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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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Illustration of selected enriched pathways in the differential network of each breast cell state.(A) S1; (B) T4; (C) the EGFR/ITGB1-T4R group; (D) the PI3K/MAPKK-T4R group; and (E) the MMP-T4R group. In each plot, the differential network for the corresponding cell state is shown. Nodes represent genes; edges represent interaction of the genes. Selected pathways enriched in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes participating in the selected pathways are also colored based on the color code for the corresponding pathway. An asterisk indicates a pathway that is significantly enriched in the corresponding differential network. The pathway names are shortened to save space. See Table S1 for detailed information about the enriched pathways in each cell state.
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pcbi-1003713-g004: Illustration of selected enriched pathways in the differential network of each breast cell state.(A) S1; (B) T4; (C) the EGFR/ITGB1-T4R group; (D) the PI3K/MAPKK-T4R group; and (E) the MMP-T4R group. In each plot, the differential network for the corresponding cell state is shown. Nodes represent genes; edges represent interaction of the genes. Selected pathways enriched in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes participating in the selected pathways are also colored based on the color code for the corresponding pathway. An asterisk indicates a pathway that is significantly enriched in the corresponding differential network. The pathway names are shortened to save space. See Table S1 for detailed information about the enriched pathways in each cell state.

Mentions: Our pathway and GO analysis showed that genes in the S1 differential network are significantly enriched with those involved in normal cellular processes, such as cell cycle, TCA cycle, and cellular respiration (Figure 4A, Tables S1A & S2A). Notably, genes involved in tube lumen formation are enriched only in S1 cells (Table S2A) while absent in the other cell states, consistent with the observation that a central lumen is always present in the acinus structure formed by S1 cells but often absent from spheres formed by reverted T4-2 cells. Furthermore, our disease relevance analysis did not find association of the genes in the S1 differential network with any disease (Table S3A). Together, these results agree with the biological fact that S1 cells are nonmalignant.


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)

Illustration of selected enriched pathways in the differential network of each breast cell state.(A) S1; (B) T4; (C) the EGFR/ITGB1-T4R group; (D) the PI3K/MAPKK-T4R group; and (E) the MMP-T4R group. In each plot, the differential network for the corresponding cell state is shown. Nodes represent genes; edges represent interaction of the genes. Selected pathways enriched in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes participating in the selected pathways are also colored based on the color code for the corresponding pathway. An asterisk indicates a pathway that is significantly enriched in the corresponding differential network. The pathway names are shortened to save space. See Table S1 for detailed information about the enriched pathways in each cell state.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003713-g004: Illustration of selected enriched pathways in the differential network of each breast cell state.(A) S1; (B) T4; (C) the EGFR/ITGB1-T4R group; (D) the PI3K/MAPKK-T4R group; and (E) the MMP-T4R group. In each plot, the differential network for the corresponding cell state is shown. Nodes represent genes; edges represent interaction of the genes. Selected pathways enriched in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes participating in the selected pathways are also colored based on the color code for the corresponding pathway. An asterisk indicates a pathway that is significantly enriched in the corresponding differential network. The pathway names are shortened to save space. See Table S1 for detailed information about the enriched pathways in each cell state.
Mentions: Our pathway and GO analysis showed that genes in the S1 differential network are significantly enriched with those involved in normal cellular processes, such as cell cycle, TCA cycle, and cellular respiration (Figure 4A, Tables S1A & S2A). Notably, genes involved in tube lumen formation are enriched only in S1 cells (Table S2A) while absent in the other cell states, consistent with the observation that a central lumen is always present in the acinus structure formed by S1 cells but often absent from spheres formed by reverted T4-2 cells. Furthermore, our disease relevance analysis did not find association of the genes in the S1 differential network with any disease (Table S3A). Together, these results agree with the biological fact that S1 cells are nonmalignant.

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