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Evaluation of models to predict BRCA germline mutations

The selection of candidates for BRCA germline mutation testing is an important clinical issue yet it remains a significant challenge. A number of risk prediction models have been developed to assist in pretest counselling. We have evaluated the performance and the inter-rater reliability of four of...

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Autores principales: Kang, H H, Williams, R, Leary, J, Ringland, C, Kirk, J, Ward, R
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2360540/
https://www.ncbi.nlm.nih.gov/pubmed/17016486
http://dx.doi.org/10.1038/sj.bjc.6603358
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author Kang, H H
Williams, R
Leary, J
Ringland, C
Kirk, J
Ward, R
author_facet Kang, H H
Williams, R
Leary, J
Ringland, C
Kirk, J
Ward, R
author_sort Kang, H H
collection PubMed
description The selection of candidates for BRCA germline mutation testing is an important clinical issue yet it remains a significant challenge. A number of risk prediction models have been developed to assist in pretest counselling. We have evaluated the performance and the inter-rater reliability of four of these models (BRCAPRO, Manchester, Penn and the Myriad-Frank). The four risk assessment models were applied to 380 pedigrees of families who had undergone BRCA1/2 mutation analysis. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operator characteristic (ROC) curve were calculated for each model. Using a greater than 10% probability threshold, the likelihood that a BRCA test result was positive in a mutation carrier compared to the likelihood that the same result would be expected in an individual without a BRCA mutation was 2.10 (95% confidence interval (CI) 1.66–2.67) for Penn, 1.74 (95% CI 1.48–2.04) for Myriad, 1.35 (95% CI 1.19–1.53) for Manchester and 1.68 (95% CI 1.39–2.03) for BRCAPRO. Application of these models, therefore, did not rule in BRCA mutation carrier status. Similar trends were observed for separate BRCA1/2 performance measures except BRCA2 assessment in the Penn model where the positive likelihood ratio was 5.93. The area under the ROC curve for each model was close to 0.75. In conclusion, the four models had very little impact on the pre-test probability of disease; there were significant clinical barriers to using some models and risk estimates varied between experts. Use of models for predicting BRCA mutation status is not currently justified for populations such as that evaluated in the current study.
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spelling pubmed-23605402009-09-10 Evaluation of models to predict BRCA germline mutations Kang, H H Williams, R Leary, J Ringland, C Kirk, J Ward, R Br J Cancer Molecular Diagnostics The selection of candidates for BRCA germline mutation testing is an important clinical issue yet it remains a significant challenge. A number of risk prediction models have been developed to assist in pretest counselling. We have evaluated the performance and the inter-rater reliability of four of these models (BRCAPRO, Manchester, Penn and the Myriad-Frank). The four risk assessment models were applied to 380 pedigrees of families who had undergone BRCA1/2 mutation analysis. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operator characteristic (ROC) curve were calculated for each model. Using a greater than 10% probability threshold, the likelihood that a BRCA test result was positive in a mutation carrier compared to the likelihood that the same result would be expected in an individual without a BRCA mutation was 2.10 (95% confidence interval (CI) 1.66–2.67) for Penn, 1.74 (95% CI 1.48–2.04) for Myriad, 1.35 (95% CI 1.19–1.53) for Manchester and 1.68 (95% CI 1.39–2.03) for BRCAPRO. Application of these models, therefore, did not rule in BRCA mutation carrier status. Similar trends were observed for separate BRCA1/2 performance measures except BRCA2 assessment in the Penn model where the positive likelihood ratio was 5.93. The area under the ROC curve for each model was close to 0.75. In conclusion, the four models had very little impact on the pre-test probability of disease; there were significant clinical barriers to using some models and risk estimates varied between experts. Use of models for predicting BRCA mutation status is not currently justified for populations such as that evaluated in the current study. Nature Publishing Group 2006-10-09 2006-10-03 /pmc/articles/PMC2360540/ /pubmed/17016486 http://dx.doi.org/10.1038/sj.bjc.6603358 Text en Copyright © 2006 Cancer Research UK https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
spellingShingle Molecular Diagnostics
Kang, H H
Williams, R
Leary, J
Ringland, C
Kirk, J
Ward, R
Evaluation of models to predict BRCA germline mutations
title Evaluation of models to predict BRCA germline mutations
title_full Evaluation of models to predict BRCA germline mutations
title_fullStr Evaluation of models to predict BRCA germline mutations
title_full_unstemmed Evaluation of models to predict BRCA germline mutations
title_short Evaluation of models to predict BRCA germline mutations
title_sort evaluation of models to predict brca germline mutations
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2360540/
https://www.ncbi.nlm.nih.gov/pubmed/17016486
http://dx.doi.org/10.1038/sj.bjc.6603358
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