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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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Simulation results comparing the performance of Treegl to a method estimating a single static network and a method estimating each network independently.In all cases, 70 networks were generated that are related by a tree lineage (See Materials and Methods for details). (A) Each network has 30 nodes and 5 samples. (B) Each network has 30 nodes and 10 samples. (C) Each network has 50 nodes and 5 samples. (D) Each network has 50 nodes and 10 samples. In all cases, Treegl (shown in blue) performs favorably to the method estimating a single static network (red) or the method estimating each network independently (green).
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pcbi-1003713-g003: Simulation results comparing the performance of Treegl to a method estimating a single static network and a method estimating each network independently.In all cases, 70 networks were generated that are related by a tree lineage (See Materials and Methods for details). (A) Each network has 30 nodes and 5 samples. (B) Each network has 30 nodes and 10 samples. (C) Each network has 50 nodes and 5 samples. (D) Each network has 50 nodes and 10 samples. In all cases, Treegl (shown in blue) performs favorably to the method estimating a single static network (red) or the method estimating each network independently (green).

Mentions: In order to evaluate how well Treegl can recover the underlying network structures for the samples in the simulation data, we compare Treegl with the static method estimating a single network and the method estimating each network independently by plotting the precision-recall curves which show the recall for different values of precision based on the network estimated by the three methods. As illustrated in Figures 3 & S2, Treegl performs favorably to the other two methods. It should also be noted that compared to the static method which produces only one network, Treegl can produce different networks related by the tree lineage. The independent method also produces different networks but it performs poorly compared to Treegl.


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)

Simulation results comparing the performance of Treegl to a method estimating a single static network and a method estimating each network independently.In all cases, 70 networks were generated that are related by a tree lineage (See Materials and Methods for details). (A) Each network has 30 nodes and 5 samples. (B) Each network has 30 nodes and 10 samples. (C) Each network has 50 nodes and 5 samples. (D) Each network has 50 nodes and 10 samples. In all cases, Treegl (shown in blue) performs favorably to the method estimating a single static network (red) or the method estimating each network independently (green).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003713-g003: Simulation results comparing the performance of Treegl to a method estimating a single static network and a method estimating each network independently.In all cases, 70 networks were generated that are related by a tree lineage (See Materials and Methods for details). (A) Each network has 30 nodes and 5 samples. (B) Each network has 30 nodes and 10 samples. (C) Each network has 50 nodes and 5 samples. (D) Each network has 50 nodes and 10 samples. In all cases, Treegl (shown in blue) performs favorably to the method estimating a single static network (red) or the method estimating each network independently (green).
Mentions: In order to evaluate how well Treegl can recover the underlying network structures for the samples in the simulation data, we compare Treegl with the static method estimating a single network and the method estimating each network independently by plotting the precision-recall curves which show the recall for different values of precision based on the network estimated by the three methods. As illustrated in Figures 3 & S2, Treegl performs favorably to the other two methods. It should also be noted that compared to the static method which produces only one network, Treegl can produce different networks related by the tree lineage. The independent method also produces different networks but it performs poorly compared to Treegl.

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