肿瘤细胞治疗研究
英文原题：Real-time monitoring of CAR T cell dynamics in tumor patient-derived organoids using the OrganoIDNet algorithm.
通过将类器官-免疫共培养与 OrganoIDNet 驱动的活细胞成像相结合，我们建立了一种自动化纵向成像检测方法，用于评估 PDAC PDO 中 CAR T 细胞介导的反应。该检测方法能够在多个效靶比下连续定量类器官数量和面积，并结合基于图像的 T 细胞邻近性评估。这些测量提供了观察期内抗原依赖性细胞毒作用和空间关联的时间分辨信息，支持其作为 CAR T 细胞开发和未来个性化免疫治疗研究的临床前平台的潜在用途。
患者来源的类器官（PDOs）为临床前药物测试提供了生理相关的3D肿瘤模型，然而用于量化对免疫治疗动态反应的稳健且自动化方法仍然有限。OrganoIDNet是一种基于深度学习的图像分析框架，能够对类器官形态进行自动化、无标记分割和纵向定量。在此，我们扩展了OrganoIDNet的应用，以评估靶向肿瘤相关抗原CD318的嵌合抗原受体（CAR）T细胞针对胰腺导管腺癌（PDAC）PDOs的活性。
使用基于Matrigel的夹层系统，将靶向CD318的CAR T细胞与PDAC PDOs共培养，并通过延时明场成像进行监测。OrganoIDNet能够实现准确的单个类器官分割，并在多个效靶比下连续定量类器官数量和面积。CAR-318 T细胞诱导强烈的、抗原依赖性细胞毒性，其特征是类器官数量和大小逐步减少，并伴随T细胞活化标志物表达增加以及终点时TIM-3表达变化。动态成像和T细胞空间分析进一步揭示了密切的T细胞-类器官相互作用和早期肿瘤细胞清除，提供关于类器官反应和T细胞邻近性的时间分辨信息，补充了常规终点检测。
BACKGROUND: Patient-derived organoids (PDOs) provide physiologically relevant 3D tumor models for preclinical drug testing, yet robust and automated methods to quantify dynamic responses to immunotherapies remain limited. OrganoIDNet is a deep learning-based image analysis framework that enables automated, label-free segmentation and longitudinal quantification of organoid morphology. Here, we extend the application of OrganoIDNet to evaluate chimeric antigen receptor (CAR) T cell activity against pancreatic ductal adenocarcinoma (PDAC) PDOs targeting the tumor-associated antigen CD318. RESULTS: CD318-directed CAR T cells were co-cultured with PDAC PDOs using a Matrigel-based sandwich system and monitored by time-lapse bright-field imaging. OrganoIDNet enabled accurate single-organoid segmentation and continuous quantification of organoid number and area across multiple effector-to-target ratios. CAR-318 T cells induced robust, antigen-dependent cytotoxicity, characterized by progressive reductions in organoid number and size and were accompanied by increased T cell activation marker expression and changes in TIM-3 expression at the endpoint. Dynamic imaging and T cell spatial analysis, further revealed close T cell-organoid interactions and early tumor cell elimination, providing time-resolved information on organoid responses and T cell proximity that complements conventional endpoint assays. CONCLUSIONS: By integrating organoid-immune co-cultures with OrganoIDNet-driven live-cell imaging, we established an automated longitudinal imaging assay for assessing CAR T cell-mediated responses in PDAC PDOs. The assay enabled continuous quantification of organoid number and area, together with image-based assessment of T cell proximity, across multiple effector-to-target ratios. These measurements provide time-resolved information on antigen-dependent cytotoxicity and spatial association during the observation period, supporting its potential utility as a preclinical platform for CAR T cell development and future personalized immunotherapy studies.
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