Abstract 5198: Semi-automated, scaffold-free organoid workflow for the T cell screening assay using AI

Abstract Among the therapies for treating cancer, immunotherapy—especially chimeric antigen receptor (CAR) T-cell therapy—is increasingly popular. CAR T cells are genetically altered T cells that when injecting back into patients, they help arm the immune system to target and destroy cancer cells. Though it has achieved much success with immunotherapy in treating blood cancers, it remains challenging in targeting solid tumors due to the intrinsic solid tumor microenvironment (TME) which suppresses the tumor-killing ability of T cells. Thus, TME plays an important role in the treatment of solid tumors and finding an approach of mimicking TME in vitro is essential for Car-T cell screening. Studies show that the 3-dimensional (3D) patient-derived organoids (PDO) model more of the physical and chemical cues present within the TME that are lacking in traditional 2D cell line cultures. They also show similar responses to drugs as original tumors, suggesting the value of using PDOs to improve therapeutic outcomes. Despite the benefits associated with the use of PDOs, there are significant barriers that hinder the widespread adoption of PDOs in drug discovery, i.e., the costly and highly labor-intensive processes associated with their growth and maintenance. To address the challenges associated with the use of PDOs in large-scale applications, a semi-automated bioreactor was developed for the large-scale expansion of assay-ready organoids. Further, to free the burden of using Matrigel for organoid culture in the assay, we developed a Matrigel-free organoid workflow to quantify the efficacy of the cells in solid tumors, automatically seeding and culturing PDOs in a novel nano film coated cell culture plate enabling spontaneous scaffold-free 3D organoid formation using a fully integrated cell culture system. Using bioreactor-expanded patient-derived colorectal cancer organoids (CRCs), activated human peripheral blood mononuclear cells (PBMCs) were added to CRCs in a 96-well plate and monitored every 1 hour for 3 days. To quantify the morphological changes induced by T cells, we first segment each organoid with the transmitted light (TL) channel using a deep-learning model and extracted morphological features from each organoid. The resulting features were then classified using a trained Random Forest model that differentiates the morphologically modified organoids from the intact organoids. Using this approach, we found that—compared to control wells—the percentage of modified organoids in stimulated PBMC-treated wells increases more rapidly, suggesting a potential live T cell efficacy assessment approach with or without labeling. The results demonstrate the utility of the scaffold-free organoids with this analysis approach in large-scale T cell screening. Citation Format: Zhisong Tong, Angeline Lim, Oksana Sirenko, Jia-Yang Chen, Hao-Wei Han, Ying C. Chang. Semi-automated, scaffold-free organoid workflow for the T cell screening assay using AI [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 5198.