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

Jianjiong Gao,Hongxin Zhang,Hannah C Johnsen,Nikolaus Schultz,David B Solit,Barry S Taylor,Brooke E Sylvester,Selcuk Onur Sumer,Chris Sander,Sizhi Paul Gao,Matthew T Chang

doi:10.1186/s13073-016-0393-x

Abstract

Many mutations in cancer are of unknown functional significance. Standard methods use statistically significant recurrence of mutations in tumor samples as an indicator of functional impact. We extend such analyses into the long tail of rare mutations by considering recurrence of mutations in clusters of spatially close residues in protein structures. Analyzing 10,000 tumor exomes, we identify more than 3000 rarely mutated residues in proteins as potentially functional and experimentally validate several in RAC1 and MAP2K1. These potential driver mutations (web resources: 3dhotspots.org and cBioPortal.org) can extend the scope of genomically informed clinical trials and of personalized choice of therapy.

Highlights

Recent large-scale sequencing efforts such as The Cancer Genome Atlas (TCGA) have revealed a complex landscape of somatic mutations in various cancer types [1]
By experimentally validating candidate functional mutations in 3D clusters in MAP2K1 and RAC1, we show that our method readily identifies previously occult rare activating mutations that could not be revealed by positional frequency analyses alone and that a subset of such mutations are potential biomarkers of sensitivity to targeted inhibitors in individual patients with cancer
In this work, we present a novel computational method for identifying mutational 3D clusters of potential functional significance with results based on the largest whole exome or genome dataset analyzed in the context of protein structures to date

Summary

Introduction

Recent large-scale sequencing efforts such as The Cancer Genome Atlas (TCGA) have revealed a complex landscape of somatic mutations in various cancer types [1]. 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 [3] and MuSiC [4] 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. It is important to develop more refined methods that at the genome scale identify infrequent mutations that are likely functional Though individually rare, these longtail mutations are present in a significant fraction of tumors and are likely key molecular events and potential drug targets [5]. Several methods exist that identify driver genes or mutations in the long tail by incorporating protein-level annotation, such as local positional clustering [7], phosphorylation sites [8], and paralogous protein domains [9]

Methods

Findings

Discussion

Conclusion