Structural and Functional Impact of Cancer-Related Missense Somatic Mutations

Abstract

A number of large scale cancer somatic genome sequencing projects are now identifying genetic alterations in cancers. Evaluation of the effects of these mutations is essential for understanding their contribution to tumorigenesis. We have used SNPs3D, a software suite originally developed for analyzing non-synonymous germ line variants, to identify single base mutations with a high impact on protein structure and function. Two machine learning methods are used, one identifying mutations that destabilize protein three dimensional structure, and the other utilizing sequence conservation, and detecting all types of effects on in vivo protein function. Incorporation of detailed structure information into the analysis allows detailed interpretation of the functional effects of mutations in specific cases. Data from a set of breast and colorectal tumors were analyzed. In known cancer genes, approaching 100% of mutations are found to impact protein function, supporting the view that these methods are appropriate for identifying driver mutations. Overall, 50% to 60% of all somatic missense mutations are predicted to have a high impact on structural stability or to more generally affect the function of the corresponding proteins. This value is similar to the fraction of all possible missense mutations that have high impact, and much higher than the corresponding one for human population SNPs, at about 30%. The majority of mutations in tumor suppressors destabilize protein structure, while mutations in oncogenes operate in more varied ways, including destabilization of the less active conformational states. The set of high impact mutations encompass the possible drivers.

Single nucleotide variations: Biological impact and theoretical interpretation

“…An increasing number of studies use the predicted impact in a variety of applications, and have reported that SNV impact predictions match experimental findings 130,132,133. Such applications include guiding mutagenesis,134,135 identifying disease associated genes in both Mendelian and common diseases,1,136–139 separating disease-causing variants from linkage disequilibrium variants,140 identifying somatic mutations that drive cancer,65,141,142 and predicting the overall phenotype of an organism 143. These applications highlight the value of SNV impact prediction and the need for further improvement.…”

“…The MutationAssessor method predicted the impact of over 10,000 nsSNVs from the COSMIC database,175 which combined with the total number of mutations in a gene and the frequency of each mutation in different tumors, ranked genes for cancer association, recovering known drivers (TP53, PTEN, etc) and suggesting many others 65. The SNPs3D method, consisting of two SVMs based on protein stability and homology respectively, was applied to about 2000 somatic mutations from colorectal and breast cancer to find that virtually all mutations in known cancer genes are predicted to impact protein function and therefore can be detected by nsSNV impact prediction methods 142. These methods produced intriguing novel predictions and may foreshadow wider use of nsSNV impact predictions to elucidate cancer mechanisms.…”

Genetic Basis of Common Human Disease: Insight into the Role of Missense SNPs from Genome-Wide Association Studies

“…A few methods make use of protein structure to directly identify high impact variants that affect protein stability, for example SNPs3D structure [26]. Use of this method to study monogenic disease [26] and cancer [27] found the majority of mutations to result in a change in protein thermodynamic stability, implying a role in folding or half-life. One of the most popular methods, Polyphen2 [18] makes use of a combination of profile and structure information.…”

“…Missense mutations, usually somatic, also play a major role in cancer [30], and application of the computational methods provides evidence that these also often have a high impact on protein function [27,31]. The role of high impact missense variants in common, complex trait, disease is less well established.…”

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

“…Several studies have demonstrated the close relationship between protein structures and their function altered by cancer-related missense mutations [96,97]. Vuong et al presented a protein pocket-based computational pipeline to study the functional consequences of somatic mutations in cancer [67].…”