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A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity
Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460199/ https://www.ncbi.nlm.nih.gov/pubmed/28588243 http://dx.doi.org/10.1038/s41598-017-03141-w |
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author | Xi, Jianing Li, Ao Wang, Minghui |
author_facet | Xi, Jianing Li, Ao Wang, Minghui |
author_sort | Xi, Jianing |
collection | PubMed |
description | Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their interaction network context. However, recent studies show that some cancer genes in different perturbed pathways are mutated in different subsets of samples. Subsequently, these genes may not display significant mutational recurrence and thus remain undiscovered even in consideration of network information. We develop a novel method called mCGfinder to efficiently detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Based on matrix decomposition framework incorporated with gene interaction network information, mCGfinder can successfully measure the significance of mutational recurrence of genes in a subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of five types of cancers, we find that the genes detected by mCGfinder are significantly enriched for known cancer genes, and yield substantially smaller p-values than other existing methods. All the results demonstrate that mCGfinder is an efficient method in detecting mutated cancer genes. |
format | Online Article Text |
id | pubmed-5460199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54601992017-06-06 A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity Xi, Jianing Li, Ao Wang, Minghui Sci Rep Article Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their interaction network context. However, recent studies show that some cancer genes in different perturbed pathways are mutated in different subsets of samples. Subsequently, these genes may not display significant mutational recurrence and thus remain undiscovered even in consideration of network information. We develop a novel method called mCGfinder to efficiently detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Based on matrix decomposition framework incorporated with gene interaction network information, mCGfinder can successfully measure the significance of mutational recurrence of genes in a subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of five types of cancers, we find that the genes detected by mCGfinder are significantly enriched for known cancer genes, and yield substantially smaller p-values than other existing methods. All the results demonstrate that mCGfinder is an efficient method in detecting mutated cancer genes. Nature Publishing Group UK 2017-06-06 /pmc/articles/PMC5460199/ /pubmed/28588243 http://dx.doi.org/10.1038/s41598-017-03141-w Text en © The Author(s) 2017 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 Xi, Jianing Li, Ao Wang, Minghui A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title | A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title_full | A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title_fullStr | A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title_full_unstemmed | A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title_short | A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
title_sort | novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460199/ https://www.ncbi.nlm.nih.gov/pubmed/28588243 http://dx.doi.org/10.1038/s41598-017-03141-w |
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