TY - JOUR
T1 - Breast cancer risk prediction in women aged 35-50 years
T2 - impact of including sex hormone concentrations in the Gail model
AU - Clendenen, Tess V.
AU - Ge, Wenzhen
AU - Koenig, Karen L.
AU - Afanasyeva, Yelena
AU - Agnoli, Claudia
AU - Brinton, Louise A.
AU - Darvishian, Farbod
AU - Dorgan, Joanne F.
AU - Eliassen, A. Heather
AU - Falk, Roni T.
AU - Hallmans, Göran
AU - Hankinson, Susan E.
AU - Hoffman-Bolton, Judith
AU - Key, Timothy J.
AU - Krogh, Vittorio
AU - Nichols, Hazel B.
AU - Sandler, Dale P.
AU - Schoemaker, Minouk J.
AU - Sluss, Patrick M.
AU - Sund, Malin
AU - Swerdlow, Anthony J.
AU - Visvanathan, Kala
AU - Zeleniuch-Jacquotte, Anne
AU - Liu, Mengling
N1 - Funding Information:
This work was supported by grant NIH R01 CA178949. Support for the individual cohorts included: The Generations Study (BGS): This work was supported by Breast Cancer Now and The Institute of Cancer Research. We acknowledge NHS funding to the Royal Marsden and The Institute of Cancer Research NIHR Biomedical Research Centre. Columbia, MO Serum Bank (CSB): This research was supported by the Intramural Research Program of the NIH, National Cancer Institute and the Department of Defense Breast Cancer Research Program (BC062367). Guernsey cohort (Guernsey): Cancer Research UK C570/A16491. Availability of data and materials: Data access policies for the Guernsey study are available on the Cancer Epidemiology Unit website at https://www.ceu. ox.ac.uk/policies2. Nurses’ Health Study (NHS): NCI UM1 CA186107; R01 CA49449. Nurses’ Health Study II (NHSII): NCI UM1 CA176726; R01 CA67262. New York University Women’s Health Study (NYUWHS): NIH R01 CA098661, UM1 CA182934 and center grants P30 CA016087 and P30 ES000260. Sister Study: This research was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005) to D.P. Sandler and the Avon Foundation (02–2012-085) to H.B. Nichols and D.P. Sandler.
Funding Information:
We thank the NCI Cohort Consortium. CLUE authors would like to thank the State of Maryland, the Maryland Cigarette Restitution Fund, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention for the funds that helped support the collection and availability of the cancer registry data. The CLUE authors would also like to thank the CLUE participants and staff at the George W. Comstock Center for Public Health Research and Prevention. NHS authors thank the participants and staff of the NHS and NHSII for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.
Publisher Copyright:
© 2019 The Author(s).
PY - 2019/3/19
Y1 - 2019/3/19
N2 - Background: Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50. Methods: In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. Results: The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. Conclusions: AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.
AB - Background: Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50. Methods: In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. Results: The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. Conclusions: AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.
KW - Anti-Müllerian hormone
KW - Breast cancer risk prediction
KW - Gail model
KW - Testosterone
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U2 - 10.1186/s13058-019-1126-z
DO - 10.1186/s13058-019-1126-z
M3 - Article
C2 - 30890167
AN - SCOPUS:85063139684
SN - 1465-5411
VL - 21
JO - Breast Cancer Research
JF - Breast Cancer Research
IS - 1
M1 - 42
ER -