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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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Diseases associated with the genes 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. Diseases associated with the genes in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes associated with a certain disease are also colored based on the color code for the corresponding disease. An asterisk indicates a disease that is significantly enriched in the corresponding differential network.
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pcbi-1003713-g005: Diseases associated with the genes 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. Diseases associated with the genes in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes associated with a certain disease are also colored based on the color code for the corresponding disease. An asterisk indicates a disease that is significantly enriched in the corresponding differential network.

Mentions: The T4-2 differential network, on the other hand, is significantly enriched with genes involved in a number of pathways important for tumor growth and progression, such as ErbB and MAPK signaling pathways, ECM-receptor interaction, and regulation of actin cytoskeleton pathways (Figure 4B, Table S1B). Moreover, disease relevance analysis showed that genes in the T4-2 differential network are associated with various cancers, such as small cell lung cancer, renal cell carcinoma, colorectal cancer, among others (Figure 5B, Table S3B), and that genes involved in the pathways such as ErbB and MAPK signaling pathways, focal adhesion, and ECM-receptor interaction (Table S1B) have significant association with pathways in cancer (FDR adjusted p-values<0.1). These data are supported by biological evidence showing that ErbB and MAPK signaling pathways, microenvironment, and integrity of tissue architecture play significant roles in the malignant T4-2 cells [4], [6]–[8]. Together, these functional results suggest that Treegl can indeed reveal biological characteristic that is specific to the states in the HMT3522 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)

Diseases associated with the genes 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. Diseases associated with the genes in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes associated with a certain disease are also colored based on the color code for the corresponding disease. An asterisk indicates a disease that is significantly enriched in the corresponding differential network.
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Related In: Results  -  Collection

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

pcbi-1003713-g005: Diseases associated with the genes 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. Diseases associated with the genes in the network (unadjusted p-values<0.05) are color-coded and shown on the right; genes associated with a certain disease are also colored based on the color code for the corresponding disease. An asterisk indicates a disease that is significantly enriched in the corresponding differential network.
Mentions: The T4-2 differential network, on the other hand, is significantly enriched with genes involved in a number of pathways important for tumor growth and progression, such as ErbB and MAPK signaling pathways, ECM-receptor interaction, and regulation of actin cytoskeleton pathways (Figure 4B, Table S1B). Moreover, disease relevance analysis showed that genes in the T4-2 differential network are associated with various cancers, such as small cell lung cancer, renal cell carcinoma, colorectal cancer, among others (Figure 5B, Table S3B), and that genes involved in the pathways such as ErbB and MAPK signaling pathways, focal adhesion, and ECM-receptor interaction (Table S1B) have significant association with pathways in cancer (FDR adjusted p-values<0.1). These data are supported by biological evidence showing that ErbB and MAPK signaling pathways, microenvironment, and integrity of tissue architecture play significant roles in the malignant T4-2 cells [4], [6]–[8]. Together, these functional results suggest that Treegl can indeed reveal biological characteristic that is specific to the states in the HMT3522 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