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Nat Genet. 2016 Aug;48(8):827-37. doi: 10.1038/ng.3586. Epub 2016 Jun 13.

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

Author information

1
McDonnell Genome Institute, Washington University, St. Louis, Missouri, USA.
2
Division of Oncology, Department of Medicine, Washington University, St. Louis, Missouri, USA.
3
Division of Nephrology, Department of Medicine, Washington University, St. Louis, Missouri, USA.
4
Department of Genetics, Washington University, St. Louis, Missouri, USA.
5
Department of Mathematics, Washington University, St. Louis, Missouri, USA.
6
Siteman Cancer Center, Washington University, St. Louis, Missouri, USA.
7
Department of Cell Biology and Physiology, Washington University, St. Louis, Missouri, USA.

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.

PMID:
27294619
PMCID:
PMC5315576
DOI:
10.1038/ng.3586
[Indexed for MEDLINE]
Free PMC Article

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