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Hum Mutat. 2017 Sep;38(9):1042-1050. doi: 10.1002/humu.23235. Epub 2017 May 16.

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

Author information

1
Department of Biomedical Sciences, University of Padova, Padova, Italy.
2
Department of Information Engineering, University of Padova, Padova, Italy.
3
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
4
Department of Genetics, Rutgers University, Piscataway, New Jersey.
5
Technical University of Munich Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.
6
BioFolD Unit, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.
7
Biocomputing Group, Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.
8
Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania.
9
Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, Padua, Italy.
10
Department of Comparative Biomedicine and Food Science, University of Padua, viale dell'Università 16, 35020, Legnaro (PD), Italy.
11
Department of Computer Science, University of Bristol, Bristol, UK.
12
Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas.
13
Department of Woman and Child Health, University of Padova, Padova, Italy.
14
Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas.
15
Department of Pharmacology, Baylor College of Medicine, Houston, Texas.
16
Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas.
17
Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden.
18
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
19
EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
20
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia.
21
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
22
Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
23
Department of Plant and Microbial Biology, University of California, Berkeley, California.
24
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.
25
CNR Institute of Neuroscience, Padova, Italy.

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 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.

KEYWORDS:

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

PMID:
28440912
PMCID:
PMC5561474
DOI:
10.1002/humu.23235
[Indexed for MEDLINE]
Free PMC Article

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