Plasma Growth Factor Gene Expression and Mammographic Breast Density in Postmenopausal Women

Breast cancer incidence increases with age and estimates show that 1 in 8 women will be diagnosed with breast cancer within their lifetime (1). Mammographic breast density (MBD), which reflects the proportion of epithelial and stromal tissues relative to adipose tissue in the breast, is a strong risk factor for breast cancer (2, 3). Women with >75% breast dense tissue have a 4- to 6-fold increased risk of breast cancer (4, 5). In addition to being a strong risk factor, MBD and breast cancer share similar biological and genetic pathways. Thus, MBD is a surrogate marker of breast cancer development and an intermediate phenotype (6, 7). MBD partially mediates the association of some risk factors like age, body mass index (BMI), history of biopsy-confirmed benign breast disease with breast cancer risk (8). Thus, deciphering the biological mechanisms that underlie MBD could have utility in understanding markers of breast cancer risk.

Growth factors stimulate cell proliferation. As such, elevated growth factor levels could be positively associated with MBD. Insulin-like growth factor (IGF)-1 regulates proliferation of mammary epithelial cells, stimulates mitosis and inhibits apoptosis (9), and has been associated with breast cancer risk (10, 11). IGF-1 binds to IGF-binding proteins (IGFBP) to facilitate its transport and increase half-life. There are conflicting reports on the associations of circulating IGF-1, IGFBP-3 with MBD. Specifically, there appears to be differential associations by menopausal status with some studies suggesting associations in premenopausal women but more limited evidence for postmenopausal women (12–15). To the best of our knowledge, no studies have, however, evaluated the associations of plasma IGF gene expression with volumetric measures of MBD. There is also a unique opportunity to clarify the association of IGF-1 and IGFBP3 with MBD in postmenopausal women.

Other growth factors that could have biological relevance include FGF, TGFβ and bone morphogenetic protein (BMP)-2. FGFs influence ductal outgrowth in the breast (16). FGFs’ binding to their receptors (17) induce matrix metalloproteinase 9, which is involved in extracellular matrix remodeling (18). The relationship between FGF gene expression and MBD in postmenopausal women is understudied (19, 20) because previous studies were in premenopausal women. TGFβ induces cellular transformation in normal fibroblasts (21) and TGFβ signaling is decreased in women with increased MBD (22, 23). BMP-2 is essential for bone formation, and its possible role in proliferation, and differentiation has been increasingly recognized (24), but to the best of our knowledge, no studies have reported on the associations of BMP-2 with MBD.

The goal of our study is to investigate for the first time the associations of growth factor (IGF-1, IGFBP-3, FGF-1, FGF-12, TGFβ1, and BMP-2) gene expression in plasma with volumetric measures of MBD in postmenopausal women. Study findings could provide new insight into the associations of growth factors with MBD in postmenopausal women because previous studies have investigated the associations of circulating protein levels and single nucleotide polymorphisms in growth factors and have focused mainly on premenopausal women.

The study was comprised of 400 postmenopausal women who were recruited during annual routine screening mammography at the Joanne Knight Breast Health Center at the Siteman Cancer Center at Washington University School of Medicine, St. Louis, Missouri between October 2017 and September 2018 (25). After excluding 32 women with error messages during conversion of their raw mammogram images to volumetric measures, our final study population consisted of 368 women (Table 1).

Inclusion criteria included: (i) age 50 to 64 years, (ii) postmenopausal, (iii) able to comply with all required study procedures and schedule, including provision of blood samples at the time of enrollment. Exclusion criteria included: (i) history of any cancer, including breast cancer; (ii) history of breast augmentation, reduction, or implants; (iii) history of denosumab use over the previous 6 months; and (iv) history of selective estrogen receptor modulators use over the previous 6 months. We defined postmenopausal as having a history of bilateral oophorectomy, age 60 or older, or if under age 60, had been amenorrheic for at least 12 months. These criteria are a modification of the National Comprehensive Cancer Network (NCCN) postmenopausal definition, which does not require measurement of serum hormone levels (26).

Study participants completed blood draw and questionnaires that ascertained breast cancer risk factors on the day of their mammogram. Blood samples were processed and stored at −80°C at the Tissue Procurement Core at Siteman Cancer Center (St. Louis, Missouri) within 60 minutes of collection. Height was measured using a stadiometer and current weight and percent body fat were measured using OMRON Full Body Sensor Body Composition Monitor and Scale model HBF-514FC. We calculated BMI by dividing current weight (kg) by height (m) squared (kg/m²).

The study was reviewed and approved by the Institutional Review Board of the Washington University School of Medicine. Written informed consent to participate in this study was provided by all the participants. This study was conducted in accordance with recognized ethical guidelines such as the U.S. Common Rule.

We performed RNA sequencing to quantify IGF-1, IGFBP-3, FGF-1, FGF-12, TGFβ1, and BMP-2 gene expression at the McDonnell Genome Institute, Washington University School of Medicine in St. Louis. Gene expression levels were measured in RNA isolated from plasma, using the NanoString “nCounter XT Codeset Gene Expression Assays” protocol (NanoString Technologies, Seattle, Washington). Hybridization of the RNA to the custom XT Codeset was performed with inputs of 100 ng (18 samples), 180 ng (1 sample), and 200 ng (384 samples). Following hybridization, samples were processed on the NanoString Prep Station where they were purified and immobilized on a sample cartridge for data collection. The output for each sample was imported into nSolver Analysis Software for quality control and analysis. Binding densities ranged from 0.09 to 0.34. Per NanoString nCounter assay manufacturer's guidelines, transcript counts were normalized using housekeeping genes.

Volumetric percent density (VPD), dense volume (DV) and non-dense volume (NDV) were measured using Volpara version 1.5 (Matakina Technology Ltd). NDV is the difference in total volume of the breast and the absolute volume of the fibroglandular tissue in the breast (cm³). Volpara measures MBD using an algorithm that determines X-ray attenuation per pixel. A conversion of the attenuation is made to estimate tissue composition and the output is a density map (27) including the mean of the cranial–caudal and mediolateral oblique views of each breast. These VPD ranges are equivalent to the standard Breast Imaging Reporting and Data System (BI-RADS, 5th edition) groups (28): (i) VPD <3.5% is equivalent to BI-RADS Group 1 and have entirely fatty breasts, (ii) VPD between 3.5 and 7.5% equates to BI-RADS Group 2 with scattered areas of fibroglandular density, (iii) VPD between 7.5 and 15.5% is equivalent to BI-RADS Group 3 with heterogeneously dense breasts, and (iv) VPD ≥15.5% is classified as BI-RADS Group 4 with extremely dense breasts (29).

Statistical analyses were performed with the NanoString nSolver Analysis System 4.0 (NanoString Technologies) using the Advanced Analysis package 2.0 and its custom analysis pipeline (30). VPD, DV, and NDV were all logarithmically transformed because of the skewed distributions. All analyses were performed on curated log₂-transformed normalized counts. We evaluated correlations between the genes controlling for age using partial Pearson correlation coefficients (Table 2).

We performed differential expression analysis to quantify plasma growth factor gene expression levels in relation to MBD adjusting for the following confounders: current age (continuous, years), BMI (continuous, kg/m²), race (Non-Hispanic white/African American/Others), menopausal hormone therapy use (yes/no), and parity and age at first birth (nulliparous; 1–2 children, <25 years; 1–2 children, 25–29 years; 1–2 children, ≥30 years; ≥3 children, <25 years; ≥3 children, ≥25 years).

We used multiple linear regression models to evaluate the associations of growth factor gene expression (predictor) with MBD measures in continuous form (outcome). The fold change in gene expression was estimated using a simplified negative binomial model; the default estimation method in NanoString nSolver Analysis System 4.0 (30). We present linear fold change and its confidence interval (CI) along with a P value. Linear fold change was calculated from log₂ fold change via the antilog function. Overexpressed genes were defined as linear fold change >1, whereas under-expressed genes were defined as linear fold change <1. These data are presented in Table 3 and Fig. 1.

We further used multinomial logistic regression models to evaluate the associations of growth factor gene expression with categories of VPD (outcome), adjusted for the above covariates and age at menarche (continuous, years). ORs and 95% CIs were calculated using Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, North Carolina). P values <0.05 were considered statistically significant in this study. These data are presented in Table 4.

The data generated in this study are not publicly available but can be made available upon request from the corresponding author.

Characteristics of study participants by VPD categories are shown in Table 1. There were differences in age at menarche, BMI, race by VPD categories. Women with VPD ≥ 15.5% attained menarche at an older age and had lower BMI than women with VPD < 3.5%. The women were similar in most of the other characteristics.

We observed significant correlations in growth factor gene expression (Table 2). The strongest positive correlations were between FGF-1 and FGF-12 (r = 0.49, P < 0.0001), and TGFβ1 and FGF-12 (r = 0.46, P < 0.0001) gene expressions. IGFBP-3 gene expression was weakly inversely correlated with FGF-1 gene expression (r = –0.17, P < 0.001) and FGF-12 gene expression (r = –0.17, P < 0.001).

In multivariable adjusted analyses using linear regression models, there was a positive association between IGF-1 gene expression and VPD (Table 3, Fig. 1). One unit increase in IGF-1 gene expression was associated with a 35% increase in VPD (1.35; 95% CI, 1.13–1.60; P = 0.001). There were suggestions that TGFβ1 was positively associated with VPD (1.04; 95% CI, 0.99–1.09; P = 0.09) while BMP-2 gene expression was inversely associated with VPD (0.87; 95% CI, 0.75–1.01; P = 0.07), but these were not statistically significant.

TGFβ1 and BMP-2 gene expression were not associated with VPD. As expected, for most of the growth factors, the associations with NDV were in the opposite direction as those observed for VPD (Fig. 1).

In multivariate adjusted analyses using multinomial logistic regression (Table 4), IGF-1 gene expression as was 193% higher (OR, 2.93; 95% CI, 1.72–4.98) in women with extremely dense breasts (VPD ≥ 15.5%) compared with women with entirely fatty breasts (VPD <3.5%). IGF-1 gene expression was not elevated among women with scattered areas of fibroglandular density (VPD, 3.5%–7.5%) and heterogeneously dense breasts (VPD, 7.5%–15.5%). TGFβ1 gene expression was 33% (OR, 1.33; 95% CI, 1.13–1.56; P = 0.0008) higher in women with extremely dense breasts than those with almost entirely fatty breasts. There were no associations between growth factor gene expression and DV or NDV (Supplementary Table S1).

To the best of our knowledge, this is the first study to investigate the associations of an array of growth factor (IGF-1, IGFBP-3, FGF-1, FGF-12, TGFβ1, and BMP-2) gene expression in plasma with volumetric measures of MBD in postmenopausal women. IGF-1 gene expression was positively associated with VPD and TGFβ1 gene expression was higher in women with extremely dense breasts compared with those with almost entirely fatty breasts. Plasma growth factor gene expression was not associated with DV and NDV.

Although there is a weak correlation between protein concentrations and mRNA levels (31–33), growth factor gene expression may provide additional biological readout because factors such as protein turnover and posttranslational modifications modulate growth factor serum levels (31–33). In addition, gene expression measures may be more stable than circulating growth factor levels (34). Growth factors could influence MBD through cellular and epithelial proliferation (35). Growth factors can also act as mitogens, inducing cell division by inhibiting factors that block cell-cycle progression and may act as survival factors by suppressing apoptosis in cells (35). Although we performed gene expression, our findings are similar to prior studies, which have shown positive associations of circulating levels with IGF-1 with MBD (36–38). Other studies have also reported associations between genetic variations in both IGF-1 and IGFPB-3 and MBD (39–41). However, ours is the first study to investigate the associations of IGF-1 and IGFBP-3 gene expression with VPD in postmenopausal women.

Premenopausal women undergo cyclical changes in hormone levels that underlie differences in epithelial proliferation in hormone-responsive organs like the breasts; hence, menopausal status could mediate the associations of growth factors with MBD. Postmenopausal women, particularly those who do not use menopausal hormone therapy, have less variation in exposure to hormones like estrogen. Estrogen signaling increases IGF-1 activity (42) and estrogen levels vary during the menstrual cycle so it is plausible that IGF-1 levels may follow the same pattern. Thus, variations in estrogen levels during the menstrual cycle may provide insight into findings showing that IGF-1 is associated with MBD in premenopausal, but not postmenopausal women (10, 43). Our study focused on the associations between growth factor gene expression and MBD in postmenopausal women.

TGFβ1 expression was higher in women with dense breasts compared with those with almost entirely fatty breasts. Prior studies in identified SNPs in TGFβ1 that are associated with MBD (23) as well as a decrease in TGFβ1 canonical signaling in women with increased MBD (22), thus, larger studies are necessary to further delineate its association with MBD. TGFβ1 has paradoxical roles in cancer, inhibiting cellular proliferation in early stages but promoting tumorigenesis in later stages of cancer (44).

Our study suggests that BMP-2 gene expression may be inversely associated with VPD. BMPs, which are members of the TGFβ superfamily, control proliferation and apoptosis by transcriptional regulation (24, 45). BMP-2 has been shown to regulate mammary gland development (46) and inhibit breast cancer cell proliferation (47). Some studies, however, suggest a paradoxical role for BMP-2 in cancer similar to that identified for TGFβ1 (24, 48). Increased fatty tissue in the breast is characteristic of low MBD (49). BMP-2 has also been characterized for its role in childhood adiposity (50) and we have shown that adiposity at age 10 is inversely associated with VPD (25). BMP-2 expression in visceral and adipose tissue is associated with obesity and SNPs in BMP-2 (rs979012) and SNP rs979012 are associated with BMI, body fat distribution, insulin biology, and total cholesterol levels (50, 51). Hence, BMP-2 requires further evaluation in MBD.

We identified correlations in the expression levels of various growth factors. IGFBP-3 gene expression was weakly inversely correlated with FGF-1 gene expression, supporting data from a previous study showing that IGFBP-3 induces downregulation of FGF transcription (52). The interplay between the growth factors may have a functional role in extracellular matrix assembly and the maintenance of extracellular matrix homeostasis (53, 54).

Our study has some limitations. The study was cross-sectional and as a result could not determine causal associations between growth factor gene expression and MBD. In addition, plasma growth factor gene expression levels and MBD change over time but we did not assay these growth factors longitudinally. Nevertheless, our study has the following strengths. We enhanced generalizability by recruiting women at their annual routine mammogram screening. We used Volpara, which has been shown to have high reliability to determine MBD (55). In addition, we assayed growth factor gene expression, rather than circulating growth factor levels, which could provide novel insights on the associations of these growth factors with MBD.

In conclusion, we identified positive associations between IGF-1 and TGFβ1 gene expression and VPD as well as findings suggestive of an inverse association of BMP-2 gene expression with VPD in postmenopausal women. Our findings require confirmation in a different study population.

No disclosures were reported.

F.A. Akinjiyan: Formal analysis, validation, investigation, visualization, writing–original draft, writing–review and editing. A. Adams: Investigation, writing–review and editing. S. Xu: Formal analysis, visualization, writing–review and editing. M. Wang: Formal analysis, visualization. A.T. Toriola: Conceptualization, resources, supervision, funding acquisition, validation, investigation, visualization, writing–review and editing.

This work was supported by funding from NIH/NCI (R21CA216515, R37CA235602 and R01CA246592) to A.T. Toriola.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.