Modulating HSF1 levels impacts expression of the estrogen receptor α and antiestrogen response

Master transcription factors control the transcriptional program and are essential to maintain cellular functions. Among them, steroid nuclear receptors, such as the estrogen receptor α (ERα), are central to the etiology of hormone-dependent cancers which are accordingly treated with corresponding endocrine therapies. However, resistance invariably arises. Here, we show that high levels of the stress response master regulator, the heat shock factor 1 (HSF1), are associated with antiestrogen resistance in breast cancer cells. Indeed, overexpression of HSF1 leads to ERα degradation, decreased expression of ERα-activated genes, and antiestrogen resistance. Furthermore, we demonstrate that reducing HSF1 levels reinstates expression of the ERα and restores response to antiestrogens. Last, our results establish a proof of concept that inhibition of HSF1, in combination with antiestrogens, is a valid strategy to tackle resistant breast cancers. Taken together, we are proposing a mechanism where high HSF1 levels interfere with the ERα-dependent transcriptional program leading to endocrine resistance in breast cancer.

Overcoming oncogene addiction in breast and prostate cancers: a comparative mechanistic overview

Prostate cancer (PCa) and breast cancer (BCa) are both hormone-dependent cancers that require the androgen receptor (AR) and estrogen receptor (ER) for growth and proliferation, respectively. Endocrine therapies that target these nuclear receptors (NRs) provide significant clinical benefit for metastatic patients. However, these therapeutic strategies are seldom curative and therapy resistance is prevalent. Because the vast majority of therapy-resistant PCa and BCa remain dependent on the augmented activity of their primary NR driver, common mechanisms of resistance involve enhanced NR signaling through overexpression, mutation, or alternative splicing of the receptor, coregulator alterations, and increased intracrine hormonal synthesis. In addition, a significant subset of endocrine therapy-resistant tumors become independent of their primary NR and switch to alternative NR or transcriptional drivers. While these hormone-dependent cancers generally employ similar mechanisms of endocrine therapy resistance, distinct differences between the two tumor types have been observed. In this review, we compare and contrast the most frequent mechanisms of antiandrogen and antiestrogen resistance, and provide potential therapeutic strategies for targeting both advanced PCa and BCa.

Proline rich 11 (PRR11) overexpression amplifies PI3K signaling and promotes antiestrogen resistance in breast cancer

The 17q23 amplicon is associated with poor outcome in ER+ breast cancers, but the causal genes to endocrine resistance in this amplicon are unclear. Here, we interrogate transcriptome data from primary breast tumors and find that among genes in 17q23, PRR11 is a key gene associated with a poor response to therapeutic estrogen suppression. PRR11 promotes estrogen-independent proliferation and confers endocrine resistance in ER+ breast cancers. Mechanistically, the proline-rich motif-mediated interaction of PRR11 with the p85α regulatory subunit of PI3K suppresses p85 homodimerization, thus enhancing insulin-stimulated binding of p110-p85α heterodimers to IRS1 and activation of PI3K. PRR11-amplified breast cancer cells rely on PIK3CA and are highly sensitive to PI3K inhibitors, suggesting that PRR11 amplification confers PI3K dependence. Finally, genetic and pharmacological inhibition of PI3K suppresses PRR11-mediated, estrogen-independent growth. These data suggest ER+/PRR11-amplified breast cancers as a novel subgroup of tumors that may benefit from treatment with PI3K inhibitors and antiestrogens.

GIP: A Gene network-based integrative approach for Inferring disease-associated signaling Pathways

Dysregulation or crosstalk of signal transduction pathways contributes to disease development. Despite the initial success of identifying causal links between source and target proteins in simple or well-studied biological systems, it remains challenging to investigate alternative pathways specifically associated with a disease. We develop a Gene network-based integrative approach for Inferring disease-associated signaling Pathways (GIP). Specifically, we identify alternative pathways given source and target proteins. GIP was applied to human breast cancer data. Experimental results showed that GIP identified biologically meaningful pathway modules associated with antiestrogen resistance.