TY - JOUR
T1 - Ontology-based prediction of cancer driver genes
AU - Althubaiti, Sara
AU - Karwath, Andreas
AU - Dallol, Ashraf
AU - Noor, Adeeb
AU - Alkhayyat, Shadi Salem
AU - Alwassia, Rolina
AU - Mineta, Katsuhiko
AU - Gojobori, Takashi
AU - Beggs, Andrew D.
AU - Schofield, Paul N.
AU - Gkoutos, Georgios V.
AU - Hoehndorf, Robert
N1 - Funding Information:
A preprint of this manuscript was submitted to BioRxiv on 27 February 2019. R.H. was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, URF/1/3790-01-01 and FCC/1/1976-08-01. S.A., P.N.S., G.V.G. and R.H. were supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. FCS/1/3657-02-01 and URF/1/3790-01-01. A.D.B. acknowledges funding from the Wellcome Trust (102732/Z/13/Z), Cancer Research UK (C31641/A23923) and the Medical Research Council (MR/M016587/1). G.V.G. acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS:1340112) as well as support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-019-53454-1
DO - 10.1038/s41598-019-53454-1
M3 - Article
C2 - 31757986
AN - SCOPUS:85075534172
VL - 9
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 17405
ER -