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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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Kaplan–Meier curves estimating the association of different expression values of three hubs in the differential networks of the breast cell states with survival of breast cancer patients.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; (C) PAPD7 in the MMP-T4R differential network.
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pcbi-1003713-g006: Kaplan–Meier curves estimating the association of different expression values of three hubs in the differential networks of the breast cell states with survival of breast cancer patients.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; (C) PAPD7 in the MMP-T4R differential network.

Mentions: In order to identify potential novel drug targets, we examined three hubs, NEBL, HBEGF, and PAPD7, whose extreme (i.e., either lower or higher) expression values are correlated with the lowest 15-year patient survival rates (35%, 30% and 34%, respectively) and also with low 10-year survival rates (60%, 42% and 56%, respectively) in the examined dataset (Figure 6). Previous evidence has shown that abnormal expression of TACE, TGFA, and AREG are associated with 62%, 61% and 54% of the 10-year survival rates respectively, and associated with 57%, 50% and 54% of the 15-year survival rates respectively, in the same cohort of the patients [12]. Our results, therefore, suggest that NEBL, HBEGF, and PAPD7, similar to TACE, TGFA, and AREG, also play important roles in breast cells.


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)

Kaplan–Meier curves estimating the association of different expression values of three hubs in the differential networks of the breast cell states with survival of breast cancer patients.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; (C) PAPD7 in the MMP-T4R differential network.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003713-g006: Kaplan–Meier curves estimating the association of different expression values of three hubs in the differential networks of the breast cell states with survival of breast cancer patients.(A) NEBL in the S1 differential network; (B) HBEGF in the T4-2 differential network; (C) PAPD7 in the MMP-T4R differential network.
Mentions: In order to identify potential novel drug targets, we examined three hubs, NEBL, HBEGF, and PAPD7, whose extreme (i.e., either lower or higher) expression values are correlated with the lowest 15-year patient survival rates (35%, 30% and 34%, respectively) and also with low 10-year survival rates (60%, 42% and 56%, respectively) in the examined dataset (Figure 6). Previous evidence has shown that abnormal expression of TACE, TGFA, and AREG are associated with 62%, 61% and 54% of the 10-year survival rates respectively, and associated with 57%, 50% and 54% of the 15-year survival rates respectively, in the same cohort of the patients [12]. Our results, therefore, suggest that NEBL, HBEGF, and PAPD7, similar to TACE, TGFA, and AREG, also play important roles in breast cells.

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