Mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome. Issue 5 (18th February 2016) - Meyer, Michael J.; Lapcevic, Ryan; Romero, Alfonso E.; Yoon, Mark; Das, Jishnu; Beltrán, Juan Felipe; Mort, Matthew; Stenson, Peter D.; Cooper, David N.; Paccanaro, Alberto; Yu, Haiyuan
Abstract : mutation3D is a new algorithm and Web server designed to find clusters of somatic cancer mutations in three‐dimensional protein structures. Users may input their mutation data in a variety of popular formats for small or large‐scale discovery of mutation hotspots across the structural proteome. Further, we demonstrate the ability of mutation3D to indicate many well‐validated driver genes as well as several new and underexplored target candidates. ABSTRACT: A new algorithm and Web server, mutation3D (http://mutation3d.org ), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8, 869 known inherited disease mutations and 2, 004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole‐genome and whole‐exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D Web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975, 000 somatic mutations reported by 6, 811 cancer sequencing studies. The mutation3D Web interface is freely available with all major browsers supported.