Cancer Biology and Advances in Treatment [1st ed.] 9783030572532, 9783030572549

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Advances in Experimental Medicine and Biology 1292 Innovations in Cancer Research and Regenerative Medicine

Phuc Van Pham  Editor

Cancer Biology and Advances in Treatment

Advances in Experimental Medicine and Biology Volume 1292 Series Editors WIM E. CRUSIO, Institut de Neurosciences Cognitives et Intégratives d’Aquitaine, CNRS and University of Bordeaux, Pessac Cedex, France HAIDONG DONG, Departments of Urology and Immunology, Rochester, Minnesota, USA JOHN D. LAMBRIS, University of Pennsylvania, Philadelphia, Pennsylvania, USA HEINFRIED H. RADEKE, Clinic of the Goethe University Frankfurt Main, Institute of Pharmacology & Toxicology, Frankfurt am Main, Hessen, Germany NIMA REZAEI, Tehran University of Medical Sciences, Research Center for Immunodeficiencies, Children’s Medical Center, Tehran, Iran JUNJIE XIAO, School of Life Science, Shanghai University, Cardiac Regeneration and Ageing Lab, Institute of Cardiovascular Sciences, Shanghai, China

Innovations in Cancer Research and Regenerative Medicine Subseries Editor PHUC VAN PHAM, Stem Cell Institute, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam

Innovations in Cancer Research and Regenerative Medicine is based on a biannual conference and its topics, and represents a major contribution to the emerging science of cancer research and regenerative medicine. The series publishes review and original research contributions, short reports, conference proceedings, and guest-edited thematic volumes. Each volume brings together some of the most preeminent scientists working on cancer biology, cancer treatment, cancer diagnosis, cancer prevention and regenerative medicine to share information on currently ongoing work which will help shape future therapies. These volumes are invaluable resources for active researchers or clinicians, those entering related fields, and professionals in industry. All contributions will be published online first and collected in book volumes. There are no publication costs. Innovations in Cancer Research and Regenerative Medicine is a subseries of Advances in Experimental Medicine and Biology, which has been publishing significant contributions in the field for over 30 years and is indexed in Medline, Scopus, EMBASE, BIOSIS, Biological Abstracts, CSA, Biological Sciences and Living Resources (ASFA-1), and Biological Sciences. The 2019 Impact Factor is 2.450. The 5 Year Impact Factor is 2.324.

More information about this subseries at http://www.springer.com/series/15740

Phuc Van Pham Editor

Cancer Biology and Advances in Treatment

Editor Phuc Van Pham Stem Cell Institute University of Science, Vietnam National University Ho Chi Minh, Vietnam

ISSN 0065-2598 ISSN 2214-8019 (electronic) Advances in Experimental Medicine and Biology ISSN 2662-3285 ISSN 2662-3293 (electronic) Innovations in Cancer Research and Regenerative Medicine ISBN 978-3-030-57253-2 ISBN 978-3-030-57254-9 (eBook) https://doi.org/10.1007/978-3-030-57254-9 # Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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

Contents

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Saleem, Mohd Bazli Ghazali, Md Azlan Mohamed Abdul Wahab, Narazah Mohd Yusoff, Hakimah Mahsin, Ch’ng Ewe Seng, Imran Abdul Khalid, Mohd Nor Gohar Rahman, and Badrul Hisham Yahaya Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root Extract on Human Breast Cancer Cell Line MCF-7 in 3D Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lam-Huyen Nguyen-Thi, Sinh Truong Nguyen, Thao Phuong Tran, Chinh-Nhan Phan-Lu, Trung The Van, and Phuc Van Pham A Novel Nonsense Mutation c.374C>G in CYP21A2 Gene of a Vietnamese Patient with Congenital Adrenal Hyperplasia . . . Chi Dung Vu, Thanh Van Ta, Ngoc-Lan Nguyen, Huy-Hoang Nguyen, Thi Kim Lien Nguyen, Thinh Huy Tran, and Van Khanh Tran Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thi Thuy Hang Tran, Duc Hinh Nguyen, Van Khanh Tran, Quy Linh Nguyen, Hong Anh Trinh, Long Hoang Luong, Van Anh Tran, Le Anh Tuan Pham, Thu Thuy Nguyen, Van Bang Nguyen, Thinh Huy Tran, and Thanh Van Ta Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and Its Cytotoxicity Effects in Tamoxifen-Resistant Breast Cancer Cells . . . . . . . . . . . . . . . . . . . Rozaina Ahmad, Noor Haida Mohd Kaus, and Shahrul Hamid Adipose-Derived Mesenchymal Stem Cells Promote Growth and Migration of Lung Adenocarcinoma Cancer Cells . . . . . . . . . Norashikin Zakaria and Badrul Hisham Yahaya

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Predictive Potential of PD-L1, TYMS, and DCC Expressions in Treatment Outcome of Colorectal Carcinoma . . . . . . . . . . . . . . Ebenyi Emeka Onwe, Fauzah Abd Ghani, Maha Abdullah, Malina Osman, Reena Rahayu Md Zin, Arimokwu Nimbi Vivian, and Norhafizah Mohtarrudin

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Clinical Trials with Cytokine-Induced Killer Cells and CAR-T Cell Transplantation for Non-small Cell Lung Cancer Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Le Van Manh Hung, Hieu Trong Ngo, and Phuc Van Pham Isopanduratin A Isolated from Boesenbergia pandurata Reduces HepG2 Hepatocellular Carcinoma Cell Proliferation in Both Monolayer and Three-Dimensional Cultures . . . . . . . . . . . . . . . . . 131 Sinh Truong Nguyen, Nghia Minh Do, Duyen Ho-Khanh Tran, Ngoc Bao To, Phuc Hong Vo, Mai Thi Thanh Nguyen, Nhan Trung Nguyen, Hai Xuan Nguyen, Kiet Dinh Truong, and Phuc Van Pham Hopea odorata Extract Can Efficiently Kill Breast Cancer Cells and Cancer Stem-Like Cells in Three-Dimensional Culture More Than in Monolayer Cell Culture . . . . . . . . . . . . . . . 145 Nhan Lu-Chinh Phan, Khuong Duy Pham, Phong Le Minh, Mai Thi-Thanh Nguyen, Ngoc Phan Kim, Kiet Dinh Truong, and Phuc Van Pham Selective Cytotoxicity of Some Plant Extracts Against Hepatocellular Carcinoma Cells but Not Mesenchymal Stem Cells: A Pilot Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Sinh Truong Nguyen, Nghia Minh Do, Phuc Hong Vo, Mai Thi Thanh Nguyen, Nhan Trung Nguyen, Hai Xuan Nguyen, Kiet Dinh Truong, and Phuc Van Pham Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

Contributors

Maha Abdullah Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Rozaina Ahmad Oncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Penang, Malaysia Nghia Minh Do Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Cancer Research Laboratory, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Fauzah Abd Ghani Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Mohd Bazli Ghazali Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Surgery Department, Seberang Jaya Hospital, Seberang Prai, Malaysia Shahrul Hamid Oncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Penang, Malaysia Le Van Manh Hung Stem Cell Institute, University of Science, Ho Chi Minh City, Viet Nam Noor Haida Mohd Kaus School of Chemical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia Imran Abdul Khalid Surgery Department, Seberang Jaya Hospital, Seberang Prai, Malaysia Ngoc Phan Kim Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Long Hoang Luong Hanoi Medical University, Hanoi, Vietnam

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Hakimah Mahsin Pathology Department, Seberang Jaya Hospital, Seberang Prai, Malaysia Phong Le Minh Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Norhafizah Mohtarrudin Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Hieu Trong Ngo Stem Cell Institute, University of Science, Ho Chi Minh City, Viet Nam Duc Hinh Nguyen Hanoi Medical University, Hanoi, Vietnam Hanoi Medical University Hospital, Hanoi, Vietnam Hai Xuan Nguyen Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Faculty of Chemistry, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Huy-Hoang Nguyen Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam Mai Thi-Thanh Nguyen Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Faculty of Chemistry, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Ngoc-Lan Nguyen Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam Nhan Trung Nguyen Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Faculty of Chemistry, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Quy Linh Nguyen Hanoi Medical University, Hanoi, Vietnam Sinh Truong Nguyen Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Thi Kim Lien Nguyen Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam Lam-Huyen Nguyen-Thi Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Thu Thuy Nguyen Hanoi Medical University, Hanoi, Vietnam

Contributors

Contributors

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Van Bang Nguyen Center for Gene and Protein Research, Vietnam Military Medical University, Hanoi, Vietnam Ebenyi Emeka Onwe Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Medical Laboratory Science Department, Ebonyi State University, Abakaliki, Nigeria Malina Osman Department of Medical Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Khuong Duy Pham Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Le Anh Tuan Pham Hanoi Medical University, Hanoi, Vietnam Phuc Van Pham Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Chinh-Nhan Phan-Lu Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Nhan Lu-Chinh Phan Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Mohd Nor Gohar Rahman Surgery Department, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia Mohamed Saleem Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Genomix Lab Sdn Bhd, Petaling Jaya, Selangor, Malaysia Ch’ng Ewe Seng Oncology and Radiological Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Thanh Van Ta Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam Hanoi Medical University, Hanoi, Vietnam Hanoi Medical University Hospital, Hanoi, Vietnam

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Ngoc Bao To Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Duyen Ho-Khanh Tran Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Thao Phuong Tran Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Thi Thuy Hang Tran Hanoi Medical University, Hanoi, Vietnam Thinh Huy Tran Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam Hanoi Medical University Hospital, Hanoi, Vietnam Van Anh Tran Hanoi Medical University, Hanoi, Vietnam Van Khanh Tran Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam Hong Anh Trinh Center for Gene and Protein Research, Vietnam Military Medical University, Hanoi, Vietnam Kiet Dinh Truong Medical Genetic Institute, Ho Chi Minh City, Vietnam Trung The Van Ho Chi Minh City Medicine and Pharmacy University, Hospital of Dermatology – Ho Chi Minh City, Ho Chi Minh City, Vietnam Arimokwu Nimbi Vivian Department of Occupational Safety and Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Phuc Hong Vo Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Cancer Research Laboratory, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Chi Dung Vu Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam Vietnam National Children Hospital Hanoi, Vietnam, Department of Medical Genetics, Metabolism & Endocrinology, Hanoi, Vietnam Md Azlan Mohamed Abdul Wahab Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Badrul Hisham Yahaya Regenerative Medicine Cluster, Advanced Medical & Dental Institute (AMDI), Universiti Sains Malaysia, Kepala Batas, Penang, Malaysia Narazah Mohd Yusoff Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia

Contributors

Contributors

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Norashikin Zakaria Regenerative Medicine Cluster, Advanced Medical & Dental Institute (AMDI), Universiti Sains Malaysia, Kepala Batas, Penang, Malaysia Reena Rahayu Md Zin Universiti Kembangsaan Malaysia, Kuala Lumpur, Malaysia

Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 1–12 https://doi.org/10.1007/5584_2018_147 # Springer International Publishing AG 2018 Published online: 24 April 2018

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients Mohamed Saleem, Mohd Bazli Ghazali, Md Azlan Mohamed Abdul Wahab, Narazah Mohd Yusoff, Hakimah Mahsin, Ch’ng Ewe Seng, Imran Abdul Khalid, Mohd Nor Gohar Rahman, and Badrul Hisham Yahaya Abstract

Approximately 5–10% of breast cancers are attributable to genetic susceptibility. Mutations in the BRCA1 and BRCA2 genes are the best known genetic factors to date. The goal of this study was to determine the structure and distribution of haplotypes of the BRCA1 and BRCA2 genes in early-onset breast

cancer patients. We enrolled 70 patients diagnosed with early-onset breast cancer. A total of 21 SNPs (11 on BRCA1 and 10 on BRCA2) and 1 dinucleotide deletion on BRCA1 were genotyped using nested allelespecific PCR methods. Linkage disequilibrium (LD) analysis was conducted, and haplotypes were deduced from the genotype data. Two tightly linked LD blocks were observed on

Mohamed Saleem and Mohd Bazli Ghazali authors contribute to the authorship (as the first author) equally. M. Saleem Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Genomix Lab Sdn Bhd, Petaling Jaya, Selangor, Malaysia e-mail: [email protected] M. B. Ghazali Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia Surgery Department, Seberang Jaya Hospital, Seberang Prai, Malaysia M. A. M. A. Wahab, N. M. Yusoff, and B. H. Yahaya (*) Regenerative Medicine Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia e-mail: [email protected]; [email protected]; [email protected]

H. Mahsin Pathology Department, Seberang Jaya Hospital, Seberang Prai, Malaysia e-mail: [email protected] C. E. Seng Oncology and Radiological Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia e-mail: [email protected] I. A. Khalid Surgery Department, Seberang Jaya Hospital, Seberang Prai, Malaysia M. N. G. Rahman Surgery Department, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia e-mail: [email protected] 1

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M. Saleem et al.

each of the BRCA1 and BRCA2 genes. Variant-free haplotypes (TAT-AG for BRCA1 and ATA-AAT for BRCA2) were observed at a frequency of more than 50% on each gene along with variable frequencies of derived haplotypes. The variant 30 -subhaplotype CGC displayed strong LD with 50 -subhaplotypes GA, AA, and GG on BRCA1 gene. Haplotypes ATA-AGT, ATC-AAT, and ATA-AAC were the variant haplotypes frequent on BRCA2 gene. Although the clinical significance of these derived haplotypes has not yet been established, it is expected that some of these haplotypes, especially the less frequent subhaplotypes, eventually will be shown to be indicative of a predisposition to earlyonset breast cancer. Keywords

BRCA1 · BRCA2 · Breast cancer · Early onset

1

Introduction

Breast cancer is ranked as the most common malignancy (18%) among Malaysians, and it is the most frequently diagnosed cancer (31.3%) in Malaysian women irrespective of their ethnic group (Lim et al. 2008). The annual incidence of breast cancer relative to other malignancies among women aged 15–49 years is 39.1%. In general, a Malaysian woman has a 1 in 20 chance of developing this complex and heterogeneous disease in her lifetime (Dahlui et al. 2011), compared with 1 in 8 in Europe (Wahid 2014). Most breast cancer cases are sporadic, but a few are ascribed to inherited germline mutations in a couple of susceptibility genes that segregate with the disease (Arver et al. 2000). Pathological germline mutations in the two tumour suppressor genes, BRCA1 and BRCA2, are the best known genetic factors known to confer a strong predisposition for familial breast and ovarian cancer in women (Wooster and Weber 2003; Breast Cancer Linkage Consortium 1997). Other less susceptible genes subsume CHEK2, ATM, and TP53.

Women who inherit mutations in either the BRCA1 or BRCA2 gene are at a significant risk of developing the disease when compared with the general population, and both contribute equally to early-on-set breast cancer (Peto et al. 1999). The disease risk in women with these mutations has been shown to increase monotonically with age, with a lifetime risk of up to 87% (Palma et al. 2006). The spectrum of mutations in BRCA1 and BRCA2 genes varies significantly between different geographic populations. Molecular susceptibility screening for early-onset breast cancer, therefore, begins with screening a given population for pathological mutations in both BRCA1 and BRCA2 in a well-defined cohort with the disease. To date, many reports of genetic associations for individual mutations give a single account of the phenotype and are mostly unreplicated in subsequent studies (Breast Cancer Association Consortium 2006; Loannidis et al. 2001). Identifying the combination of different variants on a single DNA molecule (i.e. haplotype determination) is a much more efficient and informative approach to identifying the underlying genetic mutants that increase the susceptibility to complex disease traits. The arrangement of alleles at the polymorphic loci scattered throughout the BRCA1 and BRCA2 genes is not random: Depending on the linkage disequilibrium (LD), the non-random association between these mutant alleles exists in a series of patterns called haplotypes. The linked alleles in these LD blocks co-segregate as haplotypic units of inheritance. The BRCA1 and BRCA2 genes are normally involved in the sophisticated DNA repair process, especially the homologous recombination pathway of double-strand break (DSB) repair mechanisms. The most common germline mutations in breast cancer patients occur in these two genes. The incidence of variants in the coding region of the BRCA1 and BRCA2 genes in early-onset breast cancer patients from Malaysia has been accurately determined (Toh et al. 2008). However, whether the common variants at these loci contribute to a common variant haplotype across the exons of BRCA1 and BRCA2 has not

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients

been determined in a similar cohort of early-onset breast cancer patients. It is generally accepted that haplotype renders more information than single marker analysis. In this study, we characterised the structure and distribution of haplotypes on the BRCA1 and BRCA2 genes in the Malaysian multiethnic population with early-onset breast cancer and explored the use of these haplotypes for disease predictability.

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Methods

2.1

Case Selection

Seventy randomly selected unrelated patients diagnosed with breast cancer at or before the age of 40 years were recruited into the study from the surgery department of Hospital Seberang Jaya in Penang, Malaysia. The study population was composed of 43 Malays, 19 Chinese, 5 Indians, and 3 subjects from whom ethnicity could not be ascertained. This retrospective study was approved by the Human Ethics Committee of Universiti Sains Malaysia and the Ethics Review Board of the Ministry of Health, Malaysia. Written informed consent for genetic analysis of blood was obtained from all participants at the time of phlebotomy. The medical and family histories, histopathological diagnoses, and demographic variables were obtained by review of the

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respective medical records and direct interviews. Other data gathered were lymph node status, tumour grade and size, and expression of hormone receptors.

2.2

Genetic Marker Selection

Toh Kang et al. (2008) sequenced the entire coding region and splice junctions and characterised 14 mutations in the BRCA1 gene and 17 mutations in the BRCA2 gene in a cohort of early-onset breast cancer patients from Malaysia (Toh et al. 2008). Using their findings as the source of the frequent allele distribution among Malaysian with early-onset breast cancer, we selected 11 SNPs in the BRCA1 gene and 10 SNPs in the BRCA2 genes to genotype our cohort. We also genotyped the 185delAG deletion in BRCA1 exon 2. Tables 1 and 2 provide details about these SNP markers.

2.3

Molecular Testing

For each patient, DNA was extracted from EDTA-anticoagulated blood using the DNA isolation kit from Qiagen (Hilden, Germany) according to the manufacturer’s instruction. Each SNP marker was amplified using 50 ng of genomic DNA with four primers in a single PCR reaction (Table 3). The 185delAG deletion in exon 2 of the BRCA1 gene was genotyped using

Table 1 BRCA1 mutations in women with breast cancer before the age of 40 years

1 2 3 4 5 6 7 8 9 10 11

SNP Rs1799966 Rs1060915 Rs16942 Rs16941 Rs273899691 Rs80356892 Rs16940 Rs273898682 Rs1799949 Rs4986850 Rs1800062

Genomic (NC_000017.11:g.) 43071077T > C 43082453A > G 43091983T > C 43092418T > C 43092600T > C 43092965T > C 43093220A > G 43093245T > A 43093449G > A 43093454C > T 43115746C > T

Transcript NM_007294.3:c) 4837A > G 4308T > C 3548A > G 3113A > G 2931A > G 2566T > C 2311T > C 2286A > T 2082C > T 2077G > A 114G > A

Exon 16 13 11 11 11 11 11 11 11 11 3

Allele Wild T A T T T A A T G C C

Variant C G C C C T G A A T T

MAF 0.458 0.451 0.444 0.408 0.00 0.014 0.408 0.00 0.444 0.007 0.00

HWE p value 0.719 0.563 0.426 0.063 1.0 0.014 0.381 1.0 0.426 1.0 1.0

Table 2 BRCA2 mutations in women with breast cancer before the age of 40 years

No 1 2 3 4 5 6 7 8 9 10

dbSNP number RS766173 RS79483201 Rs144848 RS1801499 RS1799944 RS1801406 RS80358589 RS543304 RS202022822 RS80358755

Genomic 500 -30

Marker position on Genomic NC_000013.11:g 32332343A > C 32332421T > A 32332592A > C 32336584T > C 32337326A > G 32337751A > G 32337800A > G 32338162T > C 32338933A > G 32339667G > A

Transcript NM_007294.3:c 856A > C 943T> A 1114A > C 2229T > C 2971A > G 3396A > G 3445A > G 3807T > C 4578A > G 5312G > A

Exon 10 10 10 11 11 11 11 11 11 11

Wild A T A T A A A T A G

Variant C A C C G G G C G A

MAF 0.064 0.014 0.364 0.114 0.015 0.321 0.00 0.15 0.0 0.0

HWE 0.479 1.0 0.489 0.072 1.0 0.192 1.0 0.939 1.0 1.0

Table 3 Oligonucleotide primers used for genotyping Ch position BRCA1 (NC_000017.11:g) 43071077T > C

Marker Rs1799966

43082453A > G

Rs1060915

43091983T > C

43092418T > C

43092600T > C

Rs16942

Rs16941

Rs273899691

43092965T > C

Rs80356892

43093220A > G

Rs16940

43093245T > A

Rs273898682

43093449G > A

Rs1799949

Name FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO

Oligos 50 >30 GTATCAGTAGTATGAGCAGCAGCTGGCCC AATTGAAAGTTGCAGAATCTGCCCCGA CCAGACACCACCATGGACATTCTTTTGT TTTCAGAGGGAACCCCTTACCTGGAATC GATTTCGCAGGTCCTCAAGGGCATAA CAGCTACCCTTCCATCATAAGTGACGCC

Tm ( C) 62.6

60.0

CAGATGACAACATGAATGACTGCCTTGG CATGGGCATTAATTGCATGAATGTGGTT GGGCTAGGACTCCTGCTAAGCTCTCATT TCTGCTGTTTTTAGCAAAAGCGTCCATAG

62.6

TAACAAGTGTTGGAAGCAGGGAAGCTCT CATGCATCTCAGGTTTGTTCTGAGACAC TTCATTAATATTGCTTGAGCTGGATT AATAACATTAGAGAAAATGTTTTTAACGG

53.8

CAATTACTTCCAGGAAGACTTTGTTTAT CCATCAAGTCATTTGTTAAAACTAAATG GATGGGAAAAAGTGGTGGTATACGAGAT CCAAATAAACATGGACTTTTACAAAACACG

60.0

GCTTGAGCTGGCTTCTTTAAAAACATTT TGTGAACAAAAGGAAGAAAATCAAGGAA TTTGAAACCTTGAATGTATTCTGCAACTG GGAAGAAAGTGAACTTGATGCTCCGT GTTTCGTTGCCTCTGAACTGAGATGATA CATTGGTACCTGGTACTGATTATGGCAC GTGCCATAATCAGTACCAGGTACAAA AAAGATCTGTAGAGAGTAGCAGTATTTAAC ATTAGACTCATTCTTTCCTTGATTTTCT GATAAAGAAAAAAAAGTACAACCAAATG ACCAATGAAATACTGCTACTCTCTACACAA GAGAAAGGGTTTTGCAAACTGAATGA TGTATTCTGCAAATACTGAGCATCAAGT AAGAAGAGTAACAAGCCAAATGAACAGA GTTAACTTCAGCTCTGGGAAAGTAGCG AATGAACAGACAAGTAAAAGACATGACCGT TCTTTGGGGTCTTCAGCATTATTAGACA AAAGCACCTAAAAAGAATAGGCTGAGGA

62.6

60.0

60.0

61.9

(continued)

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients

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Table 3 (continued) Ch position BRCA1 (NC_000017.11:g) 43093454C > T

Marker Rs4986850

43115746C > T

Rs1800062

BRCA2 (NC_000013.11:g) 32332343A > C RS766173

32332421T > A

RS79483201

32332592A > C

Rs144848

32336584T > C

RS1801499

32337326A > G

RS1799944

32337751A > G

RS1801406

32337800A > G

RS80358589

32338162T > C

RS543304

32338933A > G

RS202022822

32339667G > A

RS80358755

Name FI RI FO RO FI RI FO RO

Oligos 50 >30 CTTCAGCTCTGGGAAAGTATCGCTTTT GCCAAATGAACAGACAAGTAAAAGACCTG TGTTTTTGCCTTCCCTAGAGTGCTAACT AGCACCTAAAAAGAATAGGCTGAGGAGG CAAACTTACTTGCAAAATATGTGGTCAAAT TGATCAAGGAACCTGTCTCCACACAG AATGGAGTTGGATTTTTCGTTCTCACTT GAACTTGAGGCCTTATGTTGACTCAGTC

FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO FI RI FO RO

AAGACCACATTGGAAAGTCAATGCTAA TGTTTCATATACTTCATCTTCTAGGACGTG AAATGCCAAGTACTCAGAATAACCCTTT GGTTTTTAGATTTTTCACATTCATCAGC AAGATAGTTTTTCATTATGTTTTTCTAGAT TACTTTTTGTAGATTTTTTGTTCTGCT AATGTGCTTCTGTTTTATACTTTAACAG CTTCAGATACAAATGAGTATTTTTCTTT ACTGATCCATTAGATTCAAATGTAGTAC CTTCCACTCTCAAAGGGCTTCTGGTT CAAGACTAGGAAAAAAATTTTCCATGAA AGGTCTTTTTCTGAAATATTTTGGTCAC CTGCAGCATGTCACCCAGTACAAAAC AAGTCAGTATCACTGTATTCCACTTTTTAA GAAAAGAAGCTGTTCACAGAATGATTCT AGATTTGTGTTTTGGTTGAATTGTACCT AATACCAGAAAAAAATAATGATTACAGGG GGACCTAAGAGTCCTGCCCATTTTTT TTTTGTCTTCCAAGTAGCTAATGAAAGG CCTTTTGGCTAGGTGTTAAATTATGGTT AGTCAGTTTGAATTTACTCAGTTTAGACAA GTACTCTTCTGCAATATGTAGCTTTGC TCAAGCTCTCTGAACATAACATTAAGAA AAGATAAACTTATTGGATGTACCTCTGC GTACATTTGAAGTGCCTGAAAACCCGA TTCCTCAGAAGTGGTCTTTAAGATAGTAAC TGTGTTGAAATTGTAAATACCTTGGCAT CTAAACAGTTTCACAGCTTTTTGCAGAG TTATCTTCAAGTAAATGTCATGATTCTTTT TGATTTTCTATCTTAAACATTGAACCG TTTACTCAGTTTAGAAAACCAAGCTACA AACTTCCAAAAAAGTTAAATCTGACAAA AGGGACAACCCGAACGTGATGAAACGA TGAAAACCCAATAGAGTAGGTTCTTTTAC CAAGTGGGAAAAATATTAGTGTCGCCAA TGTACTTTAGGGTCTTTGCCCATTGATG CTCTCAAAAAATAAACTTGATTCGGA AACATTCTTCAATACTGGCTCAAGAC CAACCAGAAAGAATAAATACTGCAGATT TATTAGATATGGACAATTTAATGGCTGC

FI Forward inner, RI Reverse inner, FO Forward outer, RO Reverse outer

Tm ( C) 60.0

60.0

62.5

55.4

55.8

63.0

55.8

63.0

63.0

59.0

61.9

55.8

6

M. Saleem et al.

the allele-specific Gap-PCR assay with the following primers: forward inner (wild-type allele) 50 -TGACTTACCAGATGGGACACTC-30 , reverse inner (Gap primer complementary to 185delGA deletion) 50 -GCTATGCAGAAAATCTTAGTG30 , forward outer 50 -TTCCCGGACCACAGGA TTTG-30 , and reverse outer 50 -AGGCCTTGATT GGTGTTGGT-30 .

2.4

LD and Haplotype Analysis

Pairwise LD between the studied markers was measured using the statistic D0 . LD blocks were detected using the solid spine algorithm in Haploview software. The frequencies of haplotypes in the BRCA1 and BRCA2 genes were deduced from diploid genotypes using the accelerated partition-ligation-expectationmaximisation (EM) algorithm method as described by Qin et al. (2002) using Haploview 4.2 (Barrett et al. 2005). To evaluate deviation from Hardy-Weinberg equilibrium (HWE), we used the χ 2 test with 1 df.

3

Results

3.1

Clinical Characteristics

Histological examination showed that 56 of the 70 (80%) patients had invasive ductal cell carcinoma, 6 (8.6%) had ductal cell carcinoma in situ, and a few others presented with less common histological types, including 2 cases of mucinous and 3 cases of medullary carcinoma. Forty-six patients had cancer on the right side, and the remaining 24 had their tumour on the left side. None of the patients had history of contralateral cancer. Scarff-Bloom-Richardson grades I, II, and III were present in 8, 30, and 32 cases, respectively. Ipsilateral lymph node positivity (N+) was seen in 30 (42.8%) patients. Mastectomy was conducted in 51 (71.8%) of the patients. Twenty-three patients had a triple-negative tumour. None of the haplotypes observed in the BRCA genes showed a statistically significant

association with ( p ¼ 0.411).

3.2

triple-negative

tumours

SNP Marker Characteristics

Eleven SNPs loci were genotyped on the BRCA1 gene. Among the 70 early-onset breast cancer patients, 7.2% (5/70) had variant alleles observed in 1–3 SNP markers, and 67.1% (47/70) had variant alleles observed in 4–6 markers. Interestingly, 25.7% (18/70) of patients showed homozygosity for the wild-type allele at all 11 markers. None of the Malaysian patients had the rs80357713 (185delAG) deletion. Of the remaining 11 polymorphic markers genotyped in the BRCA1 gene, 7 were missense and 4 were synonymous mutations. Of these 11 markers, 5 were not used for haplotype determination because one (rs80356892) significantly violated HWE (χ 2 p < 0.05), and the other 4 were either not polymorphic (rs273899691, rs273898682, and rs1800062), or the minor allele frequency (MAF) was less than 1% (rs4986850). For the BRCA2 gene, at least one heterozygous individual was detected at ten SNP markers studied. However, three (rs80358589, rs202022822, and rs80358755) of the ten markers studied were removed from haplotype analysis because they were not polymorphic (MAF < 1%) in the sample. The PCR allele call rate was 100% for all of the markers studied on BRCA1 and BRCA2 genes except for rs1799944 at the BRCA2 gene, which achieved 97.1%. The cohort was assumed to be selected randomly as all markers, except for one (rs80356892), were in conformity with HW equilibrium (χ 2 p > 0.05).

3.3

LD and Haplotypes

Linkage analysis from the BRCA1 genotype data revealed two distinct LD blocks based on the threshold of D0 > 0.8 (Fig. 1). The SNPs rs1799966, rs1060915, and rs16942 were in near complete LD and belong to a 30 -subhaplotype block of 20 kb spanning from exon 16 to the 30 region of exon 11. The three

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients

7

68

57

71 71

93 64

96

94 97

B 07 09

97

Block 2 (0 kb) 7 9

01 02 03

4

rs1799949

3

rs16940

rs16941

Block 1 (20 kb) 1 2

rs16942

rs1060915

rs1799966

A

TAT .542

AG .542

CGC .437

GA .394

CGT .014

AA .050 GG .015 .99

Fig. 1 (a) Pairwise linkage disequilibrium (LD) D0 graph for the six SNPs of the BRCA1 gene as generated by Haploview. The colour scheme denotes the magnitude of D0 . (b) Haplotypes observed for the BRCA1 gene

subhaplotypes observed in this block were TAT (54.2%), CGC (43.7%), and CGT (1.4%). The second haplotype block, which spans a 229 bp region within exon 11, was represented by rs16940 and rs1799949 and had a strong LD value (D0 ¼ 0.93). Four subhaplotypes were observed in this block: AG (54.2%), GA (39.4%), AA (5%), and GG (1.5%). The two blocks were separated by SNP rs16941, which showed decaying LD with the neighbouring polymorphic markers. For the BRCA2 gene, all 70 patients genotyped for the 10 markers had at least 1 SNP positive for a variant allele. Seven markers flanking the mutations (rs766173, rs79483201, rs144848, rs1801499, rs1799944, rs1801406, and rs543304) in the BRCA2 gene were used for LD and haplotype determinations. These polymorphic markers formed two discrete LD blocks (Fig. 2). The first block containing three markers (rs766173, rs79483201, and rs144848) spans a 249 bp region on exon 10 and showed strong LD. However, the LOD score between the

markers was T) on BRCA1 and rs80358589 (3445A > G) and rs202022822 (4578A > G) on BRCA2—that were shown to have a MAF of 2.7% in the previous sample were not polymorphic in our sample due to homozygosity for wild-type allele at these loci. However, comparable allele frequencies

were observed from a geographically distant Manitoba population (Frosk et al. 2007), suggesting that the high-frequency polymorphic markers on the BRCA1 and BRCA2 genes are common in distinct global populations. The AG dinucleotide deletion at rs80357713 at codon 23 (widely known in the literature as 185delAG) found in BRCA1-linked early-onset breast cancer patients of Jewish origin (Struewing et al. 1995) was not observed in our sample cohort. Of the 70 randomly selected patients in our study, 21.4% of patients had a family history of breast cancer. This is slightly higher than the global average of 15% (Madigan et al. 1995; Colditz et al. 1993; Slattery and Kerber 1993). This value means that four out of every five women who developed early-onset breast cancer did not have an affected relative. A similar ratio, 8 out of 9, was reported from a large familial breast cancer meta-analysis including 52 epidemiological studies (Collaborative Group on Hormonal Factors in Breast Cancer 2001). Family history is a strong risk factor for breast cancer globally in every population. However, it has

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients

been consistently observed that most women with a first degree relative with the disease do not develop breast cancer in their lives, and those few who develop it are above the age of 50 when the diagnosis was first made (Collaborative Group on Hormonal Factors in Breast Cancer 2001). These findings suggest that the risk alleles and their haplotypes on BRCA genes segregated in the general population are by and large low in frequency; thus, the probability of inheriting a risk haplotype is relatively low.

5

BRCA1 Haplotypes

LD analysis of the six polymorphic markers on the BRCA1 gene in the early-onset breast cancer patients revealed that two distinct blocks of tightly linked SNP loci defined the haplotype structure of the phenotype (Fig. 1). They were clustered in two small distinct regions located at the 30 end of exon 11 (block 1), including exons 16 and 13, and within exon 11 (block 2). These blocks were separated by bi-alleles at locus rs16941 (MAF 0.408) that showed decaying LD with the surrounding SNP loci; this finding is suggestive of an evolutionary mechanism, possibly selection. Block 1 was represented by two missense mutations at rs1799966 and rs16942 and one synonymous mutation at rs1060915, whereas the block2 subhaplotype had two synonymous mutations at rs16940 and rs1799949. Together, the most common haplomolecule (TAT-AG) from both blocks formed the normal ancestral haplotype that corresponds to wild-type alleles in the references sequence NC_000017.11: g. The others were all variant haplotypes. The most common variant haplomolecule among the five interlinked loci, CGC GA, was found in nearly 40% of the BRCA1 genes studied in our sample. A structurally similar haplotype, named B1, was present at high frequency (25%) in familial breast cancer patients from the Manitoba population (Frosk et al. 2007). Despite the vast geographic and genetic distance between the two cohorts, the presence of a structurally similar variant haplotype in a similar phenogroup is phenomenal; it suggests that the haplotype, in part, has a high-risk probability for early-onset

9

breast cancer. Furthermore, the observation that this variant haplotype had a high frequency in our early-onset breast cancer patients and that it was structurally composed of all variant alleles is consistent with the hypothesis that it is likely to contribute to genetic predisposition to breast cancer and that it may be involved in the pathogenesis of tumour formation. The predicted protein derived from this haplotype (CGC GA) has a glycine, serine and arginine, and leucine and serine at those positions, which represents a difference of two amino acid substitutions compared to the wild type. The exact functional consequence of this variant haplotype, represented by five nucleotide substitutions, is not clear and may vary from truncated protein formation to inactivation of mRNA. The other lower-frequency haplotypes observed in this study may confer a greater risk for inherited susceptibility to breast cancer as these rare haplotypes may only be associated with early-onset breast cancer and not distributed in the general population.

6

BRCA2 Haplotypes

Germline mutations in the BRCA2 gene predispose carriers to early-onset breast cancer. The observation that all 70 patients had a variant allele, at least at 1 locus was a clue to the diversity of this gene in the population. Similar to BRCA1, LD analysis of the genotype data from the BRCA2 gene showed two distinct blocks interrupted by locus rs1801499, which showed decay of the non-random association with the surrounding SNP markers. As evident by the LD chart (Fig. 2), the reduced association could be due to a plausible degree of recombination at locus rs1801499, whereas the increased haplotype diversity was due to the low minor allele frequencies at rs79483201A (1.4%) and rs1799944G (1.5%). All three SNP loci that formed LD block 1 at exon 10 were composed of missense substitutions, whereas the LD block 2 located on exon 11 was composed of one missense and two synonymous loci. Together, these two LD blocks defining the variant-free haplomolecule ATA-AAT was observed at a

10

frequency of more than 52% of the BRCA2 genes studied. This mutant-free haplomolecule represents the normal and common haplotype on the BRCA2 gene that is distributed in the general population. The variant haplotypes of the BRCA2 gene were a complex of different combinations of subhaplotypes between LD block 1 (50 -subhaplotype) and block 2 (30 -subhaplotype) and are derived from the ancestral normal haplotype. As shown in Fig. 2, the normal 50 -subhaplotype ATA derived from LD block 1 combined with either AGT or AAC from the 30 -subhaplotype formed the majority of variant haplotypes. The ATA-AGT arrangement was the most common variant haplotype, and it differed from the normal haplotype by a single-nucleotide substitution of unknown significance at the second nucleotide of the 30 -subhaplotype (NM_000059.3:c. 3396A > G). The ATA-AAC arrangement differed from the normal haplotype by one nucleotide substitution at the third nucleotide of the 30 -subhaplotype (rs543304, Val1269Val), and it may not pose a risk for early-onset breast cancer because the substitution is a synonymous polymorphism. Interestingly, the variant 50 -subhaplotype ATC on block 1 complexed only with the normal 30 -subhaplotype AAT, and this combination formed another common variant haplotype structure that differed from the normal haplotype by a single missense substitution at the third nucleotide of the 50 -subhaplotype (rs144848, NM_000059.3:c. 1114A > C); this subhaplotype involves a nonconservative amino acid change in the region of the protein that interacts with histone acetyltransferase (Healey et al. 2000). Baynes et al. (2007) previously showed that this SNP (rs144848) is not associated with breast cancer risk (Baynes et al. 2007); however, in its heterozygous genotype state (AC), it has been significantly associated with epithelial ovarian cancer (Beesley et al. 2007). The rare variant haplomolecule CTA-AGT differed from the variant-free haplotype by two nucleotides, one on each subhaplotype. The rare 50 subhaplotype CTA in the BRCA2 gene with the minor allele C (MAF ¼ 0.064) at rs766173 has an asparagine to histidine substitution at position

M. Saleem et al.

289. Although its significance in breast cancer is uncertain, carriers of this allele have nonsignificant increased risk of lymphoma (Breast Cancer Linkage Consortium 1999). The discovery that BRCA gene mutations confer significant risk for familial early-onset breast cancer coupled with increased public literacy about personal genomics has led to widespread demand, especially from the relatives of index patients, for genetic profiling of BRCA1 and BRCA2 to predict the inherited risk of developing breast cancer. Some of the variant haplotypes described herein, especially the less frequent ones, may contribute to risk of early-onset breast cancer in the Malaysian population. The realisation that haplotypes play a substantial role in defining the risk of cancer comes from the fact that they are often the starting point for attempts to locate the genes involved in disease processes. Recently, haplotypes have been applied for risk assessment for various complex diseases such as non-small cell lung cancer (NSCLC) and earlyonset coronary artery disease (CAD) and myocardial infarction (MI). These studies revealed that a specific interleukin-1B haplotype correlated well with increased risk of NSCLC (Landvik et al. 2009) and a rare haplotype in LRP8 conferred significant risk of early-onset CAD/MI (Shen et al. 2014). These findings provide strong empirical support for the hypothesis that haplotype analysis could be used as a molecular screening tool to screen BRCA genes to assess potential risk and susceptibility for earlyonset breast cancer. However, the usefulness of these derived haplotypes to predict risk of earlyonset breast cancer needs to be assessed using a powerful association study.

7

Conclusion

In conclusion, we identified common and rare haplotypes of the BRCA1 and BRCA2 genes in early-onset breast cancer patients. The highfrequency haplotypes of these two genes may be regarded as the common haplotypes distributed in the Malaysian population. The less common derived haplotypes found in the breast cancer patients may serve as novel molecular markers

The BRCA1 and BRCA2 Genes in Early-Onset Breast Cancer Patients

that may be of potential significance in screening to identify patients at high risk for early-onset breast cancer. The risk associated with these haplotypes needs to be evaluated in a prospective case control study designed to validate their importance before they are used as molecular diagnostic markers for the Malaysian population. Acknowledgement Authors would like to extend nurses and technical staff in the Department of Surgery, Hospital Seberang Jaya, Ministry of Health Malaysia, Hospital Universiti Sains Malaysia (HUSM) and Clinical Trial Centre, and Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia for helping us in sample collection and patient recruitments. Conflict of Interests None

References Arver, B., Du, Q., Chen, J., Luo, L., & Lindblom, A. (2000). Hereditary breast cancer: A review. Seminars in Cancer Biology, 10, 271–288. Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2005). Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics, 21, 263–265. Baynes, C., Healey, C. S., Pooley, K. A., Scollen, S., Luben, R. N., et al. (2007). Common variants in the ATM, BRCA1, BRCA2, CHEK2 and TP53 cancer susceptibility genes are unlikely to increase breast cancer risk. Breast Cancer Research, 9, R27. Beesley, J., Jordan, S. J., Spurdle, A. B., Song, H., Ramus, S. J., et al. (2007). Association between singlenucleotide polymorphisms in hormone metabolism and DNA repair genes and epithelial ovarian cancer: Results from two Australian studies and an additional validation set. Cancer Epidemiology, Biomarkers & Prevention, 16, 2557–2565. Breast Cancer Association Consortium. (2006). Commonly studied single-nucleotide polymorphisms and breast cancer: Results from the breast cancer association consortium. Journal of the National Cancer Institute, 98, 1382–1396. Breast Cancer Linkage Consortium. (1997). Pathology of familial breast cancer: Differences between breast cancers in carriers of BRCA1 or BRCA2 mutations and sporadic cases. Lancet, 349, 1505–1510. Breast Cancer Linkage Consortium. (1999). Cancer risks in BRCA2 mutation carriers. Journal of the National Cancer Institute, 91, 1310–1316. Colditz, G. A., Willett, W. C., Hunter, D. J., Stampfer, M. J., Manson, J. E., et al. (1993). Family history, age, and risk of breast cancer. Prospective data from the Nurses’ health study. JAMA, 270, 338–343.

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Collaborative Group on Hormonal Factors in Breast Cancer. (2001). Familial breast cancer: Collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet, 358, 1389–1399. Dahlui, M., Ramli, S., & Bulgiba, A. M. (2011). Breast cancer prevention and control programs in Malaysia. Asian Pacific Journal of Cancer Prevention, 12, 1631–1634. Frosk, P., Burgess, S., Dyck, T., Jobse, R., & Spriggs, E. L. (2007). The use of ancestral haplotypes in the molecular diagnosis of familial breast cancer. Genetic Testing, 11, 208–215. Healey, C. S., Dunning, A. M., Teare, M. D., Chase, D., Parker, L., et al. (2000). A common variant in BRCA2 is associated with both breast cancer risk and prenatal viability. Nature Genetics, 26, 362–364. Landvik, N. E., Hart, K., Skaug, V., Stangeland, L. B., Haugen, A., et al. (2009). A specific interleukin-1B haplotype correlates with high levels of IL1B mRNA in the lung and increased risk of non-small cell lung cancer. Carcinogenesis, 30, 1186–1192. Lim, G. C. C., Rampal, S., & Halimah, Y. (2008). Cancer incidence in peninsular Malaysia 2003–2005. The third report of the National Cancer Registry Malaysia. Kuala Lumpur: National Cancer Registry, Malaysia. Loannidis, J. P., Ntzani, E. E., Trikalinos, T. A., & Contopoulos-Ioannidis, D. G. (2001). Replication validity of genetic association studies. Nature Genetics, 29, 306–309. Madigan, M. P., Ziegler, R. G., Benichou, J., Byrne, C., & Hoover, R. N. (1995). Proportion of breast cancer cases in the United States explained by wellestablished risk factors. Journal of the National Cancer Institute, 87, 1681–1685. Palma, M., Ristori, E., Ricevuto, E., Giannini, G., & Gulino, A. (2006). BRCA1 and BRCA2: The genetic testing and the current management options for mutation carriers. Critical Reviews in Oncology/Hematology, 57, 1–23. Peto, J., Collins, N., Barfoot, R., Seal, S., Warren, W., et al. (1999). Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. Journal of the National Cancer Institute, 91, 943–949. Qin, Z. S., Niu, T., & Liu, J. S. (2002). Partition-ligationexpectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. American Journal of Human Genetics, 71, 1242–1247. Shen, G. Q., Girelli, D., Li, L., Rao, S., Archacki, S., et al. (2014). A novel molecular diagnostic marker for familial and early-onset CAD and MI in the LRP8 gene. Circulation. Cardiovascular Genetics, 7(4), 514–520. Slattery, M. L., & Kerber, R. A. (1993). A comprehensive evaluation of family history and breast cancer risk. The Utah population database. JAMA, 270, 1563–1568. Struewing, J. P., Abeliovich, D., Peretz, T., Avishai, N., Kaback, M. M., et al. (1995). The carrier frequency of

12 the BRCA1 185delAG mutation is approximately 1 percent in Ashkenazi Jewish individuals. Nature Genetics, 11, 198–200. Toh, G. T., Kang, P., Lee, S. S., Lee, D. S., Lee, S. Y., et al. (2008). BRCA1 and BRCA2 germline mutations in Malaysian women with early-onset breast cancer without a family history. PLoS One, 3, e2024.

M. Saleem et al. Wahid, M. I. (2014). Breast cancer. Common cancer. Malaysian Oncological Society. http://www. malaysiaoncology.org/article.php?aid¼114 Wooster, R., & Weber, B. L. (2003). Breast and ovarian cancer. The New England Journal of Medicine, 348, 2339–2347.

Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 13–25 https://doi.org/10.1007/5584_2018_148 # Springer International Publishing AG 2018 Published online: 24 April 2018

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root Extract on Human Breast Cancer Cell Line MCF-7 in 3D Model Lam-Huyen Nguyen-Thi, Sinh Truong Nguyen, Thao Phuong Tran, Chinh-Nhan Phan-Lu, Trung The Van, and Phuc Van Pham Abstract

Background: Cancer is one of the leading causes of death in the world. A great deal of effort has been made to discover new agents for cancer treatment. Xao tam phan (Paramignya trimera) is a traditional medicine of Vietnam used in cancer treatment for a long time, yet there is not much scientific evidence proving its anticancer potency. The study aimed to evaluate the toxicity of Paramignya trimera extract (PTE) on multicellular tumor spheres (MCTS) of MCF-7 cells using hanging drop technique. Methods: Firstly, MCF-7 cells were seeded on hanging drop plates,

spheroid size was tracked, and growth curve was measured by MTT assay and AlamarBlue ® assay. The necrotic core of MCTS was evaluated by propidium iodide (PI) staining. Toxicity of doxorubicin (DOX) and tirapazamine (TPZ) was then tested on 3D model compared to 2D culture condition. Results: The results showed that the IC50 of DOX on 3D MCF-7 cells was nearly 50 times greater than monolayer MCF-7 cells. In contrast, TPZ (an agent which is specifically toxic under hypoxic conditions) had significantly lower IC50 in 3D condition than in 2D. The toxicity tests for PTE showed that PTE

L.-H. Nguyen-Thi, S. T. Nguyen, and C.-N. Phan-Lu Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science, VNUHCM, Ho Chi Minh City, Vietnam e-mail: [email protected]; [email protected]; [email protected] T. P. Tran Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam T. The Van Ho Chi Minh City Medicine and Pharmacy University, Hospital of Dermatology – Ho Chi Minh City, Ho Chi Minh City, Vietnam

P. Van Pham (*) Stem Cell Institute, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science, VNUHCM, Ho Chi Minh City, Vietnam Laboratory of Stem Cell Research and Application, University of Science, VNUHCM, Ho Chi Minh City, Vietnam e-mail: [email protected] 13

14

L.-H. Nguyen-Thi et al.

strongly inhibited MCF-7 cells in both 2D and 3D conditions. Interestingly, the IC50 of PTE in 3D model was remarkably lower than in 2D (IC50 value was 168.9
11.65 μg/ml compared to 260.8
16.54 μg/ml, respectively). The invasion assay showed that PTE completely inhibited invasion of MCF-7 cells at 250 μg/mL concentration. Also, flow cytometry results indicated that PTE effectively induced apoptosis in MCF-7 spheroids in 3D condition at 250 μg/mL concentration. Conclusion: The results from this study emphasize the promise of PTE in cancer therapy. Keywords

3D tumor sphere · Anticancer · Apoptosis · Extract · MCF-7 · Multicellular tumor spheres · Paramignya trimera · Tirapazamine · Toxicity · TPZ · Xao tam phan

Abbreviations DOX ECM FBS MCTS PTE PI TPZ

1

Doxorubicin Extracellular matrix Fetal bovine serum Multicellular tumor spheres Paramignya trimera extract Propidium iodide Tirapazamine

Introduction

Traditional herbal medicines have been used to improve people’s health for over a thousand years. Plant-derived compounds are being remarkably recognized as useful complementary medicines in cancer treatment (Yin et al. 2013). Xao tam phan (Paramignya trimera) is a Vietnamese herbal medicine which was shown to contain high levels of phenols, saponins, flavonoids, proanthocyanidins, and antioxidant agents (Nguyen et al. 2017). Although this herb has been widely used for the treatment of cancer

or cancer-like diseases in recent years, there has been no official publication in the literature on its anticancer effects. Cell-based assays are considered to be the foundation for drug development process, e.g., for the development of anticancer drugs. Initial promising results of new drug candidates in cellbased assays provide insight and rationale for further evaluation in animal-based models (Charoen et al. 2014; Hutmacher 2010). Although 2D cancer cell culture has a long history of usage, in anticancer drug development, there are many limitations which have been reported on in recent years (Edmondson et al. 2014). Notably, 2D cancer cell culture models fail to mimic the natural structure of a tumor. This leads to the differences in the response of monolayer cancer cells (versus natural tumors) to the tested candidate drugs (Edmondson et al. 2014). Researchers therefore developed 3D cell culture models to bridge the gap between 2D cellbased assays and preclinical animal models/trials. Thus, a large number of publications have since demonstrated the promise and benefits of 3D model (versus 2D). Notably, 3D models are able to imitate many characteristics of the in vivo tumor, including cell morphology, proliferation, cell-cell interactions, cell-ECM interactions, and gene/protein expression profiles (Nguyen et al. 2016; Sutherland 1988). The use of 3D cell models has remarkably increased in the last decade (Ferro et al. 2014). Numerous 3D cell culture systems have been developed to meet the growing need for optimal 3D cell culture, with each system having distinct advantages and disadvantages. Among the 3D cell culture systems, “hanging drop” is an effective technique for 3D cell culture that is used in drug development. The hanging drop method is popular due to its ability to generate spheroids which are homogenous in size and shape, for the number of cells required, and for its application for highthroughput screening (Kelm et al. 2003; Pham 2015; Timmins and Nielsen 2007). In recent years, 3D cell models (rather than 2D cell models) have been demonstrated to be more effective at mimicking the structure and characteristics of cancer cells in tumors, hence

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . .

more accurately reflecting the possible in vivo tumor response (Kijanska 2016; Smith et al. 2012). Being a tropical country, Vietnam has tremendous potential in cancer drug screening due to its diversity of herbs. Paramignya trimera is among the many promising candidates and is the focus of the study herein. Nonetheless, a 3D cancer cell model for testing toxicity of extracts and compounds is still relatively a new concept. This study aimed to evaluate the toxicity of Paramignya trimera extract (PTE) on microtumor spheres of MCF-7, created using hanging drop technique.

2

Materials and Methods

2.1

3D Cell Culture and Spheroid Formation

Human breast adenocarcinoma MCF-7 cells were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA), were maintained in DMEM-F12 medium supplemented with 10% fetal bovine serum (FBS; Gibco/Thermo Fisher Scientific, Waltham, MA, USA) and 1% antibiotic (Thermo Fisher Scientific), and then incubated at 37  C in a humidified atmosphere of 5% CO2. Cell culture medium was replenished every 2–3 days, and cells were passaged until they reached 65–80% confluence. For 3D culture, single-cell suspension was collected from trypsinized monolayer of cells. Cells were then diluted to obtain a concentration at 50,000 cells/mL. Cell suspensions were then seeded onto 96-well Perfecta3D® hanging drop plate (50 μL/well) (3D Biomatrix, Ann Arbor, MI, USA).

using the AxioVision microscope software (Carl Zeiss).

2.3

Measurement of Spheroid Growth

To examine the growth of spheroids over time, 2500 cells/wells were seeded into the wells. Three spheroids in three different wells were imaged every day using light microscopy (Carl Zeiss, Germany). The spheroid diameter was calculated

PI Staining for Necrotic Core Detection

Spheroids of MCF-7 cells were transferred from the hanging drop plate onto flat-bottom 96-well plates. Cells were then incubated for 1 h in 100 μL of cell culture medium supplemented with 1 μL of propidium iodide (PI; SigmaAldrich, St Louis, MO). After washing with PBS, spheroids were observed under fluorescent microscope.

2.4

Cell Viability Assay

The MTT assay and AlamarBlue® assay were used to evaluate the growth of spheroids as well as discern cell viability. Spheroids were transferred from the hanging drop plate to single wells of a low-attachment 96-well plate (Corning, Inc., Corning, NY). For the MTT assay, after 3 h of incubation with MTT (Sigma-Aldrich), formazan crystal was dissolved with 100 μL DMSO (Sigma-Aldrich), and absorbance was recorded at 570 nm. Cell viability was also assessed by AlamarBlue® assay (Thermo Fisher Scientific). The assays were conducted according to the manufacturers’ instructions. Briefly, after 3 h of incubating with AlamarBlue® at a final concentration of 10 μg/mL, plates were measured for fluorescence intensity at 535 nm excitation and 595 nm emission by a DTX 880 microplate reader (Beckman Coulter, Brea, CA).

2.5 2.2

15

Drugs Treatment on MCF-7 Spheroids

A density of 2500 cells/well was seeded for drug testing in both 2D and 3D models. For 2D cell samples, the cells were plated in 96-well plate, cultured for 24 h, and treated with gradient concentrations of the drugs. For 3D cell samples, after seeding for 3 days, MCF-7 spheroids were

16

L.-H. Nguyen-Thi et al.

transferred to low-attachment 96-well plates and treated with various concentrations of the drugs for 48 h. Spheroids were treated with DOX at the following concentrations: 10000 μM, 5000 μM, 2500 μM, 1250 μM, 600 μM, 300 μM, and 150 μM. Spheroids were treated with TPZ at the following concentrations: 500 μM, 250 μM, 125 μM, 60 μM, 30 μM, 15 μM, and 7.5μM. Lastly, spheroids were treated with PTE at the following concentrations: 2000 μg, 1000 μg, 500 μg, 250 μg, 125 μg, and 60 μg. AlamarBlue® cell viability assay was then used to determine cell viability. DOX and TPZ were chosen for our research study. Sigmoidal dose-response curves for 2D and 3D systems were generated using GraphPad Prism 7.0 (San Diego, CA, USA), and the IC50 values for each system were interpolated.

2.6

Spheroid Invasion Assay

After 3 days of cell seeding, spheroids were formed. These spheroids were then dropped onto flat-bottom 96-well plate containing media supplemented with DOX at the following concentrations: 9 μM, 4.5 μM, 2 μM, 1 μM, 0.5 μM, 0.25 μM, and 0 μM. TPZ was evaluated at the following concentrations: 500 μM, 250 μM, 125 μM, 60 μM, 30 μM, 15 μM, and 0 μM. Lastly, PTE was evaluated at the following concentrations: 2000 μg, 1000 μg, 500 μg, 250 μg, 125 μg, 60 μg, and 0μM. After 48 h of treatment, spheroids were observed to evaluate the invasion of cells surrounding the spheroids. The area of the invasion zone around the spheroids corresponding to each drug concentration was then calculated using AxioVision microscope software (Carl Zeiss, Inc.).

2.7

Apoptosis Detection Assay

The percentage of apoptosis and of necrosis of cells induced by the drugs was determined by flow cytometry using the AnnexinV-FITC Apoptosis Detection Kit (BD Bioscience, Franklin

Lakes, NJ). For spheroids, after 48 h of treatment with DOX, TPZ, or PTE, cells were trypsinized and dispersed into single-cell suspension by gentle pipetting. The cells were processed as per instructions of the AnnexinV-FITC Apoptosis Detection Kit. Flow cytometric evaluation of the cells was conducted using a flow cytometer (BD Biosciences, CA, USA), equipped with CellQuest Pro software.

2.8

Statistical Analysis

The results were presented as mean
SD. Statistical analyses were performed using GraphPad Prism 7.0 (GraphPad Software Inc., San Diego, CA, USA). P < 0.05 was considered to be statistically significant. IC50 value was calculated by GraphPad Prism 7.0 software based on the formulation: Fifty ¼ (Top+Baseline)/2 and Y ¼ Bottom + (Top-Bottom)/(1 + 10^((LogIC50-X) *HillSlope + log((Top-Bottom)/(Fifty-Bottom)-1).

3

Results

3.1

Successful Development of MCF7 Spheroids (3D Cell Culture)

From seeding 2500 cells per well, MCF-7 spheroids were easily formed 2 days of seeding and maintained a spheroid shape until day 9 (Fig. 1). Spheroid diameter increased over those 9 days (Fig. 2a). Spheroid metabolism quickly increased in the first 3 days, followed by a stable stage (from days 4 to 8) before dropping on day 9 (Fig. 2b). The results showed that the change in growth curve of MCF-7 spheroids was stable for the two different metabolism measuring methods, MTT and AlamarBlue® assays. Spheroid metabolism remarkably increased in the first 3 days, followed by a plateau from days 4 to 7; at day 8, cell metabolism rose again before dropping the next day. Previous studies have shown that the normal structure of a tumor is comprised of two main elements: necrotic core in the middle and an outer layer of viable cells (Sant and Johnston

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . .

Fig. 1 Development of microtumor spheres of MCF-7 under light microscopy. MCF-7 cells (2500 cells/well) were seeded onto a hanging drop plate. Spheroids formed after 2 days in culture. Spheroid images were captured at Fig. 2 The growth of MCF-7 spheroids. (a) Spheroid diameter increased steadily over 9 days. After seeding 2500 cells/well, MCF-7 cells easily formed spheroids at day 2. Three spheroids were imaged every day until day 9; their diameters were calculated using AxioVision software. The mean values represent the growth curve of spheroids. (b) Change in spheroid metabolism detected by MTT and AlamarBlue® assays. MTT and AlamarBlue® assays were used to evaluate the change in spheroid metabolism. AlamarBlue® assay records fluorescent intensity, while MTT assay records optical intensity. The curves of both methods were compared to verify any changes in spheroid growth. The data at each time point was presented by value
SD, the experiments were done in replicates (n ¼ 3). Data was statistically analyzed using the GraphPad Prism 7.0 (GraphPad Software, Inc.)

17

1-day intervals from day 1 to day 9 via light microscopy. Spheres retained their round shape until day 9 (Magnification: X4)

18

L.-H. Nguyen-Thi et al.

spheroids from day 2 onward. At day 3, spheroids showed a visible necrotic core in the middle; this structure was maintained until day 7 of the experiment, with live cells situated on the external side of the spheroids (Fig. 3).

2017). To test if the spheroids simulate the natural structure of the tumor, we used PI staining method to examine the spheroid dead cells. Previous studies have used a variety of methods (e.g., tumor excision then immunohistochemistry or H&E staining) to detect necrotic core of the spheroids. However, these methods cost much time, and the protocol has numerous steps which may cause spheroids to crumble. Furthermore, the spheroids are too small to be fixed by paraffin or by a frozen solution used for cold tissue slices. Therefore, we used PI fluorescence dye method to detect the formation of necrotic cores. When attached to nucleic acids, a fluorescent light is emitted at 535 nm. PI is membrane-impermeable and is generally excluded from viable cells. Hence, PI can be used to distinguish necrotic cells (in the core) from living cells (outside the spheroid). The earliest that necrotic cores could be detected was at day 2; MCF-7 tumor spheres formed after seeding of 2500 cells/well and remained as spheroids until day 7 of the study. Note that the establishment of spheroids from day 2 to 7 shows adequate duration for drug testing in 3D model, reflecting that 3D spheroids are a beneficial platform for drug testing. The results showed that dead cells were observed inside

The toxicity of PTE on MCF-7 cell line significantly differs depending on whether the MCF-7 cells are cultured in 2D or 3D (Fig. 4c). Indeed, the IC50 values of PTE on MCF-7 are significantly higher in 2D culture compared to 3D culture (260.8
16.54 vs. 168.9
11.65, respectively). This difference in toxicity is similar to that observed with TPZ in these models (Fig. 4b). Likewise, the IC50 value of TPZ in 2D culture was also significantly greater than in 3D culture (136.6
19.78 vs. 45.75
24.78, respectively). However, the reverse trend was observed for DOX treatment; in the 3D culture, the MCF-7 cells were more resistant to DOX compared to cells in 2D culture (IC50 values were 58.56
15.82 in 3D culture vs. 1.31
0.69 in 2D culture) (Fig. 4a).

Fig. 3 Spheroids of MCF-7 cells exhibited a necrotic core. Spheroids were labeled with PI at a final concentration of 1 μg/mL in culture media and incubated for half an hour. Spheroids were then observed under a fluorescent

microscope. The spheroids were seen to have a necrotic core from day 2 to day 7. Experiments were done in triplicates (n ¼ 3). The images shown above represent data from the first experiment (Magnification: X4)

3.2

Toxicity of PTE on MCF-7 Cells Cultured in 2D vs. 3D

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . .

19

Fig. 4 The inhibition of DOX, TPZ, and PTE on MCF-7 cells in 2D and 3D cultures. Spheroids were much more resistant to DOX; the IC50 value for DOX was 50-fold greater on 3D versus 2D cells. In contrast, TPZ has a stronger effect on 3D cells; the IC50 value of TPZ was nearly three times lower for 3D versus 2D. PTE strongly

inhibits the proliferation of MCF-7 cells, especially in 3D conditions; the IC50 value was significantly lower than for 2D conditions. The IC50 value is shown as mean
SD. Experiments were done in replicates (n ¼ 3). The data were analyzed by GraphPad Prism software. Statistical significance was set at P < 0.05

3.3

invasion ability of MCF-7 cells, as seen in the original control spheroids, was completely inhibited (D). This is similar to the results for 2 μM DOX (B) and 60 μM TPZ (C); the experiment was conducted once (n ¼ 1).

PTE Inhibited the Invasion Ability of MCF-7 Spheroids

The results showed that invasion of MCF-7 spheroids was completely inhibited after treatment with 2 μM of DOX (much less than the IC50 of DOX on MCF-7 in 3D), 60 μM of TPZ (more than the IC50 value of TPZ on MCF-7 in 3D), and 250 μg/ml of PTE (more than the IC50 value of PTE on MCF-7 in 3D) (Fig. 5). In the control, MCF-7 cells were seen reattaching to the culture surface and invading around many directions (A). Following treatment with 250 μg/ml plant extract (e.g., PTE), the

3.4

PTE-Induced Apoptosis of MCF-7 Spheroids

In this study, we tested the ability of drugs and plant extracts to induce apoptosis of MCF-7 spheroids. We assessed the concentrations which completely inhibited the invasion capability of

Fig. 5 Invasion-inhibiting drugs on MCF-7 in 3D. MCF-7 spheroids were treated with drugs and plant extracts, and the area of invasion zone around the spheroids was measured using AxioVision software.

(a) Control. (b) Spheroid was treated with 2 μM DOX. (c) Spheroid was treated with 60 μM TPZ. (d) Spheroid was treated with 250 μg/mL PTE. (e, f, g) The invasion area of MCF-7 cells when treated with DOX, TPZ, or PTE

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . .

MCF-7 (in spheroids) from the previous assay. Indeed, at the concentration of 250 μg/ml PTE, 98.41
0.87% of cells were induced to undergo apoptosis; most (96.89
1.30%) were in late apoptosis. It was interesting that this proportion of apoptosis is similar to the percentage of apoptotic cells induced by 60 μM TPZ (97.01
1.04%). Moreover, 250 μg/ml PTE was significantly more effective than 2 μM DOX (which only induced 78.90
1.91% apoptosis). The results indicate that DOX, TPZ, and PTE all effectively induce death of MCF-7 cells cultured in 3D condition, particularly the apoptosis process (Fig. 6). The percentage of live cells in MCTS following treatment with DOX, TPZ, or PTE was significantly lower than in untreated cells. In contrast, the proportion of apoptotic cells in the spheroids was remarkably higher than control. The number of necrotic cells induced by drug or plant extracts was not meaningfully different (Fig. 6).

4

Discussion

To date, herbal medicines have been used to treat different medical conditions and are still considered as a promising choice for treating cancers (Xu et al. 2011). In fact, nearly 80% of drugs approved by the FDA for cancer treatment during the last 30 years (1981–2010) have been natural products originating from or based on naturally occurring molecules (Newman and Cragg 2012). The multicellular tumor spheroid is an effective tool for in vitro evaluation of antitumor activity and drug efficacy (Gong et al. 2015). Threedimensional culture models provide a physiological context that is much more similar to the native tumor microenvironment than are traditional 2D cell cultures (Pampaloni et al. 2007; Schmeichel and Bissell 2003). The development of biotechnology has provided numerous techniques for 3D cell culture. Among them, generation of MCTS using hanging drop plates seems to be the first choice for cancer researchers doing high-throughput screening (Hsiao et al. 2012). This technique is particularly useful for generating MCTS which are easily controlled

21

for cell numbers and spheroid size (Kelm et al. 2003). It is also suitable for a variety of studies such as evaluating changes at the cellular and molecular levels during spheroid formation, assessing invasion and angiogenesis induced by tumor spheroids, and understanding the interplay of different cell types in coculture (Lin and Chang 2008). In this study, with 2500 MCF-7 cells/well as the seeding number, MCF-7 cells showed they were capable of forming spheroids with smooth and continuous surfaces. The mean of spheroid diameter was above 500 μm, making the spheroids suitable for drug trials since they are effectively mimic the in vivo tumor structure (Alvarez-Perez et al. 2005). During the spheroid formation, several stages of morphological change were observed. On day 1, single cells assembled together to form cell aggregates and individual cells could still be identified. At days 2–3, the cells began to fuse to form compact spheroids. After 3 days, spheroids acquired smooth and continuous surfaces. The spheroid structure remained intact until day 9 of seeding, and there was a steady increase of spheroid diameter over those 9 days. Similar results were observed by Xue Gong et al. (2015), using microwell-based agarose scaffolds to generate MCF-7 MCTS (Gong et al. 2015). The lack of oxygen and nutrition inside spheroids produces a necrotic core, similar to the hypoxic regions naturally formed within solid tumors. Although MTT assay is one of the most widely used methods for cytotoxicity screening (Mosmann 1983), it had been rarely used for drug screening on MCTS cultures since there was a lack of standardized techniques to apply this assay for MCTS (Ho et al. 2012). In a study by Wan Yong Ho et al. in 2012, the authors demonstrated that MTT assay could be suitable for high throughput screening of cytotoxicity, including for MCTS cultures – through slight modifications from the standard MTT protocol of Mosmann (1983) (Ho et al. 2012). AlamarBlue® assay has been widely used in studies of cell viability over the past five decades (Bonnier et al. 2015). Recently, this assay has gained interest, even in 3D cell culture, since it is simple, versatile

Fig. 6. Apoptosis induced in MCF-7 spheroids after treatment with DOX, TPZ, or PTE. (a) Dot plot analysis of MCF-7 spheroids following treatment with: control (PBS), DOX (2 μM), TPZ (60 μM), or PTE (250 μg) for 48 h. (b) Percentage of live/apoptotic/necrotic MCF-7 cells treated with drugs, as compared to control group. The percentage of live cells in the multicellular tumor spheres (MCTS) after

treatment with DOX, TPZ, or PTE was significantly lower than in the control (untreated) group of cells. In contrast, the proportion of apoptotic cells in the spheroids was remarkably greater than in the control. The data are presented as mean
SD; P < 0.05 was considered as statistically significant

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . .

and nontoxic for measuring cell proliferation and cytotoxicity (O’Brien et al. 2000). The results of spheroid metabolism assessed by MTT and AlamarBlue showed many similarities. However, AlamarBlue was simpler and quicker to use, and thus it is advantageous for studies involving assessment of drug toxicity. Our study indicated that MCF-7 spheroids were much more resistant to DOX. Indeed, the IC50 of DOX on MCF-7 MCTS was approximately 50-fold greater than that for 2D MCF-7 cells. This result is similar to the research findings of Xue Gong et al. in 2015. Their study has proven that the compact structure of spheroids restricts the penetration of DOX, especially when the MCTS have a diameter above 500 μm (Gong et al. 2015). Moreover, previous studies have suggested that spheroids larger than 200 μm in diameter showed heterogeneous architecture, with proliferating cells on the outside of the spheroids, and the quiescent region and necrotic core in the center (Hamilton 1998; Ivascu and Kubbies 2006). DOX mainly impacts proliferating cells. Slow-proliferating cells in the spheroids as well as quiescent cells in the inner region may explain why growth of cells in MCTS is significantly slower than in 2D (higher rate of proliferation). In addition, the lack of oxygen inside spheroids produces a hypoxic core, similar to the hypoxia condition found in solid tumors. Many anticancer drugs, including DOX, have been demonstrated to be less effective for hypoxic and acidic conditions of tumors (Kim et al. 2011; Vaupel and Mayer 2007). TPZ, on the other hand, has a stronger effect on MCF-7 cells cultured in 3D. More specifically, the IC50 of TPZ on 3D MCF-7 was nearly three times lower than that for monolayer (2D) MCF-7. TPZ is a hypoxiaactivating drug (Brown 1999; Reddy and Williamson 2009). The restoring effect of TPZ on cancer cells under low oxygen pressure was shown in past studies (Strese et al. 2013; Tung et al. 2011). Taken together with those findings, the TPZ toxicity on spheroids in our study confirmed that MCF-7 MCTS have an appearance of a hypoxia region, effectively mimicking the microenvironment of in vivo tumors. For a long time, many natural dietary agents have been used in traditional medicines, and as

23

nonconventional treatments for many diseases including cancer, but without sufficient scientific proof. Our research results indicate that PTE strongly inhibits the proliferation of breast cancer stem cells in both 2D and 3D cell culture models. Interestingly, the antiproliferative effects of the plant extracts in 3D culture were stronger than in 2D, suggesting that PTE may be a promising candidate for preclinical and clinical trials for cancer. Invasion to surrounding normal tissues is an important hallmark of malignant tumors (Vinci et al. 2015). In the control group (or low concentration of drug group), MCF-7 spheroids showed the ability to reattach to the plate surface and spread out to the area around the spheroids. The ability of invasion of MCF-7 spheroids is dosedependent (data not shown). At the concentration of 250 μg/ml PTE, the invasion of MCF-7 cells (as seen in the original spheroids) was completely inhibited. Apoptosis is an important process to remove cells which become old or damaged and to maintain homeostasis and normal function of tissue. However, cancer cells have the special ability to resist apoptosis. Anticancer drugs generally inhibit tumor cell proliferation or kill cancer cells via apoptosis and/or necrosis (Mahassni and Al-Reemi 2013). Using flow cytometry technique, the results showed that percentage of apoptotic MCF-7 cells (in spheres), when exposed to DOX, TPZ, and PTE, was significantly higher for untreated cells. This implies all of these agents have great potential to kill MCF-7 cells via apoptosis, and that PTE shows great promise as an anticancer agent. These encouraging preliminary data provide some insight for the development of novel chemotherapeutic agents, based on PTE, for the management of breast cancer.

5

Conclusion

In this study, we successfully generated a 3D model of MCF-7 that can be suitable for drug testing. MCF-7 derived spheroids exhibit many characters of in vivo microtumors, with stable growth over 9 days and presence of a necrotic core. PTE was shown to strongly inhibit the

24

proliferation MCF-7 cells, especially in 3D condition. PTE effectively constrained the invasion of MCF-7 spheroids, as well as induced apoptosis of the cells. Our findings suggest that Paramignya trimera extract (PTE) is a promising anticancer drug candidate against MCF-7 and likely other breast cancer models. Funding and Grants This research was funded by Vietnam National University, Ho Chi Minh city, Viet Nam under grant number A2015-18-01/HD-KHCN.

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L.-H. Nguyen-Thi et al. give excellent Z-factors and allow versatile formation of co-culture spheroids. Biotechnology and Bioengineering, 109, 1293–1304. Hutmacher, D. W. (2010). Biomaterials offer cancer research the third dimension. Nature Materials, 9, 90–93. Ivascu, A., & Kubbies, M. (2006). Rapid generation of single-tumor spheroids for high-throughput cell function and toxicity analysis. Journal of Biomolecular Screening, 11, 922–932. Kelm, J. M., Timmins, N. E., Brown, C. J., Fussenegger, M., & Nielsen, L. K. (2003). Method for generation of homogeneous multicellular tumor spheroids applicable to a wide variety of cell types. Biotechnology and Bioengineering, 83, 173–180. Kijanska M. K. J. (2016). In vitro 3D spheroids and microtissues: ATP-based cell viability and toxicity assays. Assay Guidance Manual [Internet]. Kim, J. W., Ho, W. J., & Wu, B. M. (2011). The role of the 3D environment in hypoxia-induced drug and apoptosis resistance. Anticancer Research, 31, 3237–3245. Lin, R. Z., & Chang, H. Y. (2008). Recent advances in three-dimensional multicellular spheroid culture for biomedical research. Biotechnology Journal, 3, 1172–1184. Mahassni, S. H., & Al-Reemi, R. M. (2013). Apoptosis and necrosis of human breast cancer cells by an aqueous extract of garden cress (Lepidium Sativum) seeds. Saudi Journal of Biological Sciences, 20, 131–139. Mosmann, T. (1983). Rapid colorimetric assay for cellular growth and survival: Application to proliferation and cytotoxicity assays. Journal of Immunological Methods, 65, 55–63. Newman, D. J., & Cragg, G. M. (2012). Natural products as sources of new drugs over the 30 years from 1981 to 2010. Journal of Natural Products, 75, 311–335. Nguyen, H. T.-L., Nguyen, S. T., & Pham, P. V. (2016). Concise review: 3D cell culture systems for anticancer drug screening. Biomedical Research and Therapy, 3, 625–632. Nguyen, V. T., Sakoff, J. A., & Scarlett, C. J. (2017). Physicochemical properties, antioxidant and antiproliferative capacities of dried leaf and its extract from Xao tam phan (Paramignya trimera). Chemistry & Biodiversity, 14. O’Brien, J., Wilson, I., Orton, T., & Pognan, F. (2000). Investigation of the Alamar blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity. European Journal of Biochemistry, 267, 5421–5426. Pampaloni, F., Reynaud, E. G., & Stelzer, E. H. (2007). The third dimension bridges the gap between cell culture and live tissue. Nature Reviews Molecular Cell Biology, 8, 839–845. Pham, P. V. (2015). Breast cancer stem cell culture and proliferation. In Breast cancer stem cells & therapy resistance. Cham: Springer. Reddy, S. B., & Williamson, S. K. (2009). Tirapazamine: A novel agent targeting hypoxic tumor cells. Expert Opinion on Investigational Drugs, 18, 77–87.

Anti-cancer Effect of Xao Tam Phan Paramignya trimera Methanol Root. . . Sant, S., & Johnston, P. A. (2017). The production of 3D tumor spheroids for cancer drug discovery. Drug Discovery Today: Technologies, 23, 27–36. Schmeichel, K. L., & Bissell, M. J. (2003). Modeling tissue-specific signaling and organ function in three dimensions. Journal of Cell Science, 116, 2377–2388. Smith, S. J., Wilson, M., Ward, J. H., Rahman, C. V., Peet, A. C., Macarthur, D. C., Rose, F. R., Grundy, R. G., & Rahman, R. (2012). Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition. PLoS One, 7, e52335. Strese, S., Fryknas, M., Larsson, R., & Gullbo, J. (2013). Effects of hypoxia on human cancer cell line chemosensitivity. BMC Cancer, 13, 331. Sutherland, R. M. (1988). Cell and environment interactions in tumor microregions: The multicell spheroid model. Science (New York, N.Y.), 240, 177–184. Timmins, N. E., & Nielsen, L. K. (2007). Generation of multicellular tumor spheroids by the hanging-drop

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method. Methods in Molecular Medicine, 140, 141–151. Tung, Y. C., Hsiao, A. Y., Allen, S. G., Torisawa, Y. S., Ho, M., & Takayama, S. (2011). High-throughput 3D spheroid culture and drug testing using a 384 hanging drop array. The Analyst, 136, 473–478. Vaupel, P., & Mayer, A. (2007). Hypoxia in cancer: Significance and impact on clinical outcome. Cancer Metastasis Reviews, 26, 225–239. Vinci, M., Box, C., & Eccles, S. A. (2015). Threedimensional (3D) tumor spheroid invasion assay. Journal of Visualized Experiments: JoVE, 1, 52686. Xu, Z., Chen, X., Zhong, Z., Chen, L., & Wang, Y. (2011). Ganoderma lucidum polysaccharides: Immunomodulation and potential anti-tumor activities. The American Journal of Chinese Medicine, 39, 15–27. Yin, S.-Y., Wei, W.-C., Jian, F.-Y., & Yang, N.-S. (2013). Therapeutic applications of herbal medicines for cancer patients. Evidence-based Complementary and Alternative Medicine, 2013, 15.

Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 27–35 https://doi.org/10.1007/5584_2018_300 # Springer Nature Switzerland AG 2018 Published online: 6 March 2019

A Novel Nonsense Mutation c.374C>G in CYP21A2 Gene of a Vietnamese Patient with Congenital Adrenal Hyperplasia Chi Dung Vu, Thanh Van Ta, Ngoc-Lan Nguyen, Huy-Hoang Nguyen, Thi Kim Lien Nguyen, Thinh Huy Tran, and Van Khanh Tran Abstract

Inactivating mutations of the CYP21A2 gene, encoding for steroid synthesis, have been reported in patients with congenital adrenal hyperplasia (CAH). We report a case of an infant who were diagnosed with CAH and presented with the severe phenotype of CAH with symptoms such as increased testicular volume, elevated of 17-hydroxyprogesteron, testosterone and progesterone. In this study, we established an assay for the detection of unusual genetic in the CYP21A2 gene in the proband and his family. A novel nonsense mutation c.374C > G which

Authors Chi Dung Vu and Thanh Van Ta have been equally contributed to this chapter. C. D. Vu Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam Vietnam National Children Hospital Hanoi, Vietnam, Department of Medical Genetics, Metabolism & Endocrinology, Hanoi, Vietnam T. Van Ta (*), T. H. Tran, and V. K. Tran Center for Gene and Protein Research, Hanoi Medical University, Hanoi, Vietnam e-mail: [email protected] N.-L. Nguyen, H.-H. Nguyen, and T. K. L. Nguyen Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi, Vietnam

caused a substitutions of Serine for a stop codon at codon 125 (p.S125*) within exon 3 was found in the proband. Parental genotype studies confirmed carrier state in the father, but the mother showed a wild allele by PCR and sequencing. This inspired us to find deletions using multiplex ligation-dependent probe amplification (MLPA) technique. The probands were found to have a large deletion in exons 1 and 3, while the mother only had deletion in exon 1. Therefore, mutation c.374C > G (p.S125*) in the proband is considered as a heterozygous deletion. This mutation caused a truncated protein which lead to the salt wasting CAH phenotype of the proband. This novel nonsense mutation expands the CYP21A2 mutation spectrum in CAH disorder. This case also highlights the need of caution when interpreting results of molecular genetic testing during genetic counseling. Keywords

Congenital adrenal hyperplasia (CAH) · CYP12A2 gene · Nonsense mutation · p.S125*

Abbreviations 17-OHP CAH

17-hydroxyprogesterone Congenital adrenal hyperplasia 27

28

MLPA NC SV SW WT

1

C. D. Vu et al.

Multiplex ligation-dependent probe amplification Nonclassical CAH Simple virilizing CAH Salt-wasting CAH Wild-type

Introduction

Congenital adrenal hyperplasia (CAH; OMIM6¼ 201910) is a group of autosomal recessive disorders affecting the steroid synthesis pathway, clinically characterized by the occurrence of salt wasting, dehydration, and simple virilization in both sexes and ambiguous genitalia in the female (White and Speiser 2000; Speiser et al. 2010; Haider et al. 2013). In Vietnam, CAH occurs with an estimated prevalence of 1 in 100,000. CAH is caused by mutations in the CYP21A2 gene. CYP21A2 gene encoding 21-hydroxylase maps to chromosome 6p21.3 and contains 10 exons which encode a protein built of 492 amino acids. The primary function of 21-hydroxylase is to regulate producing hormones called cortisol and aldosterone. This enzyme catalyzes the conversion of progesterone to 11-deoxycorticosterone in the zona glomerulosa of the human adrenal cortex and the conversion of 17α-hydroxyprogesterone to 11-deoxycortisol in the zona fasciculata (Speiser and White 2003). Over the last two decades, CAH disorder has been increasingly studied. A total of 1,340 genetic variants have been detected in the CYP21A2 gene (Simonetti et al. 2018) with 233 disease-causing variants (Concolino and Costella 2018). The detection of heterozygous deletions is difficult using routine PCR amplification of genomic DNA and direct sequencing, while multiplex ligation-dependent probe amplification (MLPA) has been proven to be an efficient and reliable technique for the copy number analysis of each exon in a gene that has been used to detect genetic disorders whose pathogenesis is related to the presence of deletions, duplications of specific genes, or presence of abnormal DNA methylation

(Chamberlain et al. 1988; Lai et al. 2006; Jankowski et al. 2008; Vrzalová et al. 2011). Recent studies (Concolino et al. 2009; Coeli et al. 2010; Xu et al. 2013; Carvalho et al. 2016) a combination of direct sequencing and MLPA were used to identify novel exonic deletions in patients with CAH. The objectives of this study were to identify a novel disease-causing mutation in the CYP21A2 gene and the deletions of copy numbers of exons of the CYP21A2 gene in a case of CAH in a Vietnamese family.

2

Proband and Methods

2.1

Proband

The proband is the first child of the healthy non-consanguineous Vietnamese parents. He was admitted to Vietnam National Children Hospital at 45 days old. At that time, he presented with diarrhea, dehydration, and mild hyperpigmentation; the size of the penis was 4 cm, and volume of two testes was 0.5 ml in the scrotum; and pulmonary and cardiac examinations were normal. Biochemical tests showed plasma 17-hydroxyprogesterone (17-OHP) level was 221.4 ng mL1 (normal range, G in CYP21A2 Gene of a Vietnamese. . .

the manufacturer’s instructions (Qiagen, Germany). The CYP12A2 gene was amplified without amplifying CYP21P1 gene using primer 21HYDF (5’-CGGGTCGGTGGGAGGGTA-30 ) and 21HYDR (5’-AGCGATCTCGCAGCACTGTGT-30 ). All PCR assays were performed in a 50 μl volume containing 100 ng of genomic DNA, 1.75 mM MgCl2, 10 mM of each deoxy-NTP, 10 pmol of each primer, and 2 U of Dream Taq DNA polymerase (Invitrogen) in the Dream Taq buffer. After denaturation step at 95  C for 4 min, the amplification was performed for 36 cycles at 95  C for 1 min, at 65  C for 2 min, and at 72  C for 3 min, with a final extension at 72  C for 4 min. Amplicons were separated on 1% agarose gel and purified by column filtration GeneJet Gel Extraction Kit (Thermo Scientific, USA). Sequencing was completed using ABI Big Dye Terminator on 3,100 Genetic Analyser (Applied Biosystems, USA). The obtained sequences were aligned with the reference CYP12A2 gene sequence which has been published in GeneBank (NM_000500) using the program CLUSTALW 1.8. Mutation was detected by screening heterozygosity of single nucleotide polymorphic markers across the CYP21A2 gene. The cDNA was numbered with +1 corresponding to the A of the ATG translation initiation codon and with codon 1 as the initiation codon. Genotypes of the parents and sibling of the proband were also performed to assist in determining and confirming the genotype of the proband.

2.3

MLPA

The SALSA MLPA P050-B2 (MRC-Holland) kit was used according to manufacturer’s recommendations. The probe mixture contains 33 different probes to amplify the CYP21A2 gene, including 5 specific probes that recognize the 5’s region and exons 1, 3, 4, 6, and 8. The specific probes for exons 3, 4, 6, and 8 contain the wild-type sequences for Del 8 bp, I172N, E6 cluster, and Q318X mutations, respectively. This probe mixture also contain 3 specific probes for amplification of the CYP21A1P gene (E1P, I2P, and E10P), 2 specific probes for amplification of

29

C4A and C4B. In addition, there are 22 typical probes for human chromosomes as control and 2 probes for chromosomes X and Y to determine gender. The 5 μl of DNA was added in the tube and denatured at 98  C for 5 min. The temperature was downed to 2  C before adding 3 μl of probe mixture. The mixture was incubated at 60  C overnight. After adding 32 μl of ligase buffer, the mixture was incubated at 54  C for 15 min and at 98  C for 5 min and then stored at 4  C. The 10 μl of ligation product was mixed with 30 μl PCR buffer and kept at 60  C before adding 10 μl of the mixture PCR master. PCR amplification was performed for 35 cycles of 95  C for 30s, 60  C for 30s, and 72  C for 1 min, followed by a final extension at 72  C for 20 min, and hold at 4  C. PCR products were sequenced directly in SEQ8000 Genetic Analyzer (Beckman Coulter, Fullerton, CA, USA), and the raw data was analyzed by using the Coffalyser 7.0 software (MRC Holland, Amsterdam, the Netherlands).

2.4

Amino Acid Conservation

The amino acid sequences of CYP21A2 from different species, including humans (Homo sapiens, P08686), wild boar (Sus scrofa, NP999598), yak (Bos mutus, NP777064), rhesus monkey (Macaca mulatta, NP001181556), dog (Canis lupus familiaris, NP001003335), chacma baboon (Papio ursinus, ABY57765), common marmoset (Callithrix jacchus, XP017826473), and red junglefowl (Gallus gallus, NP001092828), were aligned using Clustal W to determine the evolutionary conservation of wild-type amino acid residues at the position of substitutions.

3

Results

Mutations were screened at the genomic level using PCR amplification followed by direct sequencing. The proband had a homozygous c.374C > G (according to the latest Human Genome Variation Society nomenclature; the

30

C. D. Vu et al.

Fig. 1 Identification of the novel CYP21A2 mutation c.374C > G (p.S125*) in the proband family. (a) The schematic representation indicates the localization of the mutations in the CYP21A2 gene identified in the present study. The white and black boxes indicate the coding and noncoding regions, respectively. (b) The pedigree chart in the family. Squares, males; circle, female; black and

brown symbols, mutated alleles; +, wild type. (c) The proband is homozygous for mutation: the point mutation at bp 752 (corresponding to c.374C > G) in exon 3 leads to a change of serine 125 to a termination codon (p.S125*). The father and sibling are heterozygous for the p.S125* mutation, whereas the mother is homozygous as wild type (WT)

nucleotide numbering reflects coding DNA, with +1 corresponding to the A of the ATG translation initiation codon in the reference sequence) (p. S125* premature termination codon) mutation in exon 3. Genome analyses of his family revealed that the C > G mutation was heterozygous in the father and his younger brother, but the mother carried a homozygous of wild type (WT) (Fig. 1). Hence, we hypothesized that the proband may have a deletion of exon 3 in the inherited maternal allele. MLPA tracing of the proband demonstrated a reduction in the copy numbers in exon 3 in the proband but not in his mother (Fig. 2). This means

that the proband had a heterozygous deletion mutation of p.S125*. A deletion in exon 1 was observed in the proband and his mother. Therefore, we suggested that the proband inherited mutation pS125* from the father and the deletion of exon 1 from the mother. Interestingly, the proband had a deletion in exon 3. In summary, the proband carried a nonsense mutation accompanied with two deletions in exon 1 and exon 3 in the CYP21A2 gene. Amino acid sequences of CYP21A2 from different species are compared to identify the conservation areas among them. The changed amino acid (p.S125*) was in position of conservative

A Novel Nonsense Mutation c.374C>G in CYP21A2 Gene of a Vietnamese. . .

31

Fig. 2 Deletion of exons 1 and 3 of CYP21A2 gene by MLPA. Electropherograms are from a normal control, from the proband with exon 1 and 3 deletion, from the proband’s mother with an exon 1 deletion. The deletion is

apparent by approximately 50% reduction in peak area of the CYP21A2 gene exon 1- and 3-specific probe (red arrow)

Fig. 3 Alignment of amino acid sequences of CYP21A2 from different species including human (Homo sapiens, P08686), wild boar (Sus scrofa, NP999598), yak (Bos mutus, NP777064), rhesus monkey (Macaca mulatta, NP001181556), dog (Canis lupus familiaris, NP001003335), chacma baboon (Papio ursinus,

ABY57765), common marmoset (Callithrix jacchus, XP017826473), and red junglefowl (Gallus gallus, NP001092828). The position of the changed amino acid (p.S125*) in protein CYP21A2 is marked in a red dashline box

amino acid among different species (Fig. 3). The nonsense mutation p.S125* affected an amino acid located at the end of the C-helix in the CYP21 protein, which is part of the heme coordinating system.

4

Discussion

Phallic enlargement with the prepubertal testes in the proband is noted by the CAH patient (Bongiovanni and Root 1963; White and Speiser 2000). The highly elevated serum 17-OHP,

32

testosterone, and progesterone of the patient are typical diagnostics of CAH. The hyponatremia, hyperkalemia, and high level of 17-OHP and testosterone of the proband supported that the proband is classified as salt wasting. We have shown a nonsense mutation corresponding to the substitute of one nucleotide in exon 3 of the CYP21A2 gene (c.374C > G, p. S125*). The p.S125* mutation leads to a premature stop in protein synthesis resulting in a truncated protein that consists of 124 rather than 492 amino acids of WT protein. This mutation may rise to a defect in CYP21A2 enzyme that might explain the biochemical phenotype of the patient. In fact, partial gene deletions can produce completely different phenotypic effects such as salt washing, classical simple virilising, and nonclassical forms, but most of them are salt wasting (Table 1). Previously, a patient who suffered from the salt wasting form whom was compound heterozygotes for a nonsense mutation in exon 3 g.670C > A (p.Y97*) have been described (Krone et al. 1998). However, a small 10-bp deletion in exon 1 g.19_28delCTGCTGCTGC was found in a female patient with a simple virilizing phenotype in a compound heterozygous form. Previous observations in non-classic CAH in Italian patients had assumed that the enzymatic activity of 21-hydroxylase was totally lost with nonsense mutations p.Q153* and p.Q318* (Einaudi et al. 2011). To date, nonsense mutations which are rare in CAH patients with a total of 26 nonsense mutations have been reported (Table 1). Studies on genotype-phenotype correlation of patients with CAH whom harbor nonsense mutations in the CYP21A2 gene confirmed that the mutation caused a complete loss 21-hydroxylase enzymatic activity, resulting clinical manifestation of salt wasting, especially for mutations occured in the first few exons. In this study, the proband with a nonsense mutation p. S125* was diagnosed with salt-wasting CAH to confirm genotype-phenotype correlation between salt-wasting CAH with nonsense mutation in CYP21A2 gene. Direct sequencing did not detect heterozygous mutations, such as exonic deletions/duplications. The development of MLPA has facilitated the

C. D. Vu et al.

detections and duplications in patients with CAH. Only one MLPA PCR reaction can amplify whole ten exons of CYP21A2 gene. Deletion of exon 3 in the proband was easily detectable using MLPA but was not in his parents. It can be explained that the proband inherited a normal allele from his mother and a mutant allele from his father to form a heterozygous mutation carrier; however, a deletion of exon 3 in the proband leads to just one mutant allele at c.374C > G. Therefore, mutation c.374C > G (p. S125*) in exon 3 in the proband is considered as a heterozygous deletion mutation. In addition, a large deletion in exon 1 in the maternal allele supports that the deletion in exon 1 in the proband is caused by non-inherited allele from his mother. Although proband’s father and younger brother carried heterozygous mutation c.374C > G and his mother carried a deletion in exon 1 in the CYP21A2 gene, they had no apparent clinical symptoms. Therefore, the salt-wasting CAH phenotype in the proband may be explained by carrying simultaneously of a nonsense mutation (p.S125*) and two large deletions in the CYP21A2 gene which lead to significantly decreased expression of CYP21. The alignment of amino acid sequences showed that the S125 residue is conserved in CYP21A2 of human, wild boar, yak, rhesus monkey, dog, chacma baboon, common marmoset, and red junglefowl. This indicates a functionally or structurally important role of S125 in CYP. Another replacement p.K121Q in the C-helix has been reported previously to change heme coordination. As a family-based case report, limitation of this study is the lack of generalizability. Therefore, this nonsense mutation is needed to replicate in large numbers of CAH patients in the future.

5

Conclusion

Our finding indicates that c.375C > G (p.S125*) is a novel nonsense mutation that expands the genetic spectrum of CAH disease. This mutation is the fourth in exon 3 of the CYP21A2 gene. Our

A Novel Nonsense Mutation c.374C>G in CYP21A2 Gene of a Vietnamese. . .

33

Table 1 Nonsense mutations have been detected in the CYP21A2 gene No. 1

cDNA 56G > A

Protein W19

Exon 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

65G > A 66G > A 121C > T 19_28delCTGCTGCTGC 160A > T 220A > T 271A > T 291C > A 406G > T 421C > T 459C > T 481G > T 507C > A 619C > T 682C > T 784C > T 906G > A 943C > T 946C > T 952C > T

W22 W23 Q41 L48 K54 K74 R91 Y97 E136 Q141 Q153 E161 S169 Q207 Q228 Q262 W302 Q315 R316 Q318

1 1 1 1 1 2 2 3 3 3 4 4 4 5 6 7 7 8 8 8

22 23 24 25 26

1008C > G 1128C > A 1214G > A 1330C > T 1441C > T

Y336 Y376 W405 R444 Q481

8 9 9 10 10

Mutation status –

Phenotype SW

het het het het het-homo – – het – – – het – – – – – – homo –

SW SV SW SV SW SW SW SW SW SW NC SW SW SW SW SW SW SV SW SW

het het het – het

SW SW SW SW SW

Reference Kharrat et al. (2004), Bidet et al. (2009) Di Pasquale et al. (2007) Laji and Wedell (1996) Marino et al. (2011) Baradaran-Heravi et al. (2007) Concolino et al. (2009) Nunez et al. (1999) Krone et al. (1998) Simonetti et al. (2018) Krone et al. (2013) Massimi et al. (2014) Vrzalová et al. (2010) Simonetti et al. (2018) Ezquieta et al. (2002) Ohlsson et al. (1999) Levo and Partanen (1997) Lee et al. (1998), Rabbani et al. (2012) Globerman et al. (1988), Marumudi et al. (2012), Kolahdouz et al. (2016) Bernal et al. (2006) Stikkelbroeck et al. (2003) Wedell and Luthman (1993) Loidi et al. (2006) Massimi et al. (2014)

 not determined het heterozygous, homo homozygous, SW salt-wasting CAH, SV simple virilizing CAH, NC nonclassical CAH

data also highlight the importance of MLPA analysis to identify heterozygous deletions. This study provides proof of concept of the combination of direct sequencing and MLPA as a clinical tool in the evaluation of patients with undiagnosed genetic diseases. Further studies focusing characterization of this mutation in vitro expression to confirm genotype-phenotype correlation for optimal treatment and genetic counselling. Acknowledgments We thank the patients and their families for their voluntary involvement in this study. This work was supported by the Ministry of Health and National Foundation for Science and Technology Development (NAFOSTED) research fund, Vietnam.

Declaration The study was approved by the Hanoi Medical University’s scientific and ethical committees (IRB00003121, Hanoi Medical University Institutional Review Board, Hanoi, Vietnam). Informed consent of the patient’s family was obtained prior to sample collection and analysis.

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Simonetti, L., Bruque, C. D., Fernández, C. S., BenavidesMori, B., Delea, M., Kolomenski, J. E., Espeche, L. D., Buzzalino, N. D., Nadra, A. D., & Dain, L. (2018). CYP21A2 mutation update: Comprehensive analysis of databases and published genetic variants. Human Mutation, 39(1), 5–22. Speiser, P. W., & White, P. C. (2003). Congenital adrenal hyperplasia. The New England Journal of Medicine, 349(8), 776788. Speiser, P. W., Azziz, R., Baskin, L. S., Ghizzoni, L., Hensle, T. W., Merke, D. P., Meyer-Bahlburg, H. F., Miller, W. L., Montori, V. M., Oberfield, S. E., & Ritzen, M. (2010). Congenital adrenal hyperplasia due to steroid 21-hydroxylase deficiency: An Endocrine Society clinical practice guideline. The Journal of Clinical Endocrinology and Metabolism, 95(9), 4133–4160. Stikkelbroeck, N. M., Hoefsloot, L. H., de Wijs, I. J., Otten, B. J., Hermus, A. R., & Sistermans, E. A. (2003). CYP21 gene mutation analysis in 198 patients with 21-hydroxylase deficiency in The Netherlands: Six novel mutations and a specific cluster of four mutations. The Journal of Clinical Endocrinology and Metabolism, 88(8), 3852–3859. Vrzalová, Z., Hrubá, Z., St’ahlová Hrabincová, E., Pouchlá, S., Votava, F., Kolousková, S., & Fajkusová, L. (2010). Identification of CYP21A2 mutant alleles in Czech patients with 21-hydroxylase deficiency. International Journal of Molecular Medicine, 26(4), 595. Vrzalová, Z., Hrubá, Z., Hrabincová, E. S., Vrábelová, S., Votava, F., Koloušková, S., & Fajkusová, L. (2011). Chimeric CYP21A1P/CYP21A2 genes identified in Czech patients with congenital adrenal hyperplasia. European Journal of Medical Genetics, 54(2), 112–117. Wedell, A., & Luthman, H. (1993). Steroid 21-hydroxylase deficiency: Two additional mutations in salt-wasting disease and rapid screening of diseasecausing mutations. Human Molecular Genetics, 2(5), 499–504. White, P. C., & Speiser, P. W. (2000). Congenital adrenal hyperplasia due to 21-hydroxylase deficiency. Endocrine Reviews, 21(3), 245–291. Xu, Z., Chen, W., Merke, D. P., & McDonnell, N. B. (2013). Comprehensive mutation analysis of the CYP21A2 gene: An efficient multistep approach to the molecular diagnosis of congenital adrenal hyperplasia. The Journal of Molecular Diagnostics, 15(6), 745–753.

Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 37–63 https://doi.org/10.1007/5584_2018_301 # Springer Nature Switzerland AG 2018 Published online: 6 March 2019

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population Thi Thuy Hang Tran, Duc Hinh Nguyen, Van Khanh Tran, Quy Linh Nguyen, Hong Anh Trinh, Long Hoang Luong, Van Anh Tran, Le Anh Tuan Pham, Thu Thuy Nguyen, Van Bang Nguyen, Thinh Huy Tran, and Thanh Van Ta M (8.9%), and M7b1 (7.7%). The most frequently encountered SNPs in this study were A263G (100%), A73G (99.6%), 315insC (96%), 309insC (56%), C16223T (41%), and T16189C (39%). The genetic diversity was calculated at 99.83%, and the probability of random match of two individuals sharing the same mtDNA haplotype was 0.37%. Conclusion: We have assessed the genetic polymorphism of mtDNA HV1 and HV2 of 517 Kinh, Muong, Cham, and Khmer ethnic samples. The result will help in better understanding of Vietnamese’s mitochondrial genome diversity and aid in population as well as forensic science.

Abstract

Background: The sequence polymorphism of mitochondrial DNA (mtDNA) hypervariable segment 1 (HV1) and hypervariable segment 2 (HV2) is studied and applied to genetic diversity and human evolution assessment, forensic genetics, consanguinity determination, and mitochondrial disease diagnosis. Methods: The study identified the variations of HV1 and HV2 of 517 unrelated Vietnamese individuals in Kinh, Muong, Cham, and Khmer ethnic. We performed sequencing of two hypervariable segments of mitochondrial DNA: HV1 and HV2. Results: Fifty haplogroups were identified in which F1a haplogroup frequency was highest at 15.7%, followed by B5a (10.8%),

Keywords

HV2 · Hypervariable region HV1 · Mitochondrial DNA · Vietnamese

Authors Thi Thuy Hang Tran and Duc Hinh Nguyen have been equally contributed to this chapter. T. T. H. Tran, V. K. Tran, Q. L. Nguyen, L. H. Luong, V. A. Tran, L. A. T. Pham, and T. T. Nguyen Hanoi Medical University, Hanoi, Vietnam D. H. Nguyen, T. H. Tran, and T. Van Ta (*) Hanoi Medical University, Hanoi, Vietnam Hanoi Medical University Hospital, Hanoi, Vietnam e-mail: [email protected] H. A. Trinh and V. B. Nguyen Center for Gene and Protein Research, Vietnam Military Medical University, Hanoi, Vietnam

1

Introduction

Mitochondrial DNA complete sequence of 16,569 base pair (bp) was published in 1981 and subsequently revised with a few changes in base composition, which was later known as the revised Cambridge Reference Sequence (rCRS)

37

38

(Anderson et al. 1981) (Andrews et al. 1999). Mitochondrial DNA contain 37 genes, including 13 essential polypeptides, 2 ribosomal RNAs (12S and 16S), and 22 tRNA, and the control region or the displacement loop (D-loop), which span across approximately 1.1 kb. Found within the D-loop are three hypervariable regions (HV1, HV2, and HV3), the only major noncoding regions of the molecule (Andrews et al. 1999). These regions are highly polymorphic in humans, thus providing a high degree of discrimination between unrelated individuals. Analysis of these regions often reveals important information about the genetic diversity and origin of the population. Vietnam is a culturally diverse country with 54 ethnic groups. The Kinh ethnic make up most of the country’s population (85.7%), and other notable ethnic groups include Muong, Cham, and Khmer (The 2009 Population and Housing Census of Vietnam). The assessment of intraspecific mtDNA variations through sequencing of HV1 and HV2 in the mtDNA control region has become a routine practice in the past decade (Searle 2000). To take full advantage of a uniparental marker system, such as mtDNA, one needs a sufficiently resolved phylogeny that is not overly blurred by recurrent mutations. The combination of both HV1 and HV2 is useful for forensic purposes (Bandelt et al. 2002); therefor, it is important for every nation to have their database of mtDNA variants. However, mitochondrial DNA data on Vietnamese population have been understudied, and data only focus on a small subset of the population, therefor not representative enough for population as well as forensic purpose. One study has reported only the control region for the Vietnamese in Hanoi area (Irwin et al. 2008). Other studies led by MS Peng and HH Quang have analyzed mitochondrial DNA of 168 Cham and 139 Kinh individuals from Vietnam (Peng et al. 2010). In this study we carried out a comprehensive analysis of mtDNA HV1 and HV2 data on a more representative population of 517 individuals from 5 different ethnic groups across Vietnam.

T. T. H. Tran et al.

2

Methods and Materials

2.1

Samples

A total of 517 participants were recruited from 5 different ethnic groups: 206 from Kinh ethnic (106 peoples from Kinh Bac, 100 peoples from Kinh Nam), 100 from Muong ethnic, 98 from Khmer ethnic, and 113 from Cham ethnic. To ensure the participants’ Vietnamese ethnic origin, each family history was taken prior to blood collection. Informed consent was obtained from all participants prior to admission, and the study protocol was approved by the ethical committees of Hanoi Medical University (IRB00003121, Hanoi Medical University Institutional Review Board, Hanoi, Vietnam).

2.2

DNA Extraction and Amplification

Genomic DNA was extracted from 1 mL peripheral blood, and washings followed a standard phenol-chloroform method. HV1 and HV2 gene primer set used for PCR were designed by the Primer3 software. Amplification of HV1 and HV2 regions was carried out using three sets of primers encompassing the two HV1 and HV2 regions, respectively. PCR products were gel eluted and purified for sequencing. There are two sets of oligonucleotide primer for HV1 and HV2: HV1-F: 50 - CTC CAC CAT TAG CAC CCA AAG C -30 and HV1-R: 50 - CCT GAA GTA GGA ACC AGA TG -30 and HV2-F: 50 - GGT CTA TCA CCC TAT TAA CCA C -30 and HV2-R: 50 - CTG TTA AAA GTG CAT ACC GCC A -30 , respectively. Polymerase chain reaction (PCR) amplification was performed in 20 μl reaction mixture consisting 1 PCR buffer, 2.5 mM of each dNTPs, 0.2 μl of each primer, 0.5 U of Taq polymerase, and 50 ng DNA and H2O. PCR thermal cycle was performed at 94  C for 5 min, followed by 30 cycles [94  C for 30 s, 54  C for 30 s, 72  C for 30 s], 72  C for 5 min, and final hold at 15  C. PCR products underwent

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

gel electrophoresis with agarose gel standardized scale 100 bp 1%. DNA bands were stained with ethidium bromide and photographed with EC3 imaging system. PCR amplification was conducted on the GeneAmp PCR System (Applied Biosystems, Foster City, CA, USA). Amplicons were purified using Qiagen kit.

2.3

Direct Sequencing

Composition of the PCR reaction for HV1 and HV2 include 1X BigDye buffer, forward primer or reverse, and DNA and H2O. Amplification was performed on GeneAmp PCR System (Applied Biosystems, Foster City, CA, USA). The thermal cycle was performed first at 95  C for 2 min, followed by 25 cycles [95  C for 5 s, 50  C for 10 s, 60  C for 4 minutes], and final hold at 4  C. The sequencing reaction was purified by ethanol precipitation prior to sequencing. The purified products were then sequenced on an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA).

2.4

Data Analysis

The sequences were aligned and edited between positions 15,974 and 16,517 for HV1 and 8 and 431 for HV2. According to the TWGDAM (Technical Working Groups for DNA Analysis Methods), a group of forensic research laboratories in the United States that sets standards for DNA technology, the minimum sequence that will be accepted for the mtDNA database for HV1 is from position 16,024 to 16,365 and for HV2 from position 73 to 340. Each sample was sequenced in both directions (50 and 30 ) to avoid ambiguities in sequence determination. Data were analyzed using the CLC Main Workbench. The sequence samples were aligned and compared with rCRS to determine the polymorphisms. The genetic diversity and probability of random match were calculated by the formulae h ¼ (1ΣX2)n/(n1) and ΣX2, respectively; in

39

there, ΣX2 is the sum of the square of the haplotype frequencies, and n is the sample number.

3

Results

The sequence variations in HV1 and HV2 and in further polymorphic sites detected in the 517 Vietnamese individuals are shown in Table 1. The statistical data of resulted mtDNA diversity in the Vietnamese population samples is given in Tables 1 and 2. The observed mutations in these population samples were compared to rCRS, transition (39%), transversion (3.3%), insertion (1.2%), and deletion (0.6%). In this study, we found 50 haplogroups; F1a haplogroup frequency was highest which accounted for 15.7%, followed by B5a (10.8%), M haplogroup frequency was 8.9%, and M7b1 was 7.7%; other haplogroups accounted for less than 1% of the study population. A total of 247 variants were observed, 161 in HV1 and 86 in HV2 (Table 1). Nucleotide substitution (96%) was the most common, compared to insertion (2.64%) and deletion (1.32%). The most common transition observed was A ! G, while A ! C substitution was the most frequent transversion. The most common insertion encountered was an additional C residue. The most frequently encountered SNPs in this study were A263G (100%), A73G (99.6%), 315insC (96%), 309insC (56%), C16223T (41%), and T16189C (39%) (Table 3 and Fig. 1). The sequence result of HV1 and HV2 of 517 samples of 5 ethnic studies Kinh Bac, Kinh Nam, Cham, Khmer, and Muong people were obtained and analyzed. The frequency identified some common polymorphisms in HV1 and two hypervariable regions of mitochondrial DNA HV2; classification are 50 haplogroups based on location-specific polymorphisms on two hypervariable regions of mitochondrial DNA HV2 and HV1 (Table 2). As shown in Table 3, in HV1, the frequency of SNP at each ethnic population is different. In Cham and Kinh Bac population, the most common SNP is T16189C and C16223T in Khmer and Muong population; however, in Kinh Nam ethnic, the most common SNP

40

T. T. H. Tran et al.

Table 1 Mitochondrial DNA polymorphisms for HV1, HV2, and haplogroups in 517 samples of unrelated Vietnamese individuals of 4 ethnics living in Vietnam Ethnic Cham

Sample Cham07

Haplogroup B4

Cham101

B4

Cham38

B4a

Cham39

B4a

Cham53

B4a

Cham75

B4a

Cham77

B4a

Cham08

B4b

Cham31

B4c

Cham43

B4c

Cham11

B4c

Cham19

B4c

Cham21

B4c

Cham22

B4c

Cham30

B4c

Cham35

B4c

Cham40

B4c

Cham54

B4c

Cham66

B4c

Cham71

B4c

Cham78

B4c

Cham83 Cham29

B4c B4g

Cham105

B4g

Cham03

B5a

Cham12

B5a

HV1(16000+) C147T, A183C, C184A, T189C, T217C, T362C, C465T, T519A A182C, A183C, T189C, T261C, C348T, T519A T093C, C174T, A182C, A183C, T189C, T217C, T261C, T519A A182C, A183C, T189C, T217C, T261C, T519A A182C, A183C, T189C, T217C, C218T, T224C, T261C A182C, A183C, T189C, T217C, C218T, T224C, T261C A182C, A183C, T189C, T217C, C218T, T224C, T261C T136C, A183C, T189C, T217C, A241C, T519A G129A, T140C, A166C, A182C, A183C, T189C, T217C, G274A, A335G, T519A T140C, A182C, A183C, T189C, T217C, G274A, T311C, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, A183C, C184A, T189C, T217C, A235G, T362C, C465T, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, A183C, C184A, T189C, T217C, T362C, C465T, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, T183C, C184A, T189C, C201T, G213A, T217C, C232T, C270T, C292T, T519A C147T, T183C, C184A, T189C, T217C, T362C, C465T, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, A183C, C184A, T189C, T217C, A235G, T519A C147T, A183C, C184A, T189C, T217C, A235G, T362C, C465T, T519A C147T, A183C, C184A, T189C A181C, A182C, A183C, T189C, C201T, G213A, T217C, C232T, C270T, C292T, T519A A181C, A182C, A183C, T189C, G213A, T217C, C261T, C292T, T519A T140C, G153A, T178C, A183C, T189C, A207G, C266A, T519A T140C, A183C, T189C, C266A, C291T, T519A

HV2 A73G, A263G, 315C A73G, A263G, 309CC, 315C A73G, T146C, A153G, A263G, 315C 522-523d A73G, T146C, A263G, 315C, 522-523d A73G, T146C, T152C, A263G, 315C, 522-523d A73G, T146C, T152C, A263G, 315C, 522-523d A73G, T146C, T152C, A263G, 315C, 522-523d A73G, T146C, A263G, 309CC, 315C A73G, C150T, A263G, 309CC, 315C A73G, T146C, C150T, A263G, 309CC, 315C A73G, A263G, 309C, 315C A73G, A263G, 315C A73G, A263G, 309C, 315C A73G, A263G, 315C A73G, A263G, 309C, 315C A73G, A263G, 315C A73G, A263G, 315C A73G, A263G, 309C, 315C A73G, A263G, 309C, 315C A73G, A263G, 309C, 315C A73G, A263G, 315C A73G, A263G, 315C A73G, T195C, A263G, 309C, 315C, 522-523d 61A, 62A, A73G, A263G, 308-309d, 315C, 522-523d 42G, A73G, G103A, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 455 T, 522-523d (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

41

Table 1 (continued) Ethnic

Sample Cham18

Haplogroup B5a

HV1(16000+) T140C, A183C, T189C, C266A, C291T, T519A T140C, A182C, A183C, T189C, C266A, T519A T140C, A183C, T189C, C266A, T519A

Cham25

B5a

Cham28

B5a

Cham32

B5a

T140C, A182C, A183C, T189C, C261T, C266A, T519A

Cham42

B5a

T140C, A182C, A183C, T189C, C261T, C266A, T519A

Cham46

B5a

Cham48

B5a

Cham59

B5a

Cham67

B5a

Cham81

B5a

Cham86

B5a

Cham91

B5a

T140C, A182C, A183C, T189C, C266A, T519A T140C, A183C, T189C, C257A, C266A, T519A G129A, T140C, A182C, A183C, T189C, C266A, T519A T140C, C148T, A183C, T189C, T243C, C266A, T519A T140C, A183C, T189C, C257A, C266A, T519A T140C, A183C, T189C, C266A, C291T, T519A T140C, A183C, T189C, C266A, T519A

Cham103

B5a

Cham106

B5a

Cham108

B5a

Cham115

B5a

Cham94

B5b

Cham27

B5b

Cham99

B5b

Cham52

C

Cham84

D4a

G129A, T209C, C223T, A272G, T362C, T519A

Cham85

D4a

T093C, G129A, C223T, T263C, T362C, T519A

Cham69

D4e

C167T, C223T, C320T, T362C

Cham33

E

C223T, C291T,,T362C,G390A, T519A

Cham51

F

T189C, G274A, T304C, T519A

T140C, G153A, T178C, A183C, T189C, A207G, C266A, T519A T140C, C148T, A183C, T189C, T243C, C266A, T519A T140C, G153A, T178C, A183C, T189C, A207G, C266A, T519A T140C, A183C, T189C, C266A, T304C, T519A C067G, T140C, A183C, T189C, T243C, T519A C104T, C111T, T140C, A182C, A183C, T189C, C234T, T243C, C291T, C463T, T519A C104T, C111T, T140C, A182C, A183C, T189C, C234T, T243C, C291T, C463T, T519A T086C, C223T, T298C, C327T, T357C, T519A

HV2 A73G, A210G, A263G, 309C, 315C, 455 T, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d T57C, 61A, A73G, T152C, A210G, A263G, 309CC, 315C, A368G A73G, T152C, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 455C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d 42G, A73G, G103A, A210G, A263G, 309C, 315C A73G, A210G, A263G 42G, A73G, G103A, A189G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 315C, 522-523d A73G, A103G, T152C, T204C, A263G, 315C, 522-523d A73G, T131C, A263G, 309CC, 315C, 522-523d A73G, T131C, A263G, 309CC, 315C, 522-523d C64T, A73G, 249DelA, A263G, 309C, 315C A73G, T152C, 249delA, A263G, 309C, 315C, G316C, 522-523d A73G, T152C, A263G, 309C, 315C, 523CA A73G, G94A, A263G, 309C, 315C A73G, A193G, A263G, 309C, 315C (continued)

42

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample

Haplogroup

HV1(16000+)

Cham16

F1a

G129A, T172C, T304C

Cham61

F1a

G129A, T172C, T304C, T362C, T519A

Cham68

F1a

Cham88

F1a

Cham45

F1a

Cham49

F1a

Cham50

F1a

Cham89

F1a

Cham01

M

Cham06

M

G129A, A162G, T172C, T189C, T304C, T519A T093C, G129A, A162G, T172C, T304C, A399G, T519A C108T, G129A, A162G, T172A, T304C, T519A C108T, G129A, A162G, T172A, A183C, T189C, T304C, T519A C108T, G129A, A162G, T172A, T304C, T519A C108T, G129A, A162G, T172A, T304C, T519A G145A, A181C, T189C, C192T, C262T, T304C, G390A, T519A G129A, T209C, C223T, T325C

Cham15

M

C223T, C278T, T311C, C354T, T519A

Cham23

M

T093C, T209C, C223T, T224C, T263C, C278T, G319A

Cham24

M

Cham47

M

Cham55

M

Cham60

M

Cham70

M

Cham74

M

Cham82

M

G145A, A181C, T189C, C192T, C262T, T304C, T311C, G390A, T519A A129G, A183C, T189C, G213A, C218T, C223T, G274A, T519A A129G, A183C, T189C, G213A, C218T, C223T, G274A, T519A A129G, A183C, T189C, G213A, C218T, C223T, G274A, T519A G145A, A181C, A182C, C223T, C291T, T304C, T519A G145A, A181C, T189C, 192 T, C262T, T304C, G390A, T519A G129A, T209C, C223T, T325C

Cham95

M

G129A, T209C, C223T, T325C

Cham96

M

G129A, T209C, C223T, T325C

Cham100

M

G129A, T209C, C223T, T325C

Cham102

M

T093C, T209C, C223T, T224C, T263C, C278T, G319A

Cham58

M12

Cham65

M7b

G129A, C223T, C234T, C261T, C262T, G274A, C290T C223T, T297C, T311C

HV2 A73G, 249delA, A263G, 309CC, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, A210G, A263G, 309C, 315C A73G, T152C, A200G, A263G, 315C A73G, T199C, A263G, 309CC, 315C A73G, T146C, C150T, C151T, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C A73G, C150T, A263G, 309CC, 315C, 522-523d A73G, C150T, A263G, 309CC, 315C, 522-523d A73G, C150T, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C A73G, T152C, A200G, A263G, 315C A73G, T152C, A200G, A263G, 315C A73G, T152C, A200G, A263G, 315C A73G, T152C, A200G, A263G, 315C A73G, T146C, C150T, C151T, A263G, 309CC, 315C, 522-523d A73G, G143A, T152C, A263G, 309C, 315C, T318C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

43

Table 1 (continued) Ethnic

Sample

Haplogroup

HV1(16000+)

Cham97

M7b1

G129A, T189C, C192T, C223T, T297C

Cham92

M7b1

G129A, T189C, T297C

Cham114

M7b1

T093C, G129A, A183C, T189C, C223T, T297C, T311C

Cham112

M7b

C223T, C278T, C354T, G390A, T519A

Cham107

M7b

C223T, C278T, T311C, C354T, T519A

Cham20

M7c

T075C, C223T, 293 T, C295T, T519A

Cham44

M7c

T075C, C223T, 293 T, C295T, T519A

Cham113

M7c

C223T, C295T, T362C, T519A

Cham87 Cham98 Cham17

M8a M8a M9a

C184A, C223T, T298C, G319A C184A, T189C, C223T, T298C, G319A C223T, C256T, T311C, T362C, T519A

Cham10 Cham80

M9b N21

A051G, T209C, C223T, T362C, T519A G129A, C193T, C223T, T325C, T519A

Cham09

N9a

Cham14

N9a

Cham26

N9a

Cham64

N9a

Cham79

N9a

T093C, T140C, T189C, C223T, 257A, C261T, C292T, T519A T093C, T140C, T189C, C223T, 257A, C261T, C292T T093C, T189C, C223T, C256T, 257A, C261T, C292T, C354T, T519A T093C, T189C, C223T, C256T, 257A, C261T, C292T, C354T, T519A C111T, G129A, C223T, 257A, C260T, C261T

Cham02

N9a

Cham36

N9a

Cham72

N9a

Cham104

N9a

Cham109

N9a

Cham57

R

T092C, G145A, T172C, C223T, C245T, 257A, C261T, T311C, T519A T092C, G145A, T172C, C223T, C245T, 257A, C261T, T311C, T519A T092C, G145A, T172C, C223T, C245T, 257A, C261T, T311C, T519A T092C, G145A, T172C, C223T, C245T, 257A, C261T, T311C, T519A T092C, G145A, T172C, C223T, C245T, 257A, C261T, T311C, T519A C256T, C290T, C465T

Cham93

R

C256T, C290T, C465T

HV2 A73G, C150T, T199C, T204C, A263G, 309CC, 315C A73G, C150T, T199C, A202G, A263G, 309C, 315C, C332T A73G, C150T, T199C, T204C, A263G, 309C, 315C A73G, C150T, T199C, T204C, A263G, 309CC, 315C A73G, T199C, A263G, 309C, 315C A73G, T199C, A263G, 319CC, 315C A73G, T146C, T152C, T199C, A263G, 309C, 315C, 522-523d A73G, T146C, T152C, T199C, A263G, 309C, 315C, 522-523d A73G, T146C, T199C, A263G, 309CC, 315C, 522-523d A73G, A263G, 315C A73G, A263G, 315C A73G, T125G, T146C, A153G, A263G, 309C, 315C A73G, A153G, A263G, 315C A73G, C150T, T195C, A263G, 315C, 337d, 522-523d A73G, C150T, A263G, 309C, 315C A73G, C150T, A263G, 309C, 315C A73G, C150T, A263G, 309C, 315C A73G, C150T, A263G, 309C, 315C A73G, T146C, C150T, A263G, 309C, 315C A73G, C150T, T152C, A263G, 315C, 523CACA A73G, C150T, T152C, A263G, 315C, 523CACA A73G, C150T, T152C, A263G, 315C, 523CACA A73G, C150T, T152C, A263G, 315C, 523CACA A73G, C150T, T152C, A263G, 315C, 523CACA A73G, T195C, A263G, 309C, 315C, 522-523d (continued)

44

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Kinh bac

Sample

Haplogroup

HV1(16000+)

Cham63

R11

Cham111

R11

Cham04

R9b

Cham13

R9b

Cham34

R9b

Cham37

R9b

T086C, A182C, A183C, T189C, T311C, G390A, A399G, T519A T086C, A182C, A183C, T189C, T311C, G390A, A399G, T519A C221T, T249C, T288C, C301T, T304C, G390A, T519A C221T, T249C, T288C, C301T, T304C, G390A, T519A T249C, C259T, T288C, C301T, T304C, G390A, T519A C192T, T304C, A309G, G390A, T519A

Cham41

R9b

C192T, T304C, A309G, G390A, T519A

Cham56

R9b

Cham62

R9b

Cham76

R9b

Cham90

R9b

T124C, C148T, C290T, T304C, A309G, G390A, T519A C221T, T249C, T288C, C301T, T304C, G390A, T519A C221T, T249C, T288C, C301T, T304C, G390A, T519A C192T, T304C, G309A, A390G, T519A

Cham110

R9b

C192T, C193T, T304C, G309A, A390G, T519A

Khin36

B4

Khin43

B4

Khin65

B4

G129A, A182C, A183C, T189C, T217C, T362C A182C, A183C, T189C, T217C, C261T, T519A T092C, A182C, A183C, T189C, T217C, G274A, A289G, C301T, T519A

Khin72

B4

Khin78

B4

Khin37

B4a

Khin64

B4a

Khin87

B4a

Khin33

B4b

Khin16

B4c

Khin17

B4c

T140C, A182C, A183C, T189C, T217C, G274A, A305T, A335G, T519A A182C, A183C, T189C, T217C, G274A, A289G, C301T, T519A C168T, A182C, A183C, T189C, T217C, C261T, T311C, T519A A182C, A183C, T189C, C234T, C256T, C261T, T519A A182C, A183C, T189C, T217C, A219G, C261T, C286T, T519A T126C, T136A, A183C, T189C, T217C, C260T, C287T, C325T, T519A C147T, C168T, A183C, C184A, T189C, T231C, A235G, T519A C147T, C168T, A183C, C184A, T189C, T217C, T231C, A235G, T519A

HV2 A73G, T195C, A263G, 309C, 315C, 522-523d A73G, A189G, T215C, A263G, 309CCC, 315C A73G, A189G, T215C, A263G, 309CCC, 315C A73G, A263G, 315C, G329A A73G, A263G, 315C, G329A A73G, T152C, T195C, A263G, 309C, 315C, 523CA A73G, A183G, T204C, G207A, A263G, 309CC, 315C, 522-523d A73G, A183G, T204C, G207A, A263G, 309CC, 315C, 522-523d A73G, A263G, 309C, 315C A73G, A263G, 315C, G329A A73G, A263G, 315C, G329A A73G, A183G, T204C, G207A, A263G, 309CC, 315C, 522-523d A73G, A183G, T204C, G207A, A263G, 309CC, 315C, 522-523d A73G, A263G, 309CC, 522-523d A73G, A263G, 309C, 315C A73G, T152C, A183G, A244G, A263G, T310C, A374G A73G, C150T, T195C, A263G, 309CC, 315C A73G, A183G, A263G, 309CC, 315C, A374G, 522-523d A73G, T146C, A263G, 309CCC, 315C, 522-523d A73G, A263G, 309CC, 315C, 522-523d A73G, T146A, A263G, 522-523d A73G, A200G, A263G, 315C, 522-523d A73G, A263G, 309C, 315C A73G, A263G, 309C, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

45

Table 1 (continued) Ethnic

Sample Khin26

Haplogroup B4c

HV1(16000+) C147T, C168T, A183C, C184A, T189C, T217C, C234T, A235G, T519A A183C, T189C, T217C, C234T, T311C, A399G

Khin34

B4c

Khin76

B4c

Khin110

B4c

Khin5

B4g

Khin27

B4g

Khin49

B4g

Khin99

B4g

Khin3

B5

A181C, A182C, A183C, T189C, G213A, T217C, C292T, T311C, T519A C147T, A183C, C184A, T189C, T217C, A235G, C362T, T519A T093C, A181C, A182C, A183C, T189C, G213A, T217C, C242T, C261T, C287T, C292T, C301T, C355T, T519A A181C, A182C, A183C, T189C, G213A, T217C, C261T, C292T, T519A A181C, A182C, A183C, T189C, C261T, C292T, T519A T093C, A181C, A182C, A183C, T189C, G213A, T217A, C242T, C250T, C261T, C287T, C292T, C301T, C355T, T519A T140C, C187T, T189C, C256T, C266G, T519A

Khin7

B5a

T140C, A182C, T189C, C266A

Khin13

B5a

Khin31

B5a

Khin35

B5a

Khin48

B5a

Khin52

B5a

Khin58

B5a

T140C, A183C, T189C, T243C, C266A, T311C, T519A T140C, A183C, T189C, T243C, C266A, T311C, T519A T140C, C148T, A182C, A183C, T189C, T243C, C266A, T519A T140C, A183C, T189C, T249C, C266A, T519A G129A, T140C, A183C, T189C, C266A, T519A T140C, A183C, T189C, 193C, C266A, T519A

Khin67

B5a

Khin79

B5a

T140C, A183C, T189C, C234T, C266A, T519A T140C, A183C, T189C, C266A, T519A

Khin81

B5a

T140C, A183C, T189C, C266A, T519A

Khin84

B5a

T140C, A183C, T189C, C266A, T519A

Khin86

B5a

Khin98

B5a

Khin100

B5a

T140C, A183C, T189C, C266A, T304C, T519A T140C, A182C, A183C, T189C, C261T, C266A, T519A T140C, A183C, T189C, C266A, T519C

Khin60

B5b

T140C, A183C, T189C, T243C, T311C, T519A

Khin47

B6

T093C, C179A, A182C, A183C, T189C

HV2 A73G, A263G, 309C, 315C A73G, C150T, T195A, A214G, A263G, 309CC, 315C A73G, A263G, 309d, 315C, 522-523d A73G, A263G, 309C, 315C 61A, A73G, A263G, 309C, 315C, 522-523d A73G, T146C, T152C, A263G, 315C, 522-523d A73G, C150T, A263G, 309CCC, 315C, 522-523d 61A, A73G, A263G, 522-523d A73G, A93G, A210G, A263G, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 309CC, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, A210G, A263G, 315C, 522-523d A73G, A210G, A263G, 315C, 522-523d A73G, A210G, A263G, 309C, 315C, 522-523d A73G, G103A, T204C, A263G, 309CC, 315C, 522-523d A73G, C150T, A263G, 309C, 315C (continued)

46

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample Khin15

Haplogroup C

HV1(16000+) T189C, C223T, T298C, C327T, T519A

Khin97

C

T189C, C223T, T298C, C327T, T519A

Khin85

D4a

C111G, G129A, C223T, T362C

Khin95

D5a

Khin10

D5b

T092C, A164G, A182C, A183C, T189C, C223T, C266T, T362C T092C, C148T, A183C, T189C, C223T, T362C, T519A

Khin18

D5b

Khin2

F

A182C, A183C, T189C, C223T, T362C, T519A T157C, C256T, T304C, A335G

Khin77

F

T075C, T172C, T304C, T519A

Khin11

F1a

G129A, T172C, T304C, T519A

Khin14

F1a

G129A, T172C, T304C, T519A

Khin24

F1a

Khin29

F1a

Khin30

F1a

C108T, G129A, A162G, T172C, T304C, T519A G129A, A162G, T172C, T304C, A399G, T519A G129A, C218T, T304C, T311C

Khin38

F1a

G129A, A162G, T172C, T304C, T519A

Khin46

F1a

Khin50

F1a

C108T, G129A, A162G, T172C, T304C, T519A G129A, T172C, T304C, T519A

Khin54

F1a

G129A, A162G, T172C, T304C, A399G, T519A

Khin55

F1a

G129A, T172C, C287T, C295T, T304C, T519A

Khin56

F1a

Khin61

F1a

Khin63

F1a

C108T, G129A, A162G, T172C, T304C, T519A G129A, A162G, T172C, C292T, T304C, T519A G129A, T172C, C295T, T304C, T519A

Khin69

F1a

G129A, A162G, T172C, T304C, T519A

Khin74

F1a

G129A, T172C, C287T, C295T, T304C, T519A

Khin96

F1a

Khin106

F1a

C108T, G129A, A162G, T172C, T304C, T519A C108T, G129A, T172C, T304C, C365T, T519A

HV2 A73G, 249delA, A263G, 309C, 315C A73G, 249delA, A263G, 309C, 315C A73G, T152C, A263G, 309C, 315C A73G, C150T, A263G, 309CC, 315C, 522-523d A73G, C150T, T152C, G185A, A263G, 309C, 315C, 522-523d A73G, C150T, A263G, 309CCC, 315C A73G, 249delA, A263G, 315C A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, A200G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309CC, 315C, 522-523d A73G, T152C, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C, 522-523d (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

47

Table 1 (continued) Ethnic

Sample Khin109

Haplogroup F1a

Khin8

F1a1

Khin21

F1b

HV1(16000+) C108T, G129A, A162G, T172C, T304C, T519A G129A, A162G, T172C, C292T, T304C, T519A A183C, T189C, C292T, T304C, T519A

Khin19

F1c

C111T, G129A, C266T, T304C, T519A

Khin75

F3a

T093C, T249C, T298C, C355T, T362C, G390A

Khin23

M

C223T, C278T, T311C

Khin45

M

C223T, G274A

Khin105

M

C223T, C295T, T519A

Khin107

M10

Khin4

M12

T093C, G129A, C223T, T311C, T357C, A497G C148T, C223T, C234T, C261T, C290T, T519A

Khin1

M7a

T086C, G129A, T209C, C223T, A272G, T519A

Khin101 Khin66

M7a M7b

166d, T209C, C214T, C223T, C260T, T311C A235G, T311C, T356C, T519A,

Khin73

M7b

C185T, C223T, T297C

Khin108

M7b

T136C, T189C, C223T, C278T, T311C

Khin9

M7b1

G129A, C192T, T297C

Khin12

M7b1

G129A, C192T, C223T, T297C

Khin20

M7b1

G129A, T189C, C192T, C223T, T297C, T356C

Khin25

M7b1

G129A, C223T, T271C, T297C

Khin39

M7b1

G129A, C192T, C223T, C261T, T297C, T298C

Khin62

M7b1

G129A, C223T, T271C, T297C

Khin71

M7b1

G129A, T189C, T297C

Khin83

M7b1

G129A, C192T, C223T, T297C

Khin91

M7b1

G129A, C192T, C223T, T249C, T297C, T324C

HV2 A73G, 249delA, A263G, 309C, 315C, 522-523d A73G, T152A, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 315C, 522-523d A73G, T152C, 249delA, A263G, 309C, 315C, 522-523d A73G, T152C, G207A, 249delA, A263G, 309CC, 315C A73G, C150T, G203C, A263G, 315C A73G, G185A, T195C, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C, 522-523d A73G, T146C, A263G, 315C, 522-523d A73G, A263G, 309C, 315C, T318C A73G, T152C, 249delA, A263G, 315C, G316A, 522-523d A73G, A263G, 309C, 315C A73G, T146A, T199C, A263G, 309C, 315C, 522-523d A73G, C150T, T199C, T204C, A263G, C271T, 309CC, 315C A73G, C150T, T152C, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, T131C, C150T, T199C, A263G, 309C, 315C A73G, T131C, C150T, T199C, A263G, 309CC, 315C, C332T, 522-523d A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, T204C, A263G, 309C, T310C, C311T, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C, C332T (continued)

48

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Kinh Nam

Sample Khin93

Haplogroup M7b1

HV1(16000+) G129A, A163G, T189C, C223T, T297C

Khin94

M7b1

G129A, G145A, C223T, T297C, T325C

Khin103

M7b1

G129A, C192T, C223T, T297C, T324C

Khin42

M7c

C223T, C295T, T519A

Khin41

M9a

C223T, C234T, T271C, T362C

Khin53

M9a

C223T, C234T, T356C, T362C

Khin80 Khin88

M9a M9a

T093C, C223T, C234T, T271C, T362C C223T, C234T, C287T, T362C

Khin90

N

C223T, T263C, G274A, T311C, A343G, T357C, T519A

Khin6 Khin40

N9a R9

C223T, C257A, C261T, T311C T304C, A335G, T362C

Khin44 Khin92 Khin28

R9 R9 R9b

T093C, T157C, T304C T304C, T362C, T519A A284G, T304C, A309G, G390A, T519A

Khin59

R9b

T124C, C148T, T304C, A309G, G390A

Khin68

R9b

C239T, T304C, A309G, G390A, T519A

Khin82

R9b

T124C, C148T, T304C, A309G, G390A

Khin22

Z

C185T, C223T, C260T, T298C

Khin32

Z

C185T, C223T, C260T, T298C

Khin70

Z

C185T, C223T, C260T, T298C, T519A

Khin104

Z

C185T, C223T, C260T, T298C, A317C

KN23

A

38delA, T86C, G129A, T209C, C223T, A272G, C290T, T519A

KN33

A

T86C, G129A, T209C, C223T, A272G, C290T

KN67

A

KN20 KN3

B B4

KN18

B4

C071T, G129A, C223T, C234T, C262T, C263T, G274A, C290T T189C, C223T, C278T C147T, A183C, C184A, T189C, T217C, A235G, C294T C147T, A183C, C184A, T189C, T217C, A235G

HV2 A73G, C150T, T199C, T204C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C, C332T A73G, T146C, T199C, A263G, 315C, 522-523d A73G, C151T, A153G, A263G, 315C A73G, C150T, A153G, A263G, 309C, 315C, A385G A73G, A153G, A263G, 315C A73G, A153G, A263G, 309C, 315C A73G, G103A, C151T, T152C, G260A, A263G, 315C A73G, C150T, A263G, 315C A73G, C150T, T152C, A263G, 309C, 315C A73G, C151T, A263G, 315C A73G, A263G, 315C A73G, A183G, A227G, A263G, 315C, 522-523d A73G, T89C, T146C, A263G, 309CC, 315C A73G, T152C, A263G, 309CC, 315C, 522-523d A73G, T89C, T146C, A263G, 309C, 315C A73G, T152C, 249delA, A263G, 315C, 522-523d A73G, 249delA, A263G, 309C, 315C A73G, 249delA, A263G, 309C, 315C A73G, C151T, T152C, 249delA, A263G, 309C, 315C A73G, T152C, G225A, 249del, A263G, 315C, G316A A73G, T152C, G225A, 249del, A263G, 315C, G316A A73G, G143A, T152C, A263G, 309C, 315C,T317C A73G, C150T, A263G, 315C A73G, A263G, 309C, 315C A73G, A263G, 309C, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

49

Table 1 (continued) Ethnic

Sample KN26 KN31 KN35

Haplogroup B4 B4 B4

HV1(16000+) A182C,A183C, T189C, T217C A183C, T189C, T217C A183C, T189C, T217C, G274A, T311C

KN63 KN68

B4 B4

KN100

B4

KN1

B4a

KN14

B4b

KN37 KN27

B4b B5a

KN48

B5a

A182C, A183C, T189C, T217C 38delA, T140C, A182C, A183C, T189C, T217C, C242A, G274A, A335G 38delA, C147T, A183C, C184A, T189C, T217C, A235G, G518A 38delA, C67G, G129A, T157G, A182C, A183C, T189C, T217C, C261T, C354T T136C, A175C, A183C, T189C, 193insC, T217C, C218T T136C, A183C, T189C, T217C, C218T T140C, A181C, A182C, A183C, T189C, C226A, A339G T140C, A182C, A183C, T189C, C266A

KN51

B5a

KN53

B5a

T140C, A182C, A183C, T189C, 193ínC, C266A T140C, A182C, A183C, T189C, C266A

KN56

B5a

T93C, T140C, A183C, T189C, C266A

KN69

B5a

38delA, T140C, A183C, T189C, C266A

KN70

B5a

KN71

B5a

T140C, A181C, A182C, A183C, T189C, C266A T140C, A183C, T189C, 193insC, C266A

KN92

B5a

KN52 KN83

C C

T140C,T161A, A166C, A175C, A183C, T189C, C266A T093C, T172C, C218T, C223T, T298C, C327T C223T, T298C, C327T

KN87 KN89

C C

T172C, C218T, C223T, T298C, C327T C223T, T298C, C327T, C328T

KN59

D4

38del, 188C, 193C, C223T, C234T, T311C, T362C

KN46

D4a

038del, G129A, C223T, T362C

KN85

D4a

C111G, G129A, C223T, T362C

KN11 KN78

D5 F

T189C, C223T, T362C A207G, T304C, A399G

KN8

F1a

C108T, G129A, A162G, T172C, T304C

HV2 A73G, A263G, 309CC,315C A73G, G207A, A263G, 315C A73G, C150T, T195C, A263G, 309C, 315C A73G, A263G, 309C, 315C A73G, C150T, A263G, 309CC A73G, A263G, 309C, 315C A73G, A263G, 315C A73G, A263G, 315C A73G, A263G, 315C A73G, A210G, A263G, 315C A73G, A93G, A210G, A263G, 315C A73G, A210G, A263G, 309C, 315C A73G, A210G, A263G, T310C, 315C A73G, A210G, A263G, 309C, 315C A73G, C150T, A210G, A235G, A263G, T310C, 315C A73G, A210G, A263G, C308T, 310delT A73G, C150T, A210G, A235G, A263G, C308T, 309CC, 315C A73G, A210G, A263G, 309C, 315C A73G, 249del, A263G, 315C A73G, T195C, C198T, 249DelA, A263G, 309CC, 315C A73G, 249del, A263G, 315C A73G, 249del, A263G, 309CC, 315C A73G, C150T, C151T, T152C, A263G, C285T, T310C, 315C A73G, T152C, A263G, 309C, 315C A73G, T152C,C182T, A263G, 309C, 315C A73G, C150T, 309C, 315C A73G, T146C, T152C, 249del, A263G, A281G, 309C, 315C A73G, 249del, A263G, 309C, 315C (continued)

50

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample KN9

Haplogroup F1a

HV1(16000+) T108C, G129A, A162G, T172C, T304C

KN17

F1a

G129A, T172C, T304C

KN29

F1a

G129A, A162G, T172C, T304C, A399G

KN36

F1a

C108T, G129A, A162G, T172C, T304C

KN38

F1a

KN44

F1a

KN45

F1a

KN50

F1a

KN55

F1a

KN57

F1a

KN60

F1a

KN61

F1a

C108T, G129A, A162G, T172C, T304C, T311C C108T, G129A, A162G, T172C, C214T, T304C C108T, G129A, A162G, T172C, C214T, T304C 038DelA, G129A, T172C, T304C, C354T, G518A C108T, G129A, A162G, T172C, C214T, T304C, C108T, G129A, A162G, T172C, C214T, T304C, G518A, T519G C108T, G129A, A162G, T172C, C214T, T304C G129A, A162G, T172C, T304C,

KN64 KN65

F1a F1a

KN66

F1a

G129A, T189C, T172C, T304C, 38delA, C108T, G129A, A162G, T172C, C214T, T304C C108T, G129A, A162G, T172C, T304C

KN73

F1a

G129A, A162G, T172C, T304C, A399G

KN74

F1a

G129A, A162G, T172C, T304C

KN81

F1a

KN86

F1a

G129A, A162G, T172C, T243C, T304C, T311C G129A, T172C, C294T, T304C

KN88

F1a

KN91

F1a

38delA, G129A, T172C, T304C, T311C, A497G, C514T, A515C, G516A, T519A G129A, T172C, T304C

KN95

F1a

G129A, T172C, T304C

KN98

F1a

G129A, T172C, T304C, T311C

KN77

F1b

A182C, A183C, T189C, T304C

KN90

F1b

A183C, T189C, T304C

KN79 KN21

G2a M

C223T, A227G, C278T, T362C C192T, C223T

HV2 A73G, C150T, 249del, A263G, 315C A73G, C150T, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 309CC, 315C A73G, A93G, A95C, C150T, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, T195C, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 309C, 315C A73G, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, G251A, A263G, 309C, 315C A73G, C150T, A210G, A235G, A263G, 309CC, 315C A73G, 249DelA, A263G, 309CC, 315C A73G, C150T, 249DelA, A263G, 309C, 315C A73G, 249DelA, A263G, 315C A73G, T152C, 249DelA, A263G, 315C A73G, C150T, 249DelA, A263G, 315C A73G, 249DelA, A263G, 309C, 315C A73G, T152C, 249DelA, A263G, 315C A73G, 249DelA, A263G, 309CC, 315C A73G, 249DelA, A263G, 315C A73G, A263G, 309C, 315C A73G, C150T, 249DelA, A263G, 309C, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

51

Table 1 (continued) Ethnic

Sample KN28

Haplogroup M

HV1(16000+) C223T, A269G, T271C

KN32

M

C223T

KN72

M

T092C, C223T, A269G, T271C

KN82

M

C223T

KN24

M10

KN25 KN34

M10 M10

38delA, G129A, T172C, C174T, C223T, C234T, C290T, T311C, G518A, T519A C223T, C260G, T311C C167T, T311C

KN49 KN93 KN6

M10 M10 M7b

T172C, C223T, T311C, A37G, C223T, T311C 38delA, G129A, C223T, T297C,

KN58

M7b

KN75

M7b

KN76

M7b

38delA, G129A, A183C, T189C, C223T, T297C 38delA, G129A, C223T, T297C, G518A, T519G, C520T G129A, A182G, C223T, T297C, T356C

KN2

M7b1

KN4

M7b1

KN7

M7b1

KN12

M7b1

38delA, G129A, C192T, C223T, T297C, T519A 38delA, T86C, G129A, C192T, C223T, T297C, G518A 38delA, T93C, G129A, C192T, C223T, T297C, G390A, G518A, T519G, C520T C114A, G129A, C192T, T297C

KN15

M7b1

G129A, C192T, C223T, T297C

KN30

M7b1

G129A, C192T, C223T, T297C

KN42

M7b1

KN99

M7b1

G129A, C192T, C223T, T297C, G518A, T519G, C520T G129, C192T, C223T, T297C

KN16

M7c

C223T, C295T

KN39

M7c

C223T, C291T, C295T

KN54

M7c

C223T, C278T, C295T

KN62

M7c

38delA, C223T, C295T, G518A, T519G,

KN94

M7c

C223T, C261T, C295T, G518A

KN97

M7c

T75C, C223T, A293T, C295T

HV2 A73G, C150T, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C A73G, C150T, C151T, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C A73G, T125C, T127C, A263G, 309C, 315C A73G, A263G, 315C A73G, T146C, T199C, A263G, 315C A73G, T146C, A263G, 315C A73G, A263G, 315C A73G, C150T, T199C, T204C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T159C, T199C, A263G, 309C, 315C A73G, T146C, C150T, T199C, A234G, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C A73G, T131C, C150T, T195C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C A73G, T146C, T199C, A263G, 315C A73G, T146C, T199C, A263G, 315C A73G, T146C, T199C, A263G, 315C A73G, T146C, T199C, A263G, 309C, 315C A73G, T146C, T152C, T199C, A263G, 309CC, 315C (continued)

52

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Khmer

Sample KN84

Haplogroup M8a

KN5

M9

HV1(16000+) 38delA, C184T, C223T, T298C, G319A, A343G T108C, G129A, T172C, C223T, C234T, C290T

KN10 KN80 KN13

M9 M9 R

C223T, C234T, T271C, C344T, T362C G129A, T172C, C223T, C234T, C290T T93C, T157C, T304C

KN19

R

38delA, T93C, T304C

KN40

R

38delA, T288C, T304C, G390A, G518A, T519G

KN41

R

T124C, C148T, T304C, A309G, G390A

KN43

R

KN47

R

T304C, A309G, G390A, C511A, T512C, C514T, A515C, G516A, T519A C192T, T304C, A309G, G390A

KN96

R

T288C, T304C, A309G, G390A

KN22

Z

Kh 040

A

38delA, C185T, C223T, C260T, T298C, A302G, G518A, T519A T93C, C223T, C234T, C290T, A293C, G319A

Kh 026

B

Kh 124

B

Kh 052

B

Kh 155

B

Kh 083 Kh 165

B B

Kh 089

B

Kh 037

B

Kh 056

B

Kh 151

B

Kh 039

B

Kh 178

B

Kh 093

B4

T189C, C214A, C223T, G274A, T276A, C282A, T311C C04T, G042A, A183C, T189C, 193C, T209C, C223T, C291T, G390A C147T, A183C, C184A, T189C, C223T, A275G, G391A, G438A G129A, T140A, A149C, A183C, T189C, 193C, G213A, C218T, C223T, G274A T189C, C223T, G274A, T311C A183C, T189C, 193C, T209C, C223T, C291T, G390A, A166G, A183C, T189C, 193C, C223T, A275G T136A, A183C, T189C, 193C, T249C, T288C, A293G, T304C, C344T T136A, A183C, T189C, 193C, T249C, T288C, A293G, T304C, C344T T136A,A183C,T189C,G274A,T288C,T304C, T311C,G390A A054C, A70C, T136A, T140A, A183C, T189C, 193C, T249C, T288C, A293G, T304C, C344T A183C, T189C, 193C, T249C, T288C, A293G, T304C, C344T A183C, T189C, 193C, T217C, C234T, A309G, C354T

HV2 A73G, A263G, 309C, 315C A73G, G185A, A189G, A263G, 309C, 315C A73G, A153G, A263G, 315C A73G, A263G, 315C A73G, C150T, A263G, 309C, 315C A73G, T146C, T199C, A263G, 315C A73G, G143A, T146C, A183G, T204C, A263G, 309CC, 315C A73G, T146C, A263G, 309CC, 315C A73G, A183G, A263G, 315C A73G, T152C, A263G, 309C, 315C A73G, G143A, T146C, A183G, T204C, A263G, 315C A73G, T195C, A263G, 309CC, 315C A73G, A235G, A263G, A297G, 315C A73G, T146C, A263G, 315C, C418A A73G, G207A, A263G, T310C, G316A, T318C, A326G A73G, A263G, 315C A73G, C150T, A263G, 309C, 315C A73G, T146C, A263G, 315C A73G, G207A, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C A73G, T152C, A263G, 315C, G329A A73G, T152C, A263G, 315C, G329A A73G, T195C, A263G,315C, G329A A73G,T152C,A263G, 315C, G329A A73G, T152C, A263G,315C, G329A A73G, G207A, A263G, 309C, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

53

Table 1 (continued) Ethnic

Sample Kh 103

Haplogroup B4

Kh 054

B4

HV1(16000+) T93C, C147T, C150T, A183C, C184A, T189C, T217C, A235G A166C, A183C, T189C, T217C, A235G,

Kh 035

B4

T189C, T217C, G274A, T311C, A335G

Kh 072

B4

Kh 133

B4a

T93C, C147T, C149T, A183C, C184A, T189C, T217C, A235G A182C, A183C, T189C, T217C, C261T

Kh 033

B4a

A182C, A183C, T189C, T217C, T261C

Kh 084

B4b

Kh 055 Kh 162

B4b B5a

T136A, A183C, T189C, 193C, T249C, T288C, A293G, T304C, C344T T136A, A183C, T189C, T217C, A235G T140C, A183C, T189C, 193C, C266A

Kh 077

B5a

T140C, A183C, T189C, 193C, C266A, T311C

Kh 080

B5a

T140C, A183C, T189C, 193C, C266A

Kh 070

B5a

T140C, C179A, A181C, A182C, A183C, T189C, C261T, C266A, A335G

Kh 068 Kh 011

B5a B5a

T140C, A183C, A182C, T189C, C266A T140C, A182C, A183C, T189C, C266A

Kh 020

B5a

T140C, A182C, A183C, T189C,C266A

Kh 106

B5a

T140C, A182C, A183C, T189C, C266A

Kh 001

D4

Kh 116

D4

C223T, C259T, G274A, T311C, T362C, T381C, G518A C104T, C223T, C287T, T362C, T469G

Kh004

D4

C223T, C259T, G274A, T311C, 362C

Kh 058 Kh 018

D4 D4

C223T, T311C, T362C, C400T C223T, T362C

Kh 118 Kh125 Kh 012 Kh 166

D4 D4 D4 D5

Kh 082

F1a

T249C, T288C, C301T, T304C, T362C, G390A T249C, T288C, C301T, T304C, T362C, G390A T304C, T362C T136A, A175C, A182C, A183C, T189C, C223T, T362C G129A, T172C, T304C, A309G

Kh 005

F1a

Kh 127

F1a

Kh 064

F1a

C108T, G129A, A162G, T172C, C239T, T304C, C327T C108T, G129A, A162G, T172C, C245T, A284G, T304C C108T, G129A, A162G, T172C, C256T, T304C

HV2 A73G, A244G, A263G, 309C, 315C, 385DelA A73G, A263G, 309C, 315C, G207A A73G, T146C, C150T, C151T, A263G, 309C, 315C A73G, C150T, C151T, A263G, 309C, 315C A73G, T146C, A153G, A263G, 309C, 315C A73G, T146C, A263G, 309C, 315C A73G, T152C, A263G, 315C, G329A A73G, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C A73G, T152C, A210G, A263G, T310C, G316C, 318C A73G, A210G, A263G, 315C A73G, T152C, A210G, A263G, 309C, 315C A73G, T152C, A210G, A263G, 309C, 315C A73G, A210G, A263G, 309C, 315C T63C, C64T, G66A,A73G, T146C, A263G, 315C A73G, A200G, A263G, 309C, 315C T63C, C64T, G66A, A73G, T146C, A263G, 315C A73G, T146C, A263G, 315C A73G, A153G, A183G, A263G, 315C A73G,A263G,315C, G329A A73G, A263G, 309C, 315C A73G, A263G, 309C, 315C A73G, C150T, A263G, 309C, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 309C, 315C A73G, A214G, 249DelA, A263G, 309C, 315C A73G, 249DelA, A263G, T293C, 309C, 315C (continued)

54

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample Kh 157

Haplogroup F1a

Kh 170

F1a

Kh 098

F1a

HV1(16000+) C108T, G129A, A162G, T172C, C259A, T304C C108T, G129A, A162G, T172C, C266A, T304C C108T, G129A, A162G, T172C, T304C

Kh 113

F1a

C108T, G129A, A162G, T172C, T304C

Kh 173

F1a

Kh 156

F1a

C108T, G129A, A162G, T172C, T304C, A335G G129A, A162G, T172C, C214T, T304C

Kh 112

F1a

G129A, A162G, T172C, T304C

Kh 179

F1a

G129A, T172C, C295T, T304C

Kh 013

F1a

G129A, T172C, T304C

Kh 038

F1a

C108T, G129A, A162G, T172C, T304C

Kh 048

F1b

Kh 136

F1b

T136A, T140A, A183C, A184C, T189C, T304C, G390A C186T, T189C, T209C, C242T, T304C

Kh 092

G2

C214A, C223T, C256T, C278T, C362T

Kh003

G2

C223T, T249C, C259T, C278T, T311C, T362C

Kh 129

M

38delA, A219G, C223T, C290T, G518A

Kh 023

M

38delA, G129A, T209C, C223T, A272G, A322C

Kh 010 Kh 144

M M

Kh 097

M

T086C, C223T, C234T, C278T, C294T, G518A C108T, G129A, T172C, C174T, C223T, C234T, G244A, C290T C108T, G129A, T172C, C223T, C234T, C290T

Kh 142 Kh 175

M M

C223T, C239T, T263C, T381C C223T, C278T, C294T

Kh 150

M

C223T, C290T, T304C

Kh 073

M

C223T, T271C

Kh 169

M

G129A, C223T

Kh 095

M

G129A, C223T, C278T, C294T, T304C

Kh 111

M

G129A, C223T, C278T, C294T, T304C

HV2 A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, C150T, T195C, 249DelA, A263G, T310C, G316C A73G, 249DelA, A263G, 315C A73G, T217C, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, 249DelA, A263G, 315C A73G, T195C, 249DelA, A263G, 309C, 315C A73G, 249DelA, A263G, 309C, 315C A73G, 249DelA, A263G, 309C, 315C A73G, T152C, A263G, 309C, 315C A73G, A178G, A189G, A263G, 291DelA, 292-294delTTT, 315C A73G, A263G, 315C, 385DelA T58G, 56G, A73G, T152C, G225A, 249DelA, A263G, 315C, G316A A73G, C150T, A263G, 315C A73G, G185A, A263G, 309C, 315C A73G, G185A, A189G, A263G, 309C, 315C A73G, A263G, 309C, 315C A73G, C150T, T152C, A263G, 315C A73G, T159C, A263G, 309C, 315C A73G, A244G, A263G, 309C, 315C A73G, A214G, A263G, 309C, 315C A73G, C150T, A263G, C273T, 315C, C418A A73G, C150T, A263G, C273T, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

55

Table 1 (continued) Ethnic

Sample Kh 117 Kh 060 Kh 076

Haplogroup M M M

HV1(16000+) G129A, C223T, C290T G145A, C192T, C223T, C291T, T304C T124C, C193T, C223T

Kh 041

M

T124C, C223T, C234T, C261T, C290T

Kh 062

M

T86C, T129A, T209C, C223T, A272G

Kh 131

M

T93C, C148T, 183DelA, C223T

Kh 086

M10

Kh 030 Kh 027

M10 M10

C223T, T263C, G274A, T311C, A343G, T357C C223T, T311C T126C, T231C, T311C

Kh 121

M10

Kh 091

M7b

C111A, T140C, T209C, C223T, T304C, T311C, T352C, C353T C192T, C223T, T297C

Kh 009

M7b

G129A, C223T, T297C

Kh 176

M7b

T102C, G129A, C223T, T297C

Kh 022

M7b1

G129A, C192T, C223T, T297C

Kh 024

M7b1

G129A, C192T, C223T, T297C

Kh 071

M7b1

G129A, C192T, C223T, T297C

Kh 139

M7b1

G129A, C192T, C223T, T297C

Kh 065

M7b1

G129A, T189C, C192T, C223T, T297C

Kh 049

M7b1

Kh 159

M7b1

G129A, T189C, C192T, C223T, T297C, T217C, A235G G129A, T192C, C223T, T297C

Kh122

M7c

C223T, C295T

Kh 168

M7c

T75C, C223T, C278T, A293T, C295T

Kh 007 Kh 107

M7c R

Kh025

R

G145A, C291T, C295T, T304C A37G, G145A, A181G, C192T, C223T, C291T, T304C, G390A, G518A, T519A T249C, T288C, T304C, C344T

Kh 067

R9a

C260T, T298C, C355T, T362C

Kh 135

U5a

T93C, G129A, C256T, T357C, A399G

Kh 114

Z

C185T, C223T, C260T, T298C

HV2 A73G, A263G, 315C A73G, A210G, A263G, 315C A73G, C150T, T195C, A263G, 315C, 337DelA A73G, A263G, 309C, 315C, T318C A73G, T152C, G225A, 249DelA, A263G, 315C, G316A A73G, T152C, T195C, A263G, 309C, 315C A73G, A263G, T310C, G316C, T319G A73G, A263G, C332T, 315C A73G, T195C, A263G, 309C, 315C A73G, A259G, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, T204C, A263G, 309C, 315C A73G, C150T, T159C, T199C, T204C, A263G, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, 194DelC, T199C, A263G, 315C A73G, C150T, 194DelC, T199C, A263G, 315C A73G, C150T, C182T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, T146C, T199C, T204C, A263G, 309C, 315C A73G, T146C, T152C, T199C, A263G, 309C, 315C A73G, A210G, A263G, 315C A73G, A210G, A263G, 309C, 315C A73G, T217C, A263G, 315C, G329A A73G, 249DelA, A263G, 309C, 315C A73G, T131C, C150T, T199C, A263G, 315C A73G, T152C, 249DelA, A263G, 315C (continued)

56

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample Kh 163

Haplogroup Z

HV1(16000+) C185T, C223T, C260T, T298C

Muong

M1

B

M9

B

T93C, T178C, A182C, A183C, T189C,T271C, G274A C168T, A182C, A183C, T189C, A194C

M10

B

C111T, T126C, T172C, A183C, T189C

M16 M35

B B

T93C, A183C, T189C,C426G A182C, A183C, T189C, C223T, C278T

M42 M46

B B

T93C, T178C, A182C, A183C, T189C, T271C A182C, A183C, T189C

M53 M8

B B4

M25

B4

T154C, A182C, A183C, T189C A182C, A183C, T189C, T217C, G274A, T304C, G310A, 474delG A182C, A183C, T189C, T217C, A235G

M50

B4

M56

B4

M2

B4a

M86

B4a

M90 M97

B4a B4a

M41

B5

M12

B5a

M13

B5a

M14

B5a

M15

B5a

M30

B5a

M37 M64

B5a B5a

M26

C

M32

C

M71

C

A182C, A183C, T189C, 193CC, T217C, T231C, A235G, C291T, 469delT C147T, A183C, C184T, T189C, T217C, A235G A182C, A183C, T189C, G213A, T217C, C261T, C292T A182C, A183C, T189C, T217C, A240G, C261T A182C, A183C, T189C, T217C, C261T A182C, A183C, T189C, T217C, A240G, C261T T92C, G129A, T140C, A182C, A183C, T189C, A194C, C197G G84C, C95T, A113C, A116C, A149C, A183C, T189C, 193C, C266A A91T, A113C, A122T, A182C, A183C, T189C, C266A A182C, A183C, T189C, C261T, C266A A129G, T140C, A182C, A183C, T189C, C266A, A399G A182C, A183C, T189C, C266A A182C, A183C, T189C, C226A, T304C T140C, A183C, T189C, A194C, T195C, C266A A182C, A183C, T189C, C223T, T298C, C327T, 469delT C223T, A241C, G274A, T298C, C327T, G390A C223T, G274A, T298C, C327T, G390A

HV2 A73G, T152C, A214G, 249DelA, A263G, 315C A73G, T146C, A263G, 315C A73G, T146C, T217C, A263G, 309C, 315C A73G, G185A, A189G, T195C, A234G, A263G, 309C, 315C, A328G A73G, A263G, 315C A73G, T152C, A249G, A263G, 309C, 315C A73G, T146C, A263G, 315C A73G, A210G, A263G, T310C, G316C A73G, A263G, 309CC, 315C A73G, A263G, 309CC, 315C A73G, T199C, A263G, 309C, 315C A73G, A263G, 309CC, 315C A73G, A263G, 315C A73G, A263G, C308T, 310delT A73G, A263G, 315C A73G, A263G, 309CC, 315C C43T, A73G, A263G, 315C A73G, A210G, A263G, T310C A73G, A210G, A263G, 309C, 315C, A374G A73G, A210G, A263G, 309C, 315C A73G, T152C, A210G, A263G, 309C, 315C A73G, A210G, A263G, T310C, G316C A73G, A210G, A263G, 309C, 315C A73G, A210G, A263G, 315C A73G, A210G, A263G, 315C A73G, T146C, A237G, 249delA, A263G, C303A A73G, T195C, 249delA, A263G, 309C, 315C A73G, T195C, 249delA, A263G, 309C, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

57

Table 1 (continued) Ethnic

Sample M76

Haplogroup C

HV1(16000+) T172C, C223T, T298C, C327T

M80

C

M52 M68

D4 D4

38delA, T93C, T172C, C223T, T298C, T311C, C327T C223T, T362C, A367C C260T, T298C, T362C, A367C

M70

D4

T304C, T362C

M74

D4

M5

F1a

C111T, T126C, T140A, T172C, A183C, T189C, C223T, T362C G129A, T172C, T304C

M39

F1a

C108T, G129A, A126G, T172C, T304C

M43

F1a

T172C, T304C

M47

F1a

C108T, G129A, A162G, T172C, T304C

M49

F1a

G129A, T172C, C295T, T304C

M54

F1a

G129A, T172C, T304C, 469delT

M60

F1a

G129A, T172C, T304C

M65

F1a

G129A, A162G, T172C, T304C, A399G

M67

F1a

T172C, T304C, C465T

M77

F1a

38delA, G129A, T172C, C295T, T304C

M81

F1a

38delA, G129A, T172C, T304C,

M82

F1a

38delA, G129A, A162G, T172C, T304C

M84

F1a

38delA, G129A, T172C, C295T, T304C

M85

F1a

C108T, G129A, A162G, T172C, T304C

M89

F1a

G129A, T172C, T304C

M96

F1a

C108T, G129A, A162G, T172C, T304C

M22

F1b

A51G, T189C,A269G,C299T,A300G,T304C

M72

F2

T304C

M93

F2

T304C, C465T

HV2 A73G, 249delA, A263G, 315C A73G, T195C, 249delA, A263G, 315C, 368delA A73G, C194T, A263G, 315C T55C, T57C, A73G, T146C, C150T, T199C, A263G, 309CC, 315C A73G, T195C, A263G, 309C, 315C A73G, A263G, 309CC, 315C, 302C, 334delA T55C, A56C, T146C, C150T, T199C, A263G A73G, 249delA, A263G, 309CC, 315C A73G, C150T, T199C, T204C, A263G, 315C A73G, 249delA, A263G, 315C A73G, 249delA, A263G, 315C A73G, A210G, A263G, 309CC, 315C A73G, 249delA, A263G, 315C A73G, T152C, 249delA, A263G, 309C, 315C A73G, 249delA, A263G, 309C, 315C A73G, 249delA, A263G, 315C A73G, C198T, G207A, 249delA, A263G, 315C A73G, 249delA, A263G, 309C, 315C, 352delA A73G, 249delA, A263G, 309C, 315C A73G, C150T, T152C, T195C, 249delA, A263G, 315C A73G, 249delA, A263G, 309C, 315C A73G, 249delA, A263G, 309C, 315C A73G, C150T, T195C, A214G, 249delA, A263G, T310C A73G, T195C, 249delA, A263G, 315C A73G, 249delA, A263G, 309C, 315C (continued)

58

T. T. H. Tran et al.

Table 1 (continued) Ethnic

Sample M63

Haplogroup F2a

HV1(16000+) T92A, C291T, T304C

M3

G2

M6

G2

C69T, T172C, C223T, A235G, C278T, C291A, T298C, T362C 38delA, C69T, T172C, C223T, C278T, C291A, T298C, T362C

M18

G2

M20

G2

M57

G2

M73

G2

M51

M

C69T, T172C, C223T, A233G, C278T, C291A, T298C, T362C, 474delG C69T, T172C, C223T, C278T, C291A, T298C, T362C T172C, C223T, C278T, C291A, T298C, T362C, A367C C69T, T172C, C223T, T271C, G279A, C278T, C291A, T298C, T362C G129A, T209C, C223T, A272G

M55

M

C223T, A269G, T271C, 469delT

M69

M

C193T, C223T

M83

M

38delA, T86C, G129A, T209C, C223T, A272G

M95

M

T86C, G129A, T209C, C223T, A272G

M24 M79

M10 M10

M11

M7

C223T, C256T, A299G, T311C A182C, A183C, T189C, C260G, T311C, G390A, A399G, C426G T172C, C223T, C291T, T311C

M38

M7

T311C, T356C

M40

M7

T356C

M45 M78

M7 M7

A163G, T172C, C223T, C291T, T311C 38delA, T311C, T356C

M87 M17

M7 M7b

T172C, C223T, C291T, T311C G129A, C223T, T297C

M31

M7b

C223T, T297C

M44

M7b

C223T, T297C, A299G

M61

M7b

T297C, T304C, A367C, G390A, G496A

M4

M7b1

G129A, C192T, C223T, T297C

M21

M7b1

T86C, G129A, C192T, C223T, C297T

HV2 A73G, A214G, 249delA, A263G, 309CC, 315C A73G, T146C, C150T, T199C, A263, 309CC, 315C T55C, A56C, T146C, C150T, T199C, A263G, 309CC, 315C, A351G A73G, T146C, C150T, T199C, A263G, 309C, 315C A73G, T146C, C150T, T199C, A263G, 309C, 315C G53A, T55C, A73G, T146C, C150T, A263G, 309C, 315C A73G,C150T, T199C, A263G, 309CC, 315C A73G, T152C, A225G, 249delA, A263G, 315A, G316A A73G, C150T, C151T, A263G, 309C, 315C A73G, C150T, T195C, A263G, 309CC, 315C, 337delA A73G, T152C, 249delA, A263G, 315C, G316A A73G, T152C, G225A, 249delA, A263G, 315C, G316A A73G, A263G, 315C A73G, A263G, 309CC, 315C A73G, T146C, A263G, 309C, 315C A73G, T146A, T199C, A263G, 315C A73G, T146A, T199C, A263G, 309C, 315C A73G, T146C, A263G, 315C A73G, T146A, T199C, A263G, 315C, 368delA A73G, T146C, A263G, 315C A73G, T146C, T199C, A263G, 309C, 315C A73G, C150T, T199C, T204C, A263G, 309C, 315C A73G, 249delA, A263G, 309C, 315C A73G, A183G, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T152C, T199C, A263G, 315C (continued)

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

59

Table 1 (continued) Ethnic

Sample M27

Haplogroup M7b1

HV1(16000+) G129A, C192T, C223T, T297C

M29

M7b1

T86C, G129A, C192T, C223T, T297C

M33 M36

M7b1 M7b1

G129A, C192T, C223T, A289G, T297C G129A, C192T, C223T, T297C

M59

M7b1

M66

M7b1

G129A, T189C, C192T, C223T, T297C, A367C, T304C G129A, C192T, T297C

M91

M7b1

G129A, C192T, C223T, T297C, T324C

M92

M7b1

G129A, C192T, C223T, G274A, T297C, T324C

M98

M7c

C295T, G319A

M28

M8a

C184T, T189C, C223T, T298C, G319A

M94 M34

M8a N9a

C184T, T189C, C223T, T298C, G319A T172C, T189C, C201T, C223T, C257A, C261T

M100

N9a

C223T, C257A, C261T, C292T

M7

R

M19

R

T124C, C148T, A183G, T304C, A309G, G390A, 474delG C192T, T304C, A309G, G390A

M58

R

M23

R9a

M48

R9a

M62

R9a

38delA, T124C, C148T, A183C, T304C, A309G, G390A T93C, C111T, C192T, T249C, T298C, C355T, T362C, G390A C111T, C192T, T249C, T298C, C355T, T362C, A367C, G390A C260T, T298C, T362C, A367C

M75

R9a

C260T, T298C, C355T, T362C

M88

R9a

M99

R9a

T93C, C111T, C192T, T249C, T298C, C355T, T362C, G390A T189C, C221T, G274A, C295T, T298C, G319A,C355T, T362C

HV2 A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T152C, T199C, A263G, 315C A73G, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 309C, 315C A73G, C150T, T199C, A263G, 315C A73G, C150T, T199C, A263G, 309C, 315C, C332T A73G, C150T, T152C, T199C, A263G, 315C, C332T A73G, T146C, T199C, A263G, 315C A73G, T195C, A263G, 309CC, 315C A73G, A263G, 309C, 315C A73G, C150T, T195C, A263G, 309C, 315C A73G, C150T, A263G, 309C, 315C A73G, A263G, T310C A73G, T152C, A263G, 309C, 315C A73G, A263G, 309CC, 315C A73G, G207A, 249delA, A263G, 309C, 315C A73G, G207A, 249delA, A263G, 309C, 315C A73G, G207A, 249delA, A263G, 309CC, 315C A73G, T158A, G207A, 249delA, A263G, 309C, 315C A73G, G207A, 249delA, A263G, 309C, 315C A73G, A235G, 249delA, A263G, 309C, 315C

Sequences were stated by omitting the 16,000 bases; thus, 16,037 polymorphisms are stated as 37 M Muong ethnic, Kh Khmer ethnic, KN Kinh ethnic living in the South Vietnam (Kinh Nam), Khin Kinh ethnic living in North Vietnam (Kinh Bac), Cham Cham ethnic Each sequence was compared with revised Cambridge Reference Sequence, and the presence of the substitution, deletion, or insertion was recorded

Table 2 Haplogroup frequency distribution in Vietnamese population Haplogroup A B B4 B4a B4b B4c B4g B5 B5a B5b B6 C D4 D4a D4e D5 D5a D5b E F F1a F1b F1c F2 F2a F3a G2 G2a M M10 M12 M7 M7a M7b M7b1 M7c M8a M9 M9a M9b N N9a N21 R R9 R9a R9b R11 U5a Z Total

Kinh Nam (KN) 3 1 8 1 2

Khmer 1 12 5 2 2

9

8

4 1 2

8

1

1

Cham

2 5 1 14 2 18 3 1 2 1

Kinh Bac (Khin)

5 3 1 6 4 1 14 1 1 2

Muong 8 4 4

1 7

5 4

1

1 2 1 24 2

14 2

1 1 8

2 19 1 1

16 1 2 1

1 2 1 5 5

18 4

6 15 1

3 1 1

5 2 6

4 8 6 1 3

3 7 3

3 3 3 2 1 1

7

2

10 1 2

2 3 12 1

4 10 1 2

4 1 1

2 3

3 1

6 10 2

1 100

1 2 98

113

4

4 106

100

Total 4 21 24 15 6 20 6 2 56 4 1 12 13 5 1 2 1 2 1 4 81 6 1 2 1 1 8 1 46 12 2 6 2 17 40 14 5 3 5 1 1 13 1 14 3 7 14 2 1 7 517

Frequencies % (n ¼ 517) 0.77 4.06 4.64 2.9 1.16 3.86 1.16 0.39 10.83 0.7 0.19 2.32 2.51 0.97 0.19 0.39 0.19 0.39 0.19 0.77 15.67 1.16 0.19 0.39 0.19 0.19 1.56 0.19 8.9 2.32 0.39 1.16 0.39 3.29 7.74 2.71 0.97 0.58 0.97 0.19 0.19 2.51 0.19 2.71 0.58 1.35 2.70 0.39 0.19 1.35 100

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

61

Table 3 Polymorphisms frequency on HV1 and HV2 HV1a 129 172 183 189 217 223 304 311 HV2a 73 249 263 150 152 309 315

Cham 26 13 52 64 22 41 23 14 Cham 113 11 113 22 22 72 112

Kinh Bac 37 20 38 46 16 36 31 15 Kinh bac 106 31 106 25 16 67 102

Kinh nam 45 30 23 26 11 44 34 11 Kinh nam 100 32 100 28 11 48 98

Khmer 33 16 29 34 9 52 33 13 Khmer 98 21 98 21 16 45 94

Muong 30 30 29 33 8 39 28 8 Muong 98 33 100 25 11 60 92

Total 171 109 171 203 66 212 149 61 Total 515 128 517 121 76 292 498

Frequencies% (n ¼ 517) 33 21 33 39 12 41 28 11 Frequencies% (n ¼ 517) 99.6 24.7 100 23.4 14.7 56.4 96.3

a

SNP Location on cDNA sequence

is G16129A. Meanwhile, in HV2, all the ethnic populations have almost the same phenotype. The most popular SNPs in HV2 are A73G and A263G (approximately 100%) in all studied ethnics. The variant diversity in HV1 region in five Vietnamese ethnic populations was greater than that of HV2 region (Table 3). When both HV1 and HV2 variants were combined, a total of 438 haplotypes were reported, 393 of which were unique and 45 were presented in more than one individual. The genetic diversity was calculated to be 99.83%, and the probability of random match of two individuals sharing the same HV1/HV2 mtDNA haplotype was 0.37%.

4

Discussion

Mitochondrial DNA (mtDNA) is known for high mutation rates caused by the lack of protective histones, inefficient DNA repair system, and continuous exposure to oxygen radicals. The purpose of this study was to evaluate the mutation frequencies in the two HVs (HV1 and HV2) of mtDNA D-loop in five Vietnamese ethnic populations. Available mtDNA data on Vietnamese population is limited from a particular area, specially the Kinh ethnic population. In this

study, we focus on a more different population of Vietnamese, representing Vietnamese individuals from the Northern (Kinh Bac ethnic from Hanoi, Muong ethnic from Hoa Binh), Middle (Khmer ethnic from Binh Thuan), and Southern (Kinh Nam ethnic from Ho Chi Minh City, Khmer ethnic from Soc Trang). These results are very significant in the study of population genetics and evolution of the people of Vietnam. Furthermore it should aid in cancer research; mtDNA mutations were also reported to occur in human cancers; F. Miyazono has shown that mutation in the mtDNA D-loop region was detected frequently in adenocarcinomas in Barrett’s esophagus (Miyazono et al. 2002). Mutations in mitochondrial DNA are often associated with elevated level of reactive oxygen species; however, this is under further research. The genetic diversity was 99.83%, and the probability of random match of two individuals sharing the same mtDNA haplotype was 0.37%. These figures for Malaysian population were 99.47% and 0.93%, respectively (Rashid et al. 2010). In a study of Japanese population, the haplotype diversity and random match probability were estimated to be 99.69% and 0.40% (Sekiguchi et al. 2008). And for Han population in China, one study estimated it to be 99.85% and

62

T. T. H. Tran et al.

Fig. 1 Map showing the locations of Vietnamese samples collected in Vietnam

1.15%, respectively (Fu et al. 2004). The data demonstrated that the diversity of HV gene of Vietnamese people was higher than other populations in Asia. But the level of diversity of Vietnam was comparable with data from Han people in China. The higher diversity in this study can be attributed to sample collection and Vietnamese population characteristic, which is very diverse with 54 different ethnic groups. While this study only investigated five main ethnics, it already highlighted Vietnamese genetic diversity. The frequencies of SNPs observed in our study share some similarity with other Asian populations, such as Chinese, Japanese, and Korean as well as Malaysian and Indonesian,

with the most frequent SNPs being A263G (100%), A73G (99.6%), 315insC (96%), 309insC (56%), C16223T (41%), and T16189C (39%). We also identified a total of 50 haplogroups, which is comparable with Yao et al.’s (2002) study of mitochondrial genome which identified 44 named nested haplogroups in the Han (Chinese) mtDNA classification tree (Yao et al. 2002). According to HJ Jin’s study in 2009, which included the mitochondrial sequencing result of 47 Vietnamese, the most frequent haplogroup was F1a (10/47; 21.3%) (Jin et al. 2009). The result was similar in our study, in which F1a haplogroup was the most abundant with a frequency of 15.7%. However, F1a haplogroup

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population

was not abundant in other Asian population. Also from the result of HJ Jin et al.’s study, the most frequent haplogroup for Korean population was D4 44/185, for Thai population was F1b (8/40), and for Han (Chinese) population was D4 (5/40). The result suggests that while we observed some level of similarity between Vietnamese population and other Asian population, the haplogroup and SNP data have some feature unique to Vietnamese population. The study has characterized mitochondrial data from 517 Vietnamese individuals. The result showed that Vietnamese mitochondrial genome is diverse but still has unique characteristics different from other populations. This data will help in better understanding of Vietnamese’s mitochondrial genome diversity as well as to add to the data of global mitochondrial variants.

5

Conclusion

Based on the results of this study, we will continue to research and evaluate the genetic diversity of mitochondrial DNA and other ethnic groups in Vietnam to survey the genetic characteristics as well as the relationship of these mutations and the cancer progression of various ethnic people residing in Vietnam. Acknowledgments We thank the patients and their families for their voluntary involvement in this study. This work was supported by the Vietnam Ministry of Science and Technology, grant number 3045/QDBKHCN. Competing Interests The authors declare no conflict of interest.

References Anderson, S., Bankier, A. T., Barrell, B. G., de Bruijn, M. H., Coulson, A. R., Drouin, J., et al. (1981). Sequence and organization of the human mitochondrial genome. Nature, 290(5806), 457–465. Andrews, R. M., Kubacka, I., Chinnery, P. F., Lightowlers, R. N., Turnbull, D. M., & Howell, N. (1999). Reanalysis and revision of the Cambridge

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reference sequence for human mitochondrial DNA. Nature Genetics, 23(2), 147. https://doi.org/10.1038/ 13779. Bandelt, H.-J., Quintana-Murci, L., Salas, A., & Macaulay, V. (2002). The fingerprint of phantom mutations in mitochondrial DNA data. American Journal of Human Genetics, 71(5), 1150–1160. Fu, L., Yao, Y., Cong, B., & Li, S. (2004). Sequence polymorphism of mtDNA HV1, HV2 overlapping fragments and coding region 8430-8673nt in Han population of Hebei province. Zhonghua Yi Xue Yi Chuan Xue Za Zhi ¼ Zhonghua Yixue Yichuanxue Zazhi ¼ Chinese Journal of Medical Genetics, 21(5), 518–521. Irwin, J. A., Saunier, J. L., Strouss, K. M., Diegoli, T. M., Sturk, K. A., O’Callaghan, J. E., et al. (2008). Mitochondrial control region sequences from a Vietnamese population sample. International Journal of Legal Medicine, 122(3), 257–259. https://doi.org/10.1007/ s00414-007-0205-3. Jin, H. -J., Tyler-Smith, C., & Kim, W. (2009). The peopling of Korea revealed by analyses of mitochondrial DNA and Y-chromosomal markers. PLoS One, 4(1). https://doi.org/10.1371/journal.pone.0004210. Miyazono, F., Schneider, P. M., Metzger, R., WarneckeEberz, U., Baldus, S. E., Dienes, H. P., et al. (2002). Mutations in the mitochondrial DNA D-loop region occur frequently in adenocarcinoma in Barrett’s esophagus. Oncogene, 21(23), 3780–3783. https://doi.org/ 10.1038/sj.onc.1205532. Peng, M.-S., Quang, H. H., Dang, K. P., Trieu, A. V., Wang, H.-W., Yao, Y.-G., et al. (2010). Tracing the Austronesian footprint in mainland Southeast Asia: A perspective from mitochondrial DNA. Molecular Biology and Evolution, 27(10), 2417–2430. https://doi.org/ 10.1093/molbev/msq131. Rashid, N., Panneerchelvam, S., Edinur, H., Norazmi, M. N., & Zafarina, Z. (2010). Sequence polymorphisms of mtDNA HV1, HV2, and HV3 regions in the Malay population of Peninsular Malaysia. International Journal of Legal Medicine, 124, 415–426. https://doi.org/10.1007/s00414-0100469-x. Searle, J. B. (2000, August 1). Phylogeography — The history and formation of species [Comments and Opinion]. Retrieved August 15, 2018, from https://www. nature.com/articles/6887654 Sekiguchi, K., Imaizumi, K., Fujii, K., Mizuno, N., Ogawa, Y., Akutsu, T., et al. (2008). Mitochondrial DNA population data of HV1 and HV2 sequences from Japanese individuals. Legal Medicine (Tokyo, Japan), 10(5), 284–286. https://doi.org/10.1016/j. legalmed.2008.02.002. Yao, Y.-G., Kong, Q.-P., Bandelt, H.-J., Kivisild, T., & Zhang, Y.-P. (2002). Phylogeographic differentiation of mitochondrial DNA in Han Chinese. American Journal of Human Genetics, 70(3), 635–651. https:// doi.org/10.1086/338999.

Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 65–82 https://doi.org/10.1007/5584_2018_302 # Springer Nature Switzerland AG 2018 Published online: 18 December 2018

Synthesis and Characterization of PLGAPEG Thymoquinone Nanoparticles and Its Cytotoxicity Effects in TamoxifenResistant Breast Cancer Cells Rozaina Ahmad, Noor Haida Mohd Kaus, and Shahrul Hamid Abstract

Introduction: Drug resistance has been a continuous challenge in cancer treatment. The use of nanotechnology in the development of new cancer drugs has potential. One of the extensively studied compounds is thymoquinone (TQ), and this work aims to compare two types of TQ-nanoformulation and its cytotoxicity toward resistant breast cancer cells. Method: TQ-nanoparticles were prepared and optimized by using two different formulations with different drugs to PLGAPEG ratio (1:20 and 1:7) and different PLGAPEG to Pluronic F68 ratio (10:1 and 2:1). The morphology and size were determined using TEM and DLS. Characterization of particles was done using UV-VIS, ATR-IR, entrapment efficiency, and drug release. The effects of drug, polymer, and surfactants were compared between the two formulations. Cytotoxicity assay was performed using MTS assay. R. Ahmad and S. Hamid (*) Oncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Penang, Malaysia e-mail: [email protected]; [email protected] N. H. M. Kaus School of Chemical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia e-mail: [email protected]

Results: TEM finding showed 96% of particles produced with 1:7 drug to PLGAPEG were less than 90 nm in size and spherical in shape. This was confirmed with DLS which showed smaller particle size than those formed with 1:20 drug to PLGA-PEG ratio. Further analysis showed zeta potential was negatively charged which could facilitate cellular uptake as reported previously. In addition, PDI value was less than 0.1 in both formulations indicating monodispersed and less broad in size distribution. The absorption peak of PLGA-PEG-TQNps was at 255 nm. The 1:7 drug to polymer formulation was selected for further analysis where the entrapment efficiency was 79.9% and in vitro drug release showed a maximum release of TQ of 50%. Cytotoxicity result showed IC50 of TQ-nanoparticle at 20.05 μM and free TQ was 8.25 μM. Conclusion: This study showed that nanoparticle synthesized with 1:7 drug to PLGAPEG ratio and 2:1 PLGA-PEG to Pluronic F68 formed nanoparticles with less than 100 nm and had spherical shape as confirmed with DLS. This could facilitate its transportation and absorption to reach its target. There was conserved TQ stability as exhibited slow release of this volatile oil. The TQ-nanoparticles showed selective cytotoxic effect toward UACC 732 cells compared to MCF-7 breast cancer cells.

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Keywords

Breast cancer · Drug resistance · Nanoparticle · Thymoquinone

Abbreviations ATRIR DLS EE ER HER2 IC50 MDR1 MRP4 MTS

PDI RPMI TEM TQ UVVIS β-actin

1

Attenuated Total Reflectance-Infrared Spectroscopy Dynamic Light Scattering Entrapment Efficiancy estrogen receptor Human Epidermal Growth Factor Receptor 2 Half maximal inhibitory concentration Multidrug Resistance Protein 1 Multidrug Resistance-associated Protein 4 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2(4-sulfophenyl)-2H-tetrazolium Polydispersity Index Roswell Park Memorial Institute Transmission Electron Microscope thymoquinone Ultraviolet-visible spectrophotometry beta actin

Introduction

Breast cancer has been the second leading cause of death worldwide. Numerous therapies are available against this disease. However, the average 5-year survival is only 40% out of the breast cancer patients, and patients with advanced stage have lower rate of survival (Kurman 2013). Three major subtypes of breast tumors with different biologic behaviors are tumor expressing estrogen receptors (ER) and/or progesterone receptors (PR), tumors with amplification and/or overexpression of the human epidermal growth factor receptor 2 (HER2), and tumors characterized by the lack of ER/PR expression and HER2 amplification/overexpression

commonly named as triple-negative breast cancers (TNBCs). Relapse of breast cancer remains a challenge despite hormonal treatment. It has been extensively studied for its various medical benefits. Thymoquinone (TQ) is a biological active on this herb attributed to its main compound in Nigella sativa seed essential oil (Burits and Bucar 2000; Hajhashemi et al. 2004). TQ shows many benefit, and it had been reported to exhibit antioxidant (Badary et al. 2003), anti-inflammatory (El Gazzar et al. 2006), and anticancer activity (Gali-Muhtasib et al. 2008). Studies show promising antitumor activity of TQ in fibrosarcoma and squamous cell carcinoma (Ivankovic et al. 2006), colon cancer (Gali-Muhtasib et al. 2008), and prostate cancer (Yi et al. 2008). However, TQ suffers severe bioavailability issue due to its hydrophobicity, hence leading to poor solubility and instability in aqueous medium (Odeh et al. 2012). Nanotechnology enables to conserve TQ as it is a volatile oil and provides sustained release over longer duration compared to its free form. The bioavailability of other drugs have been found to be conserved within the nanoparticle compared to free form and act gradually on target cells (Kumari et al. 2010). Nano-sized vehicles have been used in drug delivery because they are suitable for intravenous application. Solid biodegradable nanoparticles have advantage over liposomes due to their increased stability and unique ability to create a controlled release of drugs (Hans and Lowman 2002). In recent years, a variety of natural and synthetic polymers have been explored for the preparation of nanoparticles, of which poly(lactic acid) (PLA), poly(glycolic acid) (PGA), and their polymers (PLGA) have been extensively investigated because of their biocompatibility and biodegradability (Bala et al. 2004). Synthetic polymers and natural macromolecules have been extensively used as colloidal materials for nanoparticle production designed for drug delivery. Synthetic polymers have the advantage of high purity and reproducibility over natural polymers. Among the synthetic polymers, the polyester family (i.e., poly(lactic acid) (PLA), poly(ε-caprolactone) (PCL), poly(glycolic acid) (PGA)) are of interest in the biomedical area because of their biocompatibility and

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

biodegradability properties. The advantage is that the drug can be delivered by water-soluble polymer platforms (Kumari et al. 2010). The physical and chemical properties of the polymers are specially synthesized to flow through the kidney and liver without getting filtered out, thus allowing the drugs to be used more effectively (Bertrand and Leroux 2012; Chau 2005). Poly(lactic-co-glycolic acid)-polyethylene glycol (PLGA-PEG) is a type of polymer that is used in a host of Food and Drug Administration (FDA)-approved therapeutic devices, owing to its biodegradability, biocompatibility, and nontoxicity (Anderson and Shive 1997; Makadia and Siegel 2011). It can be dissolved by a wide range of solvents (Makadia and Siegel 2011). The biodegradable behavior is when the PLGAPEG is taken, it will undergo hydrolysis in the body to produce the original monomer which is lactic acid and glycolic acid. They are the normal by-product of various metabolic pathways in the body (Lü et al. 2009). PLGA-PEG also have great potential in drug delivery systems as tumortargeting carriers. It can increase the stability and prolong the circulation time (Cao et al. 2016). Therefore, the scope of this work was to develop different PLGA-PEG formulations of TQ-nanoparticles as form of therapy against subtypes of drug-resistant breast cancer cells. The study included the development of PLGA/ Pluronic-based nanoparticles; characterization using ATT-IR, TEM, and SEM; drug loading efficiency; and followed by drug release measurement. Drug-resistant breast cancer cell models were developed using pulse-based method over gradual increase of chemotherapeutic drugs.

2

Materials and Methods

2.1

Media and Chemicals

cell line MCF-7 and UACC 732 were purchased from American Type Culture Collection, ATCC (Manassas, USA) (ATCC). Phosphate buffer solution was purchased from Fisher Scientific (New Hampshire, USA) and sterilized prior to use.

2.2

Development of TQ-Nanoparticle by Entrapment Method

Synthesis of nanoparticles was done using two different formulations of drug to polymer ratio and polymer to surfactant ratio. TQ-nanoparticles were then characterized to determine the optimal formulation.

3

Nanoformulation Optimization

3.1

Formulation Using 1:20 Drug to Polymer Ratio

Nanoformulation was done using 100 mg of PLGA-PEG and 5 mg of TQ that were mixed in 10 mL acetonitrile as reported previously (Ravindran et al. 2010). Formulation was added dropwise to an aqueous solution rotating at 1500 rpm containing 0.1% Pluronic F-68 as surfactant. The resulting dispersion of nanoparticles was vacuum evaporated for 39
C/1 h to eliminate the organic solvent. The nanoparticles were then centrifuged at 12000 rpm for 20 min and washed with deionized water for three times. It was later freeze dried with 5% sucrose as a cryoprotectant. The lyophilized form was stored at 4
C until use.

3.2

Thymoquinone (99% purity), PLGA-PEG, and Pluronic F68 (molecular weight of 8500 Da) were purchased from Sigma-Aldrich (Missouri, USA). Acetonitrile was purchased in analytical grade Fisher Scientific (New Hampshire, USA). Milli-Q grade water was used for the preparation of solutions. The established human breast cancer

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Formulation Using 1:7 Drug to Polymer Ratio

Comparison was done with lower drug to polymer ratio using modified method by Vega et al. (Vega et al. 2013). A total of 90 mg of PLGA-PEG and 12.5 mg of TQ were mixed in 25 mL acetonitrile. It was added dropwise into 50 mL of an aqueous pH 3.5 solution rotating at 1500 rpm that contained 10 mg/mL Pluronic F-68

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surfactant. The resulting dispersion of nanoparticles was vacuum evaporated for an hour/50
C to eliminate the organic solvent, and samples were stored at 4
C.

4

Characterization of TQ Encapsulation in the Polymeric Micelles

4.1

ATR-IR Spectroscopic Study

ATR-IR spectroscopy measurements were carried out to recognize the bio-groups that bound distinctively. During this analysis, a spot on the sample is subjected to a modulated IR beam. The sample transmittance and reflectance of the infrared rays at different frequencies were translated into an IR absorption plot consisting of reverse peaks. The TQ-nanoparticles were examined by ATR-IR spectrometer Frontier PerkinElmer (Massachusetts, USA) to confirm the drug encapsulation. The range of the scan was between 650 cm1 and 4000 cm1.

4.2

UV-VIS Spectrophotometer for Qualitative Analysis of TQ

The spectrophotometric analyses of free TQ and TQ-nanoparticle were performed on UV-VIS Spectrophotometer Shimadzu, UV 2600 (Kyoto, Japan), to determine the maximum wavelength (λmax) of TQ absorbance. The samples were scanned between 200 and 400 nm wavelengths.

4.3

Transmission Electron Microscopy (TEM)

Transmission electron microscopy was performed to observe TQ-nanoparticle morphology, shape, and size structure. The images of nanoparticles were taken from Philips CM12 transmission electron microscope (Philips Electron Optics, Eindhoven, Netherlands). The morphology was determined at an accelerating voltage of 200 KV with 1 K  1 K digital images captured using an

AMT CCD camera. Samples were prepared by adding one drop of the nanoparticle suspension to copper grids, and it was allowed to dry completely at room temperature. The samples were then negatively stained with 2% uranyl acetate prior to imaging. For sizing, three images were taken of each sample, and 30 nanoparticles were normally measured per image.

5

Physicochemical Characterization

5.1

Measurement of Particle Size and Zeta Potential

The hydrodynamic diameter, size distribution, and zeta potential were measured by dynamic light scattering technique using Malvern Zetasizer (Malvern Instruments, UK). The sample of nanoparticle suspension was diluted to 150 μM in ultrapure water (n ¼ 3). The particle size distribution of nanoparticles is reported as polydispersity index (PDI) which is a measure of the distribution broadness of the particle size. Sample volume of 1 ml was placed into a cuvette and analyzed at 25
C with an angle of 90
. Each sample was measured in triplicates.

5.2

Measurement of Entrapment Efficiency

Percentage of drug entrapped was evaluated in terms of the entrapment efficiency (EE) with respect to the overall drug loaded in the formulation. To determine the percentage of entrapment efficiency (% EE) of TQ within nanoparticle, the samples were first sonicated for 3 min and vortexed for few seconds. The drug was then separated by ultracentrifugation at 14,000 rpm at room temperature for 30 min. The resulting supernatant was collected and quantified spectrophotometrically (UV-VIS Spectrophotometer Shimadzu) at 257 nm. The percentage of drug entrapped (% EE) was calculated as in the following equation:

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

Percentage of entrapment efficiency ð%EEÞ ¼

5.3

Amount of TQ in NPs  100% Total amount of TQ

Drug Release

For drug release analysis, TQ-nanoparticles were placed in a dialysis bag (10 kDa MWCO, Thermo Scientific Inc., Rockford, IL, USA) and suspended in 200 mL phosphate buffered saline (PBS) at a pH of 7.4. To mimic the physiological condition of body systems, the samples were monitored at a constant temperature (37
C) while being continuously stirred at 200 rpm. After 30 min, 3 mL of the sample was taken and replaced with the same amount of fresh PBS medium. For each time point, three samples were taken (n ¼ 3) for measurements. The amount of free TQ released from TQ-nanoparticles was determined by UV-VIS spectrophotometer at 255 nm.

6

Breast Cancer Cell

6.1

Cell Culture

Human breast cancer cell lines MCF-7 and UACC 732 were purchased from American Tissue Culture Collection (ATCC). Cells were cultured in media containing L-glutamine RPMI 1640 (GIBCO) that was supplemented with 1 unit penicillin/streptomycin (GIBCO) and 10% heatinactivated fetal bovine serum (JR Scientific) in humidified atmosphere with 5% CO2 at 37
C. Tamoxifen resistance MCF-7 cells were developed using pulse method (Coley 2004).

6.2

Cytotoxicity

To determine the cell toxicity, the MCF-7/TAM and UACC732 cells were plated at a density of 2  103 cells/well in a 96-well plate at 37
C, under 5% CO2 for overnight. The cells were then

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treated with different concentrations of free TQ and TQ-nanoparticles (0–100 μM). After 72 h incubation, 20 μL of MTS (Promega) solution (5 mg/mL) was added into each well and incubated for another 2 h. The absorbance was determined by using a microtiter plate reader (BioTek, USA) at 490 nm. Cell viability was expressed as percentage of live cells relative to control cells. All experiments were performed in triplicates. The IC50 was generated from the doseresponse curve for MCF 7/TAM and UACC 732 cell line. The average cell viability inhibition obtained from triplicate determinations at each concentration was plotted as a dose-response curve (Table 1). The cell viability inhibition was calculated according to the following formula: Cell viability inhibition ð%Þ

¼

Control absorbance  test absorbance 100% Control absorbance

7

Results

7.1

Characterization of TQNanoparticles (1:7 and 1:20 Drug to Polymer Ratio)

7.1.1 ATR-IR Spectroscopic Study ATR-IR spectrum for samples analyzed is shown in Fig. 1. The list of functional groups based on the spectrum analysis is provided in Table 2. The samples include TQ-nanoparticle synthesized using both method samples. Raw PLGA-PEG exhibited the characteristic spectra at aliphatic C¼O and C-O-C group at 1744 and 1080 cm1 (Fig. 1a). Raw Pluronic F68 exhibited dominant absorption peak for CH (sp3) and C-O-C stretch at 2881 cm1 and 1099 cm1, respectively (Fig. 1b). The absorbance peaks on TQ in acetonitrile were CH (sp2), C¼C, aliphatic C  N, and C-O-C group at 3554 cm1, 1635 cm1, 2253 cm1 (Fig. 1c), and 1099 cm1. Raw TQ exhibited the characteristic spectra at CH (sp3) and CH (sp2) group at 2967 and 3254 cm1 (Fig. 1d). Besides that, functional group for raw TQ appeared at

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Table 1 Formulation to synthesis TQ-nanoparticles. Two formulation with different drug to polymer ratio and polymer to surfactant ratio were prepared TQ PLGA-PEG PLGA-PEG (%) Pluronic F68 Polymer to surfactant ratio Drug Drug to polymer ratio Percentage of pluronic F68 Final volume after rotary evaporation

1:20 formulation (Ravindran et al. 2010) 5 mg 100 mg 10% 1% 10:1 0.5 1:20 0.1% 10 ml Vacuum evaporated

1:7 formulation (Vega et al. 2013) 12.5 mg 90 mg 1.8% 1% 2:1 0.25 1:7 0.1% 50 ml

A B

Absorbance

C D

E F

4000

3500 1:20 TQNP

3000

2500

2000

1:7 TQNP

Raw TQ

TQ in ACN

1500 Raw PF68

1000

500

Raw PLGA-PEG

Fig. 1 Characterization of TQ-nanoparticle using ATR-IR spectrum. ATR-IR spectra of (a) Raw PLGAPEG, (b) Raw Pluronic F68, (c) TQ in Acetonitrile, (d)

Raw TQ, (e) TQ NPs 1:7 formulation, and (f) TQ-NPs 1:20 formulation. ATR-IR measurement was carried out to detect then functional groups of the compounds studied

C¼C and trans C¼C group at 1642 cm1 and 932 cm1. Meanwhile, the significant peaks for CH (sp3), aliphatic C¼O, and C-O-C stretch appeared at 2881 cm1, 1752 cm1, and 1097 cm1 as provided in Fig. 1 (e, 1:20 formulation and f, 1:7 formulation).

7.1.2

Qualitative UV-VIS Spectrophotometric Analysis UV-VIS was used to measure absorption of peaks that correlated with the type of bond in determining the functional groups within molecule. Figure 2a, b represent UV spectra of TQ-nanoparticle derived from two different

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

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Table 2 List of functional group detected in TQ-nanoparticles

Functional group sp2 CH sp3 CH Trans C¼C (strong peak) C¼C OH stretching Aliphatic C  N Aliphatic C¼O C-O-C stretch

Sample type Raw PLGAPEG – – –

Raw TQ 3254 2967 932

Raw PF 68 – 2881 –

TQ in ACN 3554 – –

1:20 formulation TQ-NP – 2878 –

1:7 formulation TQ-NPs – 2884 –

– – – 1744 1080

1642 – – – –

– – – – 1098

1635 – 2253 – 1099

– – – 1753 1088

– – – 1752 1089

formulations. Results showed that the maximum wavelength obtained was at 255 nm for controlfree TQ in acetonitrile and TQ-nanoparticles with both methods.

7.1.3

Transmission Electron Microscope (TEM) TEM was done to measure the particle size and shape using negative staining method. Figure 3a shows the TEM images of 1:20 drug to polymer formulation. The shape of particle was spherical. Measurement of particle size indicated size range between 40 and 100 nm. Figures 3a and 4a depict TEM images of different TQ-nanoparticle formulations. Histogram provided in Figs. 3b and 4b represents the size distribution of TQ-nanoparticles. Figure 3a shows the morphology of TQ-nanoparticle from 1:20 formulation in solution. Image captured showed that the morphology of the nanoparticles was spherical in shape. About 70% of the nanoparticle less than 80 nm was in between 61 and 80 nm (Fig. 3b). It was noted that all the particles were less than 100 nm in size. The negative staining of TQ-nanoparticle from 1:7 formulation showed that the morphology was regular and round in shape (Fig. 4a). Measurement of particle size indicates that most were less than 90 nm (96%) and ranged between 0 and 120 nm (Fig. 4b). 7.1.4 Dynamic Light Scattering Dynamic light scattering analysis was used to determine the size distribution profile, average size, zeta potential, and PDI value of the

nanoparticles. Results were as provided in Table 3. Data obtained that TQ-nanoparticle from 1:20 drug to polymer ratio formulation had the largest size compared to the other formulation. The average particle sizes of TQ-nanoparticle synthesized with 1:20 drug to polymer ratio formulation and 1:7 drug to polymer ratio formulation were 148.1 nm and 91.95 nm, respectively. Zeta potential test was done to measure of the magnitude of the electrostatic or charge repulsion/attraction between particle and particle stability. The zeta potential for sample 1:20 drug to polymer ratio formulation was 25.9  1.02 mV. The nanoparticles formed with 1:7 drug to polymer ratio formulation had the lowest value which was 25.1  1.07 mV.

7.1.5

Hydrodynamic Size Distribution Measurement The size distribution plot by intensity is provided in Fig. 5, where the mean size was 120 nm for TQ-nanoparticles synthesized using 1:20 drug to polymer ratio formulation and 100 nm for 1:7 drug to polymer ratio formulation. This measurement correlates with the TEM sizing for both formulations. 7.1.6

Measurement of In Vitro Drug Release and Entrapment Efficiency Table 4 summarizes the drug release and entrapment efficiency of TQ-nanoparticles prepared from 1:7 formulation with drug to polymer ratios for 1:7 (w:w). Entrapment efficiency is used to calculate the percentage of drug entrapped within

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255.0

1.0

255.0

0.8

A B

0.6 Abs

Fig. 2 UV-VIS spectra to characterize nanoparticles. The graph shows the maximum wavelength of sample TQ-nanoparticle, (a) 1:20 formulation and (b) 1:7 formulation under UV-VIS spectrophotometer. The UV-VIS spectra of the TQ is characterized by the presence of one prominent peak at 255 nm

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0.4

0.2

0.0 400 300 Wavelength (nm)

200

nanoparticle with respect to the overall drug loaded in nanoparticle, while drug release is a measure of dosage form performance under in vitro standardized condition, and it can provide insight into the in vivo performance of the drug product (Lin et al. 2016). In this study, the maximum percentage of TQ-nanoparticle released 50.3% in 3 h, respectively (Fig. 6). The TQ release was accelerated within 3 h and gradual release occurred after 5 h. The absorption reading of TQ released was taken for 1 month.

7.1.7

Cytotoxicity Effect of TQNanoparticle toward Human Breast Cancer Cells A total of 3  103 cells were seed in 96-well plates and left to adhere overnight. Culture media was replaced, and the MCF-7/TAMresistant cells, UACC 732 lapatinib, and trastuzumab-resistant cells as well as parent MCF7 cells were treated with TQ-nanoparticle and TQ for 72 h. Cytotoxicity of TQ-nanoparticle and TQ was measured using MTS assay kit (Promega, USA) for 72 h of treatment. The dose-response curve was as provided in Figs. 7 and 8. The IC50 value is 8.24 μM for MCF-7/TAM treated with TQ, while IC50 value for MCF-7 treated with TQ-nanoparticles increased up to 20.05 μM. In contrast, results showed that the UACC 732 cells were more

sensitive to TQ-nanoparticles which exhibited lower dose of inhibitory concentration.

8

Discussion

Breast cancer has been the leading cause of death among women worldwide. Systemic agents are active in 90% primary breast cancer and 50% metastases at the beginning of treatment. However, after certain duration drug resistance is reported to develop in almost 60% of patients with breast cancer. One of the causes for recurrence is underlying molecular conditions. Drugresistant genes were found to be highly expressed in cancer patients. MDR1 gene has been linked to the development of resistance in various cancers. Currently, tamoxifen is the standard treatment in ER-positive breast cancer mainly in premenopausal women. However, reports still show high percentage of recurrence. More than 25% of drugs used during the last 20 years are directly derived from plants, while the other 25% are chemically altered natural products (Vuorelaa et al. 2004). Evidences from various research have led US National Institutes of Health (NIH) to initiate clinical trial on the potential use of Nigella sativa in year 2013. Thymoquinone is the most active compound in Nigella sativa seed volatile oil. It has been reported to show various

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

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A

B

percentage (%)

70

16.66

0

0

0-20

21-40

41-60

13.33

61-80

81-100

size (nm) Fig. 3 Characterization of TQ-nanoparticle. (a) TEM images of TQ-nanoparticle using 1:20 drug to polymer ratio formulation showed homogeneous spherical shape as seen at the arrows. Nanoparticles was treated with 2%

uranyl acetate (negative staining) on copper grids. (b) Histogram plot shows the particle size distribution with range of particle sizes between 61–80 nm

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A

B

1:7 formulation

percentage (%)

63.33

26.67

0.00 0-20

3.33 21-40

41-60

61-80

3.33

3.33

81-100

101-120

saiz (nm)

Fig. 4 Characterization of TQ-nanoparticles. (a) TEM images of TQ-nanoparticle using 1:7 drug to polymer ratio formulation show homogeneous spherical shape as seen at the arrows. Nanoparticles were treated with 2%

uranyl acetate (negative staining) on copper grids. (b) Histogram plot shows the particle size distribution with range of particle sizes between 61–90 nm

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

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Table 3 DLS analysis on particle size, zeta potential and PDI Zeta potential (mV) 1:20 25.9  1.02

1:7 91.9

Fig. 5 Dynamic light scatting technique for determining the particle size distribution of (a) TQ-nanoparticle 1:20 drug to polymer ratio formulation, and (b) for TQ-nanoparticle 1:7 drug to polymer ratio formulation

A

PDI 1:20 0.11

1:7 25.1  1.07

1:7 0.103

Size Distribution by Intensity 20

Intensity (Percent)

Particle size (nm) 1:20 148.1

15

10

5

0 0.1

1

10

100

1000

10000

1000

10000

Size (d.nm)

B

Size Distribution by Intensity

Intensity (Percent)

15

10

5

0 0.1

1

10

100

Size (d.nm)

medical benefits. Its exposure to human chronic myeloid leukemia cells KBM-5 caused suppression of the tumor necrosis factor-induced NF-kappaB activation in a dose- and timedependent manner. It also inhibited activation of NF-kappaB induced by various carcinogens and inflammatory stimuli (Sethi et al. 2008). The

development of nanoparticles for treatment has been gaining interest in new drug research. The bioavailability of drug have been found to be conserved within nanoparticle compared to free form and act gradually on target cells. Therefore, study aims to develop thymoquinonenanoparticle using entrapment method. It is

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Table 4 In vitro TQ release from TQ-nanoparticle prepared from drug-to-polymer ratio of 1:7 (wt:wt) release and entrapment studies were conduct in PBS (pH 7) Sample TQ nanoparticle

Drug release 50.03%

80 Percentage of Drug Release (%)

Percentage of drug pelease (%)

Drug release for 3 days

60

70 60

Entrapment efficiency 79.93%

40 20 0

0

50

20

40

60

80

Time/ h

40 30 20 10 0 0

5

10

15

20

25

30

Time (Day) Fig. 6 In vitro TQ release from nanoparticle over a month. Drug release analysis was done and absorption of free TQ was measured using spectrophotometer.

Percentage of drug release was found to gradually decline to 30% within a month

postulated the thymoquinone nanoparticles could potentially act synergistically with tamoxifen as chemopreventive agent particularly through the modulation of drug-resistant genes. Physicochemical properties such as size, charge, morphology, and physical state are the critical factors that influence the functional performance of any nanoparticle-based delivery systems and were then systemically analyzed after preparation of TQ-Nps. Techniques used for the characterization of TQ-nanoparticles include spectroscopic, UV-VIS, particle sizing, zeta potential, transmission electron microscope, entrapment efficiency, and drug release. The synthesized nanoparticles were primarily characterized by UV-visible spectroscopy, which

is proved to be a very useful technique for the analysis of nanoparticles (Kalimuthu et al. 2008; Kalishwaralal et al. 2009). The wavelengths of absorption peaks can be correlated with the types of bonds in a given molecule and are valuable in determining the functional groups present within a molecule. In the UV-visible spectrum, a strong, broad peak, located at the maximum wavelength at 255 nm, was observed for control TQ in acetonitrile, and similar prominent peak was noted after TQ was encapsulated with PLGA-PEG in both formulations (1:20 and 1:7 drug to polymer ratio formulations), respectively. From previous study it is an agreement that TQ is characterized by the presence of one prominent peak (λmax) at 254–257 nm, known for a distinctive peak of

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

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110

90

Cell Viability (%)

70

50

free TQ 30 TQ-Np

10

0

20

40

60

80

100

-10

Drug concentration (µM) Fig. 7 Cytotoxicity effect of TQ and TQ-nanoparticle on MCF-7/TAM. Cells were treated with different concentrations (0–100 μM) for 72 h and percentage of cells viability was then measured using MTS assay. Data is presented as average mean of triplicates for each

concentration of drug (n ¼ 3). Results showed the IC50 value MCF-7/TAM treated with TQ was 8.24 μM, while IC50 value for MCF-7/TAM treated with TQ-nanoparticle was higher, which was at 20.05 μM. Higher dose of TQ-nanoparticle was needed to inhibit MCF-7/TAM cells

quinones from its analog hydroquinone, where the latter showed a peak near 290 nm Shaarani et al. (2017). Therefore, it is conclusive that both formulations successfully show the presence of TQ after the encapsulation. ATR-IR measurement is used to detect the functional groups of the chemical compounds (Nallamuthu et al. 2013). In fact, the bonds and groups of bonds will vibrate at characteristic frequencies. Infrared energy at frequencies will absorb the infrared rays of exposed molecule which are characteristic to the molecule. The infrared spectra of raw TQ, raw P68, raw PLGA-PEG, TQ with acetonitrile, and both TQ-nanoparticle formulations are shown in Fig. 1. From the page of PubChem, thymoquinone with chemical formula C10H12O2 are monoterpenes containing one ring in the isoprene chain (Information, accessed Aug. 25, 2017).

Raw TQ exhibited the characteristic spectra at CH (sp3) and CH (sp2) group at 2967 and 3254 cm1, respectively. Besides that, functional group for raw TQ appears at C¼C and trans C¼C group at 1642 cm1 and 932 cm1 (Information, accessed Aug. 25,2017). Pluronic F68 exhibited dominant absorption peak for CH (sp3) and C-OC stretch at 2881 cm1 and 1099 cm1, respectively (Maghraby and Alomrani 2009). Raw PLGA-PEG show the absorption carbonyl – C¼O stretching at 1744 cm1 and C–O stretching at 1080 cm1 (G. Lin et al. 2012; Nallamuthu et al. 2013). The absence of acetonitrile observed in TQ NPs for both formulations confirmed all solvents have fully evaporated. This was due to the C  N group with wave number at 2253 cm1 which was not present in formulations. There were no detection of C¼C and trans C¼C group which confirms that TQ was fully encapsulated within the nanoparticle as all corresponding peaks

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Cell Viability (%)

70 60 50 free TQ

40

TQ-NP

30 20 10 0 0

10

20

30

40

50

60

70

80

90

100

Drug concentration (µM) Fig. 8 Cytotoxicity effect of TQ and TQ-nanoparticle on UACC 732 breast cancer. Cells were treated with different concentrations (0–100 μM) for 72 h and percentage of cells viability was then measured using MTS assay. Data is presented as average mean of triplicates for each concentration of drug (n ¼ 3). Results showed that the IC50

value UACC 732 treated with free TQ was 7.2 μM, while IC50 value for UACC 732 treated with TQ-nanoparticle was lower at 2.5 μM. Finding suggest that UACC 732 breast cancer cells were more sensitive to TQ-nanoparticles compared to MCF-7/TAM cells

appeared similar to raw Pluronic F68 and raw PLGA-PEG samples. To determine the TQ-nanoparticle capability of forming micelles, TEM images of nanoparticle were taken. Micrograph images of TEM showed that the morphology of TQ-nanoparticle from both formulations was round in shape and had smooth surfaces. The nanoparticles had a size range between 41 and 100 nm for 1:20 drug to polymer ratio formulation and 0 and 120 nm for 1:7 drug to polymer ratio formulation. In general, the majority of TQ-nanoparticles formed with 1:20 drug to polymer ratio formulation were between 61 and 80 nm, whereas TQ-nanoparticle 1:7 drug to polymer ratio formulation had size range between 61 and 90 nm. This was in line with the previous study in which the particle size from TEM analysis of PLGA-PEG nanoparticle was 60–70 nm for PLGA-lecithinPEG nanoparticle (Chan et al. 2009). Dynamic light scattering studies further corroborated the nanoparticle size, distribution, and charge of nanoparticle.

The particle size, size distributions, and zeta potential of TQ-nanoparticle were measured in aqueous dispersion by means of DLS. The respective average particle size, zeta potential, and polydispersity index (PDI) at 25
C temperature are presented in Table 4. Based on the findings, PDI value was 0.1 in both formulations indicating monodispersed and less broad in size distribution (Holzapfel et al. 2005). Both formulations can be concluded having small in size with the narrow distribution of PDI. DLS showed different particle sizes from both formulations. TQ-nanoparticle 1:20 drug to polymer ratio formulation had bigger size than 1:7 drug to polymer ratio formulation which is 148.8 nm compared to 91.9 nm. The size of the TQ-nanoparticle could be influenced by factors such as drug and polymer ratio and concentration of TQ (Astete and Sabliov 2006). The surface charge (zeta potential) is the critical parameter on the stability of the nano-suspension and bio-adhesion of particulate systems. Furthermore electrokinetic behavior showed by zeta potential

Synthesis and Characterization of PLGA-PEG Thymoquinone Nanoparticles and. . .

is important for understanding the dispersion behavior of nanoparticles in a liquid medium (Karimian and Babaluo 2007). There is no significant difference in surface charge for both formulations. The 1:20 drug to polymer ratio formulation TQ-nanoparticle showed 25.9  1.02 mV, and 1:7 drug to polymer ratio formulation showed 25.1  1.07 mV. Nallamuthu et al. (2013) have reported recently that PLGA encapsulated TQ was at 148 nm and zeta potential was 24.8 mV (Nallamuthu et al. 2013). Both were negatively surface electric charged and indicating good dispersion stability (Hunter 2013).The negative charge of zeta potential is necessary for cellular uptake of TQ-nanoparticle as it may reduce the electrostatic barrier. This would increase the chances of cell and particle interactions and results in higher toxicity (Bhattacharya et al. 2015; Patil et al. 2007). The zeta potential can be used as a reliable guide to the magnitude of electric repulsive forces between particles (Attwood 2011). The values of between 10 and + 10 mV were considered approximately for nanoparticle, while the values that are greater than +30 mV or less than 30 mV were considered strongly cationic and strongly anionic for nanoparticle, respectively (Clogston and Patri 2011). Because most cellular membranes are negatively charged, zeta potential can affect a nanoparticle’s tendency to enter the membranes or facilitate cellular uptake. Generally, cationic particles display more toxicity associated with cell wall disruption (Clogston and Patri 2011). It was reported that the synthesis of TQ-nanoparticle with PLGA using emulsification-solvent evaporation/diffusion method showed the average size distribution was 148 nm (Nallamuthu et al. 2013). There were similar results from the UV-VIS, TEM, and ATR-IR methods for 1:20 formulation and 1:7 formulations except for DLS findings which suggest size differences. The particle size determined with DLS for 1:7 formulation TQ-nanoparticle was smaller with less than 100 nm. It was selected because generally nanoparticle with the size less than 100 nm is reported to have the ability to carry and deliver therapeutics to disease sites (Wang et al. 2012). Therefor in this study, 1:7 drug to polymer ratio

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formulation was chosen for subsequent studies involving encapsulation efficiency and drug release analysis. In addition, previous study on cell uptake also exhibited its dependency upon the nanoparticle size with smaller particles possessing greater uptake in general (Win and Feng 2005). This supports the findings reported in the literature that the extent of particle uptake is indirectly proportional to the particle size (Desai et al. 1997). The smaller size particles seem to have efficient interfacial interaction with the cell membrane compared to larger size particles. Probably the larger size particles (>1 μm) are taken up by mechanism other than endocytosis, such as fluid-phase pericytosis (Boudad et al. 2001). Furthermore, the small size particles could improve efficacy of the particle-based oral drug delivery systems (Kreuter 1991). Next, the encapsulation efficiency of TQ-nanoparticle was evaluated. In earlier reports, 63% entrapment efficiency was reported for TQ-chitosan nanoparticles (Alam et al. 2012) and 90% for TQ-liposome nanoparticles (Odeh et al. 2012). TQ-PLGA-PEG nanoparticle showed 79.93% encapsulation efficiency, respectively. Results indicated that most of the free TQ was entrapped within the nanoparticles during synthesis. The amount of TQ released throughout the study from the TQ-nanoparticle formulation was 50.3%; it exhibited an accelerated decrease after 3 h and gradual TQ release after 5 h. The initial burst release pattern might be due to some amount of the drug was absorbed on the surface or loosely bound to the inner polymer core and was lost during the initial preparation stage. Thymoquinone has been extensively studied for its various medical benefits previously. Developing TQ-nanoparticle was found to conserve this volatile oil and provided sustained drug release over longer duration compared to its free form. Nano-sized vehicles have been used in drug delivery because they are suitable for intravenous application. Solid biodegradable nanoparticles have shown their advantage over liposomes by their increased stability and unique ability to create a controlled release of drugs (Hans and Lowman 2002). In recent years, a variety of natural and synthetic polymers have been explored for

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the preparation of nanoparticles, of which poly (lactic acid) (PLA), poly(glycolic acid) (PGA), and their copolymers (PLGA) have been extensively investigated because of their biocompatibility and biodegradability (Bala et al. 2004). Synthetic polymers and natural macromolecules have been extensively researched as colloidal materials for nanoparticle production designed for drug delivery. Synthetic polymers have the advantage of high purity and reproducibility over natural polymers. Among the synthetic polymers, the polyester family (i.e., poly(lactic acid) (PLA), poly(ε-caprolactone) (PCL), poly (glycolic acid) (PGA)) are of interest in the biomedical area because of their biocompatibility and biodegradability properties. In particular, poly(lactide-co-glycolide) (PLGA) has been FDA-approved for human therapy (Anderson and Shive 1997). Breast cancer cell line cytotoxicity used in the cytotoxicity study were MCF-7/TAM and UACC 732. The cells were treated with free TQ and TQ-nanoparticle with different concentrations (0–100 μM). The IC50 MCF-7/TAM with free TQ were 8.24 μM, and IC50 with TQ-nanoparticle at different concentrations were 20.05 μM (MCF7/TAM) and 2.5 μM (UACC732).

9

Conclusion

In summary, synthesis and characterization of PLGA-based TQ-nanoparticles showed formation of spherical nanoparticles using 1:7 drug to polymer ratio as supported by DLS analysis. These particles exhibited selective cytotoxic effects toward breast cancer cell subtypes, whereby the UACC 732 cell line was more sensitive toward TQ-nanoparticle compared to the MCF-7/TAM cells.

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Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 83–95 https://doi.org/10.1007/5584_2019_464 # Springer Nature Switzerland AG 2020 Published online: 9 January 2020

Adipose-Derived Mesenchymal Stem Cells Promote Growth and Migration of Lung Adenocarcinoma Cancer Cells Norashikin Zakaria and Badrul Hisham Yahaya

Abstract

Introduction: Mesenchymal stem cells (MSCs) have been used in cancer therapy as vehicles to deliver therapeutic materials such as drugs, apoptosis inducers and cytokines due to their ability to migrate and home at the tumour site. Furthermore, MSCs have been genetically engineered to produce anticancer molecules such as TRAIL that can induce apoptosis of cancer cells. However, MSCs’ presence in the tumour microenvironment has shown to be involved in promoting tumour growth and progression. Therefore, the roles of MSCs either promoting or suppressing tumorigenesis need to be investigated. Methods: Human adiposederived MSCs (Ad-MSCs) and A549 cells are co-cultured together in indirect co-culture system using Transwell insert. Following co-culture, both cells were analysed in terms of growth rate, migration ability, apoptosis and gene expression for genes involved in migration and stemness characteristics. Results: The result shows that Ad-MSCs promoted the growth of A549 cells when indirectly co-cultured for 48 and 72 h. Furthermore, Ad-MSCs significantly enhanced the migration rate of A549 cells. The increased in migration N. Zakaria and B. H. Yahaya (*) Regenerative Medicine Cluster, Advanced Medical & Dental Institute (AMDI), Universiti Sains Malaysia, Kepala Batas, Penang, Malaysia e-mail: [email protected]; [email protected]

rate was in parallel with the significant increase of MMP9. There are no significant changes observed in the expression of TWIST2, CDH2 and CDH1, genes involved in the epithelial-tomesenchymal transition (EMT). Ad-MSCs also protect A549 cancer cells from undergoing apoptosis and increase the survival of cancer cells. Conclusion: Secretion of soluble factors from Ad-MSCs has been shown to promote the growth and metastatic characteristics of A549 cancer cells. Therefore, the use of Ad-MSCs in cancer therapy needs to be carefully evaluated in the long-term aspect. Keyword

Adipose-derived stem cells · Co-culture · Lung cancer · MSCs

Abbreviation AD AdMSCs BMMSCs cDNA CSCs EMT LCC MSCs NSCLC

Adenocarcinoma Human adipose-derived mesenchymal stem cells Bone marrow-derived mesenchymal stem cells Complementary DNA Cancer stem cells Epithelial-to-mesenchymal transition Large-cell carcinoma Mesenchymal stem cells Non-small cell lung carcinoma 83

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SCC TME TRAIL

1

N. Zakaria and B. H. Yahaya

Squamous cell carcinoma Tumour microenvironment TNF-related apoptosis-inducing ligand

Introduction

Lung cancer remains as the world’s most diagnosed cancer which accounts for 11.6% of the total cancer cases and the leading cause of world’s cancer-related mortality (Bray et al. 2018). The majority of lung cancer cases are non-small cell lung carcinoma (NSCLC) (85%) which is further sub-divided into adenocarcinoma (AD), squamous cell carcinoma (SCC) and largecell carcinoma (LCC) that comprise about 50%, 40% and < 10% of NSCLC, respectively (Travis et al. 2015). Lung cancers are treated using surgical resection and radio- and chemotherapy. However, treatment-related side effects, off-target effects and drug resistance limit the efficacies of many therapeutic options. Furthermore, metastatic cancer cells usually cannot be eliminated by traditional therapies, and recurrence in these cases is extremely likely. Therefore, new and effective therapies are needed to increase the treatment outcome. Mesenchymal stem cells (MSCs) have the ability to self-renew and to differentiate into multiple cell types. This characteristic makes MSCs a good candidate to be used in regenerative medicine. MSCs can be isolated from various sources including the umbilical cord, placenta, bone marrow and adipose tissue (Hass et al. 2011; Elahi et al. 2016). MSCs are spindle-shape-like cells with the ability to undergo osteogenic, adipogenic and chondrogenic differentiation in vitro. The cells are also positive for surface markers CD90, CD73 and CD105 and negative for CD31 (endothelial marker), CD11β, CD19, CD14, CD34, CD45 and CD79α (haematopoietic markers) and MHC class II (Dominici et al. 2006). MSCs have received much attention in cancer treatment because of its capability to migrate and home at tumour sites (Xie et al. 2017). For

example, MSCs have been used as vehicles to deliver therapeutic materials such as drugs, apoptosis inducers and cytokines to tumour sites (Krueger et al. 2018). Furthermore, genetically engineered MSCs were developed to produce anticancer molecules such as TNF-related apoptosis-inducing ligand (TRAIL) that can induce apoptosis in cancer cells (Tang et al. 2014). But the use of MSCs in cancer treatment has raised concern due to its roles in tumour microenvironment (TME) (Freese et al. 2015). MSCs found in TME have been reported to support tumour growth by suppressing immune surveillance and secreting cytokines and chemokine factors to promote cancer progression (Hill et al. 2017). Several studies have reported the dual functions of MSCs in various cancers. Ad-MSCs have been shown to promote the growth and proliferation of cancer cells such as in triple-negative breast cancer cells (Rowan et al. 2014). On the other hand, MSCs isolated from similar source have shown to inhibit the growth of melanoma cells (Ahn et al. 2015). Due to conflicting roles, it is crucial to check the effect of MSCs that will be used in cancer cell therapy. In this study, the effect of indirect co-culture between MSCs and lung cancer cells will be evaluated to better understand their roles either to promote or to suppress cancer growth. The main objective of the study is to evaluate the effect of MSCs on lung cancer cell growth to better understand its roles in either promoting or suppressing cancer growth. The adipose tissuederived MSCs (Ad-MSCs) were co-cultured with A549 lung cancer cells, and the effect of co-culture was evaluated on several aspects including cell growth, migration, apoptosis and stemness characteristics. The use of Ad-MSCs in this study was because Ad-MSCs are regarded as the ideal source of MSCs as compared to the commonly used bone marrow MSCs (BM-MSCs). In addition, Ad-MSCs are easily accessible in large quantities with inexpensive and minimally invasive procedure such as liposuction (Schneider et al. 2017). Moreover, adipose tissue usually contains more MSCs as compared to bone marrow (Mohamed-Ahmed et al. 2018). For example, MSCs constitute up to

Adipose-Derived Mesenchymal Stem Cells Promote Growth and Migration of Lung. . .

3% of all cells in adipose tissue versus 0.01% in bone marrow (Fraser et al. 2006). Ad-MSCs have attracted a lot of interest due to their convenient acquisition and regenerative capability. The use of co-culture system in this study was because its allow both cells not to have a direct contact thus to evaluate whether secreting factors released by MSCs could still be able to promote the growth of cancer cells. This co-culture model could be mimic to the microenvironment system of the cancer cells within the tumour tissue of specific organ where the organ has its own specific tissue stem cells, which can promote the growth of cancer cells from within.

2

Materials and Methods

2.1

Cell Culture

The human lung cancer cell lines A549 (ATCC® CCL-185™) and human Ad-MSCs (ATCC® PCS-500-011™) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). A549 cells were cultured in DMEM medium supplemented with 10% foetal bovine serum (FBS) and 1% penicillin/streptomycin. Ad-MSCs were cultured in CTS™ KnockOut™ DMEM medium supplemented with 10% FBS, 2 ng/mL FGF-basic, 2 mM L-glutamine and 1% Antibiotic-Antimycotic. Both cells were cultured in humidified incubator containing 5% CO2 at 37
C. Ad-MSCs were used at passage five to six and maintained in culture at confluency no greater than 70%. Unless stated, all media are from Gibco (Thermo Fisher Scientific, MA, USA).

3

Characterisation of Ad-MSCs

The characterisation of Ad-MSCs used in this study was performed by our group as previously described (Halim et al. 2014). The characterisation was done according to the guideline by the International Society for Cellular Therapy (ISCT) (Dominici et al. 2006). Ad-MSCs were characterised for the expression

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of MSC surface marker and their ability for tri-lineage differentiation. For MSC surface marker expression, approximately one million of Ad-MSCs were labelled with CD44-FITC, CD73-PE, CD90-PE, CD105-PE, CD271-FITC, CD34-FITC, CD15-FITC and CD45-PE (BD Biosciences, San Jose, CA, USA). The expression of labelled antibodies was measured using FACSCalibur flow cytometer (BD Biosciences), and the results were analysed using CellQuest software (BD Biosciences). The tri-lineage differentiation capacity of MSCs was evaluated based on their ability to differentiate into adipogenic, chondrogenic and osteogenic when cultured in differentiation induction media (PromoCell, Heidelberg, Germany). Briefly, the cells were seeded in 24-well tissue culture plates until the cells reached 80–90% confluence (for adipogenic differentiation) or 100% confluence (for chondrogenic and osteogenic differentiation). Then the respective differentiation media was added, and the cells were cultured for 14 days (adipogenic differentiation) or 21 days (chondrogenic and osteogenic differentiation) with media replenished every 3 days. The differentiation into adipocyte, osteocyte and chondrocyte cells was detected by staining with 0.3% Oil Red O, 2% Alizarin Red S and Alcian blue staining solution (Sigma-Aldrich, Munich, Germany), respectively.

4

Establishment of the Coculture System

A non-contact co-culture system of Ad-MSCs and A549 cells was established using 24-well ThinCert™ cell culture inserts with 0.4 μm transparent PET membrane (Greiner Bio-One GmbH, Frickenhausen, Germany). A549 and Ad-MSCs were cultured alone in the single-culture system or co-culture using Transwell system with the following conditions: (i) A549 (lower chamber) + Ad-MSCs (upper chamber) and (ii) Ad-MSCs (lower chamber) + A549 (upper chamber). The number of cells seeded per chamber for each group is 5  104. Cells were cultured in the aforementioned complete medium. Culture

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medium was replenished every 48 h, and cell growth state was observed under an inverted microscope.

5

Quantitative Real-Time PCR (qRT-PCR)

The RNA was extracted from cells using GeneJET RNA Purification kit (Thermo Fisher Scientific) according to the manufacturer’s instructions and stored at 80
C. RNA concentration and purity were measured using NanoDrop 2000C (Agilent Technologies, Santa Clara, CA, USA). Complementary DNA (cDNA) was synthesised from 1 μg total RNA using Tetro cDNA synthesis kit (Bioline Reagent Ltd., Humber Road, London) using a random hexamer primer and an anchored-oligo (dT) primers. Expression of selected genes was measured using TaqMan® gene expression assay (Applied Biosystems, Foster City, CA, USA), and the reaction was performed in StepOnePlus™ RT-PCR machine (Applied Biosystems). The list of gene and assay ID is given in Table 1. The relative expression of the genes was calculated using the formula 2-ΔΔCT and normalised to an endogenous housekeeping gene GAPDH.

6

7

Apoptosis Assay

The apoptosis assay was conducted using the annexin V/propidium iodide (PI) apoptosis kit (BD Bioscience). In brief, A549 and Ad-MSCs were co-cultured as described above (establishment of the co-culture system), but the percentage of serum in the medium was reduce to 2% instead of 10%. After co-culture for 24 or 48 h, the cells were harvested by trypsinisation and labelled with annexin V-FITC for 20 min followed by the addition of propidium iodide (PI) prior to FACS acquisition. Stained cells were subjected to flow cytometric analysis using a FACSCalibur instrument (BD Biosciences), and a total of 10,000 events were acquired and analysed using CellQuest software (BD Biosciences).

8

Statistical Analysis

All data were expressed as the mean  standard deviation (SD) of three independent experiments. Comparison between treatment groups was performed using t-test. P-values of 75%. The immunoreactivity score (IRS) was obtained as Σ(i  p), where i and p represent staining intensity and the proportion of cells that stained at each intensity, respectively (Li et al. 2016; Lee et al. 2013; Morgan et al. 2003). Kaplan-Meier survival curve and log-rank test were used to analyze survival times after primary tumor resection.

2.4

Statistical Analysis

Statistical evaluation was conducted with IBM SPSS version 25.0 statistical software (SPSS Inc., Chicago, IL, USA). The percentage and intensity of expression levels of the three biomarkers in CRC were obtained using

descriptive statistics (mean  SEM). Association between the biomarkers with demographic and clinicopathologic parameters was performed using chi-square, and multivariate analysis was performed using binary logistic regression and multiple linear regression to further confirm the strength of association. Kaplan-Meier analysis was used to estimate the probability of survival as a function of time, and differences in the survival of subgroups of patients were evaluated with the log-rank test. A P-value < 0.05 was considered statistically significant.

3

Results

3.1

Patients and Tumor Characteristics

Table 1 gives the relevant clinical characteristics of the 91 patients whose tumors were analyzed by IHC. The study population was divided between males (50 cases) and females (41 cases). The trend showed that the three biomarkers were detected more frequently in moderate-grade tumors with PD-L1 low expression detected in 90.6% of cases and high expression detected in 83.35% of cases. As for TYMS, low expression was detected in 85.7% of cases and high expression in 95.2% of cases. For DCC, low expression was detected in 92.9% of cases and high expression in 89.6% of cases, compared to well or poorly differentiated expression. For tumor stages, PD-L1 expression was detected more frequently in tumor stage III for both low (45.9% cases) and high (50.0%) expression levels. TYMS expression was highest in tumor stage II (45.2%) and lowest at stage III (55.1%). For DCC, low expression (42.9%) and high expression (46%) were found in tumor stage III. At the tumor site, PD-L1 expression was more frequently observed in the right tumor site for both low (77.6%) and high expression levels (83.3%). TYMS expression was seen in 83.7% of cases at low expression; high expression was seen in 71.4% of the cases at the right side of tumor site. Low DCC expression was observed in 85.7% of cases; high DCC expression was detected in 76.6% of cases

Age (years) 30 Gender Male Female Race Malay Chinese Indian Others Grade Well Moderate Poor AJCC stage Stage I Stage II Stage III Stage IV Tumor site Right Left 30 (35.3%) 55 (64.7%) 45 (52.9%) 40 (47.1%) 40 (47.1%) 38 (44.7%) 6 (7.1%) 1 (2.2%) 6 (7.1%) 77(90.6%) 2 (2.4%) 12 (14.1%) 31 (36.5%) 39 (45.9%) 3 (3.5%) 19 (22.4%) 66 (77.6%)

50 41

41 43 6 1

6 82 3

14 32 42 3

20 71

1 (16.7%) 5 (83.3%)

2 (33.35%) 1 (16.7%) 3 (50.50%) 0 (0.0%)

0 (0.0%) 5 (83.3%) 1 (16.7%)

1 (16.7%) 5 (83.3%) 0 (0.0%) 0 (0.0%)

5 (83.3%) 1 (16.7%)

0 (0.0%) 6 (100.0%)

PD-L1 expression Low High

62  11 30 61

Patient demographic and clinicopathologic parameters

Table 1 Association of biomarkers with clinicopathologic parameters

0.745

0.531

0.140

0.332

0.148

0.076

P

8 (16.3% 41 (83.7%)

8 (16.3%) 13 (26.5%) 27 (55.1%) 1 (2.0%)

5 (10.2) 42 (85.7%) 2 (4.1%)

23 (36.7%) 23 (46.9%) 2 (4.1%) 1 (2.0%)

18 (36.7%) 31 (63.3%)

17 (34.7%) 32 (65.3%)

12 (28.6%) 30 (71.4%)

6 (14.3%) 19 (45.2%) 15 (35.7%) 2 (4.8%)

1 (2.4%) 40 (95.2%) 1 (2.4%)

18 (42.9%) 20 (47.0%) 4 (9.5%) 0 (0.0%)

32 (76.2%) 18 (23.8%)

13 (31.0%) 29 (69.0%)

TYMS expression Low High

0.160

0.198

0.283

0.581

0.000

0.705

P

2 (14.3%) 12 (85.7%)

3 (21.4%) 4 (28.6%) 6 (42.9%) 1 (7.1%)

0 (0.0%) 13 (92.9%) 1 (7.1%)

4 (28.6%) 9 (64.3%) 1 (7.1%) 0 (0.0%)

9 (64.3%) 5 (35.7%)

5 (35.7%) 9 (64.3%)

18 (23.4%) 59 (76.6%)

11 (14.3%) 28 (36.4%) 36 (48.8%) 2 (2.6%)

6 (7.8%) 69 (89.6%) 2 (2.5%)

37 (48.1%) 34 (44.2%) 5 (6.5%) 1 (1.3%)

41 (53.2%) 36 (46.8%)

25 (32.5%) 52 (67.5%)

DCC expression Low High

0.450

0.711

0.397

0.531

0.445

0.812

P

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at the right tumor site. There was a significant association between TYMS expression and gender (P < 0.05), with high levels of TYMS among more males (76.2%) than females (23.8%).

showing IHC staining for the expression of PD-L1, TYMS, and DCC are shown in Figs. 2, 3 and 4, respectively.

3.3 3.2

Protein Expression of Biomarkers

PD-L1 high protein expression was detected in 6.6% (6/91) of cases, and low expression was detected in 93.4% (85/91) of cases. For TYMS expression, low expression was detected in 53.8% (49/91) of cases and high expression was detected in 46.2% (42/91) of cases. High DCC expression was detected in 84.6% (77/91) of cases and low DCC expression in 15.4% (14/91) of CRC cases, as shown in Fig. 1. Representative images Fig. 1 Proportion of programmed cell death-1 (PD-L1), thymidylate synthase (TYMS), and deleted in colon cancer (DCC) positivity in CRC cases distinguished by (1) low levels of expression and (2) high levels of expression

Fig. 2 Colon with normal epithelial mucosa, glands, and muscularis mucosa. (Hematoxylin and eosin stain, original magnification  20)

Expression of Biomarkers and Prognostic Parameters

The mean OS was 100 months and the mean disease-free survival (DFS) was 98 months (Figs. 5 to 7). Furthermore, the mean duration of OS was 22 months for patients with high PD-L1 expression and 110 months for those with low PD-L1 expression (P ¼ 0.494). The mean duration of DFS was 45 months for patients with high PD-L1 expression and 98 months for those with low PD-L1 expression (P ¼ 0.753). Patients with

Predictive Potential of PD-L1, TYMS, and DCC Expressions in Treatment. . . Fig. 3 PDL-1 protein expressions in (a) Positive control tissue with membranous staining and an accompanying cytoplasmic component (placenta) (b) CRC tissue with moderate staining (c) CRC tissue with strong staining (original magnifications  20,  40)

Fig. 4 TYMS-protein expressions in (a) Positive control tissue with strong nuclear and cytoplasmic staining (lymph node) (b) CRC tissue with moderate staining (c) CRC tissue with strong staining (original magnifications  20,  40)

103

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E. E. Onwe et al.

Fig. 5 DCC protein expressions in (a) Positive control tissue with strong cytoplasmic staining (stomach) (b) CRC case with strong cytoplasmic staining (c) CRC case with

moderate cytoplasmic staining (original magnifications  20,  40)

Survival Function Survival Function Censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

OS_Duration

Fig. 6 Mean estimate of overall survival of CRC patients

100.00 120.00

Predictive Potential of PD-L1, TYMS, and DCC Expressions in Treatment. . .

105

Survival Functions PD_L1 Low High Low-censored High-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

OS_Duration

Fig. 7 PD-L1 low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.494

low TYMS expression had a mean OS of 100 months, and those with high expression had a mean OS of 110 months (P ¼ 0.821). The mean duration of DFS for patients with high and low TYMS expression was 98 months and 100 months, respectively (P ¼ 0.439). The OS in patients with high DCC expression was 110 months and with low DCC expression was 100 months (P ¼ 0.076). DFS for patients with high DCC expression was 110 months and with low DCC expression was 98 months (P ¼ 0.404), as shown in Figs. 8, 9, 10, 11, 12 and 13.

4

Discussion

The present study evaluated the suitability of PD-L1, TYMS, and DCC as predictive biomarkers for treatment selection and prognostic biomarkers for treatment outcome in CRC by associating protein expression levels with clinical

parameters. All the CRC tissue samples used in this study were from patients who underwent curative resection without receiving any radiation therapy or chemotherapy before surgery. In this current study, we obtained the following findings after obtaining semiquantitative scores, which showed that high PD-L1 expression was detected in 6.6% of cases and low PD-L1 expression in 93.4% of cases. Previous investigations using IHC for relative proportion of expression (Droeser et al. 2013; Patel and Kurzrock 2015b; Chih-yang et al. 2018) showed that the majority of cases also had low PD-L1 expression. Furthermore, another previous study had shown that high expression of PD-L1 was predictive of good response and survival rate (Huang et al. 2015; Chae et al. 2016; Meng et al. 2015). Our results from the semiquantitative scoring showed that TYMS expression at low level was observed in 53.8% of patients; TYMS at high expression level was seen in 46.2% of the cases.

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E. E. Onwe et al. Survival Functions TYMS_2 Low High Low-censored High-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

OS_Duration

Fig. 8 TYMS low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.821

Survival Functions DCC_2 Negative Positive Negative-censored Positive-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00 120.00

OS_Duration

Fig. 9 DCC low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.076

Predictive Potential of PD-L1, TYMS, and DCC Expressions in Treatment. . .

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Survival Function Survival Function Censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

DSF_Duration

Fig. 10 Mean estimate of overall disease-free survival in CRC cases

Survival Functions PD_L1 Low High Low-censored High-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

DSF_Duration

Fig. 11 PD-L1 low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.753

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E. E. Onwe et al. Survival Functions TYMS_2 Low High Low-censored High-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

DSF_Duration

Fig. 12 TYMS low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.439

Survival Functions DCC_2 Low High Low-censored High-censored

1.0

Cum Survival

0.8

0.6

0.4

0.2

0.0 .00

20.00

40.00

60.00

80.00

100.00

120.00

DSF_Duration

Fig. 13 DCC low/high expression in CRC cases. Kaplan-Meier analysis. P ¼ 0.404

Predictive Potential of PD-L1, TYMS, and DCC Expressions in Treatment. . .

This is in-line with the investigation by Patla and Pawlega (Patla and Pawlȩga 2005), which showed that TYMS protein levels were elevated in CRC. Although discrepancies in TYMS protein expression have been reported, several independent studies have agreed that low expression level of TYMS can be a strong prognostic marker for response to 5-FU-based chemotherapy treatment of CRC (Qiu et al. 2008; Gusella and Padrini 2007). This was not observed here as no significant difference in OS or duration of DFS was seen when CRC cases with low/high expressions of TYMS were compared. There was a significant association between TYMS expression and gender (P < 0.05), where high TYMS expression had significantly higher percentage of occurrence in males (76.2%). This is similar to other investigations which have shown that gender is associated with TYMS expression (Karlberg et al. 2010; Su et al. 2019). There was high expression of DCC in most cases – 84.6% (77/91). This is in-line with a previous IHC study on DCC by Wu et al. (Morgan et al. 2003). There was high expression of the biomarkers in the age group of 60 years and above. This is in accordance with other investigations that have shown high DCC expression with age (Lee et al. 2013; Patla and Pawlȩga 2005). The log-rank test showed there was no significant correlation between the biomarkers and OS. Nevertheless, the mean OS in patients with high PD-L1 expressions was 22 months. Further, the DFS mean score in patients with high PD-L1 expression was 45 months. This is in-line with other investigations which showed good DFS in patients with high PD-L1 (Shien et al. 2016; Passiglia et al. 2016; Liu et al. 2018). Therefore, PD-L1 overexpression could be used for development of suitable candidates for anti-PD-L1 directed therapy. Further, patients with low TYMS expression showed mean OS of 100 months, which indicated better survival compared to those with low expression of with 95 months. Previous studies on the prognostic significance of TYMS protein expression in CRC patients showed that low TYMS expression correlated with improved outcome (Soong et al.

109

2008; Niedzwiecki et al. 2017). Thus, low TYMS expression could predict a good response to 5-FU treatment (Huang et al. 2013). Further, patients with high DCC expression showed OS rate of 110 months, which predicts better survival. This is in-line with previous studies that showed that high DCC expression is a strong predictive factor for better outcome in cancer (Lenka et al. 2016).

5

Conclusion

In conclusion, the results from this study suggest that PD-L1, TYMS, and DCC expressions could be used as biomarkers to stratify CRC patients who could benefit from adjuvant therapy. Acknowledgments The authors would like to acknowledge Universiti Putra Malaysia for funding this research project (Grant number GP-IPS 9666200). The authors would also like to acknowledge the support and effort of Mr. Kabeer Anatomy department, Faculty of Medicine and Health Sciences, UPM. Author Contributions Conceptualization involves those who forms the concept or idea of the research, they include E.E.O, N.M., E.E.O.; N.M and F.G.A.; methodology E.E. O.; N.M.; R.M.Z.; and F.GA, validation, N.M.; and F.G. A.; formal analysis E.E.O.; M.A.; and O.M.; investigation, E.E.O.; N.M.; F.G.A.; and M.A.; writing-original draft preparation, E.E.O.; writing-review and editing, E.E.O.; N.M.; F.G.A and M.A.; supervision, N.M.; M.A.; F.G.A. Conflicts of Interest The authors declare no conflict of interest. Funding This research was funded by Universiti Putra Malaysia (grant number GP-IPS 9666200).

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Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 113–130 https://doi.org/10.1007/5584_2020_522 # Springer Nature Switzerland AG 2020 Published online: 20 May 2020

Clinical Trials with Cytokine-Induced Killer Cells and CAR-T Cell Transplantation for Non-small Cell Lung Cancer Treatment Le Van Manh Hung, Hieu Trong Ngo, and Phuc Van Pham Abstract

The idea of utilizing the human immune system to eradicate tumors has been successfully practiced for the past decades, as reported in multiple published studies. Among cancer types, non-small cell lung cancer (NSCLC) is considered the most lethal type, leading to the necessity of finding an effective treatment for this category of cancer. Building on the success of basic and preclinical studies, numerous clinical trials of cytokine-induced killer (CIK) cells or chimera antigen receptor (CAR) T

Authors Hung Van-Manh Le and Hieu Trong Ngo have equally contributed to this chapter. L. V. M. Hung and H. T. Ngo Stem Cell Institute, University of Science, Ho Chi Minh City, Viet Nam e-mail: [email protected]; [email protected] P. Van Pham (*) Stem Cell Institute, University of Science, Ho Chi Minh City, Viet Nam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam Laboratory of Stem Cell Research and Application, University of Science, Ho Chi Minh City, Viet Nam Laboratory of Cancer Research, University of Science, Ho Chi Minh City, Viet Nam e-mail: [email protected]; [email protected]

cells for NSCLC therapy have been reported. In this review paper, we will summarize those findings in the context of clinical outcomes and adverse effects. In NSCLC, compared to CAR-T cells, CIK cells show relatively stronger antitumor efficacy and lower adverse effects. More clinical studies are needed to further confirm the clinical efficiency of both types of cellular immunotherapy. Keywords

CAR-T cells · Cytokine induced killer cells · Immune cell therapy · Non-small cell lung cancer

Abbreviations ACT CAR CIK CRS IL MHC-I NSCLC PD-1 TAAs TCR

Adoptive cell therapy Chimera antigen receptor Cytokine-induced killer Cytokine release syndrome Interleukin Major histocompatibility complex class I Non-small cell lung cancer Programmed cell death protein 1 Tumor-associated antigens T cell receptor

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L. V. M. Hung et al.

Introduction

Lung cancer remains the most common type of cancer and the leading cause of cancer-related deaths worldwide, with approximately 2 million newly diagnosed cases (11.6% of total cases) and more than 1.7 million deaths (18.4% of total cases) (Bray et al. 2018). Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases (Molina et al. 2008). The survival rate of patients with earlyonset NSCLC can be relatively high after surgery (Knight et al. 2017). However, at the time of diagnosis, most patients already exhibit disease progression, and the median survival rate is only about 18 months after diagnosis (Wang et al. 2010). Despite early discovery and development of standard therapies, the treatment of NSCLC remains inefficient, partly due to both intrinsic resistance of lung cancer cells themselves and acquired resistance by continual administration of chemotherapeutic agents (Shanker et al. 2010). Thus, novel therapeutic strategies are required to enhance therapeutic efficacy against NSCLC cells. Besides standard therapies, such as surgery, chemotherapy and radiotherapy, immunotherapy has emerged as one of the most effective remedies for cancer patients (Zhang and Chen 2018). The immune system consists of a complex network of cells, molecules, tissues and organs that work in coordination as a natural defense system for the human body. The concept of immunotherapy is based on the ability of this system to protect against diseases and harmful pathogens, including the elimination of cancerous cells (Borghaei et al. 2009; Lesterhuis et al. 2011; Swann and Smyth 2007). Immunotherapy is commonly known to consist of cytokines, immune checkpoint inhibitors, monoclonal antibodies, vaccines, and adoptive cell transfer, with each method having its own advantages and disadvantages (Oiseth and Aziz 2017). Cytokine immunotherapy using cytokines (such as IL-2) is beneficial to melanoma and

renal cell carcinoma. It can also support the proliferation and survival of cells used in adoptive cell therapy like tumor-infiltrating lymphocytes (TILs), T cell receptor (TCR)-T cells, and chimeric antigen receptor (CAR)-T cells. However, IL-2 also promotes proliferation of regulatory T cells (Tregs) and can cause severe toxicities (Jiang et al. 2016). Moreover, although immune checkpoint inhibitors show promising results in many types of cancer, the therapy can result in primary or required resistance together with severe side effects (Ribas and Wolchok 2018; de Mingo Pulido et al. 2018; LaFleur et al. 2018). Depleting or inhibiting Tregs presents as a suitable method for cancer treatment; however, this strategy has shown poor efficacy together with adverse effects. Additionally, while vaccination for both cancer prevention and treatment is beneficial in preclinical studies, side effects have still remained as the major obstacle in clinical trials (Liu and Guo 2018). In the context of this review, we focus on the recent advances in the treatment of non-small cell lung cancer by analyzing different aspects of a range of ongoing and completed clinical trials using adoptive cell therapy, including cytokine-induced killer cell (CIK)-based therapy and CAR-T cell therapy.

2

Adoptive Cell Therapy

As originally defined by the National Cancer Institute, adoptive cell therapy (ACT) is a therapy in which reactive T cells are isolated from patients and expanded ex vivo then transferred back to the patient to improve host immunity for fighting diseases. These cells recognize and eradicate tumor-associated antigens (TAAs) by their immune functions. TILs were first described by Robert Virchow in 1863; since then, TILs have been successfully cultured ex vivo and have undergone the first clinical trial by Rosenberg and colleagues (Rosenberg et al. 1994; Foppen et al. 2015). TIL-based adoptive therapy has demonstrated promising antitumor activities in

Clinical Trials with Cytokine-Induced Killer Cells and CAR-T Cell. . .

several clinical trials for patients with metastatic melanoma (Dudley et al. 2005; Rosenberg et al. 2011). However, there are various challenges for ACT especially for solid tumor treatment; these include inefficient isolation of TILs from the tumor and difficulty in T cell expansion, and the T cells are short-lived (Hawkins et al. 2010; Rosenberg and Restifo 2015). Following some success with TILs, the first generation of engineered T cells was initiated based on the idea of a specific target with TAAs. The approach of genetically engineered TCRs involves introducing antigen-specific TCR genes into isolated lymphocytes; this approach allows T cells to grow sufficiently ex vivo (Schumacher 2002). Engineered TCRs hold the ability to target specifically TAAs and have shown remarkable antitumor effects (Johnson et al. 2009; Park et al. 2011). Despite the advances in specific targeting of tumors, these strategies are hindered by the tumors’ evasion mechanisms due to major histocompatibility complex class I (MHC-I)-TCR specific interactions. MHC-I is an antigenpresenting molecule that is required for TCR recognition. Human leukocyte antigen (HLA) was observed to be downregulated in many cancers, thereby resulting in lower T cell antitumor activities (Sadelain et al. 2003; Garrido et al. 2017). These problems have led scientists to develop HLA-independent cell therapy approaches. Of these, there are two distinct approaches: CAR T cell therapy and CIK cell therapy. This review will highlight the achievements of these two approaches in clinical trials for NSCLC.

3

Cytokine-Induced Killer (CIK) Cells

CIK cells can be used as a type of adoptive cell therapy and are, in fact, being increasingly researched for the treatment of cancer. CIK cells are a heterogeneous population that were first discovered in 1991. They are generated ex vivo from human peripheral blood lymphocytes by the supplement of anti-CD3, IFN-γ, and IL-2 into the culture medium (Schmidt-Wolf et al. 1991). The

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mechanism for the cytotoxicity of CIK cells depends on the secretion of perforin/granzyme and the interaction of Fas ligands. Among the different population of CIK cells, CD3+CD56+ cells are considered the major antitumor fraction. The possible explanation for the cytotoxic effect of the CD3+CD56+ population is that it consists of a higher population of CD8+ cells and more differentiated effector cells, as well as a higher content of granzymes (Linn et al. 2009). Another advantage of CIK cells compared to other immunotherapies is their non-MHC-restricted cytotoxicity which enables the off-the-shelf production of CIK cells from both autologous and allogeneic sources (Schmidt et al. 1986; Lanier et al. 1986). Recently, numerous studies have investigated and reported on the anticancer effects of CAR-modified CIK cells and the combination of immune checkpoint inhibitors and CIK cells (Schlimper et al. 2012; Dai et al. 2016). After about a decade of preclinical research, the first CIK clinical trial was conducted in 1999 with IL-2 transfected CIK cells in patients with metastatic renal cancer, colorectal cancer, and lymphoma (Schmidt-Wolf et al. 1999). Since then, numerous basic and clinical studies have been investigated to further validate the clinical efficacy of CIK cell therapy in several types of cancers, including non-small cell lung cancer.

4

Chimeric Antigen Receptor (CAR) T Cell Therapy

Gene modified antigen receptor has gained remarkable interest for antigen-specific binding with the concepts of TCRs or CARs as new strategies against cancers. These approaches are aimed at enhancing antitumor effects via the incorporation of specific antigen receptors. Peripheral blood lymphocytes are transduced with tumor-reactive TCRs (TCR based gene therapy) or synthetic antibody-based receptors, i.e., CARs (CAR-T cell therapy) (Li et al. 2019). TCRs were first discovered by James Allison in 1982; the first genetically engineered T cells were generated by the transduction of modified alpha- and beta-chain heterodimers, which are

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two subunits of TCR, in the T cells of healthy donors. These modifications redirected T cell recognition toward tumor antigens and expressed MHC-dependent immunological actions (Rohaan et al. 2019). Development in genetic engineering advances, such as sleeping beauty or CRISPR/ Cas9, has brought increased attention from scientists to engineered T cell therapy with longer life expansion and higher gene stability (Gao et al. 2019). Different targets for TCR gene therapy have been evaluated; for instance, the feasibility of TCR gene therapy has been demonstrated and various clinically potent results have been observed in melanoma patients (Zhao and Cao 2019). However, TCR therapy faces limitations and obstacles, namely, the downregulation of MHC molecules in tumor cells as a tumor escape mechanism, thereby resulting in lower T cell antitumor activities (Jorritsma et al. 2011). Unlike TCRs, CARs directly target antigens presented on the surface of tumor cells by antigen-binding extracellular domain based on single-chain variable fragment (scFv) structure of a monoclonal antibody (D’Aloia et al. 2018). This structure allows CARs to target various types of antigens, including lipids, carbohydrates, or proteins, compared to TCRs, which are restricted to proteins (Zeltsman et al. 2017). Moreover, the CAR structure is combined of a transmembrane domain and an intracellular domain, which is a fusion of a TCR-CD3ζ intracellular signaling domain (for the production of IL-2 and lysis of target cells) with/without a co-signaling domain CD28 or 4-1BB. Since the discovery of CARs in the late 1980s, CAR-T cell therapy has had a remarkable development with four generations. Several clinical trials have been launched and positive clinical results have been documented in patients with hematologic malignancies. From CARs which were first constructed with a scFv linked to CD3ζ T cell, to the latest CAR T cell developments named “TRUCK” or “Armored” CARs, there has been continual advancement in CAR-T cell design to improve the persistence of engineered T cells in the challenging tumor microenvironment (Chmielewski and Abken 2015).

L. V. M. Hung et al.

Great successes have led two CAR-T cell therapies to become commercial products under the name YESCARTA (axicabtagene ciloleucel) with CD28 as co-stimulator and KYMRIAH (tisagenlecleucel) with 4-1BB as co-stimulator for acute lymphocytic leukemia (ALL) and non-Hodgkin lymphoma (NHL) patients. However, CAR-T cell therapy has just been explored for solid cancer treatment in recent years. Unlike hematological cancers which express unique types of antigens, solid tumors overall or non-small lung cancer cells, specifically, show a wide range of antigens. Moreover, these antigens are not only expressed on the tumor cell surface but also on the normal cell surface at a low level. To date, several candidates for targeting have been developed and launched into clinical trials phase 1 and 2 for solid tumor treatment, including MUC1 (Wei et al. 2017; Xu et al. 2015), ROR1 (Zheng et al. 2016; Specht et al. 2018), EGFR (Feng et al. 2016; Greenhalgh et al. 2016; Li et al. 2018), HER2 (Ahmed et al. 2015; Ricciardi et al. 2014), PSCA (Wei et al. 2017), FAP (Lo et al. 2015), CEA (Thistlethwaite et al. 2017), Mesothelin (Kachala et al. 2014; Morello et al. 2016), GPC3 (Li et al. 2016), PD-L1 (Reck et al. 2016), CTLA-4/PD-1 (Liu et al. 2016), and CD80/CD86 (Khalil et al. 2016). Furthermore, compared to CIK cells, only a few clinical trials utilizing CAR-T cells in NSCLC have been completed (Kiesgen et al. 2018). Unlike TCR-T cells, CAR-T cells can recognize antigens in a non-MHC-dependent manner and exert their anticancer effects through various mechanisms, including Fas and Fas ligand axis, perforin and granzyme axis, and the release of cytokines to sensitize the tumor stroma (Hong et al. 2018; Kagoya et al. 2018; Textor et al. 2014).

5

Clinical Trials

As can be seen from Table 1, several completed clinical trials have been reported on the use of CIK cells, together with dendritic cell (DC) and chemotherapy, for the treatment of different NSCLC stages over the past 10 years. In 2008, Wu, C et al. reported results from one of the first

Clinical Trials with Cytokine-Induced Killer Cells and CAR-T Cell. . .

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Table 1 Completed clinical trials of CIK and CART cells on NSCLC with articles Treatment

Clinical outcome

Patients Number of patients (treated)/ (total)

Disease stage

CIK DC/CIK + radiochemotherapy

30 (63)

IIIB

DC/CIK + chemotherapy

60

IIIB-IV

CIK/NK + chemotherapy

60 (120)

I, II, IIIA, IIIB, IV

DC/CIK + chemotherapy

28 (66)

IB

CIK + chemotherapy

87 (174)

I-IV

DC/CIK

95 (135)

III-IV

Adverse effect (AE)

References

Group with DC-CIK, 5 patients develop fever 1-2 h after reinfusion, spontaneous defervescence No significant AEs.

Zhu et al. (2015)

Common AEs: Nausea, rash, acne, pruritus, non-infective fever Lower AEs observed in patients with more immunotherapy cycles 9 patients developed chill and fever after infusion – relieve within 24 h No other toxic effect recorded

Zhong et al. (2014)

Temporary fever, chill – relieve in 12 h after treatment. No significant different between 2 groups

Li et al. (2015)

–

Li et al. (2012)

DC-CIK-CT common AEs in chemotherapy DC-CIK infusion mild fever, insomnia, skin rash – resolved after 24 h

Zhao et al. (2019)

MST, PFS, DFS, OS, ORR, CR, PR, SD, PD, DCR 6-, 12-month OS: Treated: 93.3, 83.3% Control: 90.9, 60.6% MST: 13.8 months 1, 2, 3-year OS: Treated: 60.0, 21.7, 15.0% MST: 33 months 1-, 3-, 5-year OS: Treated: 96.4, 88.1, 67.8% Control: 91.2, 65.9, 52.2% 1-, 2-year DFS: Treated: 100, 96.4% Control: 81.6, 76.3% Median PFS: Treated: 24 months Control: 12 months 3-year OS: Treated: 61% Control: 39% 1-year OS: DC-CIK-CT: 71.8% DC-CIK: 48.2% CT: 52.5% Mean OS: 17.5 months

Pan et al. (2015)

(continued)

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Table 1 (continued) Treatment

Clinical outcome

Patients Number of patients (treated)/ (total) 15 (30)

Disease stage III-IV

DC/ CIK + chemoradiotherapy

99 (507)

IIIB-IV

DC/CIK + chemotherapy

42 (84)

I-IIIA

2-year OS: Treated: 94.7% Control: 78.8% PFS: Treated: 78.6% Control: 64.3%

DC/CIK + chemotherapy

14 (28)

IIIB-IV

DC/CIK + chemotherapy

30 (60)

IIIB, IV

DC/CIK + chemotherapy

79 (157)

III

Time to progression: Treated: 6.9 months Control: 5.2 months 1, 2, 5-year OS: 64.3, 49.0, 21.0% Median PFS: Treated: 3.2 months Control: 2.6 months MST: Treated: 28 months Control: 22 months

CIK + chemotherapy

MST, PFS, DFS, OS, ORR, CR, PR, SD, PD, DCR Median PFS: Treated: 9.98 months Control: 5.44 months Median OS: Treated: 24.17 months Control: 20.19 months 2-year OS: Treated: 79.8% Control: 69.4%

Adverse effect (AE)

References

5 patients experienced transient fever – relieved in 24 h without treatment No severe adverse effect observed

Yu et al. (2017)

Fever, skin rash, insomnia, anorexia and joint soreness. AEs associated with radio/ chemotherapy were also monitored No severe adverse effects recorded Non-specific symptoms: Fever, headache after infusion, resolved spontaneously without any treatment No serious adverse effects recorded Rash, acne, pruritus, lower fatigue observed, infectious fever No serious adverse effects recorded

Zhang et al. (2016)

Chess distress: 1 case, acratia: 3 cases, pyrexia 4 cases No serious adverse effects recorded

Shi et al. (2012)

–

Zhao et al. (2014)

Li et al. (2009)

Zhong et al. (2011)

(continued)

Clinical Trials with Cytokine-Induced Killer Cells and CAR-T Cell. . .

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Table 1 (continued) Treatment

Clinical outcome

CIK + chemotherapy

Patients Number of patients (treated)/ (total) 38 (222)

DC/CIK + chemotherapy

26 (54)

IIIB, IV

DC/CIK + chemotherapy

61 (122)

III, IV

CIK + chemotherapy

42

III, IV

CIK

54

IIIB, IV

Disease stage IIIA, IIIB, IV

MST, PFS, DFS, OS, ORR, CR, PR, SD, PD, DCR MST: Treated: High MICA: 27 months Low MICA: 13 months Control: High MICA: 9 months Low MICA: 18 months Median PFS: Treated: 5.02 months Control: 3.98 months MST: Treated: 10.5 months Control: 9.9 months 1-, 2-year OS: Treated: 57.2, 27.0% Control: 37.3, 10.1% Median PFS: Cycles > ¼ 4: 39 months Cycles ¼ 3: 82.4% Cycles 100 μM (Cao et al. 2012). In this study, Pinostrobin showed its antiproliferative effect on HepG2 cells with IC50 of 422
43 nM. Compared to the previous study, Pinostrobin in this study showed strong cytotoxicity. However, Pinostrobin showed a less cytotoxic effect in 3D culture conditions compared to the other constituents – with IC50 of 3967
260 nM. Pinocembrin showed less cytotoxicity on HepG2 than did Pinostrobin in 2D conditions, but Pinocembrin demonstrated stronger cytotoxicity than Pinostrobin in 3D conditions. A previous study demonstrated that Pinocembrin induced Bax-dependent mitochondrial apoptosis in colon cancer cells (Kumar et al. 2007). Moreover, Pinocembrin demonstrated no toxicity in male rat and did not affect the phase I and II xenobiotic-metabolizing enzymes in rat liver (Punvittayagul et al. 2011). These results showed that Pinocembrin is a valuable compound to consider for further cancer treatment research. Alpinetin is one of the strongest cytotoxic compounds when tested in 2D culture conditions. In previous studies, Alpinetin induced inhibition of proliferation by arresting cells in the G2 phase and reducing migration of cells through regulation of matrix metalloproteinases MMP2 and MMP9 (Zhao et al. 2018). Alpinetin demonstrated it could sensitize drug-resistant

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lung cancer cells (Wu et al. 2015). The induction of apoptosis by Alpinetin has been demonstrated in gastric cancer and in pancreatic cancer. The antitumor activity of Alpinetin has been established by studies in 2D conditions. However, in the 3D culture conditions of HepG2 cells, Alpinetin did not affect cell viability of HepG2 cells. This shows that there is a big difference between 2D and 3D culture conditions. The compound in our study which showed the strongest cytotoxic effects on HepG2 in both 2D and 3D culture conditions was Isopanduratin A. Thus, we selected Isopanduratin A for further evaluation. In previously published studies, Panduratin A was shown to inhibit cell lines at IC50 of 10.8 μM (Cheah et al. 2011, 2013) and inhibit NF-kappa B translocation. Panduratin A targets mTOR and AMPK, leading to inhibition of autophagy and causing the cytotoxic effects on melanoma cells (Lai et al. 2018). Panduratin A was shown to induce G0/G1 phase cell cycle arrest and induce apoptosis in breast cancer cells (Liu et al. 2018), human colon cancer cells HT-29 (Yun et al. 2005), and prostate cancer cells PC3 (Yun et al. 2006). In the study herein, we demonstrated that Panduratin A possesses the ability to activate caspase 3/7, central mediators of the apoptosis process, after 2 h of incubation. Thus far, there is limited information on Panduratin A in cancer cells in 3D conditions. In this study, Panduratin A inhibited HepG2 spheres at IC50 of 744
18 nM. This 3D IC50 is almost double that of 2D IC50 (357
10 nM) but still showed significant cytotoxicity when compared to the IC50 of Pinocembrin at 2492
64 nM or the IC50 of Pinostrobin at 3967
260 nM. Especially, Alpinetin showed no cytotoxicity on HepG2 in 3D culture condition. These findings were in agreement with the PI staining assay; Panduratin A killed more HepG2 cells in spheres when compared with other compounds. Furthermore, Panduratin A (at the dose of 2500 nM) induced the breakdown of HepG2 spheres, while the other compounds did not change HepG2 sphere shape. These results may suggest that Panduratin A can regulate adhesion molecules, such as E-cadherin or Integrin, in HepG2 cells.

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Sinh Truong Nguyen et al.

Conclusion

This study has shown that Isopanduratin A isolated from Boesenbergia pandurata has a strong antitumor effect on HepG2 cells grown in both 2D and 3D culture conditions. Isopanduratin A is the strongest cytotoxic compound when compared to Pinocembrin, Pinostrobin, and Alpinetin, as evaluated on HepG2 cells. Isopanduratin A induced activation of caspase 3/7, an important indicator of apoptosis, in 2D conditions, and induced 3D HepG2 spheres to break down in shape. Overall, these results implicate Isopanduratin A as a strongly potential therapeutic agent that warrants further investigation for the treatment of hepatocellular carcinoma. Acknowledgments This work was supported by the Vietnam National University, Ho Chi Minh City, Vietnam, under grant A2015-18-01. Conflict of Interest The authors declare no conflict of interest. Author’s Contributions Hai Xuan Nguyen, Mai Thi Thanh Nguyen, and Nhan Trung Nguyen isolated the compounds. Sinh Truong Nguyen, Nghia Minh Do, Duyen Ho-Khanh Tran, Ngoc Bao To, and Phuc Hong Vo performed the experiments. Sinh Truong Nguyen analyzed the results and wrote the manuscript. Kiet Dinh Truong and Phuc Van Pham suggested the idea of the study and generally supervised the study. All authors approved the final version of manuscript.

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Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 145–155 https://doi.org/10.1007/5584_2020_524 # Springer Nature Switzerland AG 2020 Published online: 20 May 2020

Hopea odorata Extract Can Efficiently Kill Breast Cancer Cells and Cancer Stem-Like Cells in Three-Dimensional Culture More Than in Monolayer Cell Culture Nhan Lu-Chinh Phan, Khuong Duy Pham, Phong Le Minh, Mai Thi-Thanh Nguyen, Ngoc Phan Kim, Kiet Dinh Truong, and Phuc Van Pham Abstract

Introduction The breast cancer cells with CD44+CD24 phenotype are known to play an important role in tumorigenesis, drug resistance, and cancer recurrence. Breast cancer

cells with CD44+CD24 phenotype are cultured in three-dimensional (3D) stereotype showing the recapitulation of tumors in vivo such as cell differentiation, heterogeneity, and microenvironment. Using this 3D model in anti-cancer compound research results in a

N. L.-C. Phan Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected] K. D. Pham, P. Le Minh, and N. P. Kim Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected]; [email protected]; [email protected] M. T.-T. Nguyen Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Faculty of Chemistry, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected]

K. D. Truong Medical Genetic Institute, Ho Chi Minh City, Vietnam e-mail: [email protected] P. Van Pham (*) Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Laboratory of Cancer Research, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected]; [email protected]; [email protected] 145

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more accurate reflection than conventional monolayer cell culture. This study aimed to identify the antitumor activity of Hopea odorata methanol extract (HO-MeOH-E) on breast cancer cells and cancer stem-like cells in both models of three-dimensional culture (3D) and monolayer cell culture (2D). Methods HO-MeOH-E was produced from Hopea odorata plant. The VN9 breast cancer cells (VN9) were collected and expanded from the previous study. The breast cancer stem-like cells (VN9CSC) were sorted from the VN9 based on phenotype CD44+CD24 . Both VN9 and VN9CSC were used to culture in monolayer culture (2D) and organoids (3D) before they were used to treat with HO-MeOH-E. Two other anticancer drugs, doxorubicin and tirapazamine, were used as references. The antitumor activities of extracts and drugs were determined via two assays: antiproliferation using the Alamar blue assay and cell cycle assay. Results The results showed that HO-MeOHE was sensitive to both VN9 and VN9CSC in 3D more than 2D culture (IC50 on 3D organoids 144.8  2.172 μg/mL and on 2D 340.2  17.01 μg/mL for VN9CSC ( p < 0.001); IC50 on 3D organoids 2055  82.2 μg/mL and on 2D 430.6  8.612 μg/mL for VN9 ( p < 0.0001), respectively). HO-MeOH-E inhibits VN9CSC proliferation by blocking S phase and increasing the populations of apoptotic cells; this is consensus to the effect of tirapazamine (TPZ) which is used in hypoxia-activated chemotherapy. Conclusion Taken these results, HO-MeOHE has the potential effect in hypoxia-activated chemotherapy specifically on breast cancer stem-like cells with CD44+CD24 phenotype. Keywords

3D cultured · Breast cancer · Breast cancer stem cell · Hopea odorata · Hypoxia · Organoids

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Abbreviations HOMeOH-E MACS TPZ VN9 VN9CSC

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Hopea odorata methanol extract Magnetic-activated cell sorting Tirapazamine Vietnamese breast cancer cell line #9 Vietnamese breast cancer stem cell line #9

Introduction

The breast cancer stem cell population is considered one of the most important cells in targeted treatment for breast cancer (Dandawate et al. 2016; Luo et al. 2015; Li et al. 2017). One of the typical characteristics of this population can significantly be attributed to CD44-positive and CD24-negative co-expression (Rabinovich et al. 2018; Quayle et al. 2018). Numerous studies have shown that this cell population is aggressive and capable of causing tumors on mice with low amount (Shao et al. 2016; Al-Hajj et al. 2003). An important feature of this population is its high resistance to antitumor drugs (Yenigun et al. 2013; Cufi et al. 2012) and radiation (Yin and Glass 2011). Previous studies show that altered characteristics of this population, such as the CD44 knockout, have caused a differentiation of cell populations, which have led to a loss of their stemness and reduced tumorigenicity on mice (Van Phuc et al. 2011). The use of breast cancer stem cell to research antitumor agents as well as screening for new natural substances for drug development is important. Many recent studies have found new compound that have strong impacts on the breast cancer stem cell population (Cruz-Lozano et al. 2018; Choi et al. 2018; Rabacow et al. 2018). Hopea odorata belongs to the Dipterocarpaceae family, locally known as Sao den (Viet Nam). It is widely distributed in lowland forests in Vietnam and native to Southeast Asia (Nguyen et al. 2014). Different parts of this

Hopea odorata Extract Can Efficiently Kill Breast Cancer Cells and. . .

plant have been used for folk medicine, like resin for wound healing and hemostasis (Chuakul 2005), bark and leaves in treating paralysis, hemorrhoids, diarrhea, gum inflammation, and urinary incontinence (Prasad et al. 2008). Moreover, the methanol extract of Hopea odorata also powerfully suppresses the gene expression of pro-inflammatory cytokines and chemokines, such as interferon (IFN)-b, interleukin (IL)-12, and monocyte chemoattractant protein-1 (MCP-1) (Yang et al. 2013). Some recent study showed that Hopea odorata has implications for the prevention of cancer and cardiovascular disease (Zurinah 2012; Nguyen et al. 2017). Our study attempted to investigate the effect of Hopea odorata MeOH extraction from stem on breast cancer cells with CD44+/CD24 phenotype specifically on 3D culture of this cell line. The mimicking of tumor tissue in the body is emphasized as three-dimensional culture models demonstrate the ability to express similar characteristics such as differentiation, homogeneity, and distribution of different cell populations such as tolerance to hypoxia cell population (Imamura et al. 2015; Gangadhara et al. 2016; Polonio-Alcala et al. 2018; Li et al. 2018). The use of monolayer culture breast cancer cell models showed the disadvantage of screening for novel compounds, the inhibitory effects on monolayer culture models failed when tested on a three-dimensional culture model or on a pathogen animal model (Breslin and O’Driscoll 2013). This study attempts to isolate the CD44+CD24 cells from the breast cancer cell population in cell lines VN9, the Vietnamese breast cancer cell based on two of the markers CD44 and CD24. And this cell line is used to buid a three-dimensional cell culture model which is used for the determination of cancer cell proliferating inhibitory compounds either strongly toxicity compounds.

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Methods

2.1

Cell Lines

VN9 breast cancer cell line was obtained from Stem Cell Institute, VNU-HCM University of

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Science, and cultured in DMEM/F12, 10% fetal bovine serum (FBS), and 1% antibioticantimycotic (all bought from Sigma-Aldrich, Louis St., MO). VN9 is the primary cell line that was established from ascites collected from the breast carcinoma tumor of the Vietnamese women in the previous study (Pham et al. 2011).

2.2

Chemicals

Hopea odorata MeOH extraction from stem in this research was coded HO-MEOH-E and obtained from the Division of Medicinal Chemistry, Faculty of Chemistry, University of Science, Vietnam National University Ho Chi Minh City, Vietnam. Doxorubicin hydrochloride (#44583) and tirapazamine (#SML0552) were purchased from Sigma-Aldrich, Louis St., MO.

2.3

CD44+CD242 Cell Sorting

The VN9 cells were washed by phosphate buffered saline (PBS) and harvested by TrypLE (Thermo Fisher, Waltham, MA). The 107 single cells were resuspended in 80 μL sorting buffer (PBS supplemented with 0.5% fetal bovine serum (FBS) Sigma-Aldrich, Louis St., MO). 20 μL CD44 MicroBeads human (MACS Miltenyi Biotec, Germany) was added and incubated in 15 min 4  C. The CD44 MicroBeads stained cells were washed and resuspended in 500 μL sorting buffer. The cell suspension was added into the column with MACS separation (MACS Miltenyi Biotec, Germany). The CD44+ cells were collected by washing the column. This population was resuspended in 80 μL sorting buffer and 20 μL CD24 Biotin human (MACS Miltenyi Biotec, Germany). The cells were washed and resuspended in 80 μL sorting buffer and 20 μL antibiotin MicroBeads (MACS Miltenyi Biotech). The CD24-stained cells were washed and resuspended in 500 μL sorting buffer. The cell suspension was added into the column with MACS separation (MACS Miltenyi Biotec). The CD44+CD24 cells were harvested from the cell suspension passed through the column and were cultured in M171 medium (Thermo Fisher,

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Waltham, MA) with Mammary Epithelial Growth Supplement (Thermo Fisher, Waltham, MA) for maintenance of stemness.

2.4

Cancer Stem Cell Flow Cytometry

The single cells were harvested and incubated in 30 min, dark with 5 μL CD44-APC (Santa Cruz Biotechnology, Dallas, TX) and 5 μL CD24FITC (BD Pharmingen, San Diego, CA). The stained cells were washed and analyzed by FACSCalibur (BD Bioscience, San Diego, CA). The data were analyzed by CellQuest software (BD Biosciences, San Diego, CA).

2.5

Cell Proliferation Assay

Cell proliferation assay was performed by xCELLigence (Roche, Basel, Switzerland). The 96-well E-plate (Roche, Basel, Switzerland) was filled up with 50 μL fresh culture medium per well for eliminating the signal from the medium. Then 50 μL cell suspension was added at final concentration 2000 cells/well, and let them attach to the plate surface in 24 h. The cell indexes were monitored by an hour. The fresh medium was changed daily and repeated on six consecutive days. The proliferation curve and doubling time were inferred and displayed by xCELLigence RTCA software (Roche, Basel, Switzerland).

2.6

Cell Culture in 2D and 3D

Single cells were harvested and seeded in 96-well plate at final density of 1000 cells per well for growing in 5 days. The fresh medium was changed once in 2 days. VN9 cancer cells were cultured in DMEM/F12, 10% FBS, and 1% antibiotic-antimycotic (Sigma-Aldrich, Louis St., MO). VN9CSC were cultured in M171 medium (Thermo Fisher, Waltham, MA) with Mammary Epithelial Growth Supplement (Thermo Fisher, Waltham, MA). For 2D cell culture, the single cells were seeded with medium and plated on the T-flask T75 cm2 (SPL, Korea). For 3D cell culture, 1 volume of the single cell suspension was

mixed in 1 volume Matrigel (Sigma-Aldrich, Louis St., MO) on ice and placed on the edge of the well. The plate was incubated in 37  C for gel polymerization in 10 min, and 100 μL of the pre-warmed medium was added on top of the gel. The pre-warmed medium was requisite for manipulation on 3D culture to avoid melting the gel.

2.7

Cell Viability Assay

After 5 days culture, the cells and organoids were treated in 48 h with doxorubicin at 62.5 nM, 125 nM, 250 nM, 500 nM, and 2000 nM; tirapazamine at 15.625 μM, 31.25 μM, 62.5 μM, 125 μM, 250 μM, and 500 μM; and MCE18 at 31.25 μg, 62.5 μg, 125 μg, 250 μg, 500 μg, and 1000 μg. The Alamar blue (Sigma-Aldrich, Louis St., MO) was added to the well at a final concentration of 10 μg/mL and incubated in dark for 1 h. The fluorescence intensity was read using DTX880 (Beckman Coulter, Brea, CA) with excitation wavelength 535 nm and emission wavelength 595 nm and integration time 500 μs. The data was normalized to control value and IC50 values were calculated with Prism Software 7 (GraphPad Software, San Diego, CA).

2.8

Cell Cycle Assay

The single cells were harvested and fixed using ethanol 70% in 30 min. The fixed cells were washed by PBS and suspended in 100 μL PBS with 5 μL propidium iodide solution (SigmaAldrich, Louis St., MO) in dark 30 min at room temperature. The stained cells were analyzed by FACSCalibur flow cytometer (BD Bioscience). The data was analyzed by CellQuest software (BD Biosciences).

2.9

Statistical Analysis

All experiments were performed in triplication and repeated at least twice. Statistical significance was set at P < 0.05. Data were analyzed by

Hopea odorata Extract Can Efficiently Kill Breast Cancer Cells and. . .

GraphPad Prism Software 7 (GraphPad Software, San Diego, CA).

3

Results

3.1

VN9CSC Population Was Purified and Proliferated Slower Compared to the Original VN9 Population

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CD24+ cell to gain the high rate of CD44+CD24 phenotype cell which is one of the criteria for breast cancer stem cell. The VN9CSC population has 93% purified (Fig. 1) and is used for following experiments. The proliferation curve is performed in 6 days in which the cell index is monitored by hour (Fig. 2a). Result showed that the VN9CSC growth is slower than VN9 with 39.4  1.28 h and 22.4  0.31 h in doubling time, respectively (p < 0.001) (Fig. 2b).

We used the MACS technique to enrich the CD44+ cell population first and then eliminate

Fig. 1 VN9 breast cancer cell with CD44+CD24 phenotype sorting (VN9CSC). (a) The unstained cell population. (b) The original VN9 cell line. (c) The VN9CSC after sorting by MACS method

Fig. 2 VN9 and VN9CSC proliferation. (a) The proliferation curves were monitored by xCELLigence by an hour in 6 days. (b) The doubling time of VN9 and VN9CSC results in 22.4  0.31 h and 39.4  1.28 h, respectively ( p < 0.001)

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The 3D Cell Culture Resulting in Organoids Which Are More Resistant to Doxorubicin Than Monolayer Cell Culture

The VN9CSC organoids are formed after 5 days cultured in Matrigel from single cells (Fig. 3). The 3D organoids and the 2D momolayer are treated in 48 h with doxorubicin. The result showed that mean of viability of organoids is higher than monolayer on VN9CSC. The IC50 of VN9CSC on organoid is 1711  25.67 nM, but it is 98.52  1.97 on monolayer cell culture (p < 0.0001) (Fig. 4a). It is interesting that the VN9CSC is more sensitive to doxorubicin than the original cell line VN9 in both types of cultures. Data showed that two cell lines VN9 and VN9CSC are consistent in doxorubicin treatment; the IC50 of the organoids is higher than monolayer culture (Fig. 5b).

3.3

The VN9 Is More Resistant to TPZ Than VN9CSC

The TPZ is targeting into hypoxia area of organoids and makes them more sensitive. Indeed when the organoids are established, the hypoxia cell population is located inside of the cell mass. We treat the organoids and the monolayer with TPZ in 48 h. The result shows that in 3D culture as organoids, the TPZ IC50 is significantly lower

than the monolayer. The mean of cell viability is below 50% of treatment on 3D culture (Fig. 4b). In comparison of IC50 on 3D culture, VN9CSC is significantly lower than VN9 with 128  6.4 μM of VN9 and 105  1.265 μM of VN9CSC ( p < 0.0001) (Fig. 5c).

3.4

Hopea odorata MeOH Extraction (HO-MEOH-E) Has Potential Effect on VN9CSC Organoid

The HO-MEOH-E has a strong effect on breast cancer cells with CD44+CD24 phenotype; this populations have low IC50 values in comparison with the normal one (Fig. 5a). The 3D culture which creating the organoids is sensitive with HO-MEOH-E than the monolayer culture in all of cell lines. This leads to a hypothesis that HOMEOH-E has the same effects on 3D culture like TPZ.

3.5

The Cell Cycle of VN9CSC 3D Culture Population Is Changed and Induces a Presence of an Apoptotic Population when Treating the Cell with HO-MEOH-E

We used the IC50 of HO-MEOH-E treatment on organoids of VN9CSC to investigate the effects

Fig. 3 The organoid formation. (a) The single cells were seeded in Matrigel at day 0. (b) The organoids were formed after 5 days culture resulting in approximately 150 μm diameter

Hopea odorata Extract Can Efficiently Kill Breast Cancer Cells and. . .

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Fig. 4 VN9CSC cell viability in 48 h drug cytotoxic assay. (a) In doxorubicin treatment, the mean of viability from 2D is lower than 3D. (b) In tirapazamine treatment, the mean of viability from 2D is higher than 3D. (c) In HO-

MEOH-E treatment, the mean of viability from 2D is higher than 3D. For all graphs, symbols are individual replicates, bars represent the average, and error bars show SD ( p < 0.0001)

of this extract. The cell cycle shows that on 3D cell culture, the cell population is arrested at S phase. The percent of S phase under HO-MEOHE treatment increased from 9.21% (untreated) to 16%. There is a presence of apoptotic population (Fig. 6c) which is 31% in total of population of 144 μg/mL HO-MEOH-E treatment and 14.3% in total of 105 μM TPZ treatment. These changes in cell cycle under treatment of HO-MEOH-E and TPZ revealed more detail of these extract effects.

One of the cancer cell-proliferating inhibitory compounds is doxorubicin. This is the inhibitor of the activity of topoisomerase and makes the DNA not duplicate, thereby inhibiting the division and proliferation of cancer cells. Both VN9 and VN9CSC cells showed that doxorubicin was less effective on 3D models. This is consistent with many previous studies in the investigation of the antitumor activity of doxorubicin on 3D models (Imamura et al. 2015; Yildiz-Ozturk et al. 2017; Thoma et al. 2014). We raise a hypothesis that the formation of 3D organoid model could make doxorubicin difficultly permealize into the multiple layers of cell which results in resistance of doxorubicine. The results of our study demonstrated this model of 3D culture has the same characteristics as other studies. When forming three-dimensional mass such as organoids, the distribution of cancer cell populations in organoid has been well documented (Hubert et al. 2016). In the organoid as well as tumor, there is heterogeneity of cell populations (Hubert et al. 2016; Wicha 2008).

4

Discussion

Breast cancer cells with CD44+CD24 phenotype isolation resulting by the MACS method showed that this population (VN9CSC) was proliferating slower than the VN9 cell line. Some studies have also shown similar results when isolating the stem cell population with CD44+CD24 phenotype from the original cancer cell population (Richichi et al. 2013; Chen et al. 2016; Hu and Fu 2012).

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Fig. 5 Summary of the half maximal inhibitory concentration (IC50) in all cell line under treatment of all testing drugs. (a) IC50 of HO-MEOH-E treatment. (b) IC50 of

doxorubicin treatment. (c) IC50 of tirapazamine treatment. Bars represent the average and error bars show SD ( p < 0.0001)

There is a presence of the cell population using very little oxygen in the interior of the 3D cell mass which is called hypoxia (Baker et al. 2016). One of the drugs that is capable of reflecting the presence of this cell population is tirapazamine (TPZ), which acts on cells that use less oxygen (Lin et al. 2016). If the cancer cells were cultured

in three-dimensional form, they would be affected by TPZ, and culture in 2D would be the opposite. Our study uses TPZ as a reagent to initially test if there is presence of this cell population on cancer cell and cancer stem cell lines when cultured in 3D. Results showed that both cell lines VN9 and VN9CSC were more sensitive to TPZ in 3D

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Fig. 6 Morphology of organoids undergoing treatment of HO-MEOH-E and TPZ and cell cycle analysis. (a) Bright field of organoids before and after treatment with HOMEOH-E at 144 μg/mL and TPZ 105 μM in 48 h. (b)

Cell cycle flow cytometry shows the presence of apoptotic population. (c) The analysis of cell cycle. For all graphs, symbols are individual replicates, bars represent the average, and error bars show SD ( p < 0.0001)

culture. This suggests that the 3D organoids exist in a hypoxia cell population. In our study, the effect of HO-MEOH-E on VN9CSC in a three-dimensional culture altered

the cell cycle which inhibits cell proliferation by blocking S phase and increasing the populations of apoptotic cells. Previous HO-MEOH-E showed no effect on human fibroblast cells

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(Thoma et al. 2014), suggesting that HO-MEOHE is safe on cancer treatment. When comparing 2D monolayer and 3D organoid culture models, HO-MEOH-E has a stronger impact on the 3D organoid culture models. This sensitivity is similar to the effect of hypoxia-activated prodrug TPZ on the three-dimensional tumor cell model as previously reported on 3D HepG2 tumor spheroids (Hubert et al. 2016), on cancer cell lines MCF-7 (Wicha 2008), in both 3D tumor spheroids in vitro and breast tumors in vivo (Baker et al. 2016). One of the methods of killing cancer cells is hypoxia-activated chemotherapy. This research initially used HO-MEOH-E that showed an effect similar to the 3D organoid culture of TPZ on breast cancer cell enriched with CD44+CD24 phenotype.

5

Conclusion

This study uses the Vietnamese breast cancer cell with CD44+CD24 phenotype for 3D culture resulting in organoids. This model is suitable for testing breast cancer antigens. The HO-MeOH-E showed strong effects on the 3D culture model, whereas the 2D culture model was the opposite. HO-MEOH-E may be a candidate for inhibiting of Vietnamese breast cancer cell enriched with CD44+CD24 phenotype especially in hypoxiaactivated chemotherapy. Acknowledgments This work was supported by the Vietnam National University, Ho Chi Minh City, Vietnam, under grant A2015-18-01. Conflict of Interest The authors declare that there is no conflict of interests regarding the publication of this article. Author Contributions NL-CP designed the project, carried out the experiments, analyzed the data, and wrote the paper draft. KDP and PLM contributed to feasibility experiments. MTTN prepared and provided the extract. PVP, KDT, and NPK suggested the idea, analyzed the results, corrected the scientific matters and English wording, and review all paper. All authors approved the final version of manuscript for submission.

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Adv Exp Med Biol – Innovations in Cancer Research and Regenerative Medicine (2020) 3: 157–169 https://doi.org/10.1007/5584_2020_525 # Springer Nature Switzerland AG 2020 Published online: 6 June 2020

Selective Cytotoxicity of Some Plant Extracts Against Hepatocellular Carcinoma Cells but Not Mesenchymal Stem Cells: A Pilot Screening Sinh Truong Nguyen, Nghia Minh Do, Phuc Hong Vo, Mai Thi Thanh Nguyen, Nhan Trung Nguyen, Hai Xuan Nguyen, Kiet Dinh Truong, and Phuc Van Pham Abstract

Introduction Medicinal plants have been used for disease treatment throughout history, especially in Asia. Vietnam is a tropical country which possesses forests with a diversity of plants; among the plants, many have been historically used as alternative therapies for various disease treatments. In this study we aimed to evaluate the selective cytotoxicity of some plant extracts (collected from Vietnamese forests) against hepatocellular carcinoma cells HepG2, compared to adipose tissuederived mesenchymal stem cells (ADSCs).

Methods In this study, we collected nine plants and produced nine extracts from them; these included whole stem of Buchanania lucida, whole stem of Dipterocarpus turbinatus, Hopea recopei, whole stem of Shorea thorelii, bark of Shorea thorelii stem, bark of Dipterocarpus turbinatus stem, whole stem of Dipterocarpus costatus, bark of Dipterocarpus costatus stem, and rhizome of Boesenbergia pandurata. The cytotoxicity of these extracts on hepatocellular carcinoma cells and mesenchymal stem cells were determined based on IC50 values calculated using Alamar Blue assay. Based on these IC50

Sinh Truong Nguyen, Nghia Minh Do, and Phuc Hong Vo Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam

Kiet Dinh Truong Medical Genetic Institute, Ho Chi Minh City, Vietnam e-mail: [email protected]

Cancer Research Laboratory, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam

Phuc Van Pham (*) Stem Cell Institute, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam

Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected] Mai Thi Thanh Nguyen, Nhan Trung Nguyen, and Hai Xuan Nguyen Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Chemistry Department, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected]

Cancer Research Laboratory, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam Viet Nam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam Laboratory of Stem Cell Research and Application, University of Science Ho Chi Minh City, Ho Chi Minh City, Vietnam e-mail: [email protected]; [email protected]; [email protected] 157

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values, the side effect index (SEI) of extracts was determined. Only the extracts with low SEI were used in further assays to determine the apoptotic status of both hepatocellular carcinoma cells and mesenchymal stem cells via caspase 3/7 induction assay, nuclei disintegration (using Hoechst 33342 staining), and Annexin V staining assay. Results The results showed that B. pandurata extract had strong cytotoxicity toward HepG2 cells with lowest side index on mesenchymal stem cells (IC50 on HepG2 of 222
27.82 (μg/ ml) but IC50 on ADSCs of 382
16.19 (μg/ ml)). Nuclear staining showed that B. pandurata extract could induce disintegration of cell nuclei at the concentration of 400 μg/ml. After 3 h of incubation with B. pandurata extract at the concentration of 200 μg/ml, the BP extract induced caspase 3/7 activation in HepG2 cells, but not in mesenchymal stem cells. Annexin V staining showed that the BP extract induced apoptosis in HepG2 cells in a dose-dependent manner. Conclusion This study revealed that selective cytotoxicity of some extracts on cancer cells could be determined based on their IC50 values on cancer cells and on mesenchymal stem cells. B. pandurata extract displayed the lowest side effect index on mesenchymal stem cells and successfully induced apoptosis in hepatocellular carcinoma cells HepG2 via activation of caspase 3/7. Keywords

Adipose tissue-derived stem cell · Apoptosis · Boesenbergia pandurata extract · HepG2 · Selective cytotoxicity

Abbreviations ADSC AML BP

Adipose tissue-derived stem cell Acute myeloid leukemia Boesenbergia pandurata

DNA HPLC HSPCs PBS SEI

1

Deoxyribonucleic acid High Performance Liquid Chromatography Hematopoietic stem and progenitor cells Phosphate-buffered saline Side index effect

Introduction

Medicinal treatment has been used during the long human history. Many drugs have been developed from traditional plants. In 1972, artemisinin (derived from Artemisia annua L.) solved the serious problem of malaria in Vietnam. Taxol was isolated from Taxus brevifolia as a new compound and was approved by the FDA in 1992 for ovarian cancer treatment (Holmes et al. 1991; Wani and Horwitz 2014). Bicyclo was isolated from Salvia chinensis plant and approved for the treatment of chronic viral hepatitis in China (Bao and Liu 2008; Li and Liu 2004; Sun et al. 2012). Traditionally, in Asia, people usually seek out botanical treatments initially as remedies for health issues. Vietnam has a vast tropical forest, and plant-based medicines have been used in primary health care for a thousand years. Many of these plant-derived remedies have been used to treat diseases as they have antiviral and antifungal properties. There are many plants which show promise as therapeutic agents due to their anticancer activity; however, there is so much to be investigated and unveiled still. This research study aimed to identify some plants that may have potential cytotoxicity on cancer cells. We selected plants with some activity in disease treatment and non-herbal plants, including whole stem of Buchanania lucida, whole stem of Dipterocarpus turbinatus, Hopea recopei, whole stem of Shorea thorelii, bark of Shorea thorelii stem, bark of Dipterocarpus turbinatus stem, whole stem of Dipterocarpus costatus, bark of Dipterocarpus costatus stem, and rhizome of Boesenbergia pandurata. These plants were collected from the forests of the South region of

Selective Cytotoxicity of Some Plant Extracts Against Hepatocellular. . .

Vietnam and Southeast Asia; virtually none had been screened for bioactivity before, and each grew wildly in the forest. Drug discovery and development are crucial to find treatments for diseases. However, drug development is a challenging process that is expensive, time-consuming, and troubled by failures. One-third of drugs fail the first clinical phase, and approximately half of drug candidates entering clinical trials fail at some point, owing to unforeseen toxicity in humans (Hay et al. 2014; Ledford 2011). All these failures had been predicted to be nontoxic in humans based on animal models prior to beginning clinical trials. Additionally, another quarter of drug candidates fail in clinical trials because the drugs prove ineffective in humans (Marchetti and Schellens 2007). Thus, it is necessary to screen the candidates for safety and effectiveness in the early stages of the drug discovery process with in vitro testing on human cells. In this study, we used adipose stem cells as a control against cancer cells, with the aim to screen potential extracts with minimal side effects on normal cells. This should theoretically cut down the ratio of failures in future tests of the candidate drugs during the drug discovery process.

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with methanol (MeOH; 15 L, reflux, 3 h  3) (Sigma-Aldrich, St Louis, MO) to a dry extract. The MeOH extract was suspended in H2O (1.5 L) and then partitioned successively with chloroform (CHCl3; 3  1.5 L; Sigma-Aldrich) and ethyl acetate (EtOAc; 3  1.5 L; Sigma-Aldrich) to yield CHCl3, EtOAc, and H2O extracts.

2.3

Cell Lines and Cell Culture

The human hepatocellular carcinoma cell line HepG2 was obtained from American Type Culture Collection (Manassas, VA). Cells were grown in Dulbecco’s Modified Eagle’s Medium supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin-streptomycin (P/S) in a humidified incubator with 5% CO2 at 37  C; all reagents were obtained from Thermo Fisher Scientific (Waltham, MA, USA).

2.4

Adipose-Derived Mesenchymal Stem Cells

Plants were collected in the South region of Vietnam at Ma Da and Tinh Bien forest (Table 1). After collection, the plants were stored under cool conditions and brought back to the laboratory. These species were identified by Ms. Hoang Viet, Faculty of Biology, University of Science, VNU-HCM. The vouchers of specimen were deposited at the Division of Medicinal Chemistry, Faculty of Chemistry, University of Science, VNU-HCM.

Adipose-derived mesenchymal stem cells (ADSCs) were isolated from adipose tissues, collected from the hospital, with consent from the donor. Adipose tissues were kept in cool temperature in PBS solution containing 1% antibiotic and transferred to the laboratory for subsequent processing. The stromal vascular fraction (SVF) was extracted from the adipose tissue by the Cell Extraction Kit (Regenmedlab, Ho Chi Minh City, VN). Then SVF was cultured to collect ADSCs as per a previously published protocol (Van Pham et al. 2014). ADSCs were confirmed to be mesenchymal stem cells (MSCs) by the expression of markers such as CD14, CD34, CD45, CD73, CD90, CD105, and HLA-DR, as well as their differentiation potential into adipocytes, osteoblasts, and chondrocytes in vitro. All assays were performed as per the published study (Van Pham et al. 2014).

2.2

2.5

2

Materials and Methods

2.1

Plants

Extraction Method

The dried part of the plants was ground into powder. The powder (5.5 kg of powder) was extracted

Cytotoxic Assay

In brief, 2500 cells of each kind of cell (HepG2 or ADSCs) were seeded into each well of a 96-well

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Table 1 Plants and solvents used for extracts 1 2 3 4 5 6 7 8 9

Name of plant Buchanania lucida Dipterocarpus turbinatus Hopea recopei Shorea thorelii Shorea thorelii Dipterocarpus turbinatus Dipterocarpus costatus Dipterocarpus costatus Boesenbergia pandurata

tissue culture plate. After 24 h of culture, cells were treated with plant extracts in the culture medium for 48 h at the concentration of 1000, 500, 250, 125, 62.5, 31, 15, or 0 μg/ml (control). Methanol was adjusted to 0.5% in all concentrations. Cell viability was evaluated by adding Alamar Blue to a final concentration of 10 μg/mL and incubated for 45 min. The fluorescent intensity was measured by a DTX 880 machine (Beckman Coulter Inc., Brea, California, USA). The IC50 of the plant extracts on cell lines was calculated by GraphPad Prism software (GraphPad Software, Inc., La Jolla, CA), with statistical significance set as p-value