CAR-T治疗后肿瘤细胞可塑性诱导免疫逃逸的个体细胞模型

Can Zhang, Changrong Shao, Xiaopei Jiao, Yue Bai, Miao Li, Hanping Shi, Jinzhi Lei, Xiaosong Zhong
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引用次数: 0

摘要

靶向CD19的嵌合抗原受体(CAR)治疗是治疗难治性B细胞恶性肿瘤,特别是B细胞急性淋巴细胞白血病(B- all)的有效方法。大多数患者在单次输注cd19靶向car修饰T细胞(CAR-19 T细胞)后获得完全缓解;然而,许多患者在治疗后复发,其潜在机制尚不清楚。为了更好地理解肿瘤复发的机制,我们基于肿瘤细胞的异质性和可塑性以及对CAR-T治疗的异质性反应的主要假设,开发了一个基于单个细胞的计算模型。模型模拟再现了肿瘤复发的过程,并预测CAR-T应激诱导的细胞可塑性可导致B-ALL肿瘤复发。模型预测与将第二代CAR-T细胞应用于注射了NALM-6-GL白血病细胞的小鼠的实验结果一致,其中60%的小鼠在3个月内复发,复发的肿瘤保留CD19表达,但表现出高水平CD34转录的细胞亚群。计算模型表明,实验数据与CAR-T细胞诱导的肿瘤细胞向造血干细胞样细胞和骨髓样细胞的转变是相容的,这些细胞对治疗有抵抗力。所提出的计算模型框架成功地概括了个体进化动力学,并有可能通过基于早期肿瘤负荷观察和肿瘤细胞分析的模型模拟来预测CAR-T治疗的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Individual cell-based modeling of tumor cell plasticity-induced immune escape after CAR-T therapy

Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B-cell acute lymphoblastic leukemia (B-ALL). The majority of patients achieve a complete response following a single infusion of CD19-targeted CAR-modified T cells (CAR-19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell-based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR-T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR-T stress can lead to tumor relapse in B-ALL. Model predictions were in agreement with experimental results of applying the second-generation CAR-T cells to mice injected with NALM-6-GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR-T cell-induced transition of tumor cells to hematopoietic stem-like cells and myeloid-like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR-T treatment through model simulation based on early-stage observations of tumor burden and tumor cells analysis.

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CiteScore
2.80
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0.00%
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审稿时长
8 weeks
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