Abstract

Introduction

The tissue composition of the breast is reflected mammographically by the pattern of distribution of fibroglandular and fatty tissue. The higher the component of fat, the lower the mammographic density (MD). Conversely, the higher the proportion of fibroglandular tissue, the greater the density (reviewed in [1]). Since MD is a noninvasive, reliable and quantitative measure that is strongly associated with breast cancer risk [2, 3], MD is viewed as a compelling candidate for use as an intermediate marker in studies aimed at understanding breast cancer etiology and prevention [4].

Epidemiologic risk factors strongly associated with MD, such as age and body mass index (BMI), explain only 20–30% of the variation in density (reviewed in [5]). The remaining variation in MD is thought to be, in part, genetically regulated (or genetically determined) (reviewed in [6]). Increased MD has been positively associated with family history of breast cancer in most [3, 7–16], but not all [17–21], studies, which have included women with and without a personal history of breast cancer and have used a variety of methods to measure MD. Family studies [22, 23], including studies of sisters [24–27] and twins [23, 27–29], provide further support for a genetic influence on MD. In fact, a large twin study conducted in Australia and North America estimated that up to 67% of the variation in MD may be attributed to common genetic factors [29].

In contrast to the evidence from family studies, findings from gene association studies have been largely inconsistent, and the relationship between MD and BRCA1/2 mutation status is also equivocal [6]. Helvie et al. [30] were the first to suggest that MD might be greater in carriers of germline mutations in the major breast cancer susceptibility gene, BRCA1, after finding dense mammographic patterns in four of eight women with BRCA1 mutations. However, these findings provided little evidence for differences in density patterns between mutation carriers and the general population. Subsequently, four small studies have compared MD between affected (i.e., diagnosed with breast cancer) BRCA1/2 mutation carriers and women with sporadic breast cancer, offering conflicting results [31–34]. When compared with women at low risk of developing breast cancer, 30 age-matched BRCA1/2 carriers had higher percent MD in a Chicago study [35, 36]. In contrast, a European study of BRCA1/2 mutation families compared MD in 206 BRCA1/2 carriers (96 affected; 110 unaffected) with 136 noncarriers (3 affected; 133 unaffected) and found no difference in MD by mutation status [37].

We aimed to further evaluate potential differences in MD between 143 BRCA1/2 mutation carriers without breast cancer enrolled in the U.S. National Cancer Institute (NCI) Clinical Genetics Branch’s Breast Imaging Study and 119 women at low-to-average risk of developing breast cancer participating in the same study or in the NCI/National Naval Medical Center’s Susceptibility to Breast Cancer Study.

Materials and methods

Assessment of mammographic density

Participants from both studies had standardized, quantitative calculations of MD measured from digitized mammograms by the same experienced study mammographer (CKC) using an interactive computerized thresholding method developed at the NIH Clinical Center (Version 3.44, MEDx, Medical Numerics, Germantown, MD). After segmenting the breast from background, a threshold gray-level value was selected such that all pixel values above the threshold were tinted to optimally cover the breast parenchyma (Fig. 1). The summed area occupied by dense pixels divided by the total breast area constituted the percent MD. The radiologist was masked to BRCA1/2 mutation status, and the MD results from both breasts were averaged for analysis, unless the patient had only one side available for imaging. In that case, the density reading from one breast was used for analysis (n = 1 BRCA2 mutation carrier and n = 9 NCI/NNMC study participants with only one side available for MD assessment). We assessed the internal reliability of the radiologist’s readings by randomly submitting a masked set of 100 mammograms (30 from the Susceptibility to Breast Cancer Study and 70 from the Breast Imaging Study) for re-review.

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Fig. 1

Representative digitized mammogram showing tinted pixels for MD calculation. The colors (ranging from the coolest blue to the hottest red) indicate increasing density, where red is representative of the densest tissue

Among the Breast Imaging Study participants only, the study radiologist also visually scored all mammograms using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) as follows: The breast (1) is almost entirely fat; (2) contains scattered fibroglandular densities; (3) is heterogeneously dense; and (4) is extremely dense [40]. The BI-RADS classification of MD has been associated with breast cancer risk in a dose–response fashion [41–43]. Among Breast Imaging Study participants with available BI-RADS and percent MD data (n = 170), percent MD increased with increasing BI-RADS density categories as expected (Supplementary Table). For the purposes of this report, however, we used percent MD as the measure of primary interest because it was available for participants from both studies.

Results

The participant demographics by study are shown in Table 1. BRCA1/2 mutation carriers included 143 women with complete MD measures and no personal history of breast or ovarian cancer. Women determined to be at low-to-average breast cancer risk (n = 119) included (a) 29 mutation-negative women with complete MD measures and no prior breast or ovarian cancer, and (b) 90 women with complete MD measures and without cancer, a PAT score <8 and a 5-year Gail score <1.67.

Table 1

Participant risk status by study

NCI Clinical Genetic Branch’s Breast Imaging Study, 2001–2007 (n = 172)	No. women
Risk status
BRCA1	91
BRCA2	52
Mutation-negative, from mutation-positive family	29
Personal history of breast cancer	0
NCI/NNMC Susceptibility to Breast Cancer Study, 2000–2006 (n = 90)	Median (range; IQR)
Risk score
Maternal PAT	0 (0, 7; 3)
Paternal PAT	0 (0, 5; 0)
5-year Gail score [46]	1.2 (0.3, 1.6; 0.5)
Number of first-degree relatives with breast cancer	0 (0, 1; 0)
Personal history of breast cancer	0

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IQR inter-quartile range, NCI National Cancer Institute, NNMC National Naval Medical Center, PAT Pedigree Assessment Tool [47]

The BRCA1/2 mutation carriers were statistically significantly younger than the women at low-to-average breast cancer risk (Table 2). Compared with low-to-average risk women, the mutation carriers were more likely to be white, nulliparous, and/or to have a later age at first birth. In addition, the mutation carriers were more likely to have ever used oral contraceptives and to have undergone surgical menopause.

Table 2

Baseline characteristics of the study populations

Variable	Low-to-average risk of breast cancer (n = 119)		Unaffected BRCA1/2 carriers (n = 143)		P-value
	Mean (SD)	Range	Mean (SD)	Range
Age (years), mean (SD)	48.4 (9.3)	25–79	38.0 (8.6)	22–55	<.0001
Body mass index (BMI)a, mean (SD)	26.3 (5.8)	18.0–49.5	25.5 (5.4)	17.9–48.2	0.23
	n	%b	n	%
Race
Other	19	16.0	0	0.0	<.0001
White, non-Hispanic	100	84.0	143	100.0
Education level
High school	28	23.5	32	22.4	0.28
College degree	39	32.8	60	42.0
Graduate work	52	43.7	51	35.7
Cigarette smoking
Never	81	68.1	95	66.4	0.74
Former	31	26.1	36	25.2
Current	7	5.9	12	8.4
Age at menarche (years)
<12	18	15.3	19	13.4	0.91
12–13	73	61.9	90	63.4
≥14	27	22.9	33	23.2
Missing	1		1
Parous
No	31	26.1	66	46.2	0.001
Yes	88	73.9	77	53.8
Age at first birth (years)
<30	65	54.6	54	37.8	0.006
≥30 or nulliparous	54	45.4	89	62.2
Breastfed
Never	52	45.2	75	52.4	0.25
Ever	63	54.8	68	47.6
Missing	4		0
Oral contraceptives
Never	30	25.4	16	11.2	0.002
Former	78	66.1	99	69.2
Current	10	8.5	28	19.6
Missing	1		0
Menopausal status
Premenopausal	68	61.3	81	56.6	<.0001
Postmenopausal, natural	9	8.1	10	7.0
Postmenopausal, surgical	16	14.4	51	35.7
Postmenopausal, unknown	18	16.2	1	0.7
Missing	8		0
Hormone therapy
Never	81	68.6	101	70.6	0.93
Former	17	14.4	20	14.0
Current	20	16.9	22	15.4
Missing	1		0
SERMs
Never	115	97.5	133	93.0	0.24
Former	1	0.8	5	3.5
Current	2	1.7	5	3.5
Missing	1		0
Prior breast biopsy
0	83	69.7	108	75.5	0.57
1	25	21.0	25	17.5
2+	11	9.2	10	7.0

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SERM selective estrogen receptor modulator

P-values < 0.05 are in bold type

^aBMI = Weight (kg)/height² (m²)

^bMissing values were excluded from percentage calculations

The ICC for intra-observer agreement for MD assessed in the 100 paired sets using MEDx was 0.889 and in the four paired sets using Cumulus was 0.997, documenting high reliability of each method. For the 49 women with Cumulus measures, percent MD was strongly and positively correlated with that measured by MEDx (r = 0.84, P < 0.0001). Excluding the five women with problematic images from one breast did not significantly alter the results (r = 0.86, P < 0.0001).

Table 3 shows the baseline characteristics of the study populations by quartiles of percent MD. Women with higher MD had a lower BMI and were more likely to be college graduates, nulliparous, and younger, and thus were more likely to be premenopausal and to have never used menopausal hormone therapy.

Table 3

Baseline characteristics of the study populations by quartiles of mammographic density

Variable	Mammographic density (%) quartiles
	Q1 (0–25.9)		Q2 (26.0–36.3)		Q3 (36.4–46.4)		Q4 (46.8–84.6)		P-value
	Mean (SD)		Mean (SD)		Mean (SD)		Mean (SD)
Age (years)	48.5 (11.5)		42.9 (9.5)		39.2 (8.8)		40.4 (8.8)		<.0001
Body mass index (BMI)a	30.0 (7.1)		26.1 (4.7)		24.3 (3.9)		23.0 (3.4)		<.0001
	n	%b	n	%	n	%	n	%
White	60	92.3	62	93.9	62	95.4	59	89.4	0.59
College graduate	43	66.2	54	81.8	48	73.8	57	86.4	0.03
Ever smoker	21	32.3	20	30.3	24	36.9	21	31.8	0.87
Age at menarche (years)
<12	11	17.5	12	18.2	6	9.2	8	12.1	0.66
12–13	41	65.1	39	59.1	42	64.6	41	62.1
≥14	11	17.5	15	22.7	17	26.2	17	25.8
Missing	2		0		0		0
Age at first birth ≥30 years or nulliparous	29	44.6	32	48.5	38	58.5	44	66.7	0.049
Ever breastfed	36	56.3	34	53.1	29	45.3	32	49.2	0.61
Missing	1		2		1		0
Oral contraceptives
Never	12	18.8	14	21.2	11	16.9	9	13.6	0.90
Former	45	70.3	42	63.6	44	67.7	46	69.7
Current	7	10.9	10	15.2	10	15.4	11	16.7
Missing	1		0		0		0
Menopausal status
Premenopausal	25	38.5	34	54.8	48	75.0	42	66.7	0.0002
Postmenopausal, surgical	21	32.3	17	27.4	13	20.3	16	25.4
Postmenopausal, natural/type unknown	19	29.2	11	17.7	3	4.7	5	7.9
Missing	0		4		1		3
Hormone therapy
Never	31	48.4	47	71.2	55	84.6	49	74.2	0.001
Former	17	26.6	8	12.1	6	9.2	6	9.1
Current	16	25.0	11	16.7	4	6.2	11	16.7
Missing	1		0		0		0
Ever use of SERMs	3	4.7	4	6.1	2	3.1	4	6.1	0.84
Missing	1		0		0		0
Prior breast biopsy
0	43	66.2	44	66.7	52	80.0	52	78.8	0.22
1	18	27.7	15	22.7	9	13.8	8	12.1
2+	4	6.2	7	10.6	4	6.2	6	9.1

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SERM selective estrogen receptor modulator

P-values < 0.05 are in bold type

^aBMI = Weight (kg)/height² (m²)

^bMissing values were excluded from percentage calculations

While unadjusted mean MD was higher in women with BRCA1/2 mutations versus women at low-to-average risk (Table 4), there was no statistically significant difference in MD after adjusting for age and BMI. In fact, after adjustment for age and BMI, mean MD was marginally, albeit not significantly, lower among carriers than among low-to-average risk women (Fig. 2). We explored age at menarche, nulliparity, age at first birth, menopausal status, number of breast biopsies, and exposure to exogenous hormonal agents as covariates of potential interest with regard to modulating MD. Taking these factors into account did not significantly alter the results of the age-/BMI-adjusted analysis (data not shown). Results were also similar when BRCA1 and BRCA2 mutation carriers were analyzed separately. Compared with low-to-average risk women, mean MD was 2.1% lower among BRCA1 carriers and 0.94% lower among BRCA2 carriers; after adjustment for age and BMI, these differences were not statistically significant.

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Fig. 2

Unadjusted and adjusted mean (±SE) baseline MD (%) in unaffected BRCA1/2 mutation carriers (n = 143) versus women at low-to-average risk of breast cancer (n = 119)

Because age was such a strong confounder in our analysis, we conducted post hoc subgroup analyses using a restricted age range. MD is strongly and inversely associated with age, and, by virtue of the Breast Imaging Study inclusion criteria, the BRCA1/2 mutation carriers were on average approximately 10 years younger than the low-to-average risk group. Thus, if MD were truly lower among BRCA1/2 mutation carriers, such a relationship could be obscured in age-adjusted analyses. We therefore truncated age at the upper age limit of the Breast Imaging Study participants (55 years) and reanalyzed the data. Among women ≤55 years of age, mean MD did not differ between mutation carriers (n = 143) and women at low-to-average risk (n = 101) either before (P = 0.44) or after (P = 0.68) age adjustment.

Discussion

We investigated the association between MD and BRCA1/2 mutation status among women without breast cancer. Compared with women at low-to-average breast cancer risk, we observed no difference in percent MD among BRCA1 and BRCA2 mutation carriers, after accounting for age, BMI, and other potential confounders. Our results do not provide support for an independent effect of BRCA1/2 mutation status on MD.

Our null findings for a difference in MD by mutation status are consistent with those reported by three smaller studies of women with breast cancer [32–34] and the larger European study of BRCA1/2 mutation families [37]. However, our results differ from those from a small study of Asian breast cancer patients diagnosed before age 40 that found a higher BI-RADS MD score among 9 BRCA1 mutation carriers relative to 19 age-matched sporadic cases [31]. Our results also differ from a Chicago study which compared MD in 30 BRCA1/2 carriers (including 17 with breast cancer) with that in 142 women at low breast cancer risk (i.e., no family history of breast cancer and lifetime Gail risk<10%) [35, 36]. Percent MD was visually estimated, and a computerized texture analysis was performed. Breast cancer cases were not analyzed separately. Results demonstrated higher percent MD among the BRCA1/2 carriers than in the age-matched low-risk group, although a test of statistical significance was not performed and, with the exception of age, potential confounding factors were not considered in the analysis. Using a single summary score for their computerized texture features, the investigators achieved a high level of performance in distinguishing between mutation carriers and low-risk women [35]. Their results suggested that there may be additional information contained within mammographic images—not necessarily captured by current measurements of MD—which may be used to more accurately assess breast cancer risk. We are currently investigating whether these novel quantitative mammographic characteristics (e.g., texture and contrast) are associated with mutation status in the Breast Imaging Study and the Susceptibility to Breast Cancer Study participants.

Some limitations of this study deserve consideration. The technology used to measure percent MD has not been prospectively validated for its association with breast cancer risk. However, percent MD was positively associated with BI-RADS density among the Breast Imaging Study participants, inversely associated with age and BMI among all participants as expected [5], and strongly, positively correlated with MD measured by Cumulus among a subset of participants, suggesting internal and external validity for the MD measurements. Thus, our use of a quantitative, highly reliable measure of MD has proven to be a strength of the study: our ICC estimate for percent MD (ρ = 0.889) is consistent with that reported by Boyd et al. [46], who observed an ICC of ρ = 0.897 for 150 film sets in the Canadian National Breast Screening Study. The original design of our study had been to use mutation-negative women from the mutation-positive families as the comparison group, which would have provided participants known not to carry BRCA mutations, and controlled for theoretical within-family correlations in MD. However, we were unable to recruit a sufficiently large number of such women, and thus turned to the NCI/NNMC Susceptibility to Breast Cancer Study as an alternative. These participants offered the advantage of having been imaged and having MD estimated by the same mammographer as the Breast Imaging Study participants. Thus, although these women are legitimately classified as “low-to-average-risk” by all available indicators (no personal history of breast or ovarian cancer, and low scores on both the Gail and PAT models), their mutation status was not directly determined. Given that the prevalence of BRCA1/2 mutations in the general white population is estimated as from 1 in 400 to 1 in 800, the expected number of mutation carriers among the 90 NCI/NNMC Susceptibility Breast Cancer Study participants is 0.1–0.2. It is therefore unlikely that this possibility influenced our results.

Despite these limitations, this study is one of the largest to date to have evaluated the association between MD and BRCA1/2 mutation status among women without breast cancer. The study sample size achieved 80% power to detect a mean difference in MD of 5.2% between mutation carriers and low-to-average risk women. Observed mean differences in MD between the two groups were <4% in all univariate and multivariate analyses, including those restricted to women ≤55 years of age; this consistency provides compelling support for our conclusion that no association exists between mutation status and MD.

Although MD does not appear to be associated with BRCA1/2 mutation status, results from the Epidemiological Study of BRCA1 and BRCA2 mutation carriers (EMBRACE) suggest that MD is a breast cancer risk factor among mutation carriers [37], just as it is in the setting of sporadic cancer. MD ≥50% was associated with an increased odds of breast cancer (odds ratio = 2.29, 95% CI 1.23–4.26) [37]; this odds ratio is similar to that observed for the association between MD and breast cancer risk in the general population.

In conclusion, our data, plus those from prior reports, suggest that the increased risk of breast cancer in BRCA1/2 mutation carriers is not mediated through a heritable factor which modulates MD. Given the strong epidemiologic evidence for a large genetic influence on MD in the general population, ongoing genome-wide association studies may reveal novel genes which could improve our understanding of the mechanism by which MD influences susceptibility among women at risk of both hereditary and sporadic breast cancer. Additionally, prospective studies of BRCA1/2 carriers are needed to clarify the association between MD and breast cancer in this high-risk population. Such a study is being planned as an add-on to the National Ovarian Cancer Prevention and Early Detection Study (GOG-199) [49].

Supplementary Material

Acknowledgments

Abbreviations

Contributor Information

Gretchen L. Gierach, Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics and Cancer Prevention Fellowship Program, Office of Preventive Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. 6120 Executive Blvd., Suite 550, Room 5016, Rockville, MD 20892, USA.

Jennifer T. Loud, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, 6120 Executive Blvd., Room 7028, Rockville, MD 20892, USA.

Catherine K. Chow, Diagnostic Radiology Department, National Institutes of Health Clinical Center, Bethesda, MD, USA.

Sheila A. Prindiville, Coordinating Center for Clinical Trials, Office of the Director, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Jennifer Eng-Wong, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.

Peter W. Soballe, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.

Claudia Giambartolomei, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Phuong L. Mai, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Claudia E. Galbo, Department of Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.

Kathryn Nichols, Westat Corporation, Rockville, MD, USA.

Kathleen A. Calzone, Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Celine Vachon, Department of Health Sciences Research, College of Medicine, Mayo Clinic, Rochester, MN, USA.

Mitchell H. Gail, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Mark H. Greene, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

References