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Background: It is well known that the development of cancer is caused by theaccumulation of somatic mutations within the genome. For oncogenesspecifically, current research suggests that there is a small set of "driver"mutations that are primarily responsible for tumorigenesis. Further, due tosome recent pharmacological successes in treating these driver mutations andtheir resulting tumors, a variety of methods have been developed to identifypotential driver mutations using methods such as machine learning andmutational clustering. We propose a novel methodology that increases our powerto identify mutational clusters by taking into account protein tertiarystructure via a graph theoretical approach.  Results: We have designed and implemented GraphPAC (Graph Protein Amino AcidClustering) to identify mutational clustering while considering protein spatialstructure. Using GraphPAC, we are able to detect novel clusters in proteinsthat are known to exhibit mutation clustering as well as identify clusters inproteins without evidence of prior clustering based on current methods.Specifically, by utilizing the spatial information available in the ProteinData Bank (PDB) along with the mutational data in the Catalogue of SomaticMutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters inwell known oncogenes such as EGFR and KRAS. Further, by utilizing graph theoryto account for the tertiary structure, GraphPAC identifies clusters in DPP4,NRP1 and other proteins not identified by existing methods. The R package isavailable at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html  Conclusion: GraphPAC provides an alternative to iPAC and an extension tocurrent methodology when identifying potential activating driver mutations byutilizing a graph theoretic approach when considering protein tertiarystructure.

A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations