Helen Hanson

Prevalence and Penetrance of Heritable Retinoblastoma in Two Adult Population Cohorts: Implications for genomic newborn screening

This study estimates the prevalence and penetrance of heritable retinoblastoma in adult cohorts using genomic data and health records, finding lower penetrance than clinical cohorts.

Cancer Variant Interpretation Group UK (CanVIG-UK): updates on an exemplar national subspecialty multidisciplinary network

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.

Clinical validation of large-scale functional assays: insights from 2,120 gene-truthset-assay evaluations

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

'Truthsets' for clinical validation of large-scale functional assays: Practice recommendations from Cancer Variant Interpretation Group UK (CanVIG-UK)

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

Integrating enriched case data from national laboratory testing with population-based case-control analyses: a novel statistical likelihood-ratio methodology for PS4 applied to 325,345 breast cancer cases and 671,006 controls

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: an online data platform supporting collaborative diagnostic interpretation of germline variants in 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.

Availability of benign missense variant truthsets for validation of functional assays: current status and a novel systematic approach

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.

Quantifying evidence for phenotypic specificity (PP4) for syndromic phenotypes: Large-scale integration of rare germline FH variants from diagnostic laboratory testing for HLRCC (Hereditary Leiomyomatosis and Renal Cell Cancer) and renal cancer

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

Validating data from Multiplex Assays of Variant Effect (MAVEs): A CanVIG-UK National Survey of NHS Clinical Scientists

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

Breast cancer multigene germline panel testing in mainstream oncology based on clinical-public health utility (cancer mortality benefit): ESMO Precision Oncology Working Group recommendations

The paper recommends a standardized multigene germline panel for breast cancer patients based on cancer mortality benefit, including specific genes and excluding others.

Classification of Variants of Reduced Penetrance in High Penetrance Cancer Susceptibility Genes: Framework for Genetics Clinicians and Clinical Scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK)

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

The PS4-Likelihood Ratio Calculator: Flexible allocation of evidence weighting for case-control data in variant classification

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.