Protein-Structure-Guided Discovery of Functional Mutations Across 19 Cancer Types  

Oleh:

Beifang Niu ; Scott, Adam D. ; Sengupta, Sohini ; Bailey, Matthew H. ; Batra, Prag

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Nature Genetics vol. 48 no. 08 (Aug. 2016), page 827-837.
Topik: Cancer; Personalized Medicine; Software

Ketersediaan

Perpustakaan FK Nomor Panggil: N12.K Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

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.

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