Combined effects of endogenous sex hormone levels and mammographic density on postmenopausal breast cancer risk: results from the Breakthrough Generations Study

Profiling canine mammary tumors: A potential model for studying human breast cancer

Despite all clinical progress recorded in the last decades, human breast cancer (HBC) remains a major challenge worldwide both in terms of its incidence and its management. Canine mammary tumors (CMTs) share similarities with HBC and represent an alternative model for HBC. The utility of the canine model in studying HBC relies on their common features, include spontaneous development, subtype classification, mutational profile, alterations in gene expression profile, and incidence/prevalence. This review describes the similarities between CMTs and HBC regarding genomic landscape, microRNA expression alteration, methylation, and metabolomic changes occurring during mammary gland carcinogenesis. The primary purpose of this review is to highlight the advantages of using the canine model as a translational animal model for HBC research and to investigate the challenges and limitations of this approach.

Effect of baseline oestradiol serum concentration on the efficacy of anastrozole for preventing breast cancer in postmenopausal women at high risk: a case-control study of the IBIS-II prevention trial

BACKGROUND

An increased risk of breast cancer is associated with high serum concentrations of oestradiol and testosterone in postmenopausal women, but little is known about how these hormones affect response to endocrine therapy for breast cancer prevention or treatment. We aimed to assess the effects of serum oestradiol and testosterone concentrations on the efficacy of the aromatase inhibitor anastrozole for the prevention of breast cancer in postmenopausal women at high risk.

METHODS

In this case-control study we used data from the IBIS-II prevention trial, a randomised, controlled, double-blind trial in postmenopausal women aged 40-70 years at high risk of breast cancer, conducted in 153 breast cancer treatment centres across 18 countries. In the trial, women were randomly assigned (1:1) to receive anastrozole (1 mg/day, orally) or placebo daily for 5 years. In this pre-planned case-control study, the primary analysis was the effect of the baseline oestradiol to sex hormone binding globulin (SHBG) ratio (oestradiol-SHBG ratio) on the development of all breast cancers, including ductal carcinoma in situ (the primary endpoint in the trial). Cases were participants in whom breast cancer was reported after trial entry and until the cutoff on Oct 22, 2019, and who had valid blood samples and no use of hormone replacement therapy within 3 months of trial entry or during the trial. For each case, two controls without breast cancer were selected at random, matched on treatment group, age (within 2 years), and follow-up time (at least that of the matching case). For each treatment group, we applied a multinominal logistic regression likelihood-ratio trend test to assess what change in the proportion of cases was associated with a one-quartile change in hormone ratio. Controls were used only to determine quartile cutoffs. Profile likelihood 95% CIs were used to indicate the precision of estimates. A secondary analysis also investigated the effect of the baseline testosterone-SHBG ratio on breast cancer development. We also assessed relative benefit of anastrozole versus placebo (calculated as 1 - the ratio of breast cancer cases in the anastrozole group to cases in the placebo group). The trial was registered with ISRCTN (number ISRCTN31488319) and completed recruitment on Jan 31, 2012, but long-term follow-up is ongoing.

FINDINGS

3864 women were recruited into the trial between Feb 2, 2003, and Jan 31, 2012, and randomly assigned to receive anastrozole (n=1920) or placebo (n=1944). Median follow-up time was 131 months (IQR 106-156), during which 85 (4·4%) cases of breast cancer in the anastrozole group and 165 (8·5%) in the placebo group were identified. No data on gender, race, or ethnicity were collected. After exclusions, the case-control study included 212 participants from the anastrozole group (72 cases, 140 controls) and 416 from the placebo group (142 cases, 274 controls). A trend of increasing breast cancer risk with increasing oestradiol-SHBG ratio was found in the placebo group (trend per quartile 1·25 [95% CI 1·08 to 1·45], p=0·0033), but not in the anastrozole group (1·06 [0·86 to 1·30], p=0·60). A weaker effect was seen for the testosterone-SHBG ratio in the placebo group (trend 1·21 [1·05 to 1·41], p=0·011), but again not in the anastrozole group (trend 1·18 [0·96 to 1·46], p=0·11). A relative benefit of anastrozole was seen in quartile 2 (0·55 [95% CI 0·13 to 0·78]), quartile 3 (0·54 [0·22 to 0·74], and quartile 4 (0·56 [0·23 to 0·76]) of oestradiol-SHBG ratio, but not in quartile 1 (0·18 [-0·60 to 0·59]).

INTERPRETATION

These results suggest that serum hormones should be measured more routinely and integrated into risk management decisions. Measuring serum hormone concentrations is inexpensive and might help clinicians differentiate which women will benefit most from an aromatase inhibitor.

FUNDING

Cancer Research UK, National Health and Medical Research Council (Australia), Breast Cancer Research Foundation, and DaCosta Fund.

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.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Hormonal determinants of mammographic density and density change

Background

Mammographic density (MD) is a strong risk factor for breast cancer. We examined how endogenous plasma hormones are associated with average MD area (cm²) and annual MD change (cm²/year).

Methods

This study within the prospective KARMA cohort included analyses of plasma hormones of 1040 women. Hormones from the progestogen (n = 3), androgen (n = 7), oestrogen (n = 2) and corticoid (n = 5) pathways were analysed by ultra-performance supercritical fluid chromatography-tandem mass spectrometry (UPSFC-MS/MS), as well as peptide hormones and proteins (n = 2). MD was measured as a dense area using the STRATUS method (mean over the left and right breasts) and mean annual MD change over time.

Results

Greater baseline mean MD was associated with overall higher concentrations of progesterone (average + 1.29 cm² per doubling of hormone concentration), 17OH-progesterone (+ 1.09 cm²), oesterone sulphate (+ 1.42 cm²), prolactin (+ 2.11 cm²) and SHBG (+ 4.18 cm²), and inversely associated with 11-deoxycortisol (− 1.33 cm²). The association between MD and progesterone was confined to the premenopausal women only. The overall annual MD change was − 0.8 cm². Hormones from the androgen pathway were statistically significantly associated with MD change. The annual MD change was − 0.96 cm² and − 1.16 cm² lesser, for women in the highest quartile concentrations of testosterone and free testosterone, respectively, compared to those with the lowest concentrations.

Conclusions

Our results suggest that, whereas hormones from the progestogen, oestrogen and corticoid pathways drive baseline MD, MD change over time is mainly driven by androgens. This study emphasises the complexity of risk factors for breast cancer and their mechanisms of action.

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.

Mammographic breast density and its association with urinary estrogens and the fecal microbiota in postmenopausal women

BACKGROUND

Breast density, as estimated by mammography, is a strong risk factor for breast cancer in pre- and postmenopausal women, but the determinants of breast density have not yet been established. The aim of this study was to assess if urinary estrogens or gut microbiota alterations are associated with mammographic density in postmenopausal women.

METHODS

Among 54 cancer-free, postmenopausal controls in the Breast and Colon Health study, we classified low- versus high-density women with Breast Imaging Reporting and Data System (BI-RADS, 5th edition) mammographic screening data, then assessed associations with urinary estrogens and estrogen metabolites (determined by liquid chromatography/tandem mass spectrometry), and fecal microbiota alpha and beta diversity (using Illumina sequencing of 16S rRNA amplicons).

RESULTS

Multiple logistic regression revealed no significant association between breast density and fecal microbiota metrics (PD_tree P-value = 0.82; un-weighted and weighted UniFrac P = 0.92 and 0.83, respectively, both by MiRKAT). In contrast, total urinary estrogens (and all 15 estrogens/estrogen metabolites) were strongly and inversely associated with breast density (P = 0.01) after adjustment for age and body mass index.

CONCLUSION

Mammographic density was not associated with the gut microbiota, but it was inversely associated with urinary estrogen levels.

IMPACT

The finding of an inverse association between urinary estrogens and breast density in cancer-free women adds to the growing breast cancer literature on understanding the relationship between endogenous estrogens and mammographic density.

Epigenome-wide association study for lifetime estrogen exposure identifies an epigenetic signature associated with breast cancer risk

BACKGROUND

It is well established that estrogens and other hormonal factors influence breast cancer susceptibility. We hypothesized that a woman's total lifetime estrogen exposure accumulates changes in DNA methylation, detectable in the blood, which could be used in risk assessment for breast cancer.

METHODS

An estimated lifetime estrogen exposure (ELEE) model was defined using epidemiological data from EPIC-Italy (n = 31,864). An epigenome-wide association study (EWAS) of ELEE was performed using existing Illumina HumanMethylation450K Beadchip (HM450K) methylation data obtained from EPIC-Italy blood DNA samples (n = 216). A methylation index (MI) of ELEE based on 31 CpG sites was developed using HM450K data from EPIC-Italy and the Generations Study and evaluated for association with breast cancer risk in an independent dataset from the Generations Study (n = 440 incident breast cancer cases matched to 440 healthy controls) using targeted bisulfite sequencing. Lastly, a meta-analysis was conducted including three additional cohorts, consisting of 1187 case-control pairs.

RESULTS

We observed an estimated 5% increase in breast cancer risk per 1-year longer ELEE (OR = 1.05, 95% CI 1.04-1.07, P = 3 × 10-12) in EPIC-Italy. The EWAS identified 694 CpG sites associated with ELEE (FDR Q < 0.05). We report a DNA methylation index (MI) associated with breast cancer risk that is validated in the Generations Study targeted bisulfite sequencing data (ORQ4_vs_Q1 = 1.77, 95% CI 1.07-2.93, P = 0.027) and in the meta-analysis (ORQ4_vs_Q1 = 1.43, 95% CI 1.05-2.00, P = 0.024); however, the correlation between the MI and ELEE was not validated across study cohorts.

CONCLUSION

We have identified a blood DNA methylation signature associated with breast cancer risk in this study. Further investigation is required to confirm the interaction between estrogen exposure and DNA methylation in the blood.

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.

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.

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.

Circulating anti-Müllerian hormone and breast cancer risk: A study in ten prospective cohorts

A strong positive association has been observed between circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and breast cancer risk in three prospective studies. Confirming this association is important because of the paucity of biomarkers of breast cancer risk in premenopausal women. We conducted a consortium study including ten prospective cohorts that had collected blood from premenopausal women. A nested case-control design was implemented within each cohort. A total of 2,835 invasive (80%) and in situ (20%) breast cancer cases were individually matched to controls (n = 3,122) on age at blood donation. AMH was measured using a high sensitivity enzyme-linked immunoabsorbent assay. Conditional logistic regression was applied to the aggregated dataset. There was a statistically significant trend of increasing breast cancer risk with increasing AMH concentration (ptrend across quartiles <0.0001) after adjusting for breast cancer risk factors. The odds ratio (OR) for breast cancer in the top vs. bottom quartile of AMH was 1.60 (95% CI = 1.31-1.94). Though the test for interaction was not statistically significant (pinteraction  = 0.15), the trend was statistically significant only for tumors positive for both estrogen receptor (ER) and progesterone receptor (PR): ER+/PR+: ORQ4-Q1  = 1.96, 95% CI = 1.46-2.64, ptrend <0.0001; ER+/PR-: ORQ4-Q1  = 0.82, 95% CI = 0.40-1.68, ptrend  = 0.51; ER-/PR+: ORQ4-Q1  = 3.23, 95% CI = 0.48-21.9, ptrend  = 0.26; ER-/PR-: ORQ4-Q1  = 1.15, 95% CI = 0.63-2.09, ptrend  = 0.60. The association was observed for both pre- (ORQ4-Q1 = 1.35, 95% CI = 1.05-1.73) and post-menopausal (ORQ4-Q1  = 1.61, 95% CI = 1.03-2.53) breast cancer (pinteraction  = 0.34). In this large consortium study, we confirmed that AMH is associated with breast cancer risk, with a 60% increase in risk for women in the top vs. bottom quartile of AMH.

Valid and efficient subgroup analyses using nested case-control data

BACKGROUND

It is not uncommon for investigators to conduct further analyses of subgroups, using data collected in a nested case-control design. Since the sampling of the participants is related to the outcome of interest, the data at hand are not a representative sample of the population, and subgroup analyses need to be carefully considered for their validity and interpretation.

METHODS

We performed simulation studies, generating cohorts within the proportional hazards model framework and with covariate coefficients chosen to mimic realistic data and more extreme situations. From the cohorts we sampled nested case-control data and analysed the effect of a binary exposure on a time-to-event outcome in subgroups defined by a covariate (an independent risk factor, a confounder or an effect modifier) and compared the estimates with the corresponding subcohort estimates. Cohort analyses were performed with Cox regression, and nested case-control samples or restricted subsamples were analysed with both conditional logistic regression and weighted Cox regression.

RESULTS

For all studied scenarios, the subgroup analyses provided unbiased estimates of the exposure coefficients, with conditional logistic regression being less efficient than the weighted Cox regression.

CONCLUSIONS

For the study of a subpopulation, analysis of the corresponding subgroup of individuals sampled in a nested case-control design provides an unbiased estimate of the effect of exposure, regardless of whether the variable used to define the subgroup is a confounder, effect modifier or independent risk factor. Weighted Cox regression provides more efficient estimates than conditional logistic regression.

Polymorphisms in the estrogen receptor alpha gene (ESR1), daily cycling estrogen and mammographic density phenotypes

Background

Single nucleotide polymorphisms (SNPs) involved in the estrogen pathway and SNPs in the estrogen receptor alpha gene (ESR1 6q25) have been linked to breast cancer development, and mammographic density is an established breast cancer risk factor. Whether there is an association between daily estradiol levels, SNPs in ESR1 and premenopausal mammographic density phenotypes is unknown.

Methods

We assessed estradiol in daily saliva samples throughout an entire menstrual cycle in 202 healthy premenopausal women in the Norwegian Energy Balance and Breast Cancer Aspects I study. DNA was genotyped using the Illumina Golden Gate platform. Mammograms were taken between days 7 and 12 of the menstrual cycle, and digitized mammographic density was assessed using a computer-assisted method (Madena). Multivariable regression models were used to study the association between SNPs in ESR1, premenopausal mammographic density phenotypes and daily cycling estradiol.

Results

We observed inverse linear associations between the minor alleles of eight measured SNPs (rs3020364, rs2474148, rs12154178, rs2347867, rs6927072, rs2982712, rs3020407, rs9322335) and percent mammographic density (p-values: 0.002–0.026), these associations were strongest in lean women (BMI, ≤23.6 kg/m^2.). The odds of above-median percent mammographic density (>28.5 %) among women with major homozygous genotypes were 3–6 times higher than those of women with minor homozygous genotypes in seven SNPs. Women with rs3020364 major homozygous genotype had an OR of 6.46 for above-median percent mammographic density (OR: 6.46; 95 % Confidence Interval 1.61, 25.94) when compared to women with the minor homozygous genotype. These associations were not observed in relation to absolute mammographic density. No associations between SNPs and daily cycling estradiol were observed. However, we suggest, based on results of borderline significance (p values: 0.025–0.079) that the level of 17β-estradiol for women with the minor genotype for rs3020364, rs24744148 and rs2982712 were lower throughout the cycle in women with low (<28.5 %) percent mammographic density and higher in women with high (>28.5 %) percent mammographic density, when compared to women with the major genotype.

Conclusion

Our results support an association between eight selected SNPs in the ESR1 gene and percent mammographic density. The results need to be confirmed in larger studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2804-1) contains supplementary material, which is available to authorized users.

Adolescent endogenous sex hormones and breast density in early adulthood

INTRODUCTION

During adolescence the breasts undergo rapid growth and development under the influence of sex hormones. Although the hormonal etiology of breast cancer is hypothesized, it remains unknown whether adolescent sex hormones are associated with adult breast density, which is a strong risk factor for breast cancer.

METHODS

Percentage of dense breast volume (%DBV) was measured in 2006 by magnetic resonance imaging in 177 women aged 25-29 years who had participated in the Dietary Intervention Study in Children from 1988 to 1997. They had sex hormones and sex hormone-binding globulin (SHBG) measured in serum collected on one to five occasions between 8 and 17 years of age. Multivariable linear mixed-effect regression models were used to evaluate the associations of adolescent sex hormones and SHBG with %DBV.

RESULTS

Dehydroepiandrosterone sulfate (DHEAS) and SHBG measured in premenarche serum samples were significantly positively associated with %DBV (all P trend ≤0.03) but not when measured in postmenarche samples (all P trend ≥0.42). The multivariable geometric mean of %DBV across quartiles of premenarcheal DHEAS and SHBG increased from 16.7 to 22.1 % and from 14.1 to 24.3 %, respectively. Estrogens, progesterone, androstenedione, and testosterone in pre- or postmenarche serum samples were not associated with %DBV (all P trend ≥0.16).

CONCLUSIONS

Our results suggest that higher premenarcheal DHEAS and SHBG levels are associated with higher %DBV in young women. Whether this association translates into an increased risk of breast cancer later in life is currently unknown.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.gov Identifier, NCT00458588 April 9, 2007; NCT00000459 October 27, 1999.

Relationship of serum estrogens and metabolites with area and volume mammographic densities

Elevated mammographic density is a breast cancer risk factor, which has a suggestive, but unproven, relationship with increased exposure to sex steroid hormones. We examined associations of serum estrogens and estrogen metabolites with area and novel volume mammographic density measures among 187 women, ages 40-65, undergoing diagnostic breast biopsies at an academic facility in Vermont. Serum parent estrogens, estrone and estradiol, and their 2-, 4-, and 16-hydroxylated metabolites were measured using liquid chromatography-tandem mass spectrometry. Area mammographic density was measured in the breast contralateral to the biopsy using thresholding software; volume mammographic density was quantified using a density phantom. Linear regression was used to estimate associations of estrogens with mammographic densities, adjusted for age and body mass index, and stratified by menopausal status and menstrual cycle phase. Weak, positive associations between estrogens, estrogen metabolites, and mammographic density were observed, primarily among postmenopausal women. Among premenopausal luteal phase women, the 16-pathway metabolite estriol was associated with percent area (p = 0.04) and volume (p = 0.05) mammographic densities and absolute area (p = 0.02) and volume (p = 0.05) densities. Among postmenopausal women, levels of total estrogens, the sum of parent estrogens, and 2-, 4- and 16-hydroxylation pathway metabolites were positively associated with area density measures (percent: p = 0.03, p = 0.04, p = 0.01, p = 0.02, p = 0.07; absolute: p = 0.02, p = 0.02, p = 0.01, p = 0.02, p = 0.03, respectively) but not volume density measures. Our data suggest that serum estrogen profiles are weak determinants of mammographic density and that analysis of different density metrics may provide complementary information about relationships of estrogen exposure to breast tissue composition.

High-density lipoprotein-cholesterol, daily estradiol and progesterone, and mammographic density phenotypes in premenopausal women

High-density lipoprotein-cholesterol (HDL-C) may influence the proliferation of breast tumor cells, but it is unclear whether low HDL-C levels, alone or in combination with cyclic estrogen and progesterone, are associated with mammographic density, a strong predictor of breast cancer development. Fasting morning serum concentrations of HDL-C were assessed in 202 premenopausal women, 25 to 35 years of age, participating in the Norwegian Energy Balance and Breast Cancer Aspects (EBBA) I study. Estrogen and progesterone were measured both in serum, and daily in saliva, throughout an entire menstrual cycle. Absolute and percent mammographic density was assessed by a computer-assisted method (Madena), from digitized mammograms (days 7-12). Multivariable models were used to study the associations between HDL-C, estrogen and progesterone, and mammographic density phenotypes. We observed a positive association between HDL-C and percent mammographic density after adjustments (P = 0.030). When combining HDL-C, estradiol, and progesterone, we observed among women with low HDL-C (<1.39 mmol/L), a linear association between salivary 17β-estradiol, progesterone, and percent and absolute mammographic density. Furthermore, in women with low HDL-C, each one SD increase of salivary mid-menstrual 17β-estradiol was associated with an OR of 4.12 (95% confidence intervals; CI, 1.30-13.0) of having above-median percent (28.5%), and an OR of 2.5 (95% CI, 1.13-5.50) of having above-median absolute mammographic density (32.4 cm(2)). On the basis of plausible biologic mechanisms linking HDL-C to breast cancer development, our findings suggest a role of HDL-C, alone or in combination with estrogen, in breast cancer development. However, our small hypothesis generating study requires confirmation in larger studies.

Gene variations in oestrogen pathways, CYP19A1, daily 17β-estradiol and mammographic density phenotypes in premenopausal women

INTRODUCTION

High mammographic density is an established breast cancer risk factor, and circulating oestrogen influences oestrogen-regulating gene expression in breast cancer development. However, less is known about the interrelationships of common variants in the CYP19A1 gene, daily levels of oestrogens, mammographic density phenotypes and body mass index (BMI) in premenopausal women.

METHODS

Based on plausible biological mechanisms related to the oestrogen pathway, we investigated the association of single nucleotide polymorphisms (SNPs) in CYP19A1, 17β-estradiol and mammographic density in 202 premenopausal women. DNA was genotyped using the Illumina Golden Gate platform. Daily salivary 17β-estradiol concentrations were measured throughout an entire menstrual cycle. Mammographic density phenotypes were assessed using a computer-assisted method (Madena). We determined associations using multivariable linear and logistic regression models.

RESULTS

The minor alleles of rs749292 were positively (P = 0.026), and the minor alleles of rs7172156 were inversely (P = 0.002) associated with daily 17β-estradiol. We observed an 87% lower level of daily 17β-estradiol throughout a menstrual cycle in heavier women (BMI >23.6 kg/m(2)) of rs7172156 with minor genotype aa compared with major genotype AA. Furthermore, the rs749292 minor alleles were inversely associated with absolute mammographic density (P = 0.032). Lean women with rs749292 minor alleles had 70 to 80% lower risk for high absolute mammographic density (>32.4 cm(2)); Aa: odds ratio (OR) = 0.23 (95% CI 0.07 to 0.75). Lean women with rs7172156 minor homozygous genotype had OR 5.45 for high absolute mammographic density (aa: OR = 5.45 (95% CI 1.13 to 26.3)).

CONCLUSION

Our findings suggest that two SNPs in CYP19A1, rs749292 and rs7172156, are associated with both daily oestrogen levels and mammographic density phenotypes. BMI may modify these associations, but larger studies are needed.

Endogenous sex hormones and breast density in young women

BACKGROUND

Breast density is a strong risk factor for breast cancer and reflects epithelial and stromal content. Breast tissue is particularly sensitive to hormonal stimuli before it fully differentiates following the first full-term pregnancy. Few studies have examined associations between sex hormones and breast density among young women.

METHODS

We conducted a cross-sectional study among 180 women ages 25 to 29 years old who participated in the Dietary Intervention Study in Children 2006 Follow-up Study. Eighty-five percent of participants attended a clinic visit during their luteal phase of menstrual cycle. Magnetic resonance imaging measured the percentage of dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute nondense breast volume (ANDBV). Multiple-linear mixed-effect regression models were used to evaluate the association of sex hormones and sex hormone-binding globulin (SHBG) with %DBV, ADBV, and ANDBV.

RESULTS

Testosterone was significantly positively associated with %DBV and ADBV. The multivariable geometric mean of %DBV and ADBV across testosterone quartiles increased from 16.5% to 20.3% and from 68.6 to 82.3 cm(3), respectively (Ptrend ≤ 0.03). There was no association of %DBV or ADBV with estrogens, progesterone, non-SHBG-bound testosterone, or SHBG (Ptrend ≥ 0.27). Neither sex hormones nor SHBG was associated with ANDBV except progesterone; however, the progesterone result was nonsignificant in analysis restricted to women in the luteal phase.

CONCLUSIONS

These findings suggest a modest positive association between testosterone and breast density in young women.

IMPACT

Hormonal influences at critical periods may contribute to morphologic differences in the breast associated with breast cancer risk later in life.

Diverse pathomechanisms leading to the breakdown of cellular estrogen surveillance and breast cancer development: new therapeutic strategies

Recognition of the two main pathologic mechanisms equally leading to breast cancer development may provide explanations for the apparently controversial results obtained by sexual hormone measurements in breast cancer cases. Either insulin resistance or estrogen receptor (ER) defect is the initiator of pathologic processes and both of them may lead to breast cancer development. Primary insulin resistance induces hyperandrogenism and estrogen deficiency, but during these ongoing pathologic processes, ER defect also develops. Conversely, when estrogen resistance is the onset of hormonal and metabolic disturbances, initial counteraction is hyperestrogenism. Compensatory mechanisms improve the damaged reactivity of ERs; however, their failure leads to secondary insulin resistance. The final stage of both pathologic pathways is the breakdown of estrogen surveillance, leading to breast cancer development. Among premenopausal breast cancer cases, insulin resistance is the preponderant initiator of alterations with hyperandrogenism, which is reflected by the majority of studies suggesting a causal role of hyperandrogenism in breast cancer development. In the majority of postmenopausal cases, tumor development may also be initiated by insulin resistance, while hyperandrogenism is typically coupled with elevated estrogen levels within the low postmenopausal hormone range. This mild hyperestrogenism is the remnant of reactive estrogen synthesis against refractory ERs that were successfully counteracted at a younger age. When refractoriness of ERs is the initiator of pathologic processes, reactively increased estrogen levels may be found in both young and older breast cancer cases, while they may exhibit clinical symptoms of estrogen deficiency. Studies justifying a causal correlation between hyperestrogenism and tumor development compile such breast cancer cases. In conclusion, the quantitative evaluation of ER refractoriness in breast cancer cases has great importance, since the stronger the estrogen resistance, the higher the promising dose of estrogen therapy.

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

PURPOSE

Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone-binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study.

METHODS

We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting.

RESULTS

Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor-positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score).

CONCLUSION

Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening.