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

Marco Carraro; Yana Bromberg; Giovanni Minervini; Yuedong Yang; Manuel Giollo; 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 Scaini; Mauno Vihinen; Qiong Wei; Qifang Xu; Yizhou Yin; Jan Zaucha; Huiying Zhao; Yaoqi Zhou; Steven E Brenner; John Moult; Silvio C E Tosatto

doi:10.1002/humu.23235

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

Journal article

Open access

Peer reviewed

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

Marco Carraro, Yana Bromberg, Giovanni Minervini, Yuedong Yang, Manuel Giollo, Emidio Capriotti, Rita Casadio, Roland Dunbrack, Lisa Elefanti, Pietro Fariselli, …

Human mutation, Vol.38(9), pp.1042-1050

09/2017

DOI: https://doi.org/10.1002/humu.23235

PMCID: PMC5561474

PMID: 28440912

Abstract

Cell Line, Tumor

Cell Proliferation

Computational Biology - methods

Computer Simulation

Cyclin-Dependent Kinase Inhibitor p18 - chemistry

Cyclin-Dependent Kinase Inhibitor p18 - genetics

Databases, Genetic

Genetic Predisposition to Disease

Genetic Variation

Humans

Protein Stability

Machine Learning

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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Title: Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI
Creators: Marco Carraro - Department of Biomedical Sciences, University of Padova, Padova, Italy
Yana Bromberg - Technical University of Munich Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany
Giovanni Minervini - Department of Biomedical Sciences, University of Padova, Padova, Italy
Yuedong Yang - Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia
Manuel Giollo - Department of Information Engineering, University of Padova, Padova, Italy
Emidio Capriotti - BioFolD Unit, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Rita Casadio - Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Roland Dunbrack - Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania
Lisa Elefanti - Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
Pietro Fariselli - Department of Comparative Biomedicine and Food Science, University of Padua, viale dell'Università 16, 35020, Legnaro (PD), Italy
Carlo Ferrari - Department of Information Engineering, University of Padova, Padova, Italy
Julian Gough - Department of Computer Science, University of Bristol, Bristol, UK
Panagiotis Katsonis - Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas
Emanuela Leonardi - Department of Woman and Child Health, University of Padova, Padova, Italy
Olivier Lichtarge - Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas
Chiara Menin - Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
Pier Luigi Martelli - BioFolD Unit, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Abhishek Niroula - Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
Lipika R Pal - Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
Susanna Repo - EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
Maria Chiara Scaini - Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy
Mauno Vihinen - Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
Qiong Wei - Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Qifang Xu - Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Yizhou Yin - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
Jan Zaucha - Department of Computer Science, University of Bristol, Bristol, UK
Huiying Zhao - Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
Yaoqi Zhou - Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia
Steven E Brenner - Department of Plant and Microbial Biology, University of California, Berkeley, California
John Moult - Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
Silvio C E Tosatto - CNR Institute of Neuroscience, Padova, Italy
Publication Details: Human mutation, Vol.38(9), pp.1042-1050
Date published: 09/2017
Grant note: BB/G022771/1 / Biotechnology and Biological Sciences Research Council U41 HG007346 / NHGRI NIH HHS R35 GM122517 / NIGMS NIH HHS R01 GM066099 / NIGMS NIH HHS R13 HG006650 / NHGRI NIH HHS
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031666189504646