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Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model
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 w...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425605/ https://www.ncbi.nlm.nih.gov/pubmed/30890167 http://dx.doi.org/10.1186/s13058-019-1126-z |
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author | 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 |
author_facet | 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 |
author_sort | Clendenen, Tess V. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6425605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64256052019-03-29 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 Breast Cancer Res Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-19 2019 /pmc/articles/PMC6425605/ /pubmed/30890167 http://dx.doi.org/10.1186/s13058-019-1126-z Text en © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article 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 |
title | Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model |
title_full | Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model |
title_fullStr | Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model |
title_full_unstemmed | Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model |
title_short | Breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the Gail model |
title_sort | breast cancer risk prediction in women aged 35–50 years: impact of including sex hormone concentrations in the gail model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425605/ https://www.ncbi.nlm.nih.gov/pubmed/30890167 http://dx.doi.org/10.1186/s13058-019-1126-z |
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