Hormone therapy for breast cancer, with an emphasis on the pure antiestrogen fulvestrant: mode of action, antitumor efficacy and effects on bone health

Impacts of endocrine-disrupting chemicals on prostate function and cancer

Because of their historical mode of action, endocrine-disrupting chemicals (EDCs) are associated with sex-steroid receptors, namely the two estrogen receptors (ERα and ERβ) and the androgen receptor (AR). Broadly, EDCs can modulate sex-steroid receptor functions. They can also indirectly impact the androgen and estrogen pathways by influencing steroidogenesis, expression of AR or ERs, and their respective activity as transcription factors. Additionally, many of these chemicals have multiple cellular targets other than sex-steroid receptors, which results in a myriad of potential effects in humans. The current article reviews the association between prostate cancer and the endocrine-disrupting functions of four prominent EDC families: bisphenols, phthalates, phytoestrogens, and mycoestrogens. Results from both in vitro and in vivo models are included and discussed to better assess the molecular mechanisms by which EDCs can modify prostate biology. To overcome the heterogeneity of results published, we established common guidelines to properly study EDCs in the context of endocrine diseases. Firstly, the expression of sex-steroid receptors in the models used must be determined before testing. Then, in parallel to EDCs, pharmacological compounds acting as positive (agonists) and negative controls (antagonists) have to be employed. Finally, EDCs need to be used in a precise range of concentrations to modulate sex-steroid receptors and avoid off-target effects. By adequately integrating molecular endocrinology aspects in EDC studies and identifying their underlying molecular mechanisms, we will truly understand their impact on prostate cancer and distinguish those that favor the progression of the disease from those that slow down tumor development.

Effects of Alcohol and Estrogen Receptor Blockade Using ICI 182,780 on Bone in Ovariectomized Rats

BACKGROUND

Estrogen signaling is essential for the sexual dimorphism of the skeleton, is required for normal bone remodeling balance in adults, and may influence the skeletal response to alcohol. High levels of alcohol consumption lower bone mass in ovary-intact but not ovariectomized (ovx) rats. However, the extremely rapid rate of bone loss immediately following ovx may obscure the effects of alcohol. We therefore determined (i) whether heavy alcohol consumption (35% caloric intake) influences bone in sexually mature ovx rats with established cancellous osteopenia and (ii) whether ICI 182,780 (ICI), a potent estrogen receptor signaling antagonist, alters the skeletal response to alcohol.

METHODS

Three weeks following ovx, rats were randomized into 5 groups, (i) baseline, (ii) control + vehicle, (iii) control + ICI, (iv) ethanol (EtOH) + vehicle, or (v) EtOH + ICI, and treated accordingly for 4 weeks. Dual-energy X-ray absorptiometry, microcomputed tomography, blood measurements of markers of bone turnover, and gene expression in femur and uterus were used to evaluate response to alcohol and ICI.

RESULTS

Rats consuming alcohol had lower bone mass and increased fat mass. Bone microarchitecture of the tibia and gene expression in femur were altered; specifically, there was reduced accrual of cortical bone, net loss of cancellous bone, and differential expression of 19/84 genes related to bone turnover. Furthermore, osteocalcin, a marker of bone turnover, was lower in alcohol-fed rats. ICI had no effect on weight gain, body composition, or cortical bone. ICI reduced cancellous bone loss and serum CTX-1, a biochemical marker of bone resorption; alcohol antagonized the latter 2 responses. Neither alcohol nor ICI affected uterine weight or gene expression.

CONCLUSIONS

Alcohol exaggerated bone loss in ovx rats in the presence or absence of estrogen receptor blockade with ICI. The negligible effect of alcohol on uterus and limited effects of ICI on bone in alcohol-fed ovx rats suggest that estrogen receptor signaling plays a limited role in the action of alcohol on bone in a rat model for chronic alcohol abuse.

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational methods, e.g., quantitative structure-activity relationship models, have become more reliable due to bigger training sets, increased computing power, and advanced machine learning algorithms, such as multilayered artificial neural networks. Machine learning models can be used to predict compounds for endocrine disrupting capabilities, such as binding to the estrogen receptor (ER), and allow for prioritization and further testing. In this work, an exhaustive comparison of multiple machine learning algorithms, chemical spaces, and evaluation metrics for ER binding was performed on public data sets curated using in-house cheminformatics software (Assay Central). Chemical features utilized in modeling consisted of binary fingerprints (ECFP6, FCFP6, ToxPrint, or MACCS keys) and continuous molecular descriptors from RDKit. Each feature set was subjected to classic machine learning algorithms (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, Support Vector Machine) and Deep Neural Networks (DNN). Models were evaluated using a variety of metrics: recall, precision, F1-score, accuracy, area under the receiver operating characteristic curve, Cohen's Kappa, and Matthews correlation coefficient. For predicting compounds within the training set, DNN has an accuracy higher than that of other methods; however, in 5-fold cross validation and external test set predictions, DNN and most classic machine learning models perform similarly regardless of the data set or molecular descriptors used. We have also used the rank normalized scores as a performance-criteria for each machine learning method, and Random Forest performed best on the validation set when ranked by metric or by data sets. These results suggest classic machine learning algorithms may be sufficient to develop high quality predictive models of ER activity.

Awareness and current knowledge of breast cancer

Breast cancer remains a worldwide public health dilemma and is currently the most common tumour in the globe. Awareness of breast cancer, public attentiveness, and advancement in breast imaging has made a positive impact on recognition and screening of breast cancer. Breast cancer is life-threatening disease in females and the leading cause of mortality among women population. For the previous two decades, studies related to the breast cancer has guided to astonishing advancement in our understanding of the breast cancer, resulting in further proficient treatments. Amongst all the malignant diseases, breast cancer is considered as one of the leading cause of death in post menopausal women accounting for 23% of all cancer deaths. It is a global issue now, but still it is diagnosed in their advanced stages due to the negligence of women regarding the self inspection and clinical examination of the breast. This review addresses anatomy of the breast, risk factors, epidemiology of breast cancer, pathogenesis of breast cancer, stages of breast cancer, diagnostic investigations and treatment including chemotherapy, surgery, targeted therapies, hormone replacement therapy, radiation therapy, complementary therapies, gene therapy and stem-cell therapy etc for breast cancer.

Antiprogestins in gynecological diseases

Antiprogestins constitute a group of compounds, developed since the early 1980s, that bind progesterone receptors with different affinities. The first clinical uses for antiprogestins were in reproductive medicine, e.g., menstrual regulation, emergency contraception, and termination of early pregnancies. These initial applications, however, belied the capacity for these compounds to interfere with cell growth. Within the context of gynecological diseases, antiprogestins can block the growth of and kill gynecological-related cancer cells, such as those originating in the breast, ovary, endometrium, and cervix. They can also interrupt the excessive growth of cells giving rise to benign gynecological diseases such as endometriosis and leiomyomata (uterine fibroids). In this article, we present a review of the literature providing support for the antigrowth activity that antiprogestins impose on cells in various gynecological diseases. We also provide a summary of the cellular and molecular mechanisms reported for these compounds that lead to cell growth inhibition and death. The preclinical knowledge gained during the past few years provides robust evidence to encourage the use of antiprogestins in order to alleviate the burden of gynecological diseases, either as monotherapies or as adjuvants of other therapies with the perspective of allowing for long-term treatments with tolerable side effects. The key to the clinical success of antiprogestins in this field probably lies in selecting those patients who will benefit from this therapy. This can be achieved by defining the genetic makeup required - within each particular gynecological disease - for attaining an objective response to antiprogestin-driven growth inhibition therapy.Free Spanish abstractA Spanish translation of this abstract is freely available at http://www.reproduction-online.org/content/149/1/15/suppl/DC1.

B7-H1-dependent sex-related differences in tumor immunity and immunotherapy responses

CD4(+)CD25(+)Foxp3(+) regulatory T cells (Tregs) are immunopathogenic in cancers by impeding tumor-specific immunity. B7-homologue 1 (B7-H1) (CD274) is a cosignaling molecule with pleiotropic effects, including hindering antitumor immunity. In this study, we demonstrate sex-dependent, B7-H1-dependent differences in tumor immunity and response to immunotherapy in a hormone-independent cancer, murine B16 melanoma. Antitumor immunity was better in B7-H1(-/-) females versus males as a result of reduced regulatory T cell function in the B7-H1(-/-) females, and clinical response following B7-H1 blockade as tumor immunotherapy was significantly better in wild-type females than in males, owing to greater B7-H1 blockade-mediated reduction of Treg function in females. Wild-type female Tregs expressed significantly lower B7-H1 versus males but were insensitive to estrogen in vitro. Female B7-H1(-/-) Tregs were exquisitely sensitive to estrogen-mediated functional reduction in vitro, suggesting that B7-H1 effects occur before terminal Treg differentiation. Immune differences were independent of known B7-H1 ligands. Sex-dependent immune differences are seldom considered in designing immune therapy or interpreting immunotherapy treatment results. Our data demonstrate that sex is an important variable in tumor immunopathogenesis and immunotherapy responses through differential Treg function and B7-H1 signaling.

Aromatase inhibitors, efficacy and metabolic risk in the treatment of postmenopausal women with early breast cancer

The third-generation aromatase inhibitors (AIs), letrozole, anastrozole and exemestane, are becoming the first choice endocrine drugs for post-menopausal women with breast cancer, since they present greater efficacy when compared with tamoxifen in both adjuvant and metastatic setting. In particular, several large and well designed trials have suggested an important role for AIs in the adjuvant treatment of postmenopausal women with estrogen-receptor positive breast cancer either in the upfront, sequential or extended adjuvant mode. Overall, AIs are associated with a small but significant improvement in disease free survival. The expanding use of AIs in the treatment of early breast cancer means that individual patients will be exposed to the agents for longer durations, making it increasingly important to establish their long-term safety. This review focused on the effects of AIs on bone metabolism, serum lipids and cardiovascular risk. AIs have adverse effects on bone turnover with a reduction of bone mineral density and an increase in the rate of fragility fractures. With respect to tamoxifen AIs present lower thrombotic risk and a less favorable impact on lipid profile, whereas the true effects on cardiovascular risk still remain to be clarified. An adequate monitoring of bone mineral density (BMD) and lipid profile could be recommended for post-menopausal women candidate to AIs.