Identification of significantly mutated subnetworks in the breast cancer genome

Abstract Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this r...

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Autores principales:	Rasif Ajwad, Michael Domaratzki, Qian Liu, Nikta Feizi, Pingzhao Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/35eb7bde4a5b4476838f5f4ffa421d0f
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spelling	oai:doaj.org-article:35eb7bde4a5b4476838f5f4ffa421d0f2021-12-02T14:01:32ZIdentification of significantly mutated subnetworks in the breast cancer genome10.1038/s41598-020-80204-52045-2322https://doaj.org/article/35eb7bde4a5b4476838f5f4ffa421d0f2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80204-5https://doaj.org/toc/2045-2322Abstract Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.Rasif AjwadMichael DomaratzkiQian LiuNikta FeiziPingzhao HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rasif Ajwad Michael Domaratzki Qian Liu Nikta Feizi Pingzhao Hu Identification of significantly mutated subnetworks in the breast cancer genome
description	Abstract Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.
format	article
author	Rasif Ajwad Michael Domaratzki Qian Liu Nikta Feizi Pingzhao Hu
author_facet	Rasif Ajwad Michael Domaratzki Qian Liu Nikta Feizi Pingzhao Hu
author_sort	Rasif Ajwad
title	Identification of significantly mutated subnetworks in the breast cancer genome
title_short	Identification of significantly mutated subnetworks in the breast cancer genome
title_full	Identification of significantly mutated subnetworks in the breast cancer genome
title_fullStr	Identification of significantly mutated subnetworks in the breast cancer genome
title_full_unstemmed	Identification of significantly mutated subnetworks in the breast cancer genome
title_sort	identification of significantly mutated subnetworks in the breast cancer genome
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/35eb7bde4a5b4476838f5f4ffa421d0f
work_keys_str_mv	AT rasifajwad identificationofsignificantlymutatedsubnetworksinthebreastcancergenome AT michaeldomaratzki identificationofsignificantlymutatedsubnetworksinthebreastcancergenome AT qianliu identificationofsignificantlymutatedsubnetworksinthebreastcancergenome AT niktafeizi identificationofsignificantlymutatedsubnetworksinthebreastcancergenome AT pingzhaohu identificationofsignificantlymutatedsubnetworksinthebreastcancergenome
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Identification of significantly mutated subnetworks in the breast cancer genome

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