免疫检查点抑制剂（ICIs）的免疫疗法越来越多地用于治疗各种肿瘤类型。确定患者对 ICI 的反应是一项重大的临床挑战。尽管肿瘤微环境 (TME) 的组成部分可用于预测患者的预后，但对 TME 的综合评估经常被忽视。采用自上而下的方法，TME分为五层——结果、免疫作用、细胞、细胞成分和基因。利用这种结构，开发了一种名为 TME-NET 的神经网络来预测对 ICI 的反应。使用模型参数权重和细胞消融研究来研究 TME 成分的影响。该模型是使用涵盖四种癌症类型的 948 名患者组成的泛癌症队列开发和评估的，以曲线下面积 (AUC) 和准确性作为性能指标。结果表明，TME-NET 在 AUC 和准确性方面超越了支持向量机和 k-近邻等现有模型。模型参数权重可视化表明，在细胞层，Th1细胞增强免疫反应，而骨髓源性抑制细胞和M2巨噬细胞则表现出强烈的免疫抑制作用。细胞消融研究进一步证实了这些细胞的影响。在基因层，Th1细胞中的转录因子STAT4和M2巨噬细胞中的IRF4显着影响TME动态。此外，来自 Th1 细胞的细胞因子编码基因 IFNG 和来自 M2 巨噬细胞的 ARG1 对于调节 TME 内的免疫反应至关重要。免疫治疗队列的生存数据证实了这些标志物的预后能力，p 值 <0.01。总之，TME-NET 在预测免疫治疗反应方面表现良好，并为免疫治疗过程提供了可解释的见解。可以在 https://immbal.shinyapps.io/TME-NET 进行定制。© 作者 2024。由牛津大学出版社出版。

Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the Curve (AUC) and accuracy as performance metrics. Results show that TME-NET surpasses established models such as support vector machine and k-nearest neighbors in AUC and accuracy. Visualization of model parameter weights showed that at the cellular layer, Th1 cells enhance immune responses, whereas myeloid-derived suppressor cells and M2 macrophages show strong immunosuppressive effects. Cell ablation studies further confirmed the impact of these cells. At the gene layer, the transcription factors STAT4 in Th1 cells and IRF4 in M2 macrophages significantly affect TME dynamics. Additionally, the cytokine-encoding genes IFNG from Th1 cells and ARG1 from M2 macrophages are crucial for modulating immune responses within the TME. Survival data from immunotherapy cohorts confirmed the prognostic ability of these markers, with p-values <0.01. In summary, TME-NET performs well in predicting immunotherapy responses and offers interpretable insights into the immunotherapy process. It can be customized at https://immbal.shinyapps.io/TME-NET.© The Author(s) 2024. Published by Oxford University Press.