Estrogen metabolism and exposure in a genotypic-phenotypic model for breast cancer risk prediction

Cognitive function in association with high estradiol levels resulting from fertility treatment

The putative association between hormones and cognitive performance is controversial. While there is evidence that estradiol plays a neuroprotective role, hormone treatment has not been shown to improve cognitive performance. Current research is flawed by the evaluation of combined hormonal effects throughout the menstrual cycle or in the menopausal transition. The stimulation phase of a fertility treatment offers a unique model to study the effect of estradiol on cognitive function. This quasi-experimental observational study is based on data from 44 women receiving IVF in Zurich, Switzerland. We assessed visuospatial working memory, attention, cognitive bias, and hormone levels at the beginning and at the end of the stimulation phase of ovarian superstimulation as part of a fertility treatment. In addition to inter-individual differences, we examined intra-individual change over time (within-subject effects). The substantial increases in estradiol levels resulting from fertility treatment did not relate to any considerable change in cognitive functioning. As the tests applied represent a broad variety of cognitive functions on different levels of complexity and with various brain regions involved, we can conclude that estradiol does not show a significant short-term effect on cognitive function.

Urinary Estrogen Metabolites and Long-Term Mortality Following Breast Cancer

Background

Estrogen metabolite concentrations of 2-hydroxyestrone (2-OHE₁) and 16-hydroxyestrone (16-OHE₁) may be associated with breast carcinogenesis. However, no study has investigated their possible impact on mortality after breast cancer.

Methods

This population-based study was initiated in 1996–1997 with spot urine samples obtained shortly after diagnosis (mean = 96 days) from 683 women newly diagnosed with first primary breast cancer and 434 age-matched women without breast cancer. We measured urinary concentrations of 2-OHE₁ and 16-OHE₁ using an enzyme-linked immunoassay. Vital status was determined via the National Death Index (n = 244 deaths after a median of 17.7 years of follow-up). We used multivariable-adjusted Cox proportional hazards to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the estrogen metabolites-mortality association. We evaluated effect modification using likelihood ratio tests. All statistical tests were two-sided.

Results

Urinary concentrations of the 2-OHE₁ to 16-OHE₁ ratio (>median of 1.8 vs ≤median) were inversely associated with all-cause mortality (HR = 0.74, 95% CI = 0.56 to 0.98) among women with breast cancer. Reduced hazard was also observed for breast cancer mortality (HR = 0.73, 95% CI = 0.45 to 1.17) and cardiovascular diseases mortality (HR = 0.76, 95% CI = 0.47 to 1.23), although the 95% confidence intervals included the null. Similar findings were also observed for women without breast cancer. The association with all-cause mortality was more pronounced among breast cancer participants who began chemotherapy before urine collection (n = 118, HR = 0.42, 95% CI = 0.22 to 0.81) than among those who had not (n = 559, HR = 0.98, 95% CI = 0.72 to 1.34; P_interaction = .008).

Conclusions

The urinary 2-OHE₁ to 16-OHE₁ ratio may be inversely associated with long-term all-cause mortality, which may depend on cancer treatment status at the time of urine collection.

A way forward for cancer prevention therapy: personalized risk assessment

Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.

Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study

BACKGROUND

Despite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer.

METHODS

A retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia.

RESULTS

A total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497-11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606-68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861-5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142-4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851-0.903), with optimism-corrected area under the curve of 0.865.

CONCLUSION

The prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.

Review of non-clinical risk models to aid prevention of breast cancer

A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.

Translational opportunities for broad-spectrum natural phytochemicals and targeted agent combinations in breast cancer

Breast cancer (BC) prevention and therapy in the context of life-style risk factors and biological drivers is a major focus of developmental therapeutics in oncology. Obesity, alcohol, chronic estrogen signaling and smoking have distinct BC precipitating and facilitating effects that may act alone or in combination. A spectrum of signaling events including enhanced oxidative stress and changes in estrogen-receptor (ER)-dependent and -independent signaling drive the progression of BC. Breast tumors modulate ERα/ERβ ratio, upregulate proliferative pathways driven by ERα and HER2 with a parallel loss and/or downregulation of tumor suppressors such as TP53 and PTEN which together impact the efficacy of therapeutic strategies and frequently lead to emergence of drug resistance. Natural phytochemicals modulate oxidative stress, leptin, integrin, HER2, MAPK, ERK, Wnt/β-catenin and NFκB signaling along with regulating ERα and ERβ, thereby presenting unique opportunities for both primary and combinatorial interventions in BC. In this regard, this article focuses on critical analyses of the evidence from multiple studies on the efficacy of natural phytochemicals in BC. In addition, areas in which the combinations of such effective natural phytochemicals with approved and/or developing anticancer agents can be translationally beneficial are discussed to derive evidence-based inference for addressing challenges in BC control and therapy.

Protective effect and mechanism of estrogen receptor β on myocardial infarction in mice

The protective effect and the mechanism of estrogen receptor β (ERβ) on myocardial infarction (MI) in mice were explored. A total of 12 female Tg-ERβ transgenic mice and 12 non-transgenic littermate control (NLC) wild-type C57 mice were used for the present study. Both transgenic and wild-type mice had similar baseline data such as age, sex, and weight. The mouse model of MI was established by coronary artery ligation method, and the cardiac structure and function changes of the mouse were observed by ultrasonic echocardiography on days 1, 3 and 7 after the operation. RT-PCR method was used to detect the expression of collagen I, α-SMA, TGF-β mRNA in the mouse heart, and Masson staining was used to detect cardiac fibrosis. At 3 days after operation, echocardiographic posterior wall thickness at end diastole (PWTD) and end systolic PWTS of Tg-ERβ mice were significantly reduced, and left ventricular systolic diameter and left ventricular diastolic diameter significantly increased (P<0.05) compared with NLC mice. The levels of expression of Tg-ERβ cardiac tissue collagen I, α-SMA, TGF-β mRNA were significantly lower than those in the NLC mice (P<0.05). In conclusion, Tg-ERβ exerts a protective effect on MI.

Disturbed testicular expression of the estrogen-metabolizing enzymes CYP1A1 and COMT in infertile men with primary spermatogenic failure: possible negative implications on Sertoli cells

Estradiol (E₂ ) is normally metabolized to hydroxyestradiols and methoxyestradiols by CYP1A1, CYP1B1 and COMT. However, an altered production of these metabolites by a disturbed expression of these enzymes is associated with reproductive and non-reproductive pathologies. In vitro studies suggest that increased hydroxyestradiols and methoxyestradiols intratesticular generation is related to male infertility, but no studies have explored whether infertile men have a disturbed testicular expression of the enzymes that generate these E₂ metabolites. The aim of this study was to assess CYP1A1, CYP1B1 and COMT testicular expression at mRNA and protein level in men with spermatogenic impairment. Seventeen men with primary spermatogenic failure (13 with Sertoli cell-only syndrome and four with maturation arrest) and nine controls with normal spermatogenesis were subjected to testicular biopsy. mRNA was quantified using real-time RT-PCR and protein expression was evaluated using western blot and immunohistochemistry followed by integrated optic density analysis. Besides, the effects of hydroxyestradiols and methoxyestradiols on testosterone-induced transcriptional activity were evaluated in TM4 cells using a luciferase reporter assay system. Our results show that patients with Sertoli cell-only syndrome had significantly elevated COMT expression at the mRNA level, higher COMT immunoreactivity in their seminiferous tubules and increased protein expression of the soluble COMT isoform (S-COMT), whereas patients with maturation arrest had significantly elevated CYP1A1 mRNA levels and higher CYP1A1 immunoreactivity in interstitial space. Finally, 2-hydroxyestradiol decreased testosterone-induced transcriptional activity in Sertoli cells in vitro. In conclusion, male infertility is related to disturbed testicular expression of the enzymes responsible for producing hydroxyestradiols and/or methoxyestradiols. If these changes are related with increased intratesticular hydroxyestradiols and methoxyestradiols concentrations, they could elicit an impaired Sertoli cell function. Our results suggest CYP1A1 and COMT as new potential targets in treating male infertility.

Expression of ERβ gene in breast carcinoma and the relevance in neoadjuvant therapy

In the present study, we examined the expression of the estrogen receptor β (ERβ) gene in breast cancer and its relevance in neoadjuvant therapy. In total, 120 breast cancer patients who were hospitalized in the Departments of Breast Disease and Medical Oncology served as the subjects of this study. The subjects were diagnosed with breast cancer phase II to phase IIIA, as confirmed by aspiration biopsy and iconography. The patients were divided into two groups in a randomized control manner, with 60 patients in each group. The experimental group was administered the taxotere + epirubicin + cyclophosphamide (TEC) plan for 3–4 cycles of chemotherapy before the modified radical operation of breast cancer. In the control group, no TEC chemotherapy was carried out prior to operation. Instead, the breast lesion was removed directly by operation. After the operation, the IHC method was used to stain the ERβ protein in the lesion tissue. The patients were classified according to whether the basement membrane was broken through; 5 cases had non-infiltrative carcinoma and 115 cases had infiltrative carcinoma. According to the pathology of the lesion, 114 cases had breast ductal carcinoma, 2 cases had mucinous breast carcinoma (of which there were 2 cases combined with ductal carcinoma), and 4 cases had breast lobular carcinoma. The ERβ gene was found to be expressed in normal and breast cancer tissues. When ERβ gene expression was compared before and after the chemotherapy, its expression was significantly increased in breast cancer tissues, which shows a significant statistical difference (P<0.05). In the experimental group, the expression of ERβ gene in carcinoma tissue was significantly lower than that in the control group, and differences were statistically significant (P<0.05). Therefore, expression of the ERβ gene in breast carcinoma tissues was high. The application of adjuvant chemotherapy before the modified radical operation for breast carcinoma can significantly lower the level of ERβ expression. The expression levels of ERβ gene in the carcinoma tissue of the patients can be treated as the evaluation index for neoadjuvant chemotherapy. Regarding targeted therapy and corresponding drug development for breast carcinoma, ERβ can act as one of the specific drug targets.

Association Between ESR1 PvuII, XbaI, and P325P Polymorphisms and Breast Cancer Susceptibility: A Meta-Analysis

Background

Breast cancer is one of the leading causes of cancer-related deaths for women. Numerous studies have shown that single-nucleotide polymorphisms (SNPs) on the ESR1 gene are associated to this disease. However, data and conclusions are inconsistent and controversial.

Material/Methods

To investigate the association between PvuII (rs2234693), XbaI (rs9340799) and P325P (rs1801132) polymorphisms of ESR1 gene with the risk of breast cancer under different population categorizations, we searched multiple databases for data collection, and performed the meta-analysis on a total of 25 case-control studies. Three different comparison models – dominant model, recessive model, and homozygote comparison model – were applied to evaluate the association.

Results

Our results indicated that people with TT+TC or TT genotype were at a greater risk of developing breast cancer than those with CC genotype in the PvuII polymorphism. While for XbaI and P325P polymorphisms, no significance was found using any of the 3 models. Furthermore, the data were also stratified into different subgroups according to the ethnicity (white or Asian) and source of controls (hospital-based or population-based), and separate analyses were conducted to assess the association. The ethnicity subgroup assessment showed that the higher risk of breast cancer for TT genotype of PvuII polymorphism than CC genotype only occurred in Asian people, but not in white populations. For the source-stratified subgroup analysis, significant association suggested that people with TT + TC genotype were at a greater risk of developing breast cancer than those with CC genotype in the hospital-based subgroup.

Conclusions

Thus, this meta-analysis clarified the inconsistent conclusions from previous studies, conducted analyses for the entire population as well as for different subgroups using diverse population categorization strategies, and has the potential to help provide a personalized risk estimate for breast cancer susceptibility.

Multifactor dimensionality reduction analysis to elucidate the cross-talk between one-carbon and xenobiotic metabolic pathways in multi-disease models

Putatively functional polymorphisms of one-carbon and xenobiotic metabolic pathways influence susceptibility for wide spectrum of diseases. The current study was aimed to explore gene-gene interactions among these two metabolic pathways in four diseases i.e. breast cancer, systemic lupus erythematosus (SLE), coronary artery disease (CAD) and Parkinson's disease (PD). Multifactor dimensionality reduction analysis was carried out on four case-control datasets. Cross-talk was observed between one-carbon and xenobiotic pathways in breast cancer (RFC 80 G>A, COMT H108L and TYMS 5'-UTR 28 bp tandem repeat) and SLE (CYP1A1 m1, MTRR 66 A>G and GSTT1). Gene-gene interactions within one-carbon metabolic pathway were observed in CAD (GCPII 1561 C>T, SHMT 1420 C>T and MTHFR 677 C>T) and PD (cSHMT 1420 C>T, MTRR 66 A>G and RFC1 80 G>A). These interaction models showed good predictability of risk for PD (The area under the receiver operating characteristic curve (C) = 0.83) and SLE (C = 0.73); and moderate predictability of risk for breast cancer (C = 0.64) and CAD (C = 0.63). Cross-talk between one-carbon and xenobiotic pathways was observed in diseases with female preponderance. Gene-gene interactions within one-carbon metabolic pathway were observed in diseases with male preponderance.

Estrogen receptor alpha gene polymorphisms and breast cancer risk: a case-control study with meta-analysis combined

Molecular epidemiological studies have shown that gene polymorphisms of estrogen receptor alpha gene (ESR-α) are associated with breast cancer risk. However, previous results from many molecular studies have been inconsistent. In this study, we examined two polymorphisms (PvuII and XbaI RFLPs) of the ESR-α gene in 542 breast cancer cases and 1,016 controls from China. Associations between the polymorphisms and breast cancer risk were calculated with an unconditional logistic regression model. Linkage disequilibrium and haplotypes were analyzed with the SHEsis software. In addition, we also performed a systematic meta-analysis of 24 published studies evaluating the association. No significant associations were found between the PvuII polymorphism and breast cancer risk. However, a significantly decreased risk of breast cancer was observed among carriers of the XbaI 'G' allele (age-adjusted OR = 0.80; 95% CI = 0.66- 0.97) compared with carriers of the 'A' allele. Haplotype analysis showed significantly decreased cancer risk for carriers of the 'CG' haplotype (OR = 0.79; 95% CI = 0.66- 0.96). In the systematic meta-analysis, the XbaI 'G' allele was associated with an overall significantly decreased risk of breast cancer (OR = 0.90, 95% CI = 0.82- 1.00). In addition, the PvuII 'C' allele showed a 0.96- fold decreased disease risk (95% CI = 0.92- 0.99). In subgroup analysis, an association between the PvuII 'C' and XbaI 'G' alleles and breast cancer risk was significant in Asians ('C' vs. 'T': OR = 0.93, 95% CI = 0.85- 1.00; 'G' vs. 'A': OR = 0.82, 95% CI = 0.68- 0.98), but not in Euro-Americans. Thus, our results provide evidence that ESR-α polymorphisms are associated with susceptibility to breast cancer. These associations may largely depend on population characteristics and geographic location.

Estrogen receptor-dependent and independent mechanisms of breast cancer carcinogenesis

Long term exposure to estrogens is associated with an increased risk of breast cancer. The precise mechanisms responsible for estrogen mediated carcinogenesis are not well understood. The most widely accepted theory holds that estradiol (E(2)), acting through estrogen receptor alpha (ERα), stimulates cell proliferation and initiates mutations arising from replicative errors occurring during pre-mitotic DNA synthesis. The promotional effects of E(2) then support the growth of cells harboring mutations. Over a period of time, sufficient numbers of mutations accumulate to induce neoplastic transformation. Laboratory and epidemiological data also suggest that non-receptor mediated mechanisms resulting from the genotoxic effects of estrogen metabolites are involved in breast cancer development. This manuscript critically reviews existing data implicating both ER-dependent and -independent mechanisms. The weight of evidence supports the possibility that both mechanisms are involved in the carcinogenic process. In addition, estrogen metabolites likely modulate stem cell functionality and cancer progression. The roles of ER dependent and independent actions in the carcinogenic process are pertinent to the consideration of breast cancer preventative agents as anti-estrogens block only receptor mediated pathways whereas the aromatase inhibitors block both.

Cellular iron metabolism in prognosis and therapy of breast cancer

Despite many recent advances, breast cancer remains a clinical challenge. Current issues include improving prognostic evaluation and increasing therapeutic options for women whose tumors are refractory to current frontline therapies. Iron metabolism is frequently disrupted in breast cancer, and may offer an opportunity to address these challenges. Iron enhances breast tumor initiation, growth and metastases. Iron may contribute to breast tumor initiation by promoting redox cycling of estrogen metabolites. Up-regulation of iron import and down-regulation of iron export may enable breast cancer cells to acquire and retain excess iron. Alterations in iron metabolism in macrophages and other cells of the tumor microenvironment may also foster breast tumor growth. Expression of iron metabolic genes in breast tumors is predictive of breast cancer prognosis. Iron chelators and other strategies designed to limit iron may have therapeutic value in breast cancer. The dependence of breast cancer on iron presents rich opportunities for improved prognostic evaluation and therapeutic intervention.

Assessing the effects of estrogen on the dynamics of breast cancer

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.

Targeted multiplex imaging mass spectrometry with single chain fragment variable (scfv) recombinant antibodies

Recombinant scfv antibodies specific for CYP1A1 and CYP1B1 P450 enzymes were combined with targeted imaging mass spectrometry to simultaneously detect the P450 enzymes present in archived, paraffin-embedded, human breast cancer tissue sections. By using CYP1A1 and CYP1B1 specific scfv, each coupled to a unique reporter molecule (i.e., a mass tag) it was possible to simultaneously detect multiple antigens within a single tissue sample with high sensitivity and specificity using mass spectrometry. The capability of imaging multiple antigens at the same time is a significant advance that overcomes technical barriers encountered when using present day approaches to develop assays that can simultaneously detect more than a single antigen in the same tissue sample.

Dual roles of estrogen metabolism in mammary carcinogenesis

A female hormone, estrogen, is linked to breast cancer incidence. Estrogens undergo phase I and II metabolism by which they are biotransformed into genotoxic catechol estrogen metabolites and conjugate metabolites are produced for excretion or accumulation. The molecular mechanisms underlying estrogen-mediated mammary carcinogenesis remain unclear. Cell proliferation through activation of estrogen receptor (ER) by its agonist ligands and is clearly considered as one of carcinogenic mechanisms. Recent studies have proposed that reactive oxygen species generated from estrogen or estrogen metabolites are attributed to genotoxic effects and signal transduction through influencing redox sensitive transcription factors resulting in cell transformation, cell cycle, migration, and invasion of the breast cancer. Conjuguation metabolic pathway is thought to protect cells from genotoxic and cytotoxic effects by catechol estrogen metabolites. However, methoxylated catechol estrogens have been shown to induce ER-mediated signaling pathways, implying that conjugation is not a simply detoxification pathway. Dual action of catechol estrogen metabolites in mammary carcinogenesis as the ER-signaling molecules and chemical carcinogen will be discussed in this review.