Protein-structure-guided discovery of functional mutations across 19 cancer types.
Protein-structure-guided discovery of functional mutations across 19 cancer types.
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DOI：
10.1038/ng.3586
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发表时间：
2016-08
期刊：
影响因子：
30.8
通讯作者：
Ding L
中科院分区：
文献类型：
--
作者：
Niu B;Scott AD;Sengupta S;Bailey MH;Batra P;Ning J;Wyczalkowski MA;Liang WW;Zhang Q;McLellan MD;Sun SQ;Tripathi P;Lou C;Ye K;Mashl RJ;Wallis J;Wendl MC;Chen F;Ding L
Local concentrations of mutations are well-known in human cancers. However, their 3-dimensional (3D) spatial relationships 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 inter-molecular clusters, some of which showed tumor/tissue specificity. In addition, we identified 369 rare mutations from genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium recurrence mutations from genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all residing 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, drug-mutation cluster/network analysis predicted over 800 promising candidates of druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.
