Immunoassay and Nb2 lymphoma bioassay prolactin levels and mammographic density in premenopausal and postmenopausal women the Nurses' Health Studies

Hormone and receptor activator of NF-κB (RANK) pathway gene expression in plasma and mammographic breast density in postmenopausal women

Background

Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women.

Methods

We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density.

Results

One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0–15%, p value = 0.05), 10% (95% CI 0–20%, p value = 0.04) and % (95% CI 0–9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4–19%, p value = 0.003) and 7% (95% CI 0–13%, p value = 0.04) lower non-dense volume, respectively.

Conclusion

These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01522-2.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study

Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.

Early-Life and Adult Anthropometrics in Relation to Mammographic Image Intensity Variation in the Nurses' Health Studies

BACKGROUND

The V measure captures grayscale intensity variation on a mammogram and is positively associated with breast cancer risk, independent of percent mammographic density (PMD), an established marker of breast cancer risk. We examined whether anthropometrics are associated with V, independent of PMD.

METHODS

The analysis included 1,700 premenopausal and 1,947 postmenopausal women without breast cancer within the Nurses' Health Study (NHS) and NHSII. Participants recalled their body fatness at ages 5, 10, and 20 years using a 9-level pictogram (level 1: most lean) and reported weight at age 18 years, current adult weight, and adult height. V was estimated by calculating standard deviation of pixels on screening mammograms. Linear mixed models were used to estimate beta coefficients (ß) and 95% confidence intervals (CI) for the relationships between anthropometric measures and V, adjusting for confounders and PMD.

RESULTS

V and PMD were positively correlated (Spearman r = 0.60). Higher average body fatness at ages 5 to 10 years (level ≥ 4.5 vs. 1) was significantly associated with lower V in premenopausal (ß = -0.32; 95% CI, -0.48 to -0.16) and postmenopausal (ß = -0.24; 95% CI, -0.37 to -0.10) women, independent of current body mass index (BMI) and PMD. Similar inverse associations were observed with average body fatness at ages 10 to 20 years and BMI at age 18 years. Current BMI was inversely associated with V, but the associations were largely attenuated after adjustment for PMD. Height was not associated with V.

CONCLUSIONS

Our data suggest that early-life body fatness may reflect lifelong impact on breast tissue architecture beyond breast density. However, further studies are needed to confirm the results.

IMPACT

This study highlights strong inverse associations of early-life adiposity with mammographic image intensity variation.

Serum osteoprotegerin levels and mammographic density among high-risk women

PURPOSE

Mammographic density is a risk factor for breast cancer but the mechanism behind this association is unclear. The receptor activator of nuclear factor κB (RANK)/RANK ligand (RANKL) pathway has been implicated in the development of breast cancer. Given the role of RANK signaling in mammary epithelial cell proliferation, we hypothesized this pathway may also be associated with mammographic density. Osteoprotegerin (OPG), a decoy receptor for RANKL, is known to inhibit RANK signaling. Thus, it is of interest to evaluate whether OPG levels modify breast cancer risk through mammographic density.

METHODS

We quantified serum OPG levels in 57 premenopausal and 43 postmenopausal women using an enzyme-linked immunosorbent assay (ELISA). Cumulus was used to measure percent density, dense area, and non-dense area for each mammographic image. Subjects were classified into high versus low OPG levels based on the median serum OPG level in the entire cohort (115.1 pg/mL). Multivariate models were used to assess the relationship between serum OPG levels and the measures of mammographic density.

RESULTS

Serum OPG levels were not associated with mammographic density among premenopausal women (P ≥ 0.42). Among postmenopausal women, those with low serum OPG levels had higher mean percent mammographic density (20.9% vs. 13.7%; P = 0.04) and mean dense area (23.4 cm² vs. 15.2 cm²; P = 0.02) compared to those with high serum OPG levels after covariate adjustment.

CONCLUSIONS

These findings suggest that low OPG levels may be associated with high mammographic density, particularly in postmenopausal women. Targeting RANK signaling may represent a plausible, non-surgical prevention option for high-risk women with high mammographic density, especially those with low circulating OPG levels.

Residential particulate matter and distance to roadways in relation to mammographic density: results from the Nurses' Health Studies

BACKGROUND

High mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM2.5, PM2.5-10, and PM10) and distance to roadways were associated with mammographic density among women residing across the United States.

METHODS

The Nurses' Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990-1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence.

RESULTS

Recent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM2.5 within 3 years before mammogram β = 0.05, 95% CI -0.16, 0.27; PM2.5-10 β = 0, 95%, CI -0.15, 0.16; PM10 β = 0.02, 95% CI -0.10, 0.13) and postmenopausal women (PM2.5 within 3 years before mammogram β = -0.05, 95% CI -0.27, 0.17; PM2.5-10 β = -0.01, 95% CI -0.16, 0.14; PM10 β = -0.02, 95% CI -0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m3 increase in PM2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI -0.5, 7.3).

CONCLUSIONS

These findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women.

Percent mammographic density prediction: development of a model in the nurses' health studies

PURPOSE

To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk.

METHODS

Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r ² in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk.

RESULTS

In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both).

CONCLUSION

These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.

Prolactin receptor in breast cancer: marker for metastatic risk

Prolactin and prolactin receptor signaling and function are complex in nature and intricate in function. Basic, pre-clinical and translational research has opened up our eyes to the understanding that prolactin and prolactin receptor signaling function differently within different cellular contexts and microenvironmental conditions. Its multiple roles in normal physiology are subverted in cancer initiation and progression, and gradually we are teasing out the intricacies of function and therapeutic value. Recently, we observed that prolactin has a role in accelerating the time to bone metastasis in breast cancer patients and identified the mechanism by which prolactin stimulated breast cancer cell-mediated lytic osteoclast formation. The possibility that the prolactin receptor is a marker for metastasis, and specifically bone metastasis, is one that may have to be put into the context of the different variants of prolactin, different prolactin receptor isoforms and intricate signaling pathways that are regulated by the microenvironment. The more complete the picture, the better one can test biomarker identity and design clinical trials to test therapeutic intervention. This review will cover the recent advances and highlight the complexity of prolactin receptor biology.

A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density

INTRODUCTION

Mammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density.

METHODS

The study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Consortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction (P int) in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4, CYP1A1-CYP1A2, CYP1B1, ESR2, PPARG, PRL, SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density.

RESULTS

No significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted P int <0.0004) was observed with rs9358531 6.5kb 5' of PRL. Furthermore, three SNPs in PLCG2 that had previously been shown to modify the association of MHT use with breast cancer risk were found to modify also the association of MHT use with mammographic density (unadjusted P int <0.002), but solely among cases (unadjusted P int SNP×MHT×case-status <0.02).

CONCLUSIONS

The study identified potential interactions on mammographic density between current use of MHT and SNPs near PRL and in PLCG2, which require confirmation. Given the moderate size of the interactions observed, larger studies are needed to identify genetic modifiers of the association of MHT use with mammographic density.