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Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours
For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748970/ https://www.ncbi.nlm.nih.gov/pubmed/31530827 http://dx.doi.org/10.1038/s41598-019-48765-2 |
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author | Darbyshire, Madeleine du Toit, Zachary Rogers, Mark F. Gaunt, Tom R. Campbell, Colin |
author_facet | Darbyshire, Madeleine du Toit, Zachary Rogers, Mark F. Gaunt, Tom R. Campbell, Colin |
author_sort | Darbyshire, Madeleine |
collection | PubMed |
description | For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes. |
format | Online Article Text |
id | pubmed-6748970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67489702019-09-27 Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours Darbyshire, Madeleine du Toit, Zachary Rogers, Mark F. Gaunt, Tom R. Campbell, Colin Sci Rep Article For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes. Nature Publishing Group UK 2019-09-17 /pmc/articles/PMC6748970/ /pubmed/31530827 http://dx.doi.org/10.1038/s41598-019-48765-2 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 Darbyshire, Madeleine du Toit, Zachary Rogers, Mark F. Gaunt, Tom R. Campbell, Colin Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title | Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title_full | Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title_fullStr | Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title_full_unstemmed | Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title_short | Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours |
title_sort | estimating the frequency of single point driver mutations across common solid tumours |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748970/ https://www.ncbi.nlm.nih.gov/pubmed/31530827 http://dx.doi.org/10.1038/s41598-019-48765-2 |
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