Song Ruixia, Liu Fengsen, Ping Yu, Zhang Yi, Wang Liping
Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Biomark Res. 2023 Jun 2;11(1):57. doi: 10.1186/s40364-023-00498-1.
Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.
免疫检查点抑制剂(ICIs)显著改善了多种恶性肿瘤的治疗效果。然而,只有15%至60%的患者有显著反应。因此,准确识别反应者并及时给予ICI治疗是肿瘤ICI治疗中的关键问题。肿瘤学、免疫学、生物学和计算机科学交叉领域最近的快速发展为ICI疗效提供了大量预测生物标志物。这些生物标志物可能是侵入性的,也可能是非侵入性的,这取决于具体的样本采集方法。与侵入性标志物相比,许多非侵入性标志物已被证实在ICI疗效预测中具有更高的可用性和准确性。考虑到免疫治疗反应动态监测的突出优势以及广泛临床应用的潜力,我们综述了该领域的最新研究,旨在为确定可能从ICI治疗中获益最大的患者做出贡献。