Background The determinants of the response to checkpoint immunotherapy in hepatocellular carcinoma (HCC) remain poorly understood. The organisation of the immune response in the tumour microenvironment (TME) is expected to govern immunotherapy outcomes but spatial immunotypes remain poorly defined. Objective We hypothesised that the deconvolution of spatial immune network architectures could identify clinically relevant immunotypes in HCC. Design We conducted highly multiplexed imaging mass cytometry on HCC tissues from 101 patients. We performed in-depth spatial single-cell analysis in a discovery and validation cohort to deconvolute the determinants of the heterogeneity of HCC immune architecture and develop a spatial immune classification that was tested for the prediction of immune checkpoint inhibitor (ICI) therapy. Results Bioinformatic analysis identified 23 major immune, stroma, parenchymal and tumour cell types in the HCC TME. Unsupervised neighbourhood detection based on the spatial interaction of immune cells identified three immune architectures with differing involvement of immune cells and immune checkpoints dominated by either CD8 T-cells, myeloid immune cells or B- and CD4 T-cells. We used these to define three major spatial HCC immunotypes that reflect a higher level of intratumour immune cell organisation: depleted, compartmentalised and enriched. Progression-free survival under ICI therapy differed significantly between the spatial immune types with improved survival of enriched patients. In patients with intratumour heterogeneity, the presence of one enriched area governed long-term survival. Data are available in a public, open access repository. Data are available on reasonable request. The imaging mass cytometry data and cell and stroma masks supporting the findings of this study from the discovery cohort (43) and the ICI therapy cohort (44) are made publicly available by the authors for download from Zenodo under the following links respectively: and . Usage of the data should be clearly indicated and the respective repository and this article should be cited accordingly. Other inquiries may be directed to the corresponding author.

中文翻译：

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空间单细胞分析和邻域分析揭示了与肝细胞癌检查点抑制剂治疗结果相关的免疫结构的决定因素


背景 肝细胞癌 （HCC） 对检查点免疫治疗反应的决定因素仍然知之甚少。肿瘤微环境 （TME） 中免疫反应的组织有望控制免疫治疗结果，但空间免疫类型仍然不明确。目的 我们假设空间免疫网络结构的反卷积可以识别 HCC 中临床相关的免疫类型。设计 我们对 101 例患者的 HCC 组织进行了高度多重成像质谱流式细胞术。我们在发现和验证队列中进行了深入的空间单细胞分析，以解开 HCC 免疫结构异质性的决定因素，并开发一种空间免疫分类，用于预测免疫检查点抑制剂 （ICI） 治疗。结果 生物信息学分析在 HCC TME 中确定了 23 种主要免疫、基质、实质和肿瘤细胞类型。基于免疫细胞空间相互作用的无监督邻域检测确定了三种免疫结构，免疫细胞和免疫检查点参与不同，以 CD8 T 细胞、髓样免疫细胞或 B 和 CD4 T 细胞为主。我们使用这些来定义三种主要的空间 HCC 免疫类型，这些免疫类型反映了更高水平的肿瘤内免疫细胞组织：耗竭、区室化和富集。ICI 治疗下的无进展生存期在空间免疫类型之间差异显著，富集患者的生存率更高。在肿瘤内异质性患者中，一个富集区域的存在控制着长期生存。数据在公共、开放访问存储库中可用。数据可应合理要求提供。 作者分别通过以下链接公开提供来自发现队列 （43） 和 ICI 治疗队列 （44） 的成像质谱流式细胞术数据以及支持本研究结果的细胞和基质掩模，以供从 Zenodo 下载：和 。应清楚地说明数据的使用情况，并相应地引用相应的存储库和本文。其他查询可直接联系通讯作者。