The receptor activator of nuclear factor-κB (RANK) pathway plays essential roles in breast development. Mammographic density is a strong risk factor for breast cancer, especially in premenopausal women. We, therefore, investigated the associations of circulating RANK and soluble RANK ligand (sRANKL) with mammographic density in premenopausal women. Mammographic density was measured as volumetric percent density in 365 cancer-free premenopausal women (mean age, 47.5 years) attending screening mammogram at the Washington University School of Medicine (St. Louis, MO). We used linear regression models adjusted for confounders, to compare the least-square means of volumetric percent density across tertiles of circulating RANK and sRANKL. Furthermore, because RANKL levels in mammary tissue are modulated by progesterone, we stratified analyses by progesterone levels. The mean volumetric percent density increased across tertiles of circulating RANK from 8.6% in tertile 1, to 8.8% in tertile 2, and 9.5% in tertile 3 (Ptrend = 0.02). For sRANKL, the mean volumetric percent density was 8.5% in tertile 1, 9.4% in tertile 2, and 9.0% in tertile 3 (Ptrend = 0.30). However, when restricted to women with higher progesterone levels, the mean volumetric percent density increased from 9.1% in sRANKL tertile 1 to 9.5% in tertile 2, and 10.1% in tertile 3 (Ptrend = 0.01). Circulating RANK was positively associated with volumetric percent density, while circulating sRANKL was positively associated with volumetric percent density among women with higher progesterone levels. These findings support the inhibition of RANKL signaling as a pathway to reduce mammographic density and possibly breast cancer incidence in high-risk women with dense breasts.

Mammographic breast density is one of the strongest risk factors for breast cancer (1). Almost 2.4 million premenopausal women in the United States have extremely dense breasts (2), hence providing targeted prevention interventions to these women could have a substantial impact on reducing breast cancer incidence. However, the biological drivers of mammographic breast density are poorly understood. In addition, mammographic density and breast cancer share similar biological and genetic pathways (3, 4). Therefore, ascertaining biological pathways that are associated with mammographic density may identify potential targets for reducing breast density, opening up novel approaches to breast cancer prevention in premenopausal women.

The receptor activator of nuclear factor-κB (RANK) pathway is essential for bone health (5, 6). RANK is the signaling receptor for RANK ligand (RANKL), whereas osteoprotegerin (OPG) acts as the decoy receptor for RANK (7). This pathway also plays functional roles in mice mammary gland development (8–10). In particular, RANK/RANKL is essential for hormone-driven mammary epithelial proliferation (11, 12). The RANK pathway has, therefore, been suggested as a target for breast cancer chemoprevention in high-risk women (13–15). Two recent studies indicate that serum soluble RANKL (sRANKL) levels are positively associated with breast cancer risk among women with high progesterone levels (16), and women with estrogen receptor–positive (ER+) breast cancer (17).

We recently demonstrated that higher breast tissue RANKL gene expression is positively associated with mammographic density in premenopausal women (18). This study was limited to 48 women and mammographic density was based on the radiologists’ assessment using the Breast Imaging Reporting and Data (BI-RADS) quartile system. Many research studies now evaluate mammographic density using quantitative measures, with percent density (equivalent to volumetric percent density when using volumetric measures) being the strongest predictor of breast cancer risk (19). In addition, RANK and RANKL are expressed as membrane bound in tissues or in soluble form within the circulation (20), but to the best of our knowledge, there are no data yet on the associations of circulating RANK and sRANKL with mammographic density. A recent small study (N = 100) reported that OPG was associated with mammographic density in postmenopausal women, but not in premenopausal women (21). We therefore investigated the associations of circulating RANK and sRANKL with volumetric measures of mammographic density in premenopausal women. Furthermore, because RANKL levels in mammary tissue are modulated by progesterone in mice studies, we also investigated associations stratified by progesterone levels.

Study population

We recruited 383 premenopausal women who were scheduled for an annual screening mammography at the Joanne Knight Breast Health Center at the Washington University School of Medicine, and Siteman Cancer Center (St. Louis, MO) in 2016. Detailed description of the study population has been provided previously (22). Complete mammographic density data and blood samples were available for 375 women. Eligibility criteria included (i) premenopausal at the time of mammogram (defined as having had regular menstrual periods over the preceding 12 months, no prior history of bilateral oophorectomy, and no use of menopausal hormone therapy), (ii) no serious medical condition that would prevent the participant from returning for her annual mammogram in 12 months, and (iii) not pregnant. Women were excluded from the study if they had (i) history of any cancer, (ii) history of breast augmentation, reduction, or implants, (iii) history of selective estrogen receptor modulators (SERM) use during the previous 6 months. Eligible participants were asked to fast on the day of their screening mammogram appointment. On the day of screening mammogram, trained phlebotomists collected blood samples from all study participants. Hence, biomarker analyses were done on blood samples that were collected on the same day the women had their mammograms. Blood samples were processed and stored at −80°C at the Tissue Procurement Core (TPC), Siteman Cancer Center (St. Louis, MO) within 30 minutes of collection. We measured height (using a stadiometer) and current weight (OMRON Full Body Sensor Body Composition Monitor and Scale model HBF-514C) in all study participants on the same day they had their screening mammogram. Participants also completed a questionnaire (a modified version of the Predicting Risk of Breast Cancer at Screening questionnaire) with information on breast cancer risk factors and determinants of mammographic density. We did not collect data on SERM use. Study approval was granted by the Institutional Review Board of the Washington University School of Medicine, St. Louis, MO. All study participants provided informed consent.

Circulating RANK biomarkers

Circulating RANK and sRANKL levels were assayed at the Research Laboratory Service Medical Center Research Institute (Melbourne, Australia) using commercially available ELISA kits according to the manufacturers’ instructions (BioVendor for sRANKL and Abnova for RANK). Laboratory precision was monitored by the inclusion of blinded pooled quality control samples. Interassay coefficients of variation were 5.7% for RANK and 12.0% for RANKL, based on blind replicates in our samples. Intra-assay coefficients of variation were 4.4% for RANK and 9.4% for sRANKL.

Circulating progesterone levels

Circulating progesterone levels were assayed at the Department of Laboratory Medicine, Boston Children's Hospital (Boston, MA). Progesterone was measured by a competitive electrochemiluminescence immunoassay on the Roche E Modular system (Roche Diagnostics). The assay is FDA-approved for clinical use and has been used in previous studies (23, 24). The intra-assay coefficients of variation were 2.9% at 0.73 ng/mL and 0.9% at 32.4 ng/mL based on blind replicates in our samples. The inter-assay coefficients of variation were 4.8% at 0.73 ng/mL and 2.0% at 35.3 ng/mL. Sensitivity was 0.15 ng/mL and specificity was 0.81.

Mammographic density assessment

We used Volpara version 1.5 (Matakina Technology Ltd) to determine volumetric percent density, dense volume, and nondense volume (25, 26). Dense volume is the volume of fibroglandular tissue in the breast (cm3). Volumetric percent density is the ratio of the volume of fibroglandular tissue (i.e., dense volume) to the total breast volume, expressed as a percentage. Corresponding to the four categories [(a)–(d)] of the Breast Imaging Reporting and data System (BI-RADS; 5th edition), Volpara volumetric percent density measures translate to: <3.5 (a, almost entirely fatty breasts); ≥3.5 and <7.5 (b, scattered areas of fibroglandular density); ≥7.5 and <15.5 (c, heterogeneously dense breasts); ≥15.5%–34.5% (d, extremely dense breasts; ref. 27).

Statistical analyses

We summarized continuous variables using means and SDs and categorical variables using counts and percentages. We performed square-root transformations on volumetric percent density, dense volume, and nondense volume for conformation to normality. We categorized study participants into tertiles of RANK and sRANKL on the basis of the empirical distribution of the biomarkers in our study population. We used linear regression models with adjustment for confounders, to evaluate the associations between the tertiles of circulating RANK, sRANKL, and mammographic density by comparing least-square means of volumetric percent density, dense volume, and nondense volume within tertiles of circulating RANK and sRANKL concentrations. We also estimated the difference from the lowest tertile of volumetric percent density for each tertile of circulating RANK and sRANKL. In the minimally adjusted model 1, we adjusted the analyses for age (continuous) and body mass index (BMI, continuous) derived as weight (kg)/height (m2). In model 2, we additionally adjusted for family history of breast cancer in a first-degree relative (no, yes, and unknown), race (Non-Hispanic White, Black/African American, Others), age at menarche (continuous), parity (0, 1, 2, ≥3), and current alcohol intake (no, yes). We also adjusted the statistical analyses for quantity of alcohol consumed, but alcohol intake was low in our study population and the findings were similar to when we categorized alcohol intake as yes or no. We also tested the following confounders: phase of menstrual cycle, oral contraceptive use, circulating progesterone levels, and breastfeeding but they did not change the estimates by up to 10%. Phase of menstrual cycle was derived from information provided by the participants; that is, average menstrual cycle length, date of onset of their last menstrual period, and the date of the predicted onset of their next menses. We additionally evaluated the impact of other potential breast cancer risk factors such as physical activity, vitamin D intake etc., but these had no impact on mammographic density. We tested for linear trends using Wald tests by ordinally modeling the median of the tertiles. We further evaluated whether the associations of circulating RANK, sRANKL, and mammographic density varied by progesterone levels by assessing the Wald tests on the interaction term between the ordinal median biomarkers levels and progesterone levels. We then categorized study participants into two groups on the basis of median circulating progesterone levels in our study population. We classified women who had progesterone levels below the median value as having lower progesterone and women with progesterone levels above the median value as having higher progesterone. We repeated linear regression analyses stratified by lower and higher progesterone. Some women (n = 36) had circulating RANK levels reported as <2 pg/mL. We assumed that these participants had missing values and imputed their circulating RANK values by chained equation, which is based on the conditional probability of a variable, with the other covariables serving as predictors (28). Nevertheless, we performed sensitivity analyses excluding women whose RANK values were reported as <2 pg/mL. All statistical tests were two-sided, and P values < 0.05 were considered statistically significant. All analyses were done with the Statistical Analyses Systems (SAS) version 9.4 (SAS Institute).

The mean age of the study participants was 47.5 years (Table 1). The mean BMI was 30.8 kg/m2, consistent with the BMI of women attending screening mammogram at the Breast Health Center. Many were Non-Hispanic White (65.6%) and African American (29.3%). Most participants (42.1%) had volumetric percent density between 3.5%–7.5%, equivalent to BI-RADS category b.

We observed positive associations of circulating RANK with volumetric percent density (Table 2). In the age and BMI adjusted model, the mean volumetric percent density increased across tertiles of circulating RANK from 8.1% in tertile 1, to 8.4% in tertile 2, and 9.2% in tertile 3 (Ptrend = 0.01). In the full multivariable adjusted model, the mean volumetric percent density increased from 8.6% in tertile 1, to 8.8% in tertile 2, and 9.5% in tertile 3 (Ptrend = 0.02). Furthermore, adjustment for phase of menstrual cycle did not change the results. We also estimated the difference from the lowest tertile of volumetric percent density for each tertile of circulating RANK and sRANKL. Women in the highest tertile of RANK had 1.1% points higher volumetric percent density that women in the lowest tertile [95% confidence interval (CI), −0.2–2.4; Ptrend = 0.02; Supplementary Table S1]. We observed no significant associations between circulating sRANKL and volumetric percent density. In the full multivariable adjusted model, the mean volumetric percent density was 8.5% in tertile 1, 9.4% in tertile 2, and 9.0% in tertile 3 (Ptrend = 0.30). We then investigated the associations of circulating RANK and sRANKL with dense volume. As expected, the findings were in the same direction as those for volumetric percent density, but not as apparent. For RANK, the mean dense volume increased from 75.1 cm3 in tertile 1 to 76.5 cm3 in tertile 2 to 81.2 cm3 in tertile 3 (Ptrend = 0.05). For sRANKL, the mean dense volume was 75.8 cm3 in tertile 1, 75.1 cm3 in tertile 2, and 81.8 cm3 in tertile 3 (Ptrend = 0.35).

Next, we investigated the associations of circulating RANK and sRANKL with mammographic density stratified by progesterone levels. We observed an interaction between circulating sRANKL and progesterone on volumetric percent density (Pinteraction = 0.045). Circulating sRANKL was positively associated with volumetric percent density among women with higher progesterone levels (above median levels in our study population), but not among women with lower progesterone levels (Fig. 1). Among women with higher progesterone levels, the mean volumetric percent density increased from 9.1%, in tertile 1 to 9.5% in tertile 2, and 10.1% in tertile 3 (Ptrend = 0.01) in the full multivariable adjusted model. Women in the highest tertile of sRANKL had 1.3% points higher volumetric percent density than women in the lowest tertile (95% CI, −0.6–3.2; Ptrend = 0.01; Supplementary Table S1). Among women with lower progesterone levels, the mean volumetric percent density was 7.9% in tertile 1, 9.4% in tertile 2, and 7.9% in tertile 3 (Ptrend = 0.72). Likewise, the associations of RANK with volumetric percent density was more evident among women with higher progesterone levels (Ptrend = 0.03) than those with lower progesterone levels (Ptrend = 0.13; Fig. 2), but there was no interaction.

There were no statistically significant associations between RANKL, sRANKL, and breast cancer risk factors (Table 3), although BMI at age 18 appeared to decrease with increasing sRANKL concentrations. Also, 64% of women within the lowest tertile of sRANKL concentrations had a history of breast feeding compared with 68% among women within the second tertile and 74% among women within the highest tertiles.

There were no associations between both circulating RANK and sRANKL and nondense volume (Supplementary Table S2). In sensitivity analyses, we excluded women with RANK values <2 pg/mL, and the findings were similar to those reported using the full data (Supplementary Table S3).

To the best of our knowledge, this is the first large study on the associations of circulating RANK and sRANKL with mammographic density in premenopausal women. Circulating RANK was positively associated with volumetric percent density. Circulating sRANKL was positively associated with volumetric percent density among women with higher progesterone levels only.

Data on the associations of RANK pathway with mammographic density and breast cancer risk are emerging. We previously reported that higher breast tissue RANKL gene expression is positively associated with mammographic density, assessed using BI-RADS, in premenopausal women (18). In this study, we evaluated RANK within the circulation and measured mammographic density using Volpara, which enabled automated volumetric measures. Therefore, this study provides new and important insights into the associations of RANK pathway with mammographic density in premenopausal women.

It is intriguing that circulating RANK was positively associated with volumetric percent density in the overall analyses but circulating sRANKL was not. Unlike sRANKL, there is very limited data on factors that influence circulating RANK concentrations, and there are no published studies on circulating RANK and breast cancer. In a previous study, we measured circulating RANK and sRANKL using the same methods in the same lab (29). RANKL functions as a major paracrine effector via RANK. Both RANK and RANKL induce differentiation and proliferation of mammary epithelial cells via the NFkB and cyclin D1 axis (7, 12), which are key pathways involved in breast cancer development. The differential associations may be due to how these biomarkers are expressed in human tissues and factors determining such expression. RANK is constitutively expressed on mammary epithelial cells, whereas RANKL expression in mammary epithelial cells, as well as within the circulation is induced primarily by progesterone (30). Thus, progesterone levels might influence the associations of sRANKL with mammographic density, and our findings that circulating sRANKL was positively associated with volumetric percent density only among women with higher progesterone levels fits with this biological observation. Consistent with our observation for volumetric percent density, Penninger and colleagues analyzed data from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) trial and reported that circulating sRANKL was positively associated with an increased risk of breast cancer only among women with higher progesterone levels (16). Another study from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort reported that higher sRANKL concentrations were associated with increased risk of ER+ breast cancer, but there was also evidence suggestive of an inverse association with ER/PR breast cancer (17). RANKL signaling activates NFκB, and canonical NFκB pathways are most active in ER+ breast cancers (31). Taken together, these findings suggest that the positive associations of sRANKL with mammographic density and breast cancer risk appear limited to women with higher hormone concentrations.

Furthermore, RANKL signaling is the major mediator of progesterone-induced mammary epithelial proliferation (10–12, 32, 33), and progesterone-driven expansion of mammary stem cells (6, 11, 12). Progesterone is a proliferative hormone in the breast, independent of estrogen (34–41). Combined estrogen+progestin hormone therapy is classified as carcinogenic to human by the International Agency for Research on Cancer (41). Clinical trials, including the Women's Health Initiative have demonstrated that 12 months of estrogen+progestin is associated with a 5% increase in mammographic density (42, 43) and increased breast cancer risk (38).

Higher OPG levels have also been associated with lower mammographic density in high-risk postmenopausal women (21), which is biologically plausible because OPG blocks RANK activation and RANKL signaling. Nevertheless, findings from the two studies on circulating OPG and breast cancer risk are mixed. Among patients with BRCA mutations, higher OPG levels were associated with lower risk of breast cancer (44). The other study reported that higher OPG levels are associated with an increased risk of ER disease, but a decreased risk of ER+ disease (45).

Our study has the following limitations. It is observational. We measured RANK and sRANKL at the time of enrollment only. Longitudinal assessments may provide a more detailed insight on how mammographic density changes with RANKL and sRANKL over time. Nevertheless, studies have reported moderate correlations between sRANKL measurements in samples taken 1year (17), and 5years apart (46), suggesting that a single measurement of sRANKL is a good reflection of circulating levels over the short-term. We evaluated circulating sRANKL measured in the blood. We are unaware of studies that have related breast tissue RANKL levels to circulating sRANKL levels; hence, it is not clear to what extent circulating sRANKL reflects breast tissue levels. Our previous study showing that breast tissue RANKL gene expression is positively associated with mammographic density indicates circulating sRANKL concentrations could be a good surrogate for the biomarker in the breast.

Strengths of our study include the following: (i) study participants were recruited among women attending annual routine screening mammogram, which enhances generalizability. (ii) We adjusted for many potential confounders in statistical analyses. (iii) We evaluated associations stratified by progesterone levels, which modulates circulating sRANKL concentrations. (iv) We collected fasting samples from study participants, and biomarker measurements were done on aliquots that had never been thawed. (v) We assessed mammographic density using Volpara, which provides automated and robust volumetric measures of density, and has been found to be highly reproducible (25, 26, 47, 48).

In conclusion, circulating RANK concentrations were positively associated with volumetric percent density, and circulating sRANKL concentrations were positively associated with volumetric percent density among women with higher progesterone levels only. Our findings offer new insights on the biological determinants of mammographic density in premenopausal women and support the inhibition of RANKL signaling as a pathway to reduce mammographic density and possibly breast cancer incidence in high-risk women with dense breasts.

No potential conflicts of interest were disclosed.

Conception and design: A.T. Toriola, C.M. Appleton, G.A. Colditz

Development of methodology: A.T. Toriola, G.A. Colditz

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.T. Toriola, C.M. Appleton, G.A. Colditz

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.T. Toriola, X. Zong, K. Weilbaecher, G.A. Colditz

Writing, review, and/or revision of the manuscript: A.T. Toriola, C.M. Appleton, X. Zong, J. Luo, K. Weilbaecher, R.M. Tamimi, G.A. Colditz

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.T. Toriola

Study supervision: A.T. Toriola

The study is supported by funds from the Susan G. Komen Foundation (CCR15332379, to A.T. Toriola), Siteman Cancer Center Siteman Investment Program (supported by The Foundation for Barnes-Jewish Hospital Cancer Frontier Fund (BJFH CFF 3781 & 4035); NCI Cancer Center Support (grant no. P30 CA091842), Siteman Cancer Center Biostatistics Shared Resource, and Washington University School of Medicine. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support (grant #P30 CA091842). We thank patient advocate Judy Johnson for her invaluable contributions throughout the project by providing insight into the study participants’ and breast cancer survivors’ perspectives. We acknowledge the study coordinators, Kellie Imm, Linda Li, and Stephanie Niu who helped with participant recruitment and data entry.

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.

1.
Boyd
NF
,
Guo
H
,
Martin
LJ
,
Sun
L
,
Stone
J
,
Fishell
E
, et al
Mammographic density and the risk and detection of breast cancer
.
N Engl J Med
2007
;
356
:
227
36
.
2.
Sprague
BL
,
Gangnon
RE
,
Burt
V
,
Trentham-Dietz
A
,
Hampton
JM
,
Wellman
RD
, et al
Prevalence of mammographically dense breasts in the United States
.
J Natl Cancer Inst
2014
;
106
:
pii: dju255
.
3.
Varghese
JS
,
Thompson
DJ
,
Michailidou
K
,
Lindstrom
S
,
Turnbull
C
,
Brown
J
, et al
Mammographic breast density and breast cancer: evidence of a shared genetic basis
.
Cancer Res
2012
;
72
:
1478
84
.
4.
Lindstrom
S
,
Thompson
DJ
,
Paterson
AD
,
Li
J
,
Gierach
GL
,
Scott
C
, et al
Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk
.
Nat Commun
2014
;
5
:
5303
.
5.
Sigl
V
,
Penninger
JM
. 
RANKL/RANK - from bone physiology to breast cancer
.
Cytokine Growth Factor Rev
2014
;
25
:
205
14
.
6.
Gonzalez-Suarez
E
. 
RANKL inhibition: a promising novel strategy for breast cancer treatment
.
Clin Transl Oncol
2011
;
13
:
222
8
.
7.
Hanada
R
,
Hanada
T
,
Penninger
JM
. 
Physiology and pathophysiology of the RANKL/RANK system
.
Biol Chem
2010
;
391
:
1365
70
.
8.
Fata
JE
,
Kong
YY
,
Li
J
,
Sasaki
T
,
Irie-Sasaki
J
,
Moorehead
RA
, et al
The osteoclast differentiation factor osteoprotegerin-ligand is essential for mammary gland development
.
Cell
2000
;
103
:
41
50
.
9.
Asselin-Labat
M
,
Vaillant
F
,
Sheridan
J
,
Pal
B
,
Wu
D
,
Simpson
E
, et al
Control of mammary stem cell function by steroid hormone signalling
.
Nature
2010
;
465
:
798
802
.
10.
Joshi
PA
,
Jackson
HW
,
Beristain
AG
,
Di Grappa
MA
,
Mote
PA
,
Clarke
CL
, et al
Progesterone induces adult mammary stem cell expansion
.
Nature
2010
;
465
:
803
7
.
11.
Schramek
D
,
Leibbrandt
A
,
Sigl
V
,
Kenner
L
,
Pospisilik
JA
,
Lee
HJ
, et al
Osteoclast differentiation factor RANKL controls development of progestin-driven mammary cancer
.
Nature
2010
;
468
:
98
102
.
12.
Gonzalez-Suarez
E
,
Jacob
AP
,
Jones
J
,
Miller
R
,
Roudier-Meyer
MP
,
Erwert
R
, et al
RANK ligand mediates progestin-induced mammary epithelial proliferation and carcinogenesis
.
Nature
2010
;
468
:
103
7
.
13.
Koch
L
. 
Cancer: RANKL inhibition-a new weapon against breast cancer?
Nat Rev Endocrinol
2011
;
7
:
2
.
14.
Nolan
E
,
Vaillant
F
,
Branstetter
D
,
Pal
B
,
Giner
G
,
Whitehead
L
, et al
RANK ligand as a potential target for breast cancer prevention in BRCA1-mutation carriers
.
Nat Med
2016
;
22
:
933
9
.
15.
Nolan
E
,
Lindeman
GJ
,
Visvader
JE
. 
Out-RANKing BRCA1 in mutation carriers
.
Cancer Res
2017
;
77
:
595
600
.
16.
Kiechl
S
,
Schramek
D
,
Widschwendter
M
,
Fourkala
E-O
,
Zaikin
A
,
Jones
A
, et al
Aberrant regulation of RANKL/OPG in women at high risk of developing breast cancer
.
Oncotarget
2017
;
8
:
3811
25
.
17.
Sarink
D
,
Schock
H
,
Johnson
T
,
Overvad
K
,
Holm
M
,
Tjønneland
A
, et al
Circulating RANKL and RANKL/OPG and breast cancer risk by ER and PR subtype: results from the EPIC cohort
.
Cancer Prev Res
2017
;
10
:
525
34
.
18.
Toriola
AT
,
Dang
HX
,
Hagemann
IS
,
Appleton
CM
,
Colditz
GA
,
Luo
J
, et al
Increased breast tissue receptor activator of nuclear factor- kappaB ligand (RANKL) gene expression is associated with higher mammographic density in premenopausal women
.
Oncotarget
2017
;
8
:
73787
92
.
19.
Pettersson
A
,
Graff
RE
,
Ursin
G
,
dos Santos Silva
I
,
McCormack
V
,
Baglietto
L
, et al
Mammographic density phenotypes and risk of breast cancer: a meta-analysis
.
J Natl Cancer Inst
2014
;
106
:
pii: dju078
.
20.
Kanamaru
F
,
Iwai
H
,
Ikeda
T
,
Nakajima
A
,
Ishikawa
I
,
Azuma
M
. 
Expression of membrane-bound and soluble receptor activator of NF-kappaB ligand (RANKL) in human T cells
.
Immunol Lett
2004
;
94
:
239
46
.
21.
Moran
O
,
Zaman
T
,
Eisen
A
,
Demsky
R
,
Blackmore
K
,
Knight
JA
, et al
Serum osteoprotegerin levels and mammographic density among high-risk women
.
Cancer Causes Control
2018
;
29
:
507
17
.
22.
Alimujiang
A
,
Imm
KR
,
Appleton
CM
,
Colditz
GA
,
Berkey
CS
,
Toriola
AT
. 
Adiposity at age 10 and mammographic density among premenopausal women
.
Cancer Prev Res
2018
;
11
:
287
94
.
23.
Martínez-Ruiz
A
,
Sarabia-Meseguer
MD
,
Pérez-Fornieles
J
,
Vílchez
JA
,
Tovar-Zapata
I
,
Noguera-Velasco
JA
. 
Placental growth factor, soluble fms-like tyrosine kinase 1 and progesterone as diagnostic biomarkers for ectopic pregnancy and missed abortion
.
Clin Biochem
2014
;
47
:
844
7
.
24.
Wang
B
,
Mi
M
,
Wang
J
,
Wei
N
,
Zhang
Q
,
Zhu
J
, et al
Does the increase of endogenous steroid hormone levels also affect breast cancer risk in Chinese women? A case–control study in Chongqing, China
.
Int J Cancer
2009
;
124
:
1892
9
.
25.
Lee
HN
,
Sohn
YM
,
Han
KH
. 
Comparison of mammographic density estimation by Volpara software with radiologists' visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them
.
Acta Radiol
2015
;
56
:
1061
8
.
26.
Ellison-Loschmann
L
,
McKenzie
F
,
Highnam
R
,
Cave
A
,
Walker
J
,
Jeffreys
M
. 
Age and ethnic differences in volumetric breast density in new zealand women: a cross-sectional study
.
PLoS One
2013
;
8
:
e70217
.
27.
Solutions
V
.
Volpara density user manual version 1.5
.
Wellington, New Zealand
:
Volpara Solutions from Matakina Technology
; 
2013
.
28.
White
IR
,
Royston
P
,
Wood
AM
. 
Multiple imputation using chained equations: Issues and guidance for practice
.
Stat Med
2011
;
30
:
377
99
.
29.
Toriola
A
,
Liu
J
,
Ganz
P
,
Colditz
G
,
Yang
L
,
Izadi
S
, et al
Effect of weight loss on bone health in overweight/obese postmenopausal breast cancer survivors
.
Breast Cancer Res Treat
2015
;
152
:
637
43
.
30.
Karsenty
G
. 
The genetic transformation of bone biology
.
Genes Dev
1999
;
13
:
3037
51
.
31.
Nakshatri
H
,
Bhat-Nakshatri
P
,
Martin
DA
,
Goulet
RJ
,
Sledge
GW
. 
Constitutive activation of NF-kappaB during progression of breast cancer to hormone-independent growth
.
Mol Cell Biol
1997
;
17
:
3629
39
.
32.
Lee
HJ
,
Gallego-Ortega
D
,
Ledger
A
,
Schramek
D
,
Joshi
P
,
Szwarc
MM
, et al
Progesterone drives mammary secretory differentiation via RankL-mediated induction of Elf5 in luminal progenitor cells
.
Development
2013
;
140
:
1397
401
.
33.
Fernandez-Valdivia
R
,
Lydon
JP
. 
From the ranks of mammary progesterone mediators, RANKL takes the spotlight
.
Mol Cell Endocrinol
2012
;
357
:
91
100
.
34.
Anderson
E
. 
The role of oestrogen and progesterone receptors in human mammary development and tumorigenesis
.
Breast Cancer Res
2002
;
4
:
197
201
.
35.
Beral
V
. 
Breast cancer and hormone-replacement therapy in the Million Women Study
.
Lancet
2003
;
362
:
419
27
.
36.
Hofseth
LJ
,
Raafat
AM
,
Osuch
JR
,
Pathak
DR
,
Slomski
CA
,
Haslam
SZ
. 
Hormone replacement therapy with estrogen or estrogen plus medroxyprogesterone acetate is associated with increased epithelial proliferation in the normal postmenopausal breast
.
J Clin Endocrinol Metab
1999
;
84
:
4559
65
.
37.
Ross
RK
,
Paganini-Hill
A
,
Wan
PC
,
Pike
MC
. 
Effect of hormone replacement therapy on breast cancer risk: estrogen versus estrogen plus progestin
.
J Natl Cancer Inst
2000
;
92
:
328
32
.
38.
Rossouw
JE
,
Anderson
GL
,
Prentice
RL
,
LaCroix
AZ
,
Kooperberg
C
,
Stefanick
ML
, et al
Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative randomized controlled trial
.
JAMA
2002
;
288
:
321
33
.
39.
Noh
JJ
,
Maskarinec
G
,
Pagano
I
,
Cheung
LW
,
Stanczyk
FZ
. 
Mammographic densities and circulating hormones: a cross-sectional study in premenopausal women
.
Breast
2006
;
15
:
20
8
.
40.
Colditz
GA
,
Hankinson
SE
,
Hunter
DJ
,
Willett
WC
,
Manson
JE
,
Stampfer
MJ
, et al
The use of estrogens and progestins and the risk of breast cancer in postmenopausal women
.
N Engl J Med
1995
;
332
:
1589
93
.
41.
Cogliano
V
,
Grosse
Y
,
Baan
R
,
Straif
K
,
Secretan
B
,
El Ghissassi
F
, et al
Carcinogenicity of combined oestrogen-progestagen contraceptives and menopausal treatment
.
Lancet Oncol
2005
;
6
:
552
3
.
42.
McTiernan
A
,
Martin
CF
,
Peck
JD
,
Aragaki
AK
,
Chlebowski
RT
,
Pisano
ED
, et al
Estrogen-plus-progestin use and mammographic density in postmenopausal women: Women's Health Initiative randomized trial
.
J Natl Cancer Inst
2005
;
97
:
1366
76
.
43.
Greendale
GA
,
Reboussin
BA
,
Slone
S
,
Wasilauskas
C
,
Pike
MC
,
Ursin
G
. 
Postmenopausal hormone therapy and change in mammographic density
.
J Natl Cancer Inst
2003
;
95
:
30
7
.
44.
Odén
L
,
Akbari
M
,
Zaman
T
,
Singer
CF
,
Sun
P
,
Narod
SA
, et al
Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers
.
Oncotarget
2016
;
7
:
86687
94
.
45.
Fortner
RT
,
Sarink
D
,
Schock
H
,
Johnson
T
,
Tjonneland
A
,
Olsen
A
, et al
Osteoprotegerin and breast cancer risk by hormone receptor subtype: a nested case-control study in the EPIC cohort
.
BMC Med
2017
;
15
:
26
.
46.
Schett
G
,
Kiechl
S
,
Redlich
K
,
Oberhollenzer
F
,
Weger
S
,
Egger
G
, et al
Soluble RANKL and risk of nontraumatic fracture
.
JAMA
2004
;
291
:
1108
13
.
47.
Eng
A
,
Gallant
Z
,
Shepherd
J
,
McCormack
V
,
Li
J
,
Dowsett
M
, et al
Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods
.
Breast Cancer Res
2014
;
16
:
439
.
48.
Ko
SY
,
Kim
E-K
,
Kim
MJ
,
Moon
HJ
. 
Mammographic density estimation with automated volumetric breast density measurement
.
Korean J Radiol
2014
;
15
:
313
21
.