Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

中文翻译：

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从多维组学角度解读肿瘤免疫微环境：洞察下一代CAR-T细胞免疫疗法及其他疗法


肿瘤免疫微环境（TIME）由肿瘤内免疫成分组成，在肿瘤发生、进展、转移和治疗反应中发挥重要作用。嵌合抗原受体 (CAR)-T 细胞免疫疗法彻底改变了癌症治疗模式。尽管 CAR-T 细胞免疫疗法已成为血液恶性肿瘤的成功治疗方法，但它仍然是实体瘤的难题。 TIME 的异质性是导致针对实体瘤的 CAR-T 细胞免疫治疗效果不佳的原因。高度尖端技术的进步增强了我们从多组学角度对 TIME 的探索。在机器学习时代，多组学研究可以揭示TIME的特征及其免疫抵抗机制。因此，通过针对TIME中不利条件的策略，可以进一步提高CAR-T细胞免疫治疗实体瘤的临床疗效。本文旨在探讨影响TIME形成的因素，并提出通过多组学视角提高CAR-T细胞免疫治疗有效性的策略，最终目标是开发个性化治疗方法。