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与免疫检查点抑制剂诱导的免疫相关不良事件相关的生物标志物。

Biomarkers associated with immune-related adverse events induced by immune checkpoint inhibitors.

作者信息

Guo An-Jie, Deng Qing-Yuan, Dong Pan, Zhou Lian, Shi Lei

机构信息

School of Life Sciences, Chongqing University, Chongqing 400044, China.

Head and Neck Cancer Center, Chongqing University Cancer Hospital, Chongqing 400000, China.

出版信息

World J Clin Oncol. 2024 Aug 24;15(8):1002-1020. doi: 10.5306/wjco.v15.i8.1002.

Abstract

Immune checkpoint inhibitors (ICIs) constitute a pivotal class of immunotherapeutic drugs in cancer treatment. However, their widespread clinical application has led to a notable surge in immune-related adverse events (irAEs), significantly affecting the efficacy and survival rates of patients undergoing ICI therapy. While conventional hematological and imaging tests are adept at detecting organ-specific toxicities, distinguishing adverse reactions from those induced by viruses, bacteria, or immune diseases remains a formidable challenge. Consequently, there exists an urgent imperative for reliable biomarkers capable of accurately predicting or diagnosing irAEs. Thus, a thorough review of existing studies on irAEs biomarkers is indispensable. Our review commences by providing a succinct overview of major irAEs, followed by a comprehensive summary of irAEs biomarkers across various dimensions. Furthermore, we delve into innovative methodologies such as machine learning, single-cell RNA sequencing, multiomics analysis, and gut microbiota profiling to identify novel, robust biomarkers that can facilitate precise irAEs diagnosis or prediction. Lastly, this review furnishes a concise exposition of irAEs mechanisms to augment understanding of irAEs prediction, diagnosis, and treatment strategies.

摘要

免疫检查点抑制剂(ICIs)是癌症治疗中一类关键的免疫治疗药物。然而,它们在临床上的广泛应用导致免疫相关不良事件(irAEs)显著增加,严重影响了接受ICI治疗患者的疗效和生存率。虽然传统的血液学和影像学检查擅长检测器官特异性毒性,但将不良反应与病毒、细菌或免疫疾病引起的反应区分开来仍然是一项艰巨的挑战。因此,迫切需要能够准确预测或诊断irAEs的可靠生物标志物。因此,对现有关于irAEs生物标志物的研究进行全面综述是必不可少的。我们的综述首先简要概述主要的irAEs,然后全面总结不同维度的irAEs生物标志物。此外,我们深入探讨机器学习、单细胞RNA测序、多组学分析和肠道微生物群分析等创新方法,以识别能够促进irAEs精确诊断或预测的新型、可靠生物标志物。最后,本综述简要阐述了irAEs机制,以加深对irAEs预测、诊断和治疗策略的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c17/11346067/6aa4e669ee98/WJCO-15-1002-g001.jpg

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