Cargando…
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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783472878554972160 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6874647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT althubaitisara ontologybasedpredictionofcancerdrivergenes AT karwathandreas ontologybasedpredictionofcancerdrivergenes AT dallolashraf ontologybasedpredictionofcancerdrivergenes AT nooradeeb ontologybasedpredictionofcancerdrivergenes AT alkhayyatshadisalem ontologybasedpredictionofcancerdrivergenes AT alwassiarolina ontologybasedpredictionofcancerdrivergenes AT minetakatsuhiko ontologybasedpredictionofcancerdrivergenes AT gojoboritakashi ontologybasedpredictionofcancerdrivergenes AT beggsandrewd ontologybasedpredictionofcancerdrivergenes AT schofieldpauln ontologybasedpredictionofcancerdrivergenes AT gkoutosgeorgiosv ontologybasedpredictionofcancerdrivergenes AT hoehndorfrobert ontologybasedpredictionofcancerdrivergenes |