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J Natl Cancer Inst. 2015 Mar 4;107(5). pii: dju397. doi: 10.1093/jnci/dju397. Print 2015 May.

The contributions of breast density and common genetic variation to breast cancer risk.

Author information

1
Affiliations 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); Moffitt Cancer Center, Tampa, Florida (TAS); University of California at Los Angeles, Department of Medicine, Division Hematology/Oncology, David Geffen School of Medicine, Los Angeles, CA (PAF). vachon.celine@mayo.edu.
2
Affiliations 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); Moffitt Cancer Center, Tampa, Florida (TAS); University of California at Los Angeles, Department of Medicine, Division Hematology/Oncology, David Geffen School of Medicine, Los Angeles, CA (PAF).

Abstract

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 five-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 (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence 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.

PMID:
25745020
PMCID:
PMC4598340
DOI:
10.1093/jnci/dju397
[Indexed for MEDLINE]
Free PMC Article

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