iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing cancer driver genes in personal genomes

Abstract

All cancers arise as a result of the acquisition of somatic mutations that drive the disease progression. A number of computational tools have been developed to identify driver genes for a specific cancer from a group of cancer samples. However, it remains a challenge to identify driver mutations/genes for an individual patient and design drug therapies. We developed iCAGES, a novel statistical framework to rapidly analyze patient-specific cancer genomic data, prioritize personalized cancer driver events and predict personalized therapies. iCAGES includes three consecutive layers: the first layer integrates contributions from coding, non-coding and structural variations to infer driver variants. For coding mutations, we developed a radial support vector machine using manually curated mutations to predict their driver potential. The second layer identifies driver genes, by using information from the first layer and integrating prior biological knowledge on gene-gene and gene-phenotype networks. The third layer prioritizes personalized drug treatment, by classifying potential driver genes into different categories and querying drug-gene databases. Compared to currently available tools, iCAGES achieves better performance by correctly classifying point coding driver mutations (AUC=0.97, 95% CI: 0.97-0.97, significantly better than the second best tool with P=0.01) and genes (AUC=0.93, 95% CI: 0.93-0.94, significantly better than MutSigCV with P<1x10-15). We also illustrated two examples where iCAGES correctly nominated two targeted drugs for two advanced cancer patients with exceptional response, based on their somatic mutation profiles. iCAGES leverages personal genomic information and prior biological knowledge, effectively identifies cancer driver genes and predicts treatment strategies. iCAGES is available at http://icages.usc.edu.