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

Carraro, Marco ; Minervini, Giovanni ; Giollo, Manuel ; Bromberg, Yana ; Capriotti, Emidio ; Casadio, Rita ; Dunbrack, Roland ; Elefanti, Lisa ; Fariselli, Pietro and Ferrari, Carlo , et al. (2017) In Human Mutation 38(9). p.1042-1050
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bioinformatics tools, CAGI experiment, Cancer, Pathogenicity predictors, Variant interpretation
in
Human Mutation
volume
38
issue
9
pages
1042 - 1050
publisher
John Wiley & Sons Inc.
external identifiers
  • wos:000407861100002
  • pmid:28440912
  • scopus:85019402721
ISSN
1059-7794
DOI
10.1002/humu.23235
language
English
LU publication?
yes
id
cfe708cc-e93a-407a-b7b1-f55ea207fda1
date added to LUP
2017-06-26 15:32:32
date last changed
2025-01-20 17:16:21
@article{cfe708cc-e93a-407a-b7b1-f55ea207fda1,
  abstract     = {{<p>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&amp;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.</p>}},
  author       = {{Carraro, Marco and Minervini, Giovanni and Giollo, Manuel and Bromberg, Yana and Capriotti, Emidio and Casadio, Rita and Dunbrack, Roland and Elefanti, Lisa and Fariselli, Pietro and Ferrari, Carlo and Gough, Julian and Katsonis, Panagiotis and Leonardi, Emanuela and Lichtarge, Olivier and Menin, Chiara and Martelli, Pier Luigi and Niroula, Abhishek and Pal, Lipika R. and Repo, Susanna and Scaini, Maria Chiara and Vihinen, Mauno and Wei, Qiong and Xu, Qifang and Yang, Yuedong and Yin, Yizhou and Zaucha, Jan and Zhao, Huiying and Zhou, Yaoqi and Brenner, Steven E and Moult, John and Tosatto, Silvio C.E.}},
  issn         = {{1059-7794}},
  keywords     = {{Bioinformatics tools; CAGI experiment; Cancer; Pathogenicity predictors; Variant interpretation}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{1042--1050}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Human Mutation}},
  title        = {{Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI}},
  url          = {{https://lup.lub.lu.se/search/files/36614686/27398191.pdf}},
  doi          = {{10.1002/humu.23235}},
  volume       = {{38}},
  year         = {{2017}},
}