Ontology-based prediction of cancer driver genes.

Althubaiti, Sara and Karwath, Andreas and Dallol, Ashraf and Noor, Adeeb and Alkhayyat, Shadi Salem and Alwassia, Rolina and Mineta, Katsuhiko and Gojobori, Takashi and Beggs, Andrew D and Schofield, Paul N and Gkoutos, Georgios V and Hoehndorf, Robert (2019) Ontology-based prediction of cancer driver genes. Scientific reports, 9 (1). p. 17405. ISSN 2045-2322.

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Official URL: https://www.nature.com/articles/s41598-019-53454-1

Abstract

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.

Item Type: Article
Subjects: QY Clinical pathology
QZ Pathology. Oncology
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Depositing User: Mr Muneeb Liaquat
Date Deposited: 29 Nov 2019 15:35
Last Modified: 29 Nov 2019 15:35
URI: http://www.repository.uhblibrary.co.uk/id/eprint/2651

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