Monkman James, Taheri Touraj, Ebrahimi Warkiani Majid, O'Leary Connor, Ladwa Rahul, Richard Derek, O'Byrne Ken, Kulasinghe Arutha
School of Biomedical Sciences, Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4000, Australia.
Translational Research Institute, Woolloongabba, QLD 4102, Australia.
Cancers (Basel). 2020 Nov 27;12(12):3551. doi: 10.3390/cancers12123551.
Profiling the tumour microenvironment (TME) has been informative in understanding the underlying tumour-immune interactions. Multiplex immunohistochemistry (mIHC) coupled with molecular barcoding technologies have revealed greater insights into the TME. In this study, we utilised the Nanostring GeoMX Digital Spatial Profiler (DSP) platform to profile a non-small-cell lung cancer (NSCLC) tissue microarray for protein markers across immune cell profiling, immuno-oncology (IO) drug targets, immune activation status, immune cell typing, and pan-tumour protein modules. Regions of interest (ROIs) were selected that described tumour, TME, and normal adjacent tissue (NAT) compartments. Our data revealed that paired analysis ( = 18) of matched patient compartments indicate that the TME was significantly enriched in CD27, CD3, CD4, CD44, CD45, CD45RO, CD68, CD163, and VISTA relative to the tumour. Unmatched analysis indicated that the NAT ( = 19) was significantly enriched in CD34, fibronectin, IDO1, LAG3, ARG1, and PTEN when compared to the TME ( = 32). Univariate Cox proportional hazards indicated that the presence of cells expressing CD3 (hazard ratio (HR): 0.5, = 0.018), CD34 (HR: 0.53, = 0.004), and ICOS (HR: 0.6, = 0.047) in tumour compartments were significantly associated with improved overall survival (OS). We implemented both high-plex and high-throughput methodologies to the discovery of protein biomarkers and molecular phenotypes within biopsy samples, and demonstrate the power of such tools for a new generation of pathology research.
分析肿瘤微环境(TME)有助于理解潜在的肿瘤-免疫相互作用。多重免疫组织化学(mIHC)与分子条形码技术相结合,为TME提供了更深入的见解。在本研究中,我们利用Nanostring GeoMX数字空间分析平台(DSP)对非小细胞肺癌(NSCLC)组织微阵列进行分析,以检测免疫细胞分析、免疫肿瘤学(IO)药物靶点、免疫激活状态、免疫细胞分型和泛肿瘤蛋白模块中的蛋白质标志物。选择了描述肿瘤、TME和正常相邻组织(NAT)区域的感兴趣区域(ROI)。我们的数据显示,对匹配患者区域进行配对分析(n = 18)表明,相对于肿瘤,TME中CD27、CD3、CD4、CD44、CD45、CD45RO、CD68、CD163和VISTA显著富集。非配对分析表明,与TME(n = 32)相比,NAT(n = 19)中CD34、纤连蛋白、IDO1、LAG3、ARG1和PTEN显著富集。单变量Cox比例风险分析表明,肿瘤区域中表达CD3(风险比(HR):0.5,P = 0.018)、CD34(HR:0.53,P = 0.004)和ICOS(HR:0.6,P = 0.047)的细胞的存在与总生存期(OS)的改善显著相关。我们在活检样本中采用了高多重和高通量方法来发现蛋白质生物标志物和分子表型,并证明了这些工具对新一代病理学研究的作用。