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

Abstract

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 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.

Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants

“…Even the lower CI would still equate comfortably to >3 points (moderate) and >4 points (strong). 10 , 30 , 31 Of note, using BLOSUM62, in total, only 181 of 7541 variants attained PM5: 88 at the lower level (PM5_band_x) and 93 at the higher level (PM5_band_y).…”

Comparison of the frequency of loss‐of‐function LZTR1 variants between schwannomatosis patients and the general population

“…The results of clinical genetic testing play a significant role in the management of hereditary tumors, which can often provide pivotal information for screening, diagnosis, surgical recommendations, and treatment decisions (Garrett et al, 2021). We have assessed the current evidence available for published LoF LZTR1 variants identified in schwannomatosis patients, many of which were published before the widespread adoption of ACMG/AMP/ACGS classification guidelines.…”

“…Variants identified in schwannomatosis patients were classified based on the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines (Richards et al, 2015) and the Association of Clinical Genomic Science (ACGS) guidelines v4.01 2020, including updates on the application of cosegregation data (Jarvik & Browning, 2016), PVS1 classifications (Abou Tayoun et al, 2018), and the scaled points‐based system (Garrett et al, 2021; Tavtigian et al, 2018, 2020). Specific application of ACMG/AMP evidence classifiers in this study is detailed in the Supporting Information Methods and Table .…”

“…To understand the effect of LoF LZTR1 variants in schwannomatosis patients further, we compiled a robust cohort of published schwannomatosis‐associated LoF LZTR1 variants for comparison with LZTR1 LoF variants seen in the non‐cancer gnomAD dataset. The study involved a literature search of published schwannomatosis‐associated LZTR1 variants, and classification of those LoF variants based on current ACMG/AMP guidelines (Richards et al, 2015) and ACGS guidelines v4.01 2020 (https://www.acgs.uk.com/quality/best-practice-guidelines/#VariantGuidelines), including updates on the application of cosegregation data (Jarvik & Browning, 2016), ACMG classifier PVS1 (Abou Tayoun et al, 2018) and the scaled points‐based system (Garrett et al, 2021; Tavtigian et al, 2018, 2020). Subsequent data mining of the gnomAD database was used to compare the frequency of published LoF variants identified in people with schwannomatosis to those potential pathogenic variants that have been found in the general population.…”

Reclassification of clinically-detected sequence variants: Framework for genetic clinicians and clinical scientists by CanVIG-UK (Cancer Variant Interpretation Group UK)

“…1 Tavtigian et al 5,6 undertook mapping of these to a Bayesian framework in which evidence points are combined with a prior probability of pathogenicity to produce a posterior probability of pathogenicity, which determines the variant pathogenicity classification. [4][5][6] If the prior probability of a variant being P is 10%, variants with ≥10 evidence points have a >99% posterior probability of being P and are classified as P; similarly, 6 to 9 evidence points and 90% to 99% probability are classified likely pathogenic (LP); 0 to 5 evidence points and 10% to 90% probability are classified as variant of uncertain significance (VUS); -1 to -6 evidence points and 0.1% to 10% probability are classified likely benign (LB); ≤ -7 evidence points and <0.1% probability are classified benign (B).…”