Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure

Abstract

The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three dimensional protein structure may also be beneficial, because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyze ~3×105 mutations from The Cancer Genome Atlas (TCGA) and identify 216 tumor-type-specific hotspot regions. In addition to experimentally determined protein structures we consider high-quality structural models, which increases genomic coverage from ~5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes. While hotspot regions have long been known to exist in both tumor suppressor genes and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biological functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context.

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].…”

“…We have shown that the mutational 3D clusters identified by three alternative methods (Mutation3D [17], HotMAPS [18], and Hotspot3D [19]) and our method are largely complementary (Additional file 7: Figure S2). While different mutational and structural datasets used by these four tools may have led to some of the differences observed, methodological differences likely dominate.…”

“…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.…”

Precision medicine driven by cancer systems biology

“…Hotspots of somatic events of individual patient data can be compared to cancer genome databases [88]. Mutation pattern in combination with three-dimensional protein structures reveals the mutation distribution of specific functional regions and can highlight hotspots or oncogenic drivers [89–92]. The study on efficient detection of genomic drivers in malignant melanoma identified co-occurrence of a mutational and copy number hotspot on chromosome 7 including the known BRAF locus [13].…”

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

“…The Mutations tab allows the user to toggle through submitted mutations alongside statistically significant 3D mutation clusters from TCGA data; statistically significant mutation clusters are precomputed for each of 31 TCGA cancer subtypes using the HotMAPS algorithm. 13 In Figure 1C, MuPIT is located at the Annotations tab, and user-submitted mutations (shown in green) are spatially proximal to known TGFBR1 binding and active sites (cyan and blue), suggesting a potential mechanistic role for these mutations.…”