Universität zu Köln

Ernst, Corinna, Hahnen, Eric, Engel, Christoph ORCID: 0000-0002-7247-282X, Nothnagel, Michael ORCID: 0000-0001-8305-7114, Weber, Jonas, Schmutzler, Rita K. and Hauke, Jan (2018). Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics. BMC Med. Genomics, 11. LONDON: BIOMED CENTRAL LTD. ISSN 1755-8794

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Abstract

Background: The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer. Methods: We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches. Results: PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer. Conclusion: We show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Ernst, Corinna UNSPECIFIED UNSPECIFIED UNSPECIFIED Hahnen, Eric UNSPECIFIED UNSPECIFIED UNSPECIFIED Engel, Christoph UNSPECIFIED orcid.org/0000-0002-7247-282X UNSPECIFIED Nothnagel, Michael UNSPECIFIED orcid.org/0000-0001-8305-7114 UNSPECIFIED Weber, Jonas UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmutzler, Rita K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hauke, Jan UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-192385
DOI:	10.1186/s12920-018-0353-y
Journal or Publication Title:	BMC Med. Genomics
Volume:	11
Date:	2018
Publisher:	BIOMED CENTRAL LTD
Place of Publication:	LONDON
ISSN:	1755-8794
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Biology > Institute for Genetics
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SEQUENCE VARIANTS; GENETIC-VARIATION; MUTATIONS; RECOMMENDATIONS; PATHOGENICITY; ALGORITHM; IMPACT Multiple languages Genetics & Heredity Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/19238