The presence of the PvuII or the XbaI polymorphism in the estrogen receptor α gene (ESR1, 6q25) has been related to breast cancer risk; however, results are not fully consistent. To further elucidate this relation, we examined these polymorphisms in relation with mammographic density, a measure of dense tissue in the breast, which is strongly associated with breast cancer risk. For this study, 620 participants aged 49 to 68 years were selected from the Prospect-European Prospective Investigation into Cancer and Nutrition cohort. Blood samples, lifestyle– and medical questionnaire data and mammograms were available for these women. Genotyping was done using the TaqMan PCR assay and mammographic density was assessed using a computer-assisted method. Means of mammographic density were compared by ESR1 genotypes and haplotypes. The percentage density was higher in women with one or two copies of the PvuII p allele (means for Pp and pp are 37% and 36%, respectively) than in those with the PP genotype (32%, Ptrend = 0.09). Women with one or two copies of the XbaI x allele had higher mean percentage density (Xx and xx, 36% and 37%, respectively) than those with the XX genotype (31%, Ptrend < 0.01). Haplotype 1 (px) was associated with increased density, whereas haplotype 2 (PX) was associated with decreased density, both suggesting an allele-dose effect (Ptrend = 0.08 and <0.01, respectively). Similar associations were found with absolute density (Ptrend < 0.01). The findings of this study support the view that ESR1 polymorphisms may affect breast cancer risk through differences in breast density.

The appearance of the female breast on a radiographic image varies between individuals due to differences in tissue composition. On a mammogram, fat is translucent (dark) and connective and epithelial tissues are dense (light). The proportion of dense tissue in the breast is called mammographic density and high-density patterns are strongly associated with breast cancer risk (1, 2). In a study on quantitative classification of breast density and breast cancer risk, the increment in risk of breast cancer for each percentage increase in density was 2% (1).

Age, weight, menopausal status, and parity are important determinants of mammographic density but account for only a part of density variations between women (3). To explore the proportion of variation that can be explained by genetic factors, family studies were undertaken that showed a correlation between mammographic features of sisters (4). A recent study has found a strong correlation between monozygotic twin sisters and states that heritability accounts for about two thirds of variation in density (5).

In view of the important role of reproductive factors in determining mammographic density, plausible candidate genes would be those that regulate hormone synthesis, metabolism, and action of hormones, which have been investigated in a few previous studies (6-10). Another candidate gene for mammographic density, which has not been studied yet, is the gene coding for the estrogen receptor α (ESR1). ESR1 is a nuclear receptor that influences DNA transcription upon binding estrogens or other ligands (11). Estrogen receptor–estrogen interaction thus leads to stimulation of cell growth in various tissues, including breast epithelial tissue (12).

In the ESR1 gene, several DNA sequence variations have been described that are of increasing interest because of their potential association with breast cancer and other hormone-related diseases. Most frequently studied are the single nucleotide polymorphisms PvuII (also known as c.454-397T→C, IVS1-397 T/C, or rs2234693; where the T and C allele are often reported as the p and P allele, respectively) and XbaI (also known as c.454-351A→G, IVS1-351 A/G or rs9340799; where the A and G allele are often reported as the x and X allele, respectively), both located in intron 1 of the ESR1 gene (13, 14). The space between these polymorphisms is only 45 bp and, therefore, PvuII and XbaI are in strong linkage disequilibrium (15, 16).

We aimed to examine the relation between the PvuII and XbaI polymorphisms and breast cancer risk by linking these ESR1 polymorphisms to the intermediate phenotype mammographic density. The power to detect an association is greater for this quantitative trait (17). Moreover, the advantage is that the number of genetic and environmental factors influencing this intermediate phenotype is presumably smaller than the number of factors affecting the clinical end point breast cancer, which will make genetic factors easier to identify (18).

Study Population

Between 1993 and 1997, all women living in Utrecht and surroundings who were taking part in the regional population-based program of breast cancer screening were invited to participate in the Prospect-EPIC study (19), a Dutch cohort participating in the European Prospective Investigation into Cancer and Nutrition (20). Participants filled out lifestyle and food frequency questionnaires (21, 22) and donated a blood sample, which was fractionated into serum, citrate plasma, RBC, and WBC, and stored at −196°C. At the end of the inclusion period, 17,357 Caucasian women were included in the study.

For the present analysis, 620 women were randomly selected from women who had never used postmenopausal hormone therapy and were not using oral contraceptives at study intake in the Prospect cohort. These women were 49 to 68 years old at mammography.

Genotyping Analysis

Genomic DNA was extracted from WBC using the QIAamp DNA Blood Mini kit (Qiagen) according to the instructions of the manufacturer. DNA yields were quantified using a fluorescent stain (PicoGreen, Molecular Probes, Inc., Eugene, OR). Mean DNA concentration was 77 ng/μL; samples were diluted to a final DNA concentration of 5 ng/μL.

Genotyping was done using the TaqMan PCR assay (23) with fluorescent minor groove binding probes (24). The PCR reaction was done in 384-well plates, each well containing 5.0 ng DNA, 2.5 μL TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City, CA), 0.0625 μL of probe and primer solution (Assays-on-Demand, Applied Biosystems), and 2.4375 μL distilled water. PCR was initiated at 95°C for 10 minutes, followed by 40 cycles of 92°C for 15 seconds and 60°C for 60 seconds. After PCR, fluorescence was measured in an ABI 7900 HT Sequence Detector (Applied Biosystems). Samples were classified as PP, Pp, or pp (representing the CC, CT, and TT genotypes of PvuII, respectively) and as XX, Xx, or xx (representing the GG, GA, and AA genotypes of XbaI, respectively). For 22 samples with initial missing genotypes, the assay was repeated. For the PvuII genotype, 2 of 620 samples were undetermined after repeating the PCR. All participants could be genotyped for the XbaI polymorphism. The genotype data for each of the two polymorphisms were taken to infer the haplotypes of the PvuII and XbaI polymorphisms by using the PHASE software, which implements a Bayesian statistical method for reconstructing haplotypes from population genotype data (25). Subsequently, haplotypes were coded as haplotype numbers 1 through 4 in order of decreasing frequency in the population (1 = px, 2 = PX, 3 = Px, and 4 = pX) and subjects were classified as having 0, 1, or 2 copies of these haplotypes.

Mammographic Density Analysis

Mammographic density was assessed using the mediolateral oblique mammogram, which is the routine view for breast cancer screening in the Netherlands. In the past, it has been observed that the proportions of mammographic density on craniocaudal views and mediolateral oblique views and on left and right views show very strong correlation and that representative information on mammographic density is provided in a single view (26). For each study subject, the mammogram taken closest to the date of recruitment was collected and mammographic density was assessed on the left view for all women.

After digitizing the films using a laser film scanner (Lumiscan 50, Lumisys/Eastman Kodak Co., Rochester, NY), mammographic density was quantified using a computer-assisted method based on gray levels in the digitized mammogram (27). For each image, the reader first sets a threshold to determine the outside edge of the breast to discriminate between the dark area outside the breast and the lighter area within the breast. Another threshold is set to determine the area of dense tissue within the breast, which is the lightest tissue visible on the mammogram. The computer then determines the amount of pixels within the total breast area and within the dense area and calculates the percentage of dense tissue in the breast, which is the dense area divided by the total breast area multiplied by 100. In literature, the percentage of dense tissue, which is a relative measure of dense tissue, is mostly used. It may, however, be more relevant to study the absolute amount of dense tissue, which consists of connective and epithelial tissue and is regarded as the target tissue for breast cancer. We, therefore, present results on both relative and absolute measures of breast density. The absolute measure of dense tissue was calculated by multiplying the amount of pixels within the dense area with the area of one pixel, which was 0.0256 mm2.

All films were read by one observer (F.J.B. van Duijnhoven) in sets of 70 images composed of randomly ordered films. To assess the reliability of the computer-assisted method, a library set of 70 images was made, which consisted of randomly chosen films that were not included in our study. This library set was read before the first set, after the last set, and at three time points between sets, which were blinded for the reader. The images in the library set were randomly ordered every time they were read to prevent the observer from recognizing this set. This computer-assisted method to determine mammographic density has proved to be very reliable (27) and, in this study, an average intraclass correlation coefficient of 0.87 (range 0.82-0.90) for dense area and 0.93 (range 0.91-0.95) for percentage density was reached between repeated readings.

Data Analysis

Means with SDs or frequencies (where appropriate) of breast cancer risk factors were calculated for the different genotypes of PvuII and XbaI. Differences were tested by ANOVA or χ2 analysis. Menopausal status at the time of the mammogram was divided in premenopausal or postmenopausal status, where postmenopausal was defined as at least 12 consecutive months of amenorrhea. Family history of breast cancer was defined as having at least a mother or a sister diagnosed with breast cancer. Current alcohol intake was defined as grams of ethanol per day and was categorized in tertiles.

Analysis of percentage mammographic density by breast cancer risk factors was done by ANOVA.

Deviations from Hardy-Weinberg equilibrium were assessed using a goodness-of-fit χ2 test with 1 degree of freedom. The observed number of women for all possible combinations of both PvuII and XbaI genotypes was compared with the expected number of women with these combinations of genotypes, which was based on the independent frequencies of the PvuII and XbaI genotypes. Differences were tested by χ2 analysis. The allele and haplotype frequencies were used to calculate the linkage disequilibrium coefficient (D′; ref. 28). When D′ is 1, the two alleles are completely linked and when D′ is −1, the two alleles exclude each other completely.

Mean percentage mammographic density was compared between women with different genotypes and haplotypes by linear regression analysis and P values for linear trend were calculated. Covariates included in the adjusted model were age (continuous), body mass index (BMI; continuous), age at menarche (continuous), parity/age at first full-term pregnancy (three groups: nulliparous, <26 and ≥26 years), menopausal status/age at menopause (three groups: premenopausal, <49 and ≥49 years), family history of breast cancer (no/yes), previous oral contraceptive use (no/yes), smoking (three groups: current, former, and never), and alcohol consumption (tertiles). Besides the proportion of mammographically dense tissue, absolute amounts of dense tissue were assessed as well and compared between different genotype and haplotype groups. The distributions of the absolute amount of dense tissue were skewed and were, therefore, LN-transformed to approach normality. After back-transformation, geometric means were presented. The covariates in this adjusted model were the same as those in the adjusted model for percentage density.

All analyses were done with SPSS version 11.

In Table 1, breast cancer risk factors are listed according to genotypes of PvuII and XbaI. Mean BMI seemed to be slightly higher for women with the PP or XX genotype compared with the other genotypes. The mean number of children and mean age at menarche was slightly higher for women with the pp or xx genotype compared with the other genotypes. Previous use of oral contraceptives and having a family history of breast cancer were more frequent and consumption of alcohol was less frequent in women with the PP or XX genotypes compared with Pp and pp or Xx and xx genotypes.

Table 1.

Breast cancer risk factors according to genotype

Breast cancer risk factorsPvuII
XbaI
PPPpppXXXxxx
n (%) 138 (22) 314 (51) 166 (27)  76 (12) 289 (47) 255 (41)  
         
 Mean (SD) Mean (SD) Mean (SD) PMean (SD) Mean (SD) Mean (SD) P
Age at mammogram (y) 54.0 (3.8) 54.0 (3.9) 54.1 (3.8) 0.97 54.0 (3.6) 54.0 (3.9) 54.1 (3.9) 0.95 
Age at menopause (y; n = 434) 47.2 (5.1) 47.7 (5.5) 47.4 (5.6) 0.69 47.5 (5.2) 47.5 (5.4) 47.6 (5.5) 0.98 
BMI (kg/m225.9 (3.8) 25.5 (3.7) 25.5 (3.4) 0.43 26.4 (3.7) 25.5 (3.6) 25.4 (3.6) 0.09 
No. children 2.3 (1.2) 2.4 (1.1) 2.7 (1.3) <0.001 2.3 (1.4) 2.4 (1.1) 2.6 (1.3) 0.07 
Age at first delivery (y; n = 569)§ 25.2 (3.6) 25.1 (3.3) 24.8 (3.6) 0.61 25.0 (4.0) 25.1 (3.2) 24.9 (3.5) 0.82 
Age at menarche (y) 13.2 (1.6) 13.4 (1.5) 13.4 (1.5) 0.65 13.1 (1.5) 13.3 (1.5) 13.4 (1.4) 0.30 
         
 n (%) n (%) n (%) P n (%) n (%) n (%) P 
Menopausal status         
    Premenopausal 30 (22) 103 (33) 47 (29)  15 (20) 95 (33) 71 (28)  
    Postmenopausal 108 (78) 210 (67) 115 (71) 0.06 61 (80) 193 (67) 180 (72) 0.07 
Previous oral contraceptive use         
    Yes 100 (72) 222 (71) 116 (70)  56 (74) 208 (72) 176 (69)  
    No 38 (28) 92 (29) 50 (30) 0.88 20 (26) 81 (28) 79 (31) 0.64 
Family history of breast cancer         
    Yes 23 (17) 41 (13) 15 (9)  13 (18) 39 (14) 28 (11)  
    No 112 (83) 266 (87) 148 (91) 0.13 60 (82) 245 (86) 222 (89) 0.32 
Alcohol consumption (g/d)         
    <1.15 48 (35) 91 (29) 41 (25)  29 (38) 87 (30) 65 (26)  
    1.15-9.16 43 (31) 107 (34) 60 (37)  24 (32) 96 (33) 90 (36)  
    >9.16 47 (34) 116 (37) 63 (38) 0.48 23 (30) 106 (37) 98 (39) 0.32 
Smoking         
    Current 22 (16) 48 (15) 37 (22)  11 (14) 48 (17) 48 (19)  
    Former 55 (40) 111 (35) 52 (31)  26 (34) 109 (38) 84 (33)  
    Never 61 (44) 155 (49) 77 (46) 0.25 39 (51) 132 (46) 123 (48) 0.71 
Breast cancer risk factorsPvuII
XbaI
PPPpppXXXxxx
n (%) 138 (22) 314 (51) 166 (27)  76 (12) 289 (47) 255 (41)  
         
 Mean (SD) Mean (SD) Mean (SD) PMean (SD) Mean (SD) Mean (SD) P
Age at mammogram (y) 54.0 (3.8) 54.0 (3.9) 54.1 (3.8) 0.97 54.0 (3.6) 54.0 (3.9) 54.1 (3.9) 0.95 
Age at menopause (y; n = 434) 47.2 (5.1) 47.7 (5.5) 47.4 (5.6) 0.69 47.5 (5.2) 47.5 (5.4) 47.6 (5.5) 0.98 
BMI (kg/m225.9 (3.8) 25.5 (3.7) 25.5 (3.4) 0.43 26.4 (3.7) 25.5 (3.6) 25.4 (3.6) 0.09 
No. children 2.3 (1.2) 2.4 (1.1) 2.7 (1.3) <0.001 2.3 (1.4) 2.4 (1.1) 2.6 (1.3) 0.07 
Age at first delivery (y; n = 569)§ 25.2 (3.6) 25.1 (3.3) 24.8 (3.6) 0.61 25.0 (4.0) 25.1 (3.2) 24.9 (3.5) 0.82 
Age at menarche (y) 13.2 (1.6) 13.4 (1.5) 13.4 (1.5) 0.65 13.1 (1.5) 13.3 (1.5) 13.4 (1.4) 0.30 
         
 n (%) n (%) n (%) P n (%) n (%) n (%) P 
Menopausal status         
    Premenopausal 30 (22) 103 (33) 47 (29)  15 (20) 95 (33) 71 (28)  
    Postmenopausal 108 (78) 210 (67) 115 (71) 0.06 61 (80) 193 (67) 180 (72) 0.07 
Previous oral contraceptive use         
    Yes 100 (72) 222 (71) 116 (70)  56 (74) 208 (72) 176 (69)  
    No 38 (28) 92 (29) 50 (30) 0.88 20 (26) 81 (28) 79 (31) 0.64 
Family history of breast cancer         
    Yes 23 (17) 41 (13) 15 (9)  13 (18) 39 (14) 28 (11)  
    No 112 (83) 266 (87) 148 (91) 0.13 60 (82) 245 (86) 222 (89) 0.32 
Alcohol consumption (g/d)         
    <1.15 48 (35) 91 (29) 41 (25)  29 (38) 87 (30) 65 (26)  
    1.15-9.16 43 (31) 107 (34) 60 (37)  24 (32) 96 (33) 90 (36)  
    >9.16 47 (34) 116 (37) 63 (38) 0.48 23 (30) 106 (37) 98 (39) 0.32 
Smoking         
    Current 22 (16) 48 (15) 37 (22)  11 (14) 48 (17) 48 (19)  
    Former 55 (40) 111 (35) 52 (31)  26 (34) 109 (38) 84 (33)  
    Never 61 (44) 155 (49) 77 (46) 0.25 39 (51) 132 (46) 123 (48) 0.71 
*

ANOVA.

Among postmenopausal women.

Among all women.

§

Among parous women.

χ2 test.

Mean percentage density was assessed according to several breast cancer risk factors and is shown in Table 2. Mean mammographic density was highest (42.2%) in women aged 50 years or younger and declined from 37.0% in the 51 to 53 age group to 33.2% in the 54 to 56 age group and 30.7% in women aged 57 years or older (P < 0.01). Women who were premenopausal had a higher mammographic density (41.5%) than women who were postmenopausal (33.2%; P < 0.01). Mean percentage density was 43.8% for women in the lowest BMI category and 27.7% for women in the highest BMI category (P < 0.01), which is probably due to a greater amount of fat in the breast that results in a lower percentage of dense tissue. Parity also had a clear effect on mammographic density because women who had children had a lower percentage of dense breast tissue (34.9%) than women who never had children (44.1%; P < 0.01).

Table 2.

Percentage mammographic density by breast cancer risk factors

Breast cancer risk factorsnMean percentage density (95% CI)P*
Age at mammogram (y)    
    ≤50 135 42.2 (39.5-44.9)  
    51-53 167 37.0 (34.4-39.6)  
    54-56 173 33.2 (30.9-35.6)  
    ≥57 143 30.7 (28.5-32.8) <0.01 
Menopausal status/age at menopause (y)    
    Premenopausal 181 41.5 (39.2-43.9)  
    <49 201 33.3 (31.0-35.6)  
    ≥49 231 33.1 (31.2-35.0) <0.01 
Previous oral contraceptive use    
    Yes 440 35.8 (34.4-37.3)  
    No 178 35.1 (32.6-37.6) 0.59 
BMI (kg/m2   
    ≤23 179 43.8 (41.5-46.2)  
    24 98 38.5 (35.3-41.7)  
    25-27 185 32.6 (30.6-34.7)  
    ≥28 154 27.7 (25.5-29.8) <0.01 
Parity/age at first full-term pregnancy    
    Nulliparous 50 44.1 (38.9-49.2)  
    <26 336 34.8 (33.1-36.6)  
    ≥26 231 35.0 (33.0-36.9) <0.01 
Age at menarche (y)    
    ≤12 180 36.3 (33.8-38.9)  
    13 175 34.5 (32.4-36.7)  
    ≥14 253 35.8 (33.7-37.8) 0.55 
Family history of breast cancer    
    Yes 79 35.8 (32.6-39.0)  
    No 526 35.4 (34.0-36.8) 0.83 
Alcohol consumption (g/d)    
    <1.15 181 33.8 (31.4-36.1)  
    1.15-9.16 210 36.0 (33.9-38.2)  
    >9.16 225 36.8 (34.7-38.8) 0.16 
Smoking    
    Current 106 35.0 (32.0-38.0)  
    Former 218 36.3 (34.0-38.6)  
    Never 294 35.3 (33.6-37.1) 0.73 
Breast cancer risk factorsnMean percentage density (95% CI)P*
Age at mammogram (y)    
    ≤50 135 42.2 (39.5-44.9)  
    51-53 167 37.0 (34.4-39.6)  
    54-56 173 33.2 (30.9-35.6)  
    ≥57 143 30.7 (28.5-32.8) <0.01 
Menopausal status/age at menopause (y)    
    Premenopausal 181 41.5 (39.2-43.9)  
    <49 201 33.3 (31.0-35.6)  
    ≥49 231 33.1 (31.2-35.0) <0.01 
Previous oral contraceptive use    
    Yes 440 35.8 (34.4-37.3)  
    No 178 35.1 (32.6-37.6) 0.59 
BMI (kg/m2   
    ≤23 179 43.8 (41.5-46.2)  
    24 98 38.5 (35.3-41.7)  
    25-27 185 32.6 (30.6-34.7)  
    ≥28 154 27.7 (25.5-29.8) <0.01 
Parity/age at first full-term pregnancy    
    Nulliparous 50 44.1 (38.9-49.2)  
    <26 336 34.8 (33.1-36.6)  
    ≥26 231 35.0 (33.0-36.9) <0.01 
Age at menarche (y)    
    ≤12 180 36.3 (33.8-38.9)  
    13 175 34.5 (32.4-36.7)  
    ≥14 253 35.8 (33.7-37.8) 0.55 
Family history of breast cancer    
    Yes 79 35.8 (32.6-39.0)  
    No 526 35.4 (34.0-36.8) 0.83 
Alcohol consumption (g/d)    
    <1.15 181 33.8 (31.4-36.1)  
    1.15-9.16 210 36.0 (33.9-38.2)  
    >9.16 225 36.8 (34.7-38.8) 0.16 
Smoking    
    Current 106 35.0 (32.0-38.0)  
    Former 218 36.3 (34.0-38.6)  
    Never 294 35.3 (33.6-37.1) 0.73 
*

ANOVA.

Genotype distributions of PvuII and XbaI were similar to other Caucasian populations (15, 29) and were in Hardy-Weinberg equilibrium (P values were 0.65 and 0.67, respectively). In Table 3, the combinations of PvuII and XbaI genotypes are listed. Combinations PP/XX and pp/xx were frequent, and χ2 analysis showed that the observed numbers of the combined genotypes were not equal to the expected numbers as based on their independent frequencies (P < 0.01). The linkage disequilibrium analysis showed that D′ was 0.996 between the p allele and the x allele, which shows that the polymorphisms are in strong linkage disequilibrium. After separate genotyping of PvuII and XbaI, haplotypes were reconstructed. The frequencies were 52.2% for haplotype 1 (px), 35.4% for haplotype 2 (PX), 12.3% for haplotype 3 (Px), and 0.1% for haplotype 4 (pX), which are similar to haplotype frequencies in other Caucasian populations (15, 30).

Table 3.

Distribution of women according to PvuII and XbaI genotypes

XbaI
XXXxxx
PvuII    
    PP 74 (17) 53 (65) 11 (57) 
    Pp 1 (38) 236 (147) 77 (129) 
    pp 0 (20) 0 (78) 166 (68) 
XbaI
XXXxxx
PvuII    
    PP 74 (17) 53 (65) 11 (57) 
    Pp 1 (38) 236 (147) 77 (129) 
    pp 0 (20) 0 (78) 166 (68) 

NOTE: Results are expressed as no. observed (no. expected). P < 0.01, χ2 analysis.

In Table 4, mammographic densities are given according to PvuII and XbaI genotypes and to the number of copies of haplotypes 1 and 2. The unadjusted mean mammographic density was higher in women with the Pp or pp genotype (36.8% and 35.8%, respectively) than in those with the PP genotype (32.4%, Ptrend = 0.09). Similarly, the unadjusted mean mammographic density was higher in women with the Xx or xx genotype (35.6% and 37.0%, respectively) than those with the XX genotype (31.0%, Ptrend < 0.01). These results suggested that the p and x alleles were associated with higher breast densities. Therefore, haplotype 1 was taken as a “risk allele” for higher mammographic density in further analysis and the allele-dose effect was assessed by grouping individuals by the number of copies of haplotype 1. A similar strategy was taken for the analysis of haplotype 2 because opposite trends were expected. Mean mammographic density increased from 32.3% for no copies of haplotype 1 to 36.8% and 35.8% for one and two copies of haplotype 1, respectively (Ptrend = 0.08). On the contrary, mean mammographic density decreased from 37.0% for no copies of haplotype 2 to 35.6% and 30.5% for one and two copies of haplotype 2, respectively (Ptrend < 0.01). The adjusted values for mean percentage density were comparable with the unadjusted values.

Table 4.

Percentage density according to genotype and haplotype

Unadjusted
Adjusted
nMean percentage density (95% CI)Ptrend*nMean percentage density (95% CI)Ptrend
PvuII       
    PP 137 32.4 (29.7-35.1)  131 33.1 (30.7-35.5)  
    Pp 314 36.8 (35.0-38.5)  297 36.0 (34.4-37.5)  
    pp 165 35.8 (33.4-38.2) 0.09 155 36.2 (34.0-38.4) 0.08 
Xba      
    XX 75 31.0 (27.4-34.6)  71 31.8 (28.6-35.1)  
    Xx 289 35.6 (33.8-37.5)  275 35.2 (33.6-36.8)  
    xx 254 37.0 (35.0-38.9) <0.01 239 36.9 (35.1-38.6) <0.01 
Copies of haplotype 1       
    0 138 32.3 (29.7-35.0)  132 33.2 (30.8-35.6)  
    1 313 36.8 (35.0-38.6)  296 36.0 (34.4-37.5)  
    2 165 35.8 (33.4-38.2) 0.08 155 36.2 (34.0-38.4) 0.08 
Copies of haplotype 2       
    0 253 37.0 (35.0-38.9)  238 36.9 (35.1-38.6)  
    1 290 35.6 (33.7-37.4)  276 35.2 (33.5-36.8)  
    2 73 30.5 (26.8-34.1) <0.01 69 31.2 (27.9-34.5) <0.01 
Unadjusted
Adjusted
nMean percentage density (95% CI)Ptrend*nMean percentage density (95% CI)Ptrend
PvuII       
    PP 137 32.4 (29.7-35.1)  131 33.1 (30.7-35.5)  
    Pp 314 36.8 (35.0-38.5)  297 36.0 (34.4-37.5)  
    pp 165 35.8 (33.4-38.2) 0.09 155 36.2 (34.0-38.4) 0.08 
Xba      
    XX 75 31.0 (27.4-34.6)  71 31.8 (28.6-35.1)  
    Xx 289 35.6 (33.8-37.5)  275 35.2 (33.6-36.8)  
    xx 254 37.0 (35.0-38.9) <0.01 239 36.9 (35.1-38.6) <0.01 
Copies of haplotype 1       
    0 138 32.3 (29.7-35.0)  132 33.2 (30.8-35.6)  
    1 313 36.8 (35.0-38.6)  296 36.0 (34.4-37.5)  
    2 165 35.8 (33.4-38.2) 0.08 155 36.2 (34.0-38.4) 0.08 
Copies of haplotype 2       
    0 253 37.0 (35.0-38.9)  238 36.9 (35.1-38.6)  
    1 290 35.6 (33.7-37.4)  276 35.2 (33.5-36.8)  
    2 73 30.5 (26.8-34.1) <0.01 69 31.2 (27.9-34.5) <0.01 
*

Linear trend, no covariates.

Linear trend, covariates age (continuous), BMI (continuous), age at menarche (continuous), parity/age at first full-term pregnancy (three groups: nulliparous, <26 years, and ≥26 years), menopausal status (three groups: premenopausal, <49 years, and ≥49 years), family history of breast cancer (no/yes), previous oral contraceptive use (no/yes), smoking (three groups: current, former, and never), and alcohol consumption (tertiles).

In Table 5, the absolute amount of dense breast tissue is presented by genotype, represented as the amount of cm2 on the digitized mammogram. Consistent with the results of percentage density, women with the pp or xx genotype, or two copies of haplotype 1, were found to have a larger area and women with two copies of haplotype 2 were found to have a smaller area of dense tissue on their mammogram (Ptrend < 0.01), whereas total breast areas were similar in each genotype group (data not shown). The adjusted values for mean absolute density were comparable with the unadjusted values.

Table 5.

Absolute density according to genotype and haplotype

Unadjusted
Adjusted
nMean* absolute density (95% CI)PtrendnMean* absolute density (95% CI)Ptrend
PvuII       
    PP 137 35.1 (32.3-38.2)  131 35.0 (32.2-38.2)  
    Pp 314 40.7 (38.5-42.9)  297 40.0 (37.8-42.3)  
    pp 165 41.4 (38.4-44.7) <0.01 155 42.1 (38.9-45.5) <0.01 
Xba      
    XX 75 34.3 (30.6-38.5)  71 33.6 (29.9-37.8)  
    Xx 289 39.5 (37.3-41.9)  275 39.1 (36.9-41.5)  
    xx 254 41.4 (38.9-44.0) <0.01 239 41.6 (39.1-44.3) <0.01 
Copies of haplotype 1       
    0 138 35.1 (32.3-38.2)  132 35.1 (32.2-38.2)  
    1 313 40.7 (38.5-43.0)  296 40.0 (37.8-42.3)  
    2 165 41.4 (38.4-44.7) <0.01 155 42.1 (38.9-45.5) <0.01 
Copies of haplotype 2       
    0 253 41.4 (38.9-44.0)  238 41.6 (39.1-44.4)  
    1 290 39.5 (37.3-41.8)  276 39.1 (36.9-41.5)  
    2 73 34.0 (30.3-38.2) <0.01 69 33.3 (29.5-37.5) <0.01 
Unadjusted
Adjusted
nMean* absolute density (95% CI)PtrendnMean* absolute density (95% CI)Ptrend
PvuII       
    PP 137 35.1 (32.3-38.2)  131 35.0 (32.2-38.2)  
    Pp 314 40.7 (38.5-42.9)  297 40.0 (37.8-42.3)  
    pp 165 41.4 (38.4-44.7) <0.01 155 42.1 (38.9-45.5) <0.01 
Xba      
    XX 75 34.3 (30.6-38.5)  71 33.6 (29.9-37.8)  
    Xx 289 39.5 (37.3-41.9)  275 39.1 (36.9-41.5)  
    xx 254 41.4 (38.9-44.0) <0.01 239 41.6 (39.1-44.3) <0.01 
Copies of haplotype 1       
    0 138 35.1 (32.3-38.2)  132 35.1 (32.2-38.2)  
    1 313 40.7 (38.5-43.0)  296 40.0 (37.8-42.3)  
    2 165 41.4 (38.4-44.7) <0.01 155 42.1 (38.9-45.5) <0.01 
Copies of haplotype 2       
    0 253 41.4 (38.9-44.0)  238 41.6 (39.1-44.4)  
    1 290 39.5 (37.3-41.8)  276 39.1 (36.9-41.5)  
    2 73 34.0 (30.3-38.2) <0.01 69 33.3 (29.5-37.5) <0.01 
*

Geometric mean; represented as cm2 on the digitized mammogram.

Linear trend, no covariates.

Linear trend, covariates age (continuous), BMI (continuous), age at menarche (continuous), parity/age at first full-term pregnancy (three groups: nulliparous, <26 years, and ≥26 years), menopausal status (three groups: premenopausal, <49 years, and ≥49 years), family history of breast cancer (no/yes), previous oral contraceptive use (no/yes), smoking (three groups: current, former, and never), and alcohol consumption (tertiles).

In this study, the separate analyses of PvuII and XbaI polymorphisms show that the p and x alleles are associated with higher mammographic densities. The aggregated haplotype 1 shows an allele-dose effect in which percentage density increases with an increasing number of haplotype 1 copies, whereas haplotype 2 shows an opposite trend.

In addition to the association with percentage density, which is a relative measure of dense breast tissue, the PvuII and XbaI polymorphisms and haplotypes 1 and 2 were also, and even more clearly, associated with the absolute amount of dense breast tissue. This may reflect the target tissue for breast cancer better (31, 32).

It is not yet entirely clear whether the studied polymorphisms are functional variations in the ESR1 gene or markers for a functional site elsewhere in the gene. The polymorphisms are located in intron 1 of the gene. Introns do not provide the protein code but can play a role in the production of mRNA. A recent study showed that the P allele produces a potential binding site for transcription factor B-myb, which suggests that presence of this allele might either amplify ESR1 transcription or produce ESR1 isoforms that have different properties than the full-length gene product (33, 34). Another study, however, showed a slightly enhanced transcription activity in ESR1 sequences containing the PvuII and XbaI polymorphisms, where enhanced activity was highest in the fragment containing haplotype 1, followed by haplotypes 3 and 4 (35). Although these two studies contradict each other, they indicate a possible role of intron 1 sequences in the production of ESR1. However, it is possible that another polymorphic site linked to the ones studied here is the true functional sequence variation. The only way to clarify this is to identify all polymorphisms in this region and determine linkage disequilibrium between these polymorphisms to construct haplotype blocks. Once association studies have shown which haplotype carries the risk allele, functional analyses should be done to determine which of the variants in that haplotype truly contributes to the phenotype of interest.

Four studies have been published that investigated the PvuII as well as the XbaI polymorphism in relation to breast cancer risk (13, 36-38). One study reported the p allele of the PvuII polymorphism to be significantly associated with breast cancer risk (36); one showed a nonsignificantly elevated risk for the p allele (38). In two studies, the x allele of the XbaI polymorphism was significantly associated with breast cancer risk (13, 37); one showed a nonsignificantly elevated risk (36). Recently, our group (39) also found an increased breast cancer risk related to the p allele, which was borderline significant, and a nonsignificantly elevated risk for the x allele.

Onland-Moret et al. (39) combined all results from literature for the PvuII polymorphism in relation to breast cancer risk. The pooled effect was estimated by abstracting odds ratios direct from the published articles or by calculating odds ratios from the data presented in the article. The pooled estimate was assessed using the precision weighted procedure described by Greenland (40). The overall odds ratio of the studies combined was 1.14 [95% confidence interval (95% CI), 1.00-1.32] for the Pp genotype and 1.23 (1.08-1.43) for the pp genotype. When we estimated the pooled effect for the XbaI polymorphism in relation with breast cancer from all results in literature using the same method, the overall odds ratio was 1.01 (95% CI, 0.82-1.23) for the Xx genotype and 1.17 (0.96-1.44) for the xx genotype. In the study presented here, the observed variations in dense tissue between different ESR1 genotypes were small, indicating indeed that only very modest variations in breast cancer risk may be expected. The fact that the effects are small and that the populations investigated in previous studies vary in genotype frequencies and other characteristics may explain why some studies did not show a relation between these polymorphisms and breast cancer risk. However, the overall effect of all studies combined strongly suggests that the p allele and possibly also the x allele is involved in breast cancer risk.

The results of this study are consistent with most studies evaluating relations between these polymorphisms and breast cancer risk and indicate that ESR1 has a modest role in the variation of dense breast tissue and, therefore, the risk of breast cancer.

Several other polymorphisms have been investigated as genetic determinants of mammographic density (6-10, 41). The polymorphisms that were found to be associated with mammographic density were located in genes that are involved in hormone synthesis (3HSDB1 gene; ref. 6), hormone metabolism (COMT and UGT1A1 genes; refs. 7, 8), action of hormones (AIB1 gene; ref. 7), or action of growth factors (IGFBP3 gene; ref. 41). By using the intermediate phenotype mammographic density, these studies and ours have been able to identify polymorphisms that most probably play a modest role in breast cancer risk. To use the intermediate phenotype as an end point seems to be a powerful approach to determine genetic factors of a clinical end point (17, 18). Although each polymorphism by itself has only a small to modest effect, they may play an important role when combined because it is very likely that many genes and exposures act together in the development of dense breast tissue and thus breast cancer risk. Therefore, genetic determinants of mammographic density may be useful in building multigenic models for predicting breast cancer risk. In this way, subgroups of women may be identified that are at high risk for breast cancer and could benefit from intensified screening or (chemo)preventive strategies.

Grant support: Dutch Cancer Society grant UU 2002-2716.

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.

We thank Stichting Preventicon for making the mammograms from all participants available; José Drijvers, Joke Metselaar-van den Bos, Bernard Slotboom, Bert Rodenburg, and Jelmer Hoefakker for assisting in the identification and collection of the mammograms and blood samples; Arjan Barendrecht for assisting in the DNA concentration measurements; and Pascal Arp for his assistance in the Taqman PCR assays.

1
Boyd NF, Byng JW, Jong RA, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.
J Natl Cancer Inst
1995
;
87
:
670
–5.
2
Byrne C, Schairer C, Wolfe J, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status.
J Natl Cancer Inst
1995
;
87
:
1622
–9.
3
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).
Cancer Causes Control
2000
;
11
:
653
–62.
4
Pankow JS, Vachon CM, Kuni CC, et al. Genetic analysis of mammographic breast density in adult women: evidence of a gene effect.
J Natl Cancer Inst
1997
;
89
:
549
–56.
5
Boyd NF, Dite GS, Stone J, et al. Heritability of mammographic density, a risk factor for breast cancer.
N Engl J Med
2002
;
347
:
886
–94.
6
Haiman CA, Bernstein L, Berg D, Ingles SA, Salane M, Ursin G. Genetic determinants of mammographic density.
Breast Cancer Res
2002
;
4
:
R5
.
7
Haiman CA, Hankinson SE, De Vivo I, et al. Polymorphisms in steroid hormone pathway genes and mammographic density.
Breast Cancer Res Treat
2003
;
77
:
27
–36.
8
Hong CC, Thompson HJ, Jiang C, et al. Val158Met polymorphism in catechol-O-methyltransferase gene associated with risk factors for breast cancer.
Cancer Epidemiol Biomarkers Prev
2003
;
12
:
838
–47.
9
Hong CC, Thompson HJ, Jiang C, et al. Association between the T27C polymorphism in the cytochrome P450 c17α (CYP17) gene and risk factors for breast cancer.
Breast Cancer Res Treat
2004
;
88
:
217
–30.
10
Lillie EO, Bernstein L, Ingles SA, et al. Polymorphism in the androgen receptor and mammographic density in women taking and not taking estrogen and progestin therapy.
Cancer Res
2004
;
64
:
1237
–41.
11
Sommer S, Fuqua SA. Estrogen receptor and breast cancer.
Semin Cancer Biol
2001
;
11
:
339
–52.
12
Rayter Z. Steroid receptors in breast cancer.
Br J Surg
1991
;
78
:
528
–35.
13
Andersen TI, Heimdal KR, Skrede M, Tveit K, Berg K, Borresen AL. Oestrogen receptor (ESR) polymorphisms and breast cancer susceptibility.
Hum Genet
1994
;
94
:
665
–70.
14
Yaich L, Dupont WD, Cavener DR, Parl FF. Analysis of the PvuII restriction fragment-length polymorphism and exon structure of the estrogen receptor gene in breast cancer and peripheral blood.
Cancer Res
1992
;
52
:
77
–83.
15
Albagha OM, McGuigan FE, Reid DM, Ralston SH. Estrogen receptor α gene polymorphisms and bone mineral density: haplotype analysis in women from the United Kingdom.
J Bone Miner Res
2001
;
16
:
128
–34.
16
Becherini L, Gennari L, Masi L, et al. Evidence of a linkage disequilibrium between polymorphisms in the human estrogen receptor α gene and their relationship to bone mass variation in postmenopausal Italian women.
Hum Mol Genet
2000
;
9
:
2043
–50.
17
Pharoah PD, Dunning AM, Ponder BA, Easton DF. Association studies for finding cancer-susceptibility genetic variants.
Nat Rev Cancer
2004
;
4
:
850
–60.
18
Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies.
Nature
2004
;
429
:
446
–52.
19
Boker LK, van Noord PA, van der Schouw YT, et al. Prospect-EPIC Utrecht: study design and characteristics of the cohort population. European Prospective Investigation into Cancer and Nutrition.
Eur J Epidemiol
2001
;
17
:
1047
–53.
20
Riboli E, Kaaks R. The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition.
Int J Epidemiol
1997
;
26
:
S6
–14.
21
Ocke MC, Bueno-de-Mesquita HB, Goddijn HE, et al. The Dutch EPIC food frequency questionnaire. I. Description of the questionnaire, and relative validity and reproducibility for food groups.
Int J Epidemiol
1997
;
26
:
S37
–48.
22
Ocke MC, Bueno-de-Mesquita HB, Pols MA, Smit HA, Van Staveren WA, Kromhout D. The Dutch EPIC food frequency questionnaire. II. Relative validity and reproducibility for nutrients.
Int J Epidemiol
1997
;
26
:
S49
–58.
23
Livak KJ. Allelic discrimination using fluorogenic probes and the 5′ nuclease assay.
Genet Anal
1999
;
14
:
143
–9.
24
de Kok JB, Wiegerinck ET, Giesendorf BA, Swinkels DW. Rapid genotyping of single nucleotide polymorphisms using novel minor groove binding DNA oligonucleotides (MGB probes).
Hum Mutat
2002
;
19
:
554
–9.
25
Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data.
Am J Hum Genet
2001
;
68
:
978
–89.
26
Byng JW, Boyd NF, Little L, et al. Symmetry of projection in the quantitative analysis of mammographic images.
Eur J Cancer Prev
1996
;
5
:
319
–27.
27
Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities.
Phys Med Biol
1994
;
39
:
1629
–38.
28
Lewontin RC. The interaction of selection and linkage. I General considerations; heterotic models.
Genetics
1964
;
49
:
49
–67.
29
Weel AE, Uitterlinden AG, Westendorp IC, et al. Estrogen receptor polymorphism predicts the onset of natural and surgical menopause.
J Clin Endocrinol Metab
1999
;
84
:
3146
–50.
30
van Meurs JB, Schuit SC, Weel AE, et al. Association of 5′ estrogen receptor α gene polymorphisms with bone mineral density, vertebral bone area and fracture risk.
Hum Mol Genet
2003
;
12
:
1745
–54.
31
Dupont WD, Page DL. Risk factors for breast cancer in women with proliferative breast disease.
N Engl J Med
1985
;
312
:
146
–51.
32
Lochter A, Bissell MJ. Involvement of extracellular matrix constituents in breast cancer.
Semin Cancer Biol
1995
;
6
:
165
–73.
33
Herrington DM, Howard TD, Brosnihan KB, et al. Common estrogen receptor polymorphism augments effects of hormone replacement therapy on E-selectin but not C-reactive protein.
Circulation
2002
;
105
:
1879
–82.
34
Schuit SC, Oei HH, Witteman JC, et al. Estrogen receptor α gene polymorphisms and risk of myocardial infarction.
JAMA
2004
;
291
:
2969
–77.
35
Maruyama H, Toji H, Harrington CR, et al. Lack of an association of estrogen receptor α gene polymorphisms and transcriptional activity with Alzheimer disease.
Arch Neurol
2000
;
57
:
236
–40.
36
Cai Q, Shu XO, Jin F, et al. Genetic polymorphisms in the estrogen receptor α gene and risk of breast cancer: results from the Shanghai Breast Cancer Study.
Cancer Epidemiol Biomarkers Prev
2003
;
12
:
853
–9.
37
Shin A, Kang D, Nishio H, et al. Estrogen receptor α gene polymorphisms and breast cancer risk.
Breast Cancer Res Treat
2003
;
80
:
127
–31.
38
Wedren S, Lovmar L, Humphreys K, et al. Oestrogen receptor α gene haplotype and postmenopausal breast cancer risk: a case control study.
Breast Cancer Res
2004
;
6
:
R437
–49.
39
Onland-Moret NC, van Gils CH, Roest M, Grobbee DE, Peeters PH. The estrogen receptor α gene and breast cancer risk. Cancer Causes Control. In press 2005.
40
Greenland S. Meta-analysis. In: Rothman KJ, Greenland S, editors. Modern epidemiology. Philadelphia: Lippincott-Raven Publishers; 1998. p. 643–73.
41
Lai JH, Vesprini D, Zhang W, Yaffe MJ, Pollak M, Narod SA. A polymorphic locus in the promoter region of the IGFBP3 gene is related to mammographic breast density.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
573
–82.