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A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes
BACKGROUND: The rapid collection of diverse genome-scale data raises the urgent need to integrate and utilise these resources for biological discovery or biomedical applications. For example, diverse transcriptomic and gene copy number variation data are currently collected for various cancers, but...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304402/ https://www.ncbi.nlm.nih.gov/pubmed/22343619 http://dx.doi.org/10.1038/bjc.2011.584 |
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author | Dutta, B Pusztai, L Qi, Y André, F Lazar, V Bianchini, G Ueno, N Agarwal, R Wang, B Shiang, C Y Hortobagyi, G N Mills, G B Symmans, W F Balázsi, G |
author_facet | Dutta, B Pusztai, L Qi, Y André, F Lazar, V Bianchini, G Ueno, N Agarwal, R Wang, B Shiang, C Y Hortobagyi, G N Mills, G B Symmans, W F Balázsi, G |
author_sort | Dutta, B |
collection | PubMed |
description | BACKGROUND: The rapid collection of diverse genome-scale data raises the urgent need to integrate and utilise these resources for biological discovery or biomedical applications. For example, diverse transcriptomic and gene copy number variation data are currently collected for various cancers, but relatively few current methods are capable to utilise the emerging information. METHODS: We developed and tested a data-integration method to identify gene networks that drive the biology of breast cancer clinical subtypes. The method simultaneously overlays gene expression and gene copy number data on protein–protein interaction, transcriptional-regulatory and signalling networks by identifying coincident genomic and transcriptional disturbances in local network neighborhoods. RESULTS: We identified distinct driver-networks for each of the three common clinical breast cancer subtypes: oestrogen receptor (ER)+, human epidermal growth factor receptor 2 (HER2)+, and triple receptor-negative breast cancers (TNBC) from patient and cell line data sets. Driver-networks inferred from independent datasets were significantly reproducible. We also confirmed the functional relevance of a subset of randomly selected driver-network members for TNBC in gene knockdown experiments in vitro. We found that TNBC driver-network members genes have increased functional specificity to TNBC cell lines and higher functional sensitivity compared with genes selected by differential expression alone. CONCLUSION: Clinical subtype-specific driver-networks identified through data integration are reproducible and functionally important. |
format | Online Article Text |
id | pubmed-3304402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-33044022013-03-13 A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes Dutta, B Pusztai, L Qi, Y André, F Lazar, V Bianchini, G Ueno, N Agarwal, R Wang, B Shiang, C Y Hortobagyi, G N Mills, G B Symmans, W F Balázsi, G Br J Cancer Translational Therapeutics BACKGROUND: The rapid collection of diverse genome-scale data raises the urgent need to integrate and utilise these resources for biological discovery or biomedical applications. For example, diverse transcriptomic and gene copy number variation data are currently collected for various cancers, but relatively few current methods are capable to utilise the emerging information. METHODS: We developed and tested a data-integration method to identify gene networks that drive the biology of breast cancer clinical subtypes. The method simultaneously overlays gene expression and gene copy number data on protein–protein interaction, transcriptional-regulatory and signalling networks by identifying coincident genomic and transcriptional disturbances in local network neighborhoods. RESULTS: We identified distinct driver-networks for each of the three common clinical breast cancer subtypes: oestrogen receptor (ER)+, human epidermal growth factor receptor 2 (HER2)+, and triple receptor-negative breast cancers (TNBC) from patient and cell line data sets. Driver-networks inferred from independent datasets were significantly reproducible. We also confirmed the functional relevance of a subset of randomly selected driver-network members for TNBC in gene knockdown experiments in vitro. We found that TNBC driver-network members genes have increased functional specificity to TNBC cell lines and higher functional sensitivity compared with genes selected by differential expression alone. CONCLUSION: Clinical subtype-specific driver-networks identified through data integration are reproducible and functionally important. Nature Publishing Group 2012-03-13 2012-02-16 /pmc/articles/PMC3304402/ /pubmed/22343619 http://dx.doi.org/10.1038/bjc.2011.584 Text en Copyright © 2012 Cancer Research UK https://creativecommons.org/licenses/by/4.0/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 https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Translational Therapeutics Dutta, B Pusztai, L Qi, Y André, F Lazar, V Bianchini, G Ueno, N Agarwal, R Wang, B Shiang, C Y Hortobagyi, G N Mills, G B Symmans, W F Balázsi, G A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title | A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title_full | A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title_fullStr | A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title_full_unstemmed | A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title_short | A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
title_sort | network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes |
topic | Translational Therapeutics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304402/ https://www.ncbi.nlm.nih.gov/pubmed/22343619 http://dx.doi.org/10.1038/bjc.2011.584 |
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