Progesterone receptors in normal breast development and breast cancer

Progesterone receptor impairs immune respond and down-regulates sensitivity to anti-LAG3 in breast cancer

BACKGROUND

Progesterone receptor (PR) serves as a crucial prognostic and predictive marker in breast cancer. Nonetheless, the interplay between PR and the tumor immune microenvironment remains inadequately understood. This investigation employs bioinformatics analyses, mouse models, and clinical specimens to elucidate the impact of PR on immune microenvironment and identify potential targets for immunotherapy, furnishing valuable guidance for clinical practice.

METHODS

Analysis of immune infiltration score by Xcell between PR-positive and PR-negative breast cancer tumors. Construction of overexpression mouse progesterone receptor (mPgr) EMT-6 cell was to explore the tumor immune microenvironment. Furthermore, anti- Lymphocyte-activation gene 3 (LAG3) therapy aimed to investigate whether PR could influence the effectiveness of immune treatments.

RESULTS

Overexpression mPgr inhibited tumor growth in vitro, but promoted tumor growth in Balb/c mouse. Flow cytometry showed that the proportion and cytotoxicity of CD8+T cells in tumor of overexpressing mPgr group were significantly reduced. The significant reduction in overexpressing mPgr group was found in the proportions of LAG3+CD8+ T cells and LAG3+ Treg T cells. Anti-LAG3 treatment resulted in reduced tumor growth in EV group mouse rather than in overexpressing mPgr group. Patents derived tumor fragment (PDTF) also showed higher anti-tumor ability of CD3+T cell in patents' tumor with PR <20% after anti-human LAG3 treatment in vitro.

CONCLUSIONS

The mPgr promotes tumor growth by downregulating the infiltration and function of cytotoxic cell. LAG3 may be a target of ER-positive breast cancer immunotherapy. The high expression of PR hinders the sensitivity to anti-LAG3 treatment.

Progesterone Receptor Signaling Promotes Cancer Associated Fibroblast Mediated Tumorigenicity in ER+ Breast Cancer

Breast cancer progression involves intricate interactions between cancer cells and the tumor microenvironment (TME). This study elucidates the critical role of progesterone receptor (PR) signaling in mediating the protumorigenic effects of cancer-associated fibroblasts (CAFs) on estrogen receptor-positive (ER+) luminal breast cancer cells. We demonstrate that CAFs produce physiologically relevant levels of estrogen and progesterone, which significantly contribute to breast cancer tumorigenicity. Specifically, CAF conditioned media (CM) promoted PR-dependent anchorage-independent growth, tumorsphere formation/stem cell expansion, and CD44 upregulation. CAF cells formed co-clusters more frequently with PR+ breast cancer cells relative to PR-null models. While both PR isoforms mediated these actions, PR-A was a dominant driver of tumorsphere formation/stemness, while PR-B induced robust CD44 expression and CAF/tumor cell co-cluster formation. CD44 knockdown impaired CAF/tumor cell co-clustering. Fibroblast growth factor 2 (FGF2), also secreted by CAFs, phosphorylated PR (Ser294) in a MAPK-dependent manner and activated PR to enhance CD44 expression and breast cancer tumorigenicity. The FGF receptor (FGFR) inhibitor PD173074 diminished CAF- and FGF2-dependent PR activation, tumorsphere formation, and co-clustering. In summary, this study reveals a novel mechanism through which stromal CAFs orchestrate elevated PR signaling in ER+ luminal breast cancer via secretion of both progesterone and FGF2, a potent activator of ERK1/2. Understanding tumor cell/TME interactions provides insights into potential therapeutic strategies aimed at disrupting PR- and/or FGF2/FGFR-dependent signaling pathways to prevent early metastasis in patients with ER+ breast cancer.

Antiprogestins for breast cancer treatment: We are almost ready

The development of antiprogestins was initially a gynecological purpose. However, since mifepristone was developed, its application for breast cancer treatment was immediately proposed. Later, new compounds with lower antiglucocorticoid and antiandrogenic effects were developed to be applied to different pathologies, including breast cancer. We describe herein the studies performed in the breast cancer field with special focus on those reported in recent years, ranging from preclinical biological models to those carried out in patients. We highlight the potential use of antiprogestins in breast cancer prevention in women with BRCA1 mutations, and their use for breast cancer treatment, emphasizing the need to elucidate which patients will respond. In this sense, the PR isoform ratio has emerged as a possible tool to predict antiprogestin responsiveness. The effects of combined treatments of antiprogestins together with other drugs currently used in the clinic, such as tamoxifen, CDK4/CDK6 inhibitors or pembrolizumab in preclinical models is discussed since it is in this scenario that antiprogestins will be probably introduced. Finally, we explain how transcriptomic or proteomic studies, that were carried out in different luminal breast cancer models and in breast cancer samples that responded or were predicted to respond to the antiprogestin therapy, show a decrease in proliferative pathways. Deregulated pathways intrinsic of each model are discussed, as well as how these analyses may contribute to a better understanding of the mechanisms involved.

Predisposing and Overall Effects of Reproductive Hormones on Breast Cancer: A Review

Cancer, the second leading cause of mortality worldwide, has been the subject of extensive and quickly changing scientific study and practice. Cancer remains a mystery despite the enormous effort put into understanding the genesis of cancerous cells, the development of malignant tissues, and the process by which they propagate and recur. Cells from humans that have been recruited by cancer and, to some extent, changed into pathogenic organisms or the foundation of tumors serve as agents of destruction. Understanding cancers leads to challenging philosophical issues since they undermine and use multicellular organization processes. Cancer metastasizing cells adopt new phenotypes while discarding previous behaviors. The absence of comprehensive knowledge of this has hampered the development of therapeutics for metastatic illness. For systems-level experimental and computational metastasis modeling, integrating these complex and interconnected features continues to be a problem because metastasis has typically been studied in separate physiological compartments. Lung, breast, and prostate cancers accounted for the bulk of the 18 million new cases of cancer that were diagnosed in 2018. The most frequent cancer in women is breast cancer. Animal experimentation plays a significant role in primary and translational breast cancer research. In theory, such breast cancer models should be comparable to breast cancer in humans in terms of tumor etiology, biological behavior, pathology, and treatment response.

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

Progesterone Receptor Gene Polymorphisms and Breast Cancer Risk

The objective of this systematic review was to investigate the association between polymorphisms in the progesterone receptor gene (PGR) and breast cancer risk. A search of PubMed, Scopus, and Web of Science databases was performed in November 2021. Study characteristics, minor allele frequencies, genotype frequencies, and odds ratios were extracted. Forty studies met the eligibility criteria and included 75 032 cases and 89 425 controls. Of the 84 PGR polymorphisms reported, 7 variants were associated with breast cancer risk in at least 1 study. These polymorphisms included an Alu insertion (intron 7) and rs1042838 (Val660Leu), also known as PROGINS. Other variants found to be associated with breast cancer risk included rs3740753 (Ser344Thr), rs10895068 (+331G/A), rs590688 (intron 2), rs1824128 (intron 3), and rs10895054 (intron 6). Increased risk of breast cancer was associated with rs1042838 (Val660Leu) in 2 studies, rs1824128 (intron 3) in 1 study, and rs10895054 (intron 6) in 1 study. The variant rs3740753 (Ser344Thr) was associated with decreased risk of breast cancer in 1 study. Mixed results were reported for rs590688 (intron 2), rs10895068 (+331G/A), and the Alu insertion. In a pooled analysis, the Alu insertion, rs1042838 (Val660Leu), rs3740753 (Ser344Thr), and rs10895068 (+331G/A) were not associated with breast cancer risk. Factors reported to contribute to differences in breast cancer risk associated with PGR polymorphisms included age, ethnicity, obesity, and postmenopausal hormone therapy use. PGR polymorphisms may have a small contribution to breast cancer risk in certain populations, but this is not conclusive with studies finding no association in larger, mixed populations.

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images

To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April 2018 to March 2019. Ultrasound images and clinical information were collected accordingly. Molecular subtypes were determined from immunohistochemical (IHC) results. A CNN with VGG-based architecture was then used to predict TNBC. The model's performance was evaluated using randomized k-fold stratified cross-validation. A t-SNE analysis and saliency maps were used for model visualization. TNBC was identified in 16 of 145 (11.03%) patients. One hundred fifteen (80%) patients, 15 (10%) patients, and 15 (10%) patients formed the train, validation, and test set respectively. The deep learning system exhibits good efficacy, with an AUC of 0.86 (95% CI: 0.64, 0.95), an accuracy of 85%, a sensitivity of 86%, a specificity of 86%, and an F1-score of 0.74. In addition, the internal representation features learned by the model showed clear differentiation across molecular subtype groups. Such a deep learning system can automatically predict triple-negative breast cancer preoperatively and accurately. It may help to get to more precise and comprehensive management.

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

Breast cancer (BC) is the most common type of cancer in women and, in most cases, it is hormone-dependent (HD), thus relying on ovarian hormone activation of intracellular receptors to stimulate tumor growth. Endocrine therapy (ET) aimed at preventing hormone receptor activation is the primary treatment strategy, however, about half of the patients, develop resistance in time. This involves the development of hormone independent tumors that initially are ET-responsive (HI), which may subsequently become resistant (HIR). The mechanisms that promote the conversion of HI to HIR tumors are varied and not completely understood. The aim of this work was to characterize the metabolic adaptations accompanying this conversion through the analysis of the polar metabolomes of tumor tissue and non-compromised mammary gland from mice implanted subcutaneously with HD, HI and HIR tumors from a medroxyprogesterone acetate (MPA)-induced BC mouse model. This was carried out by nuclear magnetic resonance (NMR) spectroscopy of tissue polar extracts and data mining through multivariate and univariate statistical analysis. Initial results unveiled marked changes between global tumor profiles and non-compromised mammary gland tissues, as expected. More importantly, specific metabolic signatures were found to accompany progression from HD, through HI and to HIR tumors, impacting on amino acids, nucleotides, membrane percursors and metabolites related to oxidative stress protection mechanisms. For each transition, sets of polar metabolites are advanced as potential markers of progression, including acquisition of resistance to ET. Putative biochemical interpretation of such signatures are proposed and discussed.

New Insights in the Interaction of FGF/FGFR and Steroid Receptor Signaling in Breast Cancer

Luminal breast cancer (BrCa) has a favorable prognosis compared with other tumor subtypes. However, with time, tumors may evolve and lead to disease progression; thus, there is a great interest in unraveling the mechanisms that drive tumor metastasis and endocrine resistance. In this review, we focus on one of the many pathways that have been involved in tumor progression, the fibroblast growth factor/fibroblast growth factor receptor (FGFR) axis. We emphasize in data obtained from in vivo experimental models that we believe that in luminal BrCa, tumor growth relies in a crosstalk with the stromal tissue. We revisited the studies that illustrate the interaction between hormone receptors and FGFR. We also highlight the most frequent alterations found in BrCa cell lines and provide a short review on the trials that use FGFR inhibitors in combination with endocrine therapies. Analysis of these data suggests there are many players involved in this pathway that might be also targeted to decrease FGF signaling, in addition to specific FGFR inhibitors that may be exploited to increase their efficacy.

Progesterone receptor isoform ratio dictates antiprogestin/progestin effects on breast cancer growth and metastases: A role for NDRG1

Progesterone receptors (PR) ligands are being tested in luminal breast cancer. There are mainly two PR isoforms, PRA and PRB, and their ratio (PRA/PRB) may be predictive of antiprogestin response. Our aim was to investigate: the impact of the PR isoform ratio on metastatic behavior, the PR isoform ratio in paired primary tumors and lymph node metastases (LNM) and, the effect of antiprogestin/progestins on metastatic growth. Using murine and human metastatic models, we demonstrated that tumors with PRB > PRA (PRB-H) have a higher proliferation index but less metastatic ability than those with PRA > PRB (PRA-H). Antiprogestins and progestins inhibited metastatic burden in PRA-H and PRB-H models, respectively. In breast cancer samples, LNM retained the same PRA/PRB ratio as their matched primary tumors. Moreover, PRA-H LNM expressed higher total PR levels than the primary tumors. The expression of NDRG1, a metastasis suppressor protein, was higher in PRB-H compared with PRA-H tumors and was inversely regulated by antiprogestins/progestins. The binding of the corepressor SMRT at the progesterone responsive elements of the NDRG1 regulatory sequences, together with PRA, impeded its expression in PRA-H cells. Antiprogestins modulate the interplay between SMRT and AIB1 recruitment in PRA-H or PRB-H contexts regulating NDRG1 expression and thus, metastasis. In conclusion, we provide a mechanistic interpretation to explain the differential role of PR isoforms in metastatic growth and highlight the therapeutic benefit of using antiprogestins in PRA-H tumors. The therapeutic effect of progestins in PRB-H tumors is suggested. This article is protected by copyright. All rights reserved.

Nuclear receptors: from molecular mechanisms to therapeutics

Nuclear receptors are classically defined as ligand-activated transcription factors that regulate key functions in reproduction, development, and physiology. Humans have 48 nuclear receptors, which when dysregulated are often linked to diseases. Because most nuclear receptors can be selectively activated or inactivated by small molecules, they are prominent therapeutic targets. The basic understanding of this family of transcription factors was accelerated in the 1980s upon the cloning of the first hormone receptors. During the next 20 years, a deep understanding of hormone signaling was achieved that has translated to numerous clinical applications, such as the development of standard-of-care endocrine therapies for hormonally driven breast and prostate cancers. A 2004 issue of this journal reviewed progress on elucidating the structures of nuclear receptors and their mechanisms of action. In the current issue, we focus on the broad application of new knowledge in this field for therapy across diverse disease states including cancer, cardiovascular disease, various inflammatory diseases, the aging brain, and COVID-19.