Protein-structure-guided discovery of functional mutations across 19 cancer types

Beifang Niu, Adam D. Scott, Sohini Sengupta, Matthew H. Bailey, Prag Batra, Jie Ning, Matthew A. Wyczalkowski, Wen Wei Liang, Qunyuan Zhang, Michael D. McLellan, Sam Q. Sun, Piyush Tripathi, Carolyn Lou, Kai Ye, R. Jay Mashl, John Wallis, Michael C. Wendl, Feng Chen, Li Ding

Research output: Contribution to journalArticlepeer-review

84 Scopus citations

Abstract

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Original languageEnglish
Pages (from-to)827-837
Number of pages11
JournalNature Genetics
Volume48
Issue number8
DOIs
StatePublished - Aug 1 2016

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