Zeid Kuzbari

Background: Clear guidance is lacking regarding how ‘truthset’ variants should be used for clinical validation of functional assays, namely determining the allocatable evidence points (EPs) towards clinical classification. It is argued that assays should be validated using truthsets of missense variants, as this is the variant type for which classification is most impacted by functional data. EPs will be influenced by both the number of available ‘truthset’ variants and their concordance with assay readouts. Methods: We first reviewed 112 sets of ClinGen gene-specific classification specifications (CSPECs) to assess methodologies they applied for truthset assembly and clinical validation of assays. We then proposed differing rules regarding variant type and stringency of classification by which truthsets might be assembled using ClinVar-extracted classifications. We then examined augmentation of ClinVar-classified truthsets with ‘proxy-clinical’ benign-classified missense variants systematically assembled applying ACMG/AMP rules (of differing stringencies). In total, these constituted 70 basic approaches to ClinVar-based truthset assembly, which we applied to VHL, BRCA1, BRCA2 and RAD51C . We additionally analysed the impact on the size of the truthsets of changing the specified phenotypes against which ClinVar classification had been submitted. We then applied these truthsets to quantify concordance and allocatable EPs for five large-scale multiplexed functional assays for VHL , BRCA1 , BRCA2 , and RAD51C . Results: The EPs from clinical validation of each assay varied widely according to which truthset was used across 2,120 permutations of gene-truthset-assay combinations. For example, sequentially applying 700 different ClinVar-based truthsets to 2,268 VHL assay variant readouts (70 basic ClinVar-based approaches, augmented by examining 5 different phenotypes for each basic approach, and separate validation against two defined deleterious zones), the evidence strength allocatable for pathogenicity ranged from nil to strong evidence (0.0 to 5.6 EPs); for benignity it ranged from supporting to strong evidence (-1.5 to -6.4 EPs). Clinical validation using truthsets comprising just ClinVar-classified missense variants typically resulted in lower EPs than truthsets comprising protein truncating (PTV) and synonymous variants; this was more due to paucity of ClinVar-classified missense truthset variants than poorer concordance. Augmentation with larger ‘proxy-clinical’ benign-classified missense truthsets typically improved evidence allocatable for pathogenicity, with improved power negating modest reduction in concordance. Conclusions: EPs can be improved by augmentation with systematically-generated ‘proxy-clinical’ benign-classified missense variants and/or reduction of truthset stringency. Explicit prescriptive clinical guidance is urgently required to improve consistency in clinical validation of functional assays and consequent evidence application for clinical variant classification.

Background: Large-scale functional assays, including multiplex assays of variant effect, have substantial potential to resolve variants of uncertain significance (VUS), particularly for rare missense variants where clinical and population evidence are limited. The ClinGen assay-level clinical validation framework described by Brnich et al provided baseline guidance for the use of functional data for variant classification. However, clear consensus regarding construction of variant ‘truthsets’ by which to clinically validate functional data remains lacking. Methods: CanVIG-UK developed consensus recommendations for truthset construction through an iterative national consultation process involving the CanVIG Steering Advisory Group (CStAG), wider CanVIG-UK membership, and engagement with international functional genomics experts. Consultation was based on previous analyses of 2,120 truthset constructions examining the impact of truthset composition on evidence point allocation within the ClinGen assay-level clinical validation framework. Results: Across several consultations, CanVIG-UK established nine guiding principles and seven best-practice recommendations for assay-level clinical validation, using the assumed context of an assay for a cancer susceptibility gene where loss-of-function is the mechanism of pathogenicity. The principal recommendation stipulates, where assays are intended for use in interpretation of largely missense variants, the truthset used to validate should comprise only missense variants. Rather than mixtures of different variant types which may serve to over-estimate assay performance. Additional recommendations support option for relaxation of truthset stringency to improve power, augmentation of benign missense truthsets with systematically derived ‘proxy-clinical’ benign variants, independent clinical validation separate from assayist-defined validation, and careful evaluation of missense score distributions against that of protein-truncating and synonymous variants. Guidance is also provided for scenarios with limited pathogenic truthset availability and for assays reporting multiple deleterious zones or readouts. Conclusions: The CanVIG-UK principles and recommendations for truthset construction upon the ClinGen assay-level clinical validation framework, while aiming to form a baseline for future discussion regarding other functional and disease contexts and helping to address the gap between publication of new data and routine clinical implementation.

Interpretation of germline variants in cancer susceptibility genes (CSGs) requires the collation of variant-level data from diverse sources, as well as the assembly of comprehensive clinical data, often necessitating sharing of information between genomic testing centers. Although a number of variant interpretation tools exist, there remains a need for a CSG-focused platform tailored to the diverse range of ClinGen variant curation expert panel guidance in these difficult-to-interpret genes. Here, we describe CanVar-UK, a freely-accessible web platform to assist in the interpretation of germline CSG variants. CanVar-UK contains variant-level data for over 1.7 million single nucleotide variants, comprising all coding variants in 115 established CSGs. These data include: in silico scores from 11 tools of clinical relevance; population allele frequencies from gnomAD v4.1 and case counts from NHS genomic testing via linkage to the National Disease Registration Service; variant-level readouts from 31 different functional and splicing studies across 13 CSGs; genetic epidemiology studies of the BRCA1/2 genes; and live linkage to existing consensus classifications in the ClinVar database. CanVar-UK additionally has a diagnostic discussion forum functionality, via which users are able to email the rest of the user base with queries and/or suggested classifications, facilitating the exchange of clinical and classification data between diagnostic centers. Already widely used by the NHS clinical workforce in the CSG space (with 879 registered NHS users), CanVar-UK has a rapidly growing international user base, with 607 registered users based outside the UK. We believe CanVar-UK to be an invaluable resource for germline CSG variant interpretation.

Purpose: Germline deletions affecting the Y-chromosomal gr/gr region were reported in 2005 as associated with susceptibility to testicular germ cell tumor (TGCT), a highly heritable tumor type that is the most common cancer type affecting adult men under the age of 45. Attempts to replicate this association have been equivocal, primarily due to limited power. Methods: Here, we compare and validate two computational approaches to gr/gr deletion calling in high-, low– and ultra-low-coverage whole genome sequencing data, applying these to two datasets from UK Biobank and the TECAC consortium. We generate dataset-specific effect size estimates for the gr/gr deletion-TGCT association using Firth’s bias-reduced logistic regression across a total of 198,306 men of European-like ancestry (2231 with and 196,075 without TGCT). Results: Upon random-effects meta-analysis of estimated effect sizes in the two datasets, we found no significant association between gr/gr deletion status and TGCT risk (combined odds ratio=1.24, 95% CI=0.74-2.07, p=0.42), nor upon stratification of seminoma and non-seminoma/mixed histological subtypes. Conclusion: Our analysis suggests gr/gr deletion status alone is likely not predictive of TGCT risk in population-scale analyses of European-like individuals; however, the importance of other proposed determinants of gr/gr deletion impact, including Y-haplogroups and semen phenotype, remains unexplored at scale.