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.

Details

Title
Ontology-based prediction of cancer driver genes
Author
Althubaiti, Sara 1   VIAFID ORCID Logo  ; Karwath, Andreas 2 ; Dallol, Ashraf 3   VIAFID ORCID Logo  ; Noor, Adeeb 4 ; Shadi Salem Alkhayyat 5 ; Alwassia, Rolina 6 ; Mineta, Katsuhiko 1   VIAFID ORCID Logo  ; Gojobori, Takashi 7 ; Beggs, Andrew D 8   VIAFID ORCID Logo  ; Schofield, Paul N 9   VIAFID ORCID Logo  ; Gkoutos, Georgios V 10 ; Hoehndorf, Robert 1   VIAFID ORCID Logo 

1  Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 
2  College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, United Kingdom 
3  Centre of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia 
4  Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia 
5  Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia 
6  Radiation Oncology Unit, King Abdulaziz University Hospital, Jeddah, Saudi Arabia 
7  Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 
8  College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom 
9  Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, United Kingdom 
10  College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, United Kingdom; NIHR Experimental Cancer Medicine Centre, Birmingham, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK; MRC Health Data Research UK (HDR UK) Midlands, Birmingham, United Kingdom 
Pages
1-9
Publication year
2019
Publication date
Nov 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41598-019-53454-1
ProQuest document ID
2317039264
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.