Open Access

Hormone metabolism pathway genes and mammographic density change after quitting estrogen and progestin combined hormone therapy in the California Teachers Study

  • Eunjung Lee1Email author,
  • Jianning Luo2,
  • Yu-Chen Su1,
  • Juan Pablo Lewinger1,
  • Fredrick R Schumacher1,
  • David Van Den Berg1,
  • Anna H Wu1,
  • Leslie Bernstein2 and
  • Giske Ursin1, 3, 4
Breast Cancer Research201416:477

https://doi.org/10.1186/s13058-014-0477-8

Received: 10 November 2013

Accepted: 11 November 2014

Published: 11 December 2014

Abstract

Introduction

Mammographic density (MD) is a strong biomarker of breast cancer risk. MD increases after women start estrogen plus progestin therapy (EPT) and decreases after women quit EPT. A large interindividual variation in EPT-associated MD change has been observed, but few studies have investigated genetic predictors of the EPT-associated MD change. Here, we evaluate the association between polymorphisms in hormone metabolism pathway genes and MD changes when women quit EPT.

Methods

We collected mammograms before and after women quit EPT and genotyped 405 tagging single nucleotide polymorphisms (SNPs) in 30 hormone metabolism pathway genes in 284 non-Hispanic white participants of the California Teachers Study (CTS). Participants were ages 49 to 71 years at time of mammography taken after quitting EPT. We assessed percent MD using a computer-assisted method. MD change was calculated by subtracting MD of an ‘off-EPT’ mammogram from MD of an ‘on-EPT’ (that is baseline) mammogram. Linear regression analysis was used to investigate the SNP-MD change association, adjusting for the baseline ‘on-EPT’ MD, age and BMI at time of baseline mammogram, and time interval and BMI change between the two mammograms. An overall pathway and gene-level summary was obtained using the adaptive rank truncated product (ARTP) test. We calculated ‘P values adjusted for correlated tests (P ACT)’ to account for multiple testing within a gene.

Results

The strongest associations were observed for rs7489119 in SLCO1B1, and rs5933863 in ARSC. SLCO1B1 and ARSC are involved in excretion and activation of estrogen metabolites of EPT, respectively. MD change after quitting was 4.2% smaller per minor allele of rs7489119 (P = 0.0008; P ACT = 0.018) and 1.9% larger per minor allele of rs5933863 (P = 0.013; P ACT = 0.025). These individual SNP associations did not reach statistical significance when we further used Bonferroni correction to consider the number of tested genes. The pathway level summary ARTP P value was not statistically significant.

Conclusions

Data from this longitudinal study of EPT quitters suggest that genetic variation in two hormone metabolism pathway genes, SLCO1B1 and ARSC, may be associated with change in MD after women stop using EPT. Larger longitudinal studies are needed to confirm our findings.

Introduction

Mammographic density (MD) is a measure of the amount of epithelium and stroma relative to the amount of fat tissue in the breast. MD is one of the strongest biomarkers of breast cancer risk [1] and is associated with a number of breast cancer risk factors [2]. Combined estrogen plus progestin therapy (EPT) is an established risk factor of breast cancer [3]-[9]. Data from a randomized clinical trial, the Women’s Health Initiative (WHI), confirmed that an EPT regimen consisting of conjugated equine estrogens (CEE) and medroxyprogesterone acetate (MPA) [9] increased breast cancer risk. EPT use has also been associated with a substantial increase in MD, by 3 to 5% in the Postmenopausal Estrogen/Progestin Interventions (PEPI) trial, depending on the regimen [10],[11], and by approximately 7% in the WHI trial [12]. In both studies, a large interindividual variation was observed [10]-[12]. In the PEPI trial, approximately 20% of women in the EPT group experienced a one-step increase in BI-RADS grade, which represents 14 to 18% increase in density [10]. This enormous change was not observed in others in the EPT group, but some increase was observed in a larger number of women [10],[11]. Importantly, the increase in MD was positively correlated with post-treatment increases in serum levels of estrone (E1) [13], estrone sulfate (E1S) [14], and progestogen levels [15]. These findings suggest that genetic factors influencing absorption and metabolism of EPT may be important in predicting EPT-associated MD change.

Similarly, MD decreases after discontinuing hormone therapy [16],[17]. In a randomized trial of short-term cessation of hormone therapy, MD significantly decreased in the EPT cessation group, but with an interindividual variation in MD decrease [17],[18]. In the EPT cessation group, MD decreased by ≥7.5% in 24% of women; decreased by 3% to <7.5% in another 24%; changed little (<3%) in 30%; and increased by >3% in 23% [18]. None of the measured lifestyle factors modified the magnitude of the MD decrease, and the authors concluded that genetic factors could be important determinants. Considering that increases in MD are associated with higher breast cancer risk [19] and that decreases in MD are associated with reduced breast cancer risk [19],[20], identifying genetic determinants of MD change associated with EPT use and quitting is important to understand breast cancer risk in current or former EPT users.

While there have been efforts to identify genetic determinants of MD in large cross-sectional samples [21],[22], few studies have investigated the genetic predictors of longitudinal MD changes associated with EPT use. In a small sample from a clinical trial of EPT use where four single nucleotide polymorphisms (SNPs) in four hormone pathway genes were investigated, two SNPs (Val432Leu in CYP1B1 and L311V in AKR1C4) were associated with MD change after starting EPT [23]. A longitudinal study based on the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort suggested that genetic variation in progesterone receptor gene (PGR) modified the effect of hormone therapy on MD change [24]. Data from the PEPI trial did not support this association: none of the 23 tested tagging SNPs in the PGR were associated with MD change in EPT treatment arms [15]. Each of these studies investigated less than five genes [15],[23],[24], and only the PEPI study genotyped more than two SNPs in each gene [15]. In the current study, we investigated the association between 405 SNPs in 30 hormone metabolism pathway genes and MD change after stopping EPT use using data from the California Teachers Study (CTS) mammographic density subcohort.

Materials and methods

California Teachers Study (CTS)

The CTS is a prospective cohort of 133,479 current and former female public school teachers and other public school professionals, who were members of the California State Teachers Retirement System in 1995. At baseline, cohort participants completed a mailed questionnaire providing detailed information on reproductive history, oral contraceptive use, hormone therapy use and personal medical history including any previous diagnosis of cancer. A detailed description of the CTS is available [25]. The CTS was approved by the Institutional Review Board at each collaborating institution: the Cancer Prevention Institute of California, the University of California, Irvine, the University of Southern California, and the City of Hope in accord with assurances filed with and approved by the US Department of Health and Human Services.

Mammographic density substudy

The CTS mammographic density substudy was conducted to evaluate the role of genetic polymorphisms on MD changes occurring after women initiate or stop using EPT. We selected 1,420 women from among those who responded to a follow-up questionnaire collected around 2000 to 2001 and who were aged 40 to 60 years at cohort enrollment, lived in California and had not had a cancer diagnosis since enrolling in the study, newly initiated EPT use during the interval between enrollment and completion of the third questionnaire collected around 2000 to 2001, had a mammogram in the past two years, and were not participating in another CTS substudy. These initial selection criteria were used to recruit women who were most likely to have had recent mammograms before and after they started EPT use. Characteristics of the 1,420 women who were selected are described in Additional file 1. Since it was assumed that many in the CTS stopped using EPT following the publication of WHI results in June 2002 [26], the eligibility criteria described above were also expected to identify women who had mammograms before and after they quit EPT. We sent out letters to 1,420 CTS participants to solicit participation in this mammogram substudy and to inform women we would be contacting them by telephone. We spoke with 1,272 women (89.6%), and identified 21 women who were ineligible for this study (seven with a previous cancer diagnosis and 14 who did not meet the mammography screening criterion). Of the 1,251 eligible women, 247 women did not participate in this mammographic density substudy. Reasons for non-participation were refusals (n = 111), not providing written informed consent (n = 134), withdrawal of consent (n = 1), and not completing telephone interview (n = 1); a total of 1,004 women were included in the study. We conducted a telephone interview with eligible participants to obtain updated information on menstrual history and hormone use. We collected mammograms for 993 of the 1,004 women, and excluded 29 because we could not determine whether they were using hormone therapy at the time of mammogram. On average, eight mammograms for each participant were available to us. Some mammography facilities enclosed information on menstrual history and hormone use of each woman at the time of each mammogram. This information was available for approximately 50% of all participants and for approximately 40% of their mammograms. When we compared this information with the interview-based hormone therapy information, approximately 40% of these women had some inconsistency in the years of hormone therapy use between the two data sources. About 67% of the inconsistencies was due to ±1 year difference in the year when they started or stopped using hormone therapy; several years of difference were noted in other women. We considered the information collected from medical records at the time of mammography to be more accurate than women’s recall at the time of study enrollment (telephone interview), and therefore used the information from the mammography facilities when available. The mammographic density substudy of the CTS was approved by the University of Southern California institutional review board, and all participants provided written informed consent.

Selection of mammograms

To determine MD changes following the changes in women’s EPT use, we intended to select one ‘on-EPT mammogram’ taken while the participant was using EPT and one ‘off-EPT mammogram’ taken while the participant was not using any type of hormone therapy, either EPT or ET. Many EPT users in the CTS quit EPT use after the publication of WHI results in June 2002 [26]. We also realize that many women cannot recall exactly when they stopped hormone therapy. We therefore decided to select the on-EPT mammogram that was taken before July 1 2002, to minimize misclassification of EPT use status. When selecting off-EPT mammograms, we preferred mammograms that were taken after 2003. The implication was that we would mostly measure MD changes after ‘stopping’ EPT use rather than changes after ‘starting’ EPT use. Second, it has been shown that EPT-related MD changes occur predominantly within the first 12 months after starting EPT use and remain constant for at least the next two years [10]. Therefore, we preferred to select off-EPT mammograms taken as close as possible to the year of on-EPT mammograms but with at least a one-year interval between the on-EPT and off-EPT mammograms. After applying these criteria, we selected on-EPT mammograms of 578 women and off-EPT mammograms of 757 women. Both an on-EPT mammogram and an off-EPT mammogram were available for 422 women, comprising the longitudinal set of this mammogram substudy.

Construction of EPT quitters’ dataset

Of the 422 in the longitudinal set, 371 women were quitters, and 51 women were starters. Many women start using EPT during their menopausal transition, when ovarian estrogen levels are decreasing but are not as low as in postmenopausal women [27],[28]. It is possible that off-EPT mammograms of some EPT starters were taken while they were still undergoing the menopausal transition, although women reported that they were postmenopausal at that time. In such cases, the measurement of MD change before and after starting EPT use (that is increasing hormone levels) would be complicated by the MD change associated with the menopausal transition (that is decreasing hormone levels). Upon inspection of the menopausal status data for the starters, we could not exclude the possibility that the off-EPT mammogram of the majority of the starters were taken before they became completely postmenopausal. If this was the case, the data from these starters are likely to measure a decline in MD when going from a pre- or perimenopausal off-EPT state to a postmenopausal state on EPT, rather than to measure an increase in MD when going from a postmenopausal off-EPT state to a postmenopausal on-EPT state. Therefore, we decided to restrict the current study to the 371 EPT quitters in the longitudinal set.

Mammographic density assessment

The methods for MD measurements used for this study have been described previously [11],[13]. We digitized the images using a Cobrascan CX812T scanner (Radiographic Digital Imaging, Torrance, CA, USA) at a resolution of 150 pixels/inch (59 dots/cm). MD was assessed by one of the authors (GU) on scanned images using the USC Madena method, a validated computer-assisted, quantitative technique [10]. The total area of the breast was assessed by a research assistant trained by GU. MD, or the percent mammographic density, was calculated as the absolute density divided by the total area of the breast (as a percent). Readers were blinded to EPT use and which mammograms belonged to the same patient. We estimated change in MD by subtracting MD of off-EPT mammogram from that of on-EPT mammogram. For quality assurance, we included random blinded duplicates of 183 mammograms. Correlation between duplicate MD measures was excellent (R = 0.96).

Specimen collection and DNA extraction

We mailed an Oragene DNA self-collection kit (DNA Genotek, Kanata, ON, Canada) to the participant and return postage-paid mailing materials. Of the 371 women eligible for the current study, 328 women provided sufficient amount of samples for DNA extraction. We extracted DNA using the Oragene protocol (DNA Genotek).

Tagging SNP selection and genotyping

We used the same tagging SNPs selected for a CTS-nested case-control study of breast cancer [29]. Briefly, we selected linkage disequilibrium (LD) tagging SNPs across each gene, 20 kb upstream of 5′ untranslated region (UTR) and 10 kb downstream of 3′ UTR, using the Snagger software [30] and the TagSNPs program [31],[32]. We aimed to capture all common SNPs (minor allele frequency (MAF) of at least 5%) in whites with minimum pairwise r2 of at least 0.80. For some genes, due to space limitation of our genotyping platform, we included a few selected SNPs instead of complete lists of tagging SNPs.

Genotyping of the selected 455 SNPs were performed using the Illumina Golden Gate Assay (Illumina, Inc., San Diego, CA, USA) in the University of Southern California Core Facility. We excluded 38 SNPs with a call rate <90%, five SNPs with Hardy-Weinberg equilibrium P value <0.001, and seven SNPs with MAF < 1%, leaving 405 SNPs for analyses. About 6% of the 328 genotyped samples (n = 19) had a genotyping success rate (call rate) of less than 90% and were excluded from the analyses. The majority of the remaining 309 samples were non-Hispanic whites (n = 284). Due to the small sample size of women in other ethnic groups, we restricted the analysis to 284 non-Hispanic whites. The genotyping concordance rate was >99.9% based on 15 duplicate samples with a call rate >90%.

Statistical analysis

We used linear regression to examine the association between genotype and change in MD (that is ‘on-EPT density’ minus ‘off-EPT density’), adjusting for age and body mass index (BMI) (kg/m2) at on-EPT mammogram, time interval and BMI change between the two mammograms, and ‘on-EPT density’. These adjustment variables were chosen because age and BMI are strong determinants of MD [1], and baseline MD could be related to the absolute level of MD decrease [18]. An additive genetic model was used, which estimate the difference in the outcome variable (that is change in MD) per copy of the minor allele of each SNP (that is modeled as 0, 1, or 2 copies of the minor allele). The individual SNP P values were corrected for multiple testing within each gene using the P ACT method [33], which takes into account correlation due to LD. In order to capture gene and pathway level effects that may not be detectable through any single SNP, we also performed gene-based and pathway-based tests using the adaptive rank truncated product (ARTP) method [34]. ARTP adaptively combines single SNP P values within a gene region or a pathway to obtain a single test statistic for the gene or pathway and assesses significance of the test by a permutation procedure. Unlike a multiple testing procedure like P ACT, which accounts for multiple SNP tests in order to properly control the type I error, ARTP combines information across SNPs within a gene or a pathway in order to increase the power to detect a gene or pathway level effect.

Imputation

For a gene region that contains the most significant finding, we imputed genotype data for all SNPs reported in the 1000 Genomes project [35]. As the reference panel, we used the haplotypes of 1,092 samples (all populations) from the release version 3 of the 1000 Genomes Project Phase I [35]. Combining reference data from all populations helps improve imputation accuracy of low-frequency variants [36]. The haplotypes were phased using SHAPEIT2 [37]. We imputed genotypes in the selected gene region to the 1000 Genomes reference panel using IMPUTE2 [38]. We used the ‘certainty’ and ‘info’ metrics as the imputation quality measure, with cutpoints of >0.9 and >0.5 for each metric, respectively. We also excluded imputed SNPs with MAF < 0.01. We used the genotype probabilities in a dosage format as opposed to the best genotype calls in the association tests.

Parity induces substantial proliferation, differentiation, and subsequent involution of breast tissue cells [39]. It is known that nulliparous women have higher MD than parous women [40],[41], and that the association between MD and breast cancer risk may be stronger in nulliparous women than in parous women [42]. Therefore, we conducted exploratory analyses stratified by parity. Statistical tests for interaction were evaluated by introducing product terms with genotype and conducting Wald tests. We also conducted a subgroup analysis restricted to women who were aged 56 or older at time of on-EPT mammogram. This analysis helps to exclude the possibility that the genotype-MD change association is mainly driven by the MD changes related to menopausal transition rather than EPT-related MD changes. In a large population-based US study, >97% of those with natural menopause were postmenopausal by age 56 years [7]. Longitudinal data from the Minnesota Breast Cancer Family Study also suggest that non-hormone users show large decline in MD until her early 50s, and the age-related decline slows down from her late 50s [43]. We also performed subgroup analyses and statistical tests of interaction by baseline BMI (<25 kg/m2, ≥25 kg/ m2) and BMI change (<±1 kg/m2, greater than 1 kg/m2 increase or decrease in BMI) between the two mammograms.

Results

Women who were included in the final analysis (284 non-Hispanic white EPT quitters) were similar to those interviewed but not included in the analysis with respect to several factors except menopausal status (Table 1). Those included in the analysis were more likely to be peri- or postmenopausal at cohort enrollment. The time interval between the two mammograms was ≤3 years for about 50% of the 284 EPT quitters, and >3 to 5 years for another 37% (Table 1). Mean (± standard deviation (SD)) of the MD change between the two mammograms was 4.0% (±7.0%). The absolute amount of MD decrease was positively associated with baseline age (regression coefficient = 0.20, P = 0.04), baseline BMI (regression coefficient = 0.19, P = 0.016), baseline MD (regression coefficient = 0.17, P < 0.0001), longer time interval (regression coefficient = 0.43, P = 0.078), and larger BMI increase (regression coefficient = 0.46, P = 0.056) between the two mammograms, when all of these variables were included in the regression model.
Table 1

Comparison of characteristics of women participating in the California Teachers Study mammographic density substudy who were included in the analyses with those of participants who were excluded from the analyses

Characteristics

Interviewed participants not included in the current analysis (n = 720)

Interviewed participants included in the current analysis (non-Hispanic white EPT quitters) (n = 284)

P values

At cohort enrollment

   

Age (mean ± SD)

49.9 ± 4.2 (range 40-60)

50.5 ± 3.8 (range 41-60)

0.07

BMI (kg/m2)

24.6 ± 4.8 (range 17-50)

24.7 ± 4.9 (range 17-47)

0.68

White (N (%))

624 (87%)

284 (100%)

 

Nulliparous women (N (%))

141 (20%)

63 (22%)

0.33

Menopausal status

   

 Premenopausal

432 (60%)

155 (55%)

0.012

 Perimenopausal

87 (12%)

55 (19%)

 

 Postmenopausal

201 (28%)

74 (26%)

 

Ever had breast biopsy (N (%))

108 (15%)

40 (14%)

0.71

Positive 1st degree family history of breast cancer (N (%))

64 (9%)

37 (13%)

0.053

At mammogram substudy enrollment

   

Age at interview (mean ± SD)

62 ± 4.0 (range 52-71)

62 ± 3.6 (range 53-72)

0.11

BMI (kg/m2)

26.2 ± 5.2 (range 17-49)

25.7 ± 5.0 (range 18-47)

0.16

Positive 1st degree family history of breast cancer (N (%))

112 (16%)

51 (18%)

0.43

Number of mammograms in the past 10 years

8.8 ± 2.3 (range 2-20)

8.9 ± 2.2 (range 2-18)

0.59

At time of mammography evaluated for density

   

Age at time of mammogram while taking EPT

 

56 ± 4 (range 45-67)

 

Age at time of mammogram while off EPT

 

60 ± 5 (range 49-71)

 

BMI at time of mammogram while taking EPT

 

25.7 ± 5.2 (range 18-47)

 

BMI at time of mammogram while off EPT

 

25.8 ± 5.0 (range 18-48)

 

Years on EPT at time of mammogram while taking EPT

   

 <1 year

 

15 (5%)

 

 1- < 4 years

 

121 (43%)

 

 4- < 7 years

 

112 (39%)

 

 ≥7 years

 

36 (13%)

 

Time interval between two mammograms (years)

   

 ≤3 years

 

141 (50%)

 

 4-5 years

 

106 (37%)

 

 6-9 years

 

37 (13%)

 

EPT, estrogen and progestin combined therapy; BMI, body mass index.

The overall ARTP test for the entire set of 405 SNPs in the hormone metabolism pathway genes was not statistically significant (P = 0.49; data not shown). When we applied this method to each gene separately, the gene-level ARTP test P values were 0.02 and 0.04, respectively, for ARSC and SLCO1B1 (Table 2), suggesting that genetic variation in ARSC and SLCO1B1 may be associated with EPT-associated MD change. However, when we considered the number of genes tested and applied a Bonferroni correction, these gene-level ARTP test P values were not statistically significant.
Table 2

Gene-level summary P values associated with mammographic density change after quitting EPT use

Gene

Number of SNPs genotyped

Gene-level association P value *

AKR1C4

1

0.44

AR

6

0.39

ARSC

2

0.02

COMT

21

0.14

CYP11A

12

0.64

CYP19A1

2

0.80

CYP1A1/CYP1A2

5

0.97

CYP1B1

15

0.61

CYP21A2

2

0.13

CYP2C9

16

0.73

CYP3A4

3

0.46

ESR1

13

0.81

ESR2

22

0.68

HSD17B1

5

0.11

HSD17B2

24

>0.99

HSD17B4

24

0.49

HSD17B5/AKR1C3

38

0.85

HSD3B1

4

0.14

HSD3B2

10

0.97

PGR

32

0.81

SHBG

13

0.60

SLCO1B1 (SLC21A6)

38

0.04

SRD5A1

24

0.80

SULT1A1/SULT1A2

6

0.26

SULT1E1

18

0.50

UGT1A8

43

0.99

UGT2B17

1

0.63

UGT2B7

5

0.52

*Based on the adaptive rank truncated product (ARTP) statistics. EPT, estrogen and progestin combined therapy; SNP, single nucleotide polymorphisms.

When we tested the association between MD and each individual SNP, only two SNPs, rs7489119 in SLCO1B1 and rs5933863 in ARSC, showed statistically significant associations after correcting for multiple testing within each gene (Table 3). The MD decrease after quitting EPT was 4.2% smaller per minor allele (A allele) of rs7489119 (in SLCO1B1). The least squares mean of MD change for CC genotype carriers was 4.5% (that is 4.5% decrease in MD after quitting EPT), adjusted for the covariates described in the Materials and Methods section. In contrast, the least squares mean for the CA or AA genotype carriers was −0.2% (that is 0.2% increase in MD after quitting EPT; data not shown). When we performed association tests for 575 imputed SNPs in the SLCO1B1 region, only rs79640916 and rs78854974 were associated with MD change with P values of 0.0006 and 0.0007 (not corrected for multiple testing), respectively. Rs79640916 and rs78854974 are located in the intron regions of SLCO1B1, 23 kb and 40 kb away from rs7489119, and in LD with rs7489119 (r2 = 0.65 for both SNPs) in European populations based on 1000 Genomes data [35]. None of the other imputed SNPs were associated with MD change with a P value <0.001 (Additional file 2).
Table 3

SNPs that are statistically significantly associated with EPT-associated mammographic density change after correcting for multiple testing *

SNP (major/minor allele)

Gene

Minor allele frequency

N (WW/WV/VV)

beta

SE

P

P ACT

rs7489119 (C/A)

SLCO1B1

0.041

258/23/1

−4.22

1.25

0.0008

0.018

rs5933863 (G/A)

ARSC

0.15

200/79/5

1.87

0.75

0.013

0.025

*Based on linear regression model adjusting for age and BMI (kg/m2) at time of on-EPT mammogram, time interval and BMI change between the two mammograms, and mammographic density of on-EPT mammogram. Additive genetic model was used. Multiple testing corrected P value; P ACT (P values adjusted for correlated tests) within each gene was calculated using the methods by Conneely and Boehnke [33]. EPT, estrogen and progestin combined therapy; SNP, single nucleotide polymorphisms; SE, standard error; BMI, body mass index.

For rs5933863 (ARSC), the MD decrease after quitting EPT was 1.9% larger per minor allele (A allele) (Table 3). The least squares means for GG carriers was 3.4% while it was 5.7% for GA or AA carriers (data not shown). In other words, GG carriers had 3.4% decrease in MD after quitting EPT, and GA or AA carriers had 5.7% decrease in MD after quitting EPT. However, if we further applied Bonferroni corrections considering the number of genes tested, none of these associations remained statistically significant. The results for all investigated SNPs are presented in Additional file 3.

In our exploratory subset analysis restricted to 64 nulliparous women, rs2077647 in ESR1 and rs9605030 in COMT were associated with EPT-related density changes, which remained statistically significant after correcting for multiple testing at the gene level (both P ACT < 0.05; Table 4). The minor allele of rs2077647 was associated with 3.9% larger density decrease after quitting EPT. The minor allele of rs9605030 was associated with 4.5% larger density decrease after quitting EPT. P values for interaction with parity for these two SNPs were 0.003 and 0.011, respectively (uncorrected for multiple testing).
Table 4

SNPs that are statistically significantly associated with EPT-associated mammographic density change in either nulliparous (n = 63) or parous (n = 219) women after correcting for multiple testing at gene level

SNP

 

Minor

Nulliparous

(n = 63)

  

Parous

(n = 219)

   

(major/minor allele)

Gene

allele frequency

N (WW/WV/VV)

Beta (SE)

P

P ACT *

N (WW/WV/VV)

Beta (SE)

P

P ACT *

P for interaction

rs2077647 (T/C)

ESR1

0.49

18/30/15

3.92 (1.06)

0.0005

0.006

56/118/43

−0.40 (0.63)

0.53

>0.99

0.003

rs9605030 (C/T)

COMT

0.14

40/21/2

4.47 (1.42)

0.0025

0.043

166/48/4

0.69 (0.88)

0.44

>0.99

0.011

rs5933863 (G/A)

ARSC

0.15

46/17/0

1.26 (1.88)

0.51

0.75

152/62/5

2.00 (0.81)

0.014

0.027

0.82

Based on linear regression model adjusting for age and BMI (kg/m2) at time of on-EPT mammogram, time interval and BMI change between the two mammograms, and mammographic density of on-EPT mammogram. Additive genetic model was used. *Multiple testing corrected P value; P ACT (P values adjusted for correlated tests) within each gene was calculated using the methods by Conneely and Boehnke [33]. P values for interaction were not corrected for multiple testing. SNP, single nucleotide polymorphisms; EPT, estrogen and progestin combined therapy; SE, standard error; BMI, body mass index.

Among 219 parous women, only rs5933863 in ARSC had a gene-level P ACT <0.05, but this SNP showed similar magnitude of association with MD change in nulliparous women (P for interaction = 0.82; Table 4).

When the analysis was restricted to women who were aged 56 or older at the time of their on-EPT mammogram (n = 143), the association with rs7489119 (SLCO1B1) became stronger: the minor allele of rs7489119 was associated with 6.2% smaller density decrease after quitting EPT (P = 0.0003; P ACT = 0.007; data not shown). The association with rs5933863 (ARSC) was similar in magnitude, although this association was not statistically significant (P ACT >0.05; data not shown).

EPT-related MD changes occur predominantly within the first 12 months after starting EPT use and remain rather constant for at least the next two years [10]. When we excluded 15 women whose on-EPT mammogram was taken within one year from starting EPT, the results were essentially identical to those we have presented. Further adjustment for duration of EPT at time of on-EPT mammogram did not change the results. We did not observe any evidence of effect modification by BMI at baseline or BMI change between the two mammograms.

Discussion

In this longitudinal study of EPT quitters, overall genetic variation in the 30 hormone metabolism pathway genes was not associated with density change. However, at the gene-level, we found some evidence that two hormone metabolism pathway genes, namely SLCO1B1 and ARSC, were associated with MD change after women quit EPT use. The few SNPs previously proposed to be associated with EPT-associated density change, including SNPs in PGR (rs10895068) [24], AKR1C4 (rs17134592) [23], CYP1B1 (rs1056836) [23], were not associated with MD change in this study. Rs10895068 and rs17134592 were genotyped in this study, and rs1056836 was tagged with three SNPs in LD (r2 = 0.74-0.76). To our knowledge, this study is the first to systematically investigate hormone metabolism pathway genes as genetic determinants of longitudinally assessed MD change after women quit EPT use.

Data from WHI and PEPI clinical trials have shown that EPT use for one year increased MD by 3 to 7% [10]-[12]. In a randomized trial, two-month suspension of EPT decreased MD to a larger extent (by 2.8%) than was observed in the comparison group who continued EPT use [17]. However, in all studies, large interindividual variation in the MD change was noted after introducing [10]-[12] or stopping EPT use [17]. Increases and decreases in MD have been associated with higher and lower, respectively, risk of breast cancer in a study of women who were not using hormone therapy [19]. In addition, in a nested case-control study within a breast cancer prevention trial, decrease in MD was a good predictor of tamoxifen-induced reduction in breast cancer risk [20]. Further, genetic variation in a tamoxifen metabolizing enzyme CYP2D6 has been associated with MD change following tamoxifen treatment [44]. Thus it seems reasonable that understanding the genetic determinants of the interindividual variability in MD changes in response to EPT use or quitting can help predict breast cancer risk in EPT users or former users.

Our finding that one SNP (rs7489119) in SLCO1B1 may be involved in determining EPT-related MD change is novel and biologically plausible. SLCO1B1, also known as SLC21A6, is a solute carrier organic anion transporter gene expressed in the liver. SLCO1B1 transports a variety of endogenous and exogenous substrates from the blood into the hepatocytes [45], including estradiol-17β-glucuronide and estrone-3-sulfate (E1S) [46],[47]. E1S is a major component of conjugated equine estrogens, the estrogen component of the predominant EPT regimens in the US (at least prior to 2002) [48]. Two [46],[49] of the three [46],[49],[50] experimental studies reported that variant forms of SLCO1B1 showed reduced uptake of E1S and estradiol glucuronide. Specifically, polymorphisms leading to amino acid changes within the transmembrane-spanning domains such as rs56101265 (Phe73Leu), rs56061388 (Val82Ala), rs4149056 (Val174Ala), and rs55901008 (Ile353Thr) [46],[49] and those within extracellular loop 5 such as rs56387224 (Asn432Asp), rs72559748 (Asp462Gly), and rs59502379 (Gly488Ala) [49], were shown to affect the uptake kinetics. The MAF of rs4149056 (Val174Ala) was 0.14 in Europeans [49], but MAFs of the rest were less than or equal to 0.02 [49]. Rs4149056 was associated with blood E1S levels in Europeans [46]. However, rs4149056 was not in LD with rs7489119 (r2 = 0.01) in European populations based on HapMap data [51], and was not associated with MD change in our study using imputed genotype data. The imputation certainty and info scores for rs4149056 were 0.98 and 0.92, respectively. These observations suggest that rs4149056 is unlikely to be a causal variant of the observed association for rs7489119. We could not check LD between rs7489119 and the other functional SNPs in SLCO1B1 because genotype data for these SNPs are not publicly available or monomorphic in Europeans [35].

More recently, using an independent data from a breast cancer case-control study nested within CTS, our group reported that EPT use modified the effect of SLCO1B1 SNPs on breast cancer risk [29]. We found that rs4149013, a SNP located near the 5’ end of SLCO1B1 with unknown functional significance, was associated with breast cancer risk, and this association was restricted to EPT users (odds ratio (OR) = 2.3 per minor allele) [29]. This SNP did not show an association with EPT-related MD change in the current analysis, and is not in LD with rs7489119 (r2 = 0.003).

Arylsulfatase C (ARSC), also known as steroid sulfatase (STS), is expressed in the liver and breast, and converts E1S into biologically active E1 [52]. Rs5933863 is located in 3’ UTR of ARSC. To our knowledge, no data exist regarding the functional significance of rs5933863 or other SNPs in ARSC.

Our current finding that PGR genetic variation is not associated with EPT-related MD change is consistent with the results from our own previous report based on PEPI trial data [15], although an earlier longitudinal study based on EPIC data showed that rs10895068 (+331 G/A) of the two tested PGR SNPs showed an association [24].

Our exploratory analysis in nulliparous women suggests that ESR1 and COMT SNPs (rs2077647, and rs9605030, respectively) may be associated with EPT-associated MD changes in this subset. Although we had limited sample size for this subset analysis and these observations may be chance findings, our data are novel and warrant evaluation in larger studies of nulliparous women, who have higher MD [40],[41] and higher breast cancer risk than parous women [53]. It has been hypothesized that breast tissue cells of nulliparous women may be subject to greater damage from carcinogens than the cells of parous women [42], as nulliparous breast tissue cells have not undergone parity-induced proliferation, differentiation, and involution [39].

After the publication of WHI trial results in 2002 [9], EPT use markedly dropped in the US [54]; this was immediately followed by decreases in breast cancer incidence [26],[55]. It was reported that the elevated breast cancer risk in the WHI EPT group decreased rapidly after terminating the intervention, and the hazard ratio representing the risk of breast cancer associated with EPT use became approximately 1.0 within two years of cessation of exposure [56]. However, the large individual variation in MD decrease after stopping hormone therapy [18] suggests that the magnitude of risk reduction after stopping EPT may vary. Given that individual characteristics of women (for example age, BMI, parity, family history of breast cancer) are not related to the variability in MD changes [18], our findings that genetic factors may determine the amount of change in MD after quitting EPT require confirmation in future studies. Further, whether these predictors of MD decline, also predict an increase in MD when starting EPT, must be verified. The clinical management guidelines published in 2014 recommend using the lowest effective dose for the shortest duration for management of menopausal symptoms [57]. For women who consider this therapy to curtail menopausal symptoms, it would be beneficial to know whether they are at higher risk of experiencing MD changes resulting from EPT use. Such findings could help to identify a subgroup of women who should avoid EPT use.

In this study we were able to comprehensively investigate hormone metabolism pathway genes as determinants of EPT-related MD changes. A strength of our study is that one experienced investigator estimated MD in all of the mammograms using a standardized and validated method; further, both sets of mammograms from the same woman were evaluated within the same batch. A limitation of our study is that we only studied non-Hispanic white women. In addition, we did not have a comparison group who continued to use EPT or who never used EPT. Thus, it is possible that the genetic predictors of change in MD that we identified may not be specifically associated with changes following cessation of EPT, but also associated with MD reductions following aging.

Conclusions

Data from this longitudinal study of EPT quitters suggest that genetic variation in two hormone metabolism pathway genes, SLCO1B1 and ARSC, may be associated with change in MD after women quit EPT use. Larger longitudinal studies are needed to confirm our findings.

Additional files

Abbreviations

ARSC: 

arylsulfatase C

ARTP: 

adaptive rank truncated product

BMI: 

body mass index

CEE: 

conjugated equine estrogens

CTS: 

California Teachers Study

E1: 

estrone

E1S: 

estrone sulfate

EPIC: 

European Prospective Investigation into Cancer and Nutrition

EPT: 

estrogen plus progestin therapy

LD: 

linkage disequilibrium

MAF: 

minor allele frequency

MD: 

mammographic density

MPA: 

medroxyprogesterone acetate

PEPI: 

Postmenopausal Estrogen/Progestin Interventions

PGR

progesterone receptor gene

SNP: 

single nucleotide polymorphism

WHI: 

Women’s Health Initiative

UTR: 

untranslated region

Declarations

Acknowledgments

Grant/Financial Support: this research was supported by P01 CA017054. The parent CTS study was supported by grants R01 CA77398 and K05 CA136967 from the National Cancer Institute and contract 97–10500 from the California Breast Cancer Research Fund. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California, Department of Public Health the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred.

Authors’ Affiliations

(1)
Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center
(2)
Department of Population Sciences, Beckman Research Institute
(3)
Department of Nutrition, University of Oslo
(4)
Cancer Registry of Norway

References

  1. McCormack VA, dos Santos SI: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006, 15: 1159-1169. 10.1158/1055-9965.EPI-06-0034.View ArticlePubMedGoogle Scholar
  2. Boyd NF, Martin LJ, Rommens JM, Paterson AD, Minkin S, Yaffe MJ, Stone J, Hopper JL: Mammographic density: a heritable risk factor for breast cancer. Methods Mol Biol. 2009, 472: 343-360. 10.1007/978-1-60327-492-0_15.View ArticlePubMedGoogle Scholar
  3. Collaborative Group on Hormonal Factors in Breast Cancer: Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Lancet. 1997, 350: 1047-1059. 10.1016/S0140-6736(97)08233-0.View ArticleGoogle Scholar
  4. 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: 328-332. 10.1093/jnci/92.4.328.View ArticlePubMedGoogle Scholar
  5. Schairer C, Lubin J, Troisi R, Sturgeon S, Brinton L, Hoover R: Menopausal estrogen and estrogen-progestin replacement therapy and breast cancer risk. Jama. 2000, 283: 485-491. 10.1001/jama.283.4.485.View ArticlePubMedGoogle Scholar
  6. Olsson HL, Ingvar C, Bladstrom A: Hormone replacement therapy containing progestins and given continuously increases breast carcinoma risk in Sweden. Cancer. 2003, 97: 1387-1392. 10.1002/cncr.11205.View ArticlePubMedGoogle Scholar
  7. Weiss LK, Burkman RT, Cushing-Haugen K, Voigt LF, Simon MS, Daling JR, Norman SA, Bernstein L, Ursin G, Marchbanks P, Strom BL, Berlin JA, Weber AL, Liff JM, Wingo PA, McDonald JA, Malone KE, Folger SG, Spirtas R: Hormone replacement therapy regimens and breast cancer risk. Obstet Gynecol. 2002, 100: 1148-1158. 10.1016/S0029-7844(02)02502-4.PubMedGoogle Scholar
  8. Reeves GK, Beral V, Green J, Gathani T, Bull D: Hormonal therapy for menopause and breast-cancer risk by histological type: a cohort study and meta-analysis. Lancet Oncol. 2006, 7: 910-918. 10.1016/S1470-2045(06)70911-1.View ArticlePubMedGoogle Scholar
  9. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, Jackson RD, Beresford SA, Howard BV, Johnson KC, Kotchen JM, Ockene J: 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: 321-333. 10.1001/jama.288.3.321.View ArticlePubMedGoogle Scholar
  10. Greendale GA, Reboussin BA, Sie A, Singh HR, Olson LK, Gatewood O, Bassett LW, Wasilauskas C, Bush T, Barrett-Connor E: Effects of estrogen and estrogen-progestin on mammographic parenchymal density. Postmenopausal Estrogen/Progestin Interventions (PEPI) Investigators. Ann Intern Med. 1999, 130: 262-269. 10.7326/0003-4819-130-4_Part_1-199902160-00003.View ArticlePubMedGoogle Scholar
  11. 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: 30-37. 10.1093/jnci/95.1.30.View ArticlePubMedGoogle Scholar
  12. McTiernan A, Martin CF, Peck JD, Aragaki AK, Chlebowski RT, Pisano ED, Wang CY, Brunner RL, Johnson KC, Manson JE, Lewis CE, Kotchen JM, Hulka BS: Estrogen-plus-progestin use and mammographic density in postmenopausal women: Women's Health Initiative randomized trial. J Natl Cancer Inst. 2005, 97: 1366-1376. 10.1093/jnci/dji279.View ArticlePubMedGoogle Scholar
  13. Ursin G, Palla SL, Reboussin BA, Slone S, Wasilauskas C, Pike MC, Greendale GA: Post-treatment change in serum estrone predicts mammographic percent density changes in women who received combination estrogen and progestin in the Postmenopausal Estrogen/Progestin Interventions (PEPI) Trial. J Clin Oncol. 2004, 22: 2842-2848. 10.1200/JCO.2004.03.120.View ArticlePubMedGoogle Scholar
  14. Crandall CJ, Guan M, Laughlin GA, Ursin GA, Stanczyk FZ, Ingles SA, Barrett-Connor E, Greendale GA: Increases in serum estrone sulfate level are associated with increased mammographic density during menopausal hormone therapy. Cancer Epidemiol Biomarkers Prev. 2008, 17: 1674-1681. 10.1158/1055-9965.EPI-07-2779.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Lee E, Ingles SA, Van Den Berg D, Wang W, Lavallee C, Huang MH, Crandall CJ, Stanczyk FZ, Greendale GA, Ursin G: Progestogen levels, progesterone receptor gene polymorphisms, and mammographic density changes: results from the Postmenopausal Estrogen/Progestin Interventions Mammographic Density Study. Menopause. 2012, 19: 302-310. 10.1097/gme.0b013e3182310f9f.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Rutter CM, Mandelson MT, Laya MB, Seger DJ, Taplin S: Changes in breast density associated with initiation, discontinuation, and continuing use of hormone replacement therapy. JAMA. 2001, 285: 171-176. 10.1001/jama.285.2.171.View ArticlePubMedGoogle Scholar
  17. Buist DS, Anderson ML, Reed SD, Aiello Bowles EJ, Fitzgibbons ED, Gandara JC, Seger D, Newton KM: Short-term hormone therapy suspension and mammography recall: a randomized trial. Ann Intern Med. 2009, 150: 752-765. 10.7326/0003-4819-150-11-200906020-00003.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Lowry SJ, Aiello Bowles EJ, Anderson ML, Buist DS: Predictors of breast density change after hormone therapy cessation: results from a randomized trial. Cancer Epidemiol Biomarkers Prev. 2011, 20: 2309-2312. 10.1158/1055-9965.EPI-11-0629.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DS, Vacek PM, Smith-Bindman R, Yankaskas B, Carney PA, Ballard-Barbash R: Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst. 2007, 99: 386-395. 10.1093/jnci/djk066.View ArticlePubMedGoogle Scholar
  20. Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, Forbes JF, Warren RM: Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst. 2011, 103: 744-752. 10.1093/jnci/djr079.View ArticlePubMedGoogle Scholar
  21. Lindstrom S, Vachon CM, Li J, Varghese J, Thompson D, Warren R, Brown J, Leyland J, Audley T, Wareham NJ, Loos RJ, Paterson AD, Rommens J, Waggott D, Martin LJ, Scott CG, Pankratz VS, Hankinson SE, Hazra A, Hunter DJ, Hopper JL, Southey MC, Chanock SJ, Silva Idos S, Liu J, Eriksson L, Couch FJ, Stone J, Apicella C, Czene K, et al: Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet. 2011, 43: 185-187. 10.1038/ng.760.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Vachon CM, Scott CG, Fasching PA, Hall P, Tamimi RM, Li J, Stone J, Apicella C, Odefrey F, Gierach GL, Jud SM, Heusinger K, Beckmann MW, Pollan M, Fernandez-Navarro P, Gonzalez-Neira A, Benitez J, van Gils CH, Lokate M, Onland-Moret NC, Peeters PH, Brown J, Leyland J, Varghese JS, Easton DF, Thompson DJ, Luben RN, Warren RM, Wareham NJ, Loos RJ, et al: Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2012, 21: 1156-1166. 10.1158/1055-9965.EPI-12-0066.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Lord SJ, Mack WJ, Van Den Berg D, Pike MC, Ingles SA, Haiman CA, Wang W, Parisky YR, Hodis HN, Ursin G: Polymorphisms in genes involved in estrogen and progesterone metabolism and mammographic density changes in women randomized to postmenopausal hormone therapy: results from a pilot study. Breast Cancer Res. 2005, 7: R336-R344. 10.1186/bcr999.View ArticlePubMedPubMed CentralGoogle Scholar
  24. van Duijnhoven FJ, Peeters PH, Warren RM, Bingham SA, Uitterlinden AG, van Noord PA, Monninkhof EM, Grobbee DE, van Gils CH: Influence of estrogen receptor alpha and progesterone receptor polymorphisms on the effects of hormone therapy on mammographic density. Cancer Epidemiol Biomarkers Prev. 2006, 15: 462-467. 10.1158/1055-9965.EPI-05-0754.View ArticlePubMedGoogle Scholar
  25. Bernstein L, Allen M, Anton-Culver H, Deapen D, Horn-Ross PL, Peel D, Pinder R, Reynolds P, Sullivan-Halley J, West D, Wright W, Ziogas A, Ross RK: High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States). Canc Causes Contr. 2002, 13: 625-635. 10.1023/A:1019552126105.View ArticleGoogle Scholar
  26. Marshall SF, Clarke CA, Deapen D, Henderson K, Largent J, Neuhausen SL, Reynolds P, Ursin G, Horn-Ross PL, Stram DO, Templeman C, Bernstein L: Recent breast cancer incidence trends according to hormone therapy use: the California Teachers Study cohort. Breast Cancer Res. 2010, 12: R4-10.1186/bcr2467.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Rannevik G, Jeppsson S, Johnell O, Bjerre B, Laurell-Borulf Y, Svanberg L: A longitudinal study of the perimenopausal transition: altered profiles of steroid and pituitary hormones, SHBG and bone mineral density. Maturitas. 1995, 21: 103-113. 10.1016/0378-5122(94)00869-9.View ArticlePubMedGoogle Scholar
  28. Longcope C, Franz C, Morello C, Baker R, Johnston CC: Steroid and gonadotropin levels in women during the peri-menopausal years. Maturitas. 1986, 8: 189-196. 10.1016/0378-5122(86)90025-3.View ArticlePubMedGoogle Scholar
  29. Lee E, Schumacher F, Lewinger JP, Neuhausen SL, Anton-Culver H, Horn-Ross PL, Henderson KD, Ziogas A, Van Den Berg D, Bernstein L, Ursin G: The association of polymorphisms in hormone metabolism pathway genes, menopausal hormone therapy, and breast cancer risk: a nested case–control study in the California Teachers Study cohort. Breast Cancer Res. 2011, 13: R37-10.1186/bcr2859.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Edlund CK, Lee WH, Li D, Van Den Berg DJ, Conti DV: Snagger: a user-friendly program for incorporating additional information for tagSNP selection. BMC Bioinformatics. 2008, 9: 174-10.1186/1471-2105-9-174.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Canzian F, Cox DG, Setiawan VW, Stram DO, Ziegler RG, Dossus L, Beckmann L, Blanche H, Barricarte A, Berg CD, Bingham S, Buring J, Buys SS, Calle EE, Chanock SJ, Clavel-Chapelon F, Delancey JO, Diver WR, Dorronsoro M, Haiman CA, Hallmans G, Hankinson SE, Hunter DJ, Husing A, Isaacs C, Khaw KT, Kolonel LN, Kraft P, Le Marchand L, Lund E, et al: Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium. Hum Mol Genet. 2010, 19: 3873-3884. 10.1093/hmg/ddq291.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Stram DO, Haiman CA, Hirschhorn JN, Altshuler D, Kolonel LN, Henderson BE, Pike MC: Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study. Hum Hered. 2003, 55: 27-36. 10.1159/000071807.View ArticlePubMedGoogle Scholar
  33. Conneely KN, Boehnke M: So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. Am J Hum Genet. 2007, 81: 1158-1168. 10.1086/522036.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Yu K, Li Q, Bergen AW, Pfeiffer RM, Rosenberg PS, Caporaso N, Kraft P, Chatterjee N: Pathway analysis by adaptive combination of P-values. Genet Epidemiol. 2009, 33: 700-709. 10.1002/gepi.20422.View ArticlePubMedPubMed CentralGoogle Scholar
  35. The 1000 Genomes Project Consortium: A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-1073. 10.1038/nature09534.View ArticlePubMed CentralGoogle Scholar
  36. Howie B, Marchini J, Stephens M: Genotype imputation with thousands of genomes. G3 (Bethesda, Md). 2011, 1: 457-470. 10.1534/g3.111.001198.View ArticleGoogle Scholar
  37. Delaneau O, Marchini J, the 1000 Genomes Project Consortium: Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nat Comm. 2014, 5: 3934-10.1038/ncomms4934. doi:10.1038/ncomms4934View ArticleGoogle Scholar
  38. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR: Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012, 44: 955-959. 10.1038/ng.2354.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Russo J, Moral R, Balogh GA, Mailo D, Russo IH: The protective role of pregnancy in breast cancer. Breast Cancer Res. 2005, 7: 131-142. 10.1186/bcr1029.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Vachon CM, Kuni CC, Anderson K, Anderson VE, Sellers TA: Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (United States). Canc Causes Contr. 2000, 11: 653-662. 10.1023/A:1008926607428.View ArticleGoogle Scholar
  41. Stone J, Warren RM, Pinney E, Warwick J, Cuzick J: Determinants of percentage and area measures of mammographic density. Am J Epidemiol. 2009, 170: 1571-1578. 10.1093/aje/kwp313.View ArticlePubMedGoogle Scholar
  42. Woolcott CG, Koga K, Conroy SM, Byrne C, Nagata C, Ursin G, Vachon CM, Yaffe MJ, Pagano I, Maskarinec G: Mammographic density, parity and age at first birth, and risk of breast cancer: an analysis of four case–control studies. Breast Cancer Res Treat. 2012, 132: 1163-1171. 10.1007/s10549-011-1929-9.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Kelemen LE, Pankratz VS, Sellers TA, Brandt KR, Wang A, Janney C, Fredericksen ZS, Cerhan JR, Vachon CM: Age-specific trends in mammographic density: the Minnesota Breast Cancer Family Study. Am J Epidemiol. 2008, 167: 1027-1036. 10.1093/aje/kwn063.View ArticlePubMedGoogle Scholar
  44. Li J, Czene K, Brauch H, Schroth W, Saladores P, Li Y, Humphreys K, Hall P: Association of CYP2D6 metabolizer status with mammographic density change in response to tamoxifen treatment. Breast Cancer Res. 2013, 15: R93-10.1186/bcr3495.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Kalliokoski A, Niemi M: Impact of OATP transporters on pharmacokinetics. Br J Pharmacol. 2009, 158: 693-705. 10.1111/j.1476-5381.2009.00430.x.View ArticlePubMedPubMed CentralGoogle Scholar
  46. van der Deure WM, Friesema EC, de Jong FJ, de Rijke YB, de Jong FH, Uitterlinden AG, Breteler MM, Peeters RP, Visser TJ: Organic anion transporter 1B1: an important factor in hepatic thyroid hormone and estrogen transport and metabolism. Endocrinology. 2008, 149: 4695-4701. 10.1210/en.2008-0169.View ArticlePubMedGoogle Scholar
  47. Tamai I, Nozawa T, Koshida M, Nezu J, Sai Y, Tsuji A: Functional characterization of human organic anion transporting polypeptide B (OATP-B) in comparison with liver-specific OATP-C. Pharm Res. 2001, 18: 1262-1269. 10.1023/A:1013077609227.View ArticlePubMedGoogle Scholar
  48. Speroff L, Fritz MA: Clinical gynecologic endocrinology and infertility. 2005, Lippincott Williams & Wilkins, Philadelphia, 7Google Scholar
  49. Tirona RG, Leake BF, Merino G, Kim RB: Polymorphisms in OATP-C: identification of multiple allelic variants associated with altered transport activity among European- and African-Americans. J Biol Chem. 2001, 276: 35669-35675. 10.1074/jbc.M103792200.View ArticlePubMedGoogle Scholar
  50. Nozawa T, Nakajima M, Tamai I, Noda K, Nezu J, Sai Y, Tsuji A, Yokoi T: Genetic polymorphisms of human organic anion transporters OATP-C (SLC21A6) and OATP-B (SLC21A9): allele frequencies in the Japanese population and functional analysis. J Pharmacol Exp Ther. 2002, 302: 804-813. 10.1124/jpet.302.2.804.View ArticlePubMedGoogle Scholar
  51. International Hapmap Project: The International HapMap Project. Nature. 2003, 426: 789-796. 10.1038/nature02168.View ArticleGoogle Scholar
  52. Purohit A, Foster PA: Steroid sulfatase inhibitors for estrogen- and androgen-dependent cancers. J Endocrinol. 2011, 212: 99-110. 10.1530/JOE-11-0266.View ArticlePubMedGoogle Scholar
  53. Collaborative Group on Hormonal Factors in Breast Cancer: Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002, 360: 187-195. 10.1016/S0140-6736(02)09454-0.View ArticleGoogle Scholar
  54. Hersh AL, Stefanick ML, Stafford RS: National use of postmenopausal hormone therapy: annual trends and response to recent evidence. JAMA. 2004, 291: 47-53. 10.1001/jama.291.1.47.View ArticlePubMedGoogle Scholar
  55. Kerlikowske K, Miglioretti DL, Buist DS, Walker R, Carney PA: Declines in invasive breast cancer and use of postmenopausal hormone therapy in a screening mammography population. J Natl Cancer Inst. 2007, 99: 1335-1339. 10.1093/jnci/djm111.View ArticlePubMedGoogle Scholar
  56. Chlebowski RT, Kuller LH, Prentice RL, Stefanick ML, Manson JE, Gass M, Aragaki AK, Ockene JK, Lane DS, Sarto GE, Rajkovic A, Schenken R, Hendrix SL, Ravdin PM, Rohan TE, Yasmeen S, Anderson G: Breast cancer after use of estrogen plus progestin in postmenopausal women. N Engl J Med. 2009, 360: 573-587. 10.1056/NEJMoa0807684.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Committee on Practice Bulletins-Gynecology: ACOG Practice Bulletin No. 141: management of menopausal symptoms. Obstet Gynecol. 2014, 123: 202-216. 10.1097/01.AOG.0000441353.20693.78.View ArticleGoogle Scholar

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