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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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An example demonstrating the difference between a Markov network and a correlation network.(A) A true network, in which R is the regulator of A, B, and C. (B) A clique graph produced by a correlation network. Since all the genes in (A) are correlated with one another, the correlation network cannot distinguish between indirect and direct relationships and thus connects all the nodes. (C) A correct graph recovered by a Markov network. The Markov network recovers true relationship of the nodes because it uses conditional independence to determine the presence of an edge.
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pcbi-1003713-g002: An example demonstrating the difference between a Markov network and a correlation network.(A) A true network, in which R is the regulator of A, B, and C. (B) A clique graph produced by a correlation network. Since all the genes in (A) are correlated with one another, the correlation network cannot distinguish between indirect and direct relationships and thus connects all the nodes. (C) A correct graph recovered by a Markov network. The Markov network recovers true relationship of the nodes because it uses conditional independence to determine the presence of an edge.

Mentions: We model a gene network as a Markov network [25], which is a graph where is the set of vertices (genes), and is the set of edges. Genes and do not have an edge between them if and only if they are conditionally independent given the values of all other genes. We contrast this with a correlation network (a common approach for modeling gene networks), in which and are connected if their marginal pairwise correlation is greater than a certain threshold. Correlation can be effective when analyzing a pair of genes in isolation. However, when studying the dependence between two genes in the context of other genes, correlation can confound direct/indirect relationships, thus producing undesirable results (Figure 2).


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)

An example demonstrating the difference between a Markov network and a correlation network.(A) A true network, in which R is the regulator of A, B, and C. (B) A clique graph produced by a correlation network. Since all the genes in (A) are correlated with one another, the correlation network cannot distinguish between indirect and direct relationships and thus connects all the nodes. (C) A correct graph recovered by a Markov network. The Markov network recovers true relationship of the nodes because it uses conditional independence to determine the presence of an edge.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003713-g002: An example demonstrating the difference between a Markov network and a correlation network.(A) A true network, in which R is the regulator of A, B, and C. (B) A clique graph produced by a correlation network. Since all the genes in (A) are correlated with one another, the correlation network cannot distinguish between indirect and direct relationships and thus connects all the nodes. (C) A correct graph recovered by a Markov network. The Markov network recovers true relationship of the nodes because it uses conditional independence to determine the presence of an edge.
Mentions: We model a gene network as a Markov network [25], which is a graph where is the set of vertices (genes), and is the set of edges. Genes and do not have an edge between them if and only if they are conditionally independent given the values of all other genes. We contrast this with a correlation network (a common approach for modeling gene networks), in which and are connected if their marginal pairwise correlation is greater than a certain threshold. Correlation can be effective when analyzing a pair of genes in isolation. However, when studying the dependence between two genes in the context of other genes, correlation can confound direct/indirect relationships, thus producing undesirable results (Figure 2).

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