mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome

Abstract

A new algorithm and web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from non-functional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole-genome and whole-exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975,000 somatic mutations reported by 6,811 cancer sequencing studies. The mutation3D web interface is freely available with all major browsers supported.

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

“…However, the methods differ in detail, e.g., in the tumor sets analyzed, the definition of 3D clusters, and the statistical test applied, and so they produce different lists of candidate functional mutations. For example, Mutation3D identified 399 mutated residues in 75 genes as likely functional [17], HotMAPS identified 398 mutated residues in 91 genes [18], and Hotspot3D identified 14,929 mutated residues in 2466 genes [19], whereas our method identified 3404 mutated residues in 503 genes (Additional file 6: Table S5 and Additional file 7: Figure S2). Somewhat surprisingly, only 15 mutated residues were identified by all four methods, all of which were also previously identified as single-residue hotspots [6].…”

“…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.…”

Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma

“…42 Mutation 3D 43 was used to model mutations onto known protein structures and identify significant clusters.…”

A Novel Methodology for Structural, Functional and Toxicological Analysis of Mutant Angiogenin Protein in Human

“…This tool is intended to be utilized for the identification of groups of amino acid substitutions appearing from somatic cancer mutations across several patients in direction to determine fuel downstream suggestions and functional hotspots [25].…”