Identification of novel targets for breast cancer by exploring gene switches on a genome scale
1 Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
2 Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
3 Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA
4 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824,USA
BMC Genomics 2011, 12:547 doi:10.1186/1471-2164-12-547Published: 3 November 2011
An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.
We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.
Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.