~150 protein molecules at the same time, allowing it to detect both mRNA and protein expression (Van and Blank 2019). After incubating and conjugating tissue samples with visualization markers and oligonucleotide tags, some regions of interest (ROIs) are defined by the user (Kulasinghe et al. 2020). Using ultraviolet (UV) exposure, the oligo tags are cleaved and released from the ROIs and collected into a 96-well plate. Eventually, those collected oligos are counted or sequenced using the nCounter system or an NGS platform (such as Illumina) to generate spatially resolved data from the tissues (Merritt et al. 2019). The DSP preserves tissue integrity by utilizing UV-photocleavable signals (Zugazagoitia et al. 2020). Also, the instrument could be run on both FFPEs and fresh-frozen tissue samples (Zugazagoitia et al. 2020). Therefore, the technology characterizes analytes spatially located within the tissue, resulting in tumor and TME heterogeneity deciphering (Toki et al. 2019; Cabrita et al. 2020). Furthermore, DSP provides high-quality, multispectral images of tumor tissue samples (Helmink et al. 2020). By combining genomic detection technologies (Digital Optical Barcodes and NGS) and microfluidic sampling, the NanoString DSP is a platform that transforms traditional IHC into a modern genomic/proteomic profiling technology (Merritt et al. 2019; Kulasinghe et al. 2021).

4

Conclusion

The comprehensive characterization of the tumor microenvironment will aid in the discovery of cellular and tissue compartment (tumor/stroma) specific biomarker signatures that may predict outcome to therapies such as immune checkpoint blockade, resulting in a personalized medicine approach. The integration of single cell and spatial proteomic and transcriptomic datasets will enable multidimensional characterization of the tumors – ultimately leading to new insights into the tumor biology. While promising, the ground truths are being developed across multiple tissue and tumor types. Ultimately, these data have the potential to digitize pathology assessments and provide tools to understand treatment resistance.

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Acknowledgments AK is supported by an NHMRC Fellowship (APP1157741), Cure Cancer (APP1182179), and the University of Queensland Frazer Institute. KOB is supported by the Princess Alexandra Hospital Foundation (PARF). Conflict of Interest The authors declare no conflict of interest.

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Role of Mesenchymal Stem/Stromal Cells in Cancer Development Marta E. Castro-Manrreza

and Ignacio Martínez

Abstract

The tumor stroma is now recognized as a key factor in cancer development, as is the need for a deeper understanding of the underlying mechanisms for improving therapies for this group of diseases. As components of the tumor microenvironment, mesenchymal stem cells (MSCs) not only have differentiation potential but also secrete trophic factors and regulate the immune response. In particular, their immunoregulatory capacity, which allows them to modify the behavior of immune cells and to control the inflammatory microenvironment, is relevant in cancer development because MSCs increase immune evasion. Immune evasion, together with the differentiation potential of MSCs and their secretion of trophic factors, favors tumor cell proliferation, survival, dispersion, and metastasis. During these processes, MSCs can interact with tumor cells through soluble factors, cell-cell contact, and extracellular vesicles. These interactions between neoplastic cells and MSCs reciprocally affect their behavior, thus promoting a permissive microenvironment for tumor growth. This chapter describes the different mechanisms involved in the aforementioned events. Keywords

Cancer · Exosomes · Extracellular vesicles · Immune cells · Immune evasion · Immunoregulation · Inflammation · Mesenchymal stem cells · Microvesicles · Tumor microenvironment

M. E. Castro-Manrreza (✉) Immunology and Stem Cells Laboratory, Multidisciplinary Unit of Experimental Research Zaragoza, FES Zaragoza, National Autonomous University of Mexico, Mexico City, Mexico e-mail: [email protected] I. Martínez Biomedical Research Institute, National Autonomous University of Mexico, Mexico City, Mexico # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_103 Published online: 7 January 2023

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Introduction

Despite advances in basic knowledge and their application in the development of new cancer therapies, curing oncological diseases remains a challenge. Most chemotherapy, radiotherapy, and immunotherapy treatments target neoplastic cells. However, several studies have demonstrated the importance of the tumor stroma in cancer initiation, progression, and metastasis. Therefore, an in-depth understanding of the interactions between stroma components is essential for developing new therapies aimed not only at transformed cells but also at stroma constituents, approaches that could reduce nonresponse rates in patients and avoid remission. The tumor microenvironment includes transformed cells and stromal components: immune cells, fibroblasts, endothelial cells, pericytes, and mesenchymal stem/stromal cells (MSCs). These cells secrete growth factors, cytokines, chemokines, peptides, metalloproteases, and extracellular matrix components, all of which help generate an adequate microenvironment for tumor growth (Dvorak 2015). The tumor stroma can also act as a physical and metabolic barrier that reduces the efficacy of therapies against transformed cells (Chen et al. 2021). Additionally, neoplastic cells and stromal cells mutually modify their behavior through mechanisms involving cell-cell contact, secreted autocrine and paracrine factors, and extracellular vesicles (EVs) (Vallabhaneni et al. 2015). The inflammatory process also plays a key role in all stages of cancer. The areas where tumors develop, referred to as “wounds that do not heal” (Dvorak 2015), are characterized by the presence of immune cells such as neutrophils, natural killer cells (NK), M2-type macrophages, myeloid-derived suppressor cells (MDSCs), dendritic cells (DCs) and T lymphocytes with regulatory phenotype, as well as the cytokines tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), interleukin-1 (IL-1), IL-6, IL-8, IL-17, and transforming growth factor-β (TGF-β). This inflammatory microenvironment favors the recruitment of MSCs to the tumor stroma, which, through different mechanisms, can generate a protumorigenic environment (Vallabhaneni et al. 2016; Shi et al. 2017; Chen et al. 2021). Below, we describe in detail MSCs and their biological functions, subsequently explaining how these cells favor cancer development.

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Mesenchymal Stem/Stromal Cells

MSCs are adult multipotent stem cells, initially identified in the bone marrow (BM-MSCs) (Friedenstein et al. 1974), with the capacity for self-renewal and proliferation. In in vitro cultures, they are observed as cells adherent to the plastic, generating colony-forming units. In the human body, MSCs are distributed in various tissues, including skeletal muscle, adipose tissue, skin, and dental (dental pulp, periodontal ligament, apical papilla, dental follicle, and gingival tissue), perinatal (umbilical cord, umbilical cord blood, synovial membranes, amniotic fluid, and placenta), and fetal (liver, bone marrow, lung, arteries, and veins) tissues (Castro-Manrreza and Montesinos 2015; Mushahary et al. 2018). Importantly, the

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populations of MSCs derived from different tissues are heterogeneous in their proliferative capacity and biological functions (Viswanathan et al. 2021). Issues related to this heterogeneity could perhaps be solved with a specific marker for these cells; unfortunately, none is available thus far. Nevertheless, the International Society for Cellular Therapy has established that MSCs populations cultured in vitro must meet specific criteria: must be positive for CD105, CD73, and CD90 markers; express low levels of MHC-I and be negative for MHC-II, CD11b, CD19, CD14, CD34, CD45, and CD31; they must also have adipogenic, osteogenic, and chondrogenic differentiation capacity (Dominici et al. 2006). To date, the various MSCs populations studied also have three relevant and variable biological properties, namely, differentiation potential, secretion of trophic factors, and immunoregulatory capacity. For this reason, these cells may help maintain the homeostasis of an organism by stimulating wound repair and tissue regeneration, in addition to regulating inflammation. Alterations in their functions have been associated with the development of inflammatory, degenerative, and autoimmune diseases (Castro-Manrreza et al. 2019; López-García and CastroManrreza 2021). Furthermore, through different mechanisms, the three biological properties mentioned above can contribute to the development of cancer. Given the importance of inflammation in neoplastic disease progression, we first analyze the immunoregulatory capacity of MSCs derived from healthy tissues and then describe the immunoregulatory mechanisms identified in tumor-derived MSCs; lastly, other mechanisms whereby MSCs favor the development of cancer are broadly discussed.

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Immunoregulatory Properties of MSCs

Generally, MSCs remain resting, with minimal or no expression of immunoregulatory molecules. However, several in vitro and in vivo studies have shown that the presence of inflammatory cytokines in the microenvironment as IFN-γ, TNF-α, IL-1α, IL-1β e IL-17 (Aggarwal and Pittenger 2005; Ryan et al. 2007; Han et al. 2014) induces MSCs “priming” or “activation,” thus increasing the expression of immunoregulatory molecules. Several studies have shown that these cytokines can act alone or in combination, which has allowed the establishment of a more complete picture of the impact that the microenvironment has on the functions of these cells and a better understanding of their role in the development of different diseases (Montesinos et al. 2020; López-García and Castro-Manrreza 2021). Activated MSCs can regulate different biological processes in immune cells, such as neutrophils, monocytes, macrophages, natural killer (NK) cells, dendritic cells (DCs), B cells, and T cells, through the following mechanisms: secretion of immunoregulatory molecules, cell-cell contact, and EVs release (Meisel et al. 2004; Aggarwal and Pittenger 2005; Jiang et al. 2005; Krampera et al. 2006’ Ryan et al. 2007; Castro-Manrreza et al. 2014; López-García and Castro-Manrreza 2021; Zheng et al. 2021). For several years, research mainly focused on the analysis of immunoregulatory molecules secreted from MSCs. Subsequently, several reports have demonstrated the importance of mechanisms involving cell-cell contact, albeit

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with several limitations due to the low frequency of MSCs in body tissues (e.g., in bone marrow, there is one MSC for every 10,000 mononuclear cells). For this reason, the importance of the EVs released by these cells, which could mediate communication between MSCs and immune cells, has recently been raised. Details of each of these mechanisms are provided below. (a) Immunoregulation mechanisms mediated by secreted factors The ability of MSCs to generate an immunoregulatory environment is due, in part, to the paracrine action of mediators, cytokines, and growth factors released into the extracellular space; these factors directly interact with immune cells and modify their microenvironment. In addition, MSCs modify their environment through different enzymes, such as intracellular indoleamine-2-3-dioxygenase (IDO), inducible nitric oxide synthase (iNOS), and ectonucleotidase CD73 (López-García and Castro-Manrreza 2021), whose activities also contribute to immunoregulation. IDO is one of the main molecules involved in the immunoregulatory function of MSCs. This enzyme catabolizes tryptophan, depleting this amino acid from the medium and generating metabolites such as kynurenine, thereby affecting the function of immune cells through both events. IDO activity reduces the proliferation and cytotoxic activity of NK cells, inhibits DC maturation, and induces monocyte polarization toward M2-type immunoregulatory macrophages, which produce IL-10 (Wang et al. 2006). IDO activity also suppresses activated T-cell proliferation and IFN-γ, TNF-α, and IL-17 secretion (Wang et al. 2016); stimulates Foxp3+ and IL-10 + IFN-γ + CD4+ or Tr1 regulatory T-cell differentiation; and inhibits the differentiation of Th1- and Th17-type populations (Meisel et al. 2004; Krampera et al. 2006; Yu et al. 2019). Additionally, tryptophan depletion by IDO is the main mechanism by which MSCs suppress B-cell proliferation (Luk et al. 2017). Although IFN-γ is the main cytokine that induces IDO expression in MSCs, a synergistic effect has been reported between TNF-α and IFN-γ and between IFN-γ and IL-1β (López-García and Castro-Manrreza 2021). PGE2 is another effector molecule that contributes to the immunoregulatory capacity of MSCs. PGE2 is a lipid mediator derived from arachidonic acid conversion into prostaglandin by COX1 and COX2 enzymes (Aggarwal and Pittenger 2005; Ryan et al. 2007). When MSCs are exposed to an inflammatory environment, PGE2 secretion increases significantly, negatively affecting monocyte differentiation to DCs, as well as the proliferation and cytotoxic activity of NK cells (Spaggiari et al. 2009; Spaggiari and Moretta 2013). PGE2 also suppresses T-cell proliferation and favors CD4 + CD25 + Foxp3+ and Tr1 regulatory T-cell differentiation and an M2 phenotype in macrophages (Sheng et al. 2008; Hsu et al. 2013). In addition, MSCs constitutively produce TGF-β, which suppresses NK cell proliferation (Sotiropoulou et al. 2006) and, in conjunction with PGE2, favors CD4 + CD25 + Foxp3+ regulatory T-cell differentiation. However, for this to occur, direct contact between MSCs and T cells is an indispensable prerequisite (English et al. 2009). This evidences the connections between immunoregulatory mechanisms mediated by secreted factors and those requiring cell-cell contact. This

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coordination has been observed mainly among molecules involved in regulatory T-cell generation, specifically between IL-10 and human leukocyte antigen-G (HLA-G), which will be addressed later. (b) Immunoregulation mechanisms mediated by cell-cell contact The importance of cell-cell contact for adequate immunoregulation has been recently recognized. In this context, the following molecules expressed in the membrane of MSCs have been implicated in these immunoregulation mechanisms: programmed death-ligand 1 (PD-L1), Jagged-1, intercellular adhesion molecule 1 (CD54/ICAM-1), vascular cell adhesion molecule 1 (VCAM-1), CD200, CD40, and HLA-G (Liotta et al. 2008; Liu et al. 2011; Yang et al. 2012; Li et al. 2019a, b, c). The last is a nonclassical HLA molecule characterized by a limited allelic polymorphism and whose expression is restricted to specific tissues. Some isoforms are membrane-bound (HLA-G1, -G2, -G3, and -G4), and others are soluble (HLA-G5, -G6, and -G7). MSCs express the HLA-G1 and HLA-G5 isoforms. Although both molecules affect T-cell proliferation and stimulate CD4 + CD25 + Foxp3+ regulatory T-cell differentiation, HLA-G1 is mainly involved in the early contact between MSCs and activated T cells. This event is essential for inducing IL-10 secretion in these leukocytes, in turn favoring HLA-G1 and HLA-G5 expression in MSCs and thus establishing a positive feedback mechanism. Besides, IL-10 participates in the generation of regulatory T lymphocytes and increases the expression of PD-1 in these cells, which favors its immunoregulatory function (Yan et al. 2014). This cytokine also decreases the maturation of DCs and their ability to produce IL-12 (Liu et al. 2013). Due to the above, it has been suggested that the initial contact between MSCs and T cells is critical for triggering immunoregulation (Rizzo et al. 2008; Selmani et al. 2008; Giuliani et al. 2011; Castro-Manrreza et al. 2014; Najar et al. 2015). Moreover, this contact mediated by HLA-G1 on the surface of MSCs is essential for decreasing NK-cell cytotoxicity (DelaRosa et al. 2009; Spaggiari et al. 2009; Serejo et al. 2019). Another molecule involved in these immunoregulation mechanisms is PD-L1. The PD-1/PD-L1 pathway generates inhibitory signals in activated T cells, exerting suppressive effects under persistent stimulation of an antigen. Therefore, this pathway has an important role in developing tolerance, preventing autoimmunity, terminating the immune response to avoid tissue damage, and establishing the immunoregulatory microenvironment of tumors (Francisco et al. 2010). PD-L1 expression significantly increases in the membrane of MSCs treated with IFN-γ (López-García and Castro-Manrreza 2021). Through this process, these cells inhibit the differentiation and maturation of DCs, affecting their ability to present antigens and appropriately activate T cells. PD-L1 on the surface of MSCs acts directly on T cells, decreasing their proliferation and secretion of TNF-α, IFN-γ, and IL-17; it also negatively affects the differentiation of Th17 cells (Loke and Allison 2003; Sheng et al. 2008; Tipnis et al. 2010; Chinnadurai et al. 2014; Jang et al. 2014; Yan et al. 2014; Davies et al. 2017) and increasing the number of Foxp3+ regulatory T cells (Tipnis et al. 2010; Amarnath et al. 2011).

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Another important mechanism whereby MSCs exert immunoregulation is the Notch/Jagged pathway. Through this pathway, MSCs help suppresses-activated T-cell cytokine proliferation and secretion (Liotta et al. 2008), and regulatory T-cell differentiation (Cahill et al. 2015). Besides, the interaction between MSCs and CD34+ hematopoietic progenitor cells, through this pathway, generates a population of regulatory DCs, characterized by high IL-10 and low IL-12 secretion and by a decreased capacity to activate T cells and to stimulate the differentiation of alloantigen-specific regulatory T cells (Li et al. 2008). Recently, the importance of ICAM-I/CD54 in the interaction between MSCs and immune cells has been recognized (Ghannam et al. 2010; Ren et al. 2010; Rubtsov et al. 2017). ICAM-1 is an adhesion molecule that facilitates leukocyte recruitment to inflammation sites and whose expression increases in endothelial cells, fibroblasts, monocytes, DCs, and lymphocytes when these cells are exposed to proinflammatory cytokines, such as IL-1, TNF-α, and IFN-γ (Bui et al. 2020). ICAM-1 is part of the immunological synapse between antigen-presenting cells and T cells, modulating the activation and differentiation of T cells (Engelhardt and Krummel 2008; Schittenhelm et al. 2017). In this regard, ICAM-1 is involved in establishing memory CD8+ T cells (Scholer et al. 2008; Cox et al. 2013), and suppressing Th17 cell and DC differentiation (Podgrabinska et al. 2009; Schittenhelm et al. 2017). Therefore, ICAM-1 is currently considered an important regulator of the immune response. Low ICAM-1 levels have been observed in resting MSCs, but its expression increases markedly when MSCs are exposed to an inflammatory environment (Tang et al. 2018; Li et al. 2019a, b, c), which facilitates MSCs interaction with immune cells. In addition, this adhesion molecule is polarized toward sites of contact between MSCs with M1 macrophages, inducing their differentiation into M2 antiinflammatory macrophages (Espagnolle et al. 2017). This interaction also favors the immunoregulatory capacity of MSCs by increasing CD200 expression, which facilitates macrophage reprogramming toward an anti-inflammatory phenotype (Li et al. 2019a, b, c) and DC maintenance in an immature state (Zhao et al. 2021). Similarly, ICAM-1 promotes the adhesion of MSCs to DCs, a process that also inhibits the maturation and differentiation of DCs (Tang et al. 2018). As previously mentioned, the effect of MSCs on T-cell proliferation and differentiation requires direct contact between the two cell types (Castro-Manrreza et al. 2014; Najar et al. 2015; Bulati et al. 2020; López-García and Castro-Manrreza 2021). In this regard, blocking ICAM-1 in MSCs almost re-establishes the proliferative capacity of T cells and prevents the differentiation of regulatory subpopulations (Ren et al. 2010; Rubtsov et al. 2017). In animal models of inflammatory diseases, administration of MSCs overexpressing ICAM-1 suppresses Th1 and Th17 cell differentiation and TNF-α, IFN-γ, and IL-17 production. In addition, regulatory cell populations expressing IL-10 and Foxp3 are induced (Ghannam et al. 2010; Li et al. 2019a, b, c). Notwithstanding these observations, the ability of MSCs to establish direct contact with immune cells once administered in the body has recently been questioned. Therefore, research efforts have been directed toward EVs, which can act as mediators of communication between MSCs and immune cells.

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(c) Mecanismos de inmunoregulación mediados por vesículas extracelulares EVs are produced by all cells in the body and are recognized as a relevant intercellular communication mechanism (Tkach and Théry 2016). The most accepted classification is based on EV size and biogenesis. The smallest structures, i.e., exosomes, have a size of 40–100 nm and are derived from multivesicular bodies that fuse with the cell membrane to allow release into the extracellular space. Microvesicles (MVs) are 100–1000 nm in size and originate from protrusions on the surface of the cell membrane; therefore, they retain many characteristics of their parental cells. Lastly, apoptotic bodies are EVs with a diameter of 1000–4000 nm released from apoptotic cells (Lee et al. 2012; Wang et al. 2014; Matula et al. 2016). Both exosomes and MVs can reach the bloodstream and the lymphatic system, through which they reach distant sites, where they can interact with target cells. These structures transport different biomolecules (ligands, receptors, cytokines, and enzymes, among others) with which they modify the biological behavior of target cells through direct contact (Lee et al. 2012; Wang et al. 2014; Collino et al. 2017; Reis et al. 2018). Importantly, given the lack of a characteristic marker for each of these structures, isolating specific populations and accurately dissecting their function are very difficult tasks. International reports still sow confusion regarding EV nomenclature and extraction and characterization methods. Therefore, in this work, the generic term EVs will be used to refer to the study of exosomes and MVs, specifying the name of any of these structures when the method used for their extraction in the cited study allows such a classification. Resting MSCs release EVs that transport molecules such as CD73, PD-L1, Gal-1, TGF-β, and ICAM-1 on their surface (Favaro et al. 2014; Matula et al. 2016; Harting et al. 2018; Franco da Cunha et al. 2020; Montesinos et al. 2020). In addition, when MSCs are exposed to an inflammatory environment, they release a greater number of EVs, and the EV content is modified. These EVs can be captured and incorporated by immune cells, including granulocytes, NK and mast cells, monocytes, macrophages, DCs, and T and B cells (Di Trapani et al. 2016; Harting et al. 2018; Khare et al. 2018; Reis et al. 2018; López-García and Castro-Manrreza 2021). Some studies have shown that EVs have an immunoregulatory effect, although to a lesser extent than that observed with whole cells. EVs reduce NK cell proliferation and antigen capture by immature DCs, which can affect their maturation. They also favor monocyte differentiation toward M2 macrophages (Di Trapani et al. 2016; Chamberlain et al. 2019). While on T lymphocytes, EVs-MSCs decrease the proliferation and differentiation of Th1 subpopulations, as well as the secretion of IFN-γ and IL-17, increase the differentiation of regulatory T populations, and stimulating IL-10, TGF-β, PGE2, and Foxp3 expression (Favaro et al. 2014; Khare et al. 2018; Serejo et al. 2019; Dal Collo et al. 2020). However, some studies have reported that EVs do not affect T-cell function (Matula et al. 2016). This has generated controversy, highlighting the importance of continuing to investigate this topic. Additionally, EVs suppress B cell proliferation, differentiation, and IgM, IgA, and IgG antibody production (Khare et al. 2018).

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MSCs exposed to proinflammatory cytokines, such as TNF-α and IFN-γ, release EVs with an increased capacity to interact with immune cells, a process that is associated with ICAM-1 enrichment (Di Trapani et al. 2016; Harting et al. 2018; Montesinos et al. 2020). In addition, the increased immunoregulatory capacity of MSCs is linked to COX2, PGE2, and IDO or elevated PD-L1 levels (Harting et al. 2018; Zhang et al. 2018; Li et al. 2021). Given that EVs have been identified in most body fluids, these structures can function as diagnostic markers for cancer and other diseases.

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Immunoregulator Mechanisms Observed in MSCs Derived from Tumors

Since 1986, studies have proposed that tumor cells need an adequate environment for growth and immune system evasion, which is provided by the stroma (Dvorak 2015). The tumor microenvironment is characterized by persistent inflammation and by the presence of pro- and anti-inflammatory mediators, whose balance affects tumor progression and is generally tilted toward an immunoregulatory environment. In this context, the participation of MSCs becomes relevant, because both BM-MSCs from healthy subjects that are subsequently exposed to a tumor microenvironment and MSCs derived from tumors show immunoregulatory activity, which can favor the oncogenic process. The cellular components of the tumor microenvironment secrete numerous enzymes, growth factors, cytokines, and chemokines, among other molecules. These molecules attract different cell types, including MSCs. The interaction of chemokines with their receptors in MSCs stimulates the migration of MSCs to tumors, where they transform into a phenotype known as tumor-associated MSCs (TA-MSCs), which have a greater capacity to stimulate tumor growth than MSCs derived from healthy tissues or BM (Ren et al. 2012; Mathew et al. 2016) (Fig. 1). CC-chemokine ligand 2 (CCL2), RANTES/CCL5, CXC-chemokine ligand 8 (CXCL8), CXCL12, and CXCL16 are among the cytokines involved in recruiting MSCs to the tumor microenvironment. In particular, CXCL12 (also known as SDF1) acts on BM-MSCs through its interaction with CXCR4 (Fig. 1). On the other hand, it has been observed that CXCL8/IL-8 secreted by an oral squamous cell carcinoma cell line increases the expression of CXCR2 in MSCs and their migration. The same mechanism was observed in BM-MSCs migration in a murine model. Furthermore, high CXCL8 expression was detected in histological sections of oral squamous cell carcinoma samples (Meng et al. 2020). Together, these data indicate that this chemokine is relevant to MSCs recruitment to the tumor microenvironment. MSCs derived from pancreatic cancer also show higher TGF-β and COX-2 mRNA expression than those derived from a healthy pancreas (Mathew et al. 2016), potentially enhancing BM-MSCs recruitment to the tumor microenvironment. This hypothesis is supported by in vitro studies in which TGF-β increased murine BM-MSCs migration toward tumor cells. This effect was associated with an increase in N-cadherin expression in the tumor cells. Similarly, breast cancer tumor cells attract MSCs by producing TGF-β (Choi et al. 2021).

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Fig. 1 MSCs are components of the tumor stroma and stimulate cancer development. Different biomolecules found in the tumor microenvironment promote the recruitment of MSCs and their transformation into TA-MSCs. The proinflammatory cytokines IFN-γ, TNF-α, IL-1, and IL-17 activate MSCs, which increases the expression of immunoregulatory molecules; these molecules can be secreted, expressed on the membrane, or transported in EVs (exosomes and microvesicles) released from MSCs. Through these mechanisms, MSCs regulate the proliferation, differentiation, maturation, and effector function of immune cells, favoring the generation of an immunosuppressive environment and the evasion of the immune response

High concentrations of proinflammatory cytokines promote lymphocyte function-associated antigen-1 (LFA-1), ICAM-I, and VCAM-I expression in MSCs (Kansy et al. 2014; Wu et al. 2019) (Fig. 2), thus facilitating their interaction with the endothelium and subsequent extravasation to the tumor stroma. The importance of an inflammatory environment in inducing the immunoregulatory capacity of MSCs was demonstrated in a murine melanoma model in which the injection of melanoma cells in an allogenic context only established a tumor when co-administered with MSCs (Djouad et al. 2003). This finding indicates that alloantigens of transformed cells can be recognized by the mouse immune system, leading to their elimination. However, pro-inflammatory cytokines released during this immune response activate MSCs, which, by deploying their immunoregulatory mechanisms, contribute to immune system evasion. In this regard, an in vitro study showed that BM-MSCs protect hepatocellular carcinoma cells from lysis by immune cells and that this effect is stronger when MSCs are preactivated with IFN-γ. IFN-γ preactivation is associated with increased IDO expression and activity because the inactivation of IDO eliminates the protective capacity of MSCs on tumor cells (Chinnadurai et al. 2021). Additionally, when exposed to IL-17 and IFN-γ, TA-MSCs increase the expression of CCL2, CCL5, CCL7, and CCL20 chemokines involved in the recruitment of monocytes/macrophages and MDSCs by melanoma, which favors its development

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Fig. 2 Immunoregulatory mechanisms displayed by TA-MSCs in the tumor microenvironment. The tumor microenvironment induces the recruitment of immune cells and MSCs. The pro-inflammatory environment (pink dots and red letters) activates MSCs (blue cells), triggering the expression of immunoregulatory molecules and chemokines (blue letter), which stimulate the recruitment of immune cells to the tumor stroma (a). IL-6, IL-8, and IL-10 secretion and EVs released by TA-MSCs stimulate an M2 phenotype in macrophages, which has been associated with increased tumor development (b). Furthermore, EVs released from TA-MSCs can be captured by transformed cells (pink cells) facilitating their proliferation, migration capacity, and viability. Similarly, transformed cells release EVs, which modify the behavior of MSCs and immune cells (c). In addition, MSCs help transformed cells to evade the immune response through increased expression of IDO, PDL-1, PGE2, TGF-β, and ADO production, which decrease T-cell proliferation and induce a regulatory phenotype (d)

(Lou et al. 2021), by generating an immunosuppressive environment, in part through a positive-feedback mechanism between MSCs and macrophages. In this regard, M1 macrophages are capable of polarizing MSCs toward an immunoregulatory phenotype in which MSCs increase macrophage recruitment through CCL2

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secretion and polarize macrophages toward an M2 phenotype through IL-10 and IL-6 secretion, with increased arginase 1 and CD206 expression and decreased iNOS expression (Mathew et al. 2016). Interestingly, a positive correlation between MSCs infiltration and M2 macrophage levels has been observed in breast tumor samples (Biswas et al. 2019) (Fig. 2). In addition to the above, in vitro assays have shown that co-culturing MSCs with macrophages, both isolated from gastric tumor tissue, increases the ability of MSCs to induce M2 macrophage polarization. In supernatants from these co-cultures, the concentrations of the proinflammatory cytokines TNF-α, CXCL10/IP-10, RANTES, and MIP-1α are decreased, and the concentrations of IL-6, IL-10, vascular endothelial growth factor (VEGF), and MCP-1 are increased; these changes are related to increased tumor development, an event that is mainly associated with the release of IL-6 and IL-8 from TA-MSCs. Notably, the presence of M2 macrophages in tumors has been associated with metastasis and poor patient prognosis. Macrophages are the main producers of IL-1, a cytokine that is involved in tumor development, as shown by significant increases in IL-1β in most cancer types. An in vivo model showed that breast cancer cells injected into IL-1β-deficient mice can form tumors, which subsequently revert. This effect was associated with decreased CCL2 levels, lower monocytes recruitment and differentiation, and decreased immunosuppression. In addition, there was an increase in the proportion of CD11b + DCs, which secrete IL-12, generating an antitumor response involving CD8 T-cell activation and TNF-α and IFN-γ expression (Kaplanov et al. 2019). The impact of IL-1 on malignant neoplasm development may partly result from its effects on MSCs because IL-1α and IL-1β expressed by colon and breast carcinoma cell lines increase PGE2 expression in MSCs. The latter acts in an autocrine manner and, in combination with IL-1, induces the expression of anti-inflammatory molecules such as IL-6, IL-8, and CXCL1 by MSCs (Li et al. 2012). IL-1β and TNF-α produced by breast cancer cells also activate the NFκB pathway in MSCs, stimulating chemokine expression like CCL2/MCP-1, CXCL1, CXCL6, and CXCL8, which attracts immune cells and favors tumor development (Kansy et al. 2014; Escobar et al. 2015; Katanov et al. 2015). Some studies have reported that MSCs derived from breast cancer showed higher TGF-β, PGE2, VEGF expression, and IDO activity than their healthy counterparts. Furthermore, these tumor-associated MSCs may stimulate peripheral blood mononuclear cell (PBMC) proliferation. Interestingly, the treatment of PBMCs, not activated or activated with PHA, with the conditioned medium of tumor-associated MSCs, significantly increases TGF-β, IL-10, and PGE2 secretion; such an effect is not observed with MSCs derived from healthy tissues (Sineh Sepehr et al. 2020). In particular, TGF-β released by MSCs has been associated with the generation of regulatory T cells that protect transformed cells from breast cancer (Patel et al. 2010). Likewise, it has been observed that TA-MSCs derived from gastric cancer also increase the differentiation of regulatory T lymphocytes and suppress the proliferation of Th17 cells (Wang et al. 2017) (Fig. 2). Moreover, cervical cancer MSCs have been reported to have high levels of CD39 and CD73, ectonucleotidases responsible for converting adenosine monophosphate to

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adenosine (ADO). The high production of ADO by the TA-MSCs has been associated with a greater capacity to reduce both proliferation and activation of CD8+ T lymphocytes (de Lourdes Mora-García et al. 2016). Furthermore, the co-culture of TA-MSCs with cervical cancer cell lines increases the production of TGF-β1, which is consumed in an autocrine manner by tumor cells to increase the expression of CD73 on their surface, increasing the production of ADO (Ávila-Ibarra et al. 2019). The highest amount in this molecule is associated with the generation of an immunosuppressive environment mediated by regulatory T cells (Schneider et al. 2021). In addition to the participation of secreted factors, direct contact between transformed cells and MSCs is an important event that favors tumor progression. In this regard, the interaction between breast cancer cells or colorectal cancer cells and MSCs increases RANTES expression in MSCs. This event precedes an increase in the secretion of other chemokines (Karnoub et al. 2007) (Fig. 2). Additionally, in a murine melanoma model, MSCs treated with IL-17 favored tumor growth due to a significant increase in PD-L1 expression through iNOS induction (Wang et al. 2020). This event significantly decreases the proliferation of T lymphocytes, which favors the proliferation of transformed cells (Chen et al. 2018). It has been observed that the expression of PD-L1 in MSCs increases when they are exposed to IFN-γ and IL-17, alone or in combination with TNF-α, in which case a synergistic effect is observed. All this evidence highlights the importance of the tumor microenvironment in inducing the immunoregulatory capacity of MSCs.

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Participation of EV-MSCs in Cancer Development

EVs released from MSCs (EV-MSCs) can accelerate or slow cancer progression (Vallabhaneni et al. 2015; Attar-Schneider et al. 2020). These structures can induce signaling pathways in tumor cells and may even be internalized by tumor cells (Zhang et al. 2019; Attar-Schneider et al. 2020; Dabbah et al. 2020). In turn, EVs can be released from transformed cells or other cell components of the stroma, potentially modifying the functions of MSCs, as well as favoring the maintenance of an appropriate microenvironment for tumor development (Lopatina et al. 2020) (Fig. 2). Administration of EVs released from BM-MSCs (EV-BM-MSCs) in a murine melanoma model stimulates tumor development. These structures induce increased cell proliferation, decreased apoptosis, and increased expression of CD206 in tumor tissue. Furthermore, in in vitro assays, EV-BM-MSCs were internalized by macrophages, promoting macrophage differentiation toward an M2 phenotype, thereby inhibiting apoptosis and favoring the migration and invasion capacity of a melanoma cell line (Yang et al. 2022). Likewise, exosomes released from MSCs associated with breast cancer promote monocytic MDSC differentiation toward M2-type immunoregulatory macrophages, which show increased PD-L1, IL-10, CD206, and CD163 expression; arginase activity; and capacity to suppress T-cell proliferation (Fig. 2). In a murine model

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of breast cancer, the administration of these structures also favors tumor growth and epithelial-mesenchymal transition (EMT), which is accompanied by increased Slug and Snail expression and decreased E-cadherin expression. In such events, the PD-1/ PD-L1 pathway is involved because PD-L1 inhibition significantly decreases tumor size and macrophage polarization. In turn, CD4 and CD8 T cells obtained from the lymph nodes of the breast cancer-model mice showed lower viability and IFN-γ production and increased PD-1 expression (Biswas et al. 2019). Additionally, glioblastoma stem-like cells release EVs that transport PD-L1, mediating T-cell immunoregulation by blocking T-cell activation and proliferation (Ricklefs et al. 2018). These results indicate the relevance of the PD-L1/PD-1 pathway in cancer immune evasion. In vitro studies have shown that the MVs released by BM-MSCs of patients with multiple myeloma (MM) can be captured by transformed cells, subsequently activating signaling pathways in the latter and thus leading to an increase in their proliferation and migration. This interaction is mediated by integrin α4β1 (very late antigen-4/VLA-4), which is a dimer composed of CD49d (alpha 4) and CD29 (beta 1). MSCs derived from patients with MM release a greater number of CD49d + MVs enriched with this molecule, and these MVs (CD49dhigh) can transfer CD49d to MM cell lines. Interestingly, the authors determined that the BM-MSCs of stage 2–3 MM patients release a higher percentage of CD49d + MVs than stage 1 patients (Dabbah et al. 2020). This finding suggests that these structures could be used as biomarkers of cancer progression. EVs released from BM-MSCs can be captured by breast cancer and osteosarcoma cells, increasing their survival under stress conditions. In an immunodeficient mouse model of breast cancer, EVs also significantly favored tumor development (Vallabhaneni et al. 2015). Despite contradictory reports regarding the effect of EV-MSCs on cancer stimulation or inhibition, a recent study suggested that these conflicting results are mainly due to variations in MSCs origin. MVs released from MSCs derived from cancerous lung tissue have a greater capacity to increase tumor cell viability, proliferation, migration, and autophagy than EVs derived from MSCs of healthy lungs. However, EVs released from BM-MSCs do not have these effects (Attar-Schneider et al. 2020). As already mentioned, tumor cells release EVs that influence MSCs. Exosomes released from ovarian cancer cells induce MSCs derived from healthy adipose tissue, to acquire characteristics of tumor-associated myofibroblasts with increased expression of α-SMA, SDF-1, and TGF-β expression (Cho et al. 2011). Additionally, exosomes released from ovarian cancer spheroids enriched in cancer stem cells and treated with cisplatin induce the protumorigenic activity of BM-MSCs. These exosomes increase MSCs migration and proangiogenic activity; metalloprotease production; and IL-6, IL-8, and VEGF expression. When stimulated by these exosomes, MSCs also increase ovarian cancer cell migration. These findings highlight the key role of transformed cells in the generation of a protumor environment (Vera et al. 2019).

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Other Mechanisms Used by MSCs to Stimulate Tumor Development

As already mentioned, the inflammatory microenvironment of tumors favors the recruitment of MSCs to these areas and influences their biological properties. Although this chapter emphasizes the immunoregulatory properties of MSCs, importantly, their capacity for differentiation and secretion of trophic factors contributes to tumor development because MSCs stimulate the differentiation of cancer-associated fibroblasts (CAFs), angiogenesis, tumor-initiating cell maintenance, chemoresistance, and EMT (Zhang et al. 2013; Bergfeld et al. 2014; Mele et al. 2014). CAFs are the main constituents of the tumor microenvironment, and their origin is variable; therefore, their phenotype and function are heterogeneous. Although their precise origin is unknown, some CAF populations come from endothelial cells, adipocytes, and MSCs. Regardless of their origin, CAFs have a secretory profile capable of promoting tissue remodeling and express different markers, such as α-smooth-muscle-actin (αSMA), α-fibroblast activation protein (αFAP), and fibroblast specific protein-1 (FSP-1) (Quante et al. 2011; Borriello et al. 2017; Chen et al. 2021). TNF-α and IL-1β, two cytokines produced by tumor cells, induce TA-MSCs and BM-MSCs differentiation toward CAFs (Spaeth et al. 2009; Jung et al. 2013), which express proinflammatory chemokines that stimulate cancer development as CCL2/MCP-1, CXCL8/IL-8 y RANTES (Katanov et al. 2015; Chen et al. 2021). In addition, CAFs obtained from primary neuroblastoma tumors also express markers similar to those of MSCs and even show adipogenic, osteogenic, and chondrogenic differentiation potential. In vitro analysis shows that CAFs stimulate proliferation, chemoresistance, and decreased apoptosis in transformed neuroblastoma cells. In an in vivo model, these cells have a greater capacity to promote tumor grafting and growth than BM-MSCs (Borriello et al. 2017). Oxygen deficiency or hypoxia is a key characteristic of the tumor microenvironment and stimulates hypoxia inducible factor 1 alpha (HIF1α) expression, which in turn activates different genes involved in angiogenesis, including VEGF. Angiogenesis is essential for maintaining the viability and proliferation of transformed cells, and some therapeutic strategies against cancer are based on its inhibition. Numerous studies have shown that MSCs secrete VEGF whose expression increases when these cells are exposed to an inflammatory or hypoxic environment, which stimulates the expression of HIF1α, the primary regulator of VEGF (Zhang et al. 2013). The chemoresistance of transformed cells is associated with their interaction with MSCs. Communication between BM-MSCs and leukemia cells, through VLA-4 and VCAM-I, increases IL-8, IL-6, CCL2, and VCAM-1 expression in BM-MSCs. Such molecules are associated with increased transformed cell survival (Jacamo et al. 2014). It has been proposed that in tumors there is a small subpopulation of cells, with characteristics of stem cells, called cancer stem cells, with the capacity for selfrenewal and the ability to initiate tumors. These cells are responsible for chemoresistance and metastasis, and their population is promoted or maintained, at least in part, through PGE2 secretion from MSCs (Li et al. 2012). IL-8 secreted from

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MSCs derived from gastric cancer tumors increases PD-L1 expression in transformed cells, favoring their ability to evade the immune response. Increased PD-L1 expression in these cells has also been associated with the induction of cancer stem cell characteristics and with increased chemoresistance (Sun et al. 2020). The maintenance of such stem cell characteristics has also been associated with EMT. EMT, which occurs naturally during embryonic development, is a mechanism adopted by tumor cells to promote their proliferation, invasion capacity, and migration. EMT is relevant because the main cause of cancer-related death is the spread of the primary tumor through a process termed metastasis, which allows the secondary colonization of tissues (Zheng et al. 2021). All these events are stimulated by MSCs, via secretion of CXCL12/SDF-1, CCL1, CCL8/IL-8, IL-6, PGE2, and TGF-β (Li et al. 2012; Jung et al. 2013; Woosley et al. 2019; Meng et al. 2020). In vitro experiments have shown that TGF-β secreted from BM-MSCs increases EMT, proliferation, and migration in an oral squamous cell carcinoma line (Meng et al. 2020) and breast cancer cells (Woosley et al. 2019). Additionally, upon direct contact between colorectal cancer cells and BM-MSCs, the expression of genes associated with EMT (TWIST, SNAIL1, SNAIL2, ZEB1, N-CAD) increases in transformed cells, which acquire a mesenchymal morphology and exhibit reduced E-cadherin expression, a process that is not observed when contact between these two cell types is prevented. Therefore, such effects may be mediated by TGF-β expressed in the membrane of MSCs, whose levels increase when BM-MSCs are co-cultured with tumor cells. Moreover, TGF-β inhibitors prevent EMT and changes in gene expression associated with such an event (Mele et al. 2014; Takigawa et al. 2017). In addition to the above, gastric cancer MSCs exposed to tumor macrophages increase EMT and the migration and invasion capacity of transformed cells. Based on a murine model of gastric cancer, macrophages are essential for the ability of MSCs to promote tumor development (Li et al. 2019a, b, c). Under hypoxic conditions, BM-MSCs release miRNA-enriched exosomes (miR-193a, miR-210-3p, and miR-5100) that increase the migration and invasion of lung cancer cells. Besides, transformed cells present the morphological characteristics of EMT, increased expression levels of mesenchymal markers, such as vimentin and N-cadherin, and decreased expression of the epithelial marker E-cadherin. Furthermore, in a murine model of lung cancer, these miRNA-enriched exosomes favor tumor growth, invasion, and metastasis (Zhang et al. 2019). Apparently, miRNAs transported in exosomes are transferred to transformed cells, where they induce signaling pathways involved in the above processes. Finally, primary tumors facilitate the formation of a premetastatic niche by secreting cytokines, chemokines, growth factors, and EVs. Some studies have indicated that MSCs participate in this process. Zheng et al. (2021) observed that lung MSCs (L-MSCs) promote breast cancer metastasis and that this capacity is positively correlated with tumor progression because L-MSCs in the premetastatic and metastatic niche exacerbate metastasis more strongly than those derived from an environment characterized by the presence of only an adenoma. This greater metastasis-inducing capacity of MSCs is associated with their higher expression of complement component 3 (C3). This molecule mediates neutrophil recruitment to

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the lung and therefore the formation of neutrophil extracellular traps (NETs). NETs are characterized by the release of proteases, cytotoxic enzymes, and DNA into the extracellular space, thus favoring remodeling of the extracellular matrix and metastasis. This remodeling is reflected in the increase in metastatic nodules in a murine model. Interestingly, serum C3 levels are higher in patients with metastatic tumors than in healthy individuals (Zheng et al. 2021).

7

Conclusion

TA-MSCs are components of the tumor stroma and promote an anti-inflammatory environment through different immunoregulatory mechanisms. TA-MSCs can increase the recruitment of immune cells to sites of tumor development, affecting immune cell proliferation, differentiation, and effector function. Furthermore, by secreting growth factors, TA-MSCs can promote angiogenesis and the proliferation, survival, and chemoresistance of transformed cells. In addition, MSCs can differentiate into certain CAFs populations, which also play a key role in tumor development. At all times, the secretion of factors, direct cell-cell contact, and the release of EVs maintain a bidirectional communication between different components of the tumor microenvironment. Thus, MSCs may be an important therapeutic target for cancer treatment. Similarly, by exploiting their tropism toward tumor microenvironments, MSCs could be used as therapeutic vehicles against transformed cells and other tumor stroma components. Acknowledgments This work was supported by a grant from the National Council for Science and Technology (CONACYT) to Marta E Castro-Manrreza (Grant No. PN 2016-3067). We appreciate the collaboration of Carlos Paredes Monsalvo in the cell shape design. Compliance with Ethical Standards The authors declare no conflict of interest.

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Cancer-Associated Fibroblasts and Their Role in Cancer Progression Lukáš Lacina, Pavol Szabo, Ivo Klepáček, Michal Kolář, and Karel Smetana, Jr.

Abstract

Cancer incidence is increasing worldwide, partially due to the population ageing. This can be linked to advanced and accessible medical care. This trend represents a challenge for healthcare systems in many countries. Development of new diagnostic procedures and therapeutic approaches seems to be necessary for sensible care for therapeutically fragile elderly patients. The cancer microenvironment, especially cancer-associated fibroblasts, represents a promising target for therapeutic manipulation, which has not yet been fully exploited. The chapter summarises data about the origin, markers and biological properties of cancer-associated fibroblasts. The position of cancer-associated fibroblasts in the tumour cellular ecosystem has been established, and their influence on cancer cell proliferation, differentiation, migration and therapeutic resistance is widely recognised.

L. Lacina (✉) Institute of Anatomy, First Faculty of Medicine, Charles University, Praha 2, Czech Republic BIOCEV, First Faculty of Medicine, Charles University, Vestec, Czech Republic Department of Dermatovenereology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic e-mail: [email protected] P. Szabo · I. Klepáček Institute of Anatomy, First Faculty of Medicine, Charles University, Praha 2, Czech Republic M. Kolář Institute of Molecular Genetics, Czech Academy of Sciences, Prague 4, Czech Republic K. Smetana, Jr. Institute of Anatomy, First Faculty of Medicine, Charles University, Praha 2, Czech Republic BIOCEV, First Faculty of Medicine, Charles University, Vestec, Czech Republic # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_79 Published online: 24 November 2022

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Cancer-associated fibroblasts can potentially result from transition from a broad panel of cell types. The most relevant mechanism seems to be recruitment of normal tissue fibroblasts and mesenchymal stem cells. This is achieved by means of paracrine secretion from cancer cells or via secreted exosomes. CAFs are heterogeneous and represent a potent source of growth factors, pro-inflammatory cytokines, chemokines and also exosomes that significantly activate proliferation and migration of cancer cells. Cancer-associated fibroblasts represent a biologically potent and non-malignant population of cells in malignant tumours. CAF detection and phenotypic and functional characterisation in the distinct types of tumours can refine diagnostics. Moreover, CAFs are an available target for therapeutic interventions, which can potentially advance oncological therapy. Keywords

Cancer microenvironment · Cancer-associated fibroblasts · CXCL-8 · Exosome · IL-6 · Mesenchymal stem cells · TGF-β · α-smooth muscle actin

1

Introduction

Cancer represents an important medical, sociological and economic issue. The cumulative number of patients suffering from malignant disease of any type is increasing worldwide. In this context, causative analysis of this non-favourable trend highlighted ageing as an aspect of primary importance (Smetana et al. 2016). However, there are several key factors to be discussed in closer detail. These include (a) damage to the macromolecules, including DNA, by reactive radicals, (aa) reduced activity of the gene repair machinery and (aaa) reduced number of adult tissue stem cells. The human life span is of limited duration, which seems to be developmentally programmed. Historically, it was significantly shorter than we see nowadays in developed countries (Cagan et al. 2022; Smetana et al. 2017). We can speculate that the reason for such prolonged lifetime expectancy can lie in the remarkable progress in the quality of medical care. Importantly, this improved care also became widely accessible to the general population. The coincidence of these two factors seems to be responsible for such significant prolongation. With a popular phrase (Warman 1890), this adds some “years to your life”, but not necessarily “life to your years”. Due to our population’s prolonged survival, we expect that the number of patients suffering from cancer will even increase (Bray et al. 2021). Flattening of the incidence curve and stagnation of the numbers is yet an unmet goal in many cancer types. Moreover, the high cancer incidence in the elderly population represents a real therapeutic challenge. These aged patients are therapeutically fragile. Due to various comorbidities and age-related pathologies, they frequently do not tolerate aggressive oncological therapy well. Current major treatment options for cancer include

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surgery, cytotoxic chemotherapy, radiation therapy, endocrine therapy, molecular targeted therapy and most recently immunotherapy. Indeed, many classical, e.g. chemotherapeutic agents, are highly toxic. Nevertheless, the more progressive methods, e.g. immunotherapy, are also associated with a plethora of adverse effects. Avoiding the side effects of therapy is a particularly important task in the care of elderly cancer patients. However, finding the optimal balance between reduced toxicity and maximal effectiveness is challenging. Simple dose reduction can promptly lead to a suboptimal therapeutic response; even more worryingly, it can also help establish acquired resistance. Therefore, a combination of, e.g., a low-dosed “soft” chemotherapy with antiHER2 inhibitors, was proven to be particularly beneficial to this frail population (Wildiers et al. 2022). Malignant cells possess many remarkable features increasing their chance of surviving the cataclysmic impact of oncological therapy. These biological features of therapy-resistant cells significantly overlap with those observed in stem cells. These features, alone or in combinations, include heterogeneity, plasticity, selfrenewal capacity and tumour-initiating capacity (de Angelis et al. 2019). However, every stem cell vitally requires a specific tissue microenvironment known as the niche to maintain its stemness (Lacina et al. 2015). Out of this comfort zone, cancer cell becomes vulnerable. This can have certain therapeutic implications. All organs and tissues contain a variable number of fibroblasts. According to the classical morphological explanation, the fibroblast role was purely structural. Fibroblasts and their product – extracellular matrix (ECM) – were seen as nothing more than a 3D scaffold for cells of other types, mainly epithelial. The current view on the role of the fibroblast in organ function is contrastingly highly complex. Besides ECM, fibroblasts also produce a broad spectrum of bioactive factors that actively participate in the control of organ morphogenesis and also its function (de Groot et al. 2021). This more dynamic view was based on the progress in embryology, where observation of the exchange of bioactive molecules between the epithelial bud and condensed mesenchyme represents the leading mechanism of the development of many organs, systems that can be easily monitored in the model of hair follicle/teeth development (Kollar 1970; Santosh and Jones 2014; Taghiabadi et al. 2020; Zhang et al. 2019). The function of many organs depends on the presence and correct function of the adult tissue stem cells that are important for the self-renewal and repair in case of their damage (Mannino et al. 2022). Solid malignant tumours share various features with and can be paralleled to body organs. However, this similarity is somewhat caricatured or lampooned concerning the architecture and function. Like organs, tumours are also principally composed of malignant cells and supportive stroma. Stromal blood vessels supply the tumour with oxygen and nutrients. Moreover, the stromal vessels are an easy entry point for interaction with the immune system – namely, various populations of infiltrating leucocytes (Almagro et al. 2022). The structural scaffold of the stroma is mainly provided by cancer-associated fibroblasts (CAFs). Collectively, all these distinct cell types and their products form the unique microenvironment important for the correct

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function of cancer stem cells and stimulation of the cancer growth and spreading (Aramini et al. 2022). When comparing the proportion of distinct types of non-cancerous cells in the tumour site, CAFs represent the most numerous structural cell type in the cancer ecosystem (Saw et al. 2022). Routinely, this evidence is mainly based on the immunohistochemical visualisation of myofibroblasts. More likely, the actual figure can be even higher because not all CAFs exhibit this marker. Detailed investigation of the position of CAFs in the cancer ecosystem demonstrated the important control role of CAFs in cancer cell proliferation and migration during the metastatic spread (Vokurka et al. 2022). Unfortunately, their central role in tumour biology is not yet reflected by their therapeutic targeting. At this point, we can hypothesise that modulation of the mutual exchange of information between cancerous and non-cancerous cells within the tumour ecosystem represents an obvious target. As CAFs represent a numerous and biologically highly relevant group of non-cancerous cells present in the stroma of virtually all solid tumours, this concept could be widely applicable across various cancer types. Therefore, therapeutic targeting of CAFs or their product, alone or in combinations, could be therapeutically promising. In this chapter, we will analyse CAFs, including their origin and functional properties as a background for potential therapy.

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Fibroblasts and Their Origin with Specificity to CAFs

2.1

Normal Fibroblasts and Their Origin

There is an exponentially growing summa of available data focusing on the physiological roles of fibroblasts in various human organs. With emphasis on the skin, this topic has been extensively reviewed elsewhere by Vokurka and co-workers recently (2022). Briefly, fibroblasts are usually spindle-shaped cells. In the tissues, fibroblasts are surrounded by copious amounts of ECM produced by these cells. To provide ECM turnover, fibroblasts also secrete proteolytic enzymes and are thus responsible for the ECM dynamic remodelling under physiological and pathological conditions. Besides these structural aspects, fibroblasts also have regulatory functions in the tissue. To reach this goal, fibroblasts also produce a broad panel of growth factors, cytokines and chemokines. Numerically, fibroblasts are the most common cell type in connective tissue. However, fibroblasts also occur in the majority of organs. In this position, the basement membrane separates fibroblasts from the epithelial component, known as the parenchyma. Fibroblast can be isolated from adult tissues and expanded in vitro for further analyses. Because of the absence of a single fibroblast-specific marker, fibroblast isolation is dependent on the combination of several positive and negative markers simultaneously. Concerning their phenotypic identification, they are positive for intermediate filament molecule vimentin. These cells must also be negative for CD31/CD34 (present in endothelial cells), CD45/CD68 (positive in leucocytes), MELAN-A/HMB-45 (typical in melanocytes) and keratins (typical of epithelial

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cells) (Dvořánková et al. 2019; Williams and Thornton 2020). Of course, detection of other markers can be employed to distinguish the fibroblast subtypes. From the embryological point of view, fibroblasts in humans originate from two embryonic primordia: mesoderm and neural crest (Blentic et al. 2008; LeBleu and Neilson 2020; Noisa and Raivio 2014). Neural-crest-originated fibroblasts from the ectomesenchyme are mainly present in the facial region of the head. It is known that mesoderm-derived fibroblasts have site-specific HOX expression, and it is a cellautonomous and epigenetically maintained feature (Rinn et al. 2008). On the contrary, fibroblasts of the ectomesenchymal origin are characterised by almost no activity of HOX genes (Živicová et al. 2017). This developmental aspect might have several clinically relevant implications. It is known that neural-crest-originated cells residing in the hair follicles exhibit stem cell properties until adulthood (Sieber-Blum et al. 2006). This aspect of stemness maintenance can be a plausible explanation for the differences observed in wound healing in neonatal age and adulthood (Mateu et al. 2016). For this reason, it is also essential to define the differences between fibroblasts and mesenchymal stem cells (MSCs). These cells are found in the bone marrow, Wharton jelly of the umbilical cord and tooth pulp, and very similar cells are also present in the white fat and various other tissues (Ong et al. 2021). Functionally, MSCs can be distinguished by their plasticity because MSCs can be differentiated in vitro in various mesenchymal lineages. Unlike fibroblasts, MSCs can be differentiated at least to adipocytes, chondroblasts and osteoblasts. Regarding markers, MSCs can be distinguished from the normal fibroblasts by expression of molecules such as CD73, CD90 and CD105. Simultaneously, MSCs never express CD11b, CD14, CD19, CD34, CD45 and CD79a (Pittenger et al. 2019). Mesenchymal stem cells also exhibit immunomodulatory properties. Similarly to fibroblasts, the mesenchymal stem cells represent an important precursor from which CAFs can originate.

2.2

Origin of CAFs: Effect of Factors Produced by Cancer Cells

As CAFs are present in tumours of various histological origins, similar diversity must be considered while searching for the CAF origin. As reviewed earlier, several cell types can be considered being precursors to CAFs (Vokurka et al. 2022). Noteworthy, several populations can contribute to one tumour stroma formation simultaneously, which can explain the functional and genetic heterogeneity frequently observed in the stroma (Bu et al. 2019; Novotný et al. 2020). In the most accepted view, CAFs originate from the local fibroblasts. In the vicinity of proliferating or invading malignant cells, the fibroblasts are exposed to a broad repertoire of soluble factors. Indeed, cancer cells produce many active factors in a paracrine manner. The most important factors seem to be members of the TGF-β family, but other molecules such as IL-1β, FGF, PDGF, SDF1, HDGF, FGF, IL-12, IFNγ and TNFα also affect the formation of CAFs (Vokurka et al. 2022). When exposed to this intense paracrine signalling, the local fibroblasts

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respond by a broad change of transcriptional activity (Kolář et al. 2012). Thus, cancer cells control their transition to CAFs. This recruitment can be efficiently studied in vitro using various techniques. In a simple co-culture in transwells, dermal fibroblasts acquire properties of CAFs. The co-culture-induced expression profile in normal fibroblasts reliably mirrored the transcription profile of CAFs isolated from tumours of similar origin (e.g. head and neck cancer (Kolář et al. 2012)). More recently, a similar observation was confirmed in 3D using heterogeneous spheroids as a model of cutaneous melanoma (Novotný et al. 2020). The physical features of the tumour environment can also be an immensely important aspect of in vitro cancer stroma modelling. Soluble molecules are frequently deposited in the tissue on scaffolds of ECM. Moreover, the crosslinking effect of, e.g. endogenous lectins, galectins, especially of galectin-1, on receptor molecules can support the activity of the earlier-listed molecules, mainly of TGF-β, and stimulate formation of myofibroblasts very similar to CAFs (Dvořánková et al. 2011). This is a rather common feature of tumour stroma and underlines the nonspecific character of CAFs (Dvořánková et al. 2012). Of note, TGF-β and similar proteins can also transform fibroblasts into myofibroblasts in other pathological conditions. This is particularly relevant in wound healing and autoimmune diseases such as progressive polyarthritis and covid-19 (Ghanem et al. 2021; Kruglikov and Scherer 2020; van Praet et al. 2011). Considering the other local resident cell types (Fig. 1), it was suggested that the recruitment model could also be applied to, e.g. mesenchymal stem cells, endothelial cells, pericytes, stellate cells and adipocytes (Armulik et al. 2011; Ganguly et al. 2020; Nishimichi et al. 2021; Pérez et al. 2017). If suitable precursors are not readily present to form a susceptible microenvironment for malignant cells, candidate cells can also be attracted to the tumour site from distant tissues such as the bone marrow. This signalling over a long distance requires release of bioactive molecules into the systemic circulation. The tumour stroma is then penetrated by mesenchymal stem cells or circulating fibrocytes (Abe et al. 2001; Herzog and Bucala 2010; Hill et al. 2017; Karnoub 2007). Many pro-inflammatory factors suitable for this purpose are produced by cancer cells, including chemokines such as TNFα, CXCL-8 and stromal cell-derived factor (Asokan and Bandapalli 2021; H. Gao et al. 2009). Recruitment of MSCs, or capturing exosomes produced by them, to the tumour site can also have potential therapeutic implications. It was suggested that MSCs Fig. 1 Precursors from which CAFs can originate. F-actin is stained by TRITC-labelled phalloidin (red signal) and α-smooth muscle actin is recognised by monoclonal antibody (green signal). CAFs are yellow

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could deliver a suicidal cargo, destroying cancer cells at the tumour site (Ali et al. 2021; Moreno 2021; Pastorakova et al. 2020). The aforementioned exosomes represent another communication tool in the landscape of the tumour microenvironment. Initially, exosomes were studied as nanovesicles potentially carrying a protein cargo. In such a simplistic view, exosomes somewhat extend the potential of soluble protein paracrine signalling. Exosomes usually reach around 100 nm in diameter and their cargo can contain some of approximately 40,000 confirmed proteins. Moreover, it can also potentially transfer up to 5000 mRNAs and 3000 miRNAs. MicroRNA represents a particularly important component in the exosomal cargo. Micro RNAs, namely, miR-21, miR-155-5p, miR-211, miR-222, miR-146, and protein TGF-β1 can participate in the activation of CAFs by exosomes produced by melanoma cells (Vokurka et al. 2022). Thus, production of information-bearing nanovesicles such as exosomes represents an important route for the exchange of information and can have a significantly broader impact than paracrine signalling. Until now, emphasis was placed on cancer-cell-released exosomes. Surprisingly, the data about the effect of cancer cell-derived exosomes on fibroblasts is rather scarce compared to the information on the role of mutual exchange of information between cancer cells. There is increasing evidence of fibroblast activation by exosomes (Vokurka et al. 2022). These exosomes reduce the spreading and adhesion of normal fibroblasts and CAFs, but they stimulate their migration. Interestingly, their effect is different in normal fibroblasts and CAFs. CAFs under their influence produce more inflammationsupporting factors than the exosome-treated normal dermal fibroblasts (Strnadová et al. 2022).

2.3

Cancer Cells as Precursors of CAFs

Functional and supportive stroma is greatly important for virtually all solid tumours. Therefore, the question regarding the stroma origin seems to be scientifically sound. Can the stroma formation in the tumour be understood, e.g. as evidence of cancer stem cell differentiation plasticity? Are cancer cells capable of do-it-yourself formation of vitally important stroma? First, epithelial cells can indeed acquire a mesenchymal phenotype under certain circumstances. This phenomenon of epithelial-to-mesenchymal transition (EMT) seems to be particularly important during embryonic development (Thiery et al. 2009; Wong et al. 2006). EMT can also occur postnatally, and it was hypothesised as an important factor in various organ pathologies (Haensel and Dai 2018; Kriz et al. 2011). In the context of malignant diseases, EMT seems to be particularly important during cancer cell invasion and metastasis formation (Moustakas and Heldin 2007; Sánchez-Ramírez et al. 2022; Thiery et al. 2009). However, it is a subject of still ongoing discussion whether cancer cells after epithelial-mesenchymal transition can be considered potential precursors to CAFs (Kopantzev et al. 2010; Petersen et al. 2003; Tyler and Tirosh 2021).

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With a grain of salt, we have to acknowledge that it is convenient to study EMT mainly in vitro. After transfection with HPV-16 E6/E7 oncogenes, murine lung epithelial cells also bearing activated H-ras maintain the mesenchymal phenotype typical of cells undergoing EMT. In the co-culture system, these cells can influence normal primary human keratinocytes to phenotypically mimic cancer cells (Smetana et al. 2008). On the other hand, it is significantly more challenging to find robust evidence of EMT in vivo. In highly popular immunodeficient murine models, human cancer cells can be easily grafted subcutaneously. The immunodeficient mice develop tumours, usually with a relatively modest stroma. However, immunohistochemical analysis using species-specific vimentin antibodies confirmed exclusively fibroblasts of the host origin in these tumours. No CAFs exhibiting human proteins – and thus confirming EMT involvement in the stroma formation – were observed (Dvořánková et al. 2015). Alternatively, it is known that the chorioallantoic membrane of the avian embryo also represents an excellent environment for the experimental growth of cancer cells (Ribatti 2008, 2017, 2021). Grafted human cancer cells (Fig. 2) induce here formation of cells very similar to CAFs; however, these were also confirmed to be of chick embryonic origin (Strnadová et al. 2020). These observations indicate a somewhat universal nature of the intercellular information exchange between cancer cells and non-cancerous cells in the tumour site despite the developmental and interspecies barriers.

Fig. 2 Human hypopharyngeal squamous carcinoma cells FaDu form distinct nodule in the chorioallantoic membrane of a duck embryo (upper left corner). Cancer cells marked by detection of keratin 5 (brown signal) infiltrate the chorioallantoic membrane. The group of cancer cells is surrounded by condensed embryonic fibroblasts similar to CAFs. A thrombus containing chick erythrocytes is marked by an asterisk. Counterstained by haematoxylin. Bar is 100 mm

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Recently, genomic analyses of human cancer cells in xenografts came to similar conclusions, and thus EMT as a significant origin of CAFs is highly disputable (Tyler and Tirosh 2021). It seems reasonable to conclude here that the stromal components of tumours are usually exempted from the classical schemes of tumourigenesis based on sequential malignant cell transformation. Cancer cells, due to their genetic alteration, indeed represent the essential primary elements in the cancer ecosystem that, in the second step, recruit CAFs. In the opposite direction, CAFs participate in the maintenance of cancer cell phenotype.

3

Markers of CAFs

In order to characterise CAFs as a distinct cell type of the tumour ecosystem, they must be distinguished with sufficient specificity from other cell populations in this landscape. Unfortunately, no single marker for the CAF population has yet proved itself specific and universally applicable. Moreover, the markers can differ according to the CAF origin. However, some proteins are expressed with a higher frequency in CAFs than in other types of fibroblasts (reviewed in Vokurka et al. 2022). The most prominent position among CAFs markers is traditionally held by α-smooth muscle actin (SMA)(Valach et al. 2012). Further, expression of other proteins such as periostin, podoplanin, tenascin C, PDGFRα, and S100A4 is also typical of CAFs. Great attention was also dedicated in the last decade to fibroblast-activating protein (FAP). FAP was also believed to be a CAF marker protein. It was hypothesised that its specific radioligand could be used at the clinical level for tumour visualisation. Unfortunately, the result of a clinical trial was controversial due to some false-positivity observed (Roustaei et al. 2022). This failure indicates that CAF markers are also exhibited by fibroblasts in other non-physiological situations including wound healing, and therefore their value in tumour detection at the diagnostic scale is problematic (Gál et al. 2017). However, it was suggested that development of new probes for FAP detection could result in higher specificity. A labelled polymer conjugate with a substrate recognised by FAP could be very important for this purpose (Dvořáková et al. 2017). For routine histopathological analysis, detection of CAFs is more specific, for example, by employment of detection of other markers using the procedure of multiple cell labelling, where more markers can be detected at the single-cell level.

4

CAFs Influence the Biological Properties of Cancer Cells

The evidence confirming the important role of CAFs in cancer has expanded significantly, mainly in the last two decades. It can be well illustrated by the number of articles published on this topic since 1968. The actual figure is close to 12,000 in

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mid-2022 in the PubMed database. Such exponential growth reflects the position of CAFs in tumour biology. CAFs are recently seen as key stimulators of malignant tumour progression (Lacina et al. 2015). Even ancillary co-cultivation systems demonstrated the biological effect of isotypic CAFs on cancer cells and normal keratinocytes. In co-culture, normal epithelial cells readily acquire phenotypic features typical of squamous cell carcinomas (Lacina et al., 2007a, b). Somewhat surprisingly, the effect of CAFs is not cancer-type specific. While the effect of normal fibroblasts on breast cancer cells is low, the co-culture with CAFs isolated from breast cancer, cutaneous basal cell carcinoma, head and neck squamous cell carcinoma and even melanoma affect the differentiation status of epithelial cells to a similar extent (Dvořánková et al. 2012). CAFs of different tumour origins stimulate migration of various tumour cells of different origins, such as melanoma, glioblastoma, squamous cell carcinoma and ovarian as well as breast cancer under in vitro conditions (Feng et al. 2022; Jobe et al. 2016; Kudo et al. 2022; Sun et al. 2019; Trylcova et al. 2015). Their effect is mediated by production of ECM molecules and by ECM remodelling. CAFs can generate a highly aligned matrix of collagen fibres alone (Ray et al. 2018), or fibronectin enriched (Erdogan et al. 2017), and thus generate discrete tracks that cancer cells follow in vitro. Podoplanin can be another important molecule in this scenario. This small sialomucin is frequently expressed on the surface of CAFs and various carcinomas, where it is associated with increased incidence of metastasis and poor survival (Ugorski et al. 2016). There is evidence that podoplanin expression in malignant cells can be linked to their increased migration properties in loosened ECM. To further facilitate it, podoplanin also regulates paracrine production of various pro-migratory bioactive factors (Suzuki et al. 2022). However, the reaction of mesenchymal tissue in the vicinity of a forming tumour is more diverse. Desmoplastic reaction to tumour growth denotes production of fibrous connective tissue around tumour cells. This stromal reaction can vary from predominantly cellular with numerous CAFs with little collagen to a dense, almost acellular tissue. In this view, mesenchymal cells build a tight wall around the tumour in an attempt to prevent invasion. It was speculated whether this reaction should be understood as a protective response and potentially also a biomarker of good prognosis, e.g. in colorectal cancer (Caporale et al. 2005). Desmoplasia is also present in a broader scale of tumours, e.g. in gastric carcinoma (Lee et al. 2017), melanoma (Chen et al. 2013) and breast cancer (Iacobuzio-Donahue et al. 2002). The protective role in most of them was not confirmed (Caporale et al. 2001). For consideration of the actual importance of desmoplastic stroma, pancreatic ductal adenocarcinoma can serve as an educative example of a malignant disease. Some ductal adenocarcinomas of the pancreas contain CAFs that exhibit tumoursuppressive activity (Manoukian et al. 2021). These tumours usually exhibit desmoplastic stroma that can indeed somewhat limit the migration of metastasising cells. On the other hand, desmoplastic stroma also represents a relatively impermeable barrier that prevents accessibility of cancer cells for the therapy, and these patients have statistically shorter survival (Sánchez-Ramírez et al. 2022). Such diversity can explain the individually variable outcomes observed in patient cohorts.

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Interestingly, it was suggested recently that CAFs with pro-tumour phenotype in pancreatic cancer can be switched to the tumour-inhibiting phenotype by synthetic retinoid Am80 (Iida et al. 2022). These data are somewhat difficult to interpret now and require further elucidation, but it was suggested that in this case, the effect of physical stiffness could affect the behaviour of CAFs and cancer cells (Hupfer et al. 2021). To this end, it is increasingly evident that the roles of cancer stroma are complex and multifaceted and cannot be categorised simply as helpful or harmful to tumours (Ueno et al. 2021).

4.1

CAFs Are Heterogeneous

Fibroblasts represent a strikingly heterogeneous cell type across the human body. This heterogeneity is evident even at the level of single organ under physiological conditions. This diversity of fibroblast phenotypes and functions within one organ can be exemplified, e.g. on normal mammalian skin (Rognoni and Watt 2018). Dermal fibroblasts are classified according to their position in the dermal structures. Based on this routine histological dermal stratification scheme, different fibroblasts reside in the superficial papillary dermis, deeper reticular region (Lynch and Watt 2018) and hypodermis (Haydont et al. 2020). A very distinct population of fibroblasts can be found in hair follicle dermal papilla (Driskell et al. 2011). Moreover, these fibroblast subpopulations also differ in their gene transcriptional activity and protein level. All fibroblasts are descendants of the common fibroblast progenitors. However, dermal papilla fibroblasts are necessary for hair follicle formation. Reticular layer fibroblasts participate in the wound healing, and they can differentiate to SMA exhibiting myofibroblasts very similar to CAFs. CD26positive fibroblasts can participate in skin fibrosis, and their participation in the formation of skin cancer stroma was also noted (Lynch and Watt 2018). This data highlights how fibroblast subpopulations have distinct functions (Haydont et al. 2020) and presumably also can participate in the defined pathological situation. Similar functional heterogeneity of fibroblasts was also noted in many types of pathologies, including cancer, for example, in adenocarcinoma of the pancreas (Watt and Morton 2021), head and neck cancer (Bienkowska et al. 2021), gastric cancer (Li et al. 2022), breast cancer (Elwakeel and Weigert 2021) and prostate cancer (ChallaSivaKanaka et al. 2022). In vitro, the remarkable heterogeneity of CAF can be precisely identified using heterogeneous spheroids. These 3D tumour models can be constructed from cancer cells and seemingly homogeneous primary dermal fibroblasts (Novotný et al. 2020). In co-cultures, fibroblasts acquire properties of CAFs under the influence of cancer cells. The spheroids can be consequently dissociated, and individual cells can be characterised by single-cell sequencing (Fig. 3). Fibroblasts and cancer cells can be distinguished by the expression profile of genes. This method reveals that even a simplistic spheroidal model unravels several distinct fibroblast subpopulations. Thus, one subpopulation of CAFs can be defined by production of ECM molecules. Another pool of cells is

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Fig. 3 Normal dermal fibroblasts under the influence of melanoma cells acquire properties of CAFs and differentiate into three distinct populations: ECM+ cells producing extracellular matrix components, ECM- fibroblasts stimulating inflammation and ID+ cells characterised by elevated ID gene expression. (a) Marker genes of the cell populations identified by expression profile clustering in (b)

remarkable by low production of ECM but high expression of genes of mediators necessary for inflammation. Of note, these inflammation-supporting genes are more active when fibroblasts from the sun-irradiated skin harvested from old persons were employed for the spheroid construction. This can reflect the accumulated photodamage and chronological ageing (Coppé et al. 2010). This subgroup of CAFs with active pro-inflammatory genes such as IL-6 or CXCL-12 stimulates the invasiveness of gastric cancer cells and instructs the surrounding immune cells to tolerate tumour elements (Li et al. 2022). The second subgroup not producing ECM is dependent on the TGF-β signalling cascade and represents cells with deregulation of ID genes (Novotný et al. 2020). Deregulation of these genes is associated with many pathological situations, including developmental defects and cancer formation (breast, prostate, cervical, thyroid, nasopharyngeal, colorectal, gastric, hepatocellular, pancreatic, glioblastoma, neuroblastoma, medulloblastoma, leukaemia, lymphoma) (Roschger and Cabrele 2017).

4.2

CAFs Produce Growth Factors and Inflammation-Supporting Factors

For deeper insight into the CAF biology, we have to focus on the expression profiles of normal fibroblasts and their comparison to CAFs. At both the mRNA and protein level, CAFs produce a plethora of cytokines, chemokines and growth factors (reviewed in Kodet et al. 2020). The most prominent examples include, but are not

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Fig. 4 CAFs produce growth factors and inflammation-supporting factors. (a) Heatmap shows varying gene expression intensity of selected growth factors, chemokines and interleukins in different types of CAFs and control fibroblasts (SCCF_MuF, CAFs from head and neck squamous cell carcinoma of mucosal origin; SCCF_D CAFs from dermal squamous cell carcinoma, BCCF CAFs from basocellular carcinoma, DF corresponding autologous control dermal fibroblasts, MuF control mucous fibroblasts, DF_FC control child facial dermal fibroblasts, DF_FA control adult facial dermal fibroblasts). (b) Boxplots display expression profiles of several key factors and heterogeneity of their expression in CAFs originating from different malignancies

limited to, CXCL-1, CXCL-8, CXCL-12, CXCL-16, IL-1B, IL-6, 1L17, HBEGF, BDNF, IGFBP-7, TGF-β2, GAP-43, BMP-2, BMP-6, VEGF-A, VEGF-C, CTGF, PDGFRL, LEPRE-1 and KAZALD-1. The genes encoding pro-inflammatory factors are among the most differentially expressed ones (Jobe et al. 2018; Mishra et al. 2011; Souza and Colli 2022). The overlapping profile was confirmed in a broader list of tumours (Fig. 4), for example, squamous cancer of the head and neck (Kolář et al. 2012), adenocarcinoma of the pancreas (Novák et al. 2021) or malignant melanoma (Jobe et al. 2018). Upregulated production of factors such as IL-6 and CXCL-8 seems to have a very substantial role in cancer, because both these factors participate in the control of cancer cell differentiation status and migration ability, as demonstrated in vitro across different cancer types (Daouk et al. 2020; Jobe et al. 2018; Kolář et al. 2012; Shen et al. 2020; Zhong et al. 2022). Particularly common is the extensive production of IL-6 by CAFs across different tumours, indicating its general significance. It is because IL-6 has a central role in the control of the cytokine network (Uciechowski and Dempke 2020). Interestingly, there is evidence of a regulatory mechanism between CAFs and cells of the cancer of pancreas maintaining a steady concentration of IL-6 in the cancer ecosystem. Under in vitro conditions, the increase in the production of IL-6 by pancreatic cancer cells is followed by reduced production of this molecule by co-cultured CAFs (Novák et al. 2021). This suggests a need for precise regulation of IL-6 signalling intensity in the tumours. The expression of genes for pro-inflammatory factors in CAFs is also significantly stimulated by exosomes produced by cancer cells. Interestingly, the same exosomes, on the contrary, inhibit expression of genes encoding these factors in the normal human dermal fibroblasts. This opposing response was observed for IL1A,

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IL6, CCL5 and CXCL8 (Strnadová et al. 2022), highlighting functional diversity in different fibroblasts. Further, exosomes also influence production of ECM molecules by both normal fibroblasts and CAFs. Cancer-derived exosomes negatively regulate activity of the gene for type I collagen in both normal fibroblasts and CAFs. On the other hand, exosomes stimulated activity of the gene for tenascin-C, but exclusively in CAFs (Strnadová et al. 2022). Production of tenascin-C is typical of malignant conditions, where this molecule is present in the invasive front of carcinomas. It can be linked to poor survival of patients due to the progression and metastasis (Lepucki et al. 2022; Zivicova et al. 2018). Cancer-derived exosomes can also regulate the invasiveness of tumours. To quantify the invasion, 3D spheroids prepared from melanoma cells and fibroblasts can be embedded in collagen gel with or without exosomes. In exosome-enriched microenvironment, the malignant melanoma cells indeed migrate more extensively. This result can be due to the higher activation of CAFs via exosomes compared to normal fibroblasts (Strnadová et al. 2022). Extensive paracrine secretion of bioactive factors by CAFs can influence the metabolism of cells of the breast and ductal pancreatic cancer via pathways that control expression of focal adhesion kinase. Consequently, this mechanism also regulates their switch to glycolysis. These changes are associated with the growth of tumours and migration of cancer cells (Demircioglu et al. 2020). Related to anaerobic glycolytic metabolism, CAFs can produce energy-rich molecules such as pyruvate, ketone bodies, fatty acids, lactic acid and also hydrogen peroxide, fostering a broad metabolic reprogramming of cancer cells, known as the reverse Warburg effect (Liang et al. 2022).

4.3

CAFs Influence Immune Cells in the Cancer Ecosystem

While considering the complexities of the tumour microenvironment, the immune system must also be taken into account. The cancer ecosystem undisputedly involves many types of immune cells infiltrating the tumour (Lacina et al. 2018). These immune cells are inevitably influenced by the physical features and ongoing intercellular communication across the tumour microenvironment. As a result, the immune cells can cease their antitumoural activities and sometimes even effectively support cancer growth. The complex mechanisms of tumour immune evasion exceed the scope of this chapter and were summarised recently in many excellent reviews (Jhunjhunwala et al. 2021; Tomei et al. 2021). However, one facet of this clinically relevant story is particularly noteworthy in the given context. It was a somewhat neglected issue for a long time that the effect of factors produced by CAFs act not only on cancer cells, but we must also anticipate their influence on the immune cells. In detail, CAFs predominantly suppress the antitumoural immunity. This might be exemplified by their influence on neutrophil leucocytes, macrophages, T-lymphocytes as well as NK cells (Adeshakin et al. 2022; Mao et al. 2021). Interestingly, CAFs can even present antigens and act as other more prominent types of antigen-presenting cells (Elyada et al. 2019). With reference to current oncological therapy standards, anti-PD-1/PDL-1 antibodies are a

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valuable option in anti-cancer therapy for various tumours. Only tumours sufficiently exhibiting these molecules will respond in a clinically favourable way. The expression of these immune-checkpoint molecules in cells of the cancer ecosystem seems to be associated with the activity of CAFs (Gao et al. 2021). If so, the regulation of CAF activity may participate in PD-1/PDL-1-dependent immunosuppression against cancer cells and result in the failure of anti-cancer therapy (Gorchs et al. 2019).

4.4

CAFs Stimulate the Vascularisation of Tumours

The mechanisms of neoangiogenesis have been an attractive research topic for decades. In energetically demanding conditions, such as wound healing and tumour growth, the formation of new vessels can be of crucial importance (Schiffmann et al. 2020). A high rate of metabolic turnover in cancer cells requires sufficient oxygen and nutrient supply. In the classical view, cancer cells will call for support, and local endothelial cells will respond by sprouting and neovascularisation (Gadde et al. 2020). Indeed, oxidative phosphorylation and its inhibitors were proposed and tested in trials to target cancer cell metabolism (Ashton et al. 2018). Even earlier, angiogenesis inhibitors were introduced to the market after approval to treat cancer (El-Kenawi and El-Remessy 2013). Noteworthy, CAFs produce factors that are important for the vascularisation of tumours required for their progression. VEGFA and CXCL8 could be essential from this point of view (Guo et al. 2021; Inoue et al. 2019; Watanabe et al. 2022). The proangiogenic switch of CAFs is induced by exosomes containing miR-155-5p produced by the cancer cells (Zhou et al. 2018). As expected, these proangiogenic properties are stimulated by a hypoxic environment (Kugeratski et al. 2019). CAFs are potent producers of IL-6, which activates the production of VEGFA by cancer cells (Ishii et al. 2018). The growth of tumours is accompanied by forces responsible for tissue deformation. These mechanical stimuli also exhibit a strong stimulatory effect on angiogenesis via production of supporting factors by CAFs (Sewell-Loftin et al. 2017).

4.5

The Systemic Effect of CAFs and Their Products on the Whole-Organism Scale

It is critically important to define the factors responsible for the information exchange between cells participating in the cancer ecosystem. It may be assumed that accumulation of these factors in the tumour can consequently result in leakage, and thus by their entrance into circulating body fluids, including serum, these molecules can also affect distant body parts. This can also have a systemic effect on the patient’s organism. Moreover, detection of these circulating molecules can be employed in diagnostics for monitoring the effect of therapy and disease progression. The increased serum level of these factors was observed in cutaneous malignant melanoma (Kučera et al. 2019), breast cancer (Paccagnella et al. 2022), ovarian

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Fig. 5 Expression and methylation profiles of dermal fibroblasts of an advanced stage patient suffering from melanoma. While the expression profiles (a) of autologous control fibroblasts (ACF) are more similar to control dermal fibroblasts from a distal part of the patient’s body (CDF) than to the melanoma-associated fibroblasts (MAF), their methylation profile (b) more closely resembles the profile of MAFs

cancer (Pawlik et al. 2021), renal cancer (Esteban et al. 2021) and prostate cancer (Garrido et al. 2021). Bioinformatic analysis of dermal fibroblasts from patients with advanced stages of cancer (Fig. 5) demonstrated their similarity with CAFs (Kodet et al. 2018). It can be, therefore, hypothesised that an increased level of bioactive factors can prepare the premetastatic niche as soil for cancer cells as seeds – depicting classical Steven Paget’s hypothesis (Akhtar et al. 2019). Long-term increase of serum levels of bioactive factors in cancer patients can affect their total health. It is known that the advanced stage of malignant diseases is associated with the wasting of patients leading to terminal cachexia and psychic deterioration (Loumaye and Thissen 2017; White 2017). IL-6, as well as TNFα, can significantly influence the metabolism of the liver, white fat and striated muscle,

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resulting in the rapid loss of weight, weakening due to sarcopenia and consequently death (Lacina et al. 2019a, b). Interestingly, a similar situation was also observed in elderly, including the tissue and serum elevation of the same factors, namely IL-6 (Strnadova et al. 2019). Increased production of these factors supporting the age-dependent sterile inflammation is defined as inflammaging, and it is tightly associated with reduction of the immune response (Salminen 2022). Higher age is associated with the growing incidence of malignant tumours (Smetana et al. 2016). Inflammaging seems to have some profound influence on cancer cell migration, and so it stimulates metastasis formation (Pretzsch et al. 2022). Therefore, it is important to link ageing with these medically and socially important issues.

5

Tumour Evolution and Spreading at a Single Patient Level

In theory, tumours were seen as clonal proliferation for several decades (Bronchud 2002). However, this concept was later extensively disputed, since genomic studies revealed that malignant cells of the primary tumour are genetically heterogeneous (Liu et al. 2022). This heterogeneity in well-established tumours lasting for a substantial amount of time is partially due to the remarkable genome instability observed in cancer (Tubbs and Nussenzweig 2017). This can also be a good reason for genetic diversity. In terms of Darwinistic evolutionary science, as mutations can occur spontaneously, natural selection makes certain mutations advantageous at the time of need, which is an impetus for evolution. Fitness is a critical factor in evolution. In the context of cancer, the most beneficial strategy is selection of the highest resistance to a hostile environment or even therapy (Gidoin et al. 2018). Administered therapy represents a selection pressure that swipes the less-fit clones, leading to the propagation of more resistant ones. This follows surprisingly well the scenarios known from population ecology applicable to the extinction of species (Karev and Kareva 2016). In a broader timeframe, it is also compliant with Darwinian principles (Lacina et al. 2019a, b; Noble 2021). Thus, the progression of malignant disease can be easily observed and described as a sort of evolution confined to one individual body. To fully acknowledge the complexities of the intimate relation between the tumour parenchyma and tumour stroma, co-evolutionary models were proposed (Polyak et al. 2009; Weinberg 2008). In this light, it seems beneficial to consider these two dimensions simultaneously to describe how neoplasms change in time and how tumours respond to interventions. Therefore, a classification system advocating for the use of Evo/Eco (Evolutionary/Ecological) indices to describe the actual tumour stage was proposed to personalise optimal interventions and their timing (Maley et al. 2017). However, at present it is far from everyday use in clinical oncology. Heterogeneity of tumour cells can influence primary sensitivity to oncologic therapy (Kyrochristos et al. 2022) and forms a prerequisite to later acquired

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resistance development. Initially, many studies have addressed drug resistance mechanisms in cancer cells. More recently, Straussman and co-workers (Straussman et al. 2012) coined the concept that it is actually the tumour microenvironment that confers resistance to therapy. Acquired resistance frequently limits the outcomes of modern molecular targeted therapies (Roesch 2015). Therefore, the therapeutic application of combinations of several inhibitors simultaneously is somewhat beneficial to avoiding or postponing promptly acquired resistance, as demonstrated, e.g. in malignant melanoma (Mai et al. 2015). However, targeting the tumour microenvironment via CAFs could also be a practical approach. Moreover, it would be applicable to a broad scale of cancer types. Relevant to metastasis formation, only some cells from the heterogenic pool of cancer cells are equipped suitably to leave the primary site and capable of anchoring in a distant safe harbour. Detailed genetic analysis of the primary and secondary tumours revealed significant diversity (Harbst et al. 2014). The metastatic dissemination thus can be characterised again as an evolutionary process. Moreover, occupation of so-called premetastatic niches by circulating cancer cells is another striking parallel, at the cellular level, to interactions, e.g. mutualism, known from ecology (Kodet et al. 2020). Molecular mechanisms underlying these changes are only now beginning to be understood. However, we can hypothesise that this is a particularly convenient target for treatment strategy. To conclude, cancer evolution has become an even more complex and valuable scheme since the epigenetic regulation mechanisms were considered. Many clinically relevant phenomena in oncology can be elucidated through the prism of Darwin’s postulates (Vendramin et al. 2021). These evolutionary and ecological principles should be considered in searching for novel therapeutic strategies for personalised medicine in the future (Brioli et al. 2014).

6

CAFs in Diagnostics and Anti-cancer Therapy

6.1

Targeting Cytokine Signalling Pathways

Although CAFs represent the most frequent non-malignant cell type in most cancer cases, they have not been therapeutically targeted at a clinical scale. The concept of anti-CAFs-based therapy is supported by the observation that patients suffering from colorectal cancers enriched in CAFs with specific genetic signatures have a worse prognosis than patients with tumours with fewer CAFs (Guinney et al. 2015). In this particular example, CAFs can serve as a robust prognostic marker (Zheng et al. 2021). There is a plethora of factors produced by CAFs included in this chapter. However, TGF-β, IL-6 and CXCL-8 were broadly recognised by many researchers as prominent factors increasing the therapeutic resistance of cancer cells (Bu et al. 2020). Despite these known encouraging data, CAFs are recently not in the focus of interest of the pharma industry as a target for developing new therapeutics. It is most likely due to the absence of specific surface markers of CAFs suitable for their

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targeting. From this point of view, targeting the CAF-released products, such as cytokines/chemokines or their receptors, seems to be a more promising approach. Therapeutic targeting of CAFs can even fulfil criteria for the newly defined category of anti-cancer drugs – the migrastatics (Gandalovičová et al. 2017). There are numerous indications that modulation of IL-6 signalling represents a particularly good target. This cytokine participates in the intercellular information exchange in numerous types of solid tumours. Moreover, IL-6 contributes significantly to the systemic effects of cancer, including cancer-related cachexia – the physical deterioration of patients at the terminal stage of malignant disease. Every step in the IL-6 signalling cascade can be potentially targeted. These potential targets include a) blockade of the production or release of IL-6 itself; b) inactivation of released IL-6 via inactivation; c) abrogation of IL-6 interaction with the receptor complex (membrane-bound or soluble); d) inhibition of molecules transmitting the signal downstream from the receptor to the cell nucleus, as reviewed in detailed elsewhere (Španko et al. 2021). As IL-6 is a potent pro-inflammatory molecule, many of these agents were developed and clinically tested to treat rheumatologic and autoimmune conditions. Monoclonal antibodies against IL-6 tocilizumab or sarilumab are approved for the biological therapy of rheumatoid arthritis. Tocilizumab is suitable for the therapy of Castleman disease (Aita et al. 2020). However, the results reported in clinical trials in cancer patients were less encouraging than expected (Španko et al. 2021). It was used in combination with fenretinide (synthetic retinoid) and reparixin (inhibitor of CXC chemokine receptors 1 and 2) for the therapy of oral squamous carcinoma (Mallery et al. 2019). Tocilizumab also minimises drug resistance in renal cell carcinoma, reducing the number of cancer stem cells (Ishibashi et al. 2018). This therapeutic antibody can also be employed to prevent or reduce toxicity of the therapy by checkpoint inhibitors (Kang et al. 2021). Thus, combining more targets along with anti-IL-6 seems more promising for cancer therapy. One of the possible options should be targeting of IL-6, CXCL-8, VEGFA and MFGE8 (Plzák et al. 2019). From this point of view, the combination of reparixin with tocilizumab has a significant anti-metastatic effect under experimental conditions (Jayatilaka et al. 2017, 2018).

6.2

Employment of Mesenchymal Stem Cells as Active Carriers with Suicidal Cargo

As mentioned above, MSCs represent one of the possible precursors of CAFs in tumours (H. Gao et al. 2009). Several sophisticated therapeutic strategies were proposed using MSCs, or their exosomes, respectively (Ali et al. 2021; Altaner 2008; Moreno 2021; Pastorakova et al. 2020). After transfection by herpes simplex virus thymidine kinase, these cells or their exosomes were recruited to the tumour site and could activate the inactive cytostatic drug administered systemically. This strategy can increase the concentration of the active drug locally to a desirably cytotoxic level. The elicited toxicity will thus primarily affect the tumour site,

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while other body parts will be protected from potential damage. These MSCs can also be used as a carrier of the Trojan horse – a recombinant oncolytic virus that was thus transported to glioblastoma in a mouse model with remarkable therapeutic efficiency (Ali et al. 2021; Moreno 2021). This scenario is very smart because it is based on the natural proclivity of MSCs to intrude into tumours and stimulate there the population of CAFs to foster cancer cells.

7

Conclusion

CAFs represent a heterogeneous group of cells formed from a panel of precursor elements, including local fibroblasts and mesenchymal stem cells. In general, CAFs support the proliferation and migration of cancer cells. However, rare exceptions to this general functional scheme, such as the subpopulation of CAFs in pancreatic ductal adenocarcinoma, are also known. Comparable to normal fibroblasts, CAFs produce ECM molecules and a broad spectrum of soluble molecules mediating intercellular paracrine interactions. These products form the physical landscape of the tumour microenvironment and convert this microenvironment into chronically pro-inflammatory tuning, thus fostering the cancers cells in this niche. Moreover, CAFs also produce exosomes with a regulatory potential in the tumour microenvironment. The activity of CAFs and their products results in a powerful migrationsupporting effect. This is important for metastasis formation, the most severe complication of cancer. Drugs targeting CAFs could fit well into the newly proposed category of migrastatics. Distinct stromal/CAFs transcriptomic signatures can be employed as independent prognostic factors. Potentially, their use in monitoring the efficiency of anti-cancer therapy is also possible. Especially the application of MSCs with a deadly cargo as a Trojan horse can represent an attractive therapy in the future. In general, targeting of CAFs and their products (namely, with pro-inflammatory properties) represents a challenge, because this concept could be broadly applicable in the therapy of various cancer types. Acknowledgements The preparation of the article was supported by the Ministry of Education, Youth and Sport of the Czech Republic, project “Centre for Tumour Ecology – Research of the Cancer Microenvironment Supporting Cancer Growth and Spread”, No. CZ.02.1.01/0.0/0.0/ 16_019/0000785, and by Charles University, project Cooperatio. Authors are also grateful to the League Against Cancer Prague (LPR Praha) for long-standing support. Conflict of Interest Karel Smetana, Jr. is co-inventor of US patent US 11,246,874 B1 owned by Oxygen Biotech LLC, Wilmington, DE. Other authors declare no conflict of interest.

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The Role of Tumoroids in Cancer Research Mahsa Yousefpour Marzbali and Nima Rezaei

Abstract

The optimal tumor model improves cancer diagnosis, prognosis, and therapeutics studies. In tumor modeling, it is crucial to mimic the tumor microenvironment (TME) that includes cellular heterogenicity and tumor cell communications. Traditional 2D cancer cell culture and early 3D culture had many deficiencies in imitating the genotype and phenotype of TME. However, newly developed 3D tumor organoids or tumoroids accurately recapitulate the tumor TME and its cellular components. The present chapter highlights the recent advances in tumoroid cultivation and its application in cancer research. Tumoroids have been used to study different aspects of cancer biology, such as mutational signatures of tumors and cancer-stromal cell interactions in tumorigenesis and cancer progression. They are also employed to generating living biobanks of tumors and performing drug screens and pharmacogenetic studies. Moreover, the contribution of advanced techniques such as genetic manipulation and cutting-

M. Yousefpour Marzbali Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran International Network of Stem Cell (INSC), Universal Scientific Education and Research Network (USERN), Tehran, Iran N. Rezaei (✉) Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Stockholm, Sweden e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_112 Published online: 26 January 2023

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edge analysis tools makes the tumoroid model a perfect platform for addressing the gaps in personalized medicine and drug development. Keywords

Cancer · Organoids · Preclinical models · Tumor microenvironment

1

Introduction

Cancer is a worldwide health threat that affects the life of many people around the world by its high risk of morbidity and mortality. Research on detecting and treating cancer is still ongoing, but the remarkable challenge is the lack of an appropriate tumor model that mimics tumor microenvironment (TME) components and its interactions in high accuracy. TME consists of tumor cells and stroma that include extracellular matrix (ECM), growth factors, chemokines, cytokines, and stromal cells. Traditional 2D cell culture is the primary and most widely used model for basic biology and molecular analysis in tumor research. The monolayer cultivation of the cells restricts the essential cell connections in the TME that are required for signaling cascades and gene expression. However, 2D cell culture of immortalized cell lines makes long-term investigations possible with a high rate of proliferation, but this property causes many mutations and alters the genetic heterogeneity of the primary tumor cell that can be affected by the number of passages. Then, the deficiencies of this model render it an inadequate cancer model. Later, patient-derived xenograft (PDX) derived directly from patient tumor tissue was implanted into immunedeficient mice to examine the efficacy of novel therapeutics. Although PDX models preserve the phenotype, genotype, cell composition, and molecular content of the original tumor characteristics, their disadvantages include poor heterogeneity, ethical concerns, and the need for significant cost and time (Hidalgo et al. 2014). Nowadays, the next approach is 3D tumor modeling, which includes spheroid and organoid 3D cultures to address various deficiencies in previous models. Spheroids are cellular clusters that culture floatily and provide cell-cell and cell-matrix interaction in order to avoid the restrictions of conventional monolayer cell culture. Tumor spheroids are the simplest 3D cell culture models that have been applied for tumor characterization in vitro (Costa et al. 2016). Organoids are in vitro modeling platforms that have tissue-specific multicellular and anatomical structures that recapitulate original tissues by self-patterning and morphogenesis (Sasai 2013). In addition, organoids have the following abilities – extension from a tiny amount of tissue, amenable properties for genetic engineering, aptitude for long-term culture, and cryopreservation – making them an ideal system in a broad assortment of studies in stem cell biology and disease modeling. Tumor-derived organoids are named tumor organoids or tumoroids and accurately recapitulate TME and reveal a more simulated model of original tissue heterogeneity in genotype and phenotype. Therefore, these 3D models are the most recommended systems for preclinical drug studies and personalized medicine

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(Liu et al. 2021). This chapter reviews the constituents of tumoroid, the methods that develop tumoroid culture, and the applications of tumoroids in disease modeling and therapeutic approaches.

2

Tumoroid Generation

Three-dimensional (3D) tumor culture systems are used to imitate the spatial features of the tumor microstructure and cell heterogeneity. Tumor spheroids are floated cancer cell aggregation, and scaffolds can be applied to improve spheroid cultures by anchoring cells (Nath and Devi 2016). In spheroid culture, cell sources are provided from either cell lines (Nath and Devi 2016) or tumor-derived stem cells (Ishiguro et al. 2017), and the coculture technique also accompanies the state-of-art spheroids (Modi et al. 2021). Despite the extensive use of spheroids as nonvascularized tumor models, the lack of control over their size variability and cell content makes them inappropriate in vivo tumor models. Organoids are self-organized 3D models that recapitulate tissue microstructure. In organoid cultivation, progenitor cells can be pluripotent stem cells (PSCs) such as embryonic PSCs (EPSCs) and induced PSCs (iPSCs) (Yamanaka 2007), or primary tissue-derived progenitor cells like adult stem cells (ASCs) which proliferate in a compatible extracellular matrix and the tissuespecific culture (Clevers 2019). Tumoroid reproduces a tumor model that considers cell plasticity and hierarchy in TME and has genetic heterogeneity similar to the original tumor. Tumoroids are categorized into two sets: patient-derived tumor organoids (PDTOs) that are produced from tumor tissue (Foo et al. 2022) and engineered tumor organoids that develop from genetic manipulation of normal tissue (Matano et al. 2015). The tumor cells in this model are genetically more stable and therefore make it appropriate for long-term investigations assessing the consequences. As engineered tumoroids have been demonstrated to be suitable models for the analysis of tumor growth and cancer progression, the development of organoids manifests a revolution in pre-clinical cancer models which rectifies the insufficiencies of the former (Fatehullah et al. 2016). They are economical, convenient systems that can be efficient and scalable models of tumors (Grandori and Kemp 2018).

3

Tumoroid Establishment

Advances in tumor 3D self-organized culture are based on the consideration of progenitor cell biology, understanding of ECM compositions, and the development of cell culture methods. In real tissue, stem cells are located in their own specialized niche microenvironment that comprises dynamic and specific factors that control the stemness of the cells. The tumor microenvironment consists of stromal cells, intercellular and cell-ECM interactions, and essential factors that influence tumor homeostasis. Subsequently, misregulation of homeostasis leads to tumor progression and metastasis. Therefore, generating a TME that has a similar architecture to the origin

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tumor is among the major obstacles for developing tumor organoids. Tumoroid culture systems that include the cell components and mediators recapitulate TME accurately. Recent studies have attempted to enhance tumoroid cultivation by considering important TME components, such as cellular heterogeneity and ECM constituents, as well as cutting-edge culture techniques.

3.1

TME Components: Cellular Heterogeneity

Initial Cancer Cells Acquired genetic and epigenetic changes are the primary causes of cancer (Stratton et al. 2009). Furthermore, a high rate of progenitor cell divisions increases somatic mutations that consequently lead to cancer in organs (Tomasetti and Vogelstein 2015). One of the most critical challenges for tumor modeling is finding the appropriate cell sources that genetically and phenotypically mimic primary tumor tissue and remain genetically stable. PSCs, as potent cells, can produce various types of tissues that are immature in phenotype and genotype, the same as embryonic-like tissue. Additionally, the PSC culture system was introduced as a millstone in the self-formation of 3D embryonic stem cells (ESCs) and morphogenesis of tissues (Eiraku et al. 2008, 2011). However, due to ethical considerations, researchers are increasingly using iPSCs. In the precise composition of 3D scaffolds and factors, iPSCs differentiate to form various tissues for organogenesis (Takahashi et al. 2007). Therefore, PSC-derived organoids are undesirable for cancer research due to the lack of particular gene mutations found in specific tumors. Nevertheless, iPSC technology facilitates single-cell cloning for genetic manipulation to produce cell populations with specific characteristics. Endogenous and exogenous genotoxic agents cause somatic mutations in individual tissue during their lifetime (Bregeon and Doetsch 2011). Adult stem cells as the initial cells for tumoroid culture preserve the heterogeneity and stability of genetic materials. The leading research in ASC-derived organoid culture used the Lgr5+ stem cell culture with the niche factors in Matrigel, which self-organized into the crypt-like structure (Sato et al. 2009). This study has been used as a baseline protocol for normal or tumor-derived organoid culture. Stromal Cells The TME contains stromal cells for supporting cancer cells and immunization and vascularization of tumors. The development and progression of tumor are significantly dependent on cancer-stromal interactions in TME. Tumorstromal interactions occur with the secretion of ECM and stroma-associated mediators such as proteins (growth factors and cytokines), stroma-associated RNAs (microRNA (miRNAs), long noncoding RNAs (lncRNAs)), and small transfer organelles (exosomes) (Guo and Deng 2018; Uddin and Wang 2021). Mesenchymal stem cells (MSCs) in TME stroma exhibit multilineage differentiation to mesenchymal stromal cells. Mesenchymal cells, such as fibroblasts and adipocytes, contribute to supporting tumor tissue homeostasis and cancer invasion. Cancer-associated fibroblasts (CAFs) are found in all stages of cancer and enhance

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cancer progression and metastasis associated with secreting mediators and enzymes that induce TME remodeling. Cancer-associated adipocytes (CAAs) have been shown to cross-talk with breast cancer cells, causing metabolic reprogramming, ECM remodeling, microRNAs (miRNAs), and immune cell alterations that promote tumorigenesis and cancer progression (Zhao et al. 2020). The immune system has the strange ability to both prevent and promote cancer. Tumorigenesis is influenced by abnormal immune responses that disrupt homeostasis. Also, different kinds of immune cells engage in TME that include antigen presenting cells and adaptive and innate immune cells (Yuki et al. 2020). The vascular system in an organoid is needed for either the circulation of nutrients and oxygen or to make it possible to have larger ones. So, vascularization is an important challenge in producing integrated and recapitulating tumoroids. Vascular endothelial cells and pericytes are components of the TME vascular system. Pericytes are mesenchymal-derived cells that wrap around the endothelial cells and reinforce angiogenesis. Hence, pericytes, as a critical cellular component of TME, contribute to infiltrating tumors and facilitating metastasis (Garza Treviño et al. 2019). Co-culture of iPS-derived human mesodermal progenitors (MPCs) with tumor cells to generate vascularized tumoroids that have the ability to integrate into the blood vessels of animal hosts after transplantation (Wörsdörfer et al. 2019). Furthermore, iPSCs from different sources were cocultured and produced vasculature neural organoids (Wörsdörfer et al. 2020). The Organ-On-VascularNet model provides cellular interaction and could provide possible advantages in tumor vascularization (Palikuqi et al. 2020).

3.2

TME Components: Extracellular Matrix (ECM)

A hallmark investigation in the 3D culture system revealed that basement membrane structures promote mammary gland tissue morphogenesis by polarizing epithelium and directing protein (Barcellos-Hoff et al. 1989; Petersen et al. 1992). ECM proteins of TME such as laminin, collagen, glycoproteins, and proteoglycans participate in cancer cell behavior (Nallanthighal et al. 2019). As a result, ECM protein deposition and remodeling result in cancer abnormalities. Different natural and synthetic ECMs are used in tumor modeling. Hydrogels made from natural ECM components or processes in hydrogel engineering are applied in tumoroid culture. Natural ECM that is extracted from living cells includes critical growth factors and cytokines. Classic 3D tumoroid culture techniques based on laminin-rich basement membrane extract (BME) are generally used for epithelial cell culture (Sato et al. 2009). The most widely utilized natural ECM is Matrigel, a collection of basement membrane (BM) components that are obtained from a kind of mouse tumor (Hughes et al. 2010). Matrigel is widely used for stem cell and cancer cell culture by mimicking interactions and also maintaining self-renewal and pluripotency. Also, ECM from decellularized tissues causes a tissue-specific matrix with unique properties and factors that have specific effects on cell differentiation. Hence, mammary ECM hydrogel was used as a natural source for ECM used as bioink in

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large tumoroid bioprinting (Mollica et al. 2019). Hyaluronic acid (HA) is a proteoglycan that is used extensively in 3D cell culture. The compositions comprise hyaluronic acid, which is used in breast tumoroid culture systems that accurately recapitulate in vitro tumor heterogeneity (Mazio et al. 2018). Fibrin is an ECM glycoprotein with mechanical properties easily modified by cell-produced enzyme degradation. Tailoring fibrin-based hydrogels with PEG makes them suitable for cultivating the complex tumor microenvironment and simulating tumor responses (Del Bufalo et al. 2016). Hydrogel qualities such as stiffness, stress relaxation, degradation, topology, and adhesive ligands influence stemness. Cells cultivated on or within hydrogels with tumorlike stiffness can generate models of carcinoma and chemoresistance tumors. However, ECM rigidity is essential for oncogene activation that drives tumor formation in many cancer models (Tayler and Stowers 2021).

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Tumoroid Culture

Tumoroid culture conditioning involves a set of inhibitors and growth factors. The protocols are modified based on primary cell type (ASCs or PSCs) and cell population (single cell, a homogeneous multicellular collective, or a heterogeneous coculture of diverse cells). Growth factors mediate cancer cell interactions in the originating tissue. Indeed, they support stemness features such as self-renewal and differentiation in TME. To summarize, tumoroids are produced by isolating cancer cells from tumor tissue and then cultivating them in tissue-specific media.

4.1

Cell Sources

Cell sources for tumoroid culturing could derive from primary or metastatic tissue specimens, tumor biopsies, and circulating tumor cells (CRC) (Yang et al. 2019). Setting up a guideline for culturing tumoroids from patient ASCs (Driehuis et al. 2020b) and establishing living biobanks with inter- and intratumoroid heterogenicity preserve standard methods for individual biomedical research (Pauli et al. 2017). However, contamination of tumor tissue collection with normal cells decreases the efficacy of biobanks (Driehuis et al. 2020a). Many approaches, such as genetically and nongenetically manipulated, have been used to improve tumoroid efficiency in cancer research. For example, normal tissuederived organoids are applied for genetic alteration and carcinogenesis studies. CRISPR-Cas9 is a gene-editing tool that can more efficiently knock out and knock in genes in targeted loci (Komor et al. 2017) and manipulate genes in normal organoids for tumorigenesis studies (Driehuis and Clevers 2017). Also, the cell surface remodeling by synthetic materials presents a good potential for controlling cell adhesion, intercellular interaction, and cell signaling that cause an effect on cell aggregation and cell retention (Amaral and Pasparakis 2019; Su et al. 2019).

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Conditioning Media

The main components of the tumoroid culture medium contain specific growth factors and inhibitors that are adjusted to tissue specificity. Tumoroid culture medium includes Dulbecco’s modified Eagle’s medium (ADMEM)/F12, penicillin/streptomycin, and mostly contains some additives such as primocin, GlutaMAX, HEPES, B27, N2, receptor tyrosine kinase ligands (epidermal and fibroblast growth factors), hepatocyte growth factor (HGF), gastrin, prostaglandin E2, nicotinamide, neuregulin 1, N-acetylcysteine, Wnt activators (Wnt3a and R-spondin-1), bone morphogenic protein (BMP) inhibitor noggin, TGF-β inhibitor (A-83- 01), a p38 inhibitor (SB202190), and a Rho kinase inhibitor (Y27632) (Kaushik et al. 2018). Also, studying the relationship between tumor genetic variability and growth factor requirements in tumoroid culture is useful for developing the tumor subtype library (Fujii et al. 2016). Hence, initial cell sources and tumoroid subtype determine the tumoroid formation processes and components of medium culture (Xu et al. 2018). Then, guidelines present standardized protocols for manipulating organoids in culturing, maintaining, and cryopreserving (Clinton and Mcwilliams-Koeppen 2019).

4.3

Culture Techniques

Various techniques have been applied to optimize physiologic characteristics of cell interactions and accessibility to growth factors/inhibitors, nutrients, and oxygen in a 3D tumor culture system. They are allowing primary cells to organogenesis in suspension or matrix conditions. Because malignant cells can proliferate indefinitely, the stromal cell content of primary tumor tissue is gradually lost during long-term culture. The addition of supplements and decellularization and culture systems has been developed to partially recapitulate stromal cell functions in TME. Coculture techniques are applied in tumoroid culture to provide a multicellular component. In this system, primary tumor cells are cultured with stromal cells or immune components to mimic cancer cell-stroma interactions in TME (Dijkstra et al. 2018; Xia et al. 2021). In order to improve tumoroid culture processes, additional organoid culture techniques, such as air-liquid interface (ALI) and microfluidic systems, are used for tumor cells that are embedded in supporting matrix gels. In the ALI system, matrix gel is adjacent to the culture media and, on the other hand, exposed to the air (oxygen) (Wakamatsu et al. 2022). After providing the contents of the tumor-specific culture medium and establishing a system for oxygen exposure, engineers offer the microcirculatory system technique to simulate the physiological function of the tissue properly. Microfluidic systems include parallel channels and interchannel connections to culture tumoroids in a gel and are known as tumor-on-achip models (Del Piccolo et al. 2021).

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Tumoroid in Cancer Research

The accumulation of mutations in cells leads them to acquire the tumor genotype and phenotype. Tumor cells display inter- and intra-patient tumor heterogeneity because of their distinct gene mutations and expression profiles. Therefore, accurate models with tumor-specific genomes are needed to increase the accuracy of diagnostic and therapeutic research. The tumoroid models resemble TME accurately and offer a suitable setting to study the fundamentals of tumor biology and apply them in preclinical cancer studies.

5.1

Tumoroid in Biological Research

Tumoroids have been applied to investigate tumor modeling on aspects of molecular and cellular biology such as genomics, proteomics, cellular diversity, and cross talk between cells. Indeed, they are used to assess the homeostasis of cancer in its initiation and progression.

5.1.1 Tumoroid in Molecular Biology of Cancer In recent decades, tumoroids in cancer molecular research have been used to explore processes underlying tumorigenesis and tumor heterogeneity in culture. Its application in cancer genomics research focuses on probing, identifying, and analyzing the links between genomic instability and tumorigenesis. Endogenous and exogenous mutational procedures altering the genome contribute to intratumor heterogeneity by providing mutation burden in a tumor microenvironmental context. Therefore, understanding cancer key genes and mutational pathways that affect tumor evolution has significant clinical applications. Complementary, cancer genomic data is used to inform the timing of cancer onset and progression. Hence, knowledge of specific forms of genomic alteration that might drive tumor diversity is useful in personalized medicine. Several genetic engineering tools are employed in cancer genome analysis, and when combined with tumoroids, they result in significant advancements in cancer molecular research. As a gene-editing technology, CRISPR-Cas9 is employed to investigate gene alterations associated with tumorigenesis, tumor progression, or cancer remission. The CRISPR-Cas9 technique in combination with tumor modeling has provided breakthroughs in cancer mutational analysis. This gene-editing tool peruses the mutagen exposure or mutational effects encountered in tumorigenesis in different tissues, including intestinal cancer (Takeda et al. 2019), lung cancer (Hai et al. 2020), colorectal cancer (Hu et al. 2020; Heitink et al. 2022), brain tumor (Koga et al. 2020), breast cancer (Dekkers et al. 2020; Heitink et al. 2022), retinoblastoma (Zheng et al. 2020), prostate cancer (Conteduca et al. 2020), gastric cancer (Lo et al. 2021), and ovarian cancer (Zhang et al. 2021). One of the first studies in generating genetically modified tumoroids was about using the CRISPR-Cas9 tool to produce colorectal carcinoma tumoroids from intestinal derived organoids by multiple mutations (Matano et al. 2015). The CRISPRCas9 technique is also comprehensively applied in analyzing mutational signatures and

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revealing the molecular causes of tumorigenesis by deleting the key genes involved in DNA replication and repair. Culturing human intestinal stem cells which were knocked out of the mismatch repair gene MLH1 generated colon cancer tumoroids. The results of whole-genome sequencing (WGS) analysis showed that colon cancer and breast cancer contain the same mutations (Drost et al. 2017). Additionally, this technique is used to knock out Neuropilin-2 (Nrp2), a cell survival receptor for generating murine colorectal derived tumoroids. Depleted Nrp2 tumor cells demonstrated other survival pathways, such as insulin receptor signaling and autophagy (Poghosyan et al. 2022). Furthermore, the CRISPR-Cas9 tool is associated with generating subtypes of tumoroid in living libraries for deliberating cancer initiation (Seino et al. 2018; Kawasaki et al. 2020) and tumor progression (Taha et al. 2020). Moreover, the integration of CRISPR-Cas9 and tumoroid modeling creates a platform for assessing and introducing efficient and accurate therapeutics for tumors. This platform facilitates efficient screening for testing pharmacological responses and introducing targeted therapies (Pappas et al. 2019; Hirt et al. 2022). The genetic screening of tumoroid-derived RNF43-mutant pancreatic ductal adenocarcinoma (PDAC) cells that used the CRISPR-Cas9 tool revealed the FZD5 antibody as a potential for targeted therapy (Steinhart et al. 2017). Besides, KRAS mutations upregulate autophagy in pancreatic ductal adenocarcinoma (PDAC), so inhibiting autophagy could be a targeted therapeutic. Also, the loss of function of two genes that simultaneously mediate signaling pathways showed that the inhibition of IGF1R and ERK improves the efficiency of autophagy inhibitors in PDAC tumoroid (Stalnecker et al. 2022).

5.1.2 Tumoroid in Cellular Biology of Cancer In cancer biology, tumoroid advances homeostasis and tumorigenesis studies conducted in a tumor microenvironment (Drost et al. 2016). TME maintains signaling pathways, cell heterogeneity, and intercellular interactions via cargo exchange and ECM modification. Analyzing the gene mutation and expressed protein profiles of tumoroid cargos facilitates understanding the developmental biology of malignancies and developing new drugs. The Wnt pathway is crucial for cell proliferation and is utilized in Wnt-dependent malignancy research. The mutant Wnt pathway elements are involved in tumoroid formation (Nusse and Clevers 2017). In triple-negative breast cancer and colon cancer, Wnt signaling is hyperactivated. Many studies try to introduce the drug into cancer prevention based on this signaling pathway (Koval et al. 2018). The human gastric cancer organoid library includes various GC mutations established with genetic engineering and Wnt-targeting therapy considered by genotypephenotype screening (Nanki et al. 2018). A tumoroid derived from hepatocellular carcinoma (HCC) demonstrated a link between NME7 activity and Wnt/-catenin signaling, which regulates folate metabolism (Ren et al. 2022). Tumoroids offer a unique opportunity to preserve cell heterogenicity and tumor cell communication with other cells in the tumor TME. It has also made it easier to gather and analyze the TME component in vitro. TME cells release extracellular vesicles (EVs) into the ECM and participate in tumorigenesis by transporting the vesicles and mediating intercellular communication. There are many studies in the

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developmental biology of cancer that survey the biological cargo (genomics and proteomics) of tumoroid vesicles for assessing gene mutations and expressions that cause cancer. Also, these EVs transform epithelial or mesenchymal cells into encompassing factors (Bordanaba-Florit et al. 2021). Analysis of tumoroid-derived EV revealed that APC mutation and subsequent Wnt activation promote human colorectal cancer (CRC) progression and colony formation (Szvicsek et al. 2019). In CRC, APC mutation was found to increase interferon-induced transmembrane protein 1 (Ifitm1) expression and cell proliferation and decrease EV uptake (Kelemen et al. 2021). Also, It was discovered that amphiregulin, which is transported by fibroblast-derived EVs, is a critical contributor to CRC cell proliferation (Bordanaba-Florit et al. 2021). For the tumorigenesis study, the microRNA (miRNA) content of CRC-derived exosomes was analyzed for colorectal adenoma (CRA)-carcinoma transition (Nagai et al. 2021). Also, CRA organoid-derived miRNAs were used as a diagnostic biomarker in a clinical liquid biopsy (Handa et al. 2021). Furthermore, CRC patient-derived organoids with high CD44 expression promote colon fibroblast proliferation and activation in a miRNA-independent manner (Kelemen et al. 2022). Furthermore, the depletion of ABCG1 by siRNA at the protein level in CRC tumoroid causes tumor regression by inhibiting EV release (Namba et al. 2018). The APC-knockout Barrett esophagus (BE) organoid by the CRISPR-Cas9 tool demonstrated Wnt activation and neoplastic transformation (Liu et al. 2018). EV was used as a carrier for CRISPR-Cas9 RNA-guided endonuclease (RNP) delivery for targeted gene manipulation in liver tumoroid and knockdown of WNT10B expression, which resulted in tumor growth inhibition (Zhuang et al. 2020). Besides that, the knock-out of matrix metalloproteinase 3 (MMP3) by the CRISPR/Cas9 system in tumor cells decreases tumoroid formation and the addition of MMP3-enriched EVs stimulates tumor progression (Taha et al. 2020). These studies are important for cancer therapeutic strategy. Pancreatic ductal adenocarcinoma (PDAC) tumoroid and stromal CAF coculture produce a desmoplastic microenvironment by increasing EV release that leads to chemoresistance (Xiao et al. 2022). PDAC-derived tumoroid EVs contain a unique protein profile that is upregulated related to tumorigenesis and could be employed as a diagnostic biomarker (Buenafe et al. 2022). Furthermore, the miRNA cargo of EVs in PDAC patient-derived tumoroid is distinguished from normal cell EVs, especially in the level of miR-21 and miR-195 (Zeöld et al. 2021). Esophageal adenocarcinomaderived EVs and gastric organoid coculturing exhibit neoplastic effects on normal organoids (Ke et al. 2017). Moreover, the transition of mitochondria via stromalderived EVs to glioblastoma tumoroids that increase tumor cell proliferation demonstrated the effects of the TME component on tumor progression (Salaud et al. 2020). Actually, easy access to metabolites makes tumoroid an ideal model for measuring the impact of environmental variables on tumorigenesis. Additionally, the antitumor effect of probiotics on CRC tumoroids (Sugimura et al. 2021) and immune modification in lung tumoroids that are infected by the influenza A virus are being evaluated and show the correlation between biological agents and tumorigenesis (Salgueiro et al. 2022). These findings demonstrate that ECM modifications are as important as mutational analysis in tumorigenesis.

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Tumoroid in Biomedical Research

This section presents the significance of tumoroids in biomedical research and in creating tumor subtype biobanks. Also, the following part covers the applications of tumoroid in the development of preclinical drug development studies, along with the gathering and interpretation of multi-omics data.

5.2.1 Tumoroid Biobanks Tumoroid biobanks are live collections of patient-derived tumoroids and genetically engineered tumor organoids. These biobanks were created to collect tumoroids that resemble tumor tissue in terms of their genetic and proteomic profiles. Living tumoroid biobanks preserve inter- and intratumor heterogeneity and key characterizations of tumor tissue for long-term use for state-of-the-art cancer research (Li et al. 2022). Also, conditional cell reprogramming is used for nextgeneration living tumor-derived organoid biobanks to produce inexhaustible cells for cancer research (Palechor-Ceron et al. 2019). Tumoroid biobanks were established for a variety of tumor organoids such as early-onset colorectal cancer (Yan et al. 2020), bladder cancer (Mullenders et al. 2019), gastric cancer (Seidlitz et al. 2021), and pancreatic cancer (Vaes et al. 2020). Representing a method for generating tumoroids for examining their response to different therapeutics is an advantage of tumor-derived organoids. Recent studies have introduced a rapid method for glioblastoma organoids (GBOs) biobanking that is similar to the original tumor tissue in transcriptomics and genomics characteristics (Jacob et al. 2020b) and for evaluating responses to immunotherapies by chimeric antigen receptor T (CAR-T) cells (Jacob et al. 2020a). Also, hepatocellular carcinoma (HCC)-derived tumoroids that are cultured from needle biopsies of liver cancers (Nuciforo et al. 2018) or liver tumor tissue samples of human and murine origins represent useful resources in the classification of HCC subtypes and precise diagnosis and therapy (Yu and Ma 2022). Furthermore, many studies utilize patient-derived tumoroid biobanks to provide comprehensive genomic data for precision medicine. In recent studies, the biobanks of primary gastric cancer tumoroids (Yan et al. 2018) and pediatric kidney cancer tumoroids have been established for drug screening (Calandrini et al. 2020). Besides, a neuroendocrine neoplasm-derived organoid biobank was created and used for whole-genome sequencing and transcriptome analysis. As a result, analyzing tumoroid biobank data can also reveal its genotype-phenotype concordance (Kawasaki et al. 2020). 5.2.2 Tumoroid in Therapeutics Gene mutations and transcriptome profile analysis reveal the modifications associated with tumorigenesis and cancer metastasis (Weeber et al. 2017). Tumoroids make precise diagnosis of the tumor possible by analyzing gene alteration and protein expression. Consequently, tumoroids are also used in drug screening and pharmacogenetics studies to improve preclinical therapeutic evaluation on a personalized level.

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Tumoroid studies lead to identifying pathogenesis and discovering prognostic biomarkers of aggressive tumors and recurrent cancer by using next-generation sequencing (NGS) technologies that include whole exome sequencing (WES) and RNA-sequencing of tumor cells. The accessibility of EV generated from tumoroid makes it possible to continuously monitor changes in the tumor transcriptome. Also, circulating tumor cell (CTC)-derived organoids could also be employed in real-time tumor studies (Praharaj et al. 2018). The application of tumoroid platforms for drug development has also improved in recent decades. Tumoroids are used in drug screening to assess potential drugs, optimize candidate drugs for clinical trials, and evaluate possible alternative treatments. A subset of hepatoblastoma tumoroids and normal liver organoids from the same patient represent the different antitumor activities of the BET inhibitor JQ1 drug in diverse tumoroids (Saltsman et al. 2020). The same method identified antifungal drugs as a potential treatment for biliary tract carcinoma tumoroids in a drug screening (Saito et al. 2019). Furthermore, tumoroids have contributed to the advancement and redevelopment of antitumor drugs such as neoadjuvant chemoradiation (Yao et al. 2020). Moreover, cancer tumoroids have been used in research on immunotherapy and immune checkpoint therapy. Recent studies have investigated the function of T cells in targeted therapy due to their significance in cancer immunotherapy. For this reason, T lymphocyte content was assessed in primary breast cancer tumoroids (Zumwalde et al. 2016) and in coculturing of tumoroids with autologous peripheral blood lymphocytes (Cattaneo et al. 2020). Also, immune checkpoint inhibitors such as programmed cell death protein 1 (PD-1) are examined as therapeutics with coculturing of gastric cancer tumoroids and T lymphocytes (Chakrabarti et al. 2021). In recent years, adaptive immune cells that express particular T cell receptors or chimeric antigen receptors have been developed in treatments. Due to the lack of availability of a recapitulated model of the immunosuppressive tumor microenvironment, CAR-T has not been as successful in treating solid tumors as it has been in treating hematological malignancies. Thus, tumoroid generation along with CAR-engineered T cells could improve preclinical studies in solid tumors such as pancreatic cancer (Yeo et al. 2022) and glioblastoma (Jacob et al. 2020a). Also, the viability of CAR-T cells was investigated by creating various oxygen gradients in an ovarian cancer tumoroid (Ando et al. 2019). Additionally, human tumoroids are highly effective in assessing severe acute side effects of CAR-T cell therapy (Kumari et al. 2021). The effectiveness of this preclinical model was demonstrated by the immunotherapeutic response of a bladder cancer tumoroid that expresses MUC1 and by treatment with CAR-T cells that target MUC1 (Yu et al. 2021).Another study explored CAR-NK-92 cells that targeted EGFRvIII colorectal cancer tumoroids (Schnalzger et al. 2019). Besides, tumoroids recapitulate tumor TME that is used to discover biomarkers and evaluate therapeutic effects. Stromal cells in tumoroid TME have genetic stability and close interaction with cancer cells, making them a promising target for antitumor targeted therapy (Xu et al. 2018). On the other hand, the tumoroid

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provides features of a distinct TME of the resistant tumor. Drug screening and RNA-Seq analysis of hepatocellular carcinoma tumoroids discovered acquired drug-resistant mechanisms and the mTOR signaling cascade as an effective target for this drug resistance therapy (Xian et al. 2022), and in 5-FU-resistant gastric cancer tumoroids, it was found that KHDRBS3 is crucial in the acquisition of multidrug resistance by affecting CD44 expression (Ukai et al. 2020). Tumoroids as a tumor model for personalized medicine facilitate efficient genetic and drug screening for individuals and the assessment of targeted therapies at a customized level (Veninga and Voest 2021). The contribution of biomarker discovery and drug screening reveals that the ERK inhibitor SCH772984 is a candidate treatment agent for liver cancer tumoroid (Broutier et al. 2017). Moreover, personalized medicine predicts disease progression and informs patients about treatment options. Appendiceal cancer tumoroids from patients with different grades are cultivated and exhibit variable chemosensitivity responses to 5-FU, oxaliplatin, FOLFOX, FOLFIRI, and regorafenib (Votanopoulos et al. 2019). Also, the accurate preclinical models of prostate cancer tumoroids that represent heterogeneity and predictive molecular signatures are applied in precision medicine to determine optimal treatment (Pamarthy and Sabaawy 2021). Besides these, analysis of metastasis-related heterogeneity and drug screening in breast cancer tumoroids has been employed to develop tailored targeted therapy. Thus, breast cancer tumoroids established from different specimens of a patient along with NGS sequencing have been applied to investigate intratumor heterogeneity and predict therapeutic responses (Liu et al. 2022).

5.2.3 Tumoroid and Big Data Tumoroid genomics and proteomics screening results create big data sources, and innovative data analysis strategies can make them more useful in simulation preclinical research. Tumoroid NGS includes WES, and cell RNA-sequencing is accomplished for screening gene mutation profiles and defining subtypes of a tumor. Also, tumoroid proteomics screening gives valuable data for predicting treatment efficacy in pharmacogenetic studies. Tumoroid massive-scale screening in genomics and transcriptomics and their integration with clinical data provide big data in cancer research. In recent years, tumoroid multi-omics data has brought an urgent need to apply state-of-the-art big data technologies for gathering and analysis. Organoid and CRISPR-Cas9 gene editing are important tools in gene analysis for disease modeling and represent big data that is important in next-generation precision medicine (Mundel 2017). Genome-edited human organoids that express fluorescent protein also provide advanced imaging and result in 4D cell biology data, and this massive and difficult-to-manage data volume must be managed (Schöneberg et al. 2018). Hence, big data needs infrastructure and sophisticated computational modeling to revolutionize personalized medicine. A machine-learning framework has been used for analyzing pharmacogenomic data of colorectal and bladder tumoroids to classify them based on biomarker identification and predict the drug response in the patient tumor (Kong et al. 2020).

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Conclusion

Tumor-derived organoids provide heterogeneous cell populations and high homology with primary tumors, and their convenient cultivation leads to the application of tumoroids to an exact model for preclinical study. In recent decades, tumoroid studies have had many achievements, but there are many challenges in improving tumoroid formation, such as the lack of angiogenesis and hypoxia and contamination with normal cells. Moreover, tumoroid precisely simulates tumors and engages with big data science, which needs modern technology to handle and analyze big data for accurately anticipating high-risk patients and efficient therapeutics. So, tumoroids give precise data for use in precision medicine in future cases and need pioneering technologies for making this data usable in the clinic.

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Myokine Expression in Cancer Cachexia Emilia Manole , Laura C. Ceafalan , Gisela F. Gaina Oana A. Mosoia, and Mihail E. Hinescu

,

Abstract Background

Cachexia is a characteristic of many patients with terminal cancer and has a dramatic impact on the patient’s quality of life and a reduced tolerance for cancer treatment. Unfortunately it is still underestimated and often untreated. Muscle wasting in cancer cachexia is determined by activation of the immune cells mediated by tumor and gut, by tumor-derived factors other than cytokines, and myostatin as a myokine with negative regulation of skeletal muscle mass. The reduced process of muscle regeneration and chemotherapy are other factors which contribute to muscle atrophy, weakness, and fatigue. The proportion of lean and fat mass in the body is very important in cancer treatment, a lower lean mass being associated with dose limitation therapy, discontinuation of treatment, and even a

Authors Emilia Manole and Laura C. Ceafalan have contributed equally to this work. E. Manole (✉) Laboratory of Cellular Biology, Neuroscience and Experimental Myology, “Victor Babes” National Institute of Pathology, Bucharest, Romania Pathology Department, Colentina Clinical Hospital, Research Center, Bucharest, Romania e-mail: [email protected] L. C. Ceafalan · M. E. Hinescu Laboratory of Cellular Biology, Neuroscience and Experimental Myology, “Victor Babes” National Institute of Pathology, Bucharest, Romania Faculty of Medicine, Discipline Cellular, Molecular Biology and Histology, University of Medicine and Pharmacy “Carol Davila”, Bucharest, Romania G. F. Gaina · O. A. Mosoia Laboratory of Cellular Biology, Neuroscience and Experimental Myology, “Victor Babes” National Institute of Pathology, Bucharest, Romania # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2023_138 Published online: 18 February 2023

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poor survival rate. Important potential targets for cancer cachexia therapy are myokines, defined as cytokines and proteins produced and released by skeletal muscle fibers under the action of contractile activity. Some of them are implied in energy supply in physical training; others are involved in muscle proliferation, differentiation, and regeneration; and some of them have an anticancer effect. We mention here the most studied myokines involved in muscle cachexia in cancer such as myostatin, decorin, irisin, myonectin, interleukin-6, interleukin-8, interleukin-15, follistatin, fibroblast growth factor 21, oncostatin M, musclin, and stromal derived factor 1. Their role in cancer has begun to be the subject of more and more studies, although it has not been fully highlighted. The benefit of physical exercise in cancer cachexia was demonstrated in recent years, this activity reducing the disease incidence and inhibiting the tumor growth. Skeletal muscle myokines are implicated in these effects. There are few research trying to explain how these biological processes are produced, the exact mechanisms remaining to be elucidated. Conclusion

Myokines could be considered as possible therapeutic targets in cancer cachexia. Keywords

Cancer cachexia · Muscle wasting · Myokines · Physical exercise

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Introduction

In recent years, muscle cachexia in cancer has become an increasingly discussed topic. In general, cachexia is an extremely serious syndrome that refers to anorexia, weight loss especially based on involuntary loss of muscle mass and fatty tissue, inflammation, a negative protein balance, and increased energy consumption. It occurs in many chronic diseases of which cancer occupies a special place (80% of patients with cancers develop cachexia) (Fearon et al. 2011; Manole et al. 2018). Cachexia is a characteristic of many patients with terminal cancer and is responsible for death of approximately 22% of patients because of association with a low response to chemotherapy (Dewys et al. 1980; Deans and Wigmore 2005; Tan and Fearon 2008). This syndrome has a dramatic impact on the patient’s quality of life and a reduced tolerance for cancer treatment, and unfortunately it is still underestimated and often untreated (Evans et al. 2008; von Haehling and Anker 2010) despite its association with many mechanisms, especially the inflammatory ones which contribute to a persistent catabolic status installation. The current strategy is to treat only the cancer, hoping that the cachexia syndrome will be completely reversed, but this is not valid in advanced cancers. Another option

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is to improve the nutritional intake, but the anorexia of cachectic patients is only a part of the problem, nutrition as unimodal therapy not yielding the expected results (Manole et al. 2018). In addition, treatment with radio- and chemotherapy may exacerbate the progression of cachexia (Ando et al. 2013; Laine et al. 2013). However, the studies regarding the involved mechanisms in cancer cachexia have developed even if the current treatment of this syndrome in malignant neoplasm is palliative and many anticancer products may have beneficial effects in treating cancer but worsen cachexia (Aoyagi et al. 2015). New research is needed in this area to understand this complex phenomenon and ultimately find treatment methods and therapeutic targets that prevent cancer progression but also improve the quality of patient’s life. A multidisciplinary approach to treat cachexia would be necessary: new pharmacological agents combined with diet modification and exercise, knowing that the progressive skeletal muscle wasting is governed by metabolic signaling pathways alterations and systemic inflammation dysregulation. More and more in-depth studies are needed in this direction, so that the life of cancer patients can be improved and the resistance to the harsh anticancer treatment could be more effective by supporting and participating the whole body. There is a lot of recent research that shows that the skeletal muscle can act as an endocrine organ (Iizuka et al. 2014; Schnyder and Handschin 2015), exerting its influence on other organs and body systems through myokines signaling, bioactive substances released by the skeletal muscle tissue (Pedersen 2011), an energy producer and consumer that influences the energy metabolism of the whole organism. The myokines, muscle cytokines which exert an autocrine, paracrine, and endocrine effect, act as metabolic mediators between skeletal muscle and other organs and systems such as the adipose tissue (Trayhurn et al. 2011; Pedersen and Febbraio 2012), cardiac muscle (Okita et al. 2013), liver, pancreas, and central nervous system (Gleeson 2000; Pedersen and Hojman 2012; de Castro et al. 2021) (Fig. 1). Preclinical research on in vivo cachexia models have shown that myokines can regulate muscle mass in pathological conditions (Costelli et al. 2008; Busquets et al. 2012). De Castro et al. (2021) demonstrated for the first time that the tumor could contribute in cancer cachexia with synthesis and secretion of several factors, subsequently affecting skeletal muscle biology and taking part in wasting, and that patients with cancer-associated cachexia present lower content of skeletal muscle follistatin-like protein 1, higher levels of plasma fatty acid binding protein 3 and higher tumor content of fatty acid binding protein 3, interleukin-15, and irisin, in comparison to patients with cancer who do not develop muscle cachexia. A relevant finding of these authors was that myokines were differently expressed in tumors from patients with/ without cancer cachexia. Taking into account all this facts, we consider that myokines represent one of the components of this complex mechanism of muscle cachexia in cancer that leads to the muscle weakness and muscle mass loss, and many of them have an important potential to become therapeutic targets.

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Fig. 1 Skeletal muscle cross-talking with other organs/tissues through myokines. Myokines exert autocrine, paracrine, or endocrine effects and are involved in the communication between skeletal muscle and other organs, including adipose tissue, gut, brain, liver, pancreas, bone, endothelial cells in vessels, and skin. IL-6 as myokine, with an anti-inflammatory effect, enter in circulation and take part at the communication between skeletal muscle and all other organs and systems, including muscle-muscle. Other myokines which appear in many studies regarding muscle cachexia in cancer are myostatin, decorin, irisin, FSTL-1, myonectin, FGF 21, IL-15. (All figures created with BioRender.com)

Key Points • Myokines are skeletal muscle cytokines and other peptides which are produced, expressed, and released by muscle fibers. • Myokines exert autocrine, paracrine, or endocrine effects. • Myokines are involved in the communication between skeletal muscle and other organs, including adipose tissue, gut, brain, liver, pancreas, bone, endothelial cells in vessels, and skin. The muscle cytokines are signaling also within muscle tissue. • Myokines mediate the exercise-associated anti-tumoral effects and may constitute new biomarkers in cancer evolution. • The identification of myokine role in cancer cachexia may determine novel therapeutic targets in this pathology. • Myokines as medicinal compounds could enter the treatment of cancer cachexia in the future.

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Muscle Wasting in Cancer Cachexia

Several signaling pathways contribute to muscle wasting in cancer cachexia: activation of the immune cells mediated by tumor and gut, tumor-derived factors other than cytokines, and myostatin (Argilés et al. 2019). In gut barrier dysfunction, the release of microbiota toxins, such as lipopolysaccharide (LPS), activates immune cells to synthesize and deliver cytokines which promote the activation of transcription factors leading to muscle wasting (Stewart et al. 2006). Two of tumor derived factors triggering muscle wasting, found in tumor-bearing laboratory animals and patients with cancer, are lipid mobilizing factor and proteolysis-inducing factor (PIF) (Argilés et al. 2019). Myostatin, a transforming growth factor (TGF beta) ligand that operates through activin receptor type II B (ActRIIB)-mediated signaling (Coletti 2018), is another molecule associated with muscle wasting in cancer. It was observed that the treatment with sActRIIB, a soluble form of ActRIIB, ablates the symptoms of cancer cachexia in tumor-bearing mice by inactivation of myostatin (Arthur et al. 2016; Argilés 2017). An interesting fact is that the myostatin is released not only by skeletal muscle and adipose tissue but also by cachexia-inducing tumors (Argiles et al. 2012). Another factors which contribute to muscle atrophy, accompanied by weakness and fatigue, are a reduced process of muscle regeneration (Penna et al. 2019) and anticancer cytotoxic agents. Chemotherapy toxicity for muscle can persist a long period after tumor remission (Pin et al. 2021). The proportion of lean and fat mass in the body is very important for cancer treatment, a lower lean mass being associated with dose limitation therapy, discontinuation of treatment, and even a poor survival rate (Pin et al. 2018b). In the initial phases of muscle loss in patients with cancer cachexia, the muscular fibers present a disrupted mitochondrial morphology, increased apoptosis, and dysfunctional apoptosis (de Castro et al. 2019, 2021) driven by an increased systemic inflammation (Tisdale 2009; Fearon et al. 2012). A study on patients with pancreatic ductal adenocarcinoma has shown a decrease in serum lipids, weight loss, decreased adipose tissue, and muscle mass 18–6 months before cancer diagnosis (Sah et al. 2019).

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Most Studied Myokines Involved in Cancer Muscle Cachexia

It is estimated that there are around 650 potential myokines, and this list is increasing (Khan and Ghafoor 2019). Myokines have been defined as cytokines and proteins produced and released by skeletal muscle fibers (Pedersen and Febbraio 2008) under the action of contractile activity (Schnyder and Handschin 2015). Their effects are autocrine, paracrine, or endocrine, and their receptors were found in almost all tissues and organs, such as muscle, fat, heart, brain, gut, liver, pancreas, bone, and immune cells (Pedersen et al. 2007; Pedersen and Hojman 2012). Some of them are implied in energy supply in physical training; others are involved in muscle proliferation, differentiation, and regeneration (Henningsen et al. 2010; Henningsen et al. 2011). Myokines which have an anticancer effect were also recognized (Pedersen

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et al. 2016; Hojman et al. 2018). The myokine behavior as metabolic mediators between skeletal muscle and other organs especially during exercise is shown by Schnyder and Handschin (2015). The increasing number of papers describing the role of myokines in cancer cachexia proves the growing attention given to these molecules and directs our attention to their role in this pathology. The most studied myokines regarding their involvement in muscle cachexia in cancer are myostatin, decorin, irisin, myonectin, interleukin-6, interleukin-8, interleukin-15, follistatin, and fibroblast growth factor 21 (FGF 21) (Fig. 2). Myostatin, also called growth differentiation factor 8 (GDF-8), is a member of the transforming growth factor-β (TGF-β) family, expressed in developing and adult muscular tissue. It is one of the first described myokines, and unlike other myokines that have a high level after exercise, its level is low after sustained muscular effort (Louis et al. 2007; Ruas et al. 2012; Laurentino et al. 2012). The main function of myostatin is a negative regulation of muscle mass (McPherron et al. 1997) through binding to type II activin receptors resulting in the phosphorylation of Smad2 and Smad3, leading to downstream signaling inhibition of Akt-TORC1 anabolic pathway with impact in muscle differentiation (Trendelenburg et al. 2009). It is postulated to be involved in the pathogenesis of cancer-associated cachexia (Zimmers et al. 2002), a high level of this peptide meaning less muscle mass (Joulia et al. 2003). Experimental in vivo studies on cancer cachexia models, such

Fig. 2 Dynamics of myokines in cancer cachexia. In cancer cachexia, muscle wasting occurs, and there is a reduction of many myokines compared to healthy muscle. Apart from myostatin which has an opposite effect, being increased when muscle mass is decreased, as shown by all the research in this field, there are also controversial results. Thus, irisin and IL-6 were found increased by some authors, such as Us Altay et al. (2016) and Zhang et al. (2020) for irisin and Pin et al. (2018a), Huot et al. (2020a, b), and Carson and Baltgalvis (2010) for IL-6. (All figures created with BioRender. com)

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as mice with C26 adenocarcinoma and rats with Yoshida AH-130 hepatoma, demonstrated increased myostatin levels in muscular tissue and a severe skeletal muscle wasting (Costelli et al. 2008; Bonetto et al. 2009). Myostatin was found elevated also in skeletal muscle of patients with cancer (Aversa et al. 2012); however its function in human cancer cachexia remains less clear. The studies on human patients remain controversial. A clinical trial on patients with pancreatic cancer failed to show a positive reaction in preserving muscle mass or in improving patients’ survival after the treatment with anti-myostatin antibody (Golan et al. 2018). Plasma myostatin has been reported to have a low level in patients with colorectal or lung cancer cachexia (Loumaye et al. 2015). Elevated levels of serum myostatin were associated with worse survival in patients with liver cirrhosis (Nishikawa et al. 2017). Myostatin is implicated also in muscle fiber types (fast and slow) differentiation (Wang et al. 2012), in the muscle-adipose tissue cross-talking (Allen et al. 2008), and in the muscle glucose metabolism (Cleasby et al. 2014). It influences the adipocytes physiology in an indirect manner, not leading to the reduction of adipose tissue by lipolysis as it was demonstrated in vitro and in vivo models (Stolz et al. 2006). In myostatin null mice, the reduced body fat is caused by muscle mass growth, this mice developing a massive muscular hypertrophy as a result of an accelerating myogenesis (McPherron and Lee 2002; Demontis et al. 2013) and an important reduction of fatty tissue (Schuelke et al. 2004). It was described a similar phenotype in a child with myostatin gene mutation (Schuelke et al. 2004). In the myostatindeficient mice, it was reported also a reduction in circulating leptin level although food intake was not different compared with control mice (WT) (Lin et al. 2002; McPherron and Lee 2002). Leptin is the “satiety hormone” secreted by adipocytes. Myostatin has an antagonic action with another myokine, decorin, and this is an important observation regarding the status of its potential biomarker and therapeutic target in cancer cachexia. Decorin, a chondroitin sulfate/dermatan sulfate proteoglycan of the SLRP family (Baghy et al. 2020), is released by myotubes, and its circulating level is elevated after acute physical exercise (Kanzleiter et al. 2014). However, this myokine is expressed in other tissues also, including the intestinal, adipose, and cardiac ones (Kawaguchi et al. 2020). Decorin directly binds myostatin, a strong inhibitor of muscle growth (McPherron and Lee 2002), acting antagonistic, thus representing a potential therapeutic target in muscle cachexia worthy of consideration, being able to modulate the maintenance of muscle mass. Furthermore, decorin is involved in restructuring skeletal muscle during hypertrophy (Kanzleiter et al. 2014). Kawaguchi et al. (2020) reported a serum decorin level positively correlated with serum myostatin level in a study on patients with hepatocellular carcinoma, an explanation being that decorin may be upregulated to suppress muscle atrophy in response to an increase in serum myostatin level (Kawaguchi et al. 2020). In last years, decorin was found to participate in inflammation, fibrotic disorders, and cancer. Moreover, decorin is an inhibitor of TGF beta, blocking some tyrosine kinases receptors (RTKs), such as EGFR, Met, IGF-IR, VEGFR, and PDGFR, inducing their degradation through

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initiation of their caveosomal internalization (Baghy et al. 2020). It was found also to generate cell cycle arrest and apoptosis and to exert antimetastatic and antiangiogenic effects. Decorin induces conserved catabolic processes as endothelial cells autophagy and tumor cell mitophagy being a good candidate for cancer therapy (Sainio and Järveläinen 2019; Baghy et al. 2020). In patients with hepatocellular carcinoma, the serum decorin levels were identified as an independent prognostic factor and are positively correlated with skeletal muscle mass (Bekki et al. 2018; Kawaguchi et al. 2020). Irisin was discovered in 2012 as a transmembrane protein (Boström et al. 2012), Fibronectin type III domain-containing protein 5 (FNDC5), the precursor of irisin. It is encoded by Fndc5 gene. Irisin is the cleaved soluble form of FNDC5 and is released by muscle into circulation during the proteolytic process after acutely exercising of skeletal muscle, increasing the energetic and oxidative metabolism at molecular level. Irisin has a high level during myogenesis, inducing glucose uptake, increasing the activation of p38 mitogen-activated protein kinase through AMPKα2 and GLUT4 translocation to the plasma membrane (Lee and Jun 2019). This myokine improves glucose homeostasis, inhibits lipid accumulation, and reduces body weight (Huh et al. 2014). The studies regarding irisin have been made especially in relation with obesity and muscular dystrophy. In muscle pathology research, irisin injection in a murine model induced muscle hypertrophy and improved muscle strength, reduced necrosis, and developed connective tissue (Reza et al. 2017). In patients suffering from cardiac cachexia, circulating irisin was found increased (Kalkan et al. 2018). FNDC5 was found to have high levels in adipose tissue in an animal model of gastric cancer with cachexia that had also increased levels of circulating irisin (Us Altay et al. 2016). Irisin is implied in switching from white to brown adipose tissue (Maalouf and El Khoury 2019), increasing the Uncoupling Protein 1 expression in adipocyte mitochondria (Boström et al. 2012). Some studies on patients with different types of cancer showed also high levels of irisin in lung, liver, and gastrointestinal tumors (Zhang et al. 2020). Moreover, this peptide was better expressed in the tumors of cachectic patients compared with cancer patients with stable body weight (Gaggini et al. 2017; Nowinska et al. 2019; de Castro et al. 2021). Interesting, the irisin levels in colorectal and breast cancer patients were reduced in comparison with normal subjects (Provatopoulou et al. 2015; Zhu et al. 2018). In the non-small cell lung carcinoma, irisin was identified in the cytoplasm of tumor cells and tumor stromal cells due to their higher glycolytic rate, but not in normal lung parenchyma (Nowinska et al. 2019). A recent observation showed that recombinant irisin significantly decreased the ability of breast cancer cells to proliferate and migrate, increasing the cytotoxic activity of doxorubicin, a common anti-neoplastic agent, without altering the nonmalignant cells viability (Papadopetraki et al. 2022). Research on in vitro models of prostate cancer and glioblastoma demonstrated that irisin suppressed cell growth and induced cell cycle arrest (Liu et al. 2018; Huang et al. 2020). Other studies revealed that even a small increase in irisin serum levels could limit the probability of developing breast cancer up to 90% (Provatopoulou et al. 2015), while higher levels could protect female breast cancer patients from spinal metastases (Zhang et al.

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2018). These results could be a starting point for therapeutic irisin targeting attempts in cancer cachexia. Myonectin (or CTRP15), a protein belonging to the C1q/TNF-related protein (CTRP) family, is found especially in skeletal muscle tissue, related to the nutritional metabolism, and less in circulation (Lee and Jun 2019). It was found that this myokine expression is stimulated by exercise and nutrients, and it is supposed to induce food uptake and storage in adipose tissue producing a glucose or fatty acids flux (Seldin et al. 2012; Seldin and Wong 2012). Functionally, it is similar to insulin promoting fatty acid uptake into cells (Seldin and Wong 2012). Myonectin is a regulator of cellular autophagy and could be an important player in muscle mass control. Thus, reduced and increased expression in circulating levels of myonectin are correlated with autophagy activated by starvation, respectively, autophagy suppression by nutrient supplementation after food deprivation (Seldin et al. 2013). It was shown that recombinant myonectin administration suppressed autophagy induced by starvation in mouse liver and cultured hepatocytes inducing LC3-dependent autophagosome formation, p62 degradation, and expression of critical autophagy-related genes. PI3K/Akt/mTOR signaling pathway mediates the reduction in protein degradation, and its inhibition stopped myonectin to suppress autophagy in cultured hepatocytes (Seldin et al. 2013). Other studies have shown that myonectin could be involved in mitochondrial biogenesis, being increased following mtDNA depletion in myocytes. It induces the AMPK phosphorylation causing an increased glucose uptake. These findings recommend myonectin to play a functional role in insulin resistance (Park et al. 2009; Lim et al. 2012). However myonectin was less studied in connection with cancer cachexia. More research of this peptide could highlight this potential as a therapeutic target being linked to the nutrient uptake and mitochondrial biogenesis. Fibroblast growth factor 21 (FGF 21) is a hepatokine, adipokine, and myokine. As myokine, it is induced by stress (Luo and McKeehan 2013) and has a role in glucose uptake in myotubes (Mashili et al. 2011), protecting against cardiac hypertrophy, diet-induced obesity, and insulin resistance and generating the browning of white adipose tissue. Its expression is regulated by a PI3K/Akt1 signaling pathwaydependent mechanism (Itoh 2014). There is a close relationship between mitochondrial dysfunction and FGF 21 levels in muscle. After autophagy, mitochondrial dysfunction causes an increased level of this molecule. Another research showed also that in mitochondrial respiratory chain deficiency, there is a compensatory increasing in FGF 21 level, resulting in an increased mitochondrial activity (Ji et al. 2015). It is a tight link between this myokine and adiponectin that acts as a downstream effector of FGF 21, controlling in an endocrine mode the lipid and glucose homeostasis in the muscle tissue but also in other organs, such as the liver. In turn, adiponectin regulates the influence of FGF 21 in energetic metabolism and insulin sensitivity (Holland et al. 2013; Lin et al. 2013). It was reported that deficiency of FGF21 promotes hepatocellular cancer in mice after a long-term obesogenic diet, and this myokine administration prevents hepatocarcinogenesis in vivo (Kawaguchi et al. 2020). In the study of cancer cachexia, FGF 21 is poorly addressed, and future research is needed to highlight

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its potential in therapeutic strategies as long as the energy metabolism of the muscle is essential for the whole body. Interleukin-6 (IL-6) is a very interesting and important molecule, with its dual character, acting as a pro-inflammatory cytokine and an anti-inflammatory myokine and being quite controversial in the literature. It is the first identified myokine secreted in bloodstream after muscle contraction, the most abundant and the most studied (Pedersen and Febbraio 2008). IL-6 belongs to the granulocyte colonystimulating factor-like protein family of cytokines. IL-6 signaling is complicated by its ability to operate via two mechanisms: classical signaling and trans-signaling. As cytokine, IL-6 is considered a key player in the evolution of the microenvironment of malignancy, promoting tumor growth and metastasis (Daou 2020). It also induces skeletal muscle atrophy and protein breakdown (Daou 2020). IL-6 as myokine was shown to have regulatory effects in lipid and glucose metabolism and also plays an important role in myogenesis (Severinsen and Pedersen 2020). In a genetic loss of IL-6 in vivo model, it was observed impaired muscle hypertrophy, whereas IL-6 produced by myotubes stimulated muscle cell proliferation in a paracrine mode (Serrano et al. 2008). The anti-inflammatory effect is produced by inhibition of TNF-alpha and by stimulation of IL-10, anti-inflammatory cytokine (Bordignon et al. 2022). The improving of the glucose uptake is determined through the AMPK signaling stimulation (Daou 2020). In cancer cachexia the circulating IL-6 showed elevated levels in some experimental models on mice (Pin et al. 2018a; Huot et al. 2020a, b). In human cancer cachexia IL-6 has also high levels, thus representing predictors of body weight loss (Carson and Baltgalvis 2010) and a negative prognostic factor in patients with lung cancer accompanied by cachexia (Pettersen et al. 2017). It was observed that IL-6 is often produced by cancer cells too (Strassmann et al. 1992). Using specific neutralizing antibodies against IL-6, the muscle protein hypercatabolism was reduced, and muscle mass was preserved (White et al. 2012). In cancer cachexia, IL-6 drives muscle atrophy by activating the JAK/STAT3 pathway, thus stimulating the degradation of muscle protein (Pin et al. 2021). Follistatin-like protein 1 (FSTL-1) is a transmembrane extracellular secreted glycoprotein produced mainly by cells of mesenchymal origin, including skeletal and cardiac fibers, and belongs to the BM-40/SPARC/osteonectin family (Li et al. 2020). It was found as myokine too which stimulates angiogenesis and vascularization in the skeletal muscle (Ouchi et al. 2008). In animal models with cancer cachexia, its gene expression was found to be diminished in skeletal muscle (Fontes-Oliveira et al. 2014). Although there are several studies on FSTL-1 expression in some types of human cancer, they are not necessarily related to muscle cachexia. DeCastro et al. (2021) showed for the first time that patients with cancer-associated cachexia present lower content of skeletal muscle FSTL-1 compared with weight stable cancer patients. This myokine could have a role in muscle homeostasis as its expression was reported to be higher in myotubes compared with myoblasts and its synthesis increases throughout cell

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differentiation (Miyabe et al. 2014). FSLT-1 synthesis in skeletal muscles appeared to be modulated by inflammatory stimuli as showed an in vitro study on human myotubes after treatment with IL-1β and interferon gamma where FSTL-1 was increased in culture media (Görgens et al. 2013). It was hypothesized that this reduction in FSTL-1 levels in skeletal muscles of patients with cancer cachexia may be one of the causes that determine a reduced muscle performance, fatigue, and impaired muscle regeneration (de Castro et al. 2021). Interleukin-8 (IL-8) is a member of the CXC cytokine family and is produced by muscle tissue, but also by adipose tissue and other cells such as macrophages and epithelial and endothelial cells. It was described as a chemoattractant for lymphocytes and neutrophils (Matsushima et al. 1988, 1992), and it was found to be involved in angiogenesis and tumor growth (Maeda et al. 1998). Some recent studies have shown that IL-8 is involved in cachexia, having an elevated level in the serum of the patients with this syndrome (Pfitzenmaier et al. 2003; KrzystekKorpacka et al. 2007), rather like cytokine than myokine. Another study described the contribution of this myokine in pathogenesis of gastric cancer cachexia by its genetic polymorphism (Bo et al. 2010). It was found the presence of IL-8 in muscle, not in plasma, following exercise, having a local role in angiogenesis (Szalay et al. 1997). The association of IL-8 with CXCR2 suggested its involvement in exerciseinduced neovascularization in the muscle tissue (Frydelund-Larsen et al. 2007), although the physiological function of this myokine is largely unknown. IL-8 is also produced in adipose tissue having an elevated level in obese patients (Bruun et al. 2004), a fact that should be taken into account in future research on the modulation of this myokine in cancer cachexia. Interleukin-15 (IL-15) is present in skeletal muscle and it was found to be modulated by exercise (Pedersen et al. 2007), having an anabolic effect on the muscle proteins metabolism. IL-15 reduces fat mass playing an important role in the interaction of skeletal muscle with the adipose tissue (Barra et al. 2010; Li et al. 2014). An in vitro study has shown that its overexpression induced muscle hypertrophy being involved in the synthesis and inhibition of protein degradation (Quinn et al. 2002). IL-15 overexpression is also connected with an increased mitochondrial activity and an augmentation of adipose tissue mass (Barra et al. 2014). A research conducted on patients with cancer and weight loss showed that subjects with lower baseline levels of IL-15 had a higher fat mass and indicating a possible role of IL-15 as a marker of the body composition response in cancer patients (Martínez-Hernández et al. 2012). An experimental research on a rat model with cancer cachexia treated with IL-15 showed a decrease in the rate of protein degradation without affecting protein synthesis (Carbó et al. 2000). In an experimental model of breast cancer in mice, the overexpression of IL-15 attenuated skeletal muscle fatigue (Bohlen et al. 2018). In spite of all these results, systemic administration of IL-15 increases skeletal muscle apoptosis in rats (Pistilli and Alway 2008). More of this, this myokine has been found in mice cancer models to increase the immune response against the tumor being present in the tumor microenvironment (Yu et al. 2010; Santana Carrero et al. 2019). Mice with lung cancer which received IL-15 as treatment were found with a decreased number of tumor

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nodules (Yu et al. 2010). An antiapoptotic effect of IL-15 was also shown (de Castro et al. 2021). DeCastro et al. considered that IL-15 expression seems to be correlated with tumor growth and metastatic potential, triggering along with other factors an increased tumor inflammation observed in cachexia (de Castro et al. 2021). IL-15 regulates the proliferation and maintenance of NK cells and the expansion of some subpopulations of T-cells which actively mediate immunity, such as CD4+ T-helper cells or CD8+ cytotoxic T-cells, and cell types contributing to immunological memory, such as CD8+ memory T-cells (Coletta et al. 2021; Gustafson et al. 2021). A report on patients with melanoma who received treatment with IL-15 has shown elevated NKs levels in circulation and had a complete lung metastases clearance (Conlon et al. 2015). Osteonectin (secreted protein acidic and rich in cysteine, SPARC), a matricellular protein involved in extracellular matrix-cell interactions is one of the most studied myokine in cancer (Papadopetraki et al. 2022). It is secreted in circulation in a very short time after the exercises demonstrated in humans and mice. Osteonectin was known initially as a glycoprotein in the bone, secreted by osteoblasts binding calcium. As myokine it was found to suppress colon tumorigenesis in mice via regular exercise (Aoi et al. 2013). In SPARK knockout mice, this effect was abolished (Aoi et al. 2013; Matsuo et al. 2017). It was observed a better survival in patients with digestive tract cancer with a high SPARC level after exercise, and SPARC may be a predictive biomarker only for death, but not recurrence (Akutsu et al. 2020). Oncostatin M (OSM) is a cytokine belonging to the IL-6 family, and it was demonstrated in vitro and in vivo experiments to have increased levels in muscle and in plasma after exercise (Hojman et al. 2011). More of this, it seems to have an antitumoral effect in mice (Molanouri Shamsi et al. 2019). Musclin, a peptide with high homology to natriuretic peptides, is a myokine implicated also in muscle cachexia in cancer, with a role in muscle atrophy (Re Cecconi et al. 2019). Thus, low levels of musclin were identified in atrophying myotubes, plasma, and muscles, while administration of this myokine to mice with renal cancer and muscle cachexia reduced muscle fiber atrophy. Musclin was found to enhance exercise capacity in mice by promoting mitochondrial biogenesis, driven by Ca2+-dependent activation of Akt1 and the release of OSTN (the encoding gene for musclin) transcription. Reduced musclin signaling, associated with reduced exercise capacity in mice, is recovered by treatment with recombinant musclin (Subbotina et al. 2015). Stromal-derived factor 1 (SDF1) is a myokine encoded by CXCL12 gene and triggered by exercise. It was found to be specifically and strongly downregulated only in skeletal muscles of mice with cancer tumors, but not in other pathological muscle wasting (Martinelli et al. 2016), having an anti-atrophic effect. SDF1 is involved in myogenesis, muscle regeneration, and angiogenesis (Arany et al. 2008; Bobadilla et al. 2014) and in tumor progression, metastasis, and cancer cachexia on CXCL12/CXCR4 axis (Teicher and Fricker 2010). Moreover, SDF1 levels were negatively correlated with two atrophy-related ubiquitin ligases, atrogin-1 and muscle RING-finger protein-1, in abdominal muscle of patients with cancer (Martinelli et al. 2016).

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The Role of the Most Studied Myokines in Cancer

The muscle wasting in cancer cachexia is associated with different types of cancer such as lung, pancreatic, liver, colorectal, gastric, and esophageal. It was demonstrated that cachexia affects prognosis and survival in cancer patients. Although the role of myokines in protection against cancer has not been fully highlighted, more and more preclinical studies have begun to demonstrate this role. For example, it has been proven that irisin and oncostatin M inhibit breast cancer cell viability in vitro (Hojman et al. 2011; Gannon et al. 2015) and SPARC reduces tumorigenesis in the colon of exercised mice (Aoi et al. 2013). As myokines belong to different classes of proteins, they may have multiple modes of action, controlling cell proliferation directly or antagonizing cellular ligands involved in cell proliferation and differentiation (Hojman et al. 2018). There are only few studies on myokines in cancer-cachectic patients. A complex research regarding myokines content in plasma, skeletal muscle, and tumor homogenates was made on treatment-naïve patients with gastric or colorectal stages I-IV cancer with and without cachexia (de Castro et al. 2021). Myostatin, IL-15 (related to increased tumor metabolic activity and tumor growth), FSTL-1 (implication in muscle vascularization), irisin (with implication in increased tumor metabolic activity and tumor growth), fatty acid binding protein 3 (FABP3) (a circulating muscle damage marker, with implication in tumor metabolic activity), and brainderived neurotrophic factor (BDNF) levels were measured. Patients with cachexia presented lower muscle FSTL-1expression, higher level of plasma FABP3, and higher tumor content of IL-15, FABP3, and irisin, compared with weight stable cancer patients (WSC). Lower body adiposity and systemic inflammation were associated with myokines. Regarding the inverse association of tumor FSTL-1 and muscle irisin in WSC group, this study could indicate a possible role of modulation of the skeletal muscle myokines by exercise in the prevention of tumor progression, considering that tumor FSTL-1 is related to tumor aggressiveness and that skeletal muscle irisin content is responsible for physical activity. In the cancer cachectic patients, it could be made an association between the tumor stage/muscle damage and the presence of tumor BDNF and muscle FABP3, as tumor BDNF influences tumor progression and muscle FABP3 is increased in muscle inflammation and muscle weakness (de Castro et al. 2021). These results showed that the myokine profile in skeletal muscle, plasma, and tumor is strongly affected by cachexia and that it could be a systemic scenario regarding myokines function, targeting associated receptors and pathways. Overexpression of myostatin inducing skeletal muscle atrophy and adipose tissue wasting were the first indication of a potential role of this myokine in the pathogenesis of cancer cachexia as it was shown in several preclinical models such as the Yoshida AH-130 hepatoma in rats and the C26 adenocarcinoma in mice (Pin et al. 2021). High levels of myostatin were also found in muscle tissue of cancer patients (Aversa et al. 2012).

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Irisin expression was found to be highly expressed in liver, lung, and gastrointestinal tumors and in the tumor of cachectic patients compared with cancer patients with stable body weight. Contrary, the irisin levels were reduced in colorectal and breast cancer patients compared to normal subjects (Pin et al. 2021). IL-6 in cancer-induced cachexia was extensively studied. In experimental murine preclinical models, elevated levels of IL-6 in the circulation were described (Pin et al. 2021), and in human cancer cachexia, elevated IL-6 levels could be predictors of body weight loss (Carson and Baltgalvis 2010) and a negative prognostic factor for cachectic lung cancer patients (Pettersen et al. 2017). It is important to be mentioned that IL-6 is often directly produced by cancer cells, and these myokine levels return to normal values when IL-6-producing tumors are removed from cachectic mice (Strassmann et al. 1992). IL-15 treatment decreased the number of tumor nodules in mice with lung cancer (Yu et al. 2010) being correlated with tumor growth and metastatic potential. Not only skeletal muscles suffer in cancer cachexia, but bones also, giving the tight communication between these systems, exchanging paracrine and endocrine factors (myokines, osteokines). It was revealed a bone loss in cachectic patients even in the absence of bone-metastatic disease (Pin et al. 2021). All these results may give a new perspective in the treatment of cancer cachexia in the future.

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The Benefit of Physical Exercise in Cancer Cachexia

In recent years, it is increasingly evidenced that physical exercises have beneficial effects on cancer patients reducing the disease incidence and inhibiting the tumor growth, and there are an important number of studies on this topic. For the moment, there is no standard treatment for cancer cachexia, but it was observed that physical activity could reduce the risk of developing several types of cancer and reduces mortality after cancer diagnosis, for example in colon cancer (Je et al. 2013). Skeletal muscle myokines are implicated in these effects. Although there are many studies on the effectiveness of physical exercise in prevention and treatment of cancer cachexia and even of tumors, there are few of them which try to explain the ways in which these effects are produced; the exact mechanisms implicated in these actions remain to be elucidated. Hojman et al. (2018) showed that regular exercise decreases the risk of cancer and can control the tumor development. As cytokines are implied in the skeletal muscle loss in cancer and knowing that exercise is established as an anti-inflammatory therapy, it is obvious that the physical training could attenuate the muscle cachexia process. In some preclinical models, B16F10 melanoma and randomized tumor-bearing mice, it was demonstrated that the voluntary wheel running had as effects a marked reduction of tumor volume and incidence (Hojman et al. 2018). Exercise releases

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myokines that have effect inside the muscle, but communicate with other organs and tissues also, activating many signaling pathways with a direct anti-tumoral effect or with an indirect response, mobilizing the immune system (Fig. 3). Many in vitro studies in breast, colon, lung, or prostate cancer cell lines demonstrated that the treatment of cells with exercise-conditioned human serum decreased metabolic activity and increased apoptotic rates in the cells, showing the ability of the molecules released in circulation after exercise to decrease tumor cell growth and survival (Papadopetraki et al. 2022). Myokines, including irisin, oncostatin M, and SPARC, have a potential role in the suppression of some types of tumors growth, such as breast and colon cancers (Hojman et al. 2011, 2018; Aoi et al. 2013; Manole et al. 2018) after exercise. A very interesting role has IL-6, a key player in malignancy development. As cytokine, it is a pro-inflammatory agent, inducing skeletal muscle atrophy and protein breakdown. As myokine, it has an anti-inflammatory action. It was shown that in the acute phase of exercise, IL-6 is increased by 100 times, and it was considered as a myokine released in circulation by muscular tissue, heaving an inhibitory effect on TNF-alpha and improving the glucose uptake through stimulation of AMPK signaling (Daou 2020).

Fig. 3 Physical exercise inducing myokines in cancer cachexia. After moderate physical exercise in patients with cachexia in different types of cancer, as well as in murine experimental models, a visible improvement in muscle wasting was observed, as well as a direct or indirect action on cancerous tumors that decreased in volume. The healthy status of many subjects improved considerably after exercise, which leads us to consider myokines as possible therapeutic targets in some types of cancer with muscle cachexia. (All figures created with BioRender.com)

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In human hepatocarcinoma cancer, physical activity is associated with a reduced risk of the disease (Baumeister et al. 2019) and could really improve the prognosis of this type of cancer, enhancing the skeletal muscle mass (Hashida et al. 2020). However, these studies did not address the subject of myokines. Decorin expression in hepatocarcinoma cancer was the subject of a research utilizing in silico data, as well as formalin-fixed paraffin-embedded tissue samples of tumor in a tissue microarray (Reszegi et al. 2020). The results raised the possibility that decorin acted as a tumor suppressor in liver cancer, with a decreased expression in the tumoral cells. In the same study, decorin was found to be overexpressed in a mouse model of hepatocarcinogenesis evoked by thioacetamide, and the decorin gene delivery reduced tumor formation. These results showed that decorin can be utilized as an anticancer agent (Reszegi et al. 2020). IL-6, OSM, SPARC, and irisin mediate cancer progression being directly involved in the apoptosis, proliferation, metabolism, drug resistance, and epithelial-mesenchymal transformation of cancer cells (Huang et al. 2022). Interleukins IL-6, IL-8, IL-15, and irisin stimulate lipolysis of adipose tissue, accelerate the browning of white fat, and promote glucose uptake, improving obesity-induced inflammation (Huang et al. 2022). Some myokines participate to tumor microenvironment regulation, such as immune response and angiogenesis. Experimental studies demonstrated that OSM could mediate some inhibitory effects on cancer evolution after aerobic exercise. Thus, an in vitro study showed that the incubation of human breast cancer cells with a post-exercise human serum containing OSM inhibited cell proliferation and induced apoptosis, while the blockage of OSM mitigated the anti-tumoral effects of exercise-conditioned serum (Hojman et al. 2011). In healthy subjects after aerobic effort, it was observed that the plasma myokine levels were increased. Regarding the intensity of the exercise, a continuous muscle contraction with a moderate intensity induces a higher concentration of myokines than a shorter muscular contraction but with a high intensity, which means that not every kind of physical movement is beneficial in fighting cancer cachexia.

6

Conclusion

Myokines could be essential therapeutic targets in cancer cachexia (and not only), and the modulation of their expression by some interventional methods could improve the maintenance of skeletal muscles at parameters as close as normal in different types of cancer. They are involved in complex signaling pathways and communicate with almost all organs and tissues in the body. One important interaction is with the adipokines of adipose tissue, and it has been proven that 25% of cancers are caused by obesity and a sedentary life. Modulating the composition of these molecules in cancer cachexia is essential in maintaining skeletal muscle and body fat in normal conditions. The physical exercise is very important for cachectic cancer patients as it was demonstrated that myokines are overexpressed in this conditions and that they have a beneficial effect in therapy of these patients.

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Myokines could be considered as possible therapeutic targets in personalized medicine. The prevention of cancer cachexia must be taken into account also, so that the patients could better respond to cancer treatment. Thus, moderate exercise could be a part of therapy in this pathology. There are not enough studies regarding myokines as therapy target in cancer cachexia, and more research is needed in this direction. It was shown that different myokines in skeletal muscle, plasma, and tumor have their role in muscle regeneration, reducing cachexia and improving the health of cancer patients. Disclosure of Potential Conflicts of Interest The authors declare no conflict of interest. Acknowledgments and Funding Information Funding This work was funded by Ministry of Research, Innovation and Digitalization in Romania, under Program 1 – The Improvement of the National System of Research and Development, Subprogram 1.2 – Institutional Excellence-Projects of Excellence Funding in RDI, Contract No. 31PFE/30.12.2021 and the National Program 1 N/2019/PN 19.29.02.01.

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Epigenetics in Cancer Biology Richard A. Stein and Abhi N. Deverakonda

Abstract

The term epigenetics dates back to the 1940s, when Conrad Waddington introduced it to refer to gene expression changes that occur during development and do not involve alterations in the DNA sequence. Subsequently the definition expanded beyond development, and the field became one of the most rapidly developing ones in life sciences. Advances in epigenetics transformed our understanding of cellular and molecular events that occur during development, homeostasis, and disease and helped explain processes that have long fascinated and puzzled scientists, such as the link between inflammation and disease, the intricacies of memory formation and maintenance, and the connection between the social environment/social adversity and chronic disease risk. Epigenetically mediated gene expression changes were described in a broad group of medical conditions, including cancer and neurodegenerative, metabolic, autoimmune, psychiatric, and cardiovascular diseases and, of these, the most advanced understanding of their contribution, so far, has occurred for cancer. Changes in DNA methylation, histone posttranslational modifications, and microRNA alterations, described in a broad group of human cancers, were implicated in all stages of carcinogenesis, including initiation, progression, invasion, and metastasis. The discovery of epigenetic biomarkers facilitated novel strategies for the early detection of disease, helped better monitor progression, therapeutic response, and prognosis, and revolutionized personalized medicine. Moreover, the reversible nature of epigenetic marks opened the possibility to therapeutically reverse aberrant gene expression patterns and catalyzed the emergence of epigenetic drugs. Besides their promise as monotherapies, epigenetic drugs show R. A. Stein (✉) · A. N. Deverakonda Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_86 Published online: 1 January 2023

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considerable interest thanks to the possibility to combine them with other cancer therapeutic modalities, such as chemotherapy, hormone therapy, and radiation therapy. Keywords

Cancer · DNA methylation · Epigenetics · Gene expression · Histone posttranslational modifications · RNA interference

1

Introduction

In his 1940 book Organisers and Genes, Conrad Waddington describes the elements of the epigenetic landscape (Waddington 1940). In his visual metaphor, an undifferentiated cell, represented by a ball, starts rolling down an intricate system of hills and valleys. The different trajectories that the ball can follow represent the alternative fates that the cell may adopt during development. By the time the cell reaches the bottom of the landscape, it has become differentiated (Goel et al. 2021; Coomer et al. 2022; MacArthur 2022). Building on this concept, in 1942 Waddington introduced the term epigenetics (Waddington 1942) to describe gene expression changes that occur during development without altering the sequence of the DNA. Referring to the complexity of developmental processes that connect the genotype to the phenotype, Waddington effectively connected the fields of genetics and embryology (Waddington 2012). Previously, in 1924, Hans Spemann and Hilde Mangold found that grafting a piece of an amphibian blastopore’s dorsal lip from a donor embryo to the ventral side of a recipient embryo caused the neighboring tissues to change their fate and start the development of a new organism (Gorodilov Iu 2001). The cells of the dorsal lip of the blastopore were called the organizer (Spemann and Mangold 1924; Gorodilov Iu 2001) based on their role in specifying the body axes of the developing embryo (Anderson and Stern 2016). Waddington extended these studies from amphibian embryos to avian and mammalian embryos and became interested in the chemical nature of the organizer (Hall 2015; Nicoglou 2018). Together with Joseph and Dorothy Moyle Needham, he coined the term evocator to refer to a chemical factor that is released by the organizer (Waddington et al. 1933, 1934; Needham et al. 1934; Nicoglou 2018). One of the central themes in Waddington’s work was an emphasis on understanding genetics and development together (Nicoglou 2018). Waddington defined epigenetics as the branch of biology that studies the causal interactions between genes and their products that bring the phenotype into being (Waddington 1942; Goldberg et al. 2007). Subsequently, epigenetics was defined as a stably heritable phenotype resulting from changes in a chromosome without alterations in the DNA sequence (Berger et al. 2009). An important step toward our current understanding of epigenetic processes during development occurred when Sir John Gurdon showed that fertile frogs could be obtained from differentiated intestinal cell nuclei, thus demonstrating the possibility to reprogram

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them, a finding that supported the idea that genetic information is not lost during cell differentiation, as scientists had hypothesized earlier (Gurdon and Uehlinger 1966; Alberts et al. 2002; Mitalipov and Wolf 2009). As the field of epigenetics matured, epigenetic processes were shown to play critical roles in the growth and homeostasis of every tissue and cell type in the body, in the response to environmental factors, the process of aging, and the pathogenesis of an increasing number of diseases (Matilainen et al. 2017; Robinson et al. 2021; Wagner et al. 2021). The number of articles on the topic has increased exponentially in the past half of a century (Marriott et al. 2016). While ~80 articles on epigenetics were available on PubMed in 1990 (Hamm and Costa 2015), their number increased to ~320 in 2000 (Hamm and Costa 2015) and to ~1000 in 2004 (Wigle 2011), and it is currently estimated to double every 2 years (Jirtle 2009). As of mid-2022, a PubMed search using the term “epigenetics” yields ~120,000 articles, of which >57,000 were published during the past 5 years.

2

Types of Epigenetic Changes

The three main types of epigenetic changes that were described are DNA methylation, histone posttranslational modifications/the incorporation of histone variants, and RNA interference (Moosavi and Motevalizadeh Ardekani 2016). In addition, a group of modifications analogous to epigenetic changes was described in RNA and became known as epitranscriptomic changes (Morena et al. 2018; Leonardi et al. 2019; Mongan et al. 2019; Kumar and Mohapatra 2021; Chokkalla et al. 2022).

3

DNA Methylation

DNA methylation is one of the earliest described (Jin and Liu 2018) and best understood epigenetic modifications (Weinhold 2006). It is primarily found on cytosine residues followed by guanines, which are known as CpG dinucleotides (Jang et al. 2017). The three DNA methyltransferases described in mammalian cells, Dnmt1, Dnmt3a, and Dnmt3b (Jurkowska et al. 2011), catalyze the transfer of a methyl group from S-adenosyl-L-methionine (AdoMet or SAM) to the C5 position of the cytosine residue (Jurkowska et al. 2011). This methyl group does not interfere with the Watson–Crick base pairing (Jurkowska et al. 2011). Dnmt1, a maintenance DNA methyltransferase, is specific for hemimethylated DNA, and Dnmt3a and 3b are de novo DNA methyltransferases that can methylate unmodified DNA (Chédin 2011; Jurkowska et al. 2011; Gowher and Jeltsch 2018). While DNA methylation may occur throughout the lifetime of a cell and is heritable from cell to cell, it is also reversible (Li and Zhang 2014). In addition to CpG dinucleotides, which is the most frequently described context (Patil et al. 2014), cytosine methylation can also occur in other contexts, such as CpA, CpT, and CpC, which are collectively known as non-CpG or CpH methylation (Jang et al. 2017; de Mendoza et al. 2021) and are less well understood (Patil et al.

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2014). Non-CpG methylation appears to be enriched in stem cells, particularly pluripotent stem cells (Patil et al. 2014), and in the mammalian brain (de Mendoza et al. 2021). CpG methylation is usually distributed symmetrically between the two DNA strands, but non-CpG DNA methylation is often present on only one of the two DNA strands (Shirane et al. 2013; Guo et al. 2014a, b). Johnson and Coghill described 5-methylcytosine in 1925 in DNA isolated from Mycobacterium tuberculosis (Johnson and Coghill 1925), and in 1948 Hotchkiss identified, in paper chromatography studies on calf thymus DNA, a band that he called “epicytosine,” which behaved like cytosine but was slightly shifted in its migration pattern. He also pointed out that the relation between cytosine and epicytosine, in terms of migration and absorption spectrum, is the same as the one between thymine (5-methyluracil) and uracil (Hotchkiss 1948). Subsequent studies proposed that 5-methylcytosine in the DNA of higher organisms may control gene expression during development. In 1968, Grippo et al. reported that 5-methylcytosine in the genome of developing sea urchin embryos is nonrandomly distributed and 90% is found in the CG context, and they discussed the relevance of this finding for development (Grippo et al. 1968). In 1975, Holliday and Pugh hypothesized that two different enzymes are responsible for turning gene expression on and off by methylating the DNA and pointed out that DNA methylation, which is reversible, is a more likely candidate than mutations for reversibly controlling gene expression during development (Holliday and Pugh 1975). In the same year, Riggs discussed DNA methylation in the context of X chromosome inactivation in the somatic cells of mammalian females (Riggs 1975). The diploid human genome contains about 56 million CpG sites, considering both DNA strands (Zhang and Jeltsch 2010; Vaisvila et al. 2021). In mammalian genomes, 70–80% of the CpG sites are methylated (Vaisvila et al. 2021). Some of the functions of DNA methylation include silencing retroviral and transposable elements, regulating tissue-specific gene expression, and genomic imprinting, including inactivation of the X chromosome in female mammals (Moore et al. 2013; Li and Zhang 2014). CpG methylation affects transcription through several mechanisms (Nabel et al. 2012). It increases the melting temperature of the DNA duplex (Smith et al. 2009); the methyl group that projects into the major groove of the DNA double helix may repress transcription by preventing the binding of certain transcription factors to their recognition motifs (Hu et al. 2013; Dantas Machado et al. 2015; Héberlé and Bardet 2019); and the presence of the methyl group may favor the binding of transcription factors that repress gene expression, such as MeCP2 (Jin et al. 2011a, b; Lagger et al. 2017; Long et al. 2017; Ren et al. 2018; Connolly and Zhou 2019). Two classes of promoters were described in the human genome. Approximately 72% of them have a high CpG content, and about 28% have a low CpG content (Saxonov et al. 2006). Genomic regions that are rich in CpG dinucleotides are called CpG islands (CGIs) (Vavouri and Lehner 2012; Borchiellini et al. 2019). CGIs are ~200–2000 base-pairs long, have a CpG content >50% (Li and Zhang 2014), with the ratio between the observed and the expected CpG dinucleotides greater than 0.6 (Gardiner-Garden and Frommer 1987; Irizarry et al. 2009), and are mostly unmethylated in somatic cells, which allows gene expression regulation (Zhao and Han 2009; Borchiellini et al. 2019).

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CpG methylation at transcription start sites is associated with gene silencing (Borchiellini et al. 2019). In gene bodies, CpG methylation in actively dividing cells is associated with increased gene expression (Ball et al. 2009; Aran et al. 2011; Moen et al. 2014), but this correlation was not seen in nondividing or slowly-diving cells (Guo et al. 2011), and the manner in which gene body methylation contributes to gene expression is not well understood yet (Moore et al. 2013). The Mutagenicity of Methylated CpG Sites An interesting feature of CpG dinucleotides is that they are strongly underrepresented in vertebrate genomes (Burge et al. 1992; Li and Chen 2011), including the human genome, where they occur at ~20% of the expected frequency (Sved and Bird 1990; Lander et al. 2001; Angeloni and Bogdanovic 2021). This scarcity of CpG sites is thought to be explained by the high mutagenicity of the methylated cytosines (Panchin et al. 2016). The mutagenicity of the genomic CpG dinucleotides has been known for a long time (Cooper et al. 2010). Cytosine and 5-methylcytosine in the DNA can spontaneously deaminate to form uracil and thymine, respectively (Sassa et al. 2016). Uracil can be removed from the DNA by uracil glycosylases (Fryxell and Moon 2004), but this is not the case for thymine, which is normally present in the DNA and therefore is not recognized as mutagenic. Thymine residues that form from the deamination of 5-methylcytosine are not efficiently repaired in the DNA and will pair with adenines, a mutagenic event (Duncan and Miller 1980; Lakshminarasimhan and Liang 2016) that leads to C:G to T:A transition mutations (Sassa et al. 2016). Hydrolytic deamination occurs more frequently for methylated than for unmethylated cytosines (Shen et al. 1994; Xia et al. 2012), and cytosine residues at most methylated CpG sites mutate 10–50 times faster than cytosine residues in any other context or than any other nucleotide (Walser and Furano 2010; Xia et al. 2012). It was estimated that CpG methylation increases the risk of C to T mutations by ~12-fold (Pértille et al. 2019). For this reason, 5-methylcytosine residues were referred to as hotspots for spontaneous transitions (Duncan and Miller 1980; Sassa et al. 2016). CpG to TpG transitions occur more frequently than any other point mutations (Pértille et al. 2019). A study revealed that almost one-third of 280 human p53 mutations linked to cancer were transitions at CpG hotspots (Hollstein et al. 1991), and almost 50% of the colon cancers examined had mutations at three CpG hotspots (Hollstein et al. 1996; Denissenko et al. 1997). CpG sites account for about 30% of the mutations in the germline (Hermann et al. 2004) and for many acquired somatic mutations. 5-mC and Cancer DNA methylation changes were first linked to cancer in 1983, when several genomic regions were found to be hypomethylated in cancer cells as compared to adjacent, noncancerous cells from the same patients, and metastatic cancer cells from one patient showed even more pronounced hypomethylation than primary cancer cells (Feinberg and Vogelstein 1983a, b, Gama-Sosa et al. 1983). The same year, it was reported that oncogenes are hypomethylated in primary human cancers (Feinberg and Vogelstein 1983a, b).

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Both CpG and non-CpG methylation were described in pathological contexts (Patil et al. 2014). Studies described, in many cancers, a combination of global CpG hypomethylation (Das and Singal 2004), which affects chromosomal stability, and hypermethylation at gene promoters, often tumor suppressor genes, which leads to their silencing (Baylin and Jones 2011; Pang et al. 2016). Genes shown to have promoter hypermethylation in cancer include APC, BRCA1, E-cadherin, DAPK1, MGMT, and Rb (Das and Singal 2004). CpG methylation also has prognostic value in cancer (Das and Singal 2004). For example, in patients with colorectal cancer, the levels of CDKN2A promoter methylation were correlated with a poor prognosis (Maeda et al. 2003), and p16 hypermethylation was associated with higher-stage non-small-cell lung cancer and reduced disease-free survival (Ng et al. 2002). CpG methylation can also predict the response to therapy. For example, in rectal cancer, the DNA methylation pattern of three CpG sites is informative of the response to neoadjuvant chemoradiotherapy (do Canto et al. 2020), and higher O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation from cell-free DNA at baseline was associated with a better response to preoperative chemoradiotherapy (Sun et al. 2014a, b). One of the challenges in interpreting the role of DNA methylation in cancer is distinguishing between driver DNA methylation events, which have a functional role in carcinogenesis, and passenger DNA methylation, which may be innocuous and the result of the malignancy (Kalari and Pfeifer 2010a, b; Pfeifer 2018). This distinction is particularly challenging, considering that most DNA methylation changes in the genome are passenger events (Liang and Weisenberger 2017). This situation is analogous to the difficulties in distinguishing driver genetic mutations from passenger ones in cancer cells. It was suggested that driver DNA methylation changes are more likely to occur early during carcinogenesis, and changes that occur later are more likely to be passenger methylation events. Moreover, methylation changes in genes that are more relevant for carcinogenesis, and lead to cancer phenotypes, such as the ones that cause the silencing of tumor suppressor genes or the activation of oncogenes, are more likely to be driver methylation events (Kalari and Pfeifer 2010a, b; Weisenberger et al. 2018). Mapping techniques that compare promoter DNA methylation between tumor and control cells to identify loci that are hypermethylated significantly more than expected are emerging as a novel strategy to identify DNA methylation driver events in cancer (Pan et al. 2021). 5-Hydroxymethylcytosine (5-hmC) A DNA epigenetic mark that is rapidly gaining attention is 5-hydroxymethylcytosine (5-hmC), also referred to as the sixth DNA base (Wang et al. 2014a, b). While 5-hmC was identified in the bacteriophage genetic material in 1952 (Wyatt and Cohen 1952) and in the vertebrate DNA in 1972 (Penn et al. 1972), this modification was poorly understood, not thought to have biological relevance, and comparatively more attention has focused on 5-mC (Penn et al. 1972). In 2009, it was reported that 5-hmC is present in the DNA from the mouse cerebellar Purkinje and granule neurons, where it constitutes 0.6% and 0.2% of the genetic material, respectively (Kriaucionis and Heintz 2009). Another 2009 landmark study identified the TET1 (Ten-Eleven Translocation 1) protein,

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which can oxidize 5-mC to 5-hmC (Tahiliani et al. 2009). TET enzymes are oxoglutarate- and iron-dependent dioxygenases (Branco et al. 2011). More recent studies reported that 5-hmC is broadly distributed in human cells (Song et al. 2011), regulates promoters and enhancers in human embryonic stem cells (Szulwach et al. 2011), and is involved in disease. The level of 5-hmC in mammalian cells was found to range from 20-fold differences in their 5-hmC levels, indicating that the primary 5-hmC levels are determined by the tissue type as opposed to the gene expression levels (Nestor et al. 2012). Despite the involvement of 5-hmC in development and disease, the molecular functions of this epigenetic modification are not well understood yet (Pang et al. 2016). The levels of 5-hmC are depleted in cancer (Jin et al. 2011a, b; Ficz and Gribben 2014; Pfeifer et al. 2014) and its changes were implicated in other diseases, including diabetes and its complications (Yang et al. 2019; Yuan et al. 2019; Han et al. 2021; Zampieri et al. 2021) and neurodevelopmental and psychiatric conditions (Shi et al. 2017a, b; Cheng et al. 2018; Papale et al. 2022). One of the most extensively studied contexts of 5-hmC is in brain development (Sun et al. 2014a, b; Spiers et al. 2017). Elevated 5-hmC levels were reported in the promoter of the Engrailed-2 gene in the cerebellum in individuals with autism, and this was associated with an increase in gene expression (James et al. 2014). The transcription factor Engrailed-2 is important for cerebellar development and has to be downregulated in the late intrauterine and early post-natal period (Jankowski et al. 2004; James et al. 2014). The discovery of proteins that specifically recognize and bind 5-hmC indicates that this epigenetic modification most likely has an important role and is not simply an intermediate of 5-mC demethylation, as it was once hypothesized (Yildirim et al. 2011; Wang et al. 2014a, b; Chen et al. 2017). 5-hmC and Cancer The levels of 5-hmC were first reported to globally decrease in colorectal cancer (Li and Liu 2011) and, subsequently, in several other human cancers (Pfeifer et al. 2014; Xu and Gao 2020). These include leukemia (Kroeze et al. 2014), malignant melanoma (Lian et al. 2012; Xu and Gao 2020), lung cancer (Wang et al. 2020a, b), laryngeal squamous cell carcinoma (Zhang et al. 2016), breast cancer (Wilkins et al. 2020; Xu and Gao 2020), hepatocellular carcinoma (Liu et al. 2013), gastric cancer (Park et al. 2015), bladder and kidney cancer (Chen et al. 2016; Xu and Gao 2020), and urogenital malignancies (Munari et al. 2016). A 50–90% loss of 5-hmC levels was reported in various solid tumors, and although the mechanistic basis of this process is not yet understood, explanations include the possibility that rapidly proliferating tumor cells cannot maintain this epigenetic

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modification or an impairment in their TET activity (Pfeifer et al. 2014). A study on human melanoma revealed that the decreased 5-hmC levels, which have both diagnostic and prognostic value, were caused by the downregulation of isocitrate dehydrogenase 2 (IDH2) and TET family enzymes, and their overexpression reestablished the 5-hmC levels and led to increased tumor-free survival in animal models (Lian et al. 2012). Decreased 5-hmC levels across many human cancers opened the possibility to use this epigenetic change as a biomarker (Pfeifer et al. 2014).

4

Histone Posttranslational Modifications/the Incorporation of Histone Variants

The repeating unit of the eukaryotic chromatin, the nucleosome, is made by the wrapping of 145–147 base pairs of double-stranded DNA around a histone core octamer that contains H2A, H2B, H3, and H4, each of them being present in two copies (Campos and Reinberg 2009; Luger et al. 2012; Bai and Zhou 2021). The DNA is wrapped around the histone octamer as a left-handed superhelix that forms 1.65 turns (Luger et al. 1997). Histones are highly conserved, basic proteins with an unstructured N-terminal tail and a globular C-terminal region (Khan et al. 2015). Each histone is made of three alpha-helices connected by two loops (MariñoRamírez et al. 2005). Amino acids in the N-terminal tails, and in some instances in the C-terminal tails, undergo extensive posttranslational modifications (MariñoRamírez et al. 2005; Chew et al. 2006). Histone H2A is unique in having its C-terminal tail also exposed to the surface of the nucleosome and subject to posttranslational modifications (Chew et al. 2006; Vogler et al. 2010; Wratting et al. 2012). Once considered to be simply “DNA packaging” molecules, the role of histones in dynamically regulating chromatin has been increasingly recognized and appreciated (Chinaranagari et al. 2015). Both genetic and epigenetic changes that affect histones were linked to cancer. For example, somatic histone mutations are estimated to be involved in at least 4% of human cancers (Nacev et al. 2019). The possibility to acetylate and methylate histones has been known since the early 1960s (Allfrey et al. 1964). These are two of the best-understood histone posttranslational modifications, a group of covalent changes that also includes phosphorylation, ubiquitylation, SUMOylation, glycosylation, deamination, proline isomerization, propionylation, butyrylation, crotonylation, glycosylation, and ADP-ribosylation (Chen et al. 2007; Sakabe et al. 2010; Tan et al. 2011; Chinaranagari et al. 2015; Khan et al. 2015; Ramazi et al. 2020). Posttranslational modifications were described on the core histone tails, their globular domain, and the linker histones H1 and H5 (Ahmad et al. 2011; Cohen et al. 2011; Sarg et al. 2015), they determine the extent at which the DNA is wrapped around the nucleosomes (Campos and Reinberg 2009) and they shape chromatin structure and function and gene expression (Ramazi et al. 2020). Defects in histone posttranslational modifications have been implicated in a broad group of medical conditions including

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cancer, Alzheimer’s disease, autoimmune conditions, and cardiovascular and neurodegenerative diseases (Cobos et al. 2019; Ramazi et al. 2020). Histone posttranslational modifications are added by enzymes called writers, detected by readers that have specific recognition domains, and removed by enzymes called erasers (Khan et al. 2015). Histone acetyl transferases, histone methyl transferases, and histone kinases are examples of writers; bromodomains, the Tudor domain, and the plant homeodomain (PHD) finger domain are examples of readers; and histone deacetylases, histone demethylases, and histone phosphatases are examples of erasers (Musselman et al. 2012; Lu and Wang 2013; Khan et al. 2015; Jain et al. 2020). Generally, methylation at H3K4, H3K36, and H3K79 leads to gene activation, and methylation at H3K9, H3K27, and H4K20 causes gene repression (Alam et al. 2015). Histone acetylation is usually associated with gene activation, and histone deacetylation causes gene silencing (Chinaranagari et al. 2015). Lysine residues can be mono-, di-, and trimethylated, and arginine residues can be mono- and di-methylated, and the latter one can be symmetrical or asymmetrical (Bannister and Kouzarides 2011). Histone acetyltransferases use acetyl CoA as a cofactor and transfer an acetyl group to the ε-amino group of lysine side chains (Bannister and Kouzarides 2011). This neutralizes the positive charge of the lysine residue and weakens the interaction between the histone tail and the negatively charged DNA, facilitating gene expression (Bannister and Kouzarides 2011). Histone acetylation was first described in the early 1960s (Phillips 1963), but the first histone acetyltransferase was only identified in 1995 in the ciliate protozoan Tetrahymena thermophila as a single 55-kDa polypeptide that incorporates [3H]acetate from [3H]acetyl-CoA into core histone proteins (Brownell and Allis 1995). An enzymatic activity that removes acetyl groups from histones was discovered in 1969 in calf thymus extract (Inoue and Fujimoto 1969), and the first mammalian histone deacetylase, HDAC1, was described in 1996 (Taunton et al. 1996). A study that used high-resolution mass spectrometry identified 3600 lysine acetylation sites on 1750 proteins as a result of exposure to SAHA and MS-275, a histone deacetylase inhibitor with specificity for HDAC1. The acetylated proteins detected in this study were implicated in a diverse group of cellular processes. Proteins that were preferentially acetylated included those involved in cell cycle regulation, nuclear transport, chromatin remodeling, and actin nucleation (Nishioka et al. 2008; Choudhary et al. 2009). Histone modifications are involved in an extensive crosstalk that occurs through multiple mechanisms (Kouzarides 2007; Bannister and Kouzarides 2011). The histone code hypothesis was coined to refer to combinations of histone posttranslational modifications that act in combination or sequentially and together specify alternative chromatin states and regulate chromatin function (Strahl and Allis 2000). Consequently, an increasingly influential idea in the field is that studying any single histone posttranslational modification in isolation is not truly informative about its impact on gene expression and, instead, it is critical to interrogate the effect of the combinatorial occurrence of several histone posttranslational modifications (Rando 2012). For example, phosphorylation of H3S10 and methylation of the adjacent H3K9 residue constitute a switch that regulates the binding of the heterochromatin

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protein 1 (HP1) to the chromatin, with binding being inhibited when H3S10 is phosphorylated but being enabled when H3S10 is not phosphorylated (Fischle et al. 2005). The complexity of the histone code concept makes these relationships difficult to study (Chi et al. 2010; Khan et al. 2015). Even though DNA methylation and histone posttranslational modifications are often studied independently, they influence each other extensively. For example, methylated CpG sites can recruit the methyl-CpG binding protein MeCP2, a transcriptional repressor, which can recruit histone deacetylases (Jones et al. 1998; Fuks et al. 2003; Kimura and Shiota 2003) or histone methyltransferases (Fuks et al. 2003) to further repress transcription. A study showed that treating HEK 293 cells with trichostatin A (TSA), a histone deacetylation inhibitor, leads to active DNA demethylation (Cervoni and Szyf 2001). In addition, PGC7, a nuclear polypeptide that is predominantly expressed in pluripotent stem cells and in germ cells, interacts with the TET2 and TET3 enzymes and suppresses their activity, preventing them from oxidation and demethylation, thus protecting DNA methylation (Bian and Yu 2014). In Neurospora crassa, the DIM-1 histone methyltransferase recruits the DIM-2 DNA methyltransferase that methylates cytosines (Tamaru and Selker 2001), and in mouse cells, H3K36me2 recruits the DNMT3A DNA methyltransferase to intergenic regions in an NSD1-dependent manner and maintains DNA methylation (Weinberg et al. 2019). Histone Posttranslational Modifications in Cancer In cancer cells, changes in histone posttranslational modifications were described at the level of individual genes, which can become inappropriately silenced or expressed, as well as globally (Seligson et al. 2009). One of the first global histone posttranslational modifications reported in human cancer, the global loss of H4K16 monoacetylation and H4K20 trimethylation (Fraga et al. 2005), predominantly at repetitive DNA regions, emerged as a common hallmark of human cancer cells. Other histone posttranslational modifications are specific for individual cancers. For example, global levels of H3K4me2 and H3K18 acetylation predict a higher risk of recurrence in prostate cancer (Seligson et al. 2005). Promoter hypermethylation of the retinoic acid receptor beta gene, together with hypoacetylation of the gene on histones H3 and H4, causing its silencing, was documented in prostate cancer cell lines and in cells from clinical prostate cancer samples (Nakayama et al. 2001). Other studies reported that in prostate cancer, the expression of DAB2IP (Disabled homolog 2 interacting protein), which is also known as ASK1-interacting protein-1 (AIP1) (Wang et al. 2015a, b), a tumor suppressor protein that is downregulated in several other cancer types (Wang et al. 2015a, b; Liu et al. 2016; Sun et al. 2018a, b), was epigenetically repressed by DNA methylation and histone deacetylation (Chen et al. 2003, 2005). DAB2IP is also silenced in colorectal cancer, as part of a complex formed by enhancer of zeste homolog 2 (EZH2), HDAC1, and Snail (Wang et al. 2015a, b). DAB2IP downregulation is involved in the proliferation, metastasis, apoptosis, and epithelial-to-mesenchymal transition of cancer cells (Bellazzo et al. 2017; Sun et al. 2018a, b). Another study revealed that in prostate cancer cells, EZH2, the catalytic subunit of the polycomb repressive complex 2 (PRC2), shifts the balance between

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matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) toward MMPs by trimethylating H3K27, which prevents RNA polymerase II from binding to the promoter of TIMP3 and downregulates TIMP2, promoting metastatic dissemination (Shin and Kim 2012). An extensively studied epigenetic change is the phosphorylation of serine 10 on the histone H3 tail (H3S10), which occurs as part of chromosome condensation in various eukaryotes (Wei et al. 1998; Sotero-Caio et al. 2011). H3S10 phosphorylation is involved in two distinct and opposing chromatin states: it compacts the chromatin during chromosomal condensation and sorting (Hayashi-Takanaka et al. 2009), and it can lead to chromatin relaxation and transcriptional activation (Johansen and Johansen 2006; Komar and Juszczynski 2020). During cell cycle progression, H3S10 phosphorylation is minimal during interphase (Duan et al. 2008), it starts to accumulate in late G2, and after its increase from prophase to metaphase, it starts to decrease in late anaphase and disappears upon exit from mitosis (Duan et al. 2008; Komar and Juszczynski 2020). H3S10 phosphorylation is so far the only histone posttranslational modification that is directly linked to cellular transformation (Khan et al. 2015), and it is essential for cancer initiation and progression (Choi et al. 2005; Komar and Juszczynski 2020). Increased H3S10 phosphorylation is a poor prognostic factor in several cancers, including nasopharyngeal carcinoma (Li et al. 2013a, b), glioblastoma multiforme (Pacaud et al. 2015), gastric cancer (Khan et al. 2016), and breast cancer (Skaland et al. 2007), and a study on Epstein–Barr virus-initiated nasopharyngeal carcinoma showed that a histone H3S10A mutant was associated with decreased cellular proliferation as compared to the wild type (Li et al. 2013a, b). Certain histone posttranslational modifications have a prognostic value in cancer. For example, in gastric adenocarcinoma, higher H3K9 trimethylation levels were positively associated with tumor stage, lymphatic and vascular invasion, and recurrence risk and were statistically significantly associated with poor survival rates (Park et al. 2008). H3K18 hypoacetylation was associated with a higher risk of recurrence in low-grade prostate cancer (Seligson et al. 2005) and with poor prognosis in breast (Elsheikh et al. 2009) and pancreatic (Manuyakorn et al. 2010) cancer, but an inverse relationship was found for squamous cell carcinoma of the esophagus (Tzao et al. 2009) and for glioblastoma (Liu et al. 2010). An in vitro study revealed that the adenovirus protein E1a led to a threefold global decrease in the H3K18 acetylation pattern; this effect was also caused by the Simian virus 40 (SV40) large T antigen, another DNA tumor virus transforming protein (Horwitz et al. 2008). Histone Variants and Cancer In addition to the four types of “canonical” histone proteins, H2A, H2B, H3, and H4, which are part of the nucleosome core particle (Talbert and Henikoff 2021) and peak in expression during the S phase of the cell cycle to provide the main supply of histones during replication (Szenker et al. 2011), there are paralog histones that vary from their canonical, replication-coupled counterparts and are known as histone variants (Talbert and Henikoff 2021). Histone

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variants represent a small percentage of the cellular histone content (Talbert and Henikoff 2010), and they modify nucleosome composition independently of DNA replication (Méndez-Acuña et al. 2010). The best-characterized histone variants are for H1, H2A, and H3 (Vardabasso et al. 2014). The human H2A family has several members, including the histone variants H2AX, H2A.Z, H2A.B (H2A.Bbd), and macroH2A (mH2A) (Quénet 2018). Histone H2AX In normal human fibroblasts, about 10% of H2A is H2AX (Bonner et al. 2008), but the percentage varies from 2% to 25% in various human cell types (Rogakou et al. 1998; Dickey et al. 2009). H2AX is critical for the DNA damage response (Podhorecka et al. 2010; Jeffery et al. 2021) and genomic stability (Saravi et al. 2020). Mice that lack H2AX, while viable, show genomic instability, in addition to growth retardation, male sterility, and immune deficiency (Celeste et al. 2002; Georgoulis et al. 2017). The human gene for histone H2AX, H2AFX, is on 11q23 (Bonner et al. 2008), a region on chromosome 11 that often shows genetic changes in cancers (Lee et al. 2000; Pulido et al. 2000; Siew-Gek Lee et al. 2004). In response to double-strand DNA breaks, H2AX is rapidly phosphorylated on serine 139, which is located four amino acids from the carboxyl terminus and forms γH2AX (Rogakou et al. 1999; Bonner et al. 2008; Turinetto and Giachino 2015), a process that activates checkpoint proteins that arrest cell cycle progression (Podhorecka et al. 2010). Within minutes after a DNA double-strand break is generated, up to several megabases of the DNA accumulate γH2AX foci (Rogakou et al. 1999). High levels of γH2AX were associated with larger tumor size, higher tumor grade (Varvara et al. 2019), and poor overall survival in breast cancer (Nagelkerke et al. 2011; Yang et al. 2017). Histone H2A.Z The H2A.Z histone variant was first identified in 1980 in mouse L1210 lymphocytic leukemia cells, HeLa cells, and chicken erythrocytes (West and Bonner 1980). Subsequently, it was described in Tetrahymena thermophila, where it is called hv1 (Allis et al. 1986), and in Drosophila melanogaster, where it is called H2Av (van Daal et al. 1988). A histone variant evolutionarily conserved from yeasts to humans (Valdés-Mora et al. 2012; Lamaa et al. 2020), H2A.Z is 60% identical to H2A in eukaryotes (Tang et al. 2020) it is essential for development, and its deletion in mice leads to embryonic lethality (Faast et al. 2001). The human H2A.Z was cloned in 1990 (Hatch and Bonner 1990). Two paralogs described in humans, H2A. Z.1 (H2AFZ) and H2A.Z.2 (H2AFV), are encoded by genes on different chromosomes (Tang et al. 2020) and only differ from each other by three amino acids (Dryhurst et al. 2009; Lamaa et al. 2020). No antibodies exist yet to distinguish between these two paralogs (Sales-Gil et al. 2021). H2A.Z can be incorporated into nucleosomes by an ATP-dependent chromatin remodeling mechanism, in which H2A–H2B dimers are exchanged for H2A.Z–H2B dimers (Gévry et al. 2009). A study that used UVA to induce DNA double-strand breaks reported that H2A.Z.2, but not H2A.Z.1, is recruited early to the site of damage and is required for the early

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damage response and chromatin reorganization (Nishibuchi et al. 2014). The SMYD3 histone methyltransferase, which is overexpressed in many cancers (Hamamoto et al. 2004; Peserico et al. 2015), was shown to monomethylate and dimethylate H2A.Z.1 on lysine 101. Methylated SMYD3 showed increased stability and binding to histone H3, accelerated the G1 to S cell cycle transition, and increased the proliferation of breast cancer cells (Tsai et al. 2016). The first study that analyzed in detail the nonredundant roles of the two human H2A.Z paralogs in the cell cycle reported that H2A.Z.1 regulates the expression of cell cycle genes and its deficiency causes cell cycle arrest in the G1 phase and cellular senescence, whereas H2A.Z.2 is critical for chromosome segregation (Sales-Gil et al. 2021). H2A. Z.1 was overexpressed in a large group of patients with hepatocellular carcinoma, and high expression levels correlated with poor prognosis. H2A.Z.1 appears to drive carcinogenesis by accelerating cell cycle progression and epithelial-to-mesenchymal transition (Yang et al. 2016). H2A.Z.2 is highly expressed in metastatic melanoma, mediates drug sensitivity and cell proliferation, and correlates with decreased patient survival (Vardabasso et al. 2015). While the two paralogs have overlapping functions and can compensate for each other at some genes, their functions at other genes are antagonistic (Lamaa et al. 2020). H2A.Z was found to be overexpressed in breast cancer (Hua et al. 2008), bladder cancer (Kim et al. 2013), lung cancer (Hsu et al. 2018), and prostate cancer (Slupianek et al. 2010). One of the challenges in understanding the functions of the two H2A.Z paralogs is that most studies did not clearly distinguish between them (Yang et al. 2016). H2A.Z is also regulated by acetylation, which can lead to gene dysregulation and epigenetic remodeling in the context of cancer (Valdés-Mora et al. 2012). In prostate cancer, H2A.Z was shown to undergo increased acetylation at the promoters of oncogenes and decreased acetylation at promoters of tumor suppressor genes (Valdés-Mora et al. 2012; Dalvai et al. 2013). This finding was subsequently extended to triple-negative breast cancer cell lines, when it was shown that H2A.Z and the Tip60 histone acetyltransferase control the formation of a loop between the promoter and enhancer sequences of the cyclin D1 oncogene CCND1, regulating its transcription (Dalvai et al. 2013). An important consideration with respect to H2A.Z is that merely studying its incorporation into nucleosomes, without understanding its posttranslational modifications, is insufficient to understand its relevance and its functions (Colino-Sanguino et al. 2016). Histone H2A.B H2A.B or H2A.Bbd (Barr body-deficient) (Peng et al. 2020), first characterized in humans (Chadwick and Willard 2001), is the most divergent among all histone H2A variants (Dai et al. 2018). Its name comes from the fact that it is excluded from the inactive X chromosome in mammalian females (GonzálezRomero et al. 2008). H2A.B is 115 amino acids long (González-Romero et al. 2008), and it is encoded by four genes in mice and three genes in humans, all located on the X chromosome (Ishibashi et al. 2009), and shares 48% homology with histone H2A (González-Romero et al. 2008). H2A.B is shorter than H2A. Its N-terminal tail

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lacks lysine residues and has a stretch of six arginine residues, indicating poor conservation of known posttranslational modifications, such as acetylation at N-terminal lysines (Bao et al. 2004; Bönisch and Hake 2012), and the C-terminal tail that is typical of H2A histones is absent (González-Romero et al. 2008). These differences are thought to change the interaction of H2A.B with other histones and/or with DNA (Bao et al. 2004). H2A.B is primarily expressed in the testis and at lower levels in the brain, and it appeared late in evolution (Soboleva et al. 2017; Quénet 2018). Nucleosomes that contain H2A.B wrap only about 118 base pairs of DNA around them and are more unstable than the ones containing H2A; these nucleosomes form more relaxed structures that facilitate gene transcription (Bao et al. 2004; Quénet 2018). The increased transcription is explained, in part, by the near elimination of the acidic patch that is present in H2A but greatly reduced in H2A.B (Luger et al. 1997; Chakravarthy et al. 2004; Zhou et al. 2007; Ishibashi et al. 2009). Because of the near absence of this acidic patch, the putative H2A.B mouse orthologs are called H2A. Lap1–4 (lack of acidic patch) (Sansoni et al. 2014). H2A.B localizes transiently to sites of DNA synthesis during replication and repair (Sansoni et al. 2014), and it is associated with actively transcribed euchromatin (González-Romero et al. 2008). H2A.B also regulates the efficiency of mRNA splicing (Tolstorukov et al. 2012; Soboleva et al. 2017) and is important for mammalian development (Molaro et al. 2020). The ability of H2A.B to destabilize nucleosomes opened the possibility that it may be implicated in cancer (Chew et al. 2021). The expression of H2A.B RNA was detected in Hodgkin’s lymphoma cell lines and cancer cells (Sansoni et al. 2014; Jiang et al. 2021). An analysis of the transcriptomic data from the Cancer Genome Atlas revealed that short histone H2A variants, such as H2A.B, are aberrantly expressed in various malignancies across multiple cancer types, including diffuse large B-cell lymphomas, endometrial carcinomas, urothelial bladder carcinomas, and cervical squamous cell carcinomas (Chew et al. 2021). macroH2A The macroH2A (mH2A) histone is structurally the most distinct among all histone variants (Vardabasso et al. 2014) and the most divergent from its canonical counterpart H2A (Corujo and Buschbeck 2018). Its C-terminus has a 25–30-kDa globular “macro domain,” a unique feature (Corujo and Buschbeck 2018) that makes it approximately 3 times larger than the canonical H2A (Muthurajan et al. 2011). Three isoforms, macroH2A1.1, macroH2A1.2, and macroH2A2, were described in mammals (Kozlowski et al. 2018). MacroH2A1.1 and macroH2A1.2 are generated by the mutually exclusive splicing of an exon from MACROH2A1, previously known as H2AFY, and macroH2A2 is encoded by the MACROH2A2 gene, previously known as H2AFY2 (Hsu et al. 2021). While mH2A was initially linked to the epigenetic silencing of the inactive X chromosome in female mammals (Costanzi and Pehrson 1998; Chadwick et al. 2001), it was subsequently found to contribute to gene expression silencing on additional chromosomes (Muthurajan et al. 2011).

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It was reported that mH2A acts as a tumor suppressor (Corujo and Buschbeck 2018). Depletion of mH2A was documented in several cancers, including breast cancer, prostate cancer, colon cancer, and teratoma (Hsu et al. 2021). In lung cancer, low levels of macroH2A1.1 in the tumor samples were significantly related to more likely recurrences, and a similar but weaker correlation was noted for macroH2A2, both isoforms thus emerging as a potential tool for risk stratification (Sporn et al. 2009). Malignant melanoma cells showed a global decrease of the mRNA and protein levels of the mH2A1 and mH2A2 isoforms, and the overexpression of mH2A1.2 and mH2A2 suppressed lung metastases (Kapoor et al. 2010). In mice, macroH2A1 is important for silencing endogenous murine leukemia viruses (Changolkar et al. 2008). Histone H3.3 In contrast to the canonical histone H3 variants, which are incorporated into nucleosomes in a replication-dependent manner during the S phase of the interphase during the cell cycle, the H3.3 histone variant is replication-independent (Ahmad and Henikoff 2002) and its expression occurs throughout the cell cycle (Shi et al. 2017a, b). In humans, H3.3 differs from the canonical histone H3.1 by only five amino acids (Trovato et al. 2020). Of these, S31 is located in the N-terminal tail, and the other four, A87, I89, G90, and S96, are located in the histone fold of H3.3 (Trovato et al. 2020). Despite these few differences, H3.3 binds different chaperones than canonical histone H3 variants (Szenker et al. 2011). In the human genome, the two H3.3 genes, H3F3A and H3F3B, are located outside the histone gene clusters (Shi et al. 2017a, b). During metaphase, phosphorylation of the serine residue at position 31 of H3.3, which in H3.1 and H3.2 is replaced with an alanine, is important for its localization to positions adjacent to centromeres (Hake et al. 2005). The four amino acids in the core domain are important for the recognition of this histone variant by chaperone proteins (Trovato et al. 2020). H3.3 does not significantly affect the stability of the individual nucleosomes but promotes the opening of high-order chromatin structures and facilitates gene transcription (Chen et al. 2013). In mouse embryonic stem cells, H3.3 is required for the deposition of trimethylated H3K27 (H3K27me3) marks at the promoters of developmentally regulated genes, and it is important for the establishment of bivalent promoters (Banaszynski et al. 2013), which are promoters that concomitantly harbor both activating and repressing epigenetic marks and are poised for activation during differentiation (Voigt et al. 2013). Drosophila mutants that are deficient in H3.3 have widespread transcriptional changes in the form of upregulated and downregulated genes (Sakai et al. 2009). H3.3 is also important for replication fork progression after UV DNA damage (Frey et al. 2014), and it is essential for genome integrity (Jang et al. 2015) and has multiple roles in development (Kallappagoudar et al. 2015). Depletion of H3.3 in mice leads to early embryonic lethality (Jang et al. 2015). HIRA, an H3.3 chaperone and part of the HIRA protein complex, is important for chromatin assembly in the male pronucleus and for oocyte reprogramming (Wen

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et al. 2014a, b) and could serve as an epigenetic mark to distinguish the paternal and maternal genomes in the zygote (Loppin et al. 2005). The Hira/H3.3-dependent transcription of ribosomal RNA is critical for first zygotic cleavage in mice (Lin et al. 2014) and for maintaining the chromatin landscape in embryonic stem cells (Banaszynski et al. 2013). Cathepsin L mediates the proteolytic cleavage of H3.3 to generate H3.3cs1, a product that lacks the N-terminal 21 amino acids. Cleavage at an additional site, likely between the N-terminal amino acids 9 and 14, generates H3cs2. The two cleavage products are generated during oncogene-induced and replicative senescence (Duarte et al. 2014; Blum 2015). Of these, H3cs2 appears to be specific for cellular senescence (Duarte et al. 2014). H3.3cs1 is also expressed in mouse embryonic stem cells, where it changes epigenetic signatures during differentiation (Duncan et al. 2008). The first H3.3 reader that was recently identified, BS69 (ZMYND11) selectively recognizes H3.3 trimethylated lysine 36 (H3.3K36me3) and associates with regulators of splicing (Guo et al. 2014a, b). ZMYND11 regulates RNA polymerase II elongation and is a tumor suppressor protein (Wang et al. 2014a, b). In patients with lung and prostate cancer, the highest ZMYND11 levels predicted shorter recurrence-free survival (Plotnik and Hollenhorst 2017). Several studies implicated H3.3 in tumorgenesis (Kallappagoudar et al. 2015). A study on pediatric glioblastoma multiforme reported that 31% of the tumors had mutations in the histone H3.3 N-terminal tail in amino acids involved in posttranslational modifications (Schwartzentruber et al. 2012). Another study that examined diffuse intrinsic pontine glioma found that 78% of the tumors had mutations in H3.3 or H3.1, with most mutations being in H3.3 (Wu et al. 2012). In diffuse intrinsic pontine gliomas, the K27M mutation in H3.3 defines a clinically distinct subgroup of cancer, which is associated with short survival as compared to the wild-type histone variant, underscoring the value of analyzing this mutation in clinical decisionmaking (Khuong-Quang et al. 2012). Another study pointed out that this mutation can be a marker for high-grade pediatric astrocytomas (Gielen et al. 2013). Histone H4G The newly discovered H4 variant H4G, the only H4 variant identified in the human genome (Pang et al. 2020), arises from the deletion of 15 nucleotides encoding for 5 amino acids in the C-terminus of H4 (Long et al. 2019). The remaining H4G sequence shares ~85% identity with H4 at the amino acid level (Long et al. 2019). H4G was found to be overexpressed in T-cell prolymphocytic leukemia (Dürig et al. 2007), a thyroid carcinoma cell line (Baldan et al. 2016), and a breast cancer cell line (Long et al. 2019), and downregulated in an endometrioid carcinoma cell line (Jutras et al. 2010). In breast tissue from breast cancer patients, its overexpression correlated with the progression stage (Long et al. 2019). H4G localizes to the nucleoli by interacting with nucleophosmin (NPM1), relaxes chromatin, and positively regulates rDNA expression (Long et al. 2019; Pang et al. 2020). The oncogenic features of H4G seem to be related to its ability to stimulate the synthesis of ribosomal RNA and enhance protein synthesis (Ferrand et al. 2020).

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RNA Interference

Only 1.5% of the human genome encodes proteins, and much of the remaining 98.5%, once referred to as “junk DNA,” was later found to be transcribed into RNA molecules that are collectively called “noncoding RNA” (Feng et al. 2014; Kellis et al. 2014; Palazzo and Gregory 2014). Up to 85–90% of the human genome appears to be transcribed (Pertea 2012; Hangauer et al. 2013; Agostini et al. 2021), and many of the noncoding RNA molecules (Wei et al. 2017) were shown to epigenetically modulate gene expression (Holoch and Moazed 2015; Wei et al. 2017). Based on their size, noncoding RNA molecules were divided into small noncoding RNA (sncRNA) molecules, such as siRNAs, miRNAs, and piRNAs, which usually are 200 nucleotides long (Wei et al. 2017; Diamantopoulos et al. 2018; Zhang et al. 2019a, b). Over 100 different classes of noncoding RNA molecules have been characterized to date, and, among these, microRNAs, the most studied class of sncRNAs, have attracted considerable interest (Ni and Leng 2015; Catalanotto et al. 2016). MicroRNAs are single-stranded 19–24 nucleotides long regulatory RNA molecules that target mRNA molecules (Ni and Leng 2015; Wei et al. 2017). They can bind to the 5′- or 3′-untranslated regions of their target mRNA molecules or to their coding sequence (Esquela-Kerscher and Slack 2006; O’Brien et al. 2018), and their ability to inhibit mRNA expression occurs by two major mechanisms, inhibition of mRNA translation or mRNA degradation (Li et al. 2013a, b). MicroRNAs bind mRNAs based on imperfect base pairing, and, because of that, one microRNA can target and bind tens to hundreds of mRNAs (Davis-Dusenbery and Hata 2010; Cloonan 2015; Ni and Leng 2015). The first microRNA, encoded by lin-4, was discovered in Caenorhabditis elegans in 1993 and shown to control the timing of larval development (Reinhart et al. 2000). Since then, many microRNAs have been described and linked to gene regulation in plants and animals (Bartel 2004), and some of them were implicated in the biology of various diseases, including cancer, where they were linked to virtually all facets of cancer biology (Lee and Dutta 2009; Ardekani and Naeini 2010; Rutnam and Yang 2012; Sarver et al. 2015). About 30–50% of the microRNAs identified in the human genome reside in cancer-associated genomic regions (Calin et al. 2004; Laganà et al. 2010). In 2005, the first study that linked microRNA expression changes with cancer reported that the 30-kb region on human chromosome 13 that contains miR15a and miR16–1 is deleted in over half of B cell chronic lymphocytic leukemias (Calin et al. 2002). Both microRNAs negatively regulate the Bcl2 oncogene at the posttranscriptional level (Cimmino et al. 2005). Around the same time, another study reported that microRNA expression profiles were more helpful than mRNAs in classifying poorly differentiated cancers (Lu et al. 2005). Some microRNAs are dysregulated in cancer, and they became known as oncomiRs (Esquela-Kerscher and Slack 2006). The first oncomiR to be validated is the miR-17-92 microRNA cluster that, when overexpressed in vivo in mouse lymphocytes, led to the

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development of lymphoproliferative disease, an autoimmune phenotype premature animal death. These effects appear to have been caused by the inhibition in the expression of PTEN, a tumor suppressor protein that is often mutated in human lymphomas, and BIM, a proapoptotic protein that is essential for the removal of selfreactive B and T lymphocytes and was linked, when deficient, to lymphoma formation (Xiao et al. 2008; Mogilyansky and Rigoutsos 2013). Mammalian let-7 is a well-studied yet poorly understood tumor suppressor microRNA (Chirshev et al. 2019). The family of let-7 microRNA was first identified in C. elegans and is highly conserved from worms to humans (Ma et al. 2021). In humans, the family contains ten members, let-7-a to let-7-i, derived from 13 genomic loci (Gilles and Slack 2018; Ma et al. 2021). They exert their effects through multiple mechanisms, including the repression of oncogenes such as myc, ras, and H-19, the suppression of the epithelial-to-mesenchymal transition (Chirshev et al. 2019), and inhibition of cancer stem cell characteristics (Ma et al. 2021), and their decreased expression correlates with poor prognosis (Balzeau et al. 2017). The let-7 microRNA inhibits invasion and metastasis in several cancers and is often downregulated in human cancers (Wang et al. 2015a, b). In a xenograft model, Let-7c sensitized breast cancer stem-like cells to apoptosis and the inhibition of selfrenewal that were induced by tamoxifen, illustrating its therapeutic promise (Sun et al. 2018a, b). In a mouse model, the loss of let-7 increased the non-small-cell lung cancer tumor burden, and the intratumoral administration of the microRNA reduced tumor size, providing proof of concept for the possibility of using let-7 as a therapeutic modality in lung cancer (Trang et al. 2010). Another study found decreased miR-26a expression in hepatocellular carcinoma cells, and its therapeutic delivery in a mouse model of liver cancer reduced the proliferation of cancer cells by inducing cell cycle arrest and tumor-specific apoptosis (Kota et al. 2009). Some microRNAs can be oncogenes or tumor suppressor genes depending on the cellular context (Gebeshuber et al. 2009). For example, miR-29a can function as a tumor suppressor gene in lung cancer (Liu et al. 2018a, b), hepatocellular carcinoma (Yang et al. 2021), and cervical cancer (Nan et al. 2019), but appears to be upregulated in breast cancer (Choghaei et al. 2016) and pancreatic cancer (Sun et al. 2015), thus acting as an oncogene. A transformative moment in the field was the introduction of the concept of oncogene addiction (Medina et al. 2010), which refers to the fact that a malignant tumor can become “addicted” to an oncomiR, illustrating its dependence on one or a few genes. This concept was illustrated when, in a mouse model, the overexpression of miR-21, known to target several tumor suppressor genes (Davis-Dusenbery and Hata 2010), led to a pre-B malignant lymphoid-like phenotype. This miR-21 overexpression was required for all stages of cancer development, including initiation, maintenance, and invasion, and subsequent suppression of the microRNA causes the regression of the tumor within several days (Medina et al. 2010). Two approaches were described for using microRNAs for cancer treatment. One of them involves the replacement of a microRNA that was downregulated as part of disease (Hosseinahli et al. 2018), and the other one involves the use of antagomiRs (anti-miRs) to downregulate a microRNA (Krützfeldt et al. 2005).

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RNA Methylation

An analogous process to the reversible epigenetic modification of DNA and histone proteins is the reversible methylation of RNA molecules, which have collectively become known as epitranscriptomic modifications (Kumar and Mohapatra 2021; Chokkalla et al. 2022). Adenosine, uridine, cytidine, guanosine, and ribose were found to undergo epitranscriptomic modifications, such as the addition of methyl, alkyl, or glycosyl groups. As compared to the 51 known epigenetic modifications, 172 types of epitranscriptomic modifications were described as of early to mid 2021 (Chokkalla et al. 2022). All types of RNA species identified to date have epitranscriptomic modifications (Morena et al. 2018; Leonardi et al. 2019), and the highest prevalence was described on tRNA molecules (Chokkalla et al. 2022). N6-methyladenosine (m6A) (Roundtree et al. 2017a, b), first reported in the 1970s in mammalian, plant, and viral mRNAs (Desrosiers et al. 1974; Wei et al. 1975; Krug et al. 1976; Nichols 1979), is the most prevalent mRNA modification (He and He 2021) and just one of several types of methyl modifications in RNA (Mongan et al. 2019). The N6-methyladenosine (m6A) modification of mammalian RNA was first described in the mid-1970s in cancer cells (Desrosiers et al. 1974; Wei et al. 1975, 1976; Zhu et al. 2020). The presence of m6A has several effects on mRNA stability (Wang et al. 2020a, b) and expression (He and He 2021) and impacts splicing (Xiao et al. 2016), translation (Meyer et al. 2015) and nuclear export (Roundtree et al. 2017a, b). This modification was also described on noncoding RNA species (He et al. 2020a, b; Lin et al. 2021). Several studies indicate that m6A has relevance for several diseases, including cancer (Wang et al. 2020a, b). For example, a study revealed that about 70% of endometrial tumors show reductions in m6A methylation (Liu et al. 2018a, b), and the m6A pattern was modified in circular RNAs in glioblastoma (Zhang et al. 2021a, b), cervical cancer (Hu et al. 2022), prostate cancer (Zou et al. 2022), and other malignancies (Wang et al. 2020a, b).

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The Epigenetic Switch

In 1863, Rudolf Virchow found that white blood cells are present in cancer tissues and hypothesized that cancers emerge from sites of chronic inflammation (Virchow 1863; Coussens and Werb 2002). In recent years, increasing evidence revealed that chronic inflammation, such as the one caused by certain viral, bacterial, or parasitic infections, tobacco smoke, and obesity, is involved in the development of cancer (Grivennikov et al. 2010; Takahashi et al. 2010; Deng et al. 2016; Munn 2017; Wu et al. 2020). Persistent infections or chronic inflammation were implicated in 20–25% of cancers (Hussain and Harris 2007; Francescone et al. 2014; Munn 2017; Murata 2018), and chronic inflammation was linked to several steps of carcinogenesis (Multhoff et al. 2011; Singh et al. 2019). Moreover, chronic inflammation was implicated in the etiology of several other diseases, including

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cardiovascular disease, chronic kidney disease, diabetes mellitus, and neurodegenerative diseases (Furman et al. 2019). In 2009, an experimental model of oncogenesis showed that the transient in vitro activation of the Src oncoprotein in an immortalized mammary epithelial cell line caused an epigenetic switch that converted them into stably transformed cells, which formed self-renewing multicellular structures called mammospheres that were enriched in cancer stem cells. This occurred through the activation of an NF-κBdependent inflammatory response, which activated the transcription of Lin28B, inhibiting the levels of the let-7 microRNA. The let-7 microRNA normally represses the cytokine IL-6, and its downregulation leads to increased IL-6 levels and the activation of STAT3, an oncogenic transcription factor. In a positive feedback loop, the high levels of IL-6 activated NF-κB. In this case, the inflammatory signal activated an epigenetic switch that led to the transformed phenotype, and the transformed cells propagated for several generations even in the absence of the initiating signal. This regulatory circuit was described in cancer cell lines and in patient cancer samples, strengthening its relevance for certain human cancers (Iliopoulos et al. 2009). A subsequent study identified 29 microRNAs that are differentially regulated during this transformation process, of which 22 were upregulated and 7 were downregulated. STAT3-induced activation of miR-21 and miR-181b-1 emerged as an important requirement of the transformation process (Iliopoulos et al. 2010). As part of the epigenetic switch, it is important to note the central role of inflammatory signaling in malignant transformation. The link between certain serotypes of Helicobacter pylori and gastric cancer has been recognized for several years (Uemura et al. 2001), and the link was extensively studied in a Mongolian gerbil experimental system. Long-term infection of Mongolian gerbils with H. pylori led to gastric adenocarcinoma (Honda et al. 1998; Watanabe et al. 1998), and eradication of the bacteria could lower the risk of mucosal changes, including cancer (Hirayama et al. 2002). The bacterial infection was shown to cause aberrant DNA methylation in gastric epithelial cells (Niwa et al. 2010a, b), and treatment with a DNA demethylating agent decreased DNA methylation and prevented H. pyloricaused gastric cancers (Niwa et al. 2013). Treatment of infected animals with cyclosporin A, which decreased inflammation but not bacterial colonization, blocked these methylation changes, supporting the role of inflammation in carcinogenesis (Niwa et al. 2010a, b). Importantly, studies in human clinical samples revealed that individuals infected with more virulent H. pylori strains had higher DNA methylation levels, and chronic inflammation and more advanced precancerous lesions were associated with increased methylation in several genes that were examined (Schneider et al. 2013). Chronic inflammation is a central feature of obesity and provides a mechanism to explain the connection between obesity and cancer, which has been previously known from epidemiological studies (Calle et al. 2003; Deng et al. 2016; Berger 2018a, b). Obesity is currently estimated to account for about 20% of cancer cases

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(Wolin et al. 2010; De Pergola and Silvestris 2013). It is noteworthy that in an analysis of data on invasive cancers among >14,670,000 individuals diagnosed between 1995 and 2014, obtained from >25 population-based state registries in the United States, obesity-related cancers were seen to occur in successively younger birth cohorts (Sung et al. 2019). Inflammation provides a very important and potentially reversible factor that connects obesity to cancer, a very important medical and public health consideration, considering that cancer is becoming more frequent in young adults (Berger 2018a, b).

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Epigenetics Versus Genetics in Cancer

Historically, cancer was viewed as a disease that results from genetic mutations, but, more recently, the contribution of epigenetic changes has increasingly been appreciated (Sharma et al. 2010; You and Jones 2012; Choi and Lee 2013). Epigenetic changes influence genetic changes such as mutations and, at the same time, mutations can lead to epigenetic modifications, a bidirectional interplay that is critical in shaping processes involved in development, homeostasis, and disease (Zaina et al. 2010; Choi and Lee 2013; Capp 2021). For example, hypermethylation of the MGMT promoter can lead to genetic mutations, such as G to A mutations in KRAS oncogene (Esteller et al. 2000; Shen et al. 2005). MGMT encodes O6-methylguanine DNA methyltransferase, a protein that repairs DNA alkyl adducts (Lu et al. 2015). As a “suicide” repair enzyme, MGMT transfers the methyl group at the O6 site of guanine to its cysteine residues (Yu et al. 2019). On the other hand, mutations in epigenetic modifiers may cause epigenetic changes. For example, the mutational inactivation of DNMT1, leading to genome-wide DNA methylation changes, was described in colorectal cancer (Kanai et al. 2003). Loss of function and gain of function mutations in EZH2, a lysine methyltransferase that is a subunit of the Polycomb Repressive Complex 2 (PRC2) (Yan et al. 2017; Stasik et al. 2020) and catalyzes the trimethylation of H3K27, were linked to various cancer types (Stasik et al. 2020), and the presence of some of these mutations was linked to poor prognosis (Zhang et al. 2019a, b). Somatic mutations in DROSHA were linked to changes in the microRNA profile in the context of Wilms tumor (Torrezan et al. 2014). One mutation, E1147K, was associated with 64 differentially regulated microRNAs, most of which were downregulated, but a few were upregulated (Torrezan et al. 2014). The contribution of epigenetic changes to disease is also illustrated by the fact that 10–15 years after giving up smoking, the risk of ex-smokers developing cancers of the upper digestive tract, lung, pancreas, and urinary tract was similar to that of nonsmokers, indicating that certain components of the cigarette smoke may cause modifications that are relevant to cancer, but are reversible and nongenotoxic (Wynder and Hoffmann 1976; Trosko and Upham 2005).

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Epigenetic Biomarkers

The definition of the term “biomarker” has changed over the years, and even today, no standardized definition exists (Mayeux 2004; Strimbu and Tavel 2010; Gromova et al. 2020). The World Health Organization defines a biomarker as any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease (Group 2001; Goossens et al. 2015; Mouhieddine et al. 2015; Spineti 2019). Biomarkers can be used to diagnose medical conditions, monitor disease progression of treatment, and make predictive, prognostic, or safety assessments (Mayeux 2004; Califf 2018). One of the oldest biomarkers used for diagnostic purposes was the arterial pulse (Gromova et al. 2020). Several types of epigenetic biomarkers were described, with applications for screening, diagnosis, monitoring, and prognosis and as predictors of response to treatment (Mulero-Navarro and Esteller 2008; Costa-Pinheiro et al. 2015; Kamińska et al. 2019). Cancer biomarkers can be diagnostic when they identify whether a patient has a malignancy; predictive when they predict the response to an intervention; prognostic when they provide information about disease progression or outcome; and therapeutic when they can be targeted as part of therapy (Goossens et al. 2015; Carlomagno et al. 2017). Some biomarkers may fall into more than one of these categories (Ballman 2015). Biomarkers can be general for cancer or specific to certain cancer types (Verma 2012). Epigenetic biomarkers are complementary to genetic biomarkers but also offer several unique advantages (García-Giménez et al. 2017). Some epigenetic changes occur early during cancer development (Verma 2012; Feinberg 2014; Locke et al. 2019); can provide information from individual cells or cell types (García-Giménez et al. 2017); can be monitored noninvasively from body fluids (Hoque et al. 2004; Verma 2012; Li et al. 2014); are informative about the impact of the environment, including lifestyle (García-Giménez et al. 2017); can capture the natural history and progression of a disease (García-Giménez et al. 2017); and some of them can help make prognostic predictions (Mulero-Navarro and Esteller 2008). Additionally, many epigenetic biomarkers are stable (García-Giménez et al. 2017). Some of the potential challenges and shortcomings are that many epigenetic biomarkers are nonquantitative or semiquantitative (Lorincz 2011), and the diversity of assays and the scarcity of data on their inter-laboratory reproducibility sometimes makes the interpretation of results difficult (Lorincz 2011). The increasing DNA methylation of the vimentin promoter, and silencing of the gene, were reported in cervical tissue as cells were progressing from normal to malignant, indicating the value of this epigenetic biomarker for diagnosing cervical cancer (Jung et al. 2011). Similarly, patients with hepatocellular carcinoma showed hypermethylation of the vimentin promoter in plasma circulating cell-free DNA, and the mean hypermethylation increased with the tumor stage, revealing the value of this plasma-based epigenetic biomarker for hepatocellular carcinoma (Holmila et al. 2017). Vimentin promoter hypermethylation is also correlated with poor survival in breast cancer patients (Ulirsch et al. 2013).

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Several tests that noninvasively examine epigenetic changes in specific genes were developed, and some of them have become commercially available. The qualitative detection of methylated septin 9 from cell-free DNA extracted from the plasma (Taryma-Leśniak et al. 2020) by real-time PCR is the basis of Epi proColon, a blood-based screening test (Shirley 2020) that has been marketed in Europe since 2009 (Mullard 2009; Taryma-Leśniak et al. 2020), and subsequently approved by the Chinese Food and Drug Administration in 2015 (Tepus and Yau 2020) and by the US FDA in 2006 (Anghel et al. 2021). Another product, the Epi proLung BL Reflex Assay®, was initially developed to quantitate methylation of the SHOX2 (short stature homeobox gene two) gene from bronchial lavage specimens, and later the blood-based version of the test performed a combined analysis of SHOX2 and PTGER4 (Prostaglandin E Receptor 4) methylation as a biomarker for lung cancer in patients with increased risk of the disease (Beltrán-García et al. 2019; TarymaLeśniak et al. 2020). The test received CE-IVD certification in Europe (Ilse et al. 2014; Beltrán-García et al. 2019), and it is the first test to allow the early diagnosis of lung cancer using cell-free DNA from the blood (Ilse et al. 2014). EPICUP™, an epigenetic diagnostic assay that profiled 485,577 CpG sites, and the first epigenetic test for cancers of unknown origin, showed that based on DNA methylation profiles it can identify the primary tumor origin in up to 87% of the formalin-fixed, paraffin-embedded, or frozen tissue samples that were examined (Moran et al. 2016). Cancers of unknown origin, a heterogeneous group of malignancies, represent 3–5% of all human cancers diagnosed worldwide, and patients often present with histologically confirmed metastatic dissemination (Lee and Sanoff 2020) without a known primary site (Pavlidis and Fizazi 2005) even after extensive clinical investigations (Qaseem et al. 2019). Patients are usually treated empirically, but the clinical outcome is generally poor (Kato et al. 2021). The development of EPICUP™ is positioned to provide better therapeutic options for cancers of unknown origin and possibly improve patient quality of life and survival.

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Some therapeutics that have been used for decades were later shown to also have epigenetic effects. For example, valproic acid, an eight-carbon branched-chain fatty acid first synthesized in 1882 (Chateauvieux et al. 2010), and approved by the FDA in 1978 to treat absence seizures (Browne 1980), is one of the most commonly used anti-epileptic drugs (Lunke et al. 2021) and is also used to alleviate migraines and as a mood stabilizer (Milutinovic et al. 2007). In 2001, it was shown that valproic acid inhibits class I HDAC enzymes and increases histone acetylation (Göttlicher et al. 2001; Phiel et al. 2001) and causes the CpG demethylation of specific genes (Milutinovic et al. 2007; Dong et al. 2010; Veronezi et al. 2017). One of the promises of epigenetic therapies comes from the fact that unlike in the case of genetic mutations, the reversible nature of epigenetic changes allows gene expression patterns to be reverted to their physiological states (Ahuja et al. 2016). Several epigenetic therapies have been approved as of mid-2022, and they fall into

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several classes, including DNMT inhibitors, HDAC inhibitors, EZH2 inhibitors, and siRNA-based therapies (Nepali and Liou 2021; Zhang et al. 2021a, b). DNA Methyltransferase Inhibitors The first epigenetics-based therapies for cancer were based on the inhibition of DNA methyltransferases (Ning et al. 2016). Azanucleosides, considered the first epigenetic drugs (Diesch et al. 2016), are analogs of cytidine that were developed decades ago (Pískala 1964; Pliml 1964) and showed cytostatic effects at high doses (Leone et al. 2008), but more recently attracted interest for their ability to demethylate DNA at lower doses (Jones and Taylor 1980; Diesch et al. 2016). The two azanucleosides currently approved in the United States include 5-azacytidine (azacitidine) and 5-aza-2′-deoxycytidine (decitabine) (Diesch et al. 2016). Both of them are prodrugs (Stresemann and Lyko 2008). Azacitidine, an analog of cytidine, was approved by the US Food and Drug Administration (FDA) in May 2004 for the treatment of all subtypes of myelodysplastic syndrome (Silverman et al. 1990; Silverman 1994; Silverman et al. 2002; Issa and Kantarjian 2005; Kaminskas et al. 2005). Decitabine was approved in May 2006 for the treatment of myelodysplastic syndromes and chronic myelomonocytic leukemia (Wijermans et al. 2000; Gore et al. 2006; Kantarjian et al. 2006; Jabbour et al. 2008; Steensma 2009; Garcia et al. 2010). These two therapeutics were also approved by the European Medicines Agency (EMA) for myelodysplastic syndromes, acute myeloid leukemia, and chronic myelomonocytic leukemia (Majchrzak-Celińska et al. 2021). The chemical instability, poor pharmacokinetic properties (Pappalardi et al. 2021), and lack of specificity, which can make them act on non-target genes (Majchrzak-Celińska and Baer-Dubowska 2017), are some of the shortcomings of these two therapeutics. Azacitidine has two main mechanisms of anticancer action: cytotoxicity as a result of its incorporation into DNA and RNA and DNA hypomethylation as a result of DNA methyltransferase inhibition (Kaminskas et al. 2005; Khan et al. 2012). Decitabine has a dual mechanism of action that is dose-dependent: at low doses, it inhibits DNA methylation and reactivates genes that were inappropriately silenced, and at high doses, it covalently binds DNA methyltransferase and forms DNA adducts, exhibiting cytotoxic activity (de Vos and van Overveld 2005; Saba 2007; Jabbour et al. 2008). Both drugs are thought to reactivate tumor suppressor genes (Malik and Cashen 2014). A second-generation DNA hypomethylating agent, guadecitabine (Jueliger et al. 2016; Garcia-Manero et al. 2019), not yet approved for clinical use, is a dinucleotide made of decitabine and deoxyguanosine linked by an enzymatically digestible phosphodiester bond (Issa et al. 2015; Schiffer 2018). Guadecitabine is more resistant to degradation by cytidine deaminase than decitabine (Issa et al. 2015; Garcia-Manero et al. 2019) and was developed to provide better safety and clinical efficacy than azacytidine and decitabine (Daher-Reyes et al. 2019). HDAC Inhibitors The 18 human HDAC enzymes characterized to date were classified into four classes, I, II (further subdivided into IIa and IIb), III, and IV,

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primarily based on their structure, function, cellular localization, and homology to yeast histone deacetylases (Richon 2010; McGraw 2013; Parbin et al. 2014; Park and Kim 2020). The aberrant expression of HDACs from classes I, II, and IV was linked to various malignancies (Li and Seto 2016). Vorinostat (SAHA or suberoylanilide hydroxamic acid) inhibits a subset of class I and II HDAC enzymes at low nanomolar concentrations (Richon 2010; Bubna 2015) and induces the accumulation of acetylated histone and non-histone proteins, such as proteins that regulate apoptosis, cell motility, and angiogenesis (Lee and Stephanie Huang 2013). Vorinostat exerts its anticancer activities through several mechanisms. It downregulates interleukin-10 (IL-10), an immunosuppressive cytokine (Tiffon et al. 2011) and upregulates the cyclin-dependent kinase inhibitor p21, causing cell cycle arrest and inhibiting cancer cell proliferation (Richon et al. 2000; Uehara et al. 2012; Silva et al. 2013). Additionally, it induces cell death pathways (Ruefli et al. 2001) and decreases the expression of the hypoxia-inducible factor-1 (HIF-1α) (Hutt et al. 2014), which suppresses angiogenesis (Powis and Kirkpatrick 2004). A study on several human cancer cell lines showed that vorinostat increased their susceptibility to radiation-induced apoptosis, in part by suppressing the expression of proteins involved in repairing double-stranded DNA breaks, and by prolonging the expression of the γ-H2AX foci, decreasing the ability of cancer cells to repair their DNA (Munshi et al. 2006). In a rat model of N-methylnitrosourea-induced mammary cancer, orally administered vorinostat reduced tumor incidence and the mean tumor volume without causing adverse effects (Cohen et al. 1999), and in a mouse xenograft model, it suppressed the growth of prostate cancer cells and led to a 97% decrease of the mean tumor volume compared to controls (Butler et al. 2000). A study on cutaneous T-cell lymphoma cells showed that vorinostat induced apoptosis and led to the accumulation of acetylated H2B, H3, and H4 histones (Zhang et al. 2005). In October 2006, vorinostat was the first HDAC inhibitor approved by the FDA for cancer therapy (Marks and Xu 2009) in patients with cutaneous T-cell lymphoma with progressive, persistent, or recurrent disease on or after following two systemic therapies (Duvic et al. 2007; Mann et al. 2007; Olsen et al. 2007). Romidepsin is a histone deacetylase inhibitor discovered in cultures of Chromobacterium violaceum (Smolewski and Robak 2017), a Gram-negative bacterium isolated from the soil in Yamagata Prefecture, Japan (Ueda et al. 1994). After cellular uptake, its disulfide bond is reduced by glutathione in the cytoplasm, and its zinc-binding thiol group reacts with the histone deacetylases, inhibiting their activity (VanderMolen et al. 2011; Smolewski and Robak 2017). Romidepsin was approved by the FDA in November 2009 for the treatment of cutaneous T-cell lymphoma, a heterogeneous group of non-Hodgkin’s lymphomas in which malignant mature CD4+ T lymphocytes infiltrate the skin (McGraw 2013; Bagherani and Smoller 2016; Pulitzer 2017; Brunner et al. 2020), in patients who received at least one systemic therapy previously (VanderMolen et al. 2011; Shimony et al. 2019). In June 2011, romidepsin was also approved for the treatment of peripheral T-cell lymphoma in patients who have progressed after at least one prior systemic therapeutic regimen (Barbarotta and Hurley 2015; Saleh et al. 2021).

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Belinostat, a pan-HDAC inhibitor administered intravenously, was approved by the FDA in July 2014 to treat relapsed or refractory peripheral T-cell lymphoma (Lee et al. 2015; O’Connor et al. 2015; Bondarev et al. 2021). It has a favorable safety profile compared to other HDAC inhibitors, particularly in patients with thrombocytopenia (Sawas et al. 2015). More recently, the intravitreal administration of belinostat emerged as equally effective to the current standard of care in a rabbit retinoblastoma model, but without the associated retinal toxicity (Kaczmarek et al. 2021). Panobiostat, an orally administered nonselective pan-histone HDAC approved by the FDA in February 2015, is recommended for patients with relapsed or relapsed and refractory multiple myeloma who have received at least two prior therapeutic regimens (Garnock-Jones 2015; Moore 2016). In August 2015, panobiostat was also approved by the EMA (Eleutherakis-Papaiakovou et al. 2020). In in vitro studies, panobiostat inhibits all four classes of HDACs (Eleutherakis-Papaiakovou et al. 2020). In a randomized, placebo-controlled phase 3 clinical trial, panobiostat modestly increased the median overall survival over placebo, when combined with bortezomib and dexamethasone, in participants with relapsed and refractory multiple myeloma (San-Miguel et al. 2016). Additionally, panobiostat shows synergistic effects with other therapeutics, including proteasome inhibitors, immunomodulator drugs, and monoclonal antibodies (Berdeja et al. 2021). More recently, an openlabel, randomized phase 2 study that enrolled patients with relapsed or relapsed and refractory multiple myeloma at hospitals and medical centers across 21 countries found that the safety profile of oral panobinostat administered together with subcutaneous bortezomib and oral dexamethasone was superior to previous regimens in which bortemozib was administered intravenously (Laubach et al. 2021). Tucidinostat (chidamide), an orally active selective HDAC inhibitor, suppresses the growth and invasiveness of malignant tumors and induces apoptosis (Lu et al. 2016; Shojaei et al. 2021). It was approved in 2014 by the Chinese Food and Drug Administration to treat relapsed/refractory peripheral T-cell lymphoma, and it is the first orphan drug approved in China (Shi et al. 2015; Lu et al. 2016; Chan et al. 2017). EZH2 Inhibitors Tazemetostat, the first FDA-approved oral EZH2 inhibitor, was approved by the FDA in January 2020, and it is recommended in adults and adolescents 16 years and older presenting epithelioid sarcoma that is locally advanced or metastatic or cannot be treated using complete resection. Subsequently, in June 2020, it was also approved for adult patients with relapsed or refractory follicular lymphoma harboring EZH2 mutations who received at least two previous systemic therapies and for patients who did not have any options for alternative therapies (Hoy 2020; Julia and Salles 2021; Straining and Eighmy 2022). Nausea was one of the most common adverse effects associated with tazemetostat. In addition, the drug can increase the risk of developing secondary malignancies, and animal studies indicated that it might cause fetal damage (Straining and Eighmy 2022). RNAi-Based Therapeutics Patisiran, the first siRNA-based drug (Titze-de-Almeida et al. 2020), was approved in 2018 by the FDA for the treatment of hereditary

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transthyretin-mediated (hATTR) amyloidosis (Ledford 2018), a progressive and fatal autosomal dominant disease that is characterized by the buildup of mutated and wild-type transthyretin in various organs, especially the peripheral nervous system and the heart (Adams et al. 2018; Gertz et al. 2019). Since then, several additional siRNA-based drugs have been approved. These include givosiran, approved by the FDA in November 2019 for acute hepatic porphiria (Agarwal et al. 2020; Scott 2020); lumasiran, approved by the FDA in November 2020 for primary hyperoxaluria type 1 in children and adults (Scott and Keam 2021); and inclisiran, approved by the EMA in December 2020 for adults with heterozygous familial or nonfamilial hypercholesterolemia or mixed dyslipidemia (Lamb 2021) and by the FDA in December 2021, for adults with heterozygous familial hypercholesterolemia or clinical atherosclerotic cardiovascular disease who need to lower their LDL-cholesterol levels (Al Shaer et al. 2022; Banach et al. 2022; Gangopadhyay and Gore 2022). As several of these examples show, epigenetic therapies are being explored in medical areas beyond oncology, such as metabolic diseases, viral infections, neurodegenerative diseases (Ackloo et al. 2017), inflammation (Nicodeme et al. 2010), autoimmune conditions (Ciechomska and O’Reilly 2016), cardiovascular diseases (Napoli et al. 2016; Napoli et al. 2021), and osteoporosis (Baud’huin et al. 2017). For example, in a rat model of transient cerebral ischemia, valproic acid decreased the size of the infarct and reduced neurological deficit scores, and the mechanism appeared to involve HDAC inhibition and the induction of heat shock protein 70, indicating the possibility of using HDAC inhibition as a strategy to prevent permanent brain damage after a stroke (Ren et al. 2004). In another study, HDAC inhibitors stopped the progressive neurodegeneration and reduced lethality in Drosophila models of polyglutamine disease that represent a model for Huntington’s disease (Steffan et al. 2001). Epigenetic Drugs as Part of Combination Therapies The development of resistance to chemotherapy is a major reason that renders therapeutics ineffective over time and represents a major cause of death in cancer patients (Oronsky et al. 2014; Bukowski et al. 2020; Chern and Tai 2020). Several mechanisms were described to explain the emergence of resistance to chemotherapy, and one of them involves the accumulation of epigenetic changes (Quagliano et al. 2020a, b). In addition to their use as a monotherapy (Raynal et al. 2017; Lu et al. 2020), epigenetic therapies gained substantial interest for their potential to be administered in combination with other therapeutic interventions. One of them, due to the ability of certain epigenetic compounds to resensitize cancer cells to chemotherapy, is to administer a combination of the two (Strauss and Figg 2016; Roberti et al. 2019). Resensitization to chemotherapy occurs through several mechanisms, such as disrupting pro-survival signaling pathways in cancer cells, restoring the control of cell cycle progression, enhancing the immune response, and reprogramming cellular metabolism (Quagliano et al. 2020a, b).

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A phase 2 clinical trial that enrolled patients presenting with recurrent, platinumresistant ovarian cancer found that low-dose decitabine changed DNA methylation globally and in a gene-specific manner, resensitized the cancer cells to carboplatin, and increased the response rate and the progression-free survival (Matei et al. 2012). In xenograft models of prostate cancer, azacytidine decreased DNA methylation and sensitized cells to docetaxel and cisplatin (Festuccia et al. 2009). In in vitro and in vivo studies, HDAC inhibitors resensitized non-small-cell lung cancer cells to cisplatin, an effect that occurred by acetylating and activating E2F1 and induced the expression of miR-149, which downregulated the ERCC1 (excision repair crosscomplementing 1) gene, an important component of the nucleotide excision repair pathway (He et al. 2020a, b). Low ERCC1 levels correlate with prolonged survival after cisplatin-based therapies (Lord et al. 2002), and high ERCC1 levels were associated with high resistance to cisplatin (Piljić Burazer et al. 2019). When breast cancer cell lines were exposed to clinically relevant doses of doxorubicin, several types of epigenetic changes occurred, including the hypermethylation and inactivation of the promoter of MSH2, a gene involved in DNA mismatch repair, and exposure of the doxorubicin-resistant breast cancer cells to decitabine and trichostatin A led to their resensitization (Ponnusamy et al. 2018). JQ1, a selective bromodomain inhibitor, showed synergistic effects with an anti-PD-1 antibody in lowering the lung cancer burden in mice (Adeegbe et al. 2016). Beneficial effects were also seen when combining epigenetic drugs with hormonal therapies or immunotherapies. For example, in an in vitro study, BET bromodomain inhibitors disrupted the resistance of prostate cancer cells to androgen receptor antagonists (Asangani et al. 2016), and in a phase 2 clinical trial of patients that had estrogen receptor-positive metastatic breast cancer and were progressing on endocrine therapy, combined tamoxifen and vorinostat showed promising results in reversing hormone resistance (Munster et al. 2011). In another study, even though treatment with anti-PD-1 and anti-CTLA-4 antibodies was not able to eradicate two types of immunogenic malignancies, their combination with entinostat, a selective HDAC inhibitor, and 5-AZA led to cure in most of the animals (Kim et al. 2014). Epigenetic therapies can also sensitize cancer cells to radiotherapy (Flatmark et al. 2006; Peng et al. 2021). In a study on several esophageal squamous cell carcinoma cell lines, pretreatment with valproic acid, an HDAC inhibitor, enhanced radiation-induced apoptosis through several mechanisms, including chromatin decondensation as a result of histone hyperacetylation and suppression of DNA repair (Shoji et al. 2012). In a phase 1 clinical trial of patients with recurrent high-grade gliomas, the concurrent administration of panobinostat made radiotherapy more effective than the administration of radiotherapy in isolation (Shi et al. 2016), and an in vitro study, the addition of panobiostat to proton irradiation increased the apoptotic death of hepatocellular carcinoma cells more than irradiation alone (Choi et al. 2021). Importantly, some epigenetic drugs were shown to attenuate adverse effects of radiotherapy, such as lung fibrosis or hair loss (Peng et al. 2021).

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Conclusion

The field of epigenetics started to emerge in the 1970s, expanded markedly after 2000, and rapidly became one of the most vibrant and dynamic areas in biomedical sciences. There is virtually no medical field that was not transformed by advances in epigenetics. In cancer biology, these advances revealed that in addition to genetic modifications, cancer cells harbor various local and global epigenetic changes and that the combined contributions of genetic and epigenetic factors shape the cancer phenotype. Besides providing novel insights into understanding development, cellular and tissue homeostasis, and the pathogenesis of a broad group of medical conditions, epigenetics transformed cancer biology as a field and facilitated the development of new biomarkers that are important tools in disease management. The approval of several epigenetic drugs and the possibility of using them as monotherapies or in combination with other treatments, such as chemotherapy, immunotherapy, or radiation therapy, opened attractive options for cancer treatment. These combinations promise novel strategies to prevent, overcome, or reverse therapy resistance, increase the potency of existing regimens, enhance their tolerability and safety profile, improve the quality of life, and extend patient survival.

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Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and Epigenetic Regulation Bakiye Goker Bagca

and Cigir Biray Avci

Abstract

Cancer is a substantially lethal cell disease characterized by uncontrolled cell proliferation and neoplastic progression resulting in dysfunction of the control points of the cell cycle and the programmed cell death mechanisms. In this chapter, the current place of phytochemicals as epigenetic regulators in modern molecular cancer research was reviewed. The current works, which include the possible mechanisms of phytochemicals on non-coding RNAs, DNA methylation, and histone modifications on various cancers, were reviewed. It was determined that different phytochemicals regulate gene expression by up- and down-regulation of miRNAs and lncRNAs. It was also determined that phytochemicals differentiate gene expression levels by changing acetylation and methylation patterns of histone and/or DNA molecules. Nature is a great drugstore that contains minimal side-effect phytochemicals that function with many different genetic and epigenetic mechanisms awaiting discovery. The near future outcomes from molecular biological, pharmaceutical, and pharmacokinetic studies will be the guide for maximum benefit from all these natural components to overcome cancer as epidrugs. Keywords

DNA methylation · Histone acetylation · Histone methylation · lncRNA · miRNA · Non-coding RNA · Phytochemical

B. Goker Bagca Department of Medical Biology, Faculty of Medicine, Aydin Adnan Menderes University, Aydin, Turkey C. Biray Avci (*) Department of Medical Biology, Faculty of Medicine, Ege University, Izmir, Turkey e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_44 Published online: 25 November 2022

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242

1

B. Goker Bagca and C. Biray Avci

Introduction

Cancer is a substantially lethal cell disease characterized by uncontrolled cell proliferation and neoplastic progression resulting in dysfunction of the control points of the cell cycle and the programmed cell death mechanisms. Cancer can arise from mutations in a single cell (You and Jones 2012). Although molecular cancer researches aimed at enhancing survival and improving the quality of life of patients allow new approaches to diagnosis and treatment options, about 18 million people worldwide have cancer every year, and nine million of them lose their lives. Cancer is the second cause of death after cardiovascular diseases in mortality statistics in developed countries and responsible for about 20% of all deaths (Bray et al. 2018). There are three main approaches to cancer treatment, including surgery, radiotherapy, chemotherapy, and/or hormone therapy. Although chemotherapy is the most used method in the treatment of cancer, the use of chemicals creates a number of handicaps both during the treatment process and after treatment. For example, anti-neoplastic drugs are cytotoxic to cancer cells while toxic to normal cells that multiply rapidly on the other side. Some anti-neoplastic drugs also have immunosuppressive effects. For such side effects, the therapeutic dose of chemical drugs is limited to a low threshold value (Padma 2015). Using natural components has played a major role in the fight against various diseases since ancient times. Most of the chemotherapeutic drugs used in therapy are obtained from natural sources and used directly or in semisynthetic forms. Especially, microbial products such as anthracyclines, enediynes, epothilones, statins, and rapamycin and herbal products such as vinca alkaloids, taxanes, and camptothecin are natural components used in chemotherapy (Demain and Vaishnav 2011). Hereditary and reversible regulations without any change of DNA sequence are defined as “epigenetic” which means “upon” or “above” genetic. DNA methylation, histone modifications that include acetylation and methylation, and posttranscriptional regulators defined as noncoding-RNAs including long-noncoding RNAs (lncRNAs) and microRNAs (miRNA) are elements of the epigenetic system. As important as physiological conditions, pathologies including cancer are also related to epigenetics. Therefore, epigenetic drugs (epidrugs) have become more attractive to cancer research (Kagohara et al. 2018). In this chapter, the current place of phytochemicals as epigenetic regulators in modern molecular cancer researches is reviewed.

2

Phytochemicals and MicroRNAs

MicroRNAs (miRNAs) are an endogenous, small non-coding RNA group with 20–23 nucleotides. They are post-transcriptional regulators by interacting with the 30 UTR sequence of the target gene. They are critical regulators in cancers as well as physiological conditions (Yilmaz et al. 2021). miRNAs regulated by some main phytochemicals and their target mechanisms in cancers are shown in Table 1.

Anti-proliferative Apoptosis induction

Lung

Lung Breast

Anti-proliferative Apoptosis induction Cell cycle arrest Anti-cancer Anti-stem cell

Colorectal

Caffeic acid & Caffeic acid phenethyl ester

Cell cycle arrest

Melanoma

Apigenin

miR-383-5p (up)

Anti-proliferative Apoptosis induction Anti-metastatic Enhances chemotherapy Anti-proliferative

miR-3960 (up)

miR-34a-5p (up) miR-148a (up)

miR-215-5p (up)

miR-512-3p (down)

miR-612 (up) miR-20b (up)

miR-486-3p (up)

miRNA (regulation) miR-127-3p (up)

Effect Anti-proliferative

Breast

Glioblastoma

Cancer Multiple myeloma Gastric

Anacardic acid

Phytochemical Allicin

Table 1 miRNAs regulated by some essential phytochemicals

YAP1, MYC

SNAI1 TGFΒ/SMAD2 pathway

E2F1, E2F3

CCND1, MYC, FOS, PPARG, SIN3 CDKN1A

MGMT

Affected genetic mechanism PI3K/AKT pathway ERBB4

In vitro In vitro and in vivo In vitro

In vitro and in vivo In vitro

In vitro

In vitro

In vitro

Model In vitro

Mo et al. (2020)

Aida et al. (2021) Li et al. (2015a)

(continued)

Cheng et al. (2021)

Xie et al. (2022)

Schultz et al. (2017)

Wu et al. (2020)

Lv et al. (2020)

Reference He et al. (2021a)

Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and. . . 243

Curcumin

Phytochemical Crocin

Table 1 (continued)

Multiple myeloma

Lung

Hepatocellular

Head & neck

Gastric

Bladder

Colorectal

Breast

Squamous cell carcinoma

Cancer Thyroid

Anti-proliferative Enhances chemotherapy Enhances chemotherapy Apoptosis induction Anti-proliferative Apoptosis induction Anti-proliferative Apoptosis induction Anti-metastatic Anti-proliferative Apoptosis induction Anti-proliferative

Effect Apoptosis induction Anti-proliferative Apoptosis induction Anti-proliferative Anti-stem cell

miR-101 (up)

miR-206 (up) miR-192 (up)

miR-491 (down)

miR-7 (up) miR-15a (up)

miR-33b (up)

miR-1246 (down)

miR-130a (down) miR-137 (up)

miR-21(down) miR-181a (down) miR-142-3p (up)

miR-320a (up)

miRNA (regulation) miR-34a-5p (down)

EZH2

PI3K/AKT/mTOR pathway

CDKN1A, BCL2, PI3K/AKT pathway PEG10

PTEN/AKT pathway, ATGs, PSMB5 WNT/B-catenin pathway, GLS TP53

ATG2B

Affected genetic mechanism PTPN4

In vitro

In vitro

In vitro

In vitro

In vitro

In vitro

In vitro

In vitro

In vitro

Model In vitro

Wu et al. (2018)

Jin et al. (2015), Wang et al. (2020)

Li et al. (2018)

Feng et al. (2017), Mou et al. (2017)

Sun et al. (2016)

Dou et al. (2017), Agarwal et al. (2018), Fan et al. (2022) Xu et al. (2019)

Wang et al. (2017b), Liu et al. (2020a), Park et al. (2022)

Bi et al. (2021)

Reference Tang et al. (2022)

244 B. Goker Bagca and C. Biray Avci

Bladder

Breast Lung

Hesperidin

Isothiocyanate

Lutein Oleanolic acid

Breast

Prostate

Pancreas

Lung Ovarian Multiple myeloma Head & neck Breast

Genistein

Prostate

Pancreas

Anti-proliferative Anti-proliferative

Anti-proliferative Apoptosis induction Cell cycle arrest Anti-cancer

Apoptosis induction Anti-proliferative Enhances chemotherapy Autophagy (survival) inhibition Anti-proliferative Apoptosis induction

miR-590-3p (up) miR-122 (up)

miR-99a-5p (up)

miR-1469 (up) miR-21 (down) miR-155 (down)

miR-34a (up) miR-223 (down) miR-27a miR-574 (up) miR-151 (down) miR-34a (up) miR-23b (up) miR-155 (down) miR-27a (up) miR-27a (down) miR-29b (up)

miR-143 miR-34a (up)

miR-340 (up)

IGF1R, FGFR3, MTOR CASC9 CCNG1, MEF2D

MCL1

FOXO3, PTEN, CK1, CDKN1B MET SPRY2 NFKB pathway

NOTCH1, FBW7

PGK1, FOXD3, Cell cycle related genes

XIAP

In vitro In vitro

In vitro

In vitro In vitro

In vitro In vitro In vitro

In vitro and in vivo In vitro

In vitro

In vitro

In vitro

(continued)

Zhang et al. (2021b) Zhao et al. (2015)

Lin et al. (2019)

Ma et al. (2018) Magura et al. (2021)

Avci et al. (2015), de la Parra et al. (2016) Yang et al. (2016b) Xu et al. (2013) Xie et al. (2016)

Chiyomaru et al. (2012, 2013a, b)

Xia et al. (2012, 2014), Ma et al. (2013)

Cao et al. (2017), Liu et al. (2017), Zhu et al. (2019)

Yang et al. (2017)

Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and. . . 245

Colorectal

Piceatannol

Breast

Ursolic acid

Breast

Gastric Lung

Breast Breast Pancreas Lung

Quercetin Resveratrol Sulforaphane

Melanoma

Cancer Breast

Phytochemical Oleuropein

Table 1 (continued)

Apoptosis induction Apoptosis induction Anti-metastatic Anti-proliferative Anti-cancer Apoptosis induction Anti-cancer stem cell Anti-cancer stem cell Anti-proliferative Anti-cancer stem cell Enhances chemotherapy

Effect Apoptosis induction

miR-149-5p (up)

miR-133a (up) miR-149-5p (up)

miR-499a-5p (down)

miR-146a (up) miR-34a-5p (up) miR-30a-3p (down) miR-616 (down) miR-214 (up) miR-19 (down)

miR-181a (up)

miRNA (regulation) miR-125b(up) miR-16(up) miR-34a(up) miR-221(down) miR-29a(down) miR-21(down) miR-129 (up)

MYD88

WNT/B-catenin pathway AKT1 MYD88

BAX, CASP3 TP53, E2F GJA1 WNT/B-catenin pathway, MYC

BCL2

BCL2

Affected genetic mechanism TNFRS10B, BCL2, MCL1

In vitro

In vitro In vitro

In vitro

In vitro In silico In vitro In vitro and in vivo

In vitro

In vitro

Model In vitro

Xiang et al. (2019)

Xiang et al. (2014) Chen et al. (2020b)

Mandal et al. (2021)

Tao et al. (2015) Zhou et al. (2022) Georgikou et al. (2020) Li and Zhu (2017), Li et al. (2017), Wang et al. (2017a)

Du et al. (2017)

Zhang et al. (2014)

Reference Asgharzade et al. (2020)

246 B. Goker Bagca and C. Biray Avci

Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and. . .

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Phytochemicals and lncRNAs

Long non-coding RNAs (lncRNAs) comprise the class of non-protein coding RNAs longer than 200 nucleotides in length. They play critical roles in post-transcriptional gene expression regulation by sponging miRNAs (Cesmeli et al. 2022). lncRNAs regulated by some main phytochemicals and their target mechanisms in cancers are shown in Table 2.

4

Phytochemicals and Histone Modifications

Eukaryotic long DNA molecules are organized into nucleosomes by histone proteins to protect them from destruction. Histone modifications that include acetylation and methylation of specific histone tails are one of the main epigenetic mechanisms that control DNA accessibility by regulating packaging. Histone acetylation defines the addition of an acyl group to particular lysin residues of histones. This reaction forms an accessible DNA region (euchromatin) and activates gene expression. The enzymes that catalyze histone acetylation and deacetylation are histone acetyltransferases (HATs, or lysine acetyltransferases KATs) and histone deacetylases (HDACs, or lysine deacetylases KDACs), respectively (Barnes et al. 2019). Methylation differs from acetylation in some points. It occurs on different residues of histone tails, and its unique position determines the gene transcription activation or repression. The methylation and demethylation of histones are catalyzed by histone methyltransferases and histone demethylases, respectively (Greer and Shi 2012). Acetylation and methylation profile of particular histone tails comprises unique epigenetic signature. Effects of different phytochemicals on histone modifications are one of the hot topics in the cancer research area (Table 3).

5

Phytochemicals and DNA Methylation

To add a methyl group to the C5 position of the cytosine nucleotides of the DNA molecule is named DNA methylation, which inhibits gene expression by blocking the access of the transcription factors to the target gene. DNA methyltransferases (DNMTs) catalyze methylation. It markedly occurs in CpG islands of gene promoters and intergenic regions to suppress transcription (Moore et al. 2013). Although this epigenetic mechanism also participates in cancer pathogenesis, it creates a therapy target for different phytochemicals (Table 4).

6

Conclusions

Nature is a great drugstore that contains minimal side-effect phytochemicals that function with many different genetic and epigenetic mechanisms awaiting discovery. The near future outcomes from molecular biological, pharmaceutical, pharmacokinetic

Lung

Epigallocatechin3-gallate Gallic acid

Hepatocellular

Breast

Gastric

Breast

Acute myeloid leukemia Lung

Cancer Thyroid

Delphinidin

Phytochemical Curcumin

Enhances chemotherapy Anti-proliferative Anti-metastatic

Enhances chemotherapy Anti-proliferative Apoptosis induction Enhances chemotherapy Anti-metastatic Anti-proliferative Apoptosis induction Autophagy induction Anti-cancer

Effect Anti-proliferative Apoptosis induction Enhances chemotherapy

Table 2 lncRNAs regulated by some main phytochemicals

MALAT1 (down)

NEAT1 (up)

HOTAIR (down)

H19 (down)

H19 (down)

MEG3 (up) UCA1 (down)

HOTAIR (down)

lncRNA (regulation) LINC00691 (down)

WNT/B-catenin pathway

CTR1

TP53, MYC, BAX

CDH1, CDH2

PTEN, WNT/B-catenin pathway, PI3K/AKT/mTOR pathway

miR-20a-5p/ WT1

Affected genetic mechanism PI3K/AKT/mTOR pathway

In vitro

In vitro and in vivo In vitro

In vitro

In vitro

In vitro

In vitro

Model In vitro

Shi et al. (2021)

Chen et al. (2020a)

Yang et al. (2016a)

Liu et al. (2016)

Cai et al. (2021)

Wang et al. (2018), Gao et al. (2021)

Liu et al. (2021)

Reference Li et al. (2022c)

248 B. Goker Bagca and C. Biray Avci

Anti-cancer

Lung

Sulforaphane

Anti-metastatic Apoptosis induction Anti-metastatic Anti-cancer

Prostate Gastric

Resveratrol

Anti-cancer

Anti-cancer

Pancreas

Prostate

Prostate

Anti-proliferative Cell cycle arrest

Anti-proliferative Apoptosis induction Anti-metastatic Anti-proliferative

Breast Breast

Prostate

Colorectal

Lutein Quercetin

Genistein

LINC01116 (down)

H19 (down)

AK001796 (down)

PCAT29 (up)

CASC9 (down) INXS (up) UCA1 (down) MALAT1 (down) H19 (down)

HOTAIR (down)

TTTY18 (down)

KI67, APOBEC3G, SMAD1

IL6/STAT pathway

PI3K/AKT/mTOR pathway

In vitro and in vivo In vitro and in vivo In vitro

In vitro

In vitro In vivo

In vitro and in vivo In vitro In vitro

In vitro and in vivo

Ho et al. (2017)

Luo et al. (2021)

Al Aameri et al. (2017) Yang et al. (2015)

Lu et al. (2020) Li et al. (2022b)

Zhang et al. (2021b) Rezaie et al. (2021)

Chiyomaru et al. (2013b)

Chen et al. (2020d)

Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and. . . 249

Colorectal

Prostate Myelodysplastic syndrome Lung Breast Breast

Prostate

Beta-carotene

Curcumin

Delphinidin

Capsaicin

Apigenin

Cancer type Colorectal Breast Neuroblastoma Lung Breast Prostate Melanoma Oral Bladder Gastric Gastric

Phytochemical Anacardic acid

Histone acetyltransferase activator Histone deacetylase inhibitor

KDM1 (down)

Histone demethylase inhibitor Histone acetylation Histone methylation inhibitor

HDAC1 (down), TP53 ac

EP300 (P300, KAT3B) (up)

H3K4me3 (down) H3K4me3 and H3K27me3 (down) PRMT5 (down) H4R3me (down)

H3ac and H4ac (up)

SIRT1 (down)

HDAC1 (down) H3K9ac and H3K14ac (up)

Epigenetic (regulation) EP300 (P300, KAT3B) (down) H4K12ac and H3K27ac (down)

Histone deacetylase inhibitor

Histone deacetylase inhibitor

Function Histone acetyltransferase inhibitor

Table 3 Histone modifications catalyzed by some main phytochemicals

In vitro and in vivo In vitro

In vitro In vitro and in vivo

In vitro

In vitro

In vitro and in vivo

In vitro and in vivo

Model In vitro and in vivo

Jeong et al. (2016)

Liu et al. (2020a)

Zhao et al. (2018) Chatterjee et al. (2019b), Ma et al. (2019)

Kim et al. (2019)

Jia et al. (2020)

Wang et al. (2016), Islam et al. (2019, 2021), Chang et al. (2020)

Shukla et al. (2014), Tseng et al. (2017), Yan et al. (2020)

Reference Choi et al. (2017), Liu et al. (2019a, 2020b)

250 B. Goker Bagca and C. Biray Avci

Lung

Quercetin

Breast

Prostate Colorectal Breast Hepatocellular

Resveratrol

Sulforaphane

Cervical

Pancreas

Benzyl isothiocyanate

Fisetin

Skin Colorectal Cervical Prostate Pancreas

Epigallocatechin3-gallate

Histone deacetylase inhibitor Histone acetylation Histone acetylation Histone demethylation Histone acetylation Histone demethylation Histone deacetylase inhibitor

Histone demethylation

Histone deacetylase inhibitor

H3ac and H4ac (up) HDAC6 (down)

H4R3me, H3K27me (down) H3K9ac, H3K27ac (up) HDAC (down)

EP300 (P300, KAT3B) (up) H3ac H4ac (up) HDAC (down) HMT (down)

HDAC1 and HDAC3 (down)

H3K36me (down)

H3K9ac, H3K14ac, H4K5ac, H4K12ac, H4K16ac and H3K18 ac (up) HDAC1 (down) HDAC3 (down) EZH2 (down)

In vitro

In vitro

In vitro

In vitro

In vitro and in vivo In vitro

In vitro

Yang et al. (2018), dos Santos et al. (2020), Rutz et al. (2020), Hossain et al. (2021)

Chatterjee et al. (2019a), Izquierdo-Torres et al. (2019)

Kedhari Sundaram et al. (2019)

Chuang et al. (2019)

Batra et al. (2010)

Ding et al. (2020)

Nandakumar et al. (2011), Moseley et al. (2013), Khan et al. (2015), Deb et al. (2019)

Epigenetic Regulation of Cancer by Natural Touch: Phytochemicals and. . . 251

Lung Colorectal

Curcumin

Genistein

Epigallocatechin3-gallate

Colorectal

Beta-carotene

Gastric

Gastric Ovarian Myelodysplastic syndrome Hepatocellular Prostate Lung Skin Colorectal Cervical Prostate Breast Esophageal Kidney Cervical Breast

Cancer type Breast

Phytochemical Caffeic acid

DNA hypomethylation

DNA hypomethylation

DNA hypomethylation

DNA hypomethylation

Function DNA hypomethylation

Table 4 DNA methylation catalyzed by some main phytochemicals

VDR promoter (demethylation) NEUROG1 promoter (demethylation) WIF1 promoter (demethylation) DNMT1, DNMT3A, DNMT3B (down) DNMT3A (down) DNMT3B (down) TIMP3 promoter (demethylation) SCUBE2 promoter (demethylation) P16 promoter (demethylation) CDKN2A promoter (demethylation) Global methylation (down) ATM, APC, PTEN, SERPINB5, BRCA1 promoter (demethylation) PCDH17 promoter (demethylation)

DNMT3A (down) Global methylation (down) DNMT1, DNMT3A, DNMT3B (down) DNMT1, DNMT3A (down) CDX2 promoter (demethylation) Global methylation (down) DNMT3A (down) DNMT3A (down)

Epigenetic (regulation) DNMT1 (down)

In vitro

In vitro In vitro In vitro and in vivo In vitro In vitro In vitro In vitro In vitro In vitro In vitro In vitro In vitro In vitro In vitro In vitro

In vitro In vitro

Model In vitro and in vivo In vitro

Abdalla et al. (2018) Shu et al. (2011) Gao et al. (2009) Nandakumar et al. (2011) Moseley et al. (2013) Khan et al. (2015) Deb et al. (2019) Sheng et al. (2019) Meng et al. (2017) Ji et al. (2020) Sundaram et al. (2019) Xie et al. (2014), Romagnolo et al. (2017) Yang et al. (2012)

Tong et al. (2020) Yen et al. (2019) Ma et al. (2019)

He et al. (2021b) Chen et al. (2020c)

Kim et al. (2019)

Reference Li et al. (2015b)

252 B. Goker Bagca and C. Biray Avci

Ursolic acid

Sulforaphane

Thyroid

Resveratrol

Prostate

Lung Breast

Cervical Colorectal

Lung

Colorectal Prostate Gastric Cervical

Breast Prostate

Kaempferol Lycopene Oleanolic acid Quercetin

Phenethyl isothiocyanate

Neuroblastoma Prostate Prostate

DNA hypo- and hypermethylation

DNA hypomethylation

DNA hypomethylation

DNA hypomethylation DNA hypomethylation DNA hypomethylation DNA hypomethylation

DNA hypo- and hypermethylation DNA hypomethylation

CRABP2 promoter (demethylation) DNMT1, DNMT3A, DNMT3B (down) DNMT1 (down) ZFP36 promoter (demethylation) DNMT (down) NRF2 promoter (demethylation) DNMT1 (down) miR-9-3 promoter (demethylation) Global methylation (down) DNMT1, DNMT3B (down) Change methylation pattern

CDH1 promoter (demethylation) RASSF1A promoter (demethylation) DNMT1, DNMT3A, DNMT3B (down) DACT2 promoter (demethylation) GSTP1 promoter (demethylation) TET3 (down) Global methylation (down)

CHD5 promoter demethylation ER-B promoter (demethylation) Change methylation pattern

In vitro and in vivo

In vitro In vitro

In vitro In vitro

In vitro

In vitro

In vitro In vitro In vitro In vitro

In vitro In vitro

In vivo In vitro In vitro

Li et al. (2022a)

Gao et al. (2018) Lewinska et al. (2017)

Sundaram et al. (2022) Zhou et al. (2019)

Fudhaili et al. (2019)

Lu et al. (2018) Fu et al. (2014) Lu et al. (2021) Kedhari Sundaram et al. (2019) Liu et al. (2019b)

Zhang et al. (2021a) Boyanapalli et al. (2016)

Li et al. (2012) Mahmoud et al. (2015) Wu et al. (2021)

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studies will be the guide for maximum benefit from all these natural components to overcome cancer as epidrugs. Acknowledgements None. Data Availability Statements All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Compliance with Ethical Standards All authors declared that they have no conflict of interest. This manuscript does not include any human or animal subjects.

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Telomerase Reverse Transcriptase in Humans: From Biology to Cancer Immunity Magalie Dosset, Andrea Castro, Su Xian, Hannah Carter, and Maurizio Zanetti

Abstract

Telomerase reverse transcriptase (TERT) plays a central role in the biology normal somatic cells and is a factor of primary relevance in cancer cells. Telomerase regulates telomere length and integrity during replicative cycles by adding TTAGGG sequences to the end of chromosomes. In normal cells, this process only permits a finite number of replicative events at the end of which telomere attrition triggers cellular growth arrest, senescence, and apoptosis. In transformed cancer cells, telomerase is constitutively activated, preventing telomere attrition and enabling continuous cell replication. TERT is quasi-ubiquitous in cancer cells and, consequently, is an ideal self-tumor antigen. In this review, we discuss the biology of TERT, its regulation in relation to cancer cell biology, and its putative role in immune surveillance. We place emphasis on adaptive immunity mediated by CD4 and CD8 T cells as these cells are the main contributors to immune surveillance, an immune defense against cancer cells that can be strenghtened by vaccination. We also discuss the existing data on TERT T cell responses in conjunction with immune checkpoint blockade therapy. Furthermore, since the activation of adaptive T cell immunity requires presentation of antigen by molecules of the major histocompatibility complex (MHC), we discuss the immunogenicity of TERT in relation to the MHC. We review past data generated using peptides identified through laborious approaches and provide a population

M. Dosset · M. Zanetti (*) The Laboratory of Immunology, Department of Medicine and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA e-mail: [email protected]; [email protected] A. Castro · S. Xian · H. Carter Division of Medical Genetics, Department of Medicine, Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Interdisciplinary Cancer Research, https://doi.org/10.1007/16833_2022_49 Published online: 6 December 2022

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level analysis of TERT-MHC interactions. The considerations made in this article may be helpful to direct future work aiming at developing TERT-based immunotherapies to fight ongoing cancer or prevent its development. Keywords

Aneuploidy · CD4 T cells · CD8 T cells · Endoplasmic reticulum stress · Immortality · Immune checkpoint inhibitors · MHC · Telomerase · TERT

1

Introduction

The identification and molecular characterization of the first tumor antigen in the 1990s represented a milestone in tumor immunology and the development of cancer immunotherapy (Boon et al. 1994). Tumor antigens include mutated/unique antigens (tumor-specific antigens or neoantigens), antigen expressed in specific tumors (cancer/ testis antigens), antigen preferentially overexpressed in cancer cells (differentiation antigens), and viral antigens. Except for viral antigens, self-tumor antigens – whether conserved in their germline configuration or mutated – are considered the pivots of immune surveillance. Since their expression in the thymus may have consequences in shaping the peripheral T cell repertoire and establish tolerance, there continue to exist questions as to which type of antigens best accomplish the goal of immune surveillance. In the past decade, emphasis has been placed on neoantigens (Schumacher and Schreiber 2015) but as we recently discussed (Castro et al. 2021) neoantigens as antigens for personalized immunotherapy are fraught with numerous unresolved drawbacks and still much needs to be learned about how to best use them clinically. Conserved antigens, although seemingly less promising targets due to self-tolerance, are nonetheless important players in immune surveillance and anti-tumor immunity. In 2009, a panel of experts met to assess 75 self-tumor antigens to identify the “ideal” tumor antigen (Cheever et al. 2009). Using stringent criteria such as therapeutic function, immunogenicity, oncogenicity, specificity, expression level and percent positive cells, stem cell expression, number of patients with antigen-positive cancers, number of epitopes, and cellular location of expression as the main criteria, the panel concluded that “none of the 75 antigens had all of the characteristics of the ideal cancer antigen” (Cheever et al. 2009). Telomerase reverse transcriptase (TERT), the enzyme key to maintenance of telomeres during cell division, fulfills many of the required properties. Here, we will discuss TERT biology, its relation to cancer, and its role in anti-tumor immunity based on the current information.

2

Telomerase in Human Cells

Telomerase is a holoenzyme made of protein and RNA subunits that elongates chromosomes by adding TTAGGG sequences to the end of chromosomes, the telomeres (Greider and Blackburn 1989). Their role is to protect chromosomes against degradation and inappropriate DNA recombination.

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Telomerase is highly conserved and was first described in ciliate T. Tetrahyema in 1985 (Greider and Blackburn 1985). Human telomerase was cloned in 1997 (Nakamura et al. 1997). Telomerase is a ribonucleoprotein complex with reverse transcriptase activity comprising two essential components: a catalytic subunit (TERT, Telomerase Reverse Transcriptase), and an RNA template (TR or TERC, Telomerase RNA) which includes a sequence template for telomeric DNA synthesis. The TERT subunit is a protein of 127 kDa (1132 amino acids) encoded by the TERT gene located on chromosome 5p15.33 (Nakamura et al. 1997). Although the sequence of TERT has on average only 40% homology across species, its N-terminal region is very conserved. The TR RNA template is composed of 451 nucleotides and includes the sequence (50 -CUAACCCUAAC-30 ) complementary to the singlestranded 30 end of telomeres (Feng et al. 1995). Whereas its primary sequence diverges notably across species, its secondary structure is paradoxically preserved (Chen et al. 2000). During DNA replication telomeres shorten by 50–200 base-pairs at each cell division. The absence of telomeres leads to rapid genetic instability and loss of genomic integrity (Blackburn 1991). Most normal somatic cells cannot maintain constant telomere length during cell division. Therefore, they only undergo a limited number of cell cycles (Hayflick 1965) before telomeres reach a critical length, which eventually drives cellular growth arrest, senescence and apoptosis (Bodnar et al. 1998; Vaziri and Benchimol 1998). In cancer cells telomere attrition is prevented by telomerase (Shay and Wright 2019), which imparts cancer cells with the ability to divide continuously. Since the TR template is ubiquitously expressed, the TERT subunit is considered the rate-limiting component of telomerase complex activity. Subtle changes affecting telomerase abundance or function can influence telomere length and, in turn, disease risk (Greider 2006). Because telomerase is in very low abundance, its activity is tightly regulated, and the number of telomere ends exceeds the number of telomerase molecules (Armanios and Blackburn 2012). Interestingly, growing evidence shows that there are disease processes that are caused by both short and long telomere (McNally et al. 2019). For instance mice with long telomeres develop more aggressive tumors, and have worse outcome and decreased survival (Feldser and Greider 2007; Perera et al. 2008). TERT and TR are mainly found expressed in the nucleus but, under certain conditions (i.e., oxidative stress), they can shuttle into the cytosol (Haendeler et al. 2003) and mitochondria (Singhapol et al. 2013; Zheng et al. 2019). The expression of TERT protein or its mRNA levels are generally a reliable biomarker of telomerase activity (Bodnar et al. 1998), and although telomerase expression is regulated both transcriptionally and post-translationally (Yi et al. 1999), the TERT mRNA profile strikingly correlates with telomerase activity (Wang et al. 2014). Constitutive telomerase activity is confined to cancer cells or cells that depend on self-renewal (Table 1). These include early embryonic cells, germinal cells, stem cells, gonadic cells, hair follicle bulbs, activated lymphocytes (Hodes et al. 2002; Patrick and Weng 2019), and certain subsets of epithelial cells (i.e., intestinal epithelium, basal skin layer, urothelium). In normal somatic cells telomerase activity differs among cell types and only elevated levels of TERT can warrant a

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Table 1 Telomerase and telomeres regulation in human normal and cancer cells

Immortal

Mortal

Cell type Germ cell Blastula Embryonic cell Cancer stem cells Cancer cells Adult stem cell Normal somatic cell Lymphocytes

Telomerase activity +++ +++ +++ +++

Telomere stability Stable Stable Stable Stable

Competent (+) None

Telomere length Long Long Long Shorter or equal to adjacent normal cells Shorter or equal to adjacent normal cells Intermediate Intermediate

Slow decrease Rapid decrease

Competent (+)

Intermediate

Slow decrease

+++

Stable

+++ high telomerase activity, + low telomerase activity

stabilization of telomere length, enabling self-renewal and an “immortal” status (Table 1). Thus, TERT is a regulator of the replicative lifespan of a cell. In cancer, TERT is aberrantly expressed in a ubiquitous manner, i.e., it affects nearly all cells irrespective of the tissue of origin. While TERT is expressed in cancer cells, little if any expression is detected in the corresponding normal tissue. A notable exception is the thymus (Fig. 1). Therefore, TERT is the truest embodiment of a cancer antigen as it is expressed at very high frequency in cancer cells with clonal characteristics and is key to the process of self-renewal and continuous replication of cancer cells.

3

Role of Telomerase in Oncogenesis and Cancer Progression

3.1

Basic Cancer Cell Attributes Regulated by Telomerase

Compared to normal cells, cancer cells undergo a preferential selection in term of telomere maintenance, proliferation, survival, resistance to apoptotic cell death, and ability to disseminate throughout the body. The involvement of telomerase in tumorigenesis was first demonstrated in transgenic mice knocked out for the TERT gene (González-Suárez et al. 2000; Se and Ra 2000). However, expression of TERT per se is not sufficient to convert normal cells into cancer cells. Cells with ectopic expression of TERT are still subject to contact inhibition of growth, are susceptible to serum deprivation, maintain a stable genome, and are unable to generate tumors in animal models (Hahn et al. 1999; Artandi et al. 2002; Thomas et al. 2002). Gene alterations, particularly somatic mutations that arise in protooncogenes or tumor suppressor genes, play a prominent role in the development of cancer. Thus, the activation of the enzyme telomerase enables cells that have

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Fig. 1 TERT expression across different types of human cancer. TERT expression from TCGA RNA-seq data retrieved for 23 cancer types and compared to matched normal tissues. The bottom scatterplot indicates the differences in the median of TERT expression between tumors and matched normal tissues. Darker dots indicate significant differences, while gray dots indicate non-significant differences after multiple-test adjusting using the Benjamini-Hochberg method. TCGA files were downloaded from the GDC Portal on 12/27/2017, using GDC-client v1.3.0. TCGA RNA-seq alignment files were reprocessed using sailfish software version 0.7.4 and the GRCh38 reference genome with default parameters and including all read sequence duplicates. The plot is generated using python, version 3.8.5. Packages used include seaborn, version 0.11.0; matplotlib, version 3.3.1; pandas, version 1.4.1. Statistical analyses are performed using SciPy package, version 1.7.3. Multiple-test adjustment is using statsmodels, version 0.12.0

accumulated at least two cancer-driver mutations to complete their transformation into malignant cells. For instance, the expression of TERT combined with the presence of SV40 large-T antigen and Ras oncogenes was sufficient to induce transformed cells capable of forming tumors in immunodeficient nude mice (Hahn et al. 1999; Artandi et al. 2002; Thomas et al. 2002). TERT inhibition experiments reveal that longer telomeres are advantageous for cell survival. A study analyzing telomeres length across 31 types of cancer from the TCGA database paradoxically revealed that most human cancer cells carry telomeres that are shorter (70% of cohorts) or are equal in length (30% of cohorts) compared to adjacent normal cells (Barthel et al. 2017) (Table 1). These observations have been confirmed in melanoma cells vs. nevus cells (Chiba et al. 2017), indicating that elevated and constitutive telomerase activity compensates for telomere loss in cancer cells, enabling them to escape telomere-dependent cellular senescence. Regulation of telomeres by telomerase has also been linked to the maintenance of cells with telomere-driven genomic instability (Chiba et al. 2017), suggesting that TERT upregulation is a permissive factor of tumor mutational burden (TMB) (Li et al. 2020), a marker of genome instability. Normal human fibroblasts with ectopic expression of TERT can be maintained in culture for more than 300 cycles, acquiring an immortal-like status (Smith et al. 2003; Jin et al. 2010). A comprehensive mechanistic analysis of telomerase revealed that TERT modulates the expression of genes involved in critical cellular functions, upregulating genes related to cell proliferation signals (i.e., cycline D, cycline A2,

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E2F1 proteins, EGFR, FGF) and downregulating factors driving apoptosis (i.e., TRAIL, p53) (Jin et al. 2010; Mukherjee et al. 2011). Telomerase also promotes angiogenesis by directly interacting with the vascular epithelial growth factor (VEGF) promoter, upregulating VEGF expression (Liu et al. 2016). Evidence shows that regulation of these genes depends on the presence of TERT but not its enzymatic activity. Noncanonical (extratelomeric) functions of TERT have also been reported. These include the role of telomerase in signaling cascades that influence cancer development and progression through NF-kB and the Wnt/β-catenin pathways (for review see (Li and Tergaonkar 2014)). The direct interaction between NF-kB p65 subunit (RelA) and TERT protein was demonstrated to modulate nuclear translocation of TERT and its ectopic expression (Akiyama et al. 2003), resulting in NF-kB signalingdependent increase in cancer cell proliferation and resistance to cell death (Ghosh et al. 2012). TERT interacts directly with β-catenin to increase its nuclear localization and drive epithelial to mesenchymal transition (EMT) (Liu et al. 2013), effects abrogated by pharmacological inhibition of TERT. Notably, the regulation of the Wnt/β-catenin transcriptional complex by TERT is independent of its catalytic function at telomere end. Predictably, regulation of Wnt/β-catenin facilitates the acquisition of stem-cell properties and self-renewal contributing to tumor cell heterogeneity (Mani et al. 2008). c-Myc, a known transcriptional target of Wnt/β-catenin signaling, regulates TERT expression (Wu et al. 1999) and Wnt/β-catenin signaling regulates telomerase in stem cells and cancer cells (Hoffmeyer et al. 2012). Therefore, TERT is involved in crosstalk and functional cooperativity with multiple signaling networks associated with cancer progression. Numerous reports showed that cancer stem cells (CSC) depend on TERT to selfrenew and disseminate (Clarke and Fuller 2006; Beier et al. 2011; Xu et al. 2011; Hannen and Bartsch 2018). Because CSC are drivers of tumor initiation and progression (Batlle and Clevers 2017) and the antitumor effect of conventional therapies is largely mediated by activation of cellular apoptosis, it stands to reason that TERT regulates the sensitivity of cancer cells to cancer therapies. In fact, an increased expression of TERT in tumor cells correlates positively with resistance to radiotherapy (Shin et al. 2012) or chemotherapy (Galaine et al. 2019), while TERT downregulation restores sensitivity to treatment (Dong et al. 2009). This argues for a key role by CSC in resistance to conventional therapies and cancer recurrence (Batlle and Clevers 2017). On the same vein, TERT is expressed in circulating tumor cells (CTC) shed from the primary tumor (Fizazi et al. 2007; Goldkorn et al. 2015; Zhang et al. 2021).Thus, TERT is expressed at every stage of the cancer evolution, from cancer stem/tumor initiating cell through to the malignant metastatic cancer cells (Low and Tergaonkar 2013; Hannen and Bartsch 2018), and plays an essential role in cancer cell maintenance, oncogenic properties and resistance to conventional cancer therapies (chemotherapy and radiation). Figure 2 recapitulates the central role of TERT in the regulation of most hallmarks of cancer (Low and Tergaonkar 2013).

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Fig. 2 Canonical and non-canonical role of telomerase expression in cancer cells. The basic properties of TERT combined with its ability to regulate core hallmarks of cancer make it indispensable for cancer cell growth and propagation. TERT confers transformed cells with immortal status. This involves the capacity to avoid programmed cell death through the maintenance of telomeres during cell division, the reduction of apoptosis-inducing factors (e.g., TRAIL) and the inhibition of tumor suppressors. It also drives upregulation of growth factors that allow cancer cells to proliferate, and favors the development of blood vessels, which supply tumors with oxygen and nutrients to sustain optimal growth. In addition, TERT promotes stemness and enables metastasis through the induction of EMT

3.2

TERT and the Cellular Response to Stress in Cancer Cells

Normal diploid fibroblasts overexpressing TERT are more resistant to stress-induced apoptosis (Gorbunova et al. 2002) and the transient inhibition of telomerase increases the cytotoxicity of DNA-damaging agents in a cell-cycle regulated manner (Tamakawa et al. 2010). TERT expression inhibition also abrogates cellular responses to DNA double strand breaks without affecting telomere integrity and telomerase role in telomere synthesis (Masutomi et al. 2005; Fleisig et al. 2016). These observations suggest that TERT is intimately involved in increasing tolerance to chromosomal instability, a main source of genomic abnormality in cancer cells. Furthermore, while telomere-initiated senescence reflects a permanent cell-cycle arrest upon activation of the DNA damage response (DDR) (d’Adda di Fagagna et al. 2003), the ectopic expression of telomerase in telomerase-silent cells overcomes senescence and extends cellular lifespan, with reduction of spontaneous chromosome damage in G1, and enhancement of DNA repair independently of

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telomere lengthening (Sharma et al. 2003). Telomerase also alleviates telomere replication stress in response to aneuploidy (Meena et al. 2015), suggesting that TERT may be connected to cellular stress and response to it. Cells in the tumor microenvironment are subject to constant stress which is driven by cell-intrinsic and environmental events such as hypoxia, nutrient starvation, low pH, inflammation, extreme temperatures (heat and cold), and changes in protein dynamics and quality control mechanisms (aneuploidy). The response to stress is a fundamental adaptive process of the cell. In normal untransformed somatic cells, adaptive responses to stress are physiological but fuel cell dysregulation and pathology in cancer cells if unresolvable stress does not result in cell death. Adaptive stress responses activate complex intracellular signaling cascades such as the unfolded protein response (UPR) (Walter and Ron 2011) and the integrated stress response (ISR) (Costa-Mattioli and Walter 2020), the best characterized ones. The stress response to misfolded/unfolded proteins (aka UPR) is conserved among yeast, fungi, worm, fly, coral, and vertebrate and mammalian cells (Kültz 2003; Mori 2009; Ruiz-Jones and Palumbi 2017), consistent with the conclusion of a recent analysis that a high fraction of the total proteome mass of the kingdoms of life is dedicated to protein homeostasis and folding (Müller et al. 2020). What then is the relationship between TERT and the UPR? Early reports established a functional link between pharmacologically-induced UPR and TERT activation in human cancer cell lines (Zhou et al. 2014) and that in turn TERT attenuates UPR-driven cell death (Hosoi et al. 2014). Our work on transcellular UPR showed marked translocation and accumulation of the TERT protein in the cytoplasm, but not activation of the TERT promoter, suggesting that TERT translocation most likely protects cells from apoptosis (Rodvold et al. 2017). Likewise, the ISR is activated in response to a range of stressors, including hypoxia, amino acid and glucose deprivation, viral infection, oncogene activation, and oxidative stress. The common point of convergence for all these stressors is phosphorylation of the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α) on serine 51, which is also a key hub of the UPR (Pakos-Zebrucka et al. 2016; Costa-Mattioli and Walter 2020). Oxidative stress resulting from a disproportionate production of reactive oxygen species (ROS) by mitochondria (Korshunov et al. 1997) causes TERT to translocate to the cytosol (Haendeler et al. 2003) and the mitochondria (Zheng et al. 2019) to reduce ROS production (Haendeler et al. 2003, 2009; Sharma et al. 2012) and mitigate ROS-mediated nuclear and mitochondrial DNA damage and cell death (Ahmed et al. 2008; Singhapol et al. 2013). Interestingly, CSCs have lower ROS levels than cancer cells, and this may contribute to their survival and resistance to conventional therapies. These considerations are important not only because TERT is a key regulator of cancer cells but also because TERT is a quintessential conserved tumor antigen. Arguably, any modifications of the cancer cell dependent on TERT or involving TERT as a main actor, e.g., adaptive UPR (Rutkowski et al. 2006; Rodvold et al. 2017) or induction of the β-catenin/Wnt pathway (Choi et al. 2008; Park et al. 2009),

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may have implications on immune surveillance targeting TERT in cancer cells by immune cells and possibly resistance to immune attack.

4

Regulation of TERT in Cancer Cells

Telomerase is subject to upstream regulation both at the transcriptional and post-transcriptional level. In cancer cells, most regulatory events drive TERT overexpression (Fig. 3).

4.1

TERT Gene Amplification

Copy number variation (CNV) refers to duplications or deletions of genomic sequences and is part of chromosomal aberrations that commonly occurs during cancer development (Fröhling and Döhner 2008; Shlien and Malkin 2009). Gene amplification is present in certain cancer types and is generally associated with overexpression of the amplified gene (Albertson 2006). TERT gene amplification is found at an average frequency of 2% in cancers with the highest prevalence in liposarcoma, lung, breast, thyroid, liver, and bladder cancer (Zhang et al. 2000; Gupta et al. 2021).

Fig. 3 Transcriptional and post-translational regulation of TERT. In cancer cells TERT is transcriptionally regulated by factors including TERT promoter mutations, gene amplification, TERT promoter methylation, activating factors (e.g., oncogenes) and suppressing factors (e.g., tumor suppressors). TERT transcripts can give rise to truncated splice variants. TERT expression can be regulated by micro-RNA. Finally, TERT function is modulated by post-transcriptional regulators including TERT splice variants, phosphorylation status and its interaction with factors such as TERRA, a component of the ALT system. (+) factors activating TERT or telomerase activity; (-) factors repressing TERT and telomerase activity

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TERT Promoter Regulation

The promoter of the TERT gene (pTERT) is the binding site for many transcription factors and is the target of methylation (Horikawa et al. 1999), which activates or represses the expression of TERT mRNA. Several oncogenic proteins or viral factors can drive TERT transcription. This is the case of some oncogenes such as Her2/Neu, which is preferentially overexpressed in breast cancer, c-Myc, which is upregulated in ~70% of cancers (Park et al. 2001), and the E6 protein from the human papillomavirus (HPV), the causative agent of cervical and head and neck cancers (Klingelhutz et al. 1996). Additionally, non-oncogenic factors overexpressed in cancers can also promote the activation of pTERT. Among them are the Sp1 protein (Park et al. 2001), the E26 transformation-specific (ETS) factor (Dwyer et al. 2007), Wnt/β-catenin (Hoffmeyer et al. 2012), STAT3 (Chung et al. 2013), NF-kB (Sinha-Datta et al. 2004), and activated estrogen and androgen receptors (Guo et al. 2003). The mechanism by which normal cells regulate TERT expression is not fully understood, However, TERT transcription is apparently repressed by tumor suppressor genes. Inhibition of TERT expression involves transcription factors such as Wilm’s tumor 1 (WT-1) (Oh et al. 1999), p53 (González-Suárez et al. 2002), p73 and TGF-β (Hu et al. 2006). The discovery of mutations in the TERT promoter (pTERTmut) region has provided a new mechanism for cancer-specific TERT upregulation. Somatic mutations in the TERT promoter are the most frequent genomic abnormality in non-coding regions in the human cancer genome (Weinhold et al. 2014; Melton et al. 2015). The mutations C228T and C250T located 124 bp and 146 bp upstream of the translation ATG start site of TERT are the most frequent point mutations in solid cancers. Together with other less frequent mutations they are thought to facilitate the binding of ETS/TCF family transcription factors (Hollenhorst et al. 2011; Yuan et al. 2020). Consequently, pTERTmut are often associated with upregulation of TERT transcription and translation (Chiba et al. 2017). High prevalence (> 50%) of pTERTmut is generally found in cancers such as bladder, brain, liver and skin (Vinagre et al. 2013; Gupta et al. 2021; Salimi-Jeda et al. 2021). Interestingly, pTERTmut and TERT genomic amplification seem to be mutually exclusive (Gupta et al. 2021). Chromatin accessibility is another factor impacting TERT promoter activity (Atkinson et al. 2005). Hypermethylation of the TERT promoter at the oncological hypermethylated region (THOR) was reported to activate the TERT promoter, while its unmethylation causes TERT repression regardless of pTERTmut status (Lee et al. 2019). Hypermethylation at THOR prevails in human cancers and is frequently found (>60%) especially but not exclusively in colon, blood, prostate, breast, brain, lung and bladder tumors (Lee et al. 2019). Interestingly, THOR hypermethylation tends to prevail in cancers where pTERTmut is rarely found (i.e., colon, blood, breast and prostate cancers) (Lee et al. 2019), suggesting it is part of a crucial cancer-associated mechanism of TERT overexpression.

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Post-transcriptional Regulation of TERT

In addition to transcriptional activation, TERT is subject to post-transcriptional regulation involving alternative splicing of TERT messenger RNA (mRNA). Although the causes of alternative splicing remain unknown, several independent groups proved the existence of at least 22 splicing sites, the most commonly studied being three deletion sites (a, b, g) giving rise to 7 spliced TERT mRNA isoforms (Ulaner et al. 1998; Colgin et al. 2000; Wong et al. 2014). Cancer cells with low or no telomerase activity have elevated expression of spliced variants of TERT compared to full length TERT suggesting that spliced variants may act as dominantnegative regulators. Furthermore, truncated TERT variants act as dominant negative regulators competing with wild-type TERT and inhibiting its enzymatic activity (Liu et al. 2012). However, certain spliced variants of TERT retain their ability to protect cells from apoptosis and stimulate proliferation (Listerman et al. 2013). Short non-coding RNA (microRNA, miR) have also been implicated in direct or indirect regulation of TERT in different cancers (Table 2). Most of them downregulate TERT expression. The two exceptions are miR-103 and miR-346. miR-103 increases TERT mRNA levels and TERT nuclear localization via the repression of AKAP12, a factor that contributes to the dephosphorylation of TERT resulting in reduced telomerase activity (Xia et al. 2016). miR-346 competes with miR-138 for binding to the 3’UTR region of TERT leading to increased expression (Song et al. 2015). Constitutive expression of telomerase occurs in 90% of tumors irrespective of their origin and histologic type. The remaining 10% relies on process called alternative lengthening of telomere (ALT) (Shay et al. 2012). ALT-positive cancer cells have generally low or no telomerase activity and consequently are less efficient in promoting cancer progression and metastasis (Ford et al. 2001; Chang and DePinho 2002). Telomeric repeat-containing RNA (TERRA) expression, a characteristic of ALT regulation, is believed to act as a direct inhibitor of telomerase activity through specific binding to TERT, forming base-pairs with the TR RNA template (Redon et al. 2010) hence contributing to TERT downregulation (Gao et al. 2017). Only a small fraction of cancer cells (mainly mesenchymal cells) employs the ALT system.

5

Diagnostic and Prognostic Value of TERT

Since TERT is expressed in >90% of cancer cells irrespective of cancer type, its detection is clearly of clinical interest. Several studies measuring TERT activity or mRNA expression in serum, plasma or tumors, showed that a high telomerase levels are associated with poor prognosis and greater tumor aggressiveness in cancer patients treated by chemotherapy and/or radiation in non-small cell lung carcinoma (Marchetti et al. 2002; Wu et al. 2003; Zhu et al. 2006), neuroblastoma (Poremba et al. 1999; Krams et al. 2003), breast cancer (Poremba et al. 2002), liver cancer (Oh et al. 2008), leukemia (Cogulu et al. 2004), and squamous cell head and neck cancer (Liao et al. 2004). In a recent study, pTERTmut were detected with high (100%) specificity but low (46%) sensitivity in the urine of individuals up to 10 years

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Table 2 MicroRNA regulating TERT

Micro-RNA miR-615-3p

Effect on TERT expression / telomerase activity Inhibition

miR-532

Inhibition

miR-3064

Inhibition

miR-296-5p

Inhibition

miR-512-5p

Inhibition

miR-1207-5p

Inhibition

miR-1266

Inhibition

miR-380-5p

Inhibition

miR-375

Inhibition

miR-1182

Inhibition

miR-34a

Inhibition

miR-128

Inhibition

miR-138

Inhibition

miR-346

Activation

miR-103

Activation

Direct and indirect mechanism Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Target the 3’UTR region of TERT Represses HPV, leading to increase of tumor suppressors (p21 and Rb) Alter nuclear location of TERT through 14-3-3ζ regulation Target TERT open reading frame (ORF)

Negatively regulate FoxM1/ cMyc pathway Interacts with the coding sequence of TERT mRNA Target the 3’UTR region of TERT in a AGO2-dependent manner Target the 3’UTR region of TERT in a GRSF1-dependent manner Inhibits AKAP12, resulting in increased PKCa activity

Reference Yan et al. (2018) Bai et al. (2017) Bai et al. (2017) Dinami et al. (2017) Dinami et al. (2017) Chen et al. (2014) Chen et al. (2014) Cimino-Reale et al. (2017) Jung et al. (2014)

Zhang et al. (2015), Zhou et al. (2016), Hou et al. (2018), Zong et al. (2019) Xu et al. (2015) Guzman et al. (2018) Song et al. (2015)

Song et al. (2015)

Xia et al. (2016)

prior their primary diagnosis of bladder cancer (Hosen et al. 2020). As expected, the presence of pTERTmut or pTERT methylation is also predictive of worse prognosis and cancer recurrence (Descotes et al. 2017; Hugdahl et al. 2018; Lee et al. 2019). Because TERT expression increases tolerance to genome instability during the progressive accumulation of somatic mutations, TERT expression in cancer cells is an important factor to establish tumor heterogeneity and maintainance of stemness profile in cancer-initiating/progenitor cells.

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TERT as Immune Target

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TERT-Specific T Cell Immunity

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In the past two decades numerous laboratories worldwide investigated the activation of T cells by TERT (for review see (Zanetti 2017)). Like for non-tumor antigens, the initiation of T cell immunity against TERT requires the processing of the TERT protein inside the cell, its degradation in the form of peptides that are then complexed with major histocompatibility complex (MHC) molecules (also known as human leucocyte antigen (HLA) in humans), and the display of the TERT peptide/MHC complex at the cell surface. While MHC-I molecules are ubiquitously expressed on all nucleated cells in the human body, the expression of MHC-II molecules is generally restricted to professional antigen-presenting cells (APC) (i.e., dendritic cells, macrophages, and B cells). MHC- I molecules normally present peptides of 8–10 amino acids to CD8 T cells, and MHC-II molecules present longer peptides (average 15 amino acids) to CD4 T cells. MHC molecules are frequently absent on tumor cells and most human cancers downregulate their surface expression as an escape mechanism, eluding recognition by T cells. A common mechanism of immune evasion by cancer cells involves impairment of the MHC, such as somatic mutations or loss of heterozygosity (LOH). LOH can affect all MHC molecules, or only specific alleles (for review see (Castro et al. 2021)). Somatic mutations affecting HLA and B2M are observed in around 5% of TCGA patients pan-cancer. Even sub-clonal loss of HLA could shield some tumor cells from immune elimination promoting disease progression post-therapy. However, to mitigate the effects of immune evasion by LOH, CD4 T cells can kill MHC II-negative tumor cells through the effect of IFNγ secreted by activated neighboring APC (for review see (Zanetti 2015). For decades, efforts were focused on the identification of TERT peptides capable of activating cytotoxic CD8 T cells (Zanetti et al. 2005; Vonderheide 2007). However, since CD8 T cells need help from CD4 T cells (Keene and Forman 1982) and CD4 T cells can also kill target cells (Borst et al. 2018), presentation of TERT peptides by MHC II molecules has also been actively pursued. Notably, peptides recognized by CD4 T cells are promiscuous, i.e., they can bind multiple HLA alleles, albeit with lower avidity. Efforts to identify and validate TERT peptides restricted by MHC-II molecules that induce CD4 T cell responses have been recently reviewed (Dosset et al. 2020a).

6.2

TERT-Based Immunotherapy

Owing to its widespread expression in cancer cells TERT has been the object of intense activity and numerous immunotherapy studies that evaluated the efficacy of TERT vaccination as therapeutic modality. These approaches included the use of synthetic peptides, TERT mRNA, and TERT DNA (for review see (Zanetti 2017; Dosset et al. 2020a; Fernandes et al. 2020)). With few exceptions TERT

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Table 3 Immunogenic TERT peptides evaluated in clinical trials Peptide p540 p572 a pY572 UCP1 UCP2 UCP3 UCP4 p652 p660 p691 p611 p766 p672 a

Position TERT540–548 TERT572–580 TERT572-580 TERT44–58 TERT578–592 TERT916–930 TERT1041–1055 TERT386–400 TERT660–689 TERT691–706 TERT611–626 TERT766–780 TERT672–686

Sequence ILAKFLHWL RLFFYRKSV YLFFYRKSV PAAFRALVAQCLVCV KSVWSKLQSIGIRQH GTAFVQMPAHGLFPW SLCYSILKAKNAGMS YWQMRPLFLELLGNH ALFSVLNYERARRPGLLGASVLGLDDIHRA RTFVLRVRAQDPPPE EARPALLTSRLRFIPK LTDLQPYMRQFVAHL RPGLLGASVLGLDDI

Main HLA restriction HLA-A2 HLA-A2 HLA-A2 HLA-DR HLA-DR HLA-DR HLA-DR b HLA-DP4 b HLA-DP4 b HLA-DR HLA-DP4 HLA-DR HLA-DR

Peptide modified to enhance its binding to HLA Predicted using MixMHC2pred

b

therapeutic vaccine trials focused on TERT peptides identified by assessing binding affinity to MHC molecules (Table 3). As reviewed elsewhere (Zanetti 2017) both MHC-I and MHC-II epitope-based vaccines successfully stimulated T cell responses in most cancer patients without relevant toxicity, and improved overall survival in most instances. However, despite the fact that the concomitant stimulation of CD4 and CD8 T cell epitopes provided some clinical benefit patients, no TERT vaccine has been successful at preventing cancer progression in a durable way (Zanetti 2017). Apart from considerations on vaccine design, a plausible explanation is that TERT-reactive T cells induced by vaccination are also subject to negative immune regulation in the tumor microenvironment, for example by immune checkpoints (e.g., PD-1, PD-L1, TIM3).

6.3

Immunity Against TERT and Immune Checkpoint Inhibitors

Studies suggest that systemic antitumor immunity mirrors intra-tumor T cell immunity (Chen and Mellman 2017; Huang et al. 2017), and that tumor-reactive T cells in blood and intra-tumor T cell abundance predict clinical outcome (Spitzer et al. 2017; Walker et al. 2018; Iwahori et al. 2019). Information gathered through tumor biopsies is by definition cumbersome, suggesting the need to implement non-invasive approaches such as liquid biopsy (blood) permitting repeated sampling to monitor the immune status of the patient. Constitutive TERT-specific CD8 T cell immunity in cancer patients has been assessed in only a few studies (Filaci et al. 2006) with no inference on tumor

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progression and patient survival. Systemic TERT CD4 T cell immunity has been reported in several cancers including leukemia, lung, colon, melanoma, renal and liver cancers (Dosset et al. 2012). In non-small cell lung cancer (NSCLC) (Godet et al. 2012b; Laheurte et al. 2019) and melanoma (Nardin et al. 2022), the percentage of patients with systemic CD4 T cell immunity against a pool of UCP peptides (Table 2) detected before chemotherapy correlated inversely with disease stage. Although TERT antigen expression tends to rise with disease progression (Low and Tergaonkar 2013; Hannen and Bartsch 2018), the percentage of individuals with TERT-reactive CD4 T cells drops, reflecting perhaps local or systemic immunosuppression. This has been observed repeatedly in patients with metastatic anal cancer (Kim et al. 2018; Spehner et al. 2020) and renal cancer (Beziaud et al. 2016). However, in localized or metastatic cancers of different types, the presence of pre-existing TERT-reactive CD4 T cells (Th1) is associated with better clinical outcome (Godet et al. 2012b; Beziaud et al. 2016; Laheurte et al. 2019; Spehner et al. 2020; Nardin et al. 2022), for review see (Godet et al. 2012a; Dosset et al. 2020a)). Importantly, preexisting CD4 T cells confer an advantage in response to immune checkpoint inhibitors (ICPi) (Arakawa et al. 2019; Zuazo et al. 2019; Kagamu et al. 2020; Oh et al. 2020) in NSCLC 125 and melanoma 112. Thus, the detection of systemic TERT-reactive CD4 T cells appear to be a useful tool to monitor the evolution of a patient’s antitumor immunity during cancer therapy, and a predictor of clinical response. Furthermore, the blockade of PD-1/TIM3 enhanced TERT-reactive CD4 T cell response in vitro in some patients (Laheurte et al. 2019), suggesting that an in vitro functional assay could be used as biomarker of the in vivo response to ICPi (Dosset et al. 2020a). The contribution of TERT T cell immunity in concomitance with other forms of immune therapy is beginning to be investigated. TERT overexpression in tumor cells favors their propagation and resistance to chemo/radiotherapies, but it also increases their targetability and elimination by TERT-reactive T cells. Recent studies in bladder cancer patients treated with anti-PD1 immune checkpoint therapy (de Kouchkovsky et al. 2021) or Bacillus Calmette-Guerin (BCG)-immunotherapy (Batista et al. 2020) suggest that activating pTERTmut are associated with better overall survival. In the case of anti-PD1 therapy, the hypothesis is that pTERTmut benefits responding patients who have elevated TERT expression and pre-existing T cell immunity against TERT. Therefore, ICPi therapy invigorates pre-existing T cells reactive with TERT that are part of autochthonous immune surveillance. In the case of BCG, it is possible that BCG itself increases the frequency of TERTreactive T cells. However, other possibilities should be considered such as regulation by a common polymorphism rs2853669 acting as a modifier of the effect of the mutations (Rachakonda et al. 2013), or reduction of telomerase activity in cancer cells by BCG (Saitoh et al. 2002). Further work is needed to determine the extent to which immune checkpoint blockade and BCG leverage TERT expression and pTERTmut to mobilize TERT T cells against cancer cells.

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The TERT-MHC Relationship at the Population Level

Given the relevance of TERT in immune surveillance and as a potential contributor to the effects of other forms of immunotherapy (e.g., ICPi and BCG), it is important to understand the relationship between TERT and the MHC, the master regulator of adaptive T cell immunity, at the population level. Over the past decade next generation sequencing has resulted in a dramatic increase in the number of known MHC genotypes (Robinson et al. 2020). For practical reasons, over the past two decades focus has been on the most frequent HLA alleles such as HLA-A2, which is predominant (~45%) in the Caucasian population (Gulukota and DeLisi 1996) and HLA-DR1, whose frequency ranges between 7 and 20% (Stern and Calvo-Calle 2009). This skewed participation in clinical trials emphasizes an over-representation of white patients at the expense of population diversity in participating ethnicities (Knepper and McLeod 2018). Together with equity considerations, HLA genotype frequencies vary significantly across populations (Cao et al. 2001). Therefore, a re-asessment of TERT peptides included in immunotherapeutic treatments across a wider array of HLA genotypes may reveal subpopulations with different distributions of MHC haplotypes and potential benefit. To begin to address this question at a population level, we performed an analysis of the landscape of presentable peptides across the entire (1123 aa) TERT protein for all available MHC-I (n = 2915) and MHC-II (n = 5620) alleles. For sake of simplicity, we clustered MHC-I and MHC-II alleles in supertype families (Sidney et al. 2008; Greenbaum et al. 2011). We found that all MHC-I alleles and supertypes were able to present TERT peptides spanning the majority of positions (median 90%) (Fig. 4). On the other hand, we observed greater diversity in the fraction of TERT peptides able to be presented by MHC-II alleles and supertypes (Fig. 5), with an overall >50% presentability by the main DR and DP2 supertypes, but only ~20% presentability by DRB3 and main DQ supertypes. Dual activation of CD8 and CD4 T cells is predicated on the concept that linked recognition of antigen (Bretscher and Cohn 1970) is required to yield better and more durable CD8 T cell responses (Keene and Forman 1982). For this reason, we sought to identify any positions in TERT that were well presented by both MHC-I and MHC-II alleles. While there were no positions that were presented by over 50% of MHC-I and MHC-II alleles (Figs. 4 and 5), we did identify position 770 as containing both an MHC-I (YMRQFVAHL) and MHC-II (YMRQFVAHLQETSP) peptide that can be presented by over 40% of MHC alleles. Notably, YMRQFVAHL is contained within a previously studied peptide (p. 766, see Table 3) in the context of HLA-DR (Schroers et al. 2003). Altogether, as we move into an era of increased personalized medicine, considerations of the type presented herein need to be made to ensure more inclusive and accessible treatments, specifically with regard to evaluation and incorporation of HLA genotype data in the human population.

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Fig. 4 Predicted interactions between TERT and MHC-I. (a) Lineplot of position-specific affinity scores for MHC-I across the TERT protein (1132 amino acids). Position affinity scores were calculated by taking the median NetMHCpan v4.1 (Reynisson et al. 2020) rank affinity of the best overlapping peptide as described in (Castro et al. 2021). (b) Barplot denoting the frequency of various class I supertypes across population groups obtained from Sette and Sidney (1999). (c) Barplot of the fraction of positions falling below the binding threshold (