We are developing integrated approaches for whole-genome characterization of cancer patients to improve personalized therapy.

Cancer is caused by accumulation of somatic driver mutations harbored in driver genes. When it comes to a patient, the challenge now for his/her molecular diagnostics and treatment lies in rapid and accurate identification of these driver mutations/genes harbored in his/her tumor cells from a large number of background noises of passengers, given his/her genomic information.

Second-generation sequencing technology enabled researchers to rapidly identify somatic mutations from a patient by comparing the sequence from his/her tumor with healthy tissues. Accordingly, tools were developed to help identifying these cancer culprits, using readily available personal cancer genomic information generated from second-generation sequencing. Despite the establishment of various individual computational methods, joining hands in knowledge accumulation of cancer biology networks and principles in increasing global endeavor, robust computational tools exploiting such prior knowledge to aid driver genes discovery are still underdeveloped.

To address this issue, we devised integrated CAncer GEnome Score (iCAGES), a statistical framework to prioritizes potential cancer driver genes in virtue of their biological prior association and its ensemble cancer-driving potential. iCAGES is implemented with radial Support Vector Machine (SVM) trained on somatic non-synonymous Single Nucleotide Variations (nsSNVs) from COSMIC and Uniprot databases, followed by a two-step ranking process to employ related biological prior knowledge, downstream gene-gene interaction information and genomic complexity of the cancer driver event. Using individual protein-altering point mutations, iCAGES demonstrates its accuracy in prioritizing cancer driver genes in three distinct scenarios.

In summary, iCAGES computationally leverages personal genomic driver event, with the aid of prior biological knowledge, shedding light into cancer driver genes identification, personalized drug discovery and cancer treatment.