Limits...
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.

Show MeSH

Related in: MedlinePlus

A schematic representation of the relationship of the non-reverted and various conditions of the reverted HMT3522 breast cells.The nonmalignant S1 cell is the root of the tree. It is also the parent of the malignant T4-2 cell since T4-2 cells were derived from S1. The T4-2 cells can be reverted to phenotypically normal-looking structures by treatment with various agents, such as: i) either EGFR or β1-integrin inhibitor, ii) either PI3K or MAPKK inhibitor, or iii) MMP inhibitors, they are thus represented as the parent of the various conditions of the reverted T4-2 cells. Microarray profiles were generated from each cell state represented in the tree, and gene networks specific to each state were reverse engineered using Treegl.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4109850&req=5

pcbi-1003713-g001: A schematic representation of the relationship of the non-reverted and various conditions of the reverted HMT3522 breast cells.The nonmalignant S1 cell is the root of the tree. It is also the parent of the malignant T4-2 cell since T4-2 cells were derived from S1. The T4-2 cells can be reverted to phenotypically normal-looking structures by treatment with various agents, such as: i) either EGFR or β1-integrin inhibitor, ii) either PI3K or MAPKK inhibitor, or iii) MMP inhibitors, they are thus represented as the parent of the various conditions of the reverted T4-2 cells. Microarray profiles were generated from each cell state represented in the tree, and gene networks specific to each state were reverse engineered using Treegl.

Mentions: Indeed, a number of in-depth investigations of disease models have suggested that over the course of cellular transformation in response to microenvironmental changes due to disease progression or drug-induced reversion, there may exist multiple underlying “themes” that determine each molecule's function and relationship with other molecules [4], [5]. As a result, molecular networks at each cellular stage are context-dependent and can undergo systematic rewiring (Figure 1). For example, strong evidence of alterations of various pathways have been reported in the HMT3522 progression series of breast cells when malignant T4-2 cells were phenotypically reverted by various drugs, albeit only manifested by a small number of well-known signaling molecules as discussed below [6]–[8].


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)

A schematic representation of the relationship of the non-reverted and various conditions of the reverted HMT3522 breast cells.The nonmalignant S1 cell is the root of the tree. It is also the parent of the malignant T4-2 cell since T4-2 cells were derived from S1. The T4-2 cells can be reverted to phenotypically normal-looking structures by treatment with various agents, such as: i) either EGFR or β1-integrin inhibitor, ii) either PI3K or MAPKK inhibitor, or iii) MMP inhibitors, they are thus represented as the parent of the various conditions of the reverted T4-2 cells. Microarray profiles were generated from each cell state represented in the tree, and gene networks specific to each state were reverse engineered using Treegl.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003713-g001: A schematic representation of the relationship of the non-reverted and various conditions of the reverted HMT3522 breast cells.The nonmalignant S1 cell is the root of the tree. It is also the parent of the malignant T4-2 cell since T4-2 cells were derived from S1. The T4-2 cells can be reverted to phenotypically normal-looking structures by treatment with various agents, such as: i) either EGFR or β1-integrin inhibitor, ii) either PI3K or MAPKK inhibitor, or iii) MMP inhibitors, they are thus represented as the parent of the various conditions of the reverted T4-2 cells. Microarray profiles were generated from each cell state represented in the tree, and gene networks specific to each state were reverse engineered using Treegl.
Mentions: Indeed, a number of in-depth investigations of disease models have suggested that over the course of cellular transformation in response to microenvironmental changes due to disease progression or drug-induced reversion, there may exist multiple underlying “themes” that determine each molecule's function and relationship with other molecules [4], [5]. As a result, molecular networks at each cellular stage are context-dependent and can undergo systematic rewiring (Figure 1). For example, strong evidence of alterations of various pathways have been reported in the HMT3522 progression series of breast cells when malignant T4-2 cells were phenotypically reverted by various drugs, albeit only manifested by a small number of well-known signaling molecules as discussed below [6]–[8].

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