PO.IM01.05 · 免疫学
CAR-T细胞疗法在类器官-成纤维细胞共培养模型中的综合临床前评估
Comprehensive preclinical evaluation of CAR-T cell therapeutics in organoid-fibroblast co-cultures
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:CAR-T细胞疗法的临床前评估需要能够重现复杂肿瘤微环境(TME)的模型,但传统的2D培养无法模拟基质屏障。患者来源(异种移植)类器官(PDO/PDXO)具有更高的临床相关性,但采用LDH释放等批量测定法分析3D免疫-肿瘤-基质相互作用具有挑战性。高内涵成像(HCI)克服了这些局限,能够对免疫浸润和细胞毒性进行高通量、多参数定量。我们利用基于HCI的类器官共培养平台,在标准模型以及含成纤维细胞的复杂TME模型中评估CAR-T疗法。
方法:将类器官与PBMC或工程化CAR-T细胞在适合自动化的384孔板格式中共培养。HCI分析可对不同细胞群体进行定量,并获取若干关键读出指标,包括96h时的类器官体积(通过肌动蛋白和DAPI染色)、成纤维细胞网络分支(通过Cy5染色),以及48h时的T细胞浸润。为进行头对头的先导候选筛选研究,将七种不同的靶向Claudin18.2的CAR-T候选物针对三种胰腺类器官模型进行筛选,以比较评估其细胞毒潜力。为模拟复杂的TME,将卵巢模型OV9522B与正常人肺成纤维细胞(NHLF)共培养。以不同的效靶比(E:T比:20:1、10:1、5:1)用靶向间皮素(MSLN)的CAR-T细胞处理三重共培养体系。
结果:通过将来自四位健康供者的PBMC与四种类器官模型(LU9906B、CR5082B、CR20155B、PA20077B)共培养,建立了基线移植物抗肿瘤(GvT)效应,揭示了因供者和模型而异的可变细胞毒性(5%-20%)。该平台能够对七种靶向Claudin18.2的CAR-T候选物针对三种模型进行比较和排序。此次筛选揭示了广泛的杀伤效力范围:效力最强的CAR-T在最敏感的胰腺模型中诱导了>80%的体积缩小,而效力最弱的CAR-T在应答较差的模型中仅显示约10%的缩小。最后,在三重共培养模型中,靶向MSLN的CAR-T细胞对OV9522B类器官(高MSLN表达)诱导了强效的剂量依赖性杀伤。尽管存在基质网络,仍观察到类器官体积的显著缩小(>90%)以及可视化的CAR-T接合,表明在此情况下成纤维细胞未影响细胞毒活性。
结论:将HCI与类器官共培养相整合,提供了一个综合、可扩展且具有临床相关性的细胞疗法评估平台。在存在基质成分的情况下可视化并定量靶向杀伤等关键参数是一项关键优势,能够对治疗候选物进行有把握的排序,并提供机制层面的洞见,从而降低风险并加速下一代细胞疗法的临床开发。
查看英文原文 English abstract
Introduction: Preclinical assessment of CAR-T cell therapies requires models that recapitulate the complex tumor microenvironment (TME), but conventional 2D cultures fail to model stromal barriers. Patient-derived (xenograft) organoids (PDOs/PDXOs) offer superior clinical relevance, but analyzing 3D immune-tumor-stromal interactions is challenging with bulk assays like LDH release. High-Content Imaging (HCI) overcomes these limitations, enabling high-throughput, multi-parametric quantification of immune infiltration and cytotoxicity. We utilized an HCI-based organoid co-culture platform to evaluate CAR-T therapies in both standard and complex, fibroblast-containing TME models.
Methods: Organoids were co-cultured with PBMCs or engineered CAR-T cells in a 384-well format suitable for automation. HCI analysis allowed quantification of different cell populations and several key readouts, including organoid volume (via actin and DAPI staining), fibroblast network branching (via Cy5 staining) at 96h, and T-cell infiltration at 48h. For a head-to-head lead selection study, seven distinct Claudin18.2-targeting CAR-T candidates were screened against three pancreatic organoid models to comparatively assess their cytotoxic potential. To model a complex TME, ovarian model OV9522B was co-cultured with Normal Human Lung Fibroblasts (NHLF). Triple co-cultures were treated with Mesothelin (MSLN)-targeting CAR-T cells at various Effector-to-Target (E:T) ratios (20:1, 10:1, 5:1).
Results: Baseline Graft-versus-Tumor (GvT) effects were established by co-culturing PBMCs from four healthy donors with four organoid models (LU9906B, CR5082B, CR20155B, PA20077B) revealing variable donor- and model-dependent cytotoxicity (5%-20%). The platform enabled comparison and ranking of seven Claudin18.2-targeting CAR-T candidates against three models. This screen revealed a wide range of killing potency: the most potent CAR-T induced >80% volume reduction in the most sensitive pancreatic model, while the weakest CAR-T showed ~10% reduction in a poor responsive model. Finally, in the triple co-culture model, MSLN-targeting CAR-T cells induced potent, dose-dependent killing of OV9522B organoids (high MSLN expression). Despite a stromal network, a significant reduction (>90%) in organoid volume and visualized CAR-T engagement were observed, demonstrating fibroblasts did not affect cytotoxic activity in this case.
Conclusion: Integration of HCI with organoid co-cultures provides a comprehensive, scalable, and clinically relevant platform for evaluating cell therapies. Visualizing and quantifying key parameters like targeted killing in presence of stromal components is a critical advantage and enables confident ranking of therapeutic candidates and provides mechanistic insights, de-risking and accelerating clinical development for next-generation cell therapies.
利益披露 Disclosure
Y. Ren, None..
T. Veenendaal, None..
P. Wang, None..
J. Gao, None..
J. Meng, None..
D. Yan, None..
Y. Li, None..
M. Zhang, None..
M. Kop, None..
A. Weterings, None..
M. Hornsveld, None..
G. Goverse, None..
L. Bourre, None..
P. Wang, None..
J. Zhou, None.