Leveraging Large, Clinically-Based Datasets for Cancer Gene Classification

This white paper outlines Ambry's Classifi program, which focuses on the classification of cancer predisposition genes using large, clinically-based datasets. The document details the process of gene-disease validity (GDV) assessment and variant classification, emphasizing the importance of ongoing evidence evaluation. It presents two case examples: RPS20 and CTNNA1, illustrating how internal data has led to significant updates in gene classification. For RPS20, initial limited evidence was upgraded to moderate evidence based on extensive internal case data, enhancing its inclusion in cancer testing panels. The second example, CTNNA1, highlights the confirmation of its role in diffuse gastric cancer through case-control data analysis. The paper concludes by emphasizing the critical role of internal data in validating gene-disease relationships and improving the clinical utility of genetic testing, thus supporting patient management decisions.