Departments of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Sci Rep. 2017 Oct 17;7(1):13380. doi: 10.1038/s41598-017-13942-8.
Immune-profiling is becoming an important tool to identify predictive markers for the response to immunotherapy. Our goal was to validate multiplex immunofluorescence (mIF) panels to apply to formalin-fixed and paraffin-embedded tissues using a set of immune marker antibodies, with the Opal™ 7 color Kit (PerkinElmer) in the same tissue section. We validated and we described two panels aiming to characterize the expression of PD-L1, PD-1, and subsets of tumor associated immune cells. Panel 1 included pancytokeratin (AE1/AE3), PD-L1, CD4, CD8, CD3, CD68, and DAPI, and Panel 2 included pancytokeratin, PD-1, CD45RO, granzyme B, CD57, FOXP3, and DAPI. After all primary antibodies were tested in positive and negative controls by immunohistochemistry and uniplex IF, panels were developed and simultaneous marker expressions were quantified using the Vectra 3.0™ multispectral microscopy and image analysis InForm™ 2.2.1 software (PerkinElmer).These two mIF panels demonstrated specific co-localization in different cells that can identify the expression of PD-L1 in malignant cells and macrophages, and different T-cell subpopulations. This mIF methodology can be an invaluable tool for tumor tissue immune-profiling to allow multiple targets in the same tissue section and we provide that is accurate and reproducible method when is performed carefully under pathologist supervision.
免疫分析正在成为识别免疫治疗反应预测标志物的重要工具。我们的目标是使用一组免疫标志物抗体,通过 Opal™ 7 色试剂盒(PerkinElmer)在相同的组织切片中验证并描述旨在表征 PD-L1、PD-1 和肿瘤相关免疫细胞亚群表达的多重免疫荧光(mIF)面板。面板 1 包括细胞角蛋白(AE1/AE3)、PD-L1、CD4、CD8、CD3、CD68 和 DAPI,而面板 2 包括细胞角蛋白、PD-1、CD45RO、颗粒酶 B、CD57、FOXP3 和 DAPI。在通过免疫组织化学和单重 IF 测试了所有主要抗体的阳性和阴性对照后,开发了面板,并使用 Vectra 3.0™多光谱显微镜和图像分析 InForm™ 2.2.1 软件(PerkinElmer)同时定量标记物的表达。这两个 mIF 面板在不同细胞中表现出特异性共定位,可以识别恶性细胞和巨噬细胞以及不同 T 细胞亚群中 PD-L1 的表达。这种 mIF 方法可以成为肿瘤组织免疫分析的宝贵工具,允许在同一组织切片中检测多个靶标,并且当在病理学家的监督下仔细进行时,我们提供了准确且可重复的方法。