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

• Published in: Nature genetics Vol. 48; no. 8; pp. 827 - 837
• Main Authors: Niu, Beifang, Scott, Adam D, Sengupta, Sohini, Bailey, Matthew H, Batra, Prag, Ning, Jie, Wyczalkowski, Matthew A, Liang, Wen-Wei, Zhang, Qunyuan, McLellan, Michael D, Sun, Sam Q, Tripathi, Piyush, Lou, Carolyn, Ye, Kai, Mashl, R Jay, Wallis, John, Wendl, Michael C, Chen, Feng, Ding, Li
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.08.2016; Nature Publishing Group
• Subjects: 13/1; 13/106; 631/114/794; 631/208/212/2166; 692/699/67; Agriculture; Algorithms; analysis; Animal Genetics and Genomics; Antineoplastic Agents - pharmacology; Biomedicine; Cancer; Cancer Research; Care and treatment; Cluster analysis; Clustering; Computational Biology - methods; Databases, Pharmaceutical; Databases, Protein; Drugs; Gene Expression Regulation, Neoplastic - drug effects; Gene Function; Gene mutations; Genomes; Grants; Human Genetics; Humans; Identification and classification; Methods; Models, Molecular; Mutation; Mutation - genetics; Neoplasm Proteins - chemistry; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasms - drug therapy; Neoplasms - genetics; Neoplasms - metabolism; Phosphorylation; Protein Binding; Protein Interaction Maps; Protein Structure, Tertiary; Proteins; Proteomics; Studies
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.3586

Li Ding, Feng Chen and colleagues report a pan-cancer analysis using a new computational tool, HotSpot3D, to identify mutational hotspots in the encoded three-dimensional protein structure, which suggest their functional involvement in cancer. They use a mutation–drug cluster analysis to predict more than 800 potentially druggable mutations. 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.