Erdi Kara, Trachette L Jackson, Chartese Jones, Rockford Sison, Reginald L McGee Ii
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引用次数: 0
Abstract
As an adoptive cellular therapy, Chimeric Antigen Receptor T cell (CAR T cell) therapy has shown remarkable success in hematological malignancies but only limited efficacy against solid tumors. Compared with blood cancers, solid tumors present a series of challenges that ultimately combine to neutralize the function of CAR T cells. These challenges include, but are not limited to, antigen heterogeneity - variability in the expression of the antigen on tumor cells, as well as trafficking and infiltration into the solid tumor tissue. A critical question for solving the heterogeneity problem is whether CAR T therapy induces bystander effects, such as antigen spreading. Antigen spreading occurs when CAR T cells activate other endogenous antitumor CD8 T cells against antigens that were not originally targeted. In this work, we develop a mathematical model of CAR T cell therapy for solid tumors that considers both antigen heterogeneity and bystander effects. Our model is based on in vivo treatment data that includes a mixture of target antigen-positive and target antigen-negative tumor cells. We use our model to simulate large cohorts of virtual patients to better understand the relationship involving bystander killing. We also investigate several strategies for enhancing bystander effects, thus increasing CAR T cell therapy's overall efficacy for solid tumors.
作为一种采纳性细胞疗法,嵌合抗原受体 T 细胞(CAR T 细胞)疗法在血液恶性肿瘤方面取得了显著的成功,但对实体瘤的疗效有限。与血癌相比,实体瘤面临着一系列挑战,这些挑战最终会共同中和 CAR T 细胞的功能。这些挑战包括但不限于抗原异质性--抗原在肿瘤细胞上表达的差异性,以及在实体瘤组织中的迁移和浸润。解决异质性问题的一个关键问题是 CAR T 疗法是否会诱发旁观者效应,如抗原扩散。当 CAR T 细胞激活其他内源性抗肿瘤 CD8 T 细胞对抗最初未靶向的抗原时,就会发生抗原扩散。在这项研究中,我们建立了一个考虑抗原异质性和旁观者效应的 CAR T 细胞治疗实体瘤的数学模型。我们的模型基于体内治疗数据,其中包括靶抗原阳性和靶抗原阴性肿瘤细胞的混合物。我们利用模型模拟了大量虚拟患者,以更好地理解旁观者杀伤的关系。我们还研究了增强旁观者效应的几种策略,从而提高 CAR T 细胞疗法对实体瘤的总体疗效。
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.