The estrogen signaling pathway reprograms prostate cancer cell metabolism and supports proliferation and disease progression.

Just like the androgen receptor (AR), the estrogen receptor α (ERα) is expressed in the prostate and is thought to influence prostate cancer (PCa) biology. Yet the incomplete understanding of ERα functions in PCa hinders our ability to fully comprehend its clinical relevance and restricts the repurposing of estrogen-targeted therapies for the treatment of this disease. Using 2 human PCa tissue microarray cohorts, we first demonstrate that nuclear ERα expression was heterogeneous among patients, being detected in only half of the tumors. Positive nuclear ERα levels were correlated with disease recurrence, progression to metastatic PCa, and patient survival. Using in vitro and in vivo models of the normal prostate and PCa, bulk and single-cell RNA-Seq analyses revealed that estrogens partially mimicked the androgen transcriptional response and activated specific biological pathways linked to proliferation and metabolism. Bioenergetic flux assays and metabolomics confirmed the regulation of cancer metabolism by estrogens, supporting proliferation. Using cancer cell lines and patient-derived organoids, selective estrogen receptor modulators, a pure anti-estrogen, and genetic approaches impaired cancer cell proliferation and growth in an ERα-dependent manner. Overall, our study revealed that, when expressed, ERα functionally reprogrammed PCa metabolism, was associated with disease progression, and could be targeted for therapeutic purposes.

Estrogens and endocrine-disrupting chemicals differentially impact the bioenergetic fluxes of mammary epithelial cells in two- and three-dimensional models.

Due to its sensitivity to hormonal signaling, the mammary gland is often referred to as a sentinel organ for the study of endocrine-disrupting chemicals (EDCs), environmental pollutants that can interfere with the estrogen signaling pathway and induce mammary developmental defects. If and how EDCs impact mammary epithelial cell metabolism has not yet been documented. Herein, to study how estrogens and EDCs modulate mammary gland metabolism, we performed bioenergetic flux analyses using mouse mammary epithelial organoids compared to cells grown in monolayer culture. Several EDCs were tested, including bisphenol A (BPA), its close derivative BPS, a new BPA replacement copolyester called TritanTM, and the herbicide glyphosate. We report that estrogens reprogrammed mammary epithelial cell metabolism differently when grown in two- and three-dimensional models. Specific EDCs were also demonstrated to alter bioenergetic fluxes, thus identifying a new potential adverse effect of these molecules. Notably, organoids were more sensitive to low EDC concentrations, highlighting them as a key model for screening the impact of various environmental pollutants. Mechanistically, transcriptomic analyses revealed that EDCs interfered with the regulation of estrogen target genes and the expression of metabolic genes in organoids. Furthermore, co-treatment with the anti-estrogen fulvestrant blocked these metabolic impacts of EDCs, suggesting that, at least partially, they act through modulation of the estrogen receptor activity. Finally, we demonstrate that mammary organoids can be used for long-term studies on EDC exposure to study alterations in organogenesis/morphogenesis and that past pregnancies can modulate the sensitivity of mammary epithelial organoids to specific EDCs. Overall, this study demonstrates that estrogens and EDCs modulate mammary epithelial cell metabolism in monolayer and organoid cultures. A better understanding of the metabolic impacts of EDCs will allow a better appreciation of their adverse effects on mammary gland development and function.

Preclinical models of prostate cancer - modelling androgen dependency and castration resistance in vitro, ex vivo and in vivo.

Prostate cancer is well known to be dependent on the androgen receptor (AR) for growth and survival. Thus, AR is the main pharmacological target to treat this disease. However, after an initially positive response to AR-targeting therapies, prostate cancer will eventually evolve to castration-resistant prostate cancer, which is often lethal. Tumour growth was initially thought to become androgen-independent following treatments; however, results from molecular studies have shown that most resistance mechanisms involve the reactivation of AR. Consequently, tumour cells become resistant to castration - the blockade of testicular androgens - and not independent of AR per se. However, confusion still remains on how to properly define preclinical models of prostate cancer, including cell lines. Most cell lines were isolated from patients for cell culture after evolution of the tumour to castration-resistant prostate cancer, but not all of these cell lines are described as castration resistant. Moreover, castration refers to the blockade of testosterone production by the testes; thus, even the concept of "castration" in vitro is questionable. To ensure maximal transfer of knowledge from scientific research to the clinic, understanding the limitations and advantages of preclinical models, as well as how these models recapitulate cancer cell androgen dependency and can be used to study castration resistance mechanisms, is essential.

Alternative splicing regulation by the androgen receptor in prostate cancer cells.

The androgen receptor (AR) is a transcription factor that drives prostate cancer (PCa) by modulating the expression of thousands of genes to promote proliferation and survival and to reprogram metabolism. However, how AR activation controls alternative splicing is mostly unknown. Our objective was to define its role in the transcriptome-wide regulation of alternative splicing. Three human PCa models-LNCaP, LAPC4, and 22Rv1 cells-were treated with and without androgens, and RNA was purified for deep-sequencing analyses (RNA-seq). Several bioinformatic tools were then used to study alternative splicing. We demonstrate that in the absence of androgens, alternative splicing complexity is similar among AR-positive PCa cells, with 48 % of all transcripts having various levels of alternative splicing. We also describe alternative splicing differences among cell lines, such as specific splicing of AR, REST, TSC2, and CTBP1. Interestingly, AR activation changed the alternative splicing of thousands of genes in all the PCa cell lines tested. Overlap between AR-sensitive alternative splicing events revealed that genes linked to cell metabolism are major targets for this specific modulation. These genes encode metabolic enzymes such as the prostate-specific membrane antigen, encoded by FOLH1, and the malate dehydrogenase 1 (MDH1). Overall, our study presents a comprehensive analysis of the PCa cell transcriptome and its modulation by AR, revealing a significant enrichment of metabolic genes in this AR-dependent regulation of alternative splicing.

A Systematic Study of the Impact of Estrogens and Selective Estrogen Receptor Modulators on Prostate Cancer Cell Proliferation.

The estrogen signaling pathway has been reported to modulate prostate cancer (PCa) progression through the activity of estrogen receptors α and ß (ERα and ERß). Given that selective estrogen receptor modulators (SERMs) are used to treat breast cancer, ERs have been proposed as attractive therapeutic targets in PCa. However, many inconsistencies regarding the expression of ERs and the efficacy of SERMs for PCa treatment exist, notably due to the use of ERß antibodies lacking specificity and treatments with high SERM concentrations leading to off-target effects. To end this confusion, our objective was to study the impact of estrogenic and anti-estrogenic ligands in well-studied in vitro PCa models with appropriate controls, dosages, and ER subtype-specific antibodies. When using physiologically relevant concentrations of nine estrogenic/anti-estrogenic compounds, including five SERMs, we observed no significant modulation of PCa cell proliferation. Using RNA-seq and validated antibodies, we demonstrate that these PCa models do not express ERs. In contrast, RNA-seq from PCa samples from patients have detectable expression of ERα. Overall, our study reveals that commonly used PCa models are inappropriate to study ERs and indicate that usage of alternative models is essential to properly assess the roles of the estrogen signaling pathway in PCa.