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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...

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Autores principales: Lee, Charmaine Pei Ling, Choi, Hyungwon, Soo, Khee Chee, Tan, Min-Han, Chay, Wen Yee, Chia, Kee Seng, Liu, Jenny, Li, Jingmei, Hartman, Mikael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
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.
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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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