Mutational heterogeneity in cancer and the search for new cancer genes

By Mike S. Lawrence, P. Stojanov, P. Polak, G. Kryukov, Kristian Cibulskis, A. Sivachenko, Scott L. Carter, Chip Stewart, Craig H. Mermel, S. Roberts and 54 more. Nature, 2013

1,147 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/1fdec384640f93ee44c6d5e12ecb2a36b4b8d7bb

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Citations in the corpus

Clinical sequencing defines the genomic landscape of metastatic colorectal cancer (2018, neutral): “…N/A Software and Algorithms MSIsensor Niu et al., 2014 N/A MuSiC Dees et al., 2012 http://gmt.genome.wustl.edu/packages/ genome-music/ MutSigCV 1.4 Lawrence et al., 2013 RRID:SCR_010779; http://archive.broadinstitute.org/ cancer/cga/mutsig Other cBioPortal Memorial Sloan Kettering…”
Mutations driving CLL and their evolution in progression and relapse (2015, positive): “We inferred candidate cancer genes in CLL through implementation of MutSig2CV 5,7 .”
Clonal Evolution in Breast Cancer Revealed by Single Nucleus Genome Sequencing (2014, background): “While TCGA studies 39–41 report increased mutation frequencies , it remains unclear whether these mutations accumulate over many cell divisions (at a normal error rate) or through an increased mutation rate .”
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, positive): “To further illustrate the importance of ﬁltering on biological ﬁndings, we performed signiﬁcantly mutated gene (SMG) analysis using both MutSig2CV (Lawrence et al., 2013) and MuSiC2 (Dees et al., 2012) for all KIRC variants present in the controlled-access MAF compared with those present in the…”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “Large numbers of false positives could be identified due to high mutation rates in many cancers and many false negatives due to tumor heterogeneity [Lawrence et al., 2013].”
Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic (2018, background): “these genetic alterations between individual tumors and between different tumor types [35-38].”
Quantitative Proteomics of the Cancer Cell Line Encyclopedia (2020, positive): “Although differences in total mutational burden are a hallmark of different tumor types (Lawrence et al., 2013) this explanation did not apply (Fig.”
The Cancer Stem Cell Niche: How Essential is the Niche in Regulating Stemness of Tumor Cells? (2015, positive): “Althoughmutational burden is highly variable across tumor types (Lawrence et al., 2013), a typical tumor contains two to eight driver mutations that regulate three core cellular processes: cell fate, cell survival, and genomemaintenance (Vogelstein et al., 2013).”