Progesterone receptor (PR) expression is used as a biomarker of oestrogen receptor-α (ERα) function and breast cancer prognosis. Here we show that PR is not merely an ERα-induced gene target, but is also an ERα-associated protein that modulates its behaviour. In the presence of agonist ligands, PR associates with ERα to direct ERα chromatin binding events within breast cancer cells, resulting in a unique gene expression programme that is associated with good clinical outcome. Progesterone inhibited oestrogen-mediated growth of ERα⁺ cell line xenografts and primary ERα⁺ breast tumour explants, and had increased anti-proliferative effects when coupled with an ERα antagonist. Copy number loss of PGR, the gene coding for PR, is a common feature in ERα⁺ breast cancers, explaining lower PR levels in a subset of cases. Our findings indicate that PR functions as a molecular rheostat to control ERα chromatin binding and transcriptional activity, which has important implications for prognosis and therapeutic interventions.

To model the heterogeneity of breast cancer as observed in the clinic, we employed an ex vivo model of breast tumor tissue. This methodology maintained the histological integrity of the tumor tissue in unselected breast cancers, and importantly, the explants retained key molecular markers that are currently used to guide breast cancer treatment (e.g., ER and Her2 status). The primary tumors displayed the expected wide range of positivity for the proliferation marker Ki67, and a strong positive correlation between the Ki67 indices of the primary and corresponding explanted tumor tissues was observed. Collectively, these findings indicate that multiple facets of tumor pathophysiology are recapitulated in this ex vivo model. To interrogate the potential of this preclinical model to inform determinants of therapeutic response, we investigated the cytostatic response to the CDK4/6 inhibitor, PD-0332991. This inhibitor was highly effective at suppressing proliferation in approximately 85% of cases, irrespective of ER or HER2 status. However, 15% of cases were completely resistant to PD-0332991. Marker analyses in both the primary tumor tissue and the corresponding explant revealed that cases resistant to CDK4/6 inhibition lacked the RB-tumor suppressor. These studies provide important insights into the spectrum of breast tumors that could be treated with CDK4/6 inhibitors, and defines functional determinants of response analogous to those identified through neoadjuvant studies.

Androgen receptor (AR) signaling exerts an antiestrogenic, growth-inhibitory influence in normal breast tissue, and this role may be sustained in estrogen receptor α (ERα)-positive luminal breast cancers. Conversely, AR signaling may promote growth of a subset of ERα-negative, AR-positive breast cancers with a molecular apocrine phenotype. Understanding the molecular mechanisms whereby androgens can elicit distinct gene expression programs and opposing proliferative responses in these two breast cancer phenotypes is critical to the development of new therapeutic strategies to target the AR in breast cancer.

Kerstin Meyer and colleagues analyze a breast cancer gene regulatory network generated using publicly available expression and ChIP-seq data sets. They identify a cluster of 36 regulons that are significantly enriched for known breast cancer risk-associated genes and propose the use of regulon activity for patient stratification. Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)+ luminal A or luminal B and ER− basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.