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Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models
To establish an efficient, reliable and easy to apply risk assessment tool to select families with breast and/or ovarian cancer patients for BRCA mutation testing, using available probability models. In a retrospective study of 263 families with breast and/or ovarian cancer patients, the utility of...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Nature Publishing Group
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2360521/ https://www.ncbi.nlm.nih.gov/pubmed/16909138 http://dx.doi.org/10.1038/sj.bjc.6603306 |
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author | Bodmer, D Ligtenberg, M J L van der Hout, A H Gloudemans, S Ansink, K Oosterwijk, J C Hoogerbrugge, N |
author_facet | Bodmer, D Ligtenberg, M J L van der Hout, A H Gloudemans, S Ansink, K Oosterwijk, J C Hoogerbrugge, N |
author_sort | Bodmer, D |
collection | PubMed |
description | To establish an efficient, reliable and easy to apply risk assessment tool to select families with breast and/or ovarian cancer patients for BRCA mutation testing, using available probability models. In a retrospective study of 263 families with breast and/or ovarian cancer patients, the utility of the Frank (Myriad), Gilpin (family history assessment tool) and Evans (Manchester) model was analysed, to select 49 BRCA mutation-positive families. For various cutoff levels and combinations, the sensitivity and specificity were calculated and compared. The best combinations were subsequently validated in additional sets of families. Comparable sensitivity and specificity were obtained with the Gilpin and Evans models. They appeared to be complementary to the Frank model. To obtain an optimal sensitivity, five ‘additional criteria’ were introduced that are specific for the selection of small or uninformative families. The optimal selection is made by the combination ‘Frank ⩾16% or Evans2 ⩾12 or one of five additional criteria’. The efficiency of the selection of families for mutation testing of BRCA1 and BRCA2 can be optimised by using a combination of available easy to apply risk assessment models. |
format | Text |
id | pubmed-2360521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-23605212009-09-10 Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models Bodmer, D Ligtenberg, M J L van der Hout, A H Gloudemans, S Ansink, K Oosterwijk, J C Hoogerbrugge, N Br J Cancer Genetics and Genomics To establish an efficient, reliable and easy to apply risk assessment tool to select families with breast and/or ovarian cancer patients for BRCA mutation testing, using available probability models. In a retrospective study of 263 families with breast and/or ovarian cancer patients, the utility of the Frank (Myriad), Gilpin (family history assessment tool) and Evans (Manchester) model was analysed, to select 49 BRCA mutation-positive families. For various cutoff levels and combinations, the sensitivity and specificity were calculated and compared. The best combinations were subsequently validated in additional sets of families. Comparable sensitivity and specificity were obtained with the Gilpin and Evans models. They appeared to be complementary to the Frank model. To obtain an optimal sensitivity, five ‘additional criteria’ were introduced that are specific for the selection of small or uninformative families. The optimal selection is made by the combination ‘Frank ⩾16% or Evans2 ⩾12 or one of five additional criteria’. The efficiency of the selection of families for mutation testing of BRCA1 and BRCA2 can be optimised by using a combination of available easy to apply risk assessment models. Nature Publishing Group 2006-09-18 2006-08-15 /pmc/articles/PMC2360521/ /pubmed/16909138 http://dx.doi.org/10.1038/sj.bjc.6603306 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 | Genetics and Genomics Bodmer, D Ligtenberg, M J L van der Hout, A H Gloudemans, S Ansink, K Oosterwijk, J C Hoogerbrugge, N Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title | Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title_full | Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title_fullStr | Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title_full_unstemmed | Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title_short | Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ‘easy to apply’ probability models |
title_sort | optimal selection for brca1 and brca2 mutation testing using a combination of ‘easy to apply’ probability models |
topic | Genetics and Genomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2360521/ https://www.ncbi.nlm.nih.gov/pubmed/16909138 http://dx.doi.org/10.1038/sj.bjc.6603306 |
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