Conte Martina, Xella Agata, Woodall Ryan T, Cassady Kevin A, Branciamore Sergio, Brown Christine E, Rockne Russell C
Department of Mathematical, Physical and Computer Sciences, University of Parma Parco Area delle Scienze 53/A, 43124, Parma, Italy.
Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
bioRxiv. 2025 Jan 25:2025.01.23.634499. doi: 10.1101/2025.01.23.634499.
Glioblastoma is a highly aggressive and treatment-resistant primary brain cancer. While chimeric antigen receptor (CAR) T-cell therapy has demonstrated promising results in targeting these tumors, it has not yet been curative. An innovative approach to improve CAR T-cell efficacy is to combine them with other immune modulating therapies. In this study, we investigate combination of IL-13R2 targeted CAR T-cells with an oncolytic virus (OV) and study the complex interplay between tumor cells, CAR T-cells, and OV dynamics with a novel mathematical model. We fit the model to data collected from experiments with each therapy individually and in combination to reveal determinants of therapy synergy and improved efficacy. Our analysis reveals that the virus bursting size is a critical parameter in determining the net tumor infection rate and overall combination treatment efficacy. Moreover, the model predicts that administering the oncolytic virus simultaneously with, or prior to, CAR T-cells could maximize therapeutic efficacy.
胶质母细胞瘤是一种极具侵袭性且对治疗有抗性的原发性脑癌。虽然嵌合抗原受体(CAR)T细胞疗法在靶向这些肿瘤方面已显示出有前景的结果,但尚未达到治愈效果。一种提高CAR T细胞疗效的创新方法是将其与其他免疫调节疗法相结合。在本研究中,我们研究了靶向IL - 13R2的CAR T细胞与溶瘤病毒(OV)的联合使用,并使用一种新型数学模型研究肿瘤细胞、CAR T细胞和OV动态之间的复杂相互作用。我们将该模型与从每种疗法单独及联合实验收集的数据进行拟合,以揭示治疗协同作用和提高疗效的决定因素。我们的分析表明,病毒裂解量是决定净肿瘤感染率和整体联合治疗疗效的关键参数。此外,该模型预测,与CAR T细胞同时或在其之前施用溶瘤病毒可使治疗效果最大化。