IDENTIFY CANCER DRIVER GENES THROUGH SHARED MENDELIAN DISEASE PATHOGENIC VARIANTS AND CANCER SOMATIC MUTATIONS.

Genomic sequencing studies in the past several years have yielded a large number of cancer somatic mutations. There remains a major challenge in delineating a small fraction of somatic mutations that are oncogenic drivers from a background of predominantly passenger mutations. Although computational tools have been developed to predict the functional impact of mutations, their utility is limited. In this study, we applied an alternative approach to identify potentially novel cancer drivers as those somatic mutations that overlap with known pathogenic mutations in Mendelian diseases. We hypothesize that those shared mutations are more likely to be cancer drivers because they have the established molecular mechanisms to impact protein functions. We first show that the overlap between somatic mutations in COSMIC and pathogenic genetic variants in HGMD is associated with high mutation frequency in cancers and is enriched for known cancer genes. We then attempted to identify putative tumor suppressors based on the number of distinct HGMD/COSMIC overlapping mutations in a given gene, and our results suggest that ion channels, collagens and Marfan syndrome associated genes may represent new classes of tumor suppressors. To elucidate potentially novel oncogenes, we identified those HGMD/COSMIC overlapping mutations that are not only highly recurrent but also mutually exclusive from previously characterized oncogenic mutations in each specific cancer type. Taken together, our study represents a novel approach to discover new cancer genes from the vast amount of cancer genome sequencing data.