Deriving a Mutation Index of Carcinogenicity Using Protein Structure and Protein Interfaces

Abstract

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/.

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation

“…There are various methods which predict the phenotypic effects of mutations, (30–33, 41) and some of them use structural features (31, 41). We applied four state-of-the-art independent methods to predict the impacts of mutations on CBL function: PROVEAN (30), PolyPhen-2 (31), MutationAssessor (32) and InCa (33).…”

“…We also applied four additional webtools to assess the impacts of amino acid substitutions on CBL function: PROVEAN (30), PolyPhen-2 (31), MutationAssessor (32) and InCa (33). (See Supplementary Methods for more information.…”

MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis

“…On the contrary, MSEA-domain is hypothesis-driven and provides a better interpretation of the resultant genes, since most domains are known for their functions. For example, MSEA-domain can be extended for scenarios where regions are defined using biological functional units, such as protein pockets [46], protein secondary structure units [47,48], or regulatory regions (for example, promoters, untranslated regions). In practice, we suggest the user apply both methods and combine the results, in order to find all possible mutation hotspots.…”

Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach

“…Several recent studies have demonstrated that disease-causing mutations commonly alter protein folding, protein stability, and proteinprotein interactions (PPIs), often leading to new disease phenotypes [8][9][10][11][12][13][14][15][16][17][18][19][20]. Espinosa et al [21] proposed a predictor, InCa (Index of Carcinogenicity) that integrates somatic mutation profiles from the Catalogue of Somatic Mutations in Cancer (COSMIC) database and the neutral mutations from the 1000 Genomes project into protein structure and interaction interface information. Using these data, they developed the InCa classifier model to predict cancer-related mutations with 83% specificity and 77% sensitivity.…”