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

Parikh, AP and Curtis, RE and Kuhn, I and Becker-Weimann, S and Bissell, M and Xing, EP and Wu, W (2014) Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm. PLoS Computational Biology, 10 (7). ISSN 1553-734X

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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. © 2014 Parikh et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Parikh, AP
Curtis, RE
Kuhn, I
Becker-Weimann, S
Bissell, M
Xing, EP
Wu, W
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 January 2014
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 10
Number: 7
DOI or Unique Handle: 10.1371/journal.pcbi.1003713
Schools and Programs: Dietrich School of Arts and Sciences > Computational Biology
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 24 Sep 2014 15:58
Last Modified: 29 Jan 2019 15:55


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