Abstract A35: Predicting drugs effect on transcription factor activity by regulatory network analysis of drug perturbed cellular states

Abstract

Abstract Phenotypic changes during pathophysiological processes can be efficiently captured by genome-wide gene expression signatures or molecular phenotypes. Transcription factors (TFs) are the key components of the transcriptional regulatory network that integrates intracellular and extracellular stimuli and generates a response in the form of specific gene expression signatures. We have shown the existence and dissected the components of this integrative logic layer in several pathologic and physiologic processes. Moreover, perturbation of key components of this machinery can be efficiently used to induce desired phenotypic changes. However, TFs have been rather elusive targets for pharmacological intervention. Here, by using a cell-context specific network-based approach, we generated a comprehensive map between small compounds and TF activity. We leveraged the large collection of expression profiles in response to small molecule perturbagens from CMAP to first reverse-engineer transcriptionally regulatory networks in 3 different cellular context (Breast: MCF7, Prostate:PC3 and Myeloma:HL60) by the ARACNe algorithm, and then interrogate these networks with the MARINa algorithm to identify TFs whose activity is affected by the profiled compounds. We show two different evidences that validate our method. First, we correctly identified ESR1 as the very top TF whose activity is affected by the estrogen antagonists tamoxifen, raloxifen and clomifene. Second, we selected 10 compounds from the 1,400 represented in the MCF7 CMAP dataset that our method predict as STAT3 perturbagens. From these, 4 showed a significant inhibition of STAT3 activity in a cell-based STAT3 reported assay. Citation Format: Yao Shen, Mariano Alvarez, Sergey Pampou, Jorida Coku, Andrea Califano. Predicting drugs effect on transcription factor activity by regulatory network analysis of drug perturbed cellular states [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr A36.