Dataset Information

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

ABSTRACT: Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.

SUBMITTER: Carraro M

PROVIDER: S-EPMC5561474 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

Carraro Marco M Minervini Giovanni G Giollo Manuel M Bromberg Yana Y Capriotti Emidio E Casadio Rita R Dunbrack Roland R Elefanti Lisa L Fariselli Pietro P Ferrari Carlo C Gough Julian J Katsonis Panagiotis P Leonardi Emanuela E Lichtarge Olivier O Menin Chiara C Martelli Pier Luigi PL Niroula Abhishek A Pal Lipika R LR Repo Susanna S Scaini Maria Chiara MC Vihinen Mauno M Wei Qiong Q Xu Qifang Q Yang Yuedong Y Yin Yizhou Y Zaucha Jan J Zhao Huiying H Zhou Yaoqi Y Brenner Steven E SE Moult John J Tosatto Silvio C E SCE

Human mutation 20170516 9

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 vari ...[more]

PMID: 28440912

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Project description: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.

Dataset Information

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

Publications

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.

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