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Ontology-based prediction of cancer driver genes

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...

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Autores principales: Althubaiti, Sara, Karwath, Andreas, Dallol, Ashraf, Noor, Adeeb, Alkhayyat, Shadi Salem, Alwassia, Rolina, Mineta, Katsuhiko, Gojobori, Takashi, Beggs, Andrew D., Schofield, Paul N., Gkoutos, Georgios V., Hoehndorf, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874647/
https://www.ncbi.nlm.nih.gov/pubmed/31757986
http://dx.doi.org/10.1038/s41598-019-53454-1
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author Althubaiti, Sara
Karwath, Andreas
Dallol, Ashraf
Noor, Adeeb
Alkhayyat, Shadi Salem
Alwassia, Rolina
Mineta, Katsuhiko
Gojobori, Takashi
Beggs, Andrew D.
Schofield, Paul N.
Gkoutos, Georgios V.
Hoehndorf, Robert
author_facet Althubaiti, Sara
Karwath, Andreas
Dallol, Ashraf
Noor, Adeeb
Alkhayyat, Shadi Salem
Alwassia, Rolina
Mineta, Katsuhiko
Gojobori, Takashi
Beggs, Andrew D.
Schofield, Paul N.
Gkoutos, Georgios V.
Hoehndorf, Robert
author_sort Althubaiti, Sara
collection PubMed
description 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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spelling pubmed-68746472019-12-04 Ontology-based prediction of cancer driver genes Althubaiti, Sara Karwath, Andreas Dallol, Ashraf Noor, Adeeb Alkhayyat, Shadi Salem Alwassia, Rolina Mineta, Katsuhiko Gojobori, Takashi Beggs, Andrew D. Schofield, Paul N. Gkoutos, Georgios V. Hoehndorf, Robert Sci Rep Article 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. Nature Publishing Group UK 2019-11-22 /pmc/articles/PMC6874647/ /pubmed/31757986 http://dx.doi.org/10.1038/s41598-019-53454-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Althubaiti, Sara
Karwath, Andreas
Dallol, Ashraf
Noor, Adeeb
Alkhayyat, Shadi Salem
Alwassia, Rolina
Mineta, Katsuhiko
Gojobori, Takashi
Beggs, Andrew D.
Schofield, Paul N.
Gkoutos, Georgios V.
Hoehndorf, Robert
Ontology-based prediction of cancer driver genes
title Ontology-based prediction of cancer driver genes
title_full Ontology-based prediction of cancer driver genes
title_fullStr Ontology-based prediction of cancer driver genes
title_full_unstemmed Ontology-based prediction of cancer driver genes
title_short Ontology-based prediction of cancer driver genes
title_sort ontology-based prediction of cancer driver genes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874647/
https://www.ncbi.nlm.nih.gov/pubmed/31757986
http://dx.doi.org/10.1038/s41598-019-53454-1
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