Ontology-based prediction of cancer driver genes.

Change log
Karwath, Andreas 
Noor, Adeeb 
Alkhayyat, Shadi Salem 

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.

Biomarkers, Tumor, Computational Biology, Exome, Gene Ontology, Genetic Association Studies, Genetic Predisposition to Disease, Genomics, High-Throughput Nucleotide Sequencing, Humans, Machine Learning, Molecular Sequence Annotation, Mutation, Neoplasms, Oncogenes, ROC Curve
Journal Title
Sci Rep
Conference Name
Journal ISSN
Volume Title
Springer Science and Business Media LLC
King Abdullah University of Science and Technology (KAUST) (OSR-2018-CPF-3657-04)