Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

Celine M Vachon¹, Christopher G Scott², Rulla M Tamimi^{3

4

5}, Deborah J Thompson⁶, Peter A Fasching^{7

8}, Jennifer Stone^{9

10}, Melissa C Southey^{11

12}, Stacey Winham², Sara Lindström^{13

14}, Jenna Lilyquist¹⁵, Graham G Giles^{10

16

17}, Roger L Milne^{10

11

16}, Robert J MacInnis^{10

16}, Laura Baglietto^{16

18}, Jingmei Li¹⁹, Kamila Czene²⁰, Manjeet K Bolla⁶, Qin Wang⁶, Joe Dennis⁶, Lothar Haeberle⁷, Mikael Eriksson²⁰, Peter Kraft^{3

5}, Robert Luben²¹, Nick Wareham²², Janet E Olson¹⁵, Aaron Norman¹⁵, Eric C Polley², Gertraud Maskarinec²³, Loic Le Marchand²³, Christopher A Haiman²⁴, John L Hopper¹⁰, Fergus J Couch²⁵, Douglas F Easton^{6

26}, Per Hall^{20

27}, Nilanjan Chatterjee^{28

29

30}, Montse Garcia-Closas³¹

Affiliations

¹ Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA. vachon.celine@mayo.edu.
² Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, 55905, MN, USA.
³ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
⁴ Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
⁵ Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
⁶ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
⁷ Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen Nuremberg, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, 91054, Erlangen, Germany.
⁸ Department of Medicine, Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, 90095, USA.
⁹ The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, Western Australia, 6009, Australia.
¹⁰ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
¹¹ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia.
¹² Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
¹³ Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, 98195, USA.
¹⁴ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
¹⁵ Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA.
¹⁶ Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.
¹⁷ Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
¹⁸ Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
¹⁹ Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
²⁰ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden.
²¹ Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
²² Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB1 8RN, UK.
²³ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA.
²⁴ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
²⁵ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
²⁶ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK.
²⁷ Department of Oncology, South General Hospital, 118 83, Stockholm, Sweden.
²⁸ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA.
²⁹ Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, 21218, MD, USA.
³⁰ Department of Oncology, School of Medicine, John Hopkins University, Baltimore, 21218, MD, USA.
³¹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA.

PMID: 31118087
PMCID: PMC6532188
DOI: 10.1186/s13058-019-1138-8

Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

Celine M Vachon et al. Breast Cancer Res. 2019.

. 2019 May 22;21(1):68.

doi: 10.1186/s13058-019-1138-8.

Authors

Affiliations

¹ Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA. vachon.celine@mayo.edu.
² Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, 55905, MN, USA.
³ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
⁴ Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
⁵ Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
⁶ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
⁷ Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen Nuremberg, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, 91054, Erlangen, Germany.
⁸ Department of Medicine, Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, 90095, USA.
⁹ The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, Western Australia, 6009, Australia.
¹⁰ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
¹¹ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia.
¹² Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
¹³ Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, 98195, USA.
¹⁴ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
¹⁵ Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA.
¹⁶ Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.
¹⁷ Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
¹⁸ Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
¹⁹ Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
²⁰ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden.
²¹ Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
²² Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB1 8RN, UK.
²³ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA.
²⁴ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
²⁵ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
²⁶ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK.
²⁷ Department of Oncology, South General Hospital, 118 83, Stockholm, Sweden.
²⁸ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA.
²⁹ Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, 21218, MD, USA.
³⁰ Department of Oncology, School of Medicine, John Hopkins University, Baltimore, 21218, MD, USA.
³¹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA.

PMID: 31118087
PMCID: PMC6532188
DOI: 10.1186/s13058-019-1138-8

Abstract

Background: Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk.

Methods: Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies.

Results: Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile.

Conclusions: The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Keywords: Breast cancer risk; Breast density; Genetic variation; Polygenic risk score; Risk models.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Joint association of quartiles of adjusted density phenotypes and quintiles PRS with breast cancer risk, adjusted for age, 1/BMI, and study. Quartiles adjusted percent density and PRS quintile with breast cancer risk (a). Quartiles of adjusted dense area and PRS quintile with breast cancer risk (b). PRS quintiles: quintile 1, − 1.411 to − 0.014; quintile 2, − 0.015 to 0.280; quintile 3, 0.281 to 0.542; quintile 4, 0.543 to 0.885; quintile 5, 0.886 to 2.583. Reference category is PRS quintile 3 and density quartile 2

**Fig. 2**
Tail-based test results from models with continuous adjusted density measures and PRS on breast cancer risk. Population-based studies. Models of adjusted percent density and PRS without interaction (a) and with multiplicative interaction included (b). Models with adjusted dense area and PRS without interaction (c) and with multiplicative interaction included (d)

See this image and copyright information in PMC

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Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

Affiliations

Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

Authors

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Abstract

Conflict of interest statement

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