加拿大Notch Therapeutics公司Daniel C. Kirouac研究组揭示了可预测嵌合抗原受体T细胞（CAR-T）细胞药理学和应答过程中临床差异的方法。相关论文于2023年2月27日发表在《自然—生物技术》杂志上。
在本研究中，研究人员对T细胞反应进行了数学描述，其中记忆、效应和耗竭T细胞状态之间的转换受到肿瘤抗原接合的协调调节。该模型使用了来自不同血液恶性肿瘤CAR-T产品的临床数据进行训练，并以记忆细胞周转率和效应细胞的细胞内在毒性差异作为临床反应的主要决定因素。使用机器学习工作流程，研究证明了产品内在差异可以根据输注前转录组准确预测患者的预后，并且细胞与患者肿瘤的相互作用会产生额外的药理学差异。
研究发现，转录特征优于T细胞免疫表型，可预测两种靶向CD19 CAR-T产品在三种适应症中的临床反应，这开启了预测性CAR-T产品开发的新阶段。
据悉，CAR-T的扩增和持久性在患者中差异很大，这可以预测治疗效果和细胞毒性。然而，临床效果和患者变异性背后的机制尚不清楚。
附：英文原文
Title: Deconvolution of clinical variance in CAR-T cell pharmacology and response
Author: Kirouac, Daniel C., Zmurchok, Cole, Deyati, Avisek, Sicherman, Jordan, Bond, Chris, Zandstra, Peter W.
Issue&Volume: 2023-02-27
Abstract: 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.
DOI: 10.1038/s41587-023-01687-x
Source: https://www.nature.com/articles/s41587-023-01687-x
Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex
本期文章：《自然—生物技术》：Online/在线发表
