Inclusion of endogenous hormone levels in risk prediction models of postmenopausal breast cancer

Male breast cancer differs from female breast cancer in molecular features that affect prognoses and drug responses

BACKGROUND

Male breast cancer (MBC) is a rare malignancy with a worse prognosis than female breast cancer (FBC). Current MBC treatment strategies are based on those for FBC. However, molecular differences between MBC and FBC with respect to prognosis and drug responses remain unclear.

METHODS

After controlling for confounding factors with propensity score matching (PSM), differences between MBC and FBC were comprehensively analyzed using many types of data: survival, immune microenvironments, sex hormone responses, drug sensitivity, transcriptomes, genomes, epigenomes, and proteomes.

RESULTS

Overall survival (OS) and cancer-specific survival (CSS) were both worse for MBC than for FBC. Differentially expressed mRNAs were enriched in numerous cancer-related functions and pathways, with SPAG16 and STOX1 being as the most important prognosis-related mRNAs for MBC. Competing endogenous RNA (ceRNA) and transcription factor (TF)-mRNA regulatory networks contain potential prognostic genes. Nine genes had higher mutation frequencies in MBC than in FBC. MBC shows a comparatively poor response to immunotherapy, with five proteins that promote breast cancer progression being highly expressed in MBC. MBC may be more responsive than FBC to estrogen. We detected six United States Food and Drug Administration (FDA)-approved therapeutic target genes as being differentially expressed between MBC and FBC.

CONCLUSION

The poor prognosis of MBC compared to FBC is due to numerous molecular differences and resulting drug responses.

Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis

PURPOSE

Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways.

METHODS

This analysis included 1208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex-hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders.

RESULTS

Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI 1.03 to 1.44), androstenedione (RR 1.20, 95% CI 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI 0.98 to 1.28), estrone (RR 1.21, 95% CI 0.99 to 1.48), total estradiol (RR 1.19, 95% CI 1.02 to 1.39) and free estradiol (RR 1.22, 95% CI 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR 0.83, 95% CI 0.66 to 1.05).

CONCLUSION

Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex-hormone-driven nature of postmenopausal breast cancer.

The risk of breast cancer and gynecologic malignancies after ovarian stimulation: Meta-analysis of cohort study

The effects of ovarian stimulation on breast and gynecological tumor incidence remain controversial. Therefore, the aim of this meta-analysis was to study the risk of cancer in ovarian stimulation. Of the 22713 studies initially identified, 28 were eligible for inclusion. The results revealed that the impact of ovarian cancer (RR = 1.33, [1.05; 1.69]) and cervical cancer (RR = 0.67, [0.46; 0.97]) is significant among the overall effects. In subgroup analysis, in the nulliparous population (RR = 0.81 [0.68; 0.96]) was the protective factor for the breast cancer. In the Caucasians subgroup (RR = 1.45, [1.12; 1.88]), the ovarian cancer incidence was statistically significant. In the Asian subgroup (RR = 1.51, [1.00; 2.28]), the endometrial cancer incidence was statistically significant. In the subgroup of Asians (RR = 0.55 [0.44; 0.68]) and the multiparous population (RR = 0.31, [0.21; 0.46]), them can be the statistically protective factor for the cervical cancer.

Low levels of sex hormone-binding globulin predict an increased breast cancer risk and its underlying molecular mechanisms

Sex hormone-binding globulin (SHBG) is a serum glycoprotein exhibiting the unique feature of binding sex steroids with high affinity and specificity. Over the past few decades, there have been significant breakthroughs in our understanding of the function and regulation of SHBG. The biological role of SHBG has expanded from being considered a simple sex hormone transporter to being associated with several complex physiological and pathological changes in a variety of target tissues. Many factors can affect the plasma SHBG levels, with fluctuations in circulating levels affecting the development of various diseases, such as increasing the risk of developing breast cancer. This article reviews the clinical significance of changes in circulating SHBG levels in the development of breast cancer and the possible influence of these levels on endocrine drug resistance in hormone receptor-positive breast cancer. Higher levels of plasma SHBG significantly reduce the risk of estrogen receptor-positive breast cancer, especially in postmenopausal women. Moreover, the molecular mechanisms by which SHBG affects breast cancer risk are also summarized in detail. Finally, transcriptomics and proteomics data revealed that SHBG expression in breast tissue can effectively distinguish breast cancer from normal tissue. Additionally, the association between SHBG expression levels and various classical tumor-related pathways was investigated.

Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention

PURPOSE

To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology.

METHODS

We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context.

RESULTS

Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods.

CONCLUSION

Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.

Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis

Purpose

Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways.

Methods

This analysis included 1,208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders.

Results

Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI: 1.03 to 1.44), androstenedione (RR: 1.20, 95% CI: 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI: 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI: 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI: 0.98 to 1.28), estrone (RR: 1.21, 95% CI: 0.99 to 1.48), total estradiol (RR: 1.19, 95% CI: 1.02 to 1.39) and free estradiol (RR: 1.22, 95% CI: 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR: 0.83, 95% CI: 0.66 to 1.05).

Conclusion

Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex hormone-driven nature of postmenopausal breast cancer.

Associations of gut microbiome with endogenous estrogen levels in healthy postmenopausal women

PURPOSE

The gut microbiome is a potentially important contributor to endogenous estrogen levels after menopause. In healthy postmenopausal women, we examined associations of fecal microbiome composition with levels of urinary estrogens, their metabolites, and relevant metabolic pathway ratios implicated in breast cancer risk.

METHODS

Eligible postmenopausal women (n = 164) had a body mass index (BMI) ≤ 35 kg/m2 and no history of hormone use (previous 6 months) or cancer/metabolic disorders. Estrogens were quantified in spot urine samples with liquid chromatography-high resolution mass spectrometry (corrected for creatinine). Bacterial DNA was isolated from fecal samples and the V1-V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of gut microbiome's indices of within-sample (alpha) diversity (i.e., Shannon, Chao1, and Inverse Simpson), phylogenetic diversity, and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with individual estrogens and metabolic ratios, adjusted for age and BMI.

RESULTS

In this sample of 164 healthy postmenopausal women, the mean age was 62.9 years (range 47.0-86.0). We found significant inverse associations of observed species with 4-pathway:total estrogens (p = 0.04) and 4-pathway:2-pathway (p = 0.01). Shannon index was positively associated with 2-catechols: methylated 2-catechols (p = 0.04). Chao1 was inversely associated with E1:total estrogens (p = 0.04), and 4-pathway:2-pathway (p = 0.02) and positively associated with 2-pathway:parent estrogens (p = 0.01). Phylogenetic diversity was inversely associated with 4-pathway:total estrogens (p = 0.02), 4-pathway:parent estrogens (p = 0.03), 4-pathway:2-pathway (p = 0.01), and 4-pathway:16-pathway (p = 0.03) and positively associated with 2-pathway:parent estrogens (p = 0.01). F/B ratio was not associated with any of the estrogen measures.

CONCLUSION

Microbial diversity was associated with several estrogen metabolism ratios implicated in breast cancer risk. Further studies are warranted to confirm these findings in a larger and more representative sample of postmenopausal women, particularly with enrichment of minority participants.

Prolactin levels and breast cancer risk by tumor expression of prolactin-related markers

BACKGROUND

Higher circulating prolactin has been associated with increased breast cancer risk. Prolactin binding to the prolactin receptor (PRLR) can activate the transcription factor STAT5, thus, we examined the association between plasma prolactin and breast cancer risk by tumor expression of PRLR, STAT5, and the upstream kinase JAK2.

METHODS

Using data from 745 cases and 2454 matched controls in the Nurses' Health Study, we conducted polytomous logistic regression to examine the association between prolactin (> 11 ng/mL vs. ≤ 11 ng/mL) measured within 10 years of diagnosis and breast cancer risk by PRLR (nuclear [N], cytoplasmic [C]), phosphorylated STAT5 (pSTAT5; N, C), and phosphorylated JAK2 (pJAK2; C) tumor expression. Analyses were conducted separately in premenopausal (n = 168 cases, 765 controls) and postmenopausal women (n = 577 cases, 1689 controls).

RESULTS

In premenopausal women, prolactin levels > 11 ng/mL were positively associated with risk of tumors positive for pSTAT5-N (OR 2.30, 95% CI 1.02-5.22) and pSTAT5-C (OR 1.64, 95% CI 1.01-2.65), but not tumors that were negative for these markers (OR 0.98, 95% CI 0.65-1.46 and OR 0.73, 95% CI 0.43-1.25; p-heterogeneity = 0.06 and 0.02, respectively). This was stronger when tumors were positive for both pSTAT5-N and pSTAT5-C (OR 2.88, 95% CI 1.14-7.25). No association was observed for PRLR or pJAK2 (positive or negative) and breast cancer risk among premenopausal women. Among postmenopausal women, plasma prolactin levels were positively associated with breast cancer risk irrespective of PRLR, pSTAT5, or pJAK2 expression (all p-heterogeneity ≥ 0.21).

CONCLUSION

We did not observe clear differences in the association between plasma prolactin and breast cancer risk by tumor expression of PRLR or pJAK2, although associations for premenopausal women were observed for pSTAT5 positive tumors only. While additional studies are needed, this suggests that prolactin may act on human breast tumor development through alternative pathways.

Predictive model of risk and severity of enteritis in systemic lupus erythematosus

INTRODUCTION

To describe the clinical and laboratory features of systemic lupus erythematosus (SLE) enteritis and to establish a predictive model of risk and severity of lupus enteritis (LE).

METHODS

Records of patients with SLE complaining about acute digestive symptoms were reviewed. The predictive nomogram for the diagnosis of LE was constructed by using R. The accuracy of the model was tested with correction curves. The receiver operating characteristic curve (ROC curve) program and a Decision curve analysis (DCA) were used for the verification of LE model. Receiver operating characteristic curve was also employed for evaluation of factors in the prediction of severity of LE.

RESULTS

During the eight year period, 46 patients were in the LE group, while 32 were in the non-LE group. Abdominal pain, emesis, D-dimer >5 μg/mL, hypo-C3, and anti-SSA positive remained statistically significant and were included into the prediction model. Area under the curve (AUC) of ROC curve in this model was 0.909. Correction curve indicated consistency between the predicted rate and actual diagnostic rates. The DCA showed that the LE model was of benefit. Forty-four patients were included in developing the prediction model of LE severity. Infection, SLE disease activity index (SLEDAI), CT score, and new CT score were validated as risk factors for LE severity. The AUC of the combined SLEDAI, infection and new CT score were 0.870.

CONCLUSION

The LE model exhibits good predictive ability to assess LE risk in SLE patients with acute digestive symptoms. The combination of SLEDAI, infection, and new CT score could improve the assessment of LE severity.

Linking Physical Activity to Breast Cancer via Sex Steroid Hormones, Part 2: The Effect of Sex Steroid Hormones on Breast Cancer Risk

We undertook a systematic review and appraised the evidence for an effect of circulating sex steroid hormones and sex hormone-binding globulin (SHBG) on breast cancer risk in pre- and postmenopausal women. Systematic searches identified prospective studies relevant to this review. Meta-analyses estimated breast cancer risk for women with the highest compared with the lowest level of sex hormones, and the DRMETA Stata package was used to graphically represent the shape of these associations. The ROBINS-E tool assessed risk of bias, and the GRADE system appraised the strength of evidence. In premenopausal women, there was little evidence that estrogens, progesterone, or SHBG were associated with breast cancer risk, whereas androgens showed a positive association. In postmenopausal women, higher estrogens and androgens were associated with an increase in breast cancer risk, whereas higher SHBG was inversely associated with risk. The strength of the evidence quality ranged from low to high for each hormone. Dose-response relationships between sex steroid hormone concentrations and breast cancer risk were most notable for postmenopausal women. These data support the plausibility of a role for sex steroid hormones in mediating the causal relationship between physical activity and the risk of breast cancer.See related reviews by Lynch et al., p. 11 and Swain et al., p. 16.

Prolactin: The Third Hormone in Breast Cancer

Prolactin coordinates with the ovarian steroids to orchestrate mammary development and lactation, culminating in nourishment and an increasingly appreciated array of other benefits for neonates. Its central activities in mammary epithelial growth and differentiation suggest that it plays a role(s) in breast cancer, but it has been challenging to identify its contributions, essential for incorporation into prevention and treatment approaches. Large prospective epidemiologic studies have linked higher prolactin exposure to increased risk, particularly for ER+ breast cancer in postmenopausal women. However, it has been more difficult to determine its actions and clinical consequences in established tumors. Here we review experimental data implicating multiple mechanisms by which prolactin may increase the risk of breast cancer. We then consider the evidence for role(s) of prolactin and its downstream signaling cascades in disease progression and treatment responses, and discuss how new approaches are beginning to illuminate the biology behind the seemingly conflicting epidemiologic and experimental studies of prolactin actions across diverse breast cancers.

NSG-Pro mouse model for uncovering resistance mechanisms and unique vulnerabilities in human luminal breast cancers

Most breast cancer deaths are caused by estrogen receptor-α–positive (ER+) disease. Preclinical progress is hampered by a shortage of therapy-naïve ER+ tumor models that recapitulate metastatic progression and clinically relevant therapy resistance. Human prolactin (hPRL) is a risk factor for primary and metastatic ER+ breast cancer. Because mouse prolactin fails to activate hPRL receptors, we developed a prolactin-humanized Nod-SCID-IL2Rγ (NSG) mouse (NSG-Pro) with physiological hPRL levels. Here, we show that NSG-Pro mice facilitate establishment of therapy-naïve, estrogen-dependent PDX tumors that progress to lethal metastatic disease. Preclinical trials provide first-in-mouse efficacy of pharmacological hPRL suppression on residual ER+ human breast cancer metastases and document divergent biology and drug responsiveness of tumors grown in NSG-Pro versus NSG mice. Oncogenomic analyses of PDX lines in NSG-Pro mice revealed clinically relevant therapy-resistance mechanisms and unexpected, potently actionable vulnerabilities such as DNA-repair aberrations. The NSG-Pro mouse unlocks previously inaccessible precision medicine approaches for ER+ breast cancers.

Prolactin and Risk of Epithelial Ovarian Cancer

BACKGROUND

Prolactin is synthesized in the ovaries and may play a role in ovarian cancer etiology. One prior prospective study observed a suggestive positive association between prolactin levels and risk of ovarian cancer.

METHODS

We conducted a pooled case-control study of 703 cases and 864 matched controls nested within five prospective cohorts. We used unconditional logistic regression to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI) for the association between prolactin and ovarian cancer risk. We examined heterogeneity by menopausal status at blood collection, body mass index (BMI), age, and histotype.

RESULTS

Among women with known menopausal status, we observed a positive trend in the association between prolactin and ovarian cancer risk (P _trend = 0.045; OR, quartile 4 vs. 1 = 1.34; 95% CI = 0.97-1.85), but no significant association was observed for premenopausal or postmenopausal women individually (corresponding OR = 1.38; 95% CI = 0.74-2.58; P _trend = 0.32 and OR = 1.41; 95% CI = 0.93-2.13; P _trend = 0.08, respectively; P _{heterogeneity} = 0.91). In stratified analyses, we observed a positive association between prolactin and risk for women with BMI ≥ 25 kg/m², but not BMI < 25 kg/m² (corresponding OR = 2.68; 95% CI = 1.56-4.59; P _trend < 0.01 and OR = 0.90; 95% CI = 0.58-1.40; P _trend = 0.98, respectively; P _{heterogeneity} < 0.01). Associations did not vary by age, postmenopausal hormone therapy use, histotype, or time between blood draw and diagnosis.

CONCLUSIONS

We found a trend between higher prolactin levels and increased ovarian cancer risk, especially among women with a BMI ≥ 25 kg/m².

IMPACT

This work supports a previous study linking higher prolactin with ovarian carcinogenesis in a high adiposity setting. Future work is needed to understand the mechanism underlying this association.

The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

BACKGROUND

Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS.

METHODS

Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1).

RESULTS

Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR.

CONCLUSIONS

Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

Weight Regain and Breast Cancer-Related Biomarkers Following an Exercise Intervention in Postmenopausal Women

BACKGROUND

Epidemiologic studies have reported associations between weight fluctuations and postmenopausal breast cancer risk; however, the biological markers involved in this association are unknown. This study aimed to explore the associations between breast cancer-related biomarkers and weight regain following exercise-induced weight loss.

METHODS

From the 400 participants included in the Breast Cancer and Exercise Trial in Alberta, a total of 214 lost weight during the intervention and had follow-up blood samples, body composition, and covariate measurements. Outcomes were measured at baseline, 12 months (end of the study), and 24 months (follow-up).

RESULTS

During follow-up, weight regain was 1.80 kg [95% confidence interval (CI): -0.40-3.90], and was significantly associated with increases in estradiol [treatment effect ratio (TER) = 1.03; 95% CI, 1.01-1.04], estrone (TER = 1.02; 95% CI, 1.01-1.03), free estradiol (TER = 1.04; 95% CI, 1.02-1.05), the homeostatic model assessment for insulin resistance (TER = 1.03; 95% CI, 1.02-1.05), and insulin (TER = 1.03; 95% CI, 1.01-1.04), and decreases in sex hormone-binding globulin (SHBG; TER = 0.98; 95% CI, 0.97-0.99) levels. Nonstatistically significant associations were found for glucose and C-reactive protein. Furthermore, a statistically significant linear trend of increasing levels for all biomarkers, and decreasing SHBG, across weight regain categories was found.

CONCLUSIONS

These results suggest that weight regain following exercise-induced weight loss is associated with breast cancer-related biomarker changes in postmenopausal women.

IMPACT

These findings provide evidence to support the importance of developing effective strategies to prevent weight regain and, consequently, decrease postmenopausal breast cancer risk via changes in adiposity-related biomarkers.

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case -control study

Background

In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer.

Methods

This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients.

Results

Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08.

Conclusions

These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07584-9.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07584-9.

Prolactin: A hormone with diverse functions from mammary gland development to cancer metastasis

Prolactin has a rich mechanistic set of actions and signaling in order to elicit developmental effects in mammals. Historically, prolactin has been appreciated as an endocrine peptide hormone that is responsible for final, functional mammary gland development and lactation. Multiple signaling pathways impacted upon by the microenvironment contribute to cell function and differentiation. Endocrine, autocrine and paracrine signaling are now apparent in not only mammary development, but also in cancer, and involve multiple cell types including those of the immune system. Multiple ligands agonists are capable of binding to the prolactin receptor, potentially expanding receptor function. Prolactin has an important role not only in tumorigenesis of the breast, but also in a number of hormonally responsive cancers such as prostate, ovarian and endometrial cancer, as well as pancreatic and lung cancer. Although pituitary and extra-pituitary sources of prolactin such as the epithelium are important, stromal sourced prolactin is now also being recognized as an important factor in tumor progression, all of which potentially signal to multiple cell types in the tumor microenvironment. While prolactin has important roles in milk production including calcium and bone homeostasis, in the disease state it can also affect bone homeostasis. Prolactin also impacts metastatic cancer of the breast to modulate the bone microenvironment and promote bone damage. Prolactin has a fascinating contribution in both physiologic and pathologic settings of mammals.

The Steroid Metabolome and Breast Cancer Risk in Women with a Family History of Breast Cancer: The Novel Role of Adrenal Androgens and Glucocorticoids

BACKGROUND

No study has comprehensively examined how the steroid metabolome is associated with breast cancer risk in women with familial risk.

METHODS

We examined 36 steroid metabolites across the spectrum of familial risk (5-year risk ranged from 0.14% to 23.8%) in pre- and postmenopausal women participating in the New York site of the Breast Cancer Family Registry (BCFR). We conducted a nested case-control study with 62 cases/124 controls individually matched on menopausal status, age, and race. We measured metabolites using GC-MS in urine samples collected at baseline before the onset of prospectively ascertained cases. We used conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) per doubling in hormone levels.

RESULTS

The average proportion of total steroid metabolites in the study sample were glucocorticoids (61%), androgens (26%), progestogens (11%), and estrogens (2%). A doubling in glucocorticoids (aOR = 2.7; 95% CI = 1.3-5.3) and androgens (aOR = 1.6; 95% CI = 1.0-2.7) was associated with increased breast cancer risk. Specific glucocorticoids (THE, THF αTHF, 6β-OH-F, THA, and α-THB) were associated with 49% to 161% increased risk. Two androgen metabolites (AN and 11-OH-AN) were associated with 70% (aOR = 1.7; 95% CI = 1.1-2.7) and 90% (aOR = 1.9; 95% CI = 1.2-3.1) increased risk, respectively. One intermediate metabolite of a cortisol precursor (THS) was associated with 65% (OR = 1.65; 95% CI = 1.0-2.7) increased risk. E1 and E2 estrogens were associated with 20% and 27% decreased risk, respectively.

CONCLUSIONS

Results suggest that glucocorticoids and 11-oxygenated androgens are positively associated with breast cancer risk across the familial risk spectrum.

IMPACT

If replicated, our findings suggest great potential of including steroids into existing breast cancer risk assessment tools.

Sedentary behavior and risk of breast cancer: a dose-response meta-analysis from prospective studies

BACKGROUND

Emerging studies examined the association between sedentary behavior and risk of breast cancer, however, the dose-response relationship remained unclear. We aim to explore dose-response relationship of sedentary behavior and breast cancer risk based on relevant cohort studies.

METHODS

Online database (PubMed, Web of Science, EMBASE and Cochrane Library) were searched up to March 29, 2019. Overall relative risk (RR) with 95% confidence interval (CI) were pooled, and generalized least squares (GLS) method and restricted cubic splines were applied to evaluate the linear or nonlinear relation. Attributable risk proportion (ARP) was used to assess the health hazards of sedentary behavior in different countries.

RESULTS

Eight prospective studies were included in the meta-analysis, containing 17 048 breast cancer cases and 426 506 participants. The borderline statistical association was detected between prolonged sedentary behavior and risk of breast cancer (RR 1.08, 95% CI 0.99-1.19). Linear association between sedentary and breast cancer was observed (P_nonlinearity = 0.262), and for 1 h/d increment of sedentary behavior, there was 1% increase of breast cancer risk (RR 1.01, 95% CI1.00-1.02). Similar results were also found between TV viewing and risk of breast cancer (P_nonlinearity = 0.551), with 1 h/day increment of TV viewing daily attributing to 2% increase of breast cancer risk (RR 1.02, 95% CI 1.00-1.04). Moreover, sedentary behavior may statistically increase the risk of breast cancer by 21.6% for Asian countries, 8.26% for North America.

CONCLUSIONS

Sedentary behavior was validated as a risk factor of breast cancer through dose-response analysis, especially TV viewing.

Workshop on normal reference ranges for estradiol in postmenopausal women, September 2019, Chicago, Illinois

The North American Menopause Society (NAMS) organized the Workshop on Normal Ranges for Estradiol in Postmenopausal Women from September 23 to 24, 2019, in Chicago, Illinois. The aim of the workshop was to review existing analytical methodologies for measuring estradiol in postmenopausal women and to assess existing data and study cohorts of postmenopausal women for their suitability to establish normal postmenopausal ranges. The anticipated outcome of the workshop was to develop recommendations for establishing normal ranges generated with a standardized and certified assay that could be adopted by clinical and research communities. The attendees determined that the term reference range was a better descriptor than normal range for estradiol measurements in postmenopausal women. Twenty-eight speakers presented during the workshop.

Inclusion of Endogenous Plasma Dehydroepiandrosterone Sulfate and Mammographic Density in Risk Prediction Models for Breast Cancer

BACKGROUND

Endogenous hormones and mammographic density are risk factors for breast cancer. Joint analyses of the two may improve the ability to identify high-risk women.

METHODS

This study within the KARMA cohort included prediagnostic measures of plasma hormone levels of dehydroepiandrosterone (DHEA), its sulfate (DHEAS), and mammographic density in 629 cases and 1,223 controls, not using menopausal hormones. We evaluated the area under the receiver-operating curve (AUC) for risk of breast cancer by adding DHEA, DHEAS, and mammographic density to the Gail or Tyrer-Cuzick 5-year risk scores or the CAD2Y 2-year risk score.

RESULTS

DHEAS and percentage density were independently and positively associated with breast cancer risk (P = 0.007 and P < 0.001, respectively) for postmenopausal, but not premenopausal, women. No significant association was seen for DHEA. In postmenopausal women, those in the highest tertiles of both DHEAS and density were at greatest risk of breast cancer (OR, 3.5; 95% confidence interval, 1.9-6.3) compared with the lowest tertiles. Adding DHEAS significantly improved the AUC for the Gail (+2.1 units, P = 0.008) and Tyrer-Cuzick (+1.3 units, P = 0.007) risk models. Adding DHEAS to the Gail and Tyrer-Cuzick models already including mammographic density further increased the AUC by 1.2 units (P = 0.006) and 0.4 units (P = 0.007), respectively, compared with only including density.

CONCLUSIONS

DHEAS and mammographic density are independent risk factors for breast cancer and improve risk discrimination for postmenopausal breast cancer.

IMPACT

Combining DHEAS and mammographic density could help identify women at high risk who may benefit from individualized breast cancer screening and/or preventive measures among postmenopausal women.

Predicting Circulating CA125 Levels among Healthy Premenopausal Women

BACKGROUND

Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women.

METHODS

We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (≥35 U/mL) CA125 prediction models and externally validated an abridged model restricting to available predictors among 473 premenopausal women in the European Prospective Investigation into Cancer and Nutrition Study (EPIC).

RESULTS

The final linear CA125 prediction model included age, race, tubal ligation, endometriosis, menstrual phase at blood draw, and fibroids, which explained 7% of the total variance of CA125. The correlation between observed and predicted CA125 levels based on the abridged model (including age, race, and menstrual phase at blood draw) had similar correlation coefficients in NEC (r = 0.22) and in EPIC (r = 0.22). The dichotomous CA125 prediction model included age, tubal ligation, endometriosis, prior personal cancer diagnosis, family history of ovarian cancer, number of miscarriages, menstrual phase at blood draw, and smoking status with AUC of 0.83. The abridged dichotomous model (including age, number of miscarriages, menstrual phase at blood draw, and smoking status) showed similar AUCs in NEC (0.73) and in EPIC (0.78).

CONCLUSIONS

We identified a combination of factors associated with CA125 levels in premenopausal women.

IMPACT

Our model could be valuable in identifying healthy women likely to have elevated CA125 and consequently improve its specificity for ovarian cancer screening.

Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study

BACKGROUND

The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population.

METHODS

We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants.

RESULTS

Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years.

CONCLUSIONS

Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit.

IMPACT

Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.

Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

Background

Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50.

Methods

In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.

Results

The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.

Conclusions

AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.

Prediction of fetal loss in Chinese pregnant patients with systemic lupus erythematosus: a retrospective cohort study

OBJECTIVE

To develop a predictive model for fetal loss in women with systemic lupus erythematosus (SLE).

DESIGN

A retrospective cohort study.

SETTING

Data were collected in a tertiary medical centre, located in Shanghai, China, from September 2011 to May 2017.

PARTICIPANTS

338 pregnancies with SLE were analysed retrospectively. Cases of multiple pregnancy and those in which artificial abortion was performed for personal reasons were excluded.

PRIMARY OUTCOME MEASURES

Fetal loss was the primary outcome. A stepwise regression to identify the predictors related to the fetal loss and coefficient B of each variable was used to develop a predictive model and make a corresponding risk classification. The Hosmer-Lemeshow test, Omnibus test and area under the receiver-operating characteristic curve (AUC) were used to assess the goodness-of-fit and discrimination of the predictive model. A 10-fold cross validation was used to assess the model for overfitting.

RESULTS

Unplanned pregnancies (OR 2.84, 95% CI 1.12 to 7.22), C₃ hypocomplementemia (OR 5.46, 95% CI 2.30 to 12.97) and 24 hour-urinary protein level (0.3≤protein<1.0 g/24 hours: OR 2.10, 95% CI 0.63 to 6.95; protein≥1.0 g/24 hours: OR 5.89, 95% CI 2.30 to 15.06) were selected by the stepwise regression. The Hosmer-Lemeshow test resulted in p=0.325; the Omnibus test resulted in p<0.001 and the AUC was 0.829 (95% CI 0.744 to 0.91) in the regression model. The corresponding risk score classification was divided into low risk (0-3) and high risk groups (>3), with a sensitivity of 60.5%, a specificity of 93.3%, positive likelihood ratio of 9.03 and negative likelihood ratio of 0.42.

CONCLUSIONS

A predictive model for fetal loss in women with SLE was developed using the timing of conception, C₃ complement and 24 hour-urinary protein level. This model may help clinicians in identifying women with high risk pregnancies, thereby carrying out monitoring or/and interventions for improving fetal outcomes.

The insulin-like growth factor family and breast cancer prognosis: A prospective cohort study among postmenopausal women in Denmark

OBJECTIVE

Circulating levels of insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) have been associated with breast cancer (BC) risk. The evidence in relation to BC prognosis is limited. We aimed to evaluate the association between pre-diagnostic serum levels of IGF-I, IGF-II, IGFBP-2, IGFBP-3 and BC prognosis (i.e. recurrence, BC specific mortality and all-cause mortality) among women diagnosed with BC. We hypothesized that higher serum levels of IGFs and IGFBPs were associated with poor BC prognosis and that the associations were modified by estrogen receptor (ER) status.

DESIGN

From the Danish Diet, Cancer and Health cohort, 412 postmenopausal women diagnosed with incident BC within 5 years of cohort baseline (1993-1997) were identified. Baseline serum samples were analyzed for IGF-I, IGF-II, IGFBP-2 and IGFBP-3. Follow-up was carried out through 2014 by linkage to national Danish registries. Exposures were related to BC prognosis by Cox Proportional Hazard models; effect modification by ER status was investigated and sensitivity analyses by follow-up time were made.

RESULTS

During a median of 15 years, 106 women experienced recurrence and 172 died (118 due to BC). Overall, no associations were observed between IGF-I, IGF-II, IGFBP-2, IGFBP-3 and BC prognosis and no effect modification by ER status was observed. However, higher levels of IGF-II were associated with higher BC specific mortality [Hazard Ratio (HR) (95% Confidence Intervals (CI)): 1.43 (1.01-2.04)] within 10 years of follow-up. Likewise, higher levels of IGFBP-2 were associated with higher BC specific mortality [HR (95% CI): 1.87 (1.19-2.94)] within 5 years of follow-up. In contrast, higher levels of IGFBP-3 were associated with lower risk of recurrence [HR (95% CI): 0.76 (0.60-0.97)] at 5 years of follow-up and BC specific mortality [HR (95% CI): 0.80 (0.65-0.98)] within 10 years of follow-up.

CONCLUSIONS

The present study did not support an association between higher serum levels of IGFs, IGFBPs and adverse BC prognosis. However, it is possible that the role of the IGF family in the etiology of the 5-10 year BC prognosis is different from that of longer-term BC prognosis.

Inclusion of Plasma Prolactin Levels in Current Risk Prediction Models of Premenopausal and Postmenopausal Breast Cancer

Background

Circulating plasma prolactin is associated with breast cancer risk and may improve our ability to identify high-risk women. Mammographic density is a strong risk factor for breast cancer, but the association with prolactin is unclear. We studied the association between breast cancer, established breast cancer risk factors and plasma prolactin, and improvement of risk prediction by adding prolactin.

Methods

We conducted a nested case-control study including 721 breast cancer patients and 1400 age-matched controls. Plasma prolactin levels were assayed using immunoassay and mammographic density measured by STRATUS. Odds ratios (ORs) were calculated by multivariable adjusted logistic regression, and improvement in the area under the curve for the risk of breast cancer by adding prolactin to established risk models. Statistical tests were two-sided.

Results

In multivariable adjusted analyses, prolactin was associated with risk of premenopausal (OR, top vs bottom quintile = 1.9; 1.88 (95% confidence interval [CI] = 1.08 to 3.26) but not with postmenopausal breast cancer. In postmenopausal cases prolactin increased by 10.6% per cBIRADS category (P_trend = .03). In combined analyses of prolactin and mammographic density, ORs for women in the highest vs lowest tertile of both was 3.2 (95% CI = 1.3 to 7.7) for premenopausal women and 2.44 (95% CI = 1.44 to 4.14) for postmenopausal women. Adding prolactin to current risk models improved the area under the curve of the Gail model (+2.4 units, P = .02), Tyrer-Cuzick model (+3.8, P = .02), and the CAD2Y model (+1.7, P = .008) in premenopausal women.

Conclusion

Circulating plasma prolactin and mammographic density appear independently associated with breast cancer risk among premenopausal women, and prolactin may improve risk prediction by current risk models.

Estimating the receiver operating characteristic curve in matched case control studies

The matched case-control design is frequently used in the study of complex disorders and can result in significant gains in efficiency, especially in the context of measuring biomarkers; however, risk prediction in this setting is not straightforward. We propose an inverse-probability weighting approach to estimate the predictive ability associated with a set of covariates. In particular, we propose an algorithm for estimating the summary index, area under the curve corresponding to the Receiver Operating Characteristic curve associated with a set of pre-defined covariates for predicting a binary outcome. By combining data from the parent cohort with that generated in a matched case control study, we describe methods for estimation of the population parameters of interest and the corresponding area under the curve. We evaluate the bias associated with the proposed methods in simulations by considering a range of parameter settings. We illustrate the methods in two data applications: (1) a prospective cohort study of cardiovascular disease in women, the Women's Health Study, and (2) a matched case-control study nested within the Nurses' Health Study aimed at risk prediction of invasive breast cancer.

Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case-control study

BACKGROUND

No prior study to our knowledge has examined the joint contribution of a polygenic risk score (PRS), mammographic density (MD), and postmenopausal endogenous hormone levels-all well-confirmed risk factors for invasive breast cancer-to existing breast cancer risk prediction models.

METHODS AND FINDINGS

We conducted a nested case-control study within the prospective Nurses' Health Study and Nurses' Health Study II including 4,006 cases and 7,874 controls ages 34-70 years up to 1 June 2010. We added a breast cancer PRS using 67 single nucleotide polymorphisms, MD, and circulating testosterone, estrone sulfate, and prolactin levels to existing risk models. We calculated area under the curve (AUC), controlling for age and stratified by menopausal status, for the 5-year absolute risk of invasive breast cancer. We estimated the population distribution of 5-year predicted risks for models with and without biomarkers. For the Gail model, the AUC improved (p-values < 0.001) from 55.9 to 64.1 (8.2 units) in premenopausal women (Gail + PRS + MD), from 55.5 to 66.0 (10.5 units) in postmenopausal women not using hormone therapy (HT) (Gail + PRS + MD + all hormones), and from 58.0 to 64.9 (6.9 units) in postmenopausal women using HT (Gail + PRS + MD + prolactin). For the Rosner-Colditz model, the corresponding AUCs improved (p-values < 0.001) by 5.7, 6.2, and 6.5 units. For estrogen-receptor-positive tumors, among postmenopausal women not using HT, the AUCs improved (p-values < 0.001) by 14.3 units for the Gail model and 7.3 units for the Rosner-Colditz model. Additionally, the percentage of 50-year-old women predicted to be at more than twice 5-year average risk (≥2.27%) was 0.2% for the Gail model alone and 6.6% for the Gail + PRS + MD + all hormones model. Limitations of our study included the limited racial/ethnic diversity of our cohort, and that general population exposure distributions were unavailable for some risk factors.

CONCLUSIONS

In this study, the addition of PRS, MD, and endogenous hormones substantially improved existing breast cancer risk prediction models. Further studies will be needed to confirm these findings and to determine whether improved risk prediction models have practical value in identifying women at higher risk who would most benefit from chemoprevention, screening, and other risk-reducing strategies.

Measuring estrogens in women, men, and children: Recent advances 2012-2017

The measurement of estrogens is important for diagnosing and monitoring the health of women, men, and children. For example, for postmenopausal women or women undergoing treatment for breast cancer with aromatase inhibitors, the measurement of extremely low concentrations of estrogens in serum, especially estradiol, is problematic but essential for proper medical care. Achieving superb analytical sensitivity and specificity has been and continues to be a challenge for the clinical laboratory, but is a challenge that is being taken seriously. Focusing on publications from 2012 to 2017, this review will provide an overview of recent research in the development of methods to accurately and precisely measure estrogens, including a variety of estrogen metabolites. Additionally, the latest in clinical research involving estrogen measurement in women, men, and children will be presented to provide an update on the association of estrogens with diseases or conditions such as breast cancer, precocious puberty, infertility, and pregnancy. This research update will provide context as to why estrogen measurement is important and why laboratories are working hard to support the recommendations made by the Endocrine Society regarding estrogen measurement.

Towards Prevention of Breast Cancer: What Are the Clinical Challenges?

The dramatic increase in breast cancer incidence compels a paradigm shift in our preventive efforts. There are several barriers to overcome before prevention becomes an established part of breast cancer management. The objective of this review is to identify the clinical challenges for improved breast cancer prevention and discuss current knowledge on breast cancer risk assessment methods, risk communication, ethics, and interventional efforts with the aim of covering the aspects relevant for a breast cancer prevention trial. Herein, the following five areas are discussed: (i) Adequate tools for identification of women at high risk of breast cancer suggestively entitled Prevent! Online. (ii) Consensus on the definition of high risk, which is regarded as mandatory for all risk communication and potential prophylactic interventions. (iii) Risk perception and communication regarding risk information. (iv) Potential ethical concerns relevant for future breast cancer prevention programs. (v) Risk-reducing programs involving multileveled prevention depending on identified risk. Taken together, devoted efforts from both policy makers and health care providers are warranted to improve risk assessment and risk counseling in women at risk for breast cancer to optimize the prevention of breast cancer. Cancer Prev Res; 11(5); 255-64. ©2018 AACR.

Predicting high-risk endometrioid carcinomas using proteins

Background

The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to advanced-stage diseases. We sought to develop a biomarker model that predicts EEC surgical stage at the time of clinical diagnosis.

Results

PSES was significantly correlated with surgical stage in the TCGA cohort (P < 0.0001) and in the validation cohort (P = 0.0003). Even among grade 1 or 2 tumors, PSES was significantly higher in advanced than in early stage tumors in both the TCGA (P = 0.005) and MD Anderson Cancer Center (MDACC) (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in advanced stage tumors.

Methods

Using reverse-phase protein array expression profiles of 170 antibodies for 210 EEC cases from TCGA, we constructed a Protein Scoring of EEC Staging (PSES) scheme comprising 6 proteins (3 of them phosphorylated) for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MDACC using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used.

Conclusions

PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Determinants of prolactin in postmenopausal Chinese women in Singapore

PURPOSE

Mechanistic and observational data together support a role for prolactin in breast cancer development. Determinants of prolactin in Asian populations have not been meaningfully explored, despite the lower risk of breast cancer in Asian populations.

METHODS

Determinants of plasma prolactin were evaluated in 442 postmenopausal women enrolled in the Singapore Chinese Health Study, a population-based prospective cohort study. At baseline all cohort members completed an in-person interview that elicited information on diet, menstrual and reproductive history, and lifestyle factors. One year after cohort initiation we began collecting blood samples. Quantified were plasma concentrations of prolactin, estrone, estradiol, testosterone, androstenedione, and sex hormone-binding globulin (SHBG). Analysis of covariance method was used for statistical analyses with age at blood draw, time since last meal, and time at blood draw as covariates.

RESULTS

Mean prolactin levels were 25.1% lower with older age at menarche (p value = 0.001), and 27.6% higher with greater years between menarche and menopause (p value = 0.009). Prolactin levels were also positively associated with increased sleep duration (p value = 0.005). The independent determinants of prolactin were years from menarche to menopause, hours of sleep, and the plasma hormones estrone and SHBG (all p values < 0.01).

CONCLUSION

The role of prolactin in breast cancer development may involve reproductive and lifestyle factors, such as a longer duration of menstrual cycling and sleep patterns.

Genetic and environmental factors and serum hormones, and risk of estrogen receptor-positive breast cancer in pre- and postmenopausal Japanese women

Breast cancer incidence in Japanese women has more than tripled over the past two decades. We have previously shown that this marked increase is mostly due to an increase in the estrogen receptor (ER)-positive, HER2-negative subtype. We conducted a case-control study; ER-positive, HER2-negative breast cancer patients who were diagnosed since 2011 and women without disease were recruited. Environmental factors, serum levels of testosterone and 25-hydroxyvitamin D, and common genetic variants reported as predictors of ER-positive breast cancer or found in Asian women were evaluated between patients and controls in pre- and postmenopausal women. To identify important risk predictors, risk prediction models were created by logistic regression models. In premenopausal women, two environmental factors (history of breastfeeding, and history of benign breast disease) and four genetic variants (TOX3-rs3803662, ESR1-rs2046210, 8q24-rs13281615, and SLC4A7-rs4973768) were considered to be risk predictors, whereas three environmental factors (body mass index, history of breastfeeding, and hyperlipidemia), serum levels of testosterone and 25-hydroxyvitamin D, and two genetic variants (TOX3-rs3803662 and ESR1-rs2046210) were identified as risk predictors. Inclusion of common genetic variants and serum hormone measurements as well as environmental factors improved risk assessment models. The decline in the birthrate according to recent changes of lifestyle might be the main cause of the recent notable increase in the incidence of ER-positive breast cancer in Japanese women.

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones

BACKGROUND

Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms.

METHODS

We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65).

CONCLUSION

Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort

Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. Clin Cancer Res; 23(15); 4181-9. ©2017 AACR.

Breast cancer risk prediction: an update to the Rosner-Colditz breast cancer incidence model

PURPOSE

To update and expand the Rosner-Colditz breast cancer incidence model by evaluating the contributions of more recently identified risk factors as well as predicted percent mammographic density (MD) to breast cancer risk.

METHODS

Using data from the Nurses' Health Study (NHS) and NHSII, we added adolescent somatotype (9 unit scale), vegetable intake (servings/day), breastfeeding (months), physical activity (MET-h/week), and predicted percent MD to the Rosner-Colditz model to determine whether these variables improved model discrimination. We evaluated all invasive as well as ER+/PR+, ER+/PR-, and ER-/PR- breast cancer.

RESULTS

In the NHS/NHSII, we accrued over 5200 cases of invasive breast cancer over more than 20 years of follow-up with complete data on the risk factors. Adolescent somatotype and predicted percent MD significantly improved the original Rosner-Colditz model for all invasive breast cancer (change in age-adjusted AUC = 0.020, p < 0.001). The relative risk (RR) of invasive breast cancer for a 4-unit increase in adolescent somatotype was 0.62 (95% CI 0.56, 0.70), whereas the RR for a 20-unit increase in predicted percent MD was 1.32 (95% CI 1.28, 1.36). Adolescent somatotype and predicted percent MD also significantly improved the ER+/PR+model (change in age-adjusted AUC = 0.020, p < 0.001) as well as the ER+/PR- model (change in age-adjusted AUC = 0.012, p = 0.007). Adolescent somatotype, predicted percent MD, breastfeeding, and vegetable intake improved the ER-/PR- model (change in AUC = 0.031, p < 0.0001). The RR of ER-/PR- disease for 5 vegetable servings/day increase was 0.83 (95% CI 0.70, 0.99), while the RR for every 12 months of breastfeeding was 0.88 (95% CI 0.77, 1.01). Physical activity did not improve risk classification in any model.

CONCLUSION

Adolescent somatotype and predicted percent MD significantly improved breast cancer risk classification using the Rosner-Colditz model. Further, risk factors specific to ER- disease, such as breastfeeding and vegetable intake, may also help improve risk prediction of this aggressive subtype.

Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk

PURPOSE

A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status.

METHODS

We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third.

RESULTS

Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories.

CONCLUSIONS

The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

IMPORTANCE

An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention.

OBJECTIVE

To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors.

DESIGN, SETTING, AND PARTICIPANTS

Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality.

EXPOSURES

Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors.

MAIN OUTCOMES AND MEASURES

Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking).

RESULTS

The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population.

CONCLUSIONS AND RELEVANCE

This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

The Important Roles of Steroid Sulfatase and Sulfotransferases in Gynecological Diseases

Gynecological diseases such as endometriosis, adenomyosis and uterine fibroids, and gynecological cancers including endometrial cancer and ovarian cancer, affect a large proportion of women. These diseases are estrogen dependent, and their progression often depends on local estrogen formation. In peripheral tissues, estrogens can be formed from the inactive precursors dehydroepiandrosterone sulfate and estrone sulfate. Sulfatase and sulfotransferases have pivotal roles in these processes, where sulfatase hydrolyzes estrone sulfate to estrone, and dehydroepiandrosterone sulfate to dehydroepiandrosterone, and sulfotransferases catalyze the reverse reactions. Further activation of estrone to the most potent estrogen, estradiol, is catalyzed by 17-ketosteroid reductases, while estradiol can also be formed from dehydroepiandrosterone by the sequential actions of 3β-hydroxysteroid dehydrogenase-Δ⁴-isomerase, aromatase, and 17-ketosteroid reductase. This review introduces the sulfatase and sulfotransferase enzymes, in terms of their structures and reaction mechanisms, and the regulation and different transcripts of their genes, together with the importance of their currently known single nucleotide polymorphisms. Data on expression of sulfatase and sulfotransferases in gynecological diseases are also reviewed. There are often unchanged mRNA and protein levels in diseased tissue, with higher sulfatase activities in cancerous endometrium, ovarian cancer cell lines, and adenomyosis. This can be indicative of a disturbed balance between the sulfatase and sulfotransferases enzymes, defining the potential for sulfatase as a drug target for treatment of gynecological diseases. Finally, clinical trials with sulfatase inhibitors are discussed, where two inhibitors have already concluded phase II trials, although so far with no convincing clinical outcomes for patients with endometrial cancer and endometriosis.

Addressing barriers to uptake of breast cancer chemoprevention for patients and providers

Breast cancer is the most common malignancy among women in the United States, and the primary prevention of this disease is a major public health issue. Because there are relatively few modifiable breast cancer risk factors, pharmacologic interventions with antiestrogens have the potential to significantly affect the primary prevention setting. Breast cancer chemoprevention with selective estrogen receptor modulators (SERMs) tamoxifen and raloxifene, and with aromatase inhibitors (AIs) exemestane and anastrozole, is underutilized despite several randomized controlled trials demonstrating up to a 50% to 65% relative risk reduction in breast cancer incidence among women at high risk. An estimated 10 million women in the United States meet high-risk criteria for breast cancer and are potentially eligible for chemoprevention, but less than 5% of women at high risk who are offered antiestrogens for primary prevention agree to take it. Reasons for low chemoprevention uptake include lack of routine breast cancer risk assessment in primary care, inadequate time for counseling, insufficient knowledge about antiestrogens among patients and providers, and concerns about side effects. Interventions designed to increase chemoprevention uptake, such as decision aids and incorporating breast cancer risk assessment into clinical practice, have met with limited success. Clinicians can help women make informed decisions about chemoprevention by effectively communicating breast cancer risk and enhancing knowledge about the risks and benefits of antiestrogens. Widespread adoption of chemoprevention will require a major paradigm shift in clinical practice for primary care providers (PCPs). However, enhancing uptake and adherence to breast cancer chemoprevention holds promise for reducing the public health burden of this disease.

Endogenous sex hormones and cognitive function in older women

INTRODUCTION

We examined the association between endogenous sex hormones and both objective and subjective measures of cognitive function.

METHODS

We followed 3044 women up to 23 years in a prospective cohort study. We measured plasma levels of estrone, estrone sulfate, estradiol, androstenedione, testosterone, dehydroepiandrosterone (DHEA), and dehydroepiandrosterone sulfate (DHEA-S) in 1989-1990, conducted neuropsychologic testing in 1999-2008, and inquired about subjective cognition in 2012.

RESULTS

Overall, we observed little relation between plasma levels of hormones and either neuropsychologic test performance or subjective cognition. However, after adjustment for age and education, we observed a borderline significant association of higher levels of plasma estrone with higher scores for both overall cognition (P trend = .10) and verbal memory (P trend = .08).

DISCUSSION

There were no clear associations of endogenous hormone levels at midlife and cognition in later life, although a suggested finding of higher levels of plasma estrone associated with better cognitive function merits further research.

Incorporating Biomarkers in Studies of Chemoprevention

Despite Food and Drug Administration approval of tamoxifen and raloxifene for breast cancer risk reduction and endorsement by multiple agencies, uptake of these drugs for primary prevention in the United States is only 4% for risk eligible women likely to benefit from their use. Side effects coupled with incomplete efficacy and lack of a survival advantage are the likely reasons. This disappointing uptake, after the considerable effort and expense of large Phase III cancer incidence trials required for approval, suggests that a new paradigm is required. Current prevention research is focused on (1) refining risk prediction, (2) exploring behavioral and natural product interventions, and (3) utilizing novel translational trial designs for efficacy. Risk biomarkers will play a central role in refining risk estimates from traditional models and selecting cohorts for prevention trials. Modifiable risk markers called surrogate endpoint or response biomarkers will continue to be used in Phase I and II prevention trials to determine optimal dose or exposure and likely effectiveness from an intervention. The majority of Phase II trials will continue to assess benign breast tissue for response and mechanism of action biomarkers. Co-trials are those in which human and animal cohorts receive the same effective dose and the same tissue biomarkers are assessed for modulation due to the intervention, but then additional animals are allowed to progress to cancer development. These collaborations linking biomarker modulation and cancer prevention may obviate the need for cancer incidence trials for non-prescription interventions.

Effects of exercise dose on endogenous estrogens in postmenopausal women: a randomized trial

Exercise dose comparison trials with biomarker outcomes can identify the amount of exercise required to reduce breast cancer risk and also strengthen the causal inference between physical activity and breast cancer. The Breast Cancer and Exercise Trial in Alberta (BETA) tested whether or not greater changes in estradiol (E2), estrone, and sex hormone-binding globulin (SHBG) concentrations can be achieved in postmenopausal women randomized to 12 months of HIGH (300 min/week) vs MODERATE (150 min/week) volumes of aerobic exercise. BETA included 400 inactive postmenopausal women aged 50-74 years with BMI of 22-40 kg/m(2). Blood was drawn at baseline and 6 and 12 months. Adiposity, physical fitness, diet, and total physical activity were assessed at baseline and 12 months. Intention-to-treat analyses were performed using linear mixed models. At full prescription, women exercised more in the HIGH vs MODERATE group (median min/week (quartiles 1,3): 253 (157 289) vs 137 (111 150); P<0.0001). Twelve-month changes in estrogens and SHBG were <10% on average for both groups. No group differences were found for E2, estrone, SHBG or free E2 changes (treatment effect ratios (95% CI) from linear mixed models: 1.00 (0.96-1.06), 1.02 (0.98-1.05), 0.99 (0.96-1.02), 1.01 (0.95, 1.06), respectively, representing the HIGH:MODERATE ratio of geometric mean biomarker levels over 12 months; n=382). In per-protocol analyses, borderline significantly greater decreases in total and free E2 occurred in the HIGH group. Overall, no dose effect was observed for women randomized to 300 vs 150 min/week of moderate to vigorous intensity exercise who actually performed a median of 253 vs 137 min/week. For total and free E2, the lack of differential effect may be due to modest adherence in the higher dose group.