嵌合抗原受体T细胞（CAR-T）的扩增和持续时间在患者间差异很大，预测着药效和毒性。然而，致病机制和患者差异的临床结果尚不明确。在本研究中，我们开发了一个数学描述T细胞反应的模型，其中记忆细胞、效应细胞和疲劳细胞状态之间的转换由肿瘤抗原介导的协调调控。该模型使用来自不同血液恶性肿瘤的CAR-T产品的临床数据进行训练，并确定记忆细胞的周转速率和效应因子的细胞毒性作为临床反应的主要决定因素的细胞内差异。使用机器学习工作流程，我们证明了产品内在差异可以根据预输注转录组准确预测患者的临床结果，额外的药理学变异来自细胞与患者肿瘤的相互作用。我们发现，在三个指标中的两个CD19靶向的CAR-T产品中，转录标记优于T细胞免疫表型学作为临床反应预测，从而推动了预测性CAR-T产品开发的新一阶段。©2023。作者们。

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.© 2023. The Author(s).