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Network analysis of breast cancer progression and reversal using a tree-evolving network algorithm.

Parikh AP, Curtis RE, Kuhn I, Becker-Weimann S, Bissell M, Xing EP, Wu W - PLoS Comput. Biol. (2014)

Bottom Line: We found that different breast cell states contain distinct gene networks.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.

View Article: PubMed Central - PubMed

Affiliation: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
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.

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Selected pathways or GO groups in the neighborhood of the three hubs in the differential networks of the breast cell states.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; and (C) PAPD7 in the MMP-T4R differential network. An asterisk indicates a pathway or a GO group that is significantly enriched in the corresponding differential network.
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pcbi-1003713-g007: Selected pathways or GO groups in the neighborhood of the three hubs in the differential networks of the breast cell states.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; and (C) PAPD7 in the MMP-T4R differential network. An asterisk indicates a pathway or a GO group that is significantly enriched in the corresponding differential network.

Mentions: Figure 7A shows the NEBL subnetwork in the S1 differential network. NEBL encodes a member of the nebulin family of proteins, which bind actin and are components of focal adhesion complex. Our data showed that decreased expression of NEBL is associated with 36% of 15-year survival rate for breast cancer, suggesting a protective role of this protein when overexpressed. Genes interacting with NEBL in the NEBL subnetwork are mainly involved in energy production by oxidation of organic compounds, actin and cytoskeletal protein binding, regulation of growth, and anatomical structure morphogenesis, all of which are consistent with the biological evidence suggesting involvement of nebulin in migratory cells [41].


Network analysis of breast cancer progression and reversal using a tree-evolving network algorithm.

Parikh AP, Curtis RE, Kuhn I, Becker-Weimann S, Bissell M, Xing EP, Wu W - PLoS Comput. Biol. (2014)

Selected pathways or GO groups in the neighborhood of the three hubs in the differential networks of the breast cell states.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; and (C) PAPD7 in the MMP-T4R differential network. An asterisk indicates a pathway or a GO group that is significantly enriched in the corresponding differential network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109850&req=5

pcbi-1003713-g007: Selected pathways or GO groups in the neighborhood of the three hubs in the differential networks of the breast cell states.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; and (C) PAPD7 in the MMP-T4R differential network. An asterisk indicates a pathway or a GO group that is significantly enriched in the corresponding differential network.
Mentions: Figure 7A shows the NEBL subnetwork in the S1 differential network. NEBL encodes a member of the nebulin family of proteins, which bind actin and are components of focal adhesion complex. Our data showed that decreased expression of NEBL is associated with 36% of 15-year survival rate for breast cancer, suggesting a protective role of this protein when overexpressed. Genes interacting with NEBL in the NEBL subnetwork are mainly involved in energy production by oxidation of organic compounds, actin and cytoskeletal protein binding, regulation of growth, and anatomical structure morphogenesis, all of which are consistent with the biological evidence suggesting involvement of nebulin in migratory cells [41].

Bottom Line: We found that different breast cell states contain distinct gene networks.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.

View Article: PubMed Central - PubMed

Affiliation: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus