Computational pathology is redefining cancer therapeutic target assessment, delivering clinical accuracy and reproducibility that far exceeds traditional visual scoring. For the past 7+ years, AstraZeneca has developed Quantitative Continuous Scoring (QCS), a computational pathology platform built using supervised AI/Machine Learning with expert pathologist-annotated whole slide images (WSI) as benchmarked ground truth. QCS automatically detects and segments tumor regions, tumor cells, and their subcellular compartments to quantify immunohistochemical (IHC) staining intensity on each individual tumor cell—unlocking biological insights such as target internalization and expression heterogeneity critical for predicting response to antibody-drug conjugates (ADCs). This year's workshop moves beyond QCS algorithm and biomarker development and into the real world. Attendees will hear how QCS has been implemented in practice, featuring TROP2 Normalized Membrane Ratio (NMR) as a use case of end-to-end deployment, challenges that matter most to pathologists and laboratory leaders: analytical variability through AZ's Analytical Verification Program studies, the evolving regulatory landscape for AI-based companion diagnostics, and a forward-looking vision for QCS technology. Whether you are evaluating computational pathology solutions for your institution or seeking to understand how AI-developed biomarkers will shape companion diagnostic development, this session is for you.

Learning Objectives:

1. Understand QCS platform capabilities, real-world TROP2 NMR deployment, analytical performance, regulatory landscape, and future outlook