Mutational heterogeneity in cancer and the search for new cancer-associated genes.
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.
|Year of Publication||
2013 Jul 11
|PubMed Central ID||
U54 HG003067 / HG / NHGRI NIH HHS / United States
U24 CA143845 / CA / NCI NIH HHS / United States
T32 GM007753 / GM / NIGMS NIH HHS / United States
T32 CA009172 / CA / NCI NIH HHS / United States
Intramural NIH HHS / United States
T32 CA009216 / CA / NCI NIH HHS / United States
Howard Hughes Medical Institute / United States
Z01 ES065073 / ES / NIEHS NIH HHS / United States