(692g) Identification of a Novel Target for Breast Cancer by Exploring Gene Switches On a Genome Scale

Wu, M., Michigan State University
Liu, L., Michigan State University
Chan, C., Michigan State Uiversity

An important dynamic feature that emerges from analyzing gene regulatory network is the “switch-like behavior” or “bistability”, which is a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches play pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to systematically identify gene switches. We propose a top-down mining approach to identify gene switches from microarray gene expression data. Taking advantage of the tremendous amount of expression data, our approach aims to identify bimodality, which we hypothesize is an essential characteristic of a gene switch. We perform theoretical analysis based on kinetic modeling and simulation, and provide proof-of-concept applications on both synthetic and yeast microarray datasets.

Since the state of gene switches in the genetic network governs the phenotype, we postulate that recognizing specific gene switches will enable one to identify biomarkers or molecular signatures that would be better drug targets for treating a disease. We demonstrate the utility of our mining approach with human breast cancer by analyzing a paired breast cancer/normal tissue expression dataset against the integrated human gene expression dataset. Our discovery highlights TACSTD2 as an important biomarker for both ER+ and ER– breast cancer subtypes, as well as an attractive candidate for drug therapy against the triple negative (ER–, PR– (progesterone receptor) and HER2–) subtype of breast cancer. In addition, the target has potential implications for treating drug-resistant cases that are non-responsive to ER/HER2-targeted therapies. These results demonstrate the ability of our mining approach to identify candidate biomarkers and novel therapeutic targets in cancer. We predict through annotation, sequence matching of TF sites, and TF activity estimation, a novel transcriptional mechanisms by which TACSTD2 is regulated. Our experiments in MCF-7 (ER+/PR+) and MDA-MB-231 (ER–/PR–) breast cancer cell lines confirm the functional role of this gene in breast cancer and for the first time identify a likely transcription factor involved.