George J. Burghel

CanVIG-UK evaluates the impact of its activities on variant interpretation in cancer susceptibility genes through a survey, showing high utility and usage of its consensus recommendations.

The paper evaluates how different truthset strategies affect evidence points (EPs) in clinical validation of functional assays across 2,120 gene-truthset-assay combinations.

The paper establishes consensus guidelines for constructing truthsets to validate functional assays in cancer variant interpretation, focusing on missense variants and clinical validation frameworks.

The paper combines enriched case data with population-based case-control analyses using a likelihood-ratio methodology to classify variants in breast cancer susceptibility genes.

CanVar-UK is a web platform that supports collaborative interpretation of germline variants in cancer susceptibility genes by integrating variant data, clinical information, and expert discussions.

The paper evaluates the limitations of existing benign missense variant truthsets for MAVEs and proposes a proactive-systematic framework to improve their construction for better functional evidence classification.

The paper quantifies phenotypic specificity of rare FH variants in HLRCC and renal cancer using likelihood ratios from clinical and population data.

The paper surveys NHS clinical scientists on their acceptance of MAVE data validation, finding that trusted central bodies are needed for clinical adoption.

The paper develops a framework for classifying variants of reduced penetrance in cancer susceptibility genes to improve variant interpretation consistency.

The paper introduces PS4-LRCalc, a tool that quantitatively assigns evidence for variant classification using case-control data, enabling continuous scoring instead of dichotomous PS4 scoring.

This study identifies pre-analytic factors affecting NGS success in FFPE samples for lung and ovarian cancer, highlighting DNA quality, sample type, and fixation time as key predictors.

The paper re-analyzes clinical aCGH data to identify pathogenic copy number losses, finding that 7.2% of class 3 variants were reclassified as pathogenic or likely pathogenic.