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Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study

BACKGROUND: The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large...

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Autores principales: Yang, Xin, Eriksson, Mikael, Czene, Kamila, Lee, Andrew, Leslie, Goska, Lush, Michael, Wang, Jean, Dennis, Joe, Dorling, Leila, Carvalho, Sara, Mavaddat, Nasim, Simard, Jacques, Schmidt, Marjanka K, Easton, Douglas F, Hall, Per, Antoniou, Antonis C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691822/
https://www.ncbi.nlm.nih.gov/pubmed/36162852
http://dx.doi.org/10.1136/jmg-2022-108806
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author Yang, Xin
Eriksson, Mikael
Czene, Kamila
Lee, Andrew
Leslie, Goska
Lush, Michael
Wang, Jean
Dennis, Joe
Dorling, Leila
Carvalho, Sara
Mavaddat, Nasim
Simard, Jacques
Schmidt, Marjanka K
Easton, Douglas F
Hall, Per
Antoniou, Antonis C
author_facet Yang, Xin
Eriksson, Mikael
Czene, Kamila
Lee, Andrew
Leslie, Goska
Lush, Michael
Wang, Jean
Dennis, Joe
Dorling, Leila
Carvalho, Sara
Mavaddat, Nasim
Simard, Jacques
Schmidt, Marjanka K
Easton, Douglas F
Hall, Per
Antoniou, Antonis C
author_sort Yang, Xin
collection PubMed
description BACKGROUND: The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort. METHODS: We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45–63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC). RESULTS: Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%). CONCLUSION: The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.
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spelling pubmed-96918222022-11-26 Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study Yang, Xin Eriksson, Mikael Czene, Kamila Lee, Andrew Leslie, Goska Lush, Michael Wang, Jean Dennis, Joe Dorling, Leila Carvalho, Sara Mavaddat, Nasim Simard, Jacques Schmidt, Marjanka K Easton, Douglas F Hall, Per Antoniou, Antonis C J Med Genet Cancer Genetics BACKGROUND: The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort. METHODS: We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45–63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC). RESULTS: Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%). CONCLUSION: The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening. BMJ Publishing Group 2022-12 2022-09-26 /pmc/articles/PMC9691822/ /pubmed/36162852 http://dx.doi.org/10.1136/jmg-2022-108806 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Cancer Genetics
Yang, Xin
Eriksson, Mikael
Czene, Kamila
Lee, Andrew
Leslie, Goska
Lush, Michael
Wang, Jean
Dennis, Joe
Dorling, Leila
Carvalho, Sara
Mavaddat, Nasim
Simard, Jacques
Schmidt, Marjanka K
Easton, Douglas F
Hall, Per
Antoniou, Antonis C
Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title_full Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title_fullStr Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title_full_unstemmed Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title_short Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study
title_sort prospective validation of the boadicea multifactorial breast cancer risk prediction model in a large prospective cohort study
topic Cancer Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691822/
https://www.ncbi.nlm.nih.gov/pubmed/36162852
http://dx.doi.org/10.1136/jmg-2022-108806
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