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Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI
Author(s) -
Carraro Marco,
Minervini Giovanni,
Giollo Manuel,
Bromberg Yana,
Capriotti Emidio,
Casadio Rita,
Dunbrack Roland,
Elefanti Lisa,
Fariselli Pietro,
Ferrari Carlo,
Gough Julian,
Katsonis Panagiotis,
Leonardi Emanuela,
Lichtarge Olivier,
Menin Chiara,
Martelli Pier Luigi,
Niroula Abhishek,
Pal Lipika R.,
Repo Susanna,
Scaini Maria Chiara,
Vihinen Mauno,
Wei Qiong,
Xu Qifang,
Yang Yuedong,
Yin Yizhou,
Zaucha Jan,
Zhao Huiying,
Zhou Yaoqi,
Brenner Steven E.,
Moult John,
Tosatto Silvio C. E.
Publication year - 2017
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23235
Subject(s) - biology , in silico , context (archaeology) , cdkn2a , computational biology , genetics , machine learning , gene , bioinformatics , computer science , paleontology
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.