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Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models

BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, whic...

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Autores principales: Li, Sherly X, Milne, Roger L, Nguyen-Dumont, Tu, Wang, Xiaochuan, English, Dallas R, Giles, Graham G, Southey, Melissa C, Antoniou, Antonis C, Lee, Andrew, Li, Shuai, Winship, Ingrid, Hopper, John L, Terry, Mary Beth, MacInnis, Robert J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099999/
https://www.ncbi.nlm.nih.gov/pubmed/33977228
http://dx.doi.org/10.1093/jncics/pkab021
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author Li, Sherly X
Milne, Roger L
Nguyen-Dumont, Tu
Wang, Xiaochuan
English, Dallas R
Giles, Graham G
Southey, Melissa C
Antoniou, Antonis C
Lee, Andrew
Li, Shuai
Winship, Ingrid
Hopper, John L
Terry, Mary Beth
MacInnis, Robert J
author_facet Li, Sherly X
Milne, Roger L
Nguyen-Dumont, Tu
Wang, Xiaochuan
English, Dallas R
Giles, Graham G
Southey, Melissa C
Antoniou, Antonis C
Lee, Andrew
Li, Shuai
Winship, Ingrid
Hopper, John L
Terry, Mary Beth
MacInnis, Robert J
author_sort Li, Sherly X
collection PubMed
description BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. METHODS: We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). RESULTS: When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than  .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. CONCLUSIONS: Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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spelling pubmed-80999992021-05-10 Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models Li, Sherly X Milne, Roger L Nguyen-Dumont, Tu Wang, Xiaochuan English, Dallas R Giles, Graham G Southey, Melissa C Antoniou, Antonis C Lee, Andrew Li, Shuai Winship, Ingrid Hopper, John L Terry, Mary Beth MacInnis, Robert J JNCI Cancer Spectr Article BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. METHODS: We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). RESULTS: When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than  .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. CONCLUSIONS: Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted. Oxford University Press 2021-03-02 /pmc/articles/PMC8099999/ /pubmed/33977228 http://dx.doi.org/10.1093/jncics/pkab021 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Li, Sherly X
Milne, Roger L
Nguyen-Dumont, Tu
Wang, Xiaochuan
English, Dallas R
Giles, Graham G
Southey, Melissa C
Antoniou, Antonis C
Lee, Andrew
Li, Shuai
Winship, Ingrid
Hopper, John L
Terry, Mary Beth
MacInnis, Robert J
Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title_full Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title_fullStr Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title_full_unstemmed Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title_short Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
title_sort prospective evaluation of the addition of polygenic risk scores to breast cancer risk models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099999/
https://www.ncbi.nlm.nih.gov/pubmed/33977228
http://dx.doi.org/10.1093/jncics/pkab021
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