嵌合抗原受体 (CAR) T 细胞疗法是一种有前景的癌症免疫疗法。该方法包括修改患者的 T 细胞以直接靶向抗原呈递的癌细胞。开发此类疗法的障碍之一是靶抗原异质性。人们认为肿瘤内异质性是治疗耐药和治疗失败的主要决定因素之一。虽然了解抗原异质性对于有效治疗很重要，但良好的治疗策略可以提高治疗效率。在这项工作中，我们引入了一种基于代理的模型 (ABM)，该模型建立在先前的 ABM 基础上，以合理化不同 CAR T 细胞治疗策略相对于异质肿瘤源性类器官的结果。我们发现，一剂 CAR T 细胞疗法有望减少肿瘤大小及其生长速度，但可能不足以完全消除肿瘤。此外，随着剂量的增加，游离 CAR T 细胞（即不杀死任何癌细胞的 CAR T 细胞）的数量也会增加，副作用的风险也会增加。我们测试了不同的策略来增强较小的剂量，例如增强 CAR T 细胞的长期持久性和多次给药。对于这两种方法，适当的剂量测定策略对于产生“有效且安全”的治疗结果是必要的。此外，模拟还产生了一个有趣的新现象，即抗原表达低的细胞形成盾状结构。事实证明，这个屏障可以保护抗原高表达的细胞。最后，我们测试了多抗原识别疗法，以克服抗原逃逸和异质性。我们的研究表明，较大剂量可以完全消除类器官，但多抗原识别会增加副作用的风险。因此，需要采取适当的小剂量剂量测定策略来改善结果。根据我们的结果，很明显，正确的治疗策略可以增强治疗效果。在这个方向上，我们的计算方法提供了一个框架来模拟不同情况下的治疗组合，并探索成功和不成功治疗的特征。© 2024。作者。

Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients' T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce "effective yet safe" therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.© 2024. The Author(s).