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Departments of 1 Medicine, 2 Radiology, and 3 Epidemiology and Biostatistics and 4 General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, University of California at San Francisco and 5 California Pacific Medical Center Research Institute, San Francisco, California
Requests for reprints: Elad Ziv, Department of Medicine, University of California at San Francisco, 1635 Divisadero Street, Suite 600, San Francisco, CA 04115. Phone: 415-353-9755; Fax: 415-353-9865. E-mail: eziv{at}itsa.ucsf.edu
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Abstract |
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Methods: We analyzed data from 44,811 participants in the San Francisco Mammography Registry of whom 701 developed invasive breast cancer. Mammographic density was measured using the Breast Imaging Reporting and Data System (BI-RADS) classification system (1 = almost entirely fat, 2 = scattered fibroglandular, 3 = heterogeneously dense, 4 = extremely dense). We tested for associations between mammographic density and ER-positive and ER-negative breast cancer separately. Analyses were adjusted for age, body mass index, postmenopausal hormone use, family history of breast cancer, menopausal status, parity, and race/ethnicity.
Results: Mammographic density was strongly associated with both ER-positive and ER-negative breast cancers. Compared with women with BI-RADS 2, women with BI-RADS 1 (lowest density) had a lower risk of ER-positive cancer [adjusted hazard ratio (HR), 0.28; 95% confidence interval (95% CI), 0.16-0.50] and ER-negative cancer (adjusted HR, 0.17; 95% CI, 0.04-0.70). Women with BI-RADS 4 (highest density) had an increased risk of ER-positive breast cancer (adjusted HR, 2.21; 95% CI, 1.64-3.04) and an increased risk of ER-negative breast cancer (adjusted HR, 2.21; 95% CI, 1.16-4.18).
Conclusion: Surprisingly, women with high mammographic density have an increased risk of both ER-positive and ER-negative breast cancers. The association between mammographic density and breast cancer may be due to factors besides estrogen exposure.
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Mammographic density may be influenced by estrogen. Mammographic density decreases after menopause (4). Formulations of postmenopausal hormone replacement therapy (HRT) that include estrogen plus progesterone increase mammographic density (5-7). Tamoxifen, a selective estrogen receptor (ER) modulator that has antiestrogenic effects in the breast, decreases mammographic density (8-10). Thus, high mammographic density may be associated with breast cancer because it is a marker of estrogen effects on breast tissue (11).
Risk factors for ER-positive and ER-negative breast cancers may be distinct (12). Increasing age and postmenopausal HRT have been associated with an increased risk of developing an ER-positive tumor (13, 14). Selective ER modulators are protective against ER-positive but not ER-negative breast cancers and may even increase the risk of ER-negative breast cancer risk (15, 16). If mammographic density is a risk factor for breast cancer because it represents cumulative effect of both endogenous and exogenous estrogens on the breast and estrogen is associated with an increased risk of ER-positive breast cancer, then mammographic breast density should be a stronger risk factor for ER-positive than ER-negative breast cancers. We tested this hypothesis by prospectively comparing the strength of the association between mammographic breast density and invasive ER-positive and ER-negative tumors among women who participated in the San Francisco Mammography Registry, a population-based mammography registry.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Women who had reading of mammographic density associated with at least one of their mammograms taken before January 1, 2002 were included in this study. Mammographic density measurements were done on 47% of the women in the database. The density measurements were done routinely at 11 of the 13 of the facilities participating in the registry. At these 11 facilities, density was read on >95% of the mammograms. The other two sites, which include the largest site, do not routinely assess mammographic density and were therefore excluded from the analysis. We excluded all women who had a diagnosis of breast cancer prior to their first mammographic density measurement because treatments for breast cancer including tamoxifen and radiation may alter breast density.
The data were analyzed as a cohort study. For all women included, we selected the first mammogram in the data set after January 1, 1995 as the main predictor. The last possible follow-up time was July 1, 2002. Follow-up time for women who developed invasive breast cancer was calculated as the time between their first mammogram with Breast Imaging Reporting and Data System (BI-RADS) density measurement and the date of invasive breast cancer diagnosis. Follow-up time for women who did not develop cancer was censored on July 1, 2002. The latter date was selected because it was known that completeness of cancer reporting would be >95% to the Northern California Surveillance, Epidemiology, and End Results registry prior to July 1, 2002.
Cancers occurred from <1 month after the index mammogram up to 7.5 years after the index mammogram. The mean time between mammogram and diagnosis of breast cancer was 
1.9 years. For women who were not diagnosed with breast cancer, the mean time between index mammogram and the last follow-up date was 3.8 years.
Measurements
Breast density was classified using BI-RADS categories of almost entirely fat (1), scattered fibroglandular densities (2), heterogeneously dense (3), and extremely dense (4). Demographic information and a breast health history were obtained by questionnaire at each screening examination. The questionnaire includes questions about history of breast cancer, menopausal status, parity, history of breast cancer in first-degree relatives and age at diagnosis of the woman's relative, current postmenopausal HRT use, height, and weight. Body mass index (BMI) was calculated from self-reported height and weight. We used women's self-report of their menopausal status. For women who did not report menopausal status, those ages 
55 years at the time of mammography were assumed to be postmenopausal. Women ages 50 to 54 years were considered postmenopausal if both ovaries had been removed, if they reported their periods had stopped permanently, or if they were taking HRT. Women were considered "current HRT users" if they self-reported HRT use at a screening examination.
We included all invasive breast cancers diagnosed between January 1, 1995 and July 1, 2002 and reported to the regional Northern California Surveillance, Epidemiology, and End Results program. ER and progesterone receptor (PR) status of breast cancers were collected from the regional Surveillance, Epidemiology, and End Results program. Women who developed carcinoma in situ were not included in this analysis.
Statistical Analysis
We compared age between unaffected women and women who developed ER-positive breast cancer and between unaffected women and women who developed ER-negative breast cancers using ANOVA. For all other analyses in Table 1, we adjusted for age, because baseline age was different among ER-positive cases and controls. We calculated age-adjusted rates for dichotomous and categorical variables (race/ethnicity, HRT, family history, and parity) and used logistic regression models adjusted for age alone to test the significance of the observed differences. We calculated age-adjusted means for continuous variables using linear regression models. In each case, we used women without breast cancer as the baseline group. To test the association between mammographic density and age, we used ANOVA. We adjusted all other demographic and risk factors in Table 2 for age, because age is strongly correlated with mammographic density. To test each variable for association with mammographic density and adjust the association for age, we used ordinal logistic regression models in which the outcome was BI-RADS category and each variable was tested separately with age as the only other covariate in the model. Analyses comparing baseline characteristics and mammographic density across different ethnic groups were done with Caucasians as the comparison group, because this was the largest subgroup.
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To test the strength of mammographic density and other potential risk factors as predictors of ER-positive versus ER-negative breast cancers, we generated polytomous models. In these models, we classified ER-positive and ER-negative cancers as distinct outcomes and compared the women who developed each of these with women who did not develop breast cancer. The final model included all of the same covariates included in the multivariate Cox proportional hazards models described above but also included follow-up time entered as a covariate. After estimating the model, we used Wald statistic to test the hypothesis that a particular covariate (e.g., breast density) was equivalent for both ER-positive and ER-negative breast cancers. A significant P value for this test implies that the covariate has a different strength of association for ER-positive versus ER-negative breast cancers.
Statistical analysis was done using Stata software (version 6). All statistical tests were two sided.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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4 years. In age-adjusted analyses, women who developed ER-positive cancers were more likely to be taking hormones, to have had a first-degree relative with breast cancer, to be nulliparous, and to be Caucasian compared with women who did not develop breast cancer (Table 1). Women who developed ER-negative breast cancer were more likely to have had a first-degree relative with breast cancer. Mammographic density decreased with age and was inversely related to BMI after adjustment for age (Table 2). Women who used HRT were more likely to have higher mammographic density, as were women who were nulliparous and women with a family history of breast cancer. Even after adjustment for age, postmenopausal women had significantly lower mammographic density than premenopausal women (Table 2).
Among women with the BI-RADS 1, 17 (0.5%) developed breast cancer of which 0.4% were ER positive and 0.06% were ER negative. Of the women with BI-RADS 2, 315 (1.6%) developed breast cancer of which 1.1% were ER positive and 0.29% were ER negative. Among the women with BI-RADS 3 density, 297 (1.6%) developed cancer of which 1.2% were ER positive and 0.25% were ER negative. Of the women with the highest density, 72 (2.2%) developed cancer of which 1.6% were ER positive and 0.4% were ER negative. These numbers are likely to be substantially higher than the true incidence of breast cancer in the population, because women are often diagnosed with breast cancer around the time of mammography.
In multivariate-adjusted analyses, increased mammographic density was associated with increased risk of both ER-positive and ER-negative breast cancers (Table 3). For both types of tumors, there was a progressive increase in risk with increasing density. Although the number of cancers, particularly ER-negative cancers, among women with BI-RADS 1 was low, we found that the association between density and ER-positive and ER-negative cancers was consistent among different density categories. Results adjusting for age and BMI only did not substantially differ from the results of the fully adjusted models, suggesting that other than age and BMI there were no other major confounders of the association between density and either ER-positive or ER-negative cancers.
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We also tested for an association between mammographic density and breast cancer by PR status. Of the 620 tumors tested for PR status, 451 were PR positive and 169 were PR negative. We found an increased risk of both PR-positive [adjusted hazard ratio (HR), 2.21; 95% CI, 1.58-3.11] and PR-negative (adjusted HR, 1.97; 95% CI, 1.10-3.51) breast cancer for women with BI-RADS 4 compared with women with BI-RADS 2. Among women with BI-RADS 1 density, there was decreased risk of PR-positive cancer (adjusted HR, 0.20; 95% CI, 0.10-0.40) and PR-negative cancer (adjusted HR, 0.35; 95% CI, 0.15-0.81).
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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This study is prospective, with a large sample size that includes premenopausal and postmenopausal women of diverse racial and ethnic groups. One limitation of the study is that we used a qualitative rating of breast density that was made by numerous radiologists and has limited replicability (19). However, even with a qualitative measure of breast density, the association with breast cancer risk is strong. Misclassification of the readings may attenuate the association with breast cancer, but this should not have a differential affect on the association between ER-positive and ER-negative tumors.
The ER status of tumors may be associated with distinct biological subtypes of breast cancer and genetic and risk factor profiles. Classification of tumor subtypes using gene expression profiles has identified ER status asone of the most important variables in clustering the expression profiles (20, 21). BRCA1 mutation carriers develop mostly ER-negative tumors, whereas BRCA2 mutation carriers develop mostly ER-positive tumors (22–24). ER-negative cancers are more common among younger women and among African American women (12). There may also be a differential effect of menopause on risk (25). Postmenopausal risk of ER-positive cancers continue to increase with age, whereas ER-negative cancers do not. Because mammographic density is associated with development of both ER-positive and ER-negative tumors, it suggests that mammographic breast density increases the risk of a biologically diverse and heterogeneous set of tumors.
Mammographic density is a heritable trait (26) with 60% of the variance explained by genetic factors (26, 27). In addition, first-degree relatives of women with increased mammographic density have increased risk of developing breast cancer (28). Thus, genes that determine breast density may also affect breast cancer risk. Previous studies have shown that mammographic density is associated with higher insulin-like growth factor-I levels in premenopausal women, which are in turn associated with breast cancer (29). Other growth factors that affect the breast have also been shown to be associated with mammographic density. Our results provide indirect evidence that mammographic density may be defined by a set of genes that increase the risk of a diverse set of breast tumors.
The utility of measuring mammographic density to identify women at high risk for breast cancer will depend on the efficacy of preventive interventions that can be offered to women at high risk. Current primary prevention therapies such as tamoxifen and raloxifene seem to be protective against ER-positive breast tumors but not against ER-negative tumors (15, 16). Despite their strong protective effects, selective ER modulators are not being widely used currently, possibly because of the potential harms associated with their use (30). Identifying women who are at specifically increased risk of ER-positive tumors may be important to maximize the potential benefit of selective ER modulators. Because mammographic breast density is associated with increased risk of ER-positive tumors, breast density may be useful to identify women who may benefit from tamoxifen or ralixofene. However, because women with increased density are also at increased risk of ER-negative tumors, preventive therapy with selective ER modulators may not decrease the risk of women destined to be diagnosed with ER-negative breast cancer to the same extent as women destined to have ER-positive breast cancer. The value of measuring breast density to identify women who will benefit from chemoprevention may be established by measuring breast density among participants of large trials of chemoprevention.
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Acknowledgments |
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Footnotes |
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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.
Received 4/ 2/04; revised 6/22/04; accepted 7/12/04.
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