title: Network analysis of breast cancer progression and reversal using a tree-evolving network algorithm.
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.
authors: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors: Ankur P
authors: AP
authors: Parikh
authors: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors: Ross E
authors: RE
authors: Curtis
authors: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
authors: Irene
authors: I
authors: Kuhn
authors: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
authors: Sabine
authors: S
authors: Becker-Weimann
authors: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
authors: Mina
authors: M
authors: Bissell
authors: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America.
authors: Eric P
authors: EP
authors: Xing
authors: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors: Wei
authors: W
authors: Wu
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D053208:Kaplan-Meier Estimate; D008390:Markov Chains; D020411:Oligonucleotide Array Sequence Analysis