Michael T Parsons

Clinical implications of VUS reclassification in a single-centre series from application of ACMG/AMP classification rules specified for BRCA1/2

Background BRCA1/2 testing is crucial to guide clinical decisions in patients with hereditary breast/ovarian cancer, but detection of variants of uncertain significance (VUSs) prevents proper management of carriers. The ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) BRCA1/2 Variant Curation Expert Panel (VCEP) has recently developed BRCA1/2 variant classification guidelines consistent with ClinGen processes, specified against the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular-Pathology) classification framework. Methods The ClinGen-approved BRCA1/2-specified ACMG/AMP classification guidelines were applied to BRCA1/2 VUSs identified from 2011 to 2022 in a series of patients, retrieving information from the VCEP documentation, public databases, literature and ENIGMA unpublished data. Then, we critically re-evaluated carrier families based on new results and checked consistency of updated classification with main sources for clinical interpretation of BRCA1/2 variants. Results Among 166 VUSs detected in 231 index cases, 135 (81.3%) found in 197 index cases were classified by applying BRCA1/2-specified ACMG/AMP criteria: 128 (94.8%) as Benign/Likely Benign and 7 (5.2%) as Pathogenic/Likely Pathogenic. The average time from the first report as ‘VUS’ to classification using this approach was 49.4 months. Considering that 15 of these variants found in 64 families had already been internally reclassified prior to this work, this study provided 121 new reclassifications among the 151 (80.1%) remaining VUSs, relevant to 133/167 (79.6%) families. Conclusions These results demonstrated the effectiveness of new BRCA1/2 ACMG/AMP classification guidelines for VUS classification within a clinical cohort, and their important clinical impact. Furthermore, they suggested a cadence of no more than 3 years for regular review of VUSs, which however requires time, expertise and resources.

The SeqSplice multiplexed minigene splicing assay for characterization and quantitation of variant-induced BRCA1 and BRCA2 splice isoforms

BRCA1 and BRCA2 germline variant classification is vital for clinical management of families with hereditary breast and ovarian cancer. However, clinical classification of rare variants outside of the splice donor/acceptor ±1,2-dinucleotides remains challenging, particularly for variants that induce new or cryptic splice site usage. Here, we present SeqSplice, a high-throughput RNA splicing methodology utilizing barcoded minigene constructs together with a bespoke bioinformatics pipeline for identifying and quantifying the impacts for splice-altering variants. SeqSplice exhibits excellent reproducibility across cDNA input and PCR cycle differences and is able to identify and quantitate transcripts that differed by a single base. Of the 193 BRCA1 and 72 BRCA2 variants profiled, 89% (237/265) had no publicly available RNA splicing data. Complete or near complete impact owing to splice site gain/loss is observed for 42 variants, with 30 (71%) producing alternative transcripts owing to new or cryptic splice sites. These findings are used to update our aberration type predictor called SpliceAI-10k calculator, resulting in 94% specificity and 90% sensitivity for major alternative transcripts (>50% proportion). Comparison of SeqSplice findings for 28 variants with published data shows the value and limitations of using construct-based results for variant classification. Overall, our findings inform use of construct-derived data for clinical variant classification. We show that construct-derived results for variants showing low or no splicing impact provide reliable evidence against variant pathogenicity, whereas—for variants demonstrating splicing impact—construct design and naturally occurring alternative splicing are important considerations for assigning and weighting evidence towards pathogenicity.