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

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 SundAnthony J. Swerdlow, Kala Visvanathan, Anne Zeleniuch-Jacquotte, Mengling Liu

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number42
JournalBreast Cancer Research
Volume21
Issue number1
DOIs
Publication statusPublished - Mar 19 2019

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Gonadal Steroid Hormones
Area Under Curve
Breast Neoplasms
Testosterone
Hormones
Biomarkers
ROC Curve
Meta-Analysis
Case-Control Studies
Logistic Models
Prospective Studies
Mortality
Incidence
Serum
Population

Keywords

  • Anti-Müllerian hormone
  • Breast cancer risk prediction
  • Gail model
  • Testosterone

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Breast cancer risk prediction in women aged 35-50 years : impact of including sex hormone concentrations in the Gail model. / Clendenen, Tess V.; Ge, Wenzhen; Koenig, Karen L.; Afanasyeva, Yelena; Agnoli, Claudia; Brinton, Louise A.; Darvishian, Farbod; Dorgan, Joanne F.; Eliassen, A. Heather; Falk, Roni T.; Hallmans, Göran; Hankinson, Susan E.; Hoffman-Bolton, Judith; Key, Timothy J.; Krogh, Vittorio; Nichols, Hazel B.; Sandler, Dale P.; Schoemaker, Minouk J.; Sluss, Patrick M.; Sund, Malin; Swerdlow, Anthony J.; Visvanathan, Kala; Zeleniuch-Jacquotte, Anne; Liu, Mengling.

In: Breast Cancer Research, Vol. 21, No. 1, 42, 19.03.2019.

Research output: Contribution to journalArticle

Clendenen, TV, Ge, W, Koenig, KL, Afanasyeva, Y, Agnoli, C, Brinton, LA, Darvishian, F, Dorgan, JF, Eliassen, AH, Falk, RT, Hallmans, G, Hankinson, SE, Hoffman-Bolton, J, Key, TJ, Krogh, V, Nichols, HB, Sandler, DP, Schoemaker, MJ, Sluss, PM, Sund, M, Swerdlow, AJ, Visvanathan, K, Zeleniuch-Jacquotte, A & Liu, M 2019, 'Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model', Breast Cancer Research, vol. 21, no. 1, 42. https://doi.org/10.1186/s13058-019-1126-z
Clendenen, Tess V. ; Ge, Wenzhen ; Koenig, Karen L. ; Afanasyeva, Yelena ; Agnoli, Claudia ; Brinton, Louise A. ; Darvishian, Farbod ; Dorgan, Joanne F. ; Eliassen, A. Heather ; Falk, Roni T. ; Hallmans, Göran ; Hankinson, Susan E. ; Hoffman-Bolton, Judith ; Key, Timothy J. ; Krogh, Vittorio ; Nichols, Hazel B. ; Sandler, Dale P. ; Schoemaker, Minouk J. ; Sluss, Patrick M. ; Sund, Malin ; Swerdlow, Anthony J. ; Visvanathan, Kala ; Zeleniuch-Jacquotte, Anne ; Liu, Mengling. / Breast cancer risk prediction in women aged 35-50 years : impact of including sex hormone concentrations in the Gail model. In: Breast Cancer Research. 2019 ; Vol. 21, No. 1.
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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-M{\"u}llerian 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.",
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author = "Clendenen, {Tess V.} and Wenzhen Ge and Koenig, {Karen L.} and Yelena Afanasyeva and Claudia Agnoli and Brinton, {Louise A.} and Farbod Darvishian and Dorgan, {Joanne F.} and Eliassen, {A. Heather} and Falk, {Roni T.} and G{\"o}ran Hallmans and Hankinson, {Susan E.} and Judith Hoffman-Bolton and Key, {Timothy J.} and Vittorio Krogh and Nichols, {Hazel B.} and Sandler, {Dale P.} and Schoemaker, {Minouk J.} and Sluss, {Patrick M.} and Malin Sund and Swerdlow, {Anthony J.} and Kala Visvanathan and Anne Zeleniuch-Jacquotte and Mengling Liu",
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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

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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