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Tumor Immune Microenvironment Characterization in Hepatocellular Carcinoma Identifies Four Prognostic and Immunotherapeutically Relevant Subclasses

机译:肝细胞癌中的肿瘤免疫微环境表征鉴定了四个预后和免疫治疗相关的亚类

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PurposeThe tumor microenvironment (TME) plays a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, underlying compositions and functions that drive the establishment and maintenance of the TME classifications are less-well understood.MethodsA total of 766 HCC patients from three public cohorts were clustered into four immune-related subclasses based on 13 TME signatures (11 immune-related cells and 2 immune-related pathways) calculated by MCP-counter. After analyzing the landscapes of functional annotation, methylation, somatic mutation, and clinical characteristics, we built a TME-based Support Vector Machine of 365 patients (discovery phase) and 401 patients (validation phase). We applied this SVM model on another two independent cohorts of patients who received sorafenib/pembrolizumab treatment.ResultsAbout 33% of patients displayed an immune desert pattern. The other subclasses were different in abundance of tumor infiltrating cells. The Immunogenic subclass (17%) associated with the best prognosis presented a massive T cell infiltration and an activation of immune checkpoint pathway. The 13 TME signatures showed a good potential to predict the TME classification (average AUC = 88%). Molecular characteristics of immunohistochemistry from Zhejiang cohort supported our SVM classification. The optimum response to pembrolizumab (78%) and sorafenib (81%) was observed in patients belonging to the Immunogenic subclass.ConclusionsThe HCC patients from distinct immune subclass showed significant differences in clinical prognosis and response to personalized treatment. Based on tumor transcriptome data, our workflow can help to predict the clinical outcomes and to find appropriate treatment strategies for HCC patients.
机译:purposethe肿瘤微环境(TME)在肝细胞癌(HCC)的发病机制中起着关键作用。然而,驱动TME分类的建立和维持的潜在的组合和功能是较少的良好理解。从三个公共群组中的766名HCC患者基于13个TME签名聚集成4个免疫相关亚类(11个免疫相关通过MCP-Counter计算的细胞和2个免疫相关途径。在分析功能性注释的景观后,甲基化,体细胞突变和临床特征,我们构建了一种基于TME的支持向量机365名患者(发现期)和401名患者(验证阶段)。我们在接受Sorafenib / Pembrolizumab治疗的另外两个独立的患者的患者的另一个独立队列上应用了这个SVM模型。患者33%的患者展示了一种免疫沙漠模式。其他亚类在丰富的肿瘤渗透细胞中不同。与最佳预后相关的免疫原性亚类(17%)呈现大量T细胞浸润和免疫检查点途径的激活。 13个TME签名显示出良好的潜力,以预测TME分类(平均AUC = 88%)。浙江队列免疫组化的分子特征支持我们的SVM分类。在属于免疫原性亚类的患者中观察到对Pembrolizumab(78%)和索拉非尼(81%)的最佳反应。结论来自不同免疫亚类的HCC患者患者临床预后的显着差异和对个性化治疗的反应。基于肿瘤转录组数据,我们的工作流程可以有助于预测临床结果,并找到HCC患者的适当治疗策略。

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