Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings

Runpeng Li , Prativa Sahoo , Dongrui Wang , Qixuan Wang , Christine E. Brown , Russell C. Rockne , Heyrim Cho
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引用次数: 2

Abstract

Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13Rα2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13Rα2 targeting CAR T-cells for treating glioma. We focus on extending the work of Kuznetsov et al. (1994) by considering binding of multiple CAR T-cells to a single glioma cell, and the dynamics of these multi-cellular conjugates. Our model more accurately describes experimentally observed CAR T-cell killing assay data than the models which do not consider multi-cellular conjugates. Moreover, we derive conditions in the CAR T-cell expansion rate that determines treatment success or failure. Finally, we show that our model captures distinct CAR T-cell killing dynamics from low to high antigen receptor densities in patient-derived brain tumor cells.

Abstract Image

考虑多种CAR - t细胞结合的胶质瘤细胞和CAR - t细胞相互作用的建模
嵌合抗原受体(CAR) t细胞为基础的免疫疗法已经显示出其治疗血癌的潜力,其在实体肿瘤中的应用目前正在广泛研究中。对于脑胶质瘤,各种CAR - t细胞靶点包括IL13Rα2、EGFRvIII、HER2、EphA2、GD2、B7-H3和氯毒素。在这项工作中,我们感兴趣的是建立一个靶向CAR - t细胞治疗胶质瘤的IL13Rα2的数学模型。我们专注于扩展Kuznetsov等人(1994)的工作,考虑多个CAR - t细胞与单个胶质瘤细胞的结合,以及这些多细胞偶联物的动力学。我们的模型比不考虑多细胞偶联物的模型更准确地描述了实验观察到的CAR - t细胞杀伤分析数据。此外,我们得出了CAR - t细胞扩增率决定治疗成功或失败的条件。最后,我们表明我们的模型捕获了患者来源的脑肿瘤细胞中从低到高抗原受体密度的不同CAR - t细胞杀伤动力学。
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来源期刊
Immunoinformatics (Amsterdam, Netherlands)
Immunoinformatics (Amsterdam, Netherlands) Immunology, Computer Science Applications
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