CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:CAR T Cell Locomotion in Solid Tumor Microenvironment.
CAR T Cell Locomotion in Solid Tumor Microenvironment.
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嵌合抗原受体(CAR)T细胞疗法在血液系统恶性肿瘤中取得良好疗效,展现出对抗多种癌症的潜力。不过,若要用于实体瘤,仍需显著改进。研究者逐步明确了疗法局限,并持续优化CAR设计。但即使CAR-T 细胞具备理想的杀伤功能,仍须克服肿瘤微环境(TME)造成的抑制屏障并在其中存活。本综述详细讨论TME对CAR-T 细胞迁移的重要影响,以及内在屏障如何导致免疫抑制表型和癌症进展。临床前模型必须尽可能真实地再现体内TME,才能更准确预测CAR-T 活性。动物模型极大促进了人类疾病研究,但动物照护要求高且对人体生物学的模拟并不可靠,提示体内模型不能成为评估CAR-T 疗法的唯一方法。另一方面,体外CAR-T 细胞毒性模型为单细胞机制研究提供有价值的信息,但往往缺少体内复杂性、个体间异质性和生理相关的空间维度。了解不同临床前模型的优势、局限及适用场景,有助于更可靠地预测临床结局。
The promising outcomes of chimeric antigen receptor (CAR) T cell therapy in hematologic malignancies potentiates its capability in the fight against many cancers. Nevertheless, this immunotherapy modality needs significant improvements for the treatment of solid tumors. Researchers have incrementally identified limitations and constantly pursued better CAR designs.
However, even if CAR T cells are armed with optimal killer functions, they must overcome and survive suppressive barriers imposed by the tumor microenvironment (TME). In this review, we will discuss in detail the important role of TME in CAR T cell trafficking and how the intrinsic barriers contribute to an immunosuppressive phenotype and cancer progression. It is of critical importance that preclinical models can closely recapitulate the in vivo TME to better predict CAR T activity.
Animal models have contributed immensely to our understanding of human diseases, but the intensive care for the animals and unreliable representation of human biology suggest in vivo models cannot be the sole approach to CAR T cell therapy.
On the other hand, in vitro models for CAR T cytotoxic assessment offer valuable insights to mechanistic studies at the single cell level, but they often lack in vivo complexities, inter-individual heterogeneity, or physiologically relevant spatial dimension. Understanding the advantages and limitations of preclinical models and their applications would enable more reliable prediction of better clinical outcomes.
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