Abstract 7446: Stratification of cell therapies in solid tumor organoids using deep learning-derived imaging metrics

Abstract Background. High-throughput screening of immunotherapies is crucial for identifying promising candidates against various cancer indications and accelerating the pre-clinical development of cell therapies. Two technologies have shown promise in quantifying therapy effectiveness: patient-derived organoid (PDO) models co-cultured with cell therapies, and deep learning-based computer vision, which facilitates large-scale, automated image analysis to extract quantitative metrics of treatment efficacy and cell biology effects. Here, we use deep networks to predict PDO viability from brightfield images and compute explainable features which are used to stratify a large cohort of cell therapies, tested on multiple tumoroid lines and indications. Methods. Time-lapse confocal microscopy was used to record images of a large cohort of 27 cell therapies co-cultured with 15 different patient-derived tumor organoids (PDO) lines, across 8 cancer indications (breast, colorectal, endometrial, gastric, head and neck, liver, lung, pancreas) over the course of 72 hours, and at 2 different effector-to-target concentrations. PDO viability was measured by terminal TO-PRO-3 staining. A convolutional deep network was trained to perform label-free predictions of PDO viability from brightfield images at each timepoint (N=11, 110) and evaluated on a held-out test set (N=2, 112). Deep learning segmentation models were further used to co-localize tumoroid and immune cells, and extract a set of 6 explainable features - or spatial phenotypes - which quantified 1) tumoroid area, 2) cell apoptosis intensity and 3) temporal dynamics, as well as 4) immune proliferation, 5) immune infiltration and its 6) temporal dynamics. We fitted a linear model to measure their contribution in predicting terminal viability, and used hierarchical clustering to assess associations with exposure to different therapies. Results. Our bright-field model of viability was found to be highly concordant with ground truth viability from terminal vital dye stain on a held-out test set, representing 11 TO lines across 8 distinct cancer types (Pearson’s r=0.76). A linear model of spatial phenotypes showed high correlation with terminal viability (Pearson’s r=0.70), while clustering of spatial phenotypes stratified samples into groups of non-engineered and engineered therapies with and without an additional compound. Conclusion. Our study highlights that imaging-derived metrics from brightfield imaging can quantify treatment potency and cell phenotypes in ex-vivo screens. The approach pursued here is highly scalable and permits the analysis of large scale screening experiments to assess the impact of cell therapies on PDOs. Using explainable features further allows to stratify therapies into biologically meaningful groups based on the measured phenotypic readouts. Citation Format: Luca Lonini, Madhavi Kannan, Geoffrey Schau, Stanislaw Szydlo, Vignesh Krishnaraja, Sonal Khare, Daniel Rabe, Michael Streit, Mary Flaherty, Brandon Mapes, Mark Prytyskach, Stefan Kiesgen, Gurpanna Saggu, Megan McAfee, Kate Sasser, Justin Guinney, Richard Klinghoffer, Chi-Sing Ho. Stratification of cell therapies in solid tumor organoids using deep learning-derived imaging metrics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7446.