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Mutational heterogeneity in cancer and the search for new cancer-associated genes.

ABSTRACT: Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.

SUBMITTER: Lawrence MS

PROVIDER: S-EPMC3919509 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Mutational heterogeneity in cancer and the search for new cancer-associated genes.

Lawrence Michael S MS Stojanov Petar P Polak Paz P Kryukov Gregory V GV Cibulskis Kristian K Sivachenko Andrey A Carter Scott L SL Stewart Chip C Mermel Craig H CH Roberts Steven A SA Kiezun Adam A Hammerman Peter S PS McKenna Aaron A Drier Yotam Y Zou Lihua L Ramos Alex H AH Pugh Trevor J TJ Stransky Nicolas N Helman Elena E Kim Jaegil J Sougnez Carrie C Ambrogio Lauren L Nickerson Elizabeth E Shefler Erica E Cortés Maria L ML Auclair Daniel D Saksena Gordon G Voet Douglas D Noble Michael M DiCara Daniel D Lin Pei P Lichtenstein Lee L Heiman David I DI Fennell Timothy T Imielinski Marcin M Hernandez Bryan B Hodis Eran E Baca Sylvan S Dulak Austin M AM Lohr Jens J Landau Dan-Avi DA Wu Catherine J CJ Melendez-Zajgla Jorge J Hidalgo-Miranda Alfredo A Koren Amnon A McCarroll Steven A SA Mora Jaume J Crompton Brian B Onofrio Robert R Parkin Melissa M Winckler Wendy W Ardlie Kristin K Gabriel Stacey B SB Roberts Charles W M CWM Biegel Jaclyn A JA Stegmaier Kimberly K Bass Adam J AJ Garraway Levi A LA Meyerson Matthew M Golub Todd R TR Gordenin Dmitry A DA Sunyaev Shamil S Lander Eric S ES Getz Gad G

Nature 20130616 7457

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significa ...[more]

PMID: 23770567

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Project description:BackgroundSynonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations.ResultsHere, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation.ConclusionOur study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.

Dataset Information

Mutational heterogeneity in cancer and the search for new cancer-associated genes.

Publications

Mutational heterogeneity in cancer and the search for new cancer-associated genes.

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