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Breast Cancer Research
Published in: Breast Cancer Research 1/2019

Open Access 01-12-2019 | Breast Cancer | Research article

Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model

Authors: Tess V. Clendenen, Wenzhen Ge, Karen L. Koenig, Yelena Afanasyeva, Claudia Agnoli, Louise A. Brinton, Farbod Darvishian, Joanne F. Dorgan, A. Heather Eliassen, Roni T. Falk, Göran Hallmans, Susan E. Hankinson, Judith Hoffman-Bolton, Timothy J. Key, Vittorio Krogh, Hazel B. Nichols, Dale P. Sandler, Minouk J. Schoemaker, Patrick M. Sluss, Malin Sund, Anthony J. Swerdlow, Kala Visvanathan, Anne Zeleniuch-Jacquotte, Mengling Liu

Published in: Breast Cancer Research | Issue 1/2019

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Abstract

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.
Appendix
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Metadata
Title
Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model
Authors
Tess V. Clendenen
Wenzhen Ge
Karen L. Koenig
Yelena Afanasyeva
Claudia Agnoli
Louise A. Brinton
Farbod Darvishian
Joanne F. Dorgan
A. Heather Eliassen
Roni T. Falk
Göran Hallmans
Susan E. Hankinson
Judith Hoffman-Bolton
Timothy J. Key
Vittorio Krogh
Hazel B. Nichols
Dale P. Sandler
Minouk J. Schoemaker
Patrick M. Sluss
Malin Sund
Anthony J. Swerdlow
Kala Visvanathan
Anne Zeleniuch-Jacquotte
Mengling Liu
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2019
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-019-1126-z

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