Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results
By S. Plon, Diana Eccles, D. Easton, W. D. Foulkes, M. Genuardi, M. Greenblatt, Frans B. L. Hogervorst, N. Hoogerbrugge, A. Spurdle, S. Tavtigian. Human Mutation, 2008
903 citations recorded by the source corpus.
Genetic testing of cancer susceptibility genes is now widely applied in clinical practice to predict risk of developing cancer. In general, sequence‐based testing of germline DNA is used to determine whether an individual carries a change that is clearly likely to disrupt normal gene function. Genetic testing may detect changes that are clearly pathogenic, clearly neutral, or variants of unclear clinical significance. Such variants present a considerable challenge to the diagnostic laboratory and the receiving clinician in terms of interpretation and clear presentation of the implications of the result to the patient. There does not appear to be a consistent approach to interpreting and reporting the clinical significance of variants either among genes or among laboratories. The potential for confusion among clinicians and patients is considerable and misinterpretation may lead to inappropriate clinical consequences. In this article we review the current state of sequence‐based genetic testing, describe other standardized reporting systems used in oncology, and propose a standardized classification system for application to sequence‐based results for cancer predisposition genes. We suggest a system of five classes of variants based on the degree of likelihood of pathogenicity. Each class is associated with specific recommendations for clinical management of at‐risk relatives that will depend on the syndrome. We propose that panels of experts on each cancer predisposition syndrome facilitate the classification scheme and designate appropriate surveillance and cancer management guidelines. The international adoption of a standardized reporting system should improve the clinical utility of sequence‐based genetic tests to predict cancer risk. Hum Mutat 29(11), 1282–1291, 2008. © 2008 Wiley‐Liss, Inc.
Subfields: Genetics, Pathology and Forensic Medicine
View Paper: https://www.semanticscholar.org/paper/28b690e65a5cf640d75d2259e932b706aa1a8813
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In Genetics
Citations in the corpus
- PERCH: a unified framework for disease gene prioritization (2017, neutral): “…PolyPhen2 [Adzhubei et al., 2010], SIFT [Kumar et al., 2009], FATHMM [Shihab et al., 2013], LRT [Chun and Fay, 2009], Mutation Taster [Schwarz et al., 2010], Mutation Assessor [Reva et al., 2011], PhyloP [Pollard et al., 2010], GERP++ [Davydov et al., 2010], and SiPhy [Garber et al., 2009].”
- Analysis of BRCA1 Variants in Double-Strand Break Repair by Homologous Recombination and Single-Strand Annealing (2012, background): “Recently, a new classification system for variants has been proposed [Lindor et al., 2012; Plon et al., 2008].”
- Modeling the ACMG/AMP Variant Classification Guidelines as a Bayesian Classification Framework (2018, methodology): “0, equations (1) and (2) can be combined into a single equation (5):”
- Evaluation of CADD Scores in Curated Mismatch Repair Gene Variants Yields a Model for Clinical Validation and Prioritization (2015, neutral): “The variants were classified following a five-tier system [Plon et al., 2008], with class descriptions as follows:”
- 2019 HRS expert consensus statement on evaluation, risk stratification, and management of arrhythmogenic cardiomyopathy. (2019, methodology): “Adapted from Plon et al.(96) sary; for prenatal diagnostics or a pre-implantation genetic diagnosis, the evidence for pathogenicity must be strong, and only class 5 variants are used.”
- A Guide for Functional Analysis of BRCA1 Variants of Uncertain Significance (VUS) (2012, background): “…significance), Class 2 (likely not pathogenic or of little clinical significance, Class 3 (uncertain), Class 4 (likely pathogenic), and Class 5 (definitely pathogenic) have probabilities of being pathogenic of <0.001, 0.001–0.049, 0.05–0.949, 0.95–0.99, and >0.99, respectively [Plon et al., 2008].”
- Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “…data collection [Cotton et al., 2007; Cotton et al., 2009], somatic variations [Olivier et al., 2009], interpretation and reporting of variants [Plon et al., 2008; Richards et al., 2015], data sharing [den Dun-nen et al., 2009], and nomenclature [den Dunnen and Antonarakis,…”