bioRxivCancer BiologyPreprint
BET inhibitors as a therapeutic intervention in gastrointestinal gene signature-positive castration-resistant prostate cancer.
S. Shukla, D. Li, H. Nguyen, J. Conner, G. Bayshtok, W. H. Cho, M. Pachai, N. Teri, E. Campeau, S. Attwell, P. Trojer, I. Ostrovnaya, A. Gopalan, E. Corey, P. Chi, Y. Chen
Abstract
A subgroup of castration-resistant prostate cancer (CRPC) aberrantly expresses a gastrointestinal (GI) transcriptome governed by two GI-lineage-restricted transcription factors, HNF1A and HNF4G. In this study, we found that expression of GI transcriptome in CRPC correlates with adverse clinical outcomes to androgen receptor signaling inhibitor treatment and shorter overall survival. Bromo- and extra-terminal domain inhibitors (BETi) downregulated HNF1A, HNF4G, and the GI transcriptome in multiple CRPC models, including cell lines, patient-derived organoids, and patient-derived xenografts, while AR and the androgen-dependent transcriptome were largely spared. Accordingly, BETi selectively inhibited growth of GI transcriptome-positive preclinical models of prostate cancer. Mechanistically, BETi inhibited BRD4 binding at enhancers globally, including both AR and HNF4G bound enhancers while gene expression was selectively perturbed. Restoration of HNF4G expression in the presence of BETi rescued target gene expression without rescuing BRD4 binding. This suggests that inhibition of master transcription factors expression underlies the selective transcriptional effects of BETi. SIGNIFICANCEGI transcriptome expression in CRPC is regulated by the HNF1A-HNF4G-BRD4 axis and correlates with worse clinical outcomes. Accordingly, BET inhibitors significantly reduce tumor cell growth in multiple GI-transcriptome-positive preclinical models of CRPC. Our studies point that expression of GI transcriptome could serve as a predictive biomarker to BETi therapy response.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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