Received: May 17, 2014; Revised: July 18, 2014; Accepted: October 27, 2014
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JNCI J Natl Cancer Inst (2015) 107(5): dju397
First published online March 5, 2015
1 of 4
The Contributions of Breast Density and Common
Genetic Variation to Breast Cancer Risk
Celine M.Vachon, V. ShanePankratz, Christopher G.Scott, LotharHaeberle,
EladZiv, Matthew R.Jensen, Kathleen R.Brandt, Dana H.Whaley, Janet
E.Olson, KatharinaHeusinger, Carolin C.Hack, Sebastian M.Jud, Matthias
W.Beckmann, RuedigerSchulz-Wendtland, Jeffrey A.Tice, Aaron D.Norman,
Julie M.Cunningham, Kristen S.Purrington, Douglas F.Easton,
Thomas A.Sellers, KarlaKerlikowske, Peter A.Fasching*, Fergus J.Couch*
Afliations of authors: Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic (CMV, VSP, CGS, MRJ, JEO, ADN, FJC); Department of Gynecology
and Obstetrics, University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
(LH, KH, CCH, SMJ, MWB, PAF); Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San
Francisco, CA (EZ); Departments of Medicine and Epidemiology and Biostatistics and General Internal Medicine Section, Department of Veterans Affairs and Division
of General Internal Medicine (EZ, JAT, KK); Division of Breast Imaging, Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN (KRB, DHW); Institute
of Diagnostic Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany (RS-W); Division of Experimental
Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN (JMC, FJC); Wayne State University School of Medicine
and Karmanos Cancer Institute, Detroit, MI (KSP); University of Cambridge, Centre for Cancer Genetic Epidemiology, Cambridge, UK (DFE); Moftt Cancer Center,
Tampa, Florida (TAS); University of California at Los Angeles, Department of Medicine, Division Hematology/Oncology, David Geffen School of Medicine, Los Angeles,
*Authors contributed equally to this work.
Correspondence to: Celine M.Vachon, PhD, Mayo Clinic, 200 First Street SW, Charlton Building 6–239, Rochester, MN 55905 (e-mail: email@example.com).
We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS)
breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic
regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-
prediction model while accounting for its attributable risk and compared ve-year absolute risk predictions between
models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were
independent risk factors across all three studies (Pinteraction=.23). Relative to those with scattered broglandular densities
and average PRS (2nd quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% condence interval
[CI]=1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR=0.30, 95% CI=0.18 to 0.51).
PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC=0.66 to
AUC=0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to
test calibration in the general population.
Mammographic breast density is associated with decreased
diagnostic accuracy of mammography (1,2) and increased
breast cancer risk (3,4). Recent legislation, passed in nineteen
states, mandates radiologists to communicate the importance
of breast density information to patients undergoing mam-
mography. Because 45% to 50% of women have heterogeneously
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Vachon et al. | 2 of 4
or extremely dense breasts (5), this legislation will result in
increased patient-provider discussions regarding breast screen-
ing frequency, supplemental screening strategies, and risk (6).
Additional factors for further risk stratication, especially for
women with dense breasts, are needed to inform these discus-
To date, almost 80 conrmed breast cancer susceptibility loci
have been identied (8–23) and explain approximately 14% of
familial breast cancer risk (8). Coupled with established risk fac-
tors like breast density, these loci are likely to increase the utility
and accuracy of clinical risk prediction.
We conducted two studies to evaluate the contribution
of established breast cancer susceptibility loci to the Breast
Imaging Reporting and Data System (BI-RADS) density and
breast cancer association. First, we determined whether a
polygenic risk score (PRS) composed of 76 single-nucleotide
polymorphisms (SNPs) is a statistically signicant risk fac-
tor independent of BI-RADS density in three epidemiologic
studies. Second, we examined whether the addition of this
PRS improves performance of the Breast Cancer Surveillance
Consortium (BCSC) ve-year risk-prediction model in a nested
case-control study (24).
Studies included 456 case patients and 1166 age-matched
control patients nested within the Mayo Mammography Health
Study (MMHS) cohort (25,26) and two clinic-based case-control
studies with 675 case patients and 864 frequency age-matched
control patients (Mayo Clinic Breast Cancer Study [MCBCS]),
and 512 case patients and 367 unmatched control patients
(Bavarian Breast Cancer Cases and Control Study [BBCC]), for
a total 1643 case patients and 2397 control patients (25,27,29)
(Supplementary Table 1 and Methods, available online). All
studies obtained informed consent, ethics, and institutional
BI-RADS breast density was categorized as dened in the
BI-RADS lexicon (30) into one of four categories: 1) almost
entirely fat, 2)scattered broglandular densities, 3)heterogene-
ously dense, 4)extremely dense, by expert radiologists on mam-
mograms close to (MCBCS and BBCC) or years prior (MMHS) to
diagnosis. Genotyping of the 76 SNPs (8–23) was conducted on
a custom Illumina iSelect genotyping array (8) (Supplementary
Methods, available online).
The PRS was formed using published per-minor-allele odds
ratios (ORs) corresponding to the SNP associations with overall
breast cancer (Supplementary Table2, available online) (8–23).
The log OR for each SNP was multiplied by the corresponding
number of minor alleles, summed to generate a unique PRS for
each person (31), and evaluated both as continuous (per stand-
ard deviation) and quartile measures. Logistic regression was
used to examine the association of BI-RADS density, PRS, and
their interaction with breast cancer risk, adjusting for age, 1/
BMI (4,26), and study. ORs, area under the curve (AUC) (32,33),
and 95% condence intervals (CIs) were estimated. Alikelihood
ratio test (LRT) assessed statistical signicance of associations
between PRS and breast cancer while accounting for BI-RADS,
We formed the BCSC-PRS risk model by estimating the OR
corresponding to a one-unit increase in the PRS with data from
the BBCC and MCBCS studies only, and added this estimate
directly into the original BCSC model (Supplementary Methods,
available online) (24). We then estimated ve-year risk of inva-
sive cancer for the BCSC and BCSC-PRS models within the
MMHS cohort (334 invasive case patients) using a resampling
approach to obtain fty sets of 334 genotyped control patients
whose age distribution matched the full cohort. We compared
the performance of the BCSC-PRS vs BCSC model using: 1) a
LRT, 2)AUCs and corresponding 95% CIs, 3)change in AUC and
95% CIs based on standard errors estimated from 200 bootstrap
samples. We assessed the calibration of BCSC and BCSC-PRS
ve-year risk by comparing observed and predicted numbers
of cancers, adjusting for the case-control design (34), using the
Hosmer-Lemeshow test (35) as done in other risk models using
case-control data (31,36). All analyses were performed within
the 50 resampled data sets, and results were combined across
them using approaches developed for multiple imputation (37)
(Supplementary Methods, available online). We evaluated the
net reclassication of case patients (change in true-positive
rate) and control patients (change in false-positive rate) for a
ve-year risk of 3% or greater (where there is greater absolute
benet of chemoprevention ) for the BCSC-PRS compared
with the BCSC model. Estimates and corresponding 95% CIs
were obtained using bootstrapping. All statistical tests were
BI-RADS density, adjusted for age and 1/BMI, was a statisti-
cally signicant risk factor for breast cancer within and across
studies (Table1; Supplementary Tables 3–5). The PRS was not
strongly correlated with age, BMI, or density (all statistical
correlations <.05). PRS was associated with breast cancer risk
within and across the three studies and was a statistically sig-
nicant risk factor independent of BI-RADS density (Pinteraction
=.23) (Tables 1 and 2; Supplementary Tables 3–5, available
online). PRS improved the t of models with BI-RADS density,
age, and 1/BMI (PLRT < .001), resulting in an AUC of 0.69 (95%
CI = 0.67 to 0.71) (Table 1, Supplementary Figure1, available
Importantly, the PRS further stratied risk associated with
extremely dense breasts, such that those with the lowest PRS
had an odds ratio of 0.91 (95% CI=0.53 to 1.56), while those in
the highest PRS had a 2.7-fold (95% CI=1.74 to 4.12) increased
risk compared with women with scattered broglandular
densities and average PRS (2nd quartile) (Table 2). Women
with fatty breasts and in the lowest PRS quartile had the low-
est risk (OR = 0.30, 95% CI = 0.18 to 0.51). Associations were
similar across studies (Pinteraction=.24) and within menopausal
subgroups (Pinteraction = .86) (Supplementary Table6, available
We next incorporated the OR corresponding to a one-unit
increase in the PRS estimated from the MCBCS and BBCC stud-
ies only (OR= 1.83, 95% CI = 1.59 to 2.11) into the BCSC model
and evaluated the impact within the MMHS (Supplementary
Table7, available online). The BCSC and BCSC-PRS models were
well calibrated when t on the case-control sets sampled from
MMHS (Supplementary Figure2, available online). Addition of
the PRS to the BCSC model provided a statistically signicant
improvement to the model t (PLRT < .001) and improved discrim-
ination between case patients and control patients (AUC=0.69,
95% CI = 0.64 to 0.73) compared with the BCSC model alone
(AUC=0.66, 95% CI = 0.61 to 0.70) (∆AUC=0.028, 95% CI = 0.007 to
0.049) (Supplementary Figure3, available online). The BCSC-PRS
model resulted in 36.8% of case patients exceeding the ve-year
risk threshold of 3% or greater where chemotherapy should be
discussed, compared with 25.7% of cases using the BCSC model,
for a net reclassication of 11% (95% CI= 7% to 15%) of cases.
In control patients, these numbers were 13.0% and 10.7% for
BCSC-PRS and BCSC, respectively, for a net 2% (95% CI=-1% to
5%) reclassication of control patients to a risk of 3% or greater,
although not statistically signicant (Supplementary Table 8,
available online). The increase in number of cases with the
BCSC-PRS at or above the 3% threshold may represent improved
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discrimination, but might also reect poor model calibration in
the upper tails, to some degree, especially as control patients
were also upwardly reclassied.
Our ndings that the PRS and BI-RADS density were inde-
pendent risk factors and that incorporating the PRS into the BCSC
risk model improved model t and net reclassication for case
patients suggest that both breast density and common genetic
variation are important for risk prediction. Risk models with good
discrimination and accuracy for predicting breast cancer are
important for targeted screening and prevention (39). Tamoxifen,
raloxifene, and aromatase inhibitors have been shown to be
effective for primary prevention but are rarely used in practice
(40) because of side effects and low interest of moderate-risk
women in taking prevention medication for breast cancer (41,42).
The highest-risk women may be more motivated to take preven-
tive therapies and accept their potential complications (38). Our
results demonstrate that the set of 76 SNPs improves the iden-
tication of women at the highest risk. Along with the increase
seen in AUC, the net-reclassication of 11% of case patients (95%
CI=7% to 15%) to a risk level where women are more likely to
benet from chemoprevention suggests that SNPs could be use-
The main limitation of this study was lack of independent
cohort data to check the calibration of the new BCSC-PRS model
in the general population. Further, our studies consisted of pri-
marily white women, and the translation of these ndings to
other racial and ethnic groups is unknown. A strength of our
work was independent conrmation in three data sets that PRS
adds discriminatory accuracy to BI-RADS breast density.
In summary, we found that BI-RADS breast density and a PRS
composed of 76 SNPs are both important risk factors for breast
cancer that can be incorporated into breast cancer risk models.
If these models are used to estimate population risk, rening the
high- and low-risk risk groups could result in more appropriate
tailoring of screening and prevention interventions.
This work was supported by the National Cancer Institute
(R01 CA128931, R01 CA128978, R01 CA97396, P50 CA116201, R01
CA240386, K24 CA169004, R21 CA179442, P01 CA154292, R01
CA140286, Cancer Center Support Grant CA15083) and the Breast
Cancer Research Foundation. BBCC was supported in part by
the Erlanger Leistungsbezogene Anschubsnanzierung und
Table1. Contribution of continuous and quartile polygenic risk score measures to the Breast Imaging Reporting and Data System breast
density and breast cancer association
Odds ratios (95% CIs) corresponding to BI-RADS breast density and PRS measures in 4 models*
Quartiles PRS alone
Quartiles of PRS †
Almost entirely fat 0.55 (0.45 to 0.68) –§ 0.57 (0.46 to 0.70) 0.56 (0.45 to 0.70)
1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.)
Heterogeneously dense 1.25 (1.07 to 1.46) 1.24 (1.06 to 1.45) 1.24 (1.06 to 1.45)
Extremely dense 1.77 (1.38 to 2.26) 1.75 (1.36 to 2.24) 1.73 (1.35 to 2.23)
<0.50 –§ 0.63 (0.52 to 0.78) 0.62 (0.51 to 0.77) –§
0.50–0.86 1.00 (Ref.) 1.00 (Ref.)
0.87–1.27 1.52 (1.26 to 1.83) 1.48 (1.23 to 1.79)
1.28+ 1.79 (1.50 to 2.14) 1.74 (1.45 to 2.09)
PRS continuous‡ –§ –§ –§ 1.48 (1.38 to 1.58)
AUC 0.66 (0.64 to 0.68) 0.68 (0.66 to 0.69) 0.69 (0.67 to 0.70) 0.69 (0.67 to 0.71)
* All three studies combined (n=1643 case patients, 2397 control patients). Models adjusted for age, 1/BMI, study. AUC = area under the curve; BI-RADS = Breast Imag-
ing Reporting and Data System; CI = condence interval; PRS = polygenic risk score.
† Likelihood Ratio Test PLRT < .001 for models with PRS and BI-RADS density compared with model with BI-RADS alone. All statistical tests were two-sided.
‡ Continuous PRS measure evaluated as per 1 standard deviation. Model with PRS continuous measure alone had OR=1.49 (95% CI=1.39 to 1.59) and AUC=0.68
(95% CI=0.66 to 0.69).
§ Variable not evaluated in this model.
Table2. Association (odds ratios and 95% condence intervals) of Breast Imaging Reporting and Data System breast density and polygenic
risk score with breast cancer*
Odds ratios (95% CI) corresponding to categories of BI-RADS density and PRS quartiles
Almost entirely fat Scattered broglandular densities Heterogeneously dense Extremely dense Pinteraction†
<0.50 0.30 (0.18 to 0.51) 0.65 (0.48 to 0.89) 0.80 (0.57 to 1.14) 0.91 (0.53 to 1.56)
0.50–0.86 0.44 (0.28 to 0.68) 1.00 (Ref.) 1.40 (1.02 to 1.92) 1.56 (0.94 to 2.59)
0.87–1.27 0.93 (0.62 to 1.40) 1.32 (0.99 to 1.76) 1.81 (1.34 to 2.46) 3.59 (2.26 to 5.70)
1.28+ 1.20 (0.81 to 1.78) 1.84 (1.40 to 2.42) 1.91 (1.42 to 2.57) 2.68 (1.74 to 4.12)
* All three studies combined (n=1643 case patients, 2397 control patients). Models adjusted for age, 1/BMI and study. BI-RADS = Breast Imaging Reporting and Data
System; CI = condence interval; OR = odds ratio; PRS = polygenic risk score.
† Pinteraction from logistic regression model including all three studies.
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Vachon et al. | 4 of 4
Nachwuchsfoerderung program of the Medical Faculty, University
Hospital Erlangen, Friedrich-Alexander University Erlangen-
Nuremberg. Funding for the genotyping of BBCC and MCBCS, as
well as the custom Illumina iSelect genotyping array was provided
by grants from the EU FP7 programme (Collaborative Oncological
Gene-Environment Study) and from Cancer Research UK.
The Collaborative Oncological Gene-Environment Study enabled
the genotyping for this study.
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