Abstract 3969: Quantitative spatiotemporal analysis of patient-derived organoid-based tumor-immune microenvironment model in lung cancer for immunotherapy response prediction

Abstract The tumor-immune microenvironment (TiME) plays a crucial role in cancer progression and treatment response, yet establishing physiologically relevant ex vivo models that accurately recapitulate TiME remains challenging. Here, we present a novel multi-modal analytical platform using lung cancer patient-derived organoids (PDOs) and autologous lymphocytes to model TiME, aiming to predict immunotherapy responses and guide treatment strategies. We developed an integrated analytical platform combining: (1) PDO-lymphocyte co-culture system using autologous cells isolated from lung cancer patients, (2) multiplexed immunofluorescence imaging for spatial analysis of immune cell distribution, (3) label-free holotomography for real-time visualization of TiME dynamics, and (4) comprehensive genomic profiling. The formation and architecture of TiME were validated through quantitative spatiotemporal immunophenotyping. Machine learning algorithms were employed to integrate multi-modal data and identify predictive signatures for treatment responses. Our integrated platform successfully reconstituted patient-specific TiME models that maintained key molecular and cellular characteristics of original tumors. Real-time holotomographic imaging revealed distinct patterns of immune cell migration and tumor-immune cell interactions. Quantitative spatiotemporal analysis demonstrated specific immune response signatures correlating with clinical outcomes. Integration of multi-modal data through machine learning algorithms showed significant predictive value for immunotherapy responses. The platform successfully identified differential drug responses in patient-specific models, correlating with clinical treatment outcomes. We established a novel multi-modal analytical platform for studying tumor-immune interactions in patient-specific models. The integration of spatial, temporal, and molecular analyses provides comprehensive insights into TiME dynamics and treatment responses. This platform shows promise as a predictive tool for immunotherapy outcomes and could serve as a personalized drug screening system, presenting new opportunities for patient stratification and therapeutic strategy optimization in lung cancer treatment. Citation Format: Mahn Jae Lee, Joo-Eun Lee, Dahye Lee, Duk Ki Kim, Da Hyun Kang, Chaeuk Chung. Quantitative spatiotemporal analysis of patient-derived organoid-based tumor-immune microenvironment model in lung cancer for immunotherapy response prediction [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 3969.