Abstract
BackgroundIdentifying key “driver” mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign “passenger” mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research.ResultsWe have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/html/QuartPAC.html.ConclusionQuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0963-3) contains supplementary material, which is available to authorized users.
Highlights
Identifying key “driver” mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs
Oncogenic mutations result in an increase of gene output or a destabilization of the the resulting protein while mutations within tumor suppressors lead to a reduction of gene activities that promote apoptosis or cell cycle regulation
Combined with the theory of oncogene addiction, that a small subset of so called driver genes result in runaway cellular replication and that the selective targeting of these genes can have a large impact on tumorigenesis [5, 6], the identification of such driver genes becomes critical due to the large translational benefit in the pharmacological space
Summary
Identifying key “driver” mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Several studies have shown that somatic mutations cluster within protein kinases [6, 8,9,10] and that these clusters may be a sign of positive selection for protein function and targets for therapeutic intervention [11, 12] Such frequency based approaches at identifying driver mutations are often further augmented by accounting for a variety factors such as normalizing for gene length [13], accounting for tumor type and varying background mutation rates [13, 14], as well as considering the ratio of nonsynonymous (Ka) to synonymous (Ks) mutations [15]
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