Yizhou Yin, Huiying Zhao, Julian Gough, Marco Carraro, Qiong Wei, Yana Bromberg, Silvio C. E. Tosatto, Rita Casadio, Emidio Capriotti, Mauno Vihinen, Panagiotis Katsonis, Giovanni Minervini, Lipika R. Pal, Jan Zaucha, Lisa Elefanti, Manuel Giollo, Qifang Xu, Yuedong Yang, Chiara Menin, Emanuela Leonardi, Roland Dunbrack, Pietro Fariselli, Maria Chiara Scaini, Olivier Lichtarge, John Moult, Pier Luigi Martelli, Steven E. Brenner, Susanna Repo, Yaoqi Zhou, Abhishek Niroula, Carlo Ferrari
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.
The Critical Assessment of Genome Interpretation (CAGI) experiment is aimed to define the state of 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 ten variants for the p16INK4a tumor suppressor, a kinase inhibitor coded by the CDKN2A gene. Twenty-two pathogenicity predictors were validated in terms of accuracy and reliability. Different assessment measures were combined in an overall ranking to provide robust results.
