The Role of Immune Checkpoint Inhibitors in Cancer Therapy: Mechanism and Therapeutic Advances.

IF 10.7 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
MedComm Pub Date : 2025-10-05 eCollection Date: 2025-10-01 DOI:10.1002/mco2.70412
Hengyi Chen, Hongling Yang, Lu Guo, Qingxiang Sun
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引用次数: 0

Abstract

The rapid development of immune checkpoint inhibitors has fundamentally changed the landscape of cancer treatment. These agents restore T cell-mediated antitumor immune responses by targeting key immune checkpoint molecules, thereby suppressing or eliminating tumors. However, their clinical application still faces multiple challenges, mainly including efficacy heterogeneity, drug resistance, immune-related adverse events. Furthermore, there is still a lack of reliable biomarkers for predicting efficacy and toxicity. More critically, there is absence of precise predictive models that can systematically integrate multiomics features, dynamic tumor microenvironment evolution, and patient individual differences to comprehensively address the above issues. This review systematically summarizes the latest advancements in this field. The main contents include emerging targets like lymphocyte activation gene 3, T cell immunoreceptor with immunoglobulin and tyrosine-based inhibitory motif domain, and mucin-domain-containing-3, combination strategies, and the current research status and limitations of various predictive biomarkers. Moreover, it focuses on the potential of microbiome regulation, metabolic reprogramming, and artificial intelligence-driven multiomics analysis technologies in achieving dynamic patient stratification and personalized treatment. By integrating the frontier research results and clinical insights, the review aims to provide a systematical theory framework and future directions for advancing precision immunotherapy.

免疫检查点抑制剂的快速发展从根本上改变了癌症治疗的前景。这些药物通过靶向关键免疫检查点分子来恢复T细胞介导的抗肿瘤免疫反应,从而抑制或消除肿瘤。但其临床应用仍面临多重挑战,主要包括疗效异质性、耐药、免疫相关不良事件等。此外,仍然缺乏可靠的生物标志物来预测疗效和毒性。更为关键的是,目前还缺乏能够系统整合多组学特征、肿瘤微环境动态演变和患者个体差异的精确预测模型来全面解决上述问题。本文系统地总结了这一领域的最新进展。主要内容包括淋巴细胞活化基因3、基于免疫球蛋白和酪氨酸的抑制基序结构域的T细胞免疫受体、含粘蛋白结构域-3等新兴靶点、组合策略以及各种预测性生物标志物的研究现状和局限性。此外,它还侧重于微生物组调控、代谢重编程和人工智能驱动的多组学分析技术在实现动态患者分层和个性化治疗方面的潜力。本文旨在结合前沿研究成果和临床见解,为推进精准免疫治疗提供系统的理论框架和未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.70
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0.00%
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审稿时长
10 weeks
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