重编程工程化自体 T 细胞以克服 Merkel 细胞癌患者的耐药性
Reprogramming engineered autologous T cells to overcome resistance in patients with Merkel cell carcinoma.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Time to evolve: predicting engineered T cell-associated toxicity with next-generation models.
Time to evolve: predicting engineered T cell-associated toxicity with next-generation models.
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尽管在少数恶性肿瘤中取得了有希望的临床结果,但基于工程化嵌合抗原受体和T细胞受体T细胞的疗法与严重不良事件相关,包括细胞因子释放综合征和神经毒性。这些毒性有时非常严重,以至于显著阻碍了该治疗策略的实施。长期以来,现有的临床前模型未能预测工程化T细胞输注后在人体临床试验中观察到的严重毒性。然而,近年来,人们共同努力开发模型,包括人源化小鼠模型,这些模型能够更好地再现患者中观察到的毒性。加速开发和改善CAR及TCR工程化T细胞疗法可及性(T2EVOLVE)联盟是一个公私合作伙伴关系,旨在加速临床前开发并增加癌症患者对工程化T细胞疗法的可及性。T2EVOLVE的一个关键目标是设计对临床安全性和有效性具有更高预测价值的新模型和工具,以改进和加速选择用于临床转化的先导T细胞产品。在此,我们综述了用于测试工程化T细胞安全性的现有临床前模型。我们还将强调这些模型的局限性,并提出改进它们的潜在措施。
Despite promising clinical results in a small subset of malignancies, therapies based on engineered chimeric antigen receptor and T-cell receptor T cells are associated with serious adverse events, including cytokine release syndrome and neurotoxicity. These toxicities are sometimes so severe that they significantly hinder the implementation of this therapeutic strategy. For a long time, existing preclinical models failed to predict severe toxicities seen in human clinical trials after engineered T-cell infusion.
However, in recent years, there has been a concerted effort to develop models, including humanized mouse models, which can better recapitulate toxicities observed in patients. The Accelerating Development and Improving Access to CAR and TCR-engineered T cell therapy (T2EVOLVE) consortium is a public-private partnership directed at accelerating the preclinical development and increasing access to engineered T-cell therapy for patients with cancer.
A key ambition in T2EVOLVE is to design new models and tools with higher predictive value for clinical safety and efficacy, in order to improve and accelerate the selection of lead T-cell products for clinical translation.
Herein, we review existing preclinical models that are used to test the safety of engineered T cells.
We will also highlight limitations of these models and propose potential measures to improve them.
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