Prolactin Drives a Dynamic STAT5A/HDAC6/HMGN2 Cis-Regulatory Landscape Exploitable in ER+Breast Cancer

Abstract

The hormone prolactin has been implicated in breast cancer pathogenesis and regulates chromatin engagement by the transcription factor, STAT5A. STAT5A is known to inducibly bind promoters and cis-regulatory elements genome-wide, though the mechanisms by which it exerts specificity and regulation of target gene expression remain enigmatic. We previously identified HDAC6 and HMGN2 as cofactors that facilitate prolactin-induced, STAT5A-mediated gene expression. Here, multicondition STAT5A, HDAC6, and HMGN2 chromatin immunoprecipitation and sequencing with parallel condition RNA-seq are utilized to reveal the cis-regulatory landscape and cofactor dynamics underlying prolactin-stimulated gene expression in breast cancer. We find that prolactin-regulated genes are significantly enriched for cis-regulatory elements bound by HDAC6 and HMGN2, and that inducible STAT5A binding at enhancers, rather than promoters, conveys specificity for prolactin-regulated genes. The selective HDAC6 inhibitor, ACY-241, blocks prolactin-induced STAT5A chromatin engagement at cis-regulatory elements as well as a significant proportion of prolactin-stimulated gene expression. We identify functional pathways known to contribute to the development and/or progression of breast cancer that are activated by prolactin and inhibited by ACY-241. Additionally, we find that the DNA sequences underlying shared STAT5A and HDAC6 binding sites at enhancers are differentially enriched for estrogen response elements (ESR1 and ESR2 motifs) relative to enhancers bound by STAT5A alone. Gene set enrichment analysis identifies significant overlap of ERα-regulated genes with genes regulated by prolactin, particularly prolactin-regulated genes with promoters or enhancers co-occupied by both STAT5A and HDAC6. Lastly, the therapeutic efficacy of ACY-241 is demonstrated in in vitro and in vivo breast cancer models, where we identify synergistic ACY-241 drug combinations and observe differential sensitivity of ER+ models relative to ER- models.