Mutational Heterogeneity in Cancer and the Search for New Cancer-Associated Genes

As the sample size in cancer genome studies increases, the list of genes identified as significantly mutated is likely to include more false positives; here, this problem is identified as stemming largely from mutation heterogeneity, and a new analytical methodology designed to overcome this problem is described. Cancer genomic approaches have identified scores of genes responsible for the initiation and progression of cancer. But as the sample sizes increase, the list of putatively significant genes identified by current analytical methods continues to grow and is likely to include many false positives. This study shows that this situation stems largely from mutational heterogeneity and presents a novel methodology, MutSigCV, that overcomes the problem by incorporating mutational heterogeneity into the analysis. Application of MutSigCV to more than 3,000 tumour samples from 27 different tumour types shows that mutation frequencies vary more than 1,000-fold between extreme samples both between and within tumour types. And when applied to a data set on lung cancer, MutSigCV reduced the list of significantly mutated genes from 450 to a more manageable 11, most of them previously reported to be mutated in squamous cell lung cancer. Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer1,2,3,4,5,6,7,8,9. 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.