Suppr超能文献

CD8+ T细胞亚群作为预测癌症免疫治疗中检查点治疗结果的生物标志物

CD8+ T Cell Subsets as Biomarkers for Predicting Checkpoint Therapy Outcomes in Cancer Immunotherapy.

作者信息

Casalegno Garduño Rosaely, Spitschak Alf, Pannek Tim, Pützer Brigitte M

机构信息

Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany.

Department Life, Light & Matter, University of Rostock, 18059 Rostock, Germany.

出版信息

Biomedicines. 2025 Apr 9;13(4):930. doi: 10.3390/biomedicines13040930.

Abstract

The advent of immune checkpoint blockade (ICB) has transformed cancer immunotherapy, enabling remarkable long-term outcomes and improved survival, particularly with ICB combination treatments. However, clinical benefits remain confined to a subset of patients, and life-threatening immune-related adverse effects pose a significant challenge. This limited efficacy is attributed to cancer heterogeneity, which is mediated by ligand-receptor interactions, exosomes, secreted factors, and key transcription factors. Oncogenic regulators like E2F1 and MYC drive metastatic tumor environments and intertwine with immunoregulatory pathways, impairing T cell function and reducing immunotherapy effectiveness. To address these challenges, FDA-approved biomarkers, such as tumor mutational burden (TMB) and programmed cell death-ligand 1 (PD-L1) expression, help to identify patients most likely to benefit from ICB. Yet, current biomarkers have limitations, making treatment decisions difficult. Recently, T cells-the primary target of ICB-have emerged as promising biomarkers. This review explores the relationship between cancer drivers and immune response, and emphasizes the role of CD8+ T cells in predicting and monitoring ICB efficacy. Tumor-infiltrating CD8+ T cells correlate with positive clinical outcomes in many cancers, yet obtaining tumor tissue remains complex, limiting its practical use. Conversely, circulating T cell subsets are more accessible and have shown promise as predictive biomarkers. Specifically, memory and progenitor exhausted T cells are associated with favorable immunotherapy responses, while terminally exhausted T cells negatively correlate with ICB efficacy. Ultimately, combining biomarkers enhances predictive accuracy, as demonstrated by integrating TMB/PD-L1 expression with CD8+ T cell frequency. Computational models incorporating cancer and immune signatures could further refine patient stratification, advancing personalized immunotherapy.

摘要

免疫检查点阻断(ICB)的出现改变了癌症免疫治疗,带来了显著的长期疗效并提高了生存率,尤其是在ICB联合治疗中。然而,临床益处仍仅限于一部分患者,危及生命的免疫相关不良反应构成了重大挑战。这种有限的疗效归因于癌症异质性,它由配体-受体相互作用、外泌体、分泌因子和关键转录因子介导。像E2F1和MYC这样的致癌调节因子驱动转移性肿瘤环境并与免疫调节途径相互交织,损害T细胞功能并降低免疫治疗效果。为应对这些挑战,美国食品药品监督管理局(FDA)批准的生物标志物,如肿瘤突变负荷(TMB)和程序性细胞死亡配体1(PD-L1)表达,有助于识别最有可能从ICB中获益的患者。然而,目前的生物标志物存在局限性,使得治疗决策变得困难。最近,T细胞——ICB的主要靶点——已成为有前景的生物标志物。本综述探讨了癌症驱动因素与免疫反应之间的关系,并强调了CD8+T细胞在预测和监测ICB疗效中的作用。肿瘤浸润性CD8+T细胞在许多癌症中与积极的临床结果相关,但获取肿瘤组织仍然复杂,限制了其实际应用。相反,循环T细胞亚群更容易获取,并已显示出作为预测生物标志物的潜力。具体而言,记忆性和祖细胞耗竭性T细胞与良好的免疫治疗反应相关,而终末耗竭性T细胞与ICB疗效呈负相关。最终,正如将TMB/PD-L1表达与CD8+T细胞频率相结合所证明的那样,联合使用生物标志物可提高预测准确性。纳入癌症和免疫特征的计算模型可以进一步优化患者分层,推动个性化免疫治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec89/12025007/f627302a5442/biomedicines-13-00930-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验