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


Journal

Breast cancer research : BCR
ISSN: 1465-542X
Titre abrégé: Breast Cancer Res
Pays: England
ID NLM: 100927353

Informations de publication

Date de publication:
22 05 2019
Historique:
received: 27 11 2018
accepted: 15 04 2019
entrez: 24 5 2019
pubmed: 24 5 2019
medline: 10 1 2020
Statut: epublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 31118087
doi: 10.1186/s13058-019-1138-8
pii: 10.1186/s13058-019-1138-8
pmc: PMC6532188
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

68

Subventions

Organisme : NCI NIH HHS
ID : R01 CA128931
Pays : United States
Organisme : NCI NIH HHS
ID : R25 CA092049
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA087969
Pays : United States
Organisme : Medical Research Council
ID : MR/N003284/1
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : P01 CA154292
Pays : United States
Organisme : Medical Research Council
ID : G1000143
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12015/1
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : R01 CA128978
Pays : United States
Organisme : Cancer Research UK
ID : 14136
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : UM1 CA186107
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA179442
Pays : United States
Organisme : Medical Research Council
ID : G0500300
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : R01 CA097396
Pays : United States
Organisme : NCI NIH HHS
ID : UM1 CA176726
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA116201
Pays : United States
Organisme : Medical Research Council
ID : G0401527
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : R01 CA085265
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA122340
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA164973
Pays : United States
Organisme : NCI NIH HHS
ID : K24 CA169004
Pays : United States

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Auteurs

Celine M Vachon (CM)

Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA. vachon.celine@mayo.edu.

Christopher G Scott (CG)

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, 55905, MN, USA.

Rulla M Tamimi (RM)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.
Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.

Deborah J Thompson (DJ)

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.

Peter A Fasching (PA)

Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen Nuremberg, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, 91054, Erlangen, Germany.
Department of Medicine, Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, 90095, USA.

Jennifer Stone (J)

The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, Western Australia, 6009, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.

Melissa C Southey (MC)

Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia.
Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, 3010, Australia.

Stacey Winham (S)

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, 55905, MN, USA.

Sara Lindström (S)

Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, 98195, USA.
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.

Jenna Lilyquist (J)

Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA.

Graham G Giles (GG)

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.
Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Roger L Milne (RL)

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, 3168, Australia.
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.

Robert J MacInnis (RJ)

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.

Laura Baglietto (L)

Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, 3004, Australia.
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.

Jingmei Li (J)

Human Genetics, Genome Institute of Singapore, Singapore, Singapore.

Kamila Czene (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden.

Manjeet K Bolla (MK)

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.

Qin Wang (Q)

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.

Joe Dennis (J)

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.

Lothar Haeberle (L)

Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen Nuremberg, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, 91054, Erlangen, Germany.

Mikael Eriksson (M)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden.

Peter Kraft (P)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.

Robert Luben (R)

Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.

Nick Wareham (N)

Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB1 8RN, UK.

Janet E Olson (JE)

Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA.

Aaron Norman (A)

Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA.

Eric C Polley (EC)

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, 55905, MN, USA.

Gertraud Maskarinec (G)

Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA.

Loic Le Marchand (L)

Epidemiology Program, University of Hawaii Cancer Center, Honolulu, 96813, HI, USA.

Christopher A Haiman (CA)

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.

John L Hopper (JL)

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, 3010, Australia.

Fergus J Couch (FJ)

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.

Douglas F Easton (DF)

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK.

Per Hall (P)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden.
Department of Oncology, South General Hospital, 118 83, Stockholm, Sweden.

Nilanjan Chatterjee (N)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA.
Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, 21218, MD, USA.
Department of Oncology, School of Medicine, John Hopkins University, Baltimore, 21218, MD, USA.

Montse Garcia-Closas (M)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA.

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