Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cel...

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Main Authors:	Parikh, Ankur P., Curtis, Ross E., Kuhn, Irene, Becker-Weimann, Sabine, Bissell, Mina, Xing, Eric P., Wu, Wei
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109850/

id	pubmed-4109850
recordtype	oai_dc
spelling	pubmed-41098502014-07-29 Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm Parikh, Ankur P. Curtis, Ross E. Kuhn, Irene Becker-Weimann, Sabine Bissell, Mina Xing, Eric P. Wu, Wei Research Article The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer. Public Library of Science 2014-07-24 /pmc/articles/PMC4109850/ /pubmed/25057922 http://dx.doi.org/10.1371/journal.pcbi.1003713 Text en © 2014 Parikh et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Parikh, Ankur P. Curtis, Ross E. Kuhn, Irene Becker-Weimann, Sabine Bissell, Mina Xing, Eric P. Wu, Wei
spellingShingle	Parikh, Ankur P. Curtis, Ross E. Kuhn, Irene Becker-Weimann, Sabine Bissell, Mina Xing, Eric P. Wu, Wei Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
author_facet	Parikh, Ankur P. Curtis, Ross E. Kuhn, Irene Becker-Weimann, Sabine Bissell, Mina Xing, Eric P. Wu, Wei
author_sort	Parikh, Ankur P.
title	Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
title_short	Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
title_full	Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
title_fullStr	Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
title_full_unstemmed	Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm
title_sort	network analysis of breast cancer progression and reversal using a tree-evolving network algorithm
description	The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109850/
_version_	1613117592359141376

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

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