. 2019 Mar 19;21(1):42.

doi: 10.1186/s13058-019-1126-z.

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

Tess V Clendenen¹, Wenzhen Ge¹, Karen L Koenig¹, Yelena Afanasyeva¹, Claudia Agnoli², Louise A Brinton³, Farbod Darvishian^{4

5}, Joanne F Dorgan⁶, A Heather Eliassen⁷, Roni T Falk³, Göran Hallmans⁸, Susan E Hankinson^{7

9}, Judith Hoffman-Bolton¹⁰, Timothy J Key¹¹, Vittorio Krogh², Hazel B Nichols¹², Dale P Sandler¹³, Minouk J Schoemaker^{14

15}, Patrick M Sluss¹⁶, Malin Sund¹⁷, Anthony J Swerdlow¹⁴, Kala Visvanathan^{10

18}, Anne Zeleniuch-Jacquotte^{1

5}, Mengling Liu^{19

20}

Affiliations

¹ Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.
² Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy.
³ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁴ Department of Pathology, New York University School of Medicine, New York, NY, USA.
⁵ Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA.
⁶ Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁸ Department of Biobank Research, Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
⁹ Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
¹⁰ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹¹ Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
¹² Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
¹³ Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
¹⁴ Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
¹⁵ Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
¹⁶ Department of Pathology, Harvard Medical School, Boston, MA, USA.
¹⁷ Department of Surgery, Umeå University Hospital, Umeå, Sweden.
¹⁸ Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹⁹ Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA. mengling.liu@nyumc.org.
²⁰ Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA. mengling.liu@nyumc.org.

PMID: 30890167
PMCID: PMC6425605
DOI: 10.1186/s13058-019-1126-z

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

Tess V Clendenen et al. Breast Cancer Res. 2019.

. 2019 Mar 19;21(1):42.

doi: 10.1186/s13058-019-1126-z.

Authors

Affiliations

¹ Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.
² Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy.
³ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁴ Department of Pathology, New York University School of Medicine, New York, NY, USA.
⁵ Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA.
⁶ Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁸ Department of Biobank Research, Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
⁹ Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
¹⁰ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹¹ Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
¹² Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
¹³ Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
¹⁴ Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
¹⁵ Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
¹⁶ Department of Pathology, Harvard Medical School, Boston, MA, USA.
¹⁷ Department of Surgery, Umeå University Hospital, Umeå, Sweden.
¹⁸ Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹⁹ Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA. mengling.liu@nyumc.org.
²⁰ Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA. mengling.liu@nyumc.org.

PMID: 30890167
PMCID: PMC6425605
DOI: 10.1186/s13058-019-1126-z

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.

Keywords: Anti-Müllerian hormone; Breast cancer risk prediction; Gail model; Testosterone.

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Conflict of interest statement

Ethics approval and consent to participate

Each cohort was approved by its institutional ethical review board.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

**Fig. 1**
Area under the receiver operating curve (AUC) estimates and 95% confidence intervals

**Fig. 2**
Reclassification of absolute 5-year risk of breast cancer with the addition of AMH and testosterone to the Gail model

See this image and copyright information in PMC

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

Affiliations

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

Authors

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Abstract

Conflict of interest statement

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

Figures

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Abstract

Conflict of interest statement

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