ABSTRACT/SUMMARY – Overall Systems Analysis of Epigenomic Architecture in Cancer Progression Despite anti-hormone therapies in patients, the cognate receptors ER and AR can remain functional to support oncogenic signaling for advanced progression of breast and prostate cancers. Intensive studies have uncovered cellular and biochemical changes underlying the development of hormone resistance. However, epigenetic mechanisms for establishing and maintaining a hormone-resistant phenotype remain to be explored. Our preliminary studies have found remarkably similar epigenetic machineries that regulate hormone-independent gene transcription in both breast and prostate cancers. This process has multifaceted components, involving trans- and cis-acting elements, nucleosome reorganization, and chromatin interactions. To understand this complex mechanism, the San Antonio-Ohio State University Research Center for Cancer Systems Biology (SA-OSU RCCSB) has assembled a team of 21 experimental and computational investigators, and oncologists who will study a three-tiered epigenetic framework for gene regulation. First, microenvironmental cues initiate the recruitment of a specific combination of trans-bound transcription factors (TFs), called MegaTrans TFs, to ER or AR-bound enhancers (Project 1). MegaTrans TFs are composed of diverse signaling-dependent transcription factors that activate these enhancers through receiving other signal cues without hormone stimulation. Second, this hormone-independent action requires well-orchestrated repositioning of nucleosomes, enabling maximal MegaTrans-DNA contact in target chromatin regions (Project 2). Pioneer factor FOXA1 and chromatin remodelers are also critical regulators of repositioned nucleosomes during the transition of a hormone-sensitive to -resistant phenotype. Third, this concerted action triggers chromatin movement, remotely bringing the MegaTrans/enhancer complexes in close proximity to target promoters (Project 3). Intra- and inter-chromatin interactions facilitate the formation of transcriptional architectures that efficiently and autonomously regulate ER/AR-mediated gene expression even in the absence of agonists or in the presence of antagonists. Experimental investigators will use omics-seq platforms to map combinatorial MegaTrans complexes, repositioned nucleosomes, and topologically associated domains (TADs) that spatiotemporally regulate hormone-independent transcription. Computational scientists will then use omics data to derive 3D models of DNA-eRNA-protein interacting units in subnuclear compartments of cancer cells. Back to the bench, experimental scientists will use in silico findings to validate enhancer/gene markers that predict a hormone-resistant phenotype in patient-derived xenografts (PDXs) and clinical samples. To ensure seamless data integration of the three proposed projects, a Data Analysis and Management Core will implement customized toolkits to manage computational infrastructure...