Ontology-based prediction of cancer driver genes

Research output: Contribution to journalArticlepeer-review


  • Sara Althubaiti
  • InformationAshraf Dallol
  • Adeeb Noor
  • Shadi Salem Alkhayyat
  • Rolina Alwassia
  • Katsuhiko Mineta
  • Takashi Gojobori
  • Paul N Schofield
  • Robert Hoehndorf

Colleges, School and Institutes

External organisations

  • University of Cambridge
  • King Abdullah University of Science and Technology
  • King Abdulaziz University Hospital
  • King Abdulaziz University


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.


Original languageEnglish
Article number17405
JournalScientific Reports
Publication statusPublished - 22 Nov 2019