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

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.

Linking Physical Activity to Breast Cancer Risk via the Insulin/Insulin-like Growth Factor Signaling System, Part 2: The Effect of Insulin/Insulin-like Growth Factor Signaling on Breast Cancer Risk

Perturbation of the insulin/insulin-like growth factor (IGF) signaling system is often cited as a mechanism driving breast cancer risk. A systematic review identified prospective cohort studies and Mendelian randomization studies that examined the effects of insulin/IGF signaling (IGF, their binding proteins (IGFBP), and markers of insulin resistance] on breast cancer risk. Meta-analyses generated effect estimates; risk of bias was assessed and the Grading of Recommendations Assessment, Development and Evaluation system applied to evaluate the overall quality of the evidence. Four Mendelian randomization and 19 prospective cohort studies met our inclusion criteria. Meta-analysis of cohort studies confirmed that higher IGF-1 increased risk of breast cancer; this finding was supported by the Mendelian randomization studies. IGFBP-3 did not affect breast cancer. Meta analyses for connecting-peptide and fasting insulin showed small risk increases, but confidence intervals were wide and crossed the null. The quality of evidence obtained ranged from 'very low' to 'moderate'. There were insufficient studies to examine other markers of insulin/IGF signaling. These findings do not strongly support the biological plausibility of the second part of the physical activity-insulin/IGF signaling system-breast cancer pathway. Robust conclusions cannot be drawn due to the dearth of high quality studies. See related article by Swain et al., p. 2106.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries

BACKGROUND

Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

METHODS

Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

RESULTS

Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

CONCLUSION

Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

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.

Can the epigenome contribute to risk stratification for cancer onset?

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.

Steroid Ligands, the Forgotten Triggers of Nuclear Receptor Action; Implications for Acquired Resistance to Endocrine Therapy

PURPOSE

There is strong epidemiologic evidence indicating that estrogens may not be the sole steroid drivers of breast cancer. We hypothesize that abundant adrenal androgenic steroid precursors, acting via the androgen receptor (AR), promote an endocrine-resistant breast cancer phenotype.

EXPERIMENTAL DESIGN

AR was evaluated in a primary breast cancer tissue microarray (n = 844). Androstenedione (4AD) levels were evaluated in serum samples (n = 42) from hormone receptor-positive, postmenopausal breast cancer. Levels of androgens, progesterone, and estradiol were quantified using LC/MS-MS in serum from age- and grade-matched recurrent and nonrecurrent patients (n = 6) before and after aromatase inhibitor (AI) therapy (>12 months). AR and estrogen receptor (ER) signaling pathway activities were analyzed in two independent AI-treated cohorts.

RESULTS

AR protein expression was associated with favorable progression-free survival in the total population (Wilcoxon, P < 0.001). Pretherapy serum samples from breast cancer patients showed decreasing levels of 4AD with age only in the nonrecurrent group (P < 0.05). LC/MS-MS analysis of an AI-sensitive and AI-resistant cohort demonstrated the ability to detect altered levels of steroids in serum of patients before and after AI therapy. Transcriptional analysis showed an increased ratio of AR:ER signaling pathway activities in patients failing AI therapy (t test P < 0.05); furthermore, 4AD mediated gene changes associated with acquired AI resistance.

CONCLUSIONS

This study highlights the importance of examining the therapeutic consequences of the steroid microenvironment and demonstrable receptor activation using indicative gene expression signatures.

Endogenous hormones and risk of invasive breast cancer in pre- and post-menopausal women: findings from the UK Biobank

BACKGROUND

Some endogenous hormones have been associated with breast cancer risk, but the nature of these relationships is not fully understood.

METHODS

UK Biobank was used. Hormone concentrations were measured in serum collected in 2006-2010, and in a repeat subsample (N ~ 5000) in 2012-13. Incident cancers were identified through data linkage. Cox regression models were used, and hazard ratios (HRs) corrected for regression dilution bias.

RESULTS

Among 30,565 pre-menopausal and 133,294 post-menopausal women, 527 and 2,997, respectively, were diagnosed with invasive breast cancer during a median follow-up of 7.1 years. Cancer risk was positively associated with testosterone in post-menopausal women (HR per 0.5 nmol/L increment: 1.18; 95% CI: 1.14, 1.23) but not in pre-menopausal women (pheterogeneity = 0.03), and with IGF-1 (insulin-like growth factor-1) (HR per 5 nmol/L increment: 1.18; 1.02, 1.35 (pre-menopausal) and 1.07; 1.01, 1.12 (post-menopausal); pheterogeneity = 0.2), and inversely associated with SHBG (sex hormone-binding globulin) (HR per 30 nmol/L increment: 0.96; 0.79, 1.15 (pre-menopausal) and 0.89; 0.84, 0.94 (post-menopausal); pheterogeneity = 0.4). Oestradiol, assessed only in pre-menopausal women, was not associated with risk, but there were study limitations for this hormone.

CONCLUSIONS

This study confirms associations of testosterone, IGF-1 and SHBG with breast cancer risk, with heterogeneity by menopausal status for testosterone.

Single Nucleotide Polymorphism in Prolactin Gene Is Associated With Clinical Aggressiveness and Outcome of Canine Mammary Malignant Tumors

Prolactin (PRL) is a key hormone involved in canine mammary development and tumorigenesis. In this study, the influence of a single nucleotide polymorphism (SNP) in the PRL gene (rs23932236) on the clinicopathological parameters and survival of dogs with canine mammary tumors (CMTs) was investigated. A total of 206 female dogs with spontaneous mammary tumors were enrolled in this study and circulating blood cells were genotyped. This specific SNP was associated with larger size (>3 cm diameter) for malignant tumors (P = .036), tumors with infiltrative/invasive growth pattern (P = .010), vascular invasion (P = .006), and lymph node metastasis (P = .004). Carriers of the variant allele had a shorter overall survival compared to the wild-type population with an overall survival of 18.7 months and 22.7 months, respectively (P = .004). These findings suggest that SNP rs23932236 of canine PRL gene may be used as an indicator for the development of clinically aggressive forms of CMTs.

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation

BACKGROUND

Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS).

METHODS

We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up.

RESULTS

In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: OR_{per SD} = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, OR_{per SD} = 1.37 (95% CI: 1.21-1.55) and QS, OR_{per SD} = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls.

CONCLUSION

A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk.

IMPACT

This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.

Validation of two US breast cancer risk prediction models in German women

PURPOSE

There are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman's age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman's medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use).

METHODS

We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC).

RESULTS

Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, E_BCRmod/O = 1.08 (95% confidence interval: 0.95-1.21), and E_BCRAT/O = 0.99(0.87-1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat overestimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUC_BCRmod = 0.61(0.58-0.63) and AUC_BCRAT = 0.58(0.56-0.61), with similar values for 5-year follow-up.

CONCLUSION

US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.

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.

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.

Weighted volume under the three-way receiver operating characteristic surface

It is often necessary to differentiate subjects from multiple categories using medical tests. We may then adopt statistical measures to characterize the performance of these tests. The three-way ROC analysis has been proposed to evaluate the diagnostic accuracy of medical tests with three categories, reflecting the correct classification probabilities across all possible decision thresholds. The geometry of the ROC surface is carefully studied, leading to numerical summary measures such as the volume under the surface. This paper generalizes the global volume under the surface of three-way ROC analysis to the weighted volume under the surface (WVUS) by introducing a weight function emphasizing particular regions of correct classification probabilities. This generalization practically allows researchers to calculate the diagnostic accuracy for a medical or clinical biomarker while satisfactorily high probabilities of correct classification for one or two classes are conditionally ensured. We provide the asymptotic properties of the proposed nonparametric and parametric estimators of WVUS, which could easily lend support to statistical inferences. Some simulations have been conducted to assess the proposed estimators and also to demonstrate the necessity of WVUS. A real data analysis about liver cancer illustrates our methodology.

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.

Polymorphisms in the TOX3/LOC643714 and risk of breast cancer in south China

OBJECTIVE

Breast cancer is the most widespread cancer in women, with a high mortality rate. This study aims to assess the association between single nucleotide polymorphisms (SNPs) of LOC643714 (rs12922061) and TOX3 (rs3803662) and breast cancer, as well as the clinical characteristics of tumors.

MATERIALS AND METHODS

In total, 104 breast cancer patients and 118 healthy controls were recruited to our study. The genotyping was performed by the SNP scan method. General characteristics, the clinical characteristics of tumors and reproductive factors were included in the analysis. Statistical tests included the Student t-test, the Chi-square test (X2) or Fisher's exact test, and unconditional logistic regression analysis. The receiver operating characteristic curves were used to evaluate the predictive role of rs12922061 in breast cancer.

RESULTS

The LOC643714 polymorphism was a risk factor for breast cancer under a dominant model (TT+TC vs. CC: OR 1.801; 95% CI 1.048, 3.095; statistical power=60%), recessive model (TT vs. TC + CC: OR 4.297; 95% CI 1.164, 15.867; statistical power=64%) and log-additive (TT vs. CC: OR 5.163; 95% CI 1.368, 19.485; statistical power= 73%). Furthermore, the rs12922061 polymorphism was associated with menopause status in patients ( P=0.005). No statistically significant association was found between the rs3803662 polymorphism and breast cancer in patients or healthy controls.

CONCLUSIONS

Our study found that rs12922061 of LOC643714 was related to breast cancer risk. With a limited sample size and statistical power, further multi-center studies are needed to confirm the influence of the LOC643714 polymorphisms on breast cancer based on larger populations.

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.