Genetic variation in the estrogen metabolic pathway and mammographic density as an intermediate phenotype of breast cancer

Introduction

Several studies have examined the effect of genetic variants in genes involved in the estrogen metabolic pathway on mammographic density, but the number of loci studied and the sample sizes evaluated have been small and pathways have not been evaluated comprehensively. In this study, we evaluate the association between mammographic density and genetic variants of the estrogen metabolic pathway.

Methods

A total of 239 SNPs in 34 estrogen metabolic genes were studied in 1,731 Swedish women who participated in a breast cancer case-control study, of which 891 were cases and 840 were controls. Film mammograms of the medio-lateral oblique view were digitalized and the software Cumulus was used for computer-assisted semi-automated thresholding of mammographic density. Generalized linear models controlling for possible confounders were used to evaluate the effects of SNPs on mammographic density. Results found to be nominally significant were examined in two independent populations. The admixture maximum likelihood-based global test was performed to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three subpathways for androgen synthesis, androgen-to-estrogen conversion and estrogen removal.

Results

Genetic variants of genes involved in estrogen metabolism exhibited no appreciable effect on mammographic density. None of the nominally significant findings were validated. In addition, global analyses on the overall estrogen metabolic pathway and its subpathways did not yield statistically significant results.

Conclusions

Overall, there is no conclusive evidence that genetic variants in genes involved in the estrogen metabolic pathway are associated with mammographic density in postmenopausal women.     

Mammographic breast density as a general marker of breast cancer risk.

Mammographic breast density is a strong risk factor for breast cancer but whether breast density is a general marker of susceptibility or is specific to the location of the eventual cancer is unknown. A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Mammograms on average 7 years before breast cancer were digitized, and quantitative measures of percentage density and dense area from each side and view were estimated. A regional density estimate accounting for overall percentage density was calculated from both mammogram views. Location of breast cancer and potential confounders were abstracted from medical records. Conditional logistic regression was used to estimate associations, and C-statistics were used to evaluate the strength of risk prediction. There were increasing trends in breast cancer risk with increasing quartiles of percentage density and dense area, irrespective of the side of the breast with cancer (P(trends) < 0.001). Percentage density from the ipsilateral side [craniocaudal (CC): odds ratios (ORs), 1.0 (ref), 1.7, 3.1, and 3.1; mediolateral oblique (MLO): ORs, 1.0 (ref), 1.5, 2.2, and 2.8] and the contralateral side [CC: ORs, 1.0 (ref), 1.8, 2.2, and 3.7; MLO: ORs, 1.0 (ref), 1.6, 1.9, and 2.5] similarly predicted case-control status (C-statistics, 0.64-65). Accounting for overall percentage density, density in the region where the cancer subsequently developed was not a significant risk factor [CC: 1.0 (ref), 1.3, 1.0, and 1.2; MLO: 1.0 (ref), 1.1, 1.0, and 1.1 for increasing quartiles]. Results did not change when examining mammograms 3 years on average before the cancer. Overall mammographic density seems to represent a general marker of breast cancer risk that is not specific to breast side or location of the eventual cancer.     

Mammographic density as a mediator for breast cancer risk: analytic approaches

ABSTRACT: Mammographic breast density has been found to be associated with breast cancer risk. Many of the traditional risk factors for breast cancer are themselves associated with mammographic breast density. A natural question that arises in this setting is the extent to which the effects of breast cancer risk factors are mediated by breast density and the extent to which such effects are through other pathways. We discuss analytic approaches to address these questions of mediation and also discuss how such approaches can accommodate potential interaction between risk factors and mammographic density and can accommodate case-control study designs.     

Mammographic breast density and family history of breast cancer

The association between mammographic breast density and breast cancer risk may be the result of genetic and/or environmental factors that determine breast density. We reasoned that if the genetic factors that underlie breast density increase breast cancer risk, then breast density should be associated with family history of breast cancer. Therefore, we determined the association between mammographic density and family history of breast cancer among women in the San Francisco Mammography Registry. Mammographic density was classified using the four BI-RADS criteria: 1 = almost entirely fatty, 2 = scattered fibroglandular tissue, 3 = heterogeneously dense, and 4 = extremely dense. We adjusted for age, body mass index, hormone replacement therapy use, menopause status, and personal history of breast cancer. Compared with women with BI-RADS 1 readings, women with higher breast density were more likely to have first-degree relatives with breast cancer (BI-RADS 2, odds ratio [OR] = 1.37, 95% confidence interval [CI] = 0.96 to 1.89; BI-RADS 3, OR = 1.70, 95% CI = 1.19 to 2.40; BI-RADS 4, OR = 1.70, 95% CI = 1.05 to 2.71). Thus, the genetic factors that determine breast density may determine breast cancer risk.     

Mammographic density and molecular subtypes of breast cancer

Background:

Gene expression profiling has led to a subclassification of breast cancers independent of established clinical parameters, such as the Sorlie–Perou subtypes. Mammographic density (MD) is one of the strongest risk factors for breast cancer, but it is unknown if MD is associated with molecular subtypes of this carcinoma.

Methods:

We investigated whether MD was associated with breast cancer subtypes in 110 women with breast cancer, operated in Stockholm, Sweden, during 1994 to 1996. Subtypes were defined using expression data from HGU133A+B chips. The MD of the unaffected breast was measured using the Cumulus software. We used multinomial logistic models to investigate the relationship between MD and Sorlie–Perou subtypes.

Results:

Although the distribution of molecular subtypes differed in women with high vs low MD, this was statistically non-significant (P=0.249), and further analyses revealed no association between the MD and Sorlie–Perou subtypes as a whole, nor with individual subtypes.

Conclusion:

These findings suggest that although MD is one of the strongest risk factors for breast cancer, it does not seem to be differentially associated with breast cancer molecular subtypes. However, larger studies with more comprehensive covariate information are needed to confirm these results.     

Mammographic density as a risk factor for breast cancer in a low risk population

Background: Mammographic density is a function of abundance of epithelial and connective tissue in breast. It has been identified as an independent risk factor for breast cancer in studies in western populations. We conducted a case control study to evaluate the role of mammographic density as risk factor for the development of breast cancer in Indian patients. Methods: One hundred and one cases of breast cancer and 123 healthy controls were included in the study. Mammographic density of the breast tissue of all controls and the contralateral breast of breast cancer patients was measured using a six category scale by a qualified radiologist. Results: A low prevalence of dense mammographic patterns (16.3% in controls and 26.7% in cases) was seen in the study population. Premenopausal women with breast density of 50% or more had 3.8 times risk of developing breast cancer than women with breast density of Conclusion: High mammographic density patterns are associated with an increased risk for the development of breast cancer in younger women in a low risk population, whereas no such increase in risk is seen in postmenopausal women.     

Mammographic texture resemblance generalizes as an independent risk factor for breast cancer

Introduction

Breast density has been established as a major risk factor for breast cancer. We have previously demonstrated that mammographic texture resemblance (MTR), recognizing the local texture patterns of the mammogram, is also a risk factor for breast cancer, independent of percent breast density. We examine if these findings generalize to another population.

Methods

Texture patterns were recorded in digitalized pre-diagnosis (3.7 years) film mammograms of a nested case–control study within the Dutch screening program (S1) comprising of 245 breast cancers and 250 matched controls. The patterns were recognized in the same study using cross-validation to form resemblance scores associated with breast cancer. Texture patterns from S1 were examined in an independent nested case–control study within the Mayo Mammography Health Study cohort (S2) of 226 cases and 442 matched controls: mammograms on average 8.5 years prior to diagnosis, risk factor information and percent mammographic density (PD) estimated using Cumulus were available. MTR scores estimated from S1, S2 and S1 + S2 (the latter two as cross-validations) were evaluated in S2. MTR scores were analyzed as both quartiles and continuously for association with breast cancer using odds ratios (OR) and adjusting for known risk factors including age, body mass index (BMI), and hormone usage.

Results

The mean ages of S1 and S2 were 58.0 ± 5.7 years and 55.2 ± 10.5 years, respectively. The MTR scores on S1 showed significant capability to discriminate cancers from controls (area under the operator characteristics curve (AUC) = 0.63 ± 0.02, P <0.001), which persisted after adjustment for PD. S2 showed an AUC of 0.63, 0.61, and 0.60 based on PD, MTR scores trained on S2, and MTR scores trained on S1, respectively. When adjusted for PD, MTR scores of S2 trained on S1 showed an association with breast cancer for the highest quartile alone: OR in quartiles of controls as reference; 1.04 (0.59 to 1.81); 0.95 (0.52 to 1.74); 1.84 (1.10 to 3.07) respectively. The combined continuous model with both PD and MTR scores based on S1 had an AUC of 0.66 ± 0.03.

Conclusions

The local texture patterns associated with breast cancer risk in S1 were also an independent risk factor in S2. Additional textures identified in S2 did not significantly improve risk segregation. Hence, the textural patterns that indicated elevated risk persisted under differences in X-ray technology, population demographics, follow-up time and geography.     

Mammographic breast density and survival in women with invasive breast cancer

Cancer Causes & Control - We explored the under-debate association between mammographic breast density (MBD) and survival. From the Piedmont Cancer Registry, we identified 693 invasive breast...     

Mammographic breast density: effect on imaging and breast cancer risk

Mammographic breast density has been studied for more than 30 years. Greater breast density not only is related to decreased sensitivity of mammograms because of a masking effect but also is a major independent risk factor for breast cancer. This article defines breast density and reviews literature on quantification of mammographic density that is key to future clinical and research protocols. Important influences on breast density are addressed, including age, menopausal status, exogenous hormones, and genetics of density. Young women with dense breasts benefit from digital mammographic technique. The potential use of supplemental MRI and ultrasound screening techniques in high-risk women and women with dense breasts is explored, as are potential risk reduction strategies.     

Mammographic density as a predictor of breast cancer survival: the Multiethnic Cohort

Introduction

Mammographic density, a strong predictor for breast cancer incidence, may also worsen prognosis in women with breast cancer. This prospective analysis explored the effect of prediagnostic mammographic density among 607 breast cancer cases diagnosed within the Hawaii component of the Multiethnic Cohort (MEC).

Methods

Female MEC participants, aged ≥ 50 years at cohort entry, diagnosed with primary invasive breast cancer, and enrolled in a mammographic density case-control study were part of this analysis. At cohort entry, anthropometric and demographic information was collected by questionnaire. Tumor characteristics and vital status were available through linkage with the Hawaii Tumor Registry. Multiple digitized prediagnostic mammograms were assessed for mammographic density using a computer-assisted method. Cox proportional hazards regression was applied to examine the effect of mammographic density on breast cancer survival while adjusting for relevant covariates.

Results

Of the 607 cases, 125 were diagnosed as in situ, 380 as localized, and 100 as regional/distant stage. After a mean follow-up time of 12.9 years, 27 deaths from breast cancer and 100 deaths from other causes had occurred; 71 second breast cancer primaries were diagnosed. In an overall model, mammographic density was not associated with breast cancer-specific survival (HR = 0.95 per 10%; 95%CI: 0.79-1.15), but the interaction with radiotherapy was highly significant (p = 0.006). In stratified models, percent density was associated with a reduced risk of dying from breast cancer (HR = 0.77; 95%CI: 0.60-0.99; p = 0.04) in women who had received radiation, but with an elevated risk (HR = 1.46; 95% CI: 1.00-2.14; p = 0.05) in patients who had not received radiation. High breast density predicted a borderline increase in risk for a second primary (HR = 1.72; 95% CI: 0.88-2.55; p = 0.15).

Conclusions

Assessing mammographic density in women with breast cancer may identify women with a poorer prognosis and provide them with radiotherapy to improve outcomes.     

Mammographic density,breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.     

Mammographic signs as risk factors for breast cancer.

We have carried out a case-control study to examine the relationship between mammographic signs and breast cancer. The mammographic signs assessed were prominent ducts and dysplasia. The cases were a group of 183 women with histologically verified unilateral breast cancer. The controls were a group of women attending a screening centre. Cases and controls were individually age-matched. Mammograms from the non-cancerous breast of the cases were randomly assembled with those of the controls and classified by 3 radiologists without knowledge of which films were from cases and which from controls. Mammographic dysplasia was found to be strongly associated with breast cancer, particularly in women aged less than 50. Prominent ducts were only weakly associated with breast cancer. Multivariate analysis showed that the association between dysplasia and breast cancer could not be explained on the basis of other risk factors for breast cancer, and that classification of dysplasia discriminated more strongly between cases and controls than did classification of Wolfe''s mammographic patterns. These results show that mammograms contain information about risk of breast cancer. Mammographic dysplasia is strongly associated with breast cancer, is present in a substantial proportion of patients with the disease, and may offer opportunities for prevention.     

Mammographic density and breast cancer after ductal carcinoma in situ

Women with ductal carcinoma in situ (DCIS) are at substantially increased risk for a second breast cancer, but few strong predictors for these subsequent tumors have been identified. We used Cox regression modeling to examine the association between mammographic density at diagnosis of DCIS of 504 women from the National Surgical Adjuvant Breast and Bowel Project B-17 trial and risk of subsequent breast cancer events. In this group of patients, mostly 50 years old or older, approximately 6.6% had breasts categorized as highly dense (i.e., > or =75% of the breast occupied by dense tissue). After adjusting for treatment with radiotherapy, age, and body mass index, women with highly dense breasts had 2.8 (95% confidence interval [CI] = 1.3 to 6.1) times the risk of subsequent breast cancer (DCIS or invasive), 3.2 (95% CI = 1.2 to 8.5) times the risk of invasive breast cancer, and 3.0 (95% CI = 1.2 to 7.5) times the risk of any ipsilateral breast cancer, compared with women with less than 25% of the breast occupied by dense tissue. Our results provide initial evidence that the risk of second breast cancers may be increased among DCIS patients with highly dense breasts.     

Mammographic density and breast cancer in three ethnic groups.

The extent of radiodense tissue on a mammogram (mammographic densities) is strongly associated with breast cancer risk among (non-Latina) white women, but few data exist for African-American and Asian-American women. We collected prediagnostic mammograms from 622 breast cancer patients and 443 control subjects ages 35-64 years from three different ethnic groups (whites, African Americans, and Asian Americans) who participated as cases and controls in one of two ongoing breast cancer studies. Percent and absolute mammographic density were assessed using a previously validated computer-assisted method. In all three ethnic groups combined, breast cancer risk increased with increasing percent mammographic density. After adjustment for ethnicity, age, body mass index, age at menarche, breast cancer family history, age at and number of full-term pregnancies, menopausal status, and hormone replacement therapy use, women with the highest percent density had 5-fold greater breast cancer risk than women with no density (P(trend) = 0.0001). The impact of percent density on risk was stronger for older than for younger women (>/=50 versus <50 years; P = 0.05). Risk estimates did not differ significantly by ethnicity, with breast cancer risk (95% confidence interval) increasing 15% (4-27%) in whites, 30% (5-61%) in Asian Americans, and 11% (-2-26%) in African Americans for each 10% increase in density. The trends were similar for absolute density. Our results confirm that increases in computer-assisted mammographic density measurements are associated with a strong gradient in breast cancer risk. Furthermore, our findings suggest that mammographic density is as strong a predictor of risk for African-American and Asian-American women as for white women.     

Mammographic density and estrogen receptor status of breast cancer.

BACKGROUND: The density of breast tissue on a mammogram is a strong predictor of breast cancer risk and may reflect cumulative estrogen effect on breast tissue. Endogenous and exogenous estrogen exposure increases the risk of estrogen receptor (ER)-positive breast cancer. We determined if mammographic density is associated more strongly with ER-positive breast cancer than with ER-negative breast cancer.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.     

Mammographic breast density and breast cancer: evidence of a shared genetic basis

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.     

Mammographic density and its interaction with other breast cancer risk factors in an Asian population

Background:

Joint effects of mammographic density and other risk factors on breast cancer risk remain unclear.

Methods:

From The Singapore Breast Screening Project, we selected 491 cases and 982 controls. Mammographic density was measured quantitatively. Data analysis was by conditional logistic regression.

Results:

Density was a significant risk factor, adjusting for other factors. Density of 76–100% had an odds ratio of 5.54 (95% CI 2.38–12.90) compared with 0–10%. Density had significant interactions with body mass index and oral contraceptive use (P=0.02).

Conclusions:

Percent density increases breast cancer risk in addition to effects of other risk factors, and modifies the effects of BMI and OCs.     

Mammographic density, lobular involution, and risk of breast cancer

In this review, we propose that age-related changes in mammographic density and breast tissue involution are closely related phenomena, and consider their potential relevance to the aetiology of breast cancer. We propose that the reduction in mammographic density that occurs with increasing age, parity and menopause reflects the involution of breast tissue. We further propose that age-related changes in both mammographic density and breast tissue composition are observable and measurable phenomena that resemble Pike's theoretical construct of 'breast tissue ageing'. Extensive mammographic density and delayed breast involution are both associated with an increased risk of breast cancer and are consistent with the hypothesis of the Pike model that cumulative exposure of breast tissue to hormones and growth factors that stimulate cell division, as well as the accumulation of genetic damage in breast cells, are major determinants of breast cancer incidence.     

Mammographic density and breast cancer in women from high risk families

Introduction

Mammographic density (MD) is one of the strongest determinants of sporadic breast cancer (BC). In this study, we compared MD in BRCA1/2 mutation carriers and non-carriers from BRCA1/2 mutation-positive families and investigated the association between MD and BC among BRCA1/2 mutation carriers per type of mutation and tumor subtype.

Methods

The study was carried out in 1039 female members of BRCA1 and BRCA2 mutation-positive families followed at 16 Spanish Genetic Counseling Units. Participants’ density was scored retrospectively from available mammograms by a single blinded radiologist using a 5-category scale (<10 %, 10-25 %, 25-50 %, 50-75 %, >75 %). In BC cases, we selected mammograms taken prior to diagnosis or from the contralateral breast, whereas, in non-cases, the last screening mammogram was evaluated. MD distribution in carriers and non-carriers was compared using ordinal logistic models, and the association between MD and BC in BRCA1/2 mutation carriers was studied using logistic regression. Huber-White robust estimators of variance were used to take into account correlations between family members. A similar multinomial model was used to explore this association by BC subtype.

Results

We identified and scored mammograms from 341 BRCA1, 350 BRCA2 mutation carriers and 229 non-carriers. Compared to non-carriers, MD was significantly lower among BRCA2 mutation carriers (odds ratio (OR) =0.71; P-value=0.04), but not among BRCA1 carriers (OR=0.84; P-value=0.33). MD was associated with subsequent development BC (OR per category of MD=1.45; 95 % confidence interval=1.18-1.78, P-value<0.001), with no significant differences between BRCA1 and BRCA2 mutation carriers (P-value=0.48). Finally, no statistically significant differences were observed in the association of MD with specific BC subtypes.

Conclusions

Our study, the largest to date on this issue, confirms that MD is an independent risk factor for all BC subtypes in either BRCA1 and BRCA2 mutation carriers, and should be considered a phenotype risk marker in this context.