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

Abstract

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.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

“…A collection of 624 cancer genes (Niu et al, 2016) was used to identify possible driver mutations in individual cases.…”

3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets

“…StructMAn [15] annotated the amino acid variations of single-nucleotide polymorphisms (SNPs) in the context of 3D structures. SpacePAC [16], Mutation3D [17], HotMAPS [18], and Hotspot3D [19] used 3D structures to identify mutational clusters in cancer. These efforts have generated interesting sets of candidate functional mutations and illustrate that many rare driver mutations are functionally, and potentially clinically, relevant.…”

“…Many of these 3D clusters were identified in well-characterized cancer genes, such as KRAS , BRAF , and TP53 , and include known oncogenic recurrent alleles (e.g., KRAS G12D) as well as rare long-tail alleles (e.g., KRAS D33E, which has recently been experimentally validated [20]). We were able to identify new potential driver genes as well as novel candidate driver mutations in clinically actionable cancer genes that were not detected by our mutational single-residue hotspot detection method [6] and other 3D cluster detection methods [17–19]. We experimentally tested the activating potential of rare mutations identified in 3D clusters in the MAP2K1 and RAC1 proteins, enlarging the number of biologically and potentially clinically significant alleles in these two critical effectors of activated signaling pathways in cancer.…”

Phenotypic Characterization of a Comprehensive Set of MAPK1 /ERK2 Missense Mutants

“…Computational approaches that identify significantly mutated genes or detect mutational clusters within individual genes have revealed the complex landscape of genetic alterations in cancer (Cancer Genome Atlas Research et al, 2013; International Cancer Genome et al, 2010; Kamburov et al, 2015; Lawrence et al, 2014; Niu et al, 2016; Porta-Pardo and Godzik, 2014; Tamborero et al, 2013; Vogelstein et al, 2013). As a result, tumor-associated mutations have been detected in nearly all human genes and the number of uncharacterized mutations continues to rise (Lawrence et al, 2014).…”