The treatment of many cancers centers on risk-adapted therapy. Patient stratification methodologies are, therefore, essential to the design of effective treatment strategies. Our research focus is to develop transcriptional regulatory networks of cancer that can predict a patient’s response to various therapies and their risk of progression. This approach associates mechanistic underpinnings to the various disease states of a given cancer type. Thus, it enables rational approaches to directing high-risk transcriptional states to become low-risk. In this way, we hope to not only identify the best available treatment for a given patient, but to discover new therapies for effectively modulating gene regulation.
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Plaisier, Christopher L., Sofie O’Brien, Brady Bernard, Sheila Reynolds, Zac Simon, Chad M. Toledo, Yu Ding, David J. Reiss, Patrick J. Paddison, and Nitin S. Baliga. “Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.” Cell Systems 3, no. 2 (August 2016): 172–86. https://doi.org/10.1016/j.cels.2016.06.006. Cite