Hatice Osmanbeyoglu, PhD
Postdoctoral Research Associate, Department of Computational Biology, Memorial Sloan Kettering Cancer Research Center, New York
Title: Predictive gene regulatory models for precision medicine
Abstract: Massive cancer genomics efforts have been undertaken with the hopes of personalizing cancer therapy by using targeted therapies matched to the genetics of the patient’s tumor rather than cytotoxic drugs that kill all proliferating cells. In recent “basket” clinical trials, targeted therapies are chosen based on somatic alterations affecting specific pathway genes regardless of the cancer type, e.g. patients with activating mutations in PIK3CA are eligible for treatment with PI3K inhibitors whether they have breast cancer or head and neck cancer. Data from such clinical trials shows that the presence of an “actionable mutation” is not sufficient to predict a clinical response to the corresponding targeted therapy, and it is unclear when a targeted therapeutic with efficacy in one cancer will prove useful in another. To better model the context dependent role of somatic alterations, we first applied a regularized bilinear regression model to link dysregulation of upstream signaling pathways with altered transcriptional response. We used parallel (phospho)proteomic and mRNA sequencing data across the Cancer Genome Atlas (TCGA) tumor data sets for these models. We then developed a systematic regularized regression analysis to interpret the impact of mutations and copy number events in terms of functional outcomes such as (phospho)protein and transcription factor (TF) activities. Our analysis predicted distinct dysregulated transcriptional regulators downstream of similar somatic alterations in different cancers. We validated the context-specific activity of TFs associated to mutant PIK3CA in model systems. These results have implications for the pancancer use of targeted drugs and potentially for the design of combination therapies.