AI-Powered CAR-T Analysis Could Predict Cancer Immunotherapy Response
A machine learning-based method automatically quantifies CAR-T immunological synapse quality using high-resolution images generated with a glass-supported planar lipid bilayer. Artificial neural networks combine object detection and instance segmentation to identify individual cells and measure F-actin, perforin, tumor-antigen, and pZeta signals. The system could analyze thousands of CAR-T cells and demonstrate differences between samples from a clinical responder and non-responder. The findings support CAR-T immunological synapse quality as a potential composite biomarker and provide a foundation for the proposed Synapse Predicts Efficacy assay for CAR-T development and translational cancer immunotherapy research.
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