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

Marco Carraro, Giovanni Minervini, Manuel Giollo, Yana Bromberg, Emidio Capriotti, Rita Casadio, Roland Dunbrack, Lisa Elefanti, Pietro Fariselli, Carlo Ferrari, Julian Gough, Panagiotis Katsonis, Emanuela Leonardi, Olivier Lichtarge, Chiara Menin, Pier Luigi Martelli, Abhishek Niroula, Lipika R. Pal, Susanna Repo, Maria Chiara ScainiMauno Vihinen, Qiong Wei, Qifang Xu, Yuedong Yang, Yizhou Yin, Jan Zaucha, Huiying Zhao, Yaoqi Zhou, Steven E. Brenner, John Moult, Silvio C.E. Tosatto

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1042-1050
Number of pages9
JournalHuman mutation
Issue number9
StatePublished - Sep 2017

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


  • CAGI experiment
  • bioinformatics tools
  • cancer
  • pathogenicity predictors
  • variant interpretation


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