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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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2
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Pancotti C, Rollo C, Codicè F, Birolo G, Fariselli P, Sanavia T. MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification. Bioinformatics 2024; 40:btae320. [PMID: 38754097 PMCID: PMC11139523 DOI: 10.1093/bioinformatics/btae320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/08/2024] [Accepted: 05/15/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients. RESULTS We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. AVAILABILITY AND IMPLEMENTATION MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.
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Affiliation(s)
- Corrado Pancotti
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
| | - Cesare Rollo
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
| | - Francesco Codicè
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
| | - Giovanni Birolo
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
| | - Piero Fariselli
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
| | - Tiziana Sanavia
- Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy
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3
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Otlu B, Alexandrov LB. Evaluating topography of mutational signatures with SigProfilerTopography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574683. [PMID: 38260507 PMCID: PMC10802511 DOI: 10.1101/2024.01.08.574683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.
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Affiliation(s)
- Burçak Otlu
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA, 92037, USA
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
| | - Ludmil B. Alexandrov
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA, 92037, USA
- Sanford Stem Cell Institute, University of California San Diego, La Jolla, CA 92037
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4
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:1958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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5
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Islam SA, Díaz-Gay M, Wu Y, Barnes M, Vangara R, Bergstrom EN, He Y, Vella M, Wang J, Teague JW, Clapham P, Moody S, Senkin S, Li YR, Riva L, Zhang T, Gruber AJ, Steele CD, Otlu B, Khandekar A, Abbasi A, Humphreys L, Syulyukina N, Brady SW, Alexandrov BS, Pillay N, Zhang J, Adams DJ, Martincorena I, Wedge DC, Landi MT, Brennan P, Stratton MR, Rozen SG, Alexandrov LB. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. CELL GENOMICS 2022; 2:None. [PMID: 36388765 PMCID: PMC9646490 DOI: 10.1016/j.xgen.2022.100179] [Citation(s) in RCA: 83] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 04/10/2022] [Accepted: 08/31/2022] [Indexed: 12/09/2022]
Abstract
Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.
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Affiliation(s)
- S.M. Ashiqul Islam
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Marcos Díaz-Gay
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Yang Wu
- Centre for Computational Biology and Programme in Cancer & Stem Cell Biology, Duke NUS Medical School, Singapore 169857, Singapore
| | - Mark Barnes
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Raviteja Vangara
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Erik N. Bergstrom
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Yudou He
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Mike Vella
- NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA
| | - Jingwei Wang
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Jon W. Teague
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Peter Clapham
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Sarah Moody
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Sergey Senkin
- Genetic Epidemiology Group, International Agency for Research on Cancer, Cedex 08, 69372 Lyon, France
| | - Yun Rose Li
- Departments of Radiation Oncology and Cancer Genetics, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Laura Riva
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Andreas J. Gruber
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
- Manchester Cancer Research Centre, The University of Manchester, Manchester M20 4GJ, UK
- Department of Biology, University of Konstanz, Universitaetsstrasse 10, D-78464 Konstanz, Germany
| | - Christopher D. Steele
- Research Department of Pathology, Cancer Institute, University College London, London WC1E 6BT, UK
| | - Burçak Otlu
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Azhar Khandekar
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Ammal Abbasi
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
| | - Laura Humphreys
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | | | - Samuel W. Brady
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Boian S. Alexandrov
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Nischalan Pillay
- Research Department of Pathology, Cancer Institute, University College London, London WC1E 6BT, UK
- Department of Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Trust, Stanmore, Middlesex HA7 4LP, UK
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - David J. Adams
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Iñigo Martincorena
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - David C. Wedge
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
- Manchester Cancer Research Centre, The University of Manchester, Manchester M20 4GJ, UK
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer, Cedex 08, 69372 Lyon, France
| | - Michael R. Stratton
- Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Steven G. Rozen
- Centre for Computational Biology and Programme in Cancer & Stem Cell Biology, Duke NUS Medical School, Singapore 169857, Singapore
| | - Ludmil B. Alexandrov
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA 92037, USA
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Islam MA, Versypt ANF. Mathematical Modeling of Impacts of Patient Differences on COVID-19 Lung Fibrosis Outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.11.06.515367. [PMID: 36380760 DOI: 10.1101/2020.12.13.422570] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating effects of these factors. Recent evidence suggests that patient-specific alteration of RAS homeostasis with premorbidity and the expression level of angiotensin converting enzyme 2 (ACE2), depending on age and sex, is correlated with lung fibrosis. However, conflicting evidence suggests decreases, increases, or no changes in RAS after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced fibrosis are still unknown. Here, a mathematical model is developed to quantify the systemic contribution of heterogeneous factors of RAS in the progression of lung fibrosis. Three submodels are connected-a RAS model, an agent-based COVID-19 in-host immune response model, and a fibrosis model-to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicate cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2, whereas there are no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. Reduction of ACE reduces the homeostasis of RAS including angiotensin II (ANGII), while the decrease in ACE2 increases ANGII and results in severe lung injury and fibrosis. The model explains possible mechanisms for conflicting evidence of RAS alterations in previously published studies. Also, the results show that ACE2 variations with age and sex significantly alter RAS peptides and lead to fibrosis with around 20% additional collagen deposition from systemic RAS with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.
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Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022; 23:6628783. [PMID: 35788277 PMCID: PMC9294421 DOI: 10.1093/bib/bbac246] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/06/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022] Open
Abstract
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rina Aoyama
- Showa University Graduate School of Medicine School of Medicine
| | | | - Ken Asada
- RIKEN Center for Advanced Intelligence Project
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8
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Li Z, Liang H, Zhang S, Luo W. A practical framework RNMF for exploring the association between mutational signatures and genes using gene cumulative contribution abundance. Cancer Med 2022; 11:4053-4069. [PMID: 35575002 PMCID: PMC9636515 DOI: 10.1002/cam4.4717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/04/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background Mutational signatures are somatic mutation patterns enriching operational mutational processes, which can provide abundant information about the mechanism of cancer. However, understanding of the pathogenic biological processes is still limited, such as the association between mutational signatures and genes. Methods We developed a simple and practical R package called RNMF (https://github.com/zhenzhang‐li/RNMF) for mutational signature analysis, including a key model of cumulative contribution abundance (CCA), which was designed to highlight the association between mutational signatures and genes and then applying it to a meta‐analysis of 1073 individuals with esophageal squamous cell carcinoma (ESCC). Results We revealed a number of known and previously undescribed SBS or ID signatures, and we found that APOBEC signatures (SBS2* and SBS13*) were closely associated with PIK3CA mutation, especially the E545k mutation. Furthermore, we found that age signature is closely related to the frequent mutation of TP53, of which R342* is highlighted due to strongly linked to age signature. In addition, the CCA matrix image data of genes in the signatures New, SBS3*, and SBS17b* were helpful for the preliminary evaluation of shortened survival outcome. These results can be extended to estimate the distribution of mutations or features, and study the potential impact of clinical factors. Conclusions In a word, RNMF can successfully achieve the correlation analysis of mutational signatures and genes, proving a strong theoretical basis for the study of mutational processes during tumor development.
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Affiliation(s)
- Zhenzhang Li
- College of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, China.,School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Cloud and Gene AI Research Institute, Guangzhou, China
| | - Haihua Liang
- College of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Shaoan Zhang
- College of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, China.,School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Wen Luo
- College of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, China
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Karanović S, Ardin M, Tang Z, Tomić K, Villar S, Renard C, Venturini E, Lorch AH, Lee DS, Stipančić Ž, Slade N, Vuković Brinar I, Dittrich D, Karlović K, Borovečki F, Dickman KG, Olivier M, Grollman AP, Jelaković B, Zavadil J. Molecular profiles and urinary biomarkers of upper tract urothelial carcinomas associated with aristolochic acid exposure. Int J Cancer 2022; 150:374-386. [PMID: 34569060 PMCID: PMC8627473 DOI: 10.1002/ijc.33827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/25/2021] [Accepted: 08/31/2021] [Indexed: 12/23/2022]
Abstract
Recurrent upper tract urothelial carcinomas (UTUCs) arise in the context of nephropathy linked to exposure to the herbal carcinogen aristolochic acid (AA). Here we delineated the molecular programs underlying UTUC tumorigenesis in patients from endemic aristolochic acid nephropathy (AAN) regions in Southern Europe. We applied an integrative multiomics analysis of UTUCs, corresponding unaffected tissues and of patient urines. Quantitative microRNA (miRNA) and messenger ribonucleic acid (mRNA) expression profiling, immunohistochemical analysis by tissue microarrays and exome and transcriptome sequencing were performed in UTUC and nontumor tissues. Urinary miRNAs of cases undergoing surgery were profiled before and after tumor resection. Ribonucleic acid (RNA) and protein levels were analyzed using appropriate statistical tests and trend assessment. Dedicated bioinformatic tools were used for analysis of pathways, mutational signatures and result visualization. The results delineate UTUC-specific miRNA:mRNA networks comprising 89 miRNAs associated with 1,862 target mRNAs, involving deregulation of cell cycle, deoxyribonucleic acid (DNA) damage response, DNA repair, bladder cancer, oncogenes, tumor suppressors, chromatin structure regulators and developmental signaling pathways. Key UTUC-specific transcripts were confirmed at the protein level. Exome and transcriptome sequencing of UTUCs revealed AA-specific mutational signature SBS22, with 68% to 76% AA-specific, deleterious mutations propagated at the transcript level, a possible basis for neoantigen formation and immunotherapy targeting. We next identified a signature of UTUC-specific miRNAs consistently more abundant in the patients' urine prior to tumor resection, thereby defining biomarkers of tumor presence. The complex gene regulation programs of AAN-associated UTUC tumors involve regulatory miRNAs prospectively applicable to noninvasive urine-based screening of AAN patients for cancer presence and recurrence.
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Affiliation(s)
- Sandra Karanović
- Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Center ZagrebSchool of Medicine, University of ZagrebZagrebCroatia
| | - Maude Ardin
- Epigenomics and Mechanisms BranchInternational Agency for Research on Cancer, WHOLyonFrance
| | - Zuojian Tang
- Institute for Systems GeneticsNew York University Langone Medical CenterNew YorkNew YorkUSA
- Present address:
Boehringer Ingelheim Pharmaceuticals, Inc.RidgefieldCTUSA
| | - Karla Tomić
- Department of PathologyGeneral Hospital Dr. Josip BenčevićSlavonski BrodCroatia
- Present address:
Department of PathologyÅlesund Hospital, Møre and Romsdal Health TrustÅlesundNorway
| | - Stephanie Villar
- Epigenomics and Mechanisms BranchInternational Agency for Research on Cancer, WHOLyonFrance
| | - Claire Renard
- Epigenomics and Mechanisms BranchInternational Agency for Research on Cancer, WHOLyonFrance
| | - Elisa Venturini
- Office for Collaborative ScienceNew York University Langone Medical CenterNew YorkNew YorkUSA
- Present address:
Natera, Inc.San CarlosCAUSA
| | - Adam H. Lorch
- Biochemistry and Molecular GeneticsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Daniel S. Lee
- Office for Collaborative ScienceNew York University Langone Medical CenterNew YorkNew YorkUSA
| | - Želimir Stipančić
- Department for Dialysis OdžakCounty Hospital OrašjeOdžakBosnia and Herzegovina
| | - Neda Slade
- Division of Molecular MedicineInstitute Ruđer BoškovićZagrebCroatia
| | - Ivana Vuković Brinar
- Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Center ZagrebSchool of Medicine, University of ZagrebZagrebCroatia
| | - Damir Dittrich
- Department of UrologyGeneral Hospital Dr. Josip BenčevićSlavonski BrodCroatia
| | - Krešimir Karlović
- Department of UrologyGeneral Hospital Dr. Josip BenčevićSlavonski BrodCroatia
| | - Fran Borovečki
- Department for Functional Genomics, Center for Translational and Clinical ResearchUniversity Hospital Center Zagreb, School of Medicine, University of ZagrebZagrebCroatia
| | - Kathleen G. Dickman
- Department of MedicineStony Brook UniversityStony BrookNew YorkUSA
- Department of Medicine/NephrologyStony Brook UniversityStony BrookNew YorkUSA
| | - Magali Olivier
- Epigenomics and Mechanisms BranchInternational Agency for Research on Cancer, WHOLyonFrance
| | | | - Bojan Jelaković
- Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Center ZagrebSchool of Medicine, University of ZagrebZagrebCroatia
| | - Jiri Zavadil
- Epigenomics and Mechanisms BranchInternational Agency for Research on Cancer, WHOLyonFrance
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10
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Wu Y, Chua EHZ, Ng AWT, Boot A, Rozen SG. Accuracy of mutational signature software on correlated signatures. Sci Rep 2022; 12:390. [PMID: 35013428 PMCID: PMC8748538 DOI: 10.1038/s41598-021-04207-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/17/2021] [Indexed: 11/09/2022] Open
Abstract
Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.
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Affiliation(s)
- Yang Wu
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Ellora Hui Zhen Chua
- Department of Biological Sciences, National University of Singapore, Singapore, 117558, Singapore
| | - Alvin Wei Tian Ng
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Arnoud Boot
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Steven G Rozen
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore.
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore.
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11
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Abbasi A, Alexandrov LB. Significance and limitations of the use of next-generation sequencing technologies for detecting mutational signatures. DNA Repair (Amst) 2021; 107:103200. [PMID: 34411908 PMCID: PMC9478565 DOI: 10.1016/j.dnarep.2021.103200] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 12/13/2022]
Abstract
Next generation sequencing technologies (NGS) have been critical in characterizing the genomic landscape and untangling the genetic heterogeneity of human cancer. Since its advent, NGS has played a pivotal role in identifying the patterns of somatic mutations imprinted on cancer genomes and in deciphering the signatures of the mutational processes that have generated these patterns. Mutational signatures serve as phenotypic molecular footprints of exposures to environmental factors as well as deficiency and infidelity of DNA replication and repair pathways. Since the first roadmap of mutational signatures in human cancer was generated from whole-genome and whole-exome sequencing data, there has been a growing interest to extract mutational signatures from other NGS technologies such as targeted panel sequencing, RNA sequencing, single-cell sequencing, duplex sequencing, reduced representation sequencing, and long-read sequencing. Many of these technologies have their inherent sequencing biases and produce technical artifacts that can confound the extraction of reliable and interpretable mutational signatures. In this review, we highlight the relevance, limitations, and prospects of using different NGS technologies for examining mutational patterns and for deciphering mutational signatures.
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Affiliation(s)
- Ammal Abbasi
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, 92093, USA; Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA; Moores Cancer Center, UC San Diego, La Jolla, CA, 92037, USA
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, 92093, USA; Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA; Moores Cancer Center, UC San Diego, La Jolla, CA, 92037, USA.
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12
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Abstract
The genome of a cancer contains somatic mutations that reflect the activities of endogenous and exogenous mutational processes, with each mutational process imprinting a characteristic mutational signature. Computational analysis of somatic mutations derived from next-generation sequencing data allows revealing the mutational signatures operative in a set of cancer genomes. In this chapter, we briefly review the concept of mutational signatures and the tools available for deciphering mutational signatures. Further, we provide a quick guide as well as an in-depth protocol for deciphering mutational signatures using the tool SigProfilerExtractor and review the results generated from an example dataset of cancer genomes.
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13
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Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network. Oncogene 2020; 39:5031-5041. [PMID: 32528130 PMCID: PMC7334101 DOI: 10.1038/s41388-020-1343-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/19/2020] [Accepted: 05/29/2020] [Indexed: 12/28/2022]
Abstract
Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfully revealed many known and novel signatures from whole exome data, but many still remain undiscovered. Here, we developed a deep learning approach, DeepMS, to decompose mutational signatures using 52,671,908 somatic mutations from 2780 highly curated cancer genomes with whole genome sequencing (WGS) in 37 cancer types/subtypes. With rigorous model training and comparison, we characterized 54 signatures for single base substitutions (SBSs), 11 for doublet base substitutions (DBSs) and 16 for small insertions and deletions (Indels). Compared to the previous methods, DeepMS could discover 37 SBS, 5 DBS and 9 Indel new signatures, many of which represent associations with DNA mismatch or base excision repair and cisplatin therapy mechanisms. We further developed a regression-based model to estimate the correlation between signatures and clinical and demographical phenotypes. The first deep learning model DeepMS on WGS somatic mutational profiles enable us identify more comprehensive context-based mutational signatures than traditional NMF approaches. Our work substantially expands the landscape of the naturally occurring mutational signatures in cancer genomes, and provides new insights into cancer biology.
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14
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Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, Boot A, Covington KR, Gordenin DA, Bergstrom EN, Islam SMA, Lopez-Bigas N, Klimczak LJ, McPherson JR, Morganella S, Sabarinathan R, Wheeler DA, Mustonen V, Getz G, Rozen SG, Stratton MR. The repertoire of mutational signatures in human cancer. Nature 2020; 578:94-101. [PMID: 32025018 PMCID: PMC7054213 DOI: 10.1038/s41586-020-1943-3] [Citation(s) in RCA: 1883] [Impact Index Per Article: 470.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 11/18/2019] [Indexed: 01/27/2023]
Abstract
Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
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Affiliation(s)
- Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas J Haradhvala
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Mi Ni Huang
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Alvin Wei Tian Ng
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Yang Wu
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Arnoud Boot
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Kyle R Covington
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Dmitry A Gordenin
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
| | - Erik N Bergstrom
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - S M Ashiqul Islam
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Leszek J Klimczak
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
| | - John R McPherson
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | | | - Radhakrishnan Sabarinathan
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ville Mustonen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Steven G Rozen
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore.
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore.
- SingHealth, Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore, Singapore.
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15
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Viarisio D, Robitaille A, Müller-Decker K, Flechtenmacher C, Gissmann L, Tommasino M. Cancer susceptibility of beta HPV49 E6 and E7 transgenic mice to 4-nitroquinoline 1-oxide treatment correlates with mutational signatures of tobacco exposure. Virology 2019; 538:53-60. [PMID: 31569015 DOI: 10.1016/j.virol.2019.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 09/20/2019] [Accepted: 09/23/2019] [Indexed: 12/27/2022]
Abstract
We have previously showed that a transgenic (Tg) mouse model with cytokeratin 14 promoter (K14)-driven expression of E6 and E7 from beta-3 HPV49 in the basal layer of the epidermis and of the mucosal epithelia of the digestive tract (K14 HPV49 E6/E7 Tg mice) are highly susceptible to upper digestive tract carcinogenesis upon exposure to 4-nitroquinoline 1-oxide (4NQO). Using whole-exome sequencing, we show that in K14 HPV49 E6/E7 Tg mice, development of 4NQO-induced cancers tightly correlates with the accumulation of somatic mutations in cancer-related genes. The mutational signature in 4NQO-treated mice was similar to the signature observed in humans exposed to tobacco smoking and tobacco chewing. Similar results were obtained with K14 Tg animals expressing mucosal high-risk HPV16 E6 and E7 oncogenes. Thus, beta-3 HPV49 share some functional similarities with HPV16 in Tg animals.
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Affiliation(s)
- Daniele Viarisio
- Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Alexis Robitaille
- International Agency for Research on Cancer (IARC), World Health Organization, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France
| | - Karin Müller-Decker
- Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Christa Flechtenmacher
- Department of Pathology, University Hospital of Heidelberg, Im Neuenheimer Feld 220, 69120, Heidelberg, Germany
| | - Lutz Gissmann
- Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany; Department of Botany and Microbiology (honorary MMember), King Saud University, Riyadh, Saudi Arabia
| | - Massimo Tommasino
- International Agency for Research on Cancer (IARC), World Health Organization, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France.
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16
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Meier MJ, Beal MA, Schoenrock A, Yauk CL, Marchetti F. Whole Genome Sequencing of the Mutamouse Model Reveals Strain- and Colony-Level Variation, and Genomic Features of the Transgene Integration Site. Sci Rep 2019; 9:13775. [PMID: 31551502 PMCID: PMC6760142 DOI: 10.1038/s41598-019-50302-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 09/05/2019] [Indexed: 12/30/2022] Open
Abstract
The MutaMouse transgenic rodent model is widely used for assessing in vivo mutagenicity. Here, we report the characterization of MutaMouse's whole genome sequence and its genetic variants compared to the C57BL/6 reference genome. High coverage (>50X) next-generation sequencing (NGS) of whole genomes from multiple MutaMouse animals from the Health Canada (HC) colony showed ~5 million SNVs per genome, ~20% of which are putatively novel. Sequencing of two animals from a geographically separated colony at Covance indicated that, over the course of 23 years, each colony accumulated 47,847 (HC) and 17,677 (Covance) non-parental homozygous single nucleotide variants. We found no novel nonsense or missense mutations that impair the MutaMouse response to genotoxic agents. Pairing sequencing data with array comparative genomic hybridization (aCGH) improved the accuracy and resolution of copy number variants (CNVs) calls and identified 300 genomic regions with CNVs. We also used long-read sequence technology (PacBio) to show that the transgene integration site involved a large deletion event with multiple inversions and rearrangements near a retrotransposon. The MutaMouse genome gives important genetic context to studies using this model, offers insight on the mechanisms of structural variant formation, and contributes a framework to analyze aCGH results alongside NGS data.
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Affiliation(s)
- Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.,Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Marc A Beal
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.,Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, Canada
| | - Andrew Schoenrock
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Francesco Marchetti
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.
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17
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Omichessan H, Severi G, Perduca V. Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance. PLoS One 2019; 14:e0221235. [PMID: 31513583 PMCID: PMC6741849 DOI: 10.1371/journal.pone.0221235] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/01/2019] [Indexed: 12/03/2022] Open
Abstract
Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.
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Affiliation(s)
- Hanane Omichessan
- CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif, France
- Gustave Roussy, Villejuif, France
| | - Gianluca Severi
- CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif, France
- Gustave Roussy, Villejuif, France
- Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School for Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Vittorio Perduca
- CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif, France
- Laboratoire de Mathématiques Appliquées à Paris 5—MAP5 (UMR CNRS 8145), Université Paris Descartes, Université de Paris, Paris, France
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18
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Huang PJ, Chiu LY, Lee CC, Yeh YM, Huang KY, Chiu CH, Tang P. mSignatureDB: a database for deciphering mutational signatures in human cancers. Nucleic Acids Res 2019; 46:D964-D970. [PMID: 29145625 PMCID: PMC5753213 DOI: 10.1093/nar/gkx1133] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/26/2017] [Indexed: 11/21/2022] Open
Abstract
Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼10 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases.
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Affiliation(s)
- Po-Jung Huang
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.,Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Ling-Ya Chiu
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Ching Lee
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.,Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Yuan-Ming Yeh
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.,Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Kuo-Yang Huang
- Graduate Institute of Pathology and Parasitology, National Defense Medical Center, Taipei, Taiwan
| | - Cheng-Hsun Chiu
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.,Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Petrus Tang
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.,Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan
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19
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Integrated analyses of murine breast cancer models reveal critical parallels with human disease. Nat Commun 2019; 10:3261. [PMID: 31332182 PMCID: PMC6646342 DOI: 10.1038/s41467-019-11236-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 06/27/2019] [Indexed: 12/20/2022] Open
Abstract
Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. Here, we complete whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. Through integrative in vitro and in vivo studies, we identify copy number alterations in key extracellular matrix proteins including collagen 1 type 1 alpha 1 (COL1A1) and chondroadherin (CHAD) that drive metastasis in these mouse models. In addition to copy number alterations, we observe a propensity of the tumors to modulate tyrosine kinase-mediated signaling through mutation of phosphatases such as PTPRH in the MMTV-PyMT mouse model. Mutation in PTPRH leads to increased phospho-EGFR levels and decreased latency. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers. Mouse models are an essential tool in breast cancer research. Here, the authors present the genomic and transcriptomic profiles of two widely used mouse models, revealing parallels with the human disease specifically with metastasis and treatment response.
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20
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Baez-Ortega A, Gori K. Computational approaches for discovery of mutational signatures in cancer. Brief Bioinform 2019; 20:77-88. [PMID: 28968631 DOI: 10.1093/bib/bbx082] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Indexed: 01/07/2023] Open
Abstract
The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.
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Affiliation(s)
| | - Kevin Gori
- Transmissible Cancer Group, University of Cambridge
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21
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Rogozin IB, Pavlov YI, Goncearenco A, De S, Lada AG, Poliakov E, Panchenko AR, Cooper DN. Mutational signatures and mutable motifs in cancer genomes. Brief Bioinform 2019; 19:1085-1101. [PMID: 28498882 DOI: 10.1093/bib/bbx049] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Indexed: 12/22/2022] Open
Abstract
Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.
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Affiliation(s)
- Igor B Rogozin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, USA
| | - Youri I Pavlov
- Eppley Institute for Cancer Research, University of Nebraska Medical Center, USA
| | | | | | - Artem G Lada
- Department Microbiology and Molecular Genetics, University of California, Davis, USA
| | - Eugenia Poliakov
- Laboratory of Retinal Cell and Molecular Biology, National Eye Institute, National Institutes of Health, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, USA
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22
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Goncearenco A, Rager SL, Li M, Sang QX, Rogozin IB, Panchenko AR. Exploring background mutational processes to decipher cancer genetic heterogeneity. Nucleic Acids Res 2019; 45:W514-W522. [PMID: 28472504 PMCID: PMC5793731 DOI: 10.1093/nar/gkx367] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 04/21/2017] [Indexed: 01/08/2023] Open
Abstract
Much remains unknown about the progression and heterogeneity of mutational processes in different cancers and their diagnostic and clinical potential. A growing body of evidence supports mutation rate dependence on the local DNA sequence context for various types of mutations. We propose several tools for the analysis of cancer context-dependent mutations, which are implemented in an online computational framework MutaGene. The framework explores DNA context-dependent mutational patterns and underlying somatic cancer mutagenesis, analyzes mutational profiles of cancer samples, identifies the combinations of underlying mutagenic processes including those related to infidelity of DNA replication and repair machinery, and various other endogenous and exogenous mutagenic factors. As a result, the combination of mutagenic processes can be identified in any query sample with subsequent comparison to mutational profiles derived from malignant and benign samples. In addition, mutagen or cancer-specific mutational background models are applied to calculate expected DNA and protein site mutability to decouple relative contributions of mutagenesis and selection in carcinogenesis, thus elucidating the site-specific driving events in cancer. MutaGene is freely available at https://www.ncbi.nlm.nih.gov/projects/mutagene/.
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Affiliation(s)
| | - Stephanie L Rager
- National Center for Biotechnology Information, NIH, Bethesda, MD 20894, USA.,Columbia University, School of Engineering and Applied Science, New York, NY 10027, USA
| | - Minghui Li
- National Center for Biotechnology Information, NIH, Bethesda, MD 20894, USA
| | - Qing-Xiang Sang
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
| | - Igor B Rogozin
- National Center for Biotechnology Information, NIH, Bethesda, MD 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, NIH, Bethesda, MD 20894, USA
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23
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Ham-Karim HA, Ebili HO, Bradshaw K, Richman SD, Fadhil W, Domingo E, Tomlinson I, Ilyas M. Targeted next generation sequencing reveals a common genetic pathway for colorectal cancers with chromosomal instability and those with microsatellite and chromosome stability. Pathol Res Pract 2019; 215:152445. [PMID: 31153694 DOI: 10.1016/j.prp.2019.152445] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 05/07/2019] [Indexed: 10/26/2022]
Abstract
INTRODUCTION Microsatellite stable sporadic colorectal cancers (CRCs) can be classified as either tumours with chromosomal instability (CIN+) or tumours that are 'Microsatellite and Chromosomal Stable' (MACS). The CIN + tumours are aneuploid whilst MACS are near-diploid; little else is known about their differences. We compared the mutation profiles of CIN + and MACS CRCs. METHOD Targeted Next Generation Sequencing for mutation in 26 driver genes (TruSight-26 kit) was undertaken in 46 CIN + and 35 MACSCRCs. Tumours were compared for mutation frequency, allelic imbalance and clonal heterogeneity. RESULTS Mutations were detected in 58% genes and, overall, mutation in driver genes was at expected frequencies. Comparison of classes revealed similar mutation frequencies in most genes and allelic imbalance atAPC and TP53. Differences were seen in mutation frequency in KRAS (41% CIN+ vs 68% MACS, p = 0.015) and GNAS (0% CIN+ vs 12% MACS, p = 0.032). Twenty percent CIN + CRCs harboured mutations only in TP53 - a profile not seen in the MACS tumours (p = 0.009). None of the differences were significant after multiple testing corrections. CONCLUSIONS The mutation profiles of CIN and MACS CRCs are similar. The events allowing aneuploidy (or forcing retention of diploidy) remain unknown.
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Affiliation(s)
- Hersh A Ham-Karim
- Academic Unit of Pathology and Nottingham Molecular Pathology Node, University of Nottingham, Queen's Medical Centre, UK
| | - Henry O Ebili
- Academic Unit of Pathology and Nottingham Molecular Pathology Node, University of Nottingham, Queen's Medical Centre, UK.
| | - Kirsty Bradshaw
- Centre for Medical Genetics, Nottingham University Hospitals NHS Trust, City Hospital Campus, UK
| | - Susan D Richman
- Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, Wellcome Trust Brenner Building, St James University Hospital, Leeds, UK
| | - Wakkas Fadhil
- Academic Unit of Pathology and Nottingham Molecular Pathology Node, University of Nottingham, Queen's Medical Centre, UK
| | - Enric Domingo
- Oxford Centre for Cancer Gene Research and NIHR Comprehensive Biomedical Research Centre, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Ian Tomlinson
- Institute of Cancer and Genomic Science, University of Birmingham, Birmingham, UK
| | - Mohammad Ilyas
- Academic Unit of Pathology and Nottingham Molecular Pathology Node, University of Nottingham, Queen's Medical Centre, UK
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24
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Grolleman JE, Díaz-Gay M, Franch-Expósito S, Castellví-Bel S, de Voer RM. Somatic mutational signatures in polyposis and colorectal cancer. Mol Aspects Med 2019; 69:62-72. [PMID: 31108140 DOI: 10.1016/j.mam.2019.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/13/2019] [Accepted: 05/16/2019] [Indexed: 02/04/2023]
Abstract
The somatic mutation spectrum imprinted in the genome of a tumor represents the mutational processes that have been active in that tumor. Large sequencing efforts in various cancer types have resulted in the identification of multiple mutational signatures, of which several have been linked to specific biological mechanisms. Several pan-cancer mutational signatures have been identified, while other signatures are only found in specific tissue types. Research on tumors from individuals with specific DNA repair defects has led to links between specific mutational signatures and mutational processes. Studying mutational signatures in cancers that are likely the result of a genetic predisposition may represent an interesting strategy to identify constitutional DNA repair defects, including those underlying polyposis and colorectal cancer.
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Affiliation(s)
- Judith E Grolleman
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marcos Díaz-Gay
- Gastroenterology Department, Hospital Clínic de Barcelona, August Pi I Sunyer Biomedical Research Institute, CIBER of Hepatic and Digestive Diseases, University of Barcelona, Barcelona, Spain
| | - Sebastià Franch-Expósito
- Gastroenterology Department, Hospital Clínic de Barcelona, August Pi I Sunyer Biomedical Research Institute, CIBER of Hepatic and Digestive Diseases, University of Barcelona, Barcelona, Spain
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic de Barcelona, August Pi I Sunyer Biomedical Research Institute, CIBER of Hepatic and Digestive Diseases, University of Barcelona, Barcelona, Spain
| | - Richarda M de Voer
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands.
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25
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Zhivagui M, Ng AWT, Ardin M, Churchwell MI, Pandey M, Renard C, Villar S, Cahais V, Robitaille A, Bouaoun L, Heguy A, Guyton KZ, Stampfer MR, McKay J, Hollstein M, Olivier M, Rozen SG, Beland FA, Korenjak M, Zavadil J. Experimental and pan-cancer genome analyses reveal widespread contribution of acrylamide exposure to carcinogenesis in humans. Genome Res 2019; 29:521-531. [PMID: 30846532 PMCID: PMC6442384 DOI: 10.1101/gr.242453.118] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 02/01/2019] [Indexed: 02/06/2023]
Abstract
Humans are frequently exposed to acrylamide, a probable human carcinogen found in commonplace sources such as most heated starchy foods or tobacco smoke. Prior evidence has shown that acrylamide causes cancer in rodents, yet epidemiological studies conducted to date are limited and, thus far, have yielded inconclusive data on association of human cancers with acrylamide exposure. In this study, we experimentally identify a novel and unique mutational signature imprinted by acrylamide through the effects of its reactive metabolite glycidamide. We next show that the glycidamide mutational signature is found in a full one-third of approximately 1600 tumor genomes corresponding to 19 human tumor types from 14 organs. The highest enrichment of the glycidamide signature was observed in the cancers of the lung (88% of the interrogated tumors), liver (73%), kidney (>70%), bile duct (57%), cervix (50%), and, to a lesser extent, additional cancer types. Overall, our study reveals an unexpectedly extensive contribution of acrylamide-associated mutagenesis to human cancers.
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Affiliation(s)
- Maria Zhivagui
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Alvin W T Ng
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, 169857, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore
| | - Maude Ardin
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Mona I Churchwell
- Division of Biochemical Toxicology, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA
| | - Manuraj Pandey
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Claire Renard
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Stephanie Villar
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Vincent Cahais
- Epigenetics Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Alexis Robitaille
- Infections and Cancer Biology Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Liacine Bouaoun
- Environment and Radiation Section, International Agency for Research on Cancer, Lyon 69008, France
| | - Adriana Heguy
- Department of Pathology and Genome Technology Center, New York University, Langone Medical Center, New York, New York 10016, USA
| | - Kathryn Z Guyton
- IARC Monographs Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Martha R Stampfer
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - James McKay
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Monica Hollstein
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
- Deutsches Krebsforschungszentrum, 69120 Heidelberg, Germany
- Faculty of Medicine and Health, University of Leeds, LIGHT Laboratories, Leeds LS2 9JT, United Kingdom
| | - Magali Olivier
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Steven G Rozen
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, 169857, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore
| | - Frederick A Beland
- Division of Biochemical Toxicology, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA
| | - Michael Korenjak
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
| | - Jiri Zavadil
- Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon 69008, France
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26
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Olivier M, Bouaoun L, Villar S, Robitaille A, Cahais V, Heguy A, Byrnes G, Le Calvez-Kelm F, Torres-Mejía G, Alvarado-Cabrero I, Imani-Razavi FS, Inés Sánchez G, Jaramillo R, Porras C, Rodriguez AC, Garmendia ML, Soto JL, Romieu I, Porter P, Guenthoer J, Rinaldi S. Molecular features of premenopausal breast cancers in Latin American women: Pilot results from the PRECAMA study. PLoS One 2019; 14:e0210372. [PMID: 30653559 PMCID: PMC6336331 DOI: 10.1371/journal.pone.0210372] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 12/20/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In Latin America (LA), there is a high incidence rate of breast cancer (BC) in premenopausal women, and the genomic features of these BC remain unknown. Here, we aim to characterize the molecular features of BC in young LA women within the framework of the PRECAMA study, a multicenter population-based case-control study of BC in premenopausal women. METHODS Pathological tumor tissues were collected from incident cases from four LA countries. Immunohistochemistry (IHC) was performed centrally for ER, PR, HER2, Ki67, EGFR, CK5/6, and p53 protein markers. Targeted deep sequencing was done on genomic DNA extracted from formalin-fixed, paraffin-embedded tumor tissues and their paired blood samples to screen for somatic mutations in eight genes frequently mutated in BC. A subset of samples was analyzed by exome sequencing to identify somatic mutational signatures. RESULTS The majority of cases were positive for ER or PR (168/233; 72%), and 21% were triple-negative (TN), mainly of basal type. Most tumors were positive for Ki67 (189/233; 81%). In 126 sequenced cases, TP53 and PIK3CA were the most frequently mutated genes (32.5% and 21.4%, respectively), followed by AKT1 (9.5%). TP53 mutations were more frequent in HER2-enriched and TN IHC subtypes, whereas PIK3CA/AKT1 mutations were more frequent in ER-positive tumors, as expected. Interestingly, a higher proportion of G:C>T:A mutations was observed in TP53 in PRECAMA cases compared with TCGA and METABRIC BC series (27% vs 14%). Exome-wide mutational patterns in 10 TN cases revealed alterations in signal transduction pathways and major contributions of mutational signatures caused by altered DNA repair pathways. CONCLUSIONS These pilot results on PRECAMA tumors give a preview of the molecular features of premenopausal BC in LA. Although the overall mutation burden was as expected from data in other populations, mutational patterns observed in TP53 and exome-wide suggested possible differences in mutagenic processes giving rise to these tumors compared with other populations. Further -omics analyses of a larger number of cases in the near future will enable the investigation of relationships between these molecular features and risk factors.
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Affiliation(s)
- Magali Olivier
- Section of Mechanisms of Carcinogenesis, International Agency for Research on Cancer, Lyon, France
| | - Liacine Bouaoun
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - Stephanie Villar
- Section of Mechanisms of Carcinogenesis, International Agency for Research on Cancer, Lyon, France
| | - Alexis Robitaille
- Section of Mechanisms of Carcinogenesis, International Agency for Research on Cancer, Lyon, France
| | - Vincent Cahais
- Section of Mechanisms of Carcinogenesis, International Agency for Research on Cancer, Lyon, France
| | - Adriana Heguy
- Department of Pathology and Genome Technology Center, New York University Langone Medical Center, New York, United States of America
| | - Graham Byrnes
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - Florence Le Calvez-Kelm
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer, Lyon, France
| | - Gabriela Torres-Mejía
- Center for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico
| | - Isabel Alvarado-Cabrero
- Department of Pathology, Hospital de Oncología, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Fazlollah Shahram Imani-Razavi
- Department of Pathology, UMAE Hospital de Gineco Obstetricia No. 4 "Luis Castelazo Ayala", Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Gloria Inés Sánchez
- Group Infection and Cancer, School of Medicine, University of Antioquia, Medellín, Colombia
| | | | - Carolina Porras
- Agencia Costarricense de Investigaciones Biomédicas (ACIB)-Fundación INCIENSA, Costa Rica
| | - Ana Cecilia Rodriguez
- Agencia Costarricense de Investigaciones Biomédicas (ACIB)-Fundación INCIENSA, Costa Rica
| | | | | | - Isabelle Romieu
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Peggy Porter
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States of America
| | - Jamie Guenthoer
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, United States of America
| | - Sabina Rinaldi
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
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27
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Carlson J, Li JZ, Zöllner S. Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets. BMC Genomics 2018; 19:845. [PMID: 30486787 PMCID: PMC6263557 DOI: 10.1186/s12864-018-5264-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 12/14/2022] Open
Abstract
Background The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants. Results We introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman’s memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs. Conclusions Helmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman. Electronic supplementary material The online version of this article (10.1186/s12864-018-5264-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jedidiah Carlson
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA. .,Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Jun Z Li
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
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28
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Palodhi A, Ghosh S, Biswas NK, Basu A, Majumder PP, Maitra A. Profiling of genomic alterations of mitochondrial DNA in gingivobuccal oral squamous cell carcinoma: Implications for disease progress. Mitochondrion 2018; 46:361-369. [PMID: 30261279 DOI: 10.1016/j.mito.2018.09.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 08/08/2018] [Accepted: 09/14/2018] [Indexed: 01/31/2023]
Abstract
We have identified 164 somatic mutations in mitochondrial DNA in gingivobuccal oral cancer by deep sequencing the mitochondrial genome from paired tumor and blood DNA samples from 89 patients. We have found evidence of positive selection of somatic nonsynonymous mutations. Non-synonymous mutations in mitochondrial respiratory genes were found to increase the risk of lymph node metastasis (P = 0.0028). We have observed a significant reduction in mitochondrial DNA copy number in tumor DNA of these patients compared to the DNA from adjacent normal tissue samples (P < 1 × 10-6). Analysis of transcriptome data of tumor and adjacent normal tissue revealed patients harboring mutations in mitochondrial protein-coding genes exhibited reduced expression of mitochondrial transcripts.
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Affiliation(s)
- Arindam Palodhi
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Sahana Ghosh
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India
| | | | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, West Bengal 741251, India.
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29
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Díaz-Gay M, Vila-Casadesús M, Franch-Expósito S, Hernández-Illán E, Lozano JJ, Castellví-Bel S. Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples. BMC Bioinformatics 2018; 19:224. [PMID: 29898651 PMCID: PMC6001047 DOI: 10.1186/s12859-018-2234-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 06/04/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts. RESULTS In this work we present Mutational Signatures in Cancer (MuSiCa), a new web tool based on MutationalPatterns and built using the Shiny framework in R language. By means of a simple interface suited to non-specialized researchers, it provides a comprehensive analysis of the somatic mutational status of the supplied cancer samples. It permits characterizing the profile and burden of mutations, as well as quantifying COSMIC-reported mutational signatures. It also allows classifying samples according to the above signature contributions. CONCLUSIONS MuSiCa is a helpful web application to characterize mutational signatures in cancer samples. It is accessible online at http://bioinfo.ciberehd.org/GPtoCRC/en/tools.html and source code is freely available at https://github.com/marcos-diazg/musica .
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Affiliation(s)
- Marcos Díaz-Gay
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Maria Vila-Casadesús
- Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
- Present Address: Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Sebastià Franch-Expósito
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Eva Hernández-Illán
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Juan José Lozano
- Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain
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30
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Ng AWT, Poon SL, Huang MN, Lim JQ, Boot A, Yu W, Suzuki Y, Thangaraju S, Ng CCY, Tan P, Pang ST, Huang HY, Yu MC, Lee PH, Hsieh SY, Chang AY, Teh BT, Rozen SG. Aristolochic acids and their derivatives are widely implicated in liver cancers in Taiwan and throughout Asia. Sci Transl Med 2018; 9:9/412/eaan6446. [PMID: 29046434 DOI: 10.1126/scitranslmed.aan6446] [Citation(s) in RCA: 243] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 07/31/2017] [Accepted: 09/25/2017] [Indexed: 12/21/2022]
Abstract
Many traditional pharmacopeias include Aristolochia and related plants, which contain nephrotoxins and mutagens in the form of aristolochic acids and similar compounds (collectively, AA). AA is implicated in multiple cancer types, sometimes with very high mutational burdens, especially in upper tract urothelial cancers (UTUCs). AA-associated kidney failure and UTUCs are prevalent in Taiwan, but AA's role in hepatocellular carcinomas (HCCs) there remains unexplored. Therefore, we sequenced the whole exomes of 98 HCCs from two hospitals in Taiwan and found that 78% showed the distinctive mutational signature of AA exposure, accounting for most of the nonsilent mutations in known cancer driver genes. We then searched for the AA signature in 1400 HCCs from diverse geographic regions. Consistent with exposure through known herbal medicines, 47% of Chinese HCCs showed the signature, albeit with lower mutation loads than in Taiwan. In addition, 29% of HCCs from Southeast Asia showed the signature. The AA signature was also detected in 13 and 2.7% of HCCs from Korea and Japan as well as in 4.8 and 1.7% of HCCs from North America and Europe, respectively, excluding one U.S. hospital where 22% of 87 "Asian" HCCs had the signature. Thus, AA exposure is geographically widespread. Asia, especially Taiwan, appears to be much more extensively affected, which is consistent with other evidence of patterns of AA exposure. We propose that additional measures aimed at primary prevention through avoidance of AA exposure and investigation of possible approaches to secondary prevention are warranted.
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Affiliation(s)
- Alvin W T Ng
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore
| | - Song Ling Poon
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Mi Ni Huang
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Jing Quan Lim
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore 169610, Singapore.,Lymphoma Genomic Translational Research Laboratory, Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Arnoud Boot
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Willie Yu
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Yuka Suzuki
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Saranya Thangaraju
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Cedric C Y Ng
- Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Patrick Tan
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore.,SingHealth/Duke-NUS Precision Medicine Institute, Singapore 169609, Singapore.,Genome Institute of Singapore, Singapore 138672, Singapore
| | - See-Tong Pang
- Division of Urooncology, Department of Urology, Chang Gung University and Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
| | - Hao-Yi Huang
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
| | - Ming-Chin Yu
- Department of General Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
| | - Po-Huang Lee
- Department of Surgery, National Taiwan University, Taipei 10051, Taiwan
| | - Sen-Yung Hsieh
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan.
| | - Alex Y Chang
- Johns Hopkins Singapore, Singapore 308433, Singapore.
| | - Bin T Teh
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore. .,Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore 169610, Singapore.,SingHealth/Duke-NUS Precision Medicine Institute, Singapore 169609, Singapore.,Institute of Molecular and Cell Biology, Singapore 138673, Singapore
| | - Steven G Rozen
- Centre for Computational Biology, Duke-NUS Medical School, Singapore 169857, Singapore. .,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore.,SingHealth/Duke-NUS Precision Medicine Institute, Singapore 169609, Singapore
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31
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MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med 2018; 10:33. [PMID: 29695279 PMCID: PMC5922316 DOI: 10.1186/s13073-018-0539-0] [Citation(s) in RCA: 408] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 04/04/2018] [Indexed: 12/23/2022] Open
Abstract
Background Base substitution catalogues represent historical records of mutational processes that have been active in a cell. Such processes can be distinguished by various characteristics, like mutation type, sequence context, transcriptional and replicative strand bias, genomic distribution and association with (epi)-genomic features. Results We have created MutationalPatterns, an R/Bioconductor package that allows researchers to characterize a broad range of patterns in base substitution catalogues to dissect the underlying molecular mechanisms. Furthermore, it offers an efficient method to quantify the contribution of known mutational signatures within single samples. This analysis can be used to determine whether certain DNA repair mechanisms are perturbed and to further characterize the processes underlying known mutational signatures. Conclusions MutationalPatterns allows for easy characterization and visualization of mutational patterns. These analyses willsupport fundamental research into mutational mechanisms and may ultimately improve cancer diagnosis and treatment strategies. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns. Electronic supplementary material The online version of this article (10.1186/s13073-018-0539-0) contains supplementary material, which is available to authorized users.
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Viarisio D, Müller-Decker K, Accardi R, Robitaille A, Dürst M, Beer K, Jansen L, Flechtenmacher C, Bozza M, Harbottle R, Voegele C, Ardin M, Zavadil J, Caldeira S, Gissmann L, Tommasino M. Beta HPV38 oncoproteins act with a hit-and-run mechanism in ultraviolet radiation-induced skin carcinogenesis in mice. PLoS Pathog 2018; 14:e1006783. [PMID: 29324843 PMCID: PMC5764406 DOI: 10.1371/journal.ppat.1006783] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 11/30/2017] [Indexed: 11/19/2022] Open
Abstract
Cutaneous beta human papillomavirus (HPV) types are suspected to be involved, together with ultraviolet (UV) radiation, in the development of non-melanoma skin cancer (NMSC). Studies in in vitro and in vivo experimental models have highlighted the transforming properties of beta HPV E6 and E7 oncoproteins. However, epidemiological findings indicate that beta HPV types may be required only at an initial stage of carcinogenesis, and may become dispensable after full establishment of NMSC. Here, we further investigate the potential role of beta HPVs in NMSC using a Cre-loxP-based transgenic (Tg) mouse model that expresses beta HPV38 E6 and E7 oncogenes in the basal layer of the skin epidermis and is highly susceptible to UV-induced carcinogenesis. Using whole-exome sequencing, we show that, in contrast to WT animals, when exposed to chronic UV irradiation K14 HPV38 E6/E7 Tg mice accumulate a large number of UV-induced DNA mutations, which increase proportionally with the severity of the skin lesions. The mutation pattern detected in the Tg skin lesions closely resembles that detected in human NMSC, with the highest mutation rate in p53 and Notch genes. Using the Cre-lox recombination system, we observed that deletion of the viral oncogenes after development of UV-induced skin lesions did not affect the tumour growth. Together, these findings support the concept that beta HPV types act only at an initial stage of carcinogenesis, by potentiating the deleterious effects of UV radiation. Many epidemiological and biological findings support the hypothesis that beta HPV types cooperate with UV radiation in the induction of NMSC, the most common form of human cancer. We have previously shown that K14 HPV38 E6/E7 Tg mice, when exposed to long-term UV radiation, developed NMSC, whereas WT animals subjected to identical treatments did not develop any type of skin lesions. Here, we show that the high skin cancer susceptibility of these Tg animals tightly correlates with their tendency to accumulate UV-induced mutations in genes that are frequently mutated in human NMSC. Importantly, deletion of the HPV38 E6 and E7 genes in existing skin lesions did not affect the further growth of the cancer cells. Together, these findings support the model that beta HPV infection is a co-factor in skin carcinogenesis, facilitating the accumulation of the UV-induced DNA mutations.
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Affiliation(s)
| | | | - Rosita Accardi
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Alexis Robitaille
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Matthias Dürst
- Department of Gynecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | - Katrin Beer
- Department of Gynecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | - Lars Jansen
- Department of Gynecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | | | | | | | - Catherine Voegele
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Maude Ardin
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Jiri Zavadil
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Lutz Gissmann
- Deutsches Krebsforschungszentrum, Heidelberg, Germany
- Department of Botany and Microbiology (honorary member), King Saud University, Riyadh, Saudi Arabia
| | - Massimo Tommasino
- International Agency for Research on Cancer, World Health Organization, Lyon, France
- * E-mail:
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Mathieson I, Reich D. Differences in the rare variant spectrum among human populations. PLoS Genet 2017; 13:e1006581. [PMID: 28146552 PMCID: PMC5310914 DOI: 10.1371/journal.pgen.1006581] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 02/15/2017] [Accepted: 01/12/2017] [Indexed: 12/30/2022] Open
Abstract
Mutations occur at vastly different rates across the genome, and populations, leading to differences in the spectrum of segregating polymorphisms. Here, we investigate variation in the rare variant spectrum in a sample of human genomes representing all major world populations. We find at least two distinct signatures of variation. One, consistent with a previously reported signature is characterized by an increased rate of TCC>TTC mutations in people from Western Eurasia and South Asia, likely related to differences in the rate, or efficiency of repair, of damage due to deamination of methylated guanine. We describe the geographic extent of this signature and show that it is detectable in the genomes of ancient, but not archaic humans. The second signature is private to certain Native American populations, and is concentrated at CpG sites. We show that this signature is not driven by differences in the CpG mutation rate, but is a result of the fact that highly mutable CpG sites are more likely to undergo multiple independent mutations across human populations, and the spectrum of such mutations is highly sensitive to recent demography. Both of these effects dramatically affect the spectrum of rare variants across human populations, and should be taken into account when using mutational clocks to make inference about demography.
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Affiliation(s)
- Iain Mathieson
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - David Reich
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, United States of America
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