Combining evidence for and against pathogenicity for variants in cancer susceptibility genes: CanVIG-UK consensus recommendations

Alice Garrett, Miranda Durkie, Alison Callaway, George J Burghel, Rachel Robinson, James Drummond, Bethany Torr, Cankut Cubuk, Ian R Berry, Andrew J Wallace, Sian Ellard, Diana M Eccles (+3 others)
2020 Journal of Medical Genetics  
Accurate classification of variants in cancer susceptibility genes (CSGs) is key for correct estimation of cancer risk and management of patients. Consistency in the weighting assigned to individual elements of evidence has been much improved by the American College of Medical Genetics (ACMG) 2015 framework for variant classification, UK Association for Clinical Genomic Science (UK-ACGS) Best Practice Guidelines and subsequent Cancer Variant Interpretation Group UK (CanVIG-UK) consensus
more » ... ) consensus specification for CSGs. However, considerable inconsistency persists regarding practice in the combination of evidence elements. CanVIG-UK is a national subspecialist multidisciplinary network for cancer susceptibility genomic variant interpretation, comprising clinical scientist and clinical geneticist representation from each of the 25 diagnostic laboratories/clinical genetic units across the UK and Republic of Ireland. Here, we summarise the aggregated evidence elements and combinations possible within different variant classification schemata currently employed for CSGs (ACMG, UK-ACGS, CanVIG-UK and ClinGen gene-specific guidance for PTEN, TP53 and CDH1). We present consensus recommendations from CanVIG-UK regarding (1) consistent scoring for combinations of evidence elements using a validated numerical 'exponent score' (2) new combinations of evidence elements constituting likely pathogenic' and 'pathogenic' classification categories, (3) which evidence elements can and cannot be used in combination for specific variant types and (4) classification of variants for which there are evidence elements for both pathogenicity and benignity.
doi:10.1136/jmedgenet-2020-107248 pmid:33208383 pmcid:PMC8086256 fatcat:5y77vtq7mbhhzo7i2fq5vbwz5e