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Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes

Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs m...

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Autores principales: Kerr, Iain D., Cox, Hannah C., Moyes, Kelsey, Evans, Brent, Burdett, Brianna C., van Kan, Aric, McElroy, Heather, Vail, Paris J., Brown, Krystal L., Sumampong, Dechie B., Monteferrante, Nicholas J., Hardman, Kennedy L., Theisen, Aaron, Mundt, Erin, Wenstrup, Richard J., Eggington, Julie M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5386911/
https://www.ncbi.nlm.nih.gov/pubmed/28050887
http://dx.doi.org/10.1007/s12687-016-0289-x
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author Kerr, Iain D.
Cox, Hannah C.
Moyes, Kelsey
Evans, Brent
Burdett, Brianna C.
van Kan, Aric
McElroy, Heather
Vail, Paris J.
Brown, Krystal L.
Sumampong, Dechie B.
Monteferrante, Nicholas J.
Hardman, Kennedy L.
Theisen, Aaron
Mundt, Erin
Wenstrup, Richard J.
Eggington, Julie M.
author_facet Kerr, Iain D.
Cox, Hannah C.
Moyes, Kelsey
Evans, Brent
Burdett, Brianna C.
van Kan, Aric
McElroy, Heather
Vail, Paris J.
Brown, Krystal L.
Sumampong, Dechie B.
Monteferrante, Nicholas J.
Hardman, Kennedy L.
Theisen, Aaron
Mundt, Erin
Wenstrup, Richard J.
Eggington, Julie M.
author_sort Kerr, Iain D.
collection PubMed
description Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs may be reclassified to better inform patient care when new evidence is available. It is critical that the methods used for reclassification are robust in order to prevent inappropriate medical management strategies and unnecessary, life-altering surgeries. In an effort to provide evidence for classification, several in silico algorithms have been developed that attempt to predict the functional impact of missense variants through amino acid sequence conservation analysis. We report an analysis comparing internally derived, evidence-based classifications with the results obtained from six commonly used algorithms. We compiled a dataset of 1118 variants in BRCA1, BRCA2, MLH1, and MSH2 previously classified by our laboratory’s evidence-based variant classification program. We compared internally derived classifications with those obtained from the following in silico tools: Align-GVGD, CONDEL, Grantham Analysis, MAPP-MMR, PolyPhen-2, and SIFT. Despite being based on similar underlying principles, all algorithms displayed marked divergence in accuracy, specificity, and sensitivity. Overall, accuracy ranged from 58.7 to 90.8% while the Matthews Correlation Coefficient ranged from 0.26–0.65. CONDEL, a weighted average of multiple algorithms, did not perform significantly better than its individual components evaluated here. These results suggest that the in silico algorithms evaluated here do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12687-016-0289-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-53869112017-04-25 Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes Kerr, Iain D. Cox, Hannah C. Moyes, Kelsey Evans, Brent Burdett, Brianna C. van Kan, Aric McElroy, Heather Vail, Paris J. Brown, Krystal L. Sumampong, Dechie B. Monteferrante, Nicholas J. Hardman, Kennedy L. Theisen, Aaron Mundt, Erin Wenstrup, Richard J. Eggington, Julie M. J Community Genet Original Article Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs may be reclassified to better inform patient care when new evidence is available. It is critical that the methods used for reclassification are robust in order to prevent inappropriate medical management strategies and unnecessary, life-altering surgeries. In an effort to provide evidence for classification, several in silico algorithms have been developed that attempt to predict the functional impact of missense variants through amino acid sequence conservation analysis. We report an analysis comparing internally derived, evidence-based classifications with the results obtained from six commonly used algorithms. We compiled a dataset of 1118 variants in BRCA1, BRCA2, MLH1, and MSH2 previously classified by our laboratory’s evidence-based variant classification program. We compared internally derived classifications with those obtained from the following in silico tools: Align-GVGD, CONDEL, Grantham Analysis, MAPP-MMR, PolyPhen-2, and SIFT. Despite being based on similar underlying principles, all algorithms displayed marked divergence in accuracy, specificity, and sensitivity. Overall, accuracy ranged from 58.7 to 90.8% while the Matthews Correlation Coefficient ranged from 0.26–0.65. CONDEL, a weighted average of multiple algorithms, did not perform significantly better than its individual components evaluated here. These results suggest that the in silico algorithms evaluated here do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12687-016-0289-x) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2017-01-03 2017-04 /pmc/articles/PMC5386911/ /pubmed/28050887 http://dx.doi.org/10.1007/s12687-016-0289-x Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
spellingShingle Original Article
Kerr, Iain D.
Cox, Hannah C.
Moyes, Kelsey
Evans, Brent
Burdett, Brianna C.
van Kan, Aric
McElroy, Heather
Vail, Paris J.
Brown, Krystal L.
Sumampong, Dechie B.
Monteferrante, Nicholas J.
Hardman, Kennedy L.
Theisen, Aaron
Mundt, Erin
Wenstrup, Richard J.
Eggington, Julie M.
Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title_full Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title_fullStr Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title_full_unstemmed Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title_short Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
title_sort assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5386911/
https://www.ncbi.nlm.nih.gov/pubmed/28050887
http://dx.doi.org/10.1007/s12687-016-0289-x
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