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Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction
INTRODUCTION: Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic d...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581713/ https://www.ncbi.nlm.nih.gov/pubmed/26401662 http://dx.doi.org/10.1371/journal.pone.0136650 |
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author | Lee, Charmaine Pei Ling Choi, Hyungwon Soo, Khee Chee Tan, Min-Han Chay, Wen Yee Chia, Kee Seng Liu, Jenny Li, Jingmei Hartman, Mikael |
author_facet | Lee, Charmaine Pei Ling Choi, Hyungwon Soo, Khee Chee Tan, Min-Han Chay, Wen Yee Chia, Kee Seng Liu, Jenny Li, Jingmei Hartman, Mikael |
author_sort | Lee, Charmaine Pei Ling |
collection | PubMed |
description | INTRODUCTION: Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population. METHODS: We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values. RESULTS: During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm(2), 21-30cm(2), 31-40cm(2), 41-50cm(2), 51-60cm(2), and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively. CONCLUSION: Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore. |
format | Online Article Text |
id | pubmed-4581713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45817132015-10-01 Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction Lee, Charmaine Pei Ling Choi, Hyungwon Soo, Khee Chee Tan, Min-Han Chay, Wen Yee Chia, Kee Seng Liu, Jenny Li, Jingmei Hartman, Mikael PLoS One Research Article INTRODUCTION: Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population. METHODS: We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values. RESULTS: During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm(2), 21-30cm(2), 31-40cm(2), 41-50cm(2), 51-60cm(2), and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively. CONCLUSION: Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore. Public Library of Science 2015-09-24 /pmc/articles/PMC4581713/ /pubmed/26401662 http://dx.doi.org/10.1371/journal.pone.0136650 Text en © 2015 Lee et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lee, Charmaine Pei Ling Choi, Hyungwon Soo, Khee Chee Tan, Min-Han Chay, Wen Yee Chia, Kee Seng Liu, Jenny Li, Jingmei Hartman, Mikael Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title | Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title_full | Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title_fullStr | Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title_full_unstemmed | Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title_short | Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction |
title_sort | mammographic breast density and common genetic variants in breast cancer risk prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581713/ https://www.ncbi.nlm.nih.gov/pubmed/26401662 http://dx.doi.org/10.1371/journal.pone.0136650 |
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