Best Practices for Analytically Validating Gene Expression Signatures for Oncology-based Applications
Gene expression signatures require both analytical and clinical validation, along with evidence of clinical utility, to be adopted into clinical practice as prognostic or predictive biomarkers. This webinar will focus on the aspects of analytical validation that are important for demonstrating that a gene expression signature is robust across a number of potential variables that may influence the accuracy or precision of the test. These variables include both pre-analytical variables such as sample collection and storage, as well as analytical variables such as RNA input amount or reagent lot. A recent case study of the Prosigna Breast Cancer Gene Signature Assay will be used as an example of how one may analytically characterize and subsequently validate a test.