Immunotherapy with immune checkpoint inhibitors has changed the treatment landscape in many solid tumors. Despite the unprecedent success, many patients will develop primary or secondary resistance to treatment or will hold up therapy due to the emerging immune-related toxicity. Traditionally, tissue-based immune biomarkers, such as PD-L1 expression, have been used to select patients who will benefit most from immunotherapy. However, these markers demonstrate major limitations, such as tumor heterogeneity and sample constraints. In addition, they do not reflect the dynamic interplay of tumor and hosts immune response during treatment. Peripheral blood immunomarkers offer a minimally invasive, real-time assessment of the immune system and its interaction with the tumor. Integration of traditional tissue-based and peripheral blood markers coupled with the recent developments in computational platforms, artificial intelligence, and machine learning models may provide more successful biomarkers for prognosis, prediction of immunotherapy-related outcomes, the early evaluation of forthcoming disease progression, and the prediction of the emerging immune-related adverse events. Despite the promising developments in the field of immune biomarkers, several issues including assay standardization, clinical validation, and biological variability should be addressed to improve personalized immunotherapy approaches. In this comprehensive review we provide an update on immune biomarker evolution, and we discuss the current limitations and future directions.
免疫检查点抑制剂疗法已改变多种实体瘤的治疗格局。尽管取得前所未有的成功，但许多患者会出现原发性或继发性耐药，或因新出现的免疫相关毒性而中断治疗。传统上，基于组织的免疫生物标志物（如PD-L1表达）被用于筛选最可能从免疫治疗中获益的患者。然而，这些标志物存在显著局限性，包括肿瘤异质性和样本获取限制。此外，它们无法反映治疗过程中肿瘤与宿主免疫反应的动态相互作用。外周血免疫标志物为评估免疫系统及其与肿瘤的相互作用提供了微创、实时的监测手段。将传统组织标志物与外周血标志物相结合，并借助计算平台、人工智能和机器学习模型的最新进展，有望开发出更有效的生物标志物，用于预后评估、免疫治疗疗效预测、疾病进展早期监测以及免疫相关不良事件预测。尽管免疫生物标志物领域前景广阔，但仍需解决检测标准化、临床验证和生物学变异等问题，以推动个体化免疫治疗策略的发展。本综述系统阐述了免疫生物标志物的研究进展，并探讨当前局限性与未来发展方向。
肿瘤(癌症)患者之家