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Background Recent studies suggested that crosstalk between ERα and EGFR/HER2 pathways plays a critical role in mediating endocrine therapy resistance. Several inhibitors targeting EGFR/HER2 signaling, including FDA-approved lapatinib and gefitinib as well as a novel dual tyrosine kinase inhibitor (TKI) sapitinib, showed greater therapeutic efficacies. However, how 3D chromatin landscape responds to the inhibition of EGFR/HER2 pathway remains to be elucidated. Methods In this study, we conducted in situ Hi-C and RNA-seq in two ERα+ breast cancer cell systems, 1) parental MCF7 cells and its associated tamoxifen-resistant MCF7TR cells; and 2) parental T47D cells and its associated tamoxifen-resistant T47DTR cells, before and after the treatment of sapitinib. Results We identified differential responses in topologically associated domains (TADs), looping genes and expressed genes. Interestingly, we found that many differential TADs and looping genes are reversible after sapitinib treatment, indicating that EGFR/HER2 signaling may play a role in reshaping and rewiring the high order genome organization. We further examined and recapitulated the reversible looping genes in 3D spheroids of breast cancer cells, demonstrating that 3D cell culture spheroid of breast cancer cells could be a potential preclinical breast cancer model for studying 3D chromatin regulation. Conclusions Our study has provided significant insights into our understanding of 3D genomic landscape changes in response to EGFR/HER2 Inhibition in endocrine-resistant breast cancer cells. Our data provides a rich resource for further evaluating chromatin structural responses to EGFR/HER2 targeted therapies in endocrine-resistant breast cancer cells. Our analyses suggest that these alterations of chromatin structures and transcriptional programs may provide new avenues for intervention or designing of patient selection for targeted endocrine treatment.

Progesterone and prolactin are two key hormones involved in development and remodeling of the mammary gland. As such, both hormones have been linked to breast cancer. Despite the overlap between biological processes ascribed to these two hormones, little is known about how co-expression of both hormones affects their individual actions. Progesterone and prolactin exert many of their effects on the mammary gland through activation of gene expression, either directly (progesterone, binding to the progesterone receptor [PR]) or indirectly (multiple transcription factors being activated downstream of prolactin, most notably STAT5). Using RNA-seq in T47D breast cancer cells, we characterized the gene expression programs regulated by progestin and prolactin, either alone or in combination. We found significant crosstalk and fine-tuning between the transcriptional programs executed by each hormone independently and in combination. We divided and characterized the transcriptional programs into four broad categories. All crosstalk/fine-tuning shown to be modulated by progesterone was dependent upon the expression of PR. Moreover, PR was recruited to enhancer regions of all regulated genes. Interestingly, despite the canonical role for STAT5 in transducing prolactin-signaling in the normal and lactating mammary gland, very few of the prolactin-regulated transcriptional programs fine-tuned by progesterone in this breast cancer cell line model system were in fact dependent upon STAT5. Cumulatively, these data suggest that the interplay of progesterone and prolactin in breast cancer impacts gene expression in a more complex and nuanced manner than previously thought, and likely through different transcriptional regulators than those observed in the normal mammary gland. Studying gene regulation when both hormones are present is most clinically relevant, particularly in the context of breast cancer.

Purpose: Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune-related mRNAs. If this were the case, we hypothesized that immune-related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune-related mRNA targets than non-serum miRNAs., Materials and Methods: We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA–mRNA interactions in various subsets of miRNAs (serum, non-serum) and mRNAs (immune related, nonimmune related)., Results: Immune-related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune-related mRNA targets than non-serum miRNAs; however, these two biases in immune-related mRNAs and serum miRNAs appear to be completely independent., Conclusion: Immune-related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non-serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA–immune mRNA interactions than would be expected by chance.