"Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy".

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-01-26 DOI:10.1038/s41540-024-00335-7
Louis R Joslyn, Weize Huang, Dale Miles, Iraj Hosseini, Saroja Ramanujan
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Abstract

Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (Tscm) cells as an important determinant of both expansion and persistence and suggest that Tscm-related differences contribute significantly to the observed variability in cellular kinetics among patients. We simulated in silico clinical trials using digital twins and predict that Tscm enrichment in the infused product improves persistence of the engineered T cells and could enable administration of a lower dose. Finally, we verified the broader relevance of the QSP model, the digital twins, and findings on the importance of Tscm enrichment by predicting kinetics for two patients with pancreatic cancer treated with KRAS G12D targeting T cell therapy. This work offers insight into the key role of Tscm biology on T cell kinetics and provides a quantitative framework to evaluate cellular kinetics for future efforts in the development and clinical application of TCR-engineered T cell therapies.

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"数字双胞胎阐明了 Tscm 在 TCR 工程细胞疗法的临床持续性中的关键作用"。
尽管最近在采用 T 细胞治疗癌症方面取得了进展,但了解和预测输注 T 细胞的动力学仍是一项挑战。多种因素会影响输注 T 细胞在患者体内的分布、扩增、衰减或持久性。我们开发了一种新的定量系统药理学(QSP)模型,用于实体瘤患者的 TCR 转基因 T 细胞疗法,以描述输注后内源性 T 细胞和工程 T 细胞多个记忆亚群的动力学。这些 T 细胞在血液、淋巴结、肿瘤部位和其他外周组织中进行淋巴消耗、增殖、迁移、分化和凋亡。利用该模型,我们生成了与患者匹配的数字双胞胎,它们再现了针对转移性HPV相关上皮癌患者E7的TCR工程T细胞临床试验中报告的循环T细胞动力学。对影响细胞动力学的关键参数和数字双胞胎之间差异的分析表明,干细胞样记忆 T 细胞(Tscm)是决定细胞扩增和持久性的重要因素,并表明与 Tscm 相关的差异在很大程度上导致了观察到的患者间细胞动力学差异。我们利用数字双胞胎模拟了硅学临床试验,并预测输注产品中 Tscm 的富集能提高工程 T 细胞的持久性,并能降低给药剂量。最后,我们通过预测两名接受 KRAS G12D 靶向 T 细胞疗法的胰腺癌患者的动力学,验证了 QSP 模型、数字双胞胎和 Tscm 富集重要性研究结果的广泛相关性。这项研究深入揭示了 Tscm 生物学对 T 细胞动力学的关键作用,并为今后 TCR 工程 T 细胞疗法的开发和临床应用提供了评估细胞动力学的定量框架。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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