UC Riverside

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis of copy number aberration (CNA) regions of cancer genomes, by integrating publicly available human genomic data. MAXDRIVER employs several optimization strategies to construct a heterogeneous network, by means of combining a fused gene functional similarity network, gene-disease associations and a disease phenotypic similarity network. MAXDRIVER was validated to effectively recall known associations among genes and cancers. Previously identified as well as novel driver genes were detected by scanning CNAs of breast cancer, melanoma and liver carcinoma. Three predicted driver genes (CDKN2A, AKT1, RNF139) were found common in these three cancers by comparative analysis.