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肺癌的治疗:免疫细胞能预测疗效吗?一项系统性综述。

Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review.

机构信息

Department of Medical Oncology, Paoli-Calmettes Institute, Marseille, France.

Team Immunity and Cancer, Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Marseille, France.

出版信息

Front Immunol. 2020 Jun 24;11:1036. doi: 10.3389/fimmu.2020.01036. eCollection 2020.

Abstract

The landscape for medical treatment of lung cancer has irreversibly changed since the development of immuno-oncology (IO). Yet, while immune checkpoint blockade (ICB) revealed that T lymphocytes play a major role in lung cancer, the precise dynamic of innate and adaptive immune cells induced by anticancer treatments including chemotherapy, targeted therapy, and/or ICB is poorly understood. In lung cancer, studies evaluating specific immune cell populations as predictors of response to medical treatment are scarce, and knowledge is fragmented. Here, we review the different techniques allowing the detection of immune cells in the tumor and blood (multiplex immunohistochemistry and immunofluorescence, RNA-seq, DNA methylation pattern, mass cytometry, functional tests). In addition, we present data that consider different baseline immune cell populations as predictors of response to medical treatments of lung cancer. We also review the potential for assessing dynamic changes in cell populations during treatment as a biomarker. As powerful tools for immune cell detection and data analysis are available, clinicians and researchers could increase understanding of mechanisms of efficacy and resistance in addition to identifying new targets for IO by developing translational studies that decipher the role of different immune cell populations during lung cancer treatments.

摘要

自免疫肿瘤学(IO)发展以来,肺癌的治疗格局已经发生了不可逆转的变化。然而,尽管免疫检查点阻断(ICB)表明 T 淋巴细胞在肺癌中发挥着重要作用,但对于包括化疗、靶向治疗和/或 ICB 在内的抗癌治疗所诱导的固有和适应性免疫细胞的精确动态仍知之甚少。在肺癌中,评估特定免疫细胞群体作为对治疗反应的预测因子的研究很少,而且知识也很零散。在这里,我们回顾了不同的技术,这些技术可以检测肿瘤和血液中的免疫细胞(多重免疫组化和免疫荧光、RNA-seq、DNA 甲基化模式、质谱流式细胞术、功能测试)。此外,我们还提供了一些数据,这些数据考虑了不同的基线免疫细胞群体作为预测肺癌对医疗治疗反应的指标。我们还回顾了在治疗期间评估细胞群体动态变化作为生物标志物的潜力。由于有强大的免疫细胞检测和数据分析工具,临床医生和研究人员可以通过开展转化研究来增加对疗效和耐药机制的理解,除了确定 IO 的新靶点外,还可以通过解析不同免疫细胞群体在肺癌治疗过程中的作用来识别新的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c596/7327092/3356c05dbb82/fimmu-11-01036-g0001.jpg

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