Explore approaches to classifying variants in TP53 and other hereditary cancer genes in this informative webinar. Gain an overview of current methods for interpreting germline variants, comparing ACMG codes with quantitative classification models. Learn how to mine data to determine the applicability and weight of evidence codes for specific genes. Discover insights on bioinformatic feature assessment, segregation data analysis, and functional assay calibration for genes like BRCA1/2. Examine the process of converting bioinformatic predictions to ACMG/AMP guidelines, compare tool performances, and understand the use of somatic data in classification. This comprehensive presentation, featuring Amanda Spurdle, PhD, and moderated by Sarah Campian, MS CGC, offers valuable knowledge for professionals working in genetic variant interpretation and hereditary cancer research.
Approaches to Classification of Variants in TP53 and Other Hereditary Cancer Genes - Ambry Genetics
Overview
Syllabus
Intro
Reminders
Logistics
Background - my variant classification activities
Overview
ACMG weighted qualitative classification system
Quantitative evidence: multifactorial likelihood analysis
Clinically calibrated bioinformatic information? Assess bioinformatic features of proven pathogenic and non-pathogenic variants in large datasets -Determine the proportion of pathogenic variants in a given bioinformatogroup
Segregation data
Other components of the model?
Alignment to ACMG codes?
Recent examples of calibration
BRCA1/2 functional assay calibration
Estimating Functional Assay LRs
BRCA1/2 splicing assay calibration
Strength of evidence for splicing data
Population allele frequency as a predictor
Strength of evidence for population frequency
TP53 ACMG code strengths - starting from scratch
Converting bioinformatic predictions to ACMG/AMP
Comparing tool performance
Comparing performance for tools in combination
Specifying PM5 ACMGIAMP rule for TP53
Using somatic data - somatic to germline ratio
Using somatic data - Second hit
Conclusions
Acknowledgements for work presented here
Taught by
Ambry Genetics
