3D Hotspots

[PMID:28115009] [Genome Medicine]

3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets

“Several methods are currently being used to identify driver genes based on the frequency of mutations observed in a gene across a set of tumors, e.g., MutSig and MuSiC. These methods have two limitations: (1) their unit of analysis is a gene and they do not distinguish individual driver mutations from passengers in a given gene, and (2) they are not able to detect functional mutations in infrequently mutated genes, often referred to as the “long tail” of the frequency distribution of somatic mutations in cancer.” “Here, we describe a novel method that identifies mutational 3D clusters, i.e., missense (amino-acid-changing) mutations that cluster together in 3D proximity in protein structures above a random background, with a focus on identifying rare mutations. In this largest 3D cluster analysis of whole exome or genome sequencing data in cancer to date, we analyzed more than one million somatic missense mutations in 11,119 human tumors across 32,445 protein structures from 7390 genes.”


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