Department of Precision Medicine, Terasaki Institute for Biomedical Innovation, Los Angeles, CA, United States.
Department of Pipeline Development, Biomap, Inc., San Francisco, CA, United States.
Front Immunol. 2024 Jul 8;15:1416751. doi: 10.3389/fimmu.2024.1416751. eCollection 2024.
Tissue-resident memory T cells (T) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of T-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique T signatures, through which we inferred the abundance of T in NSCLC. We discovered that T abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, T signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A T-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher T infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the T signature revealed low T abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between T and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.
组织驻留记忆 T 细胞（T 细胞）是驻留在外周组织中的长寿记忆 T 细胞的一个专门亚群。然而，T 相关免疫监视对肿瘤免疫微环境（TIME）和各种非小细胞肺癌（NSCLC）患者人群中肿瘤进展的影响仍有待阐明。我们对多个独立的 NSCLC 患者样本单细胞和批量 RNA-seq 数据集进行了全面分析，生成了可靠的、独特的 T 特征，通过这些特征，我们推断了 NSCLC 中 T 的丰度。我们发现，T 的丰度与 TIME 中的 CD4+T 辅助 1 细胞、M1 巨噬细胞和静止树突状细胞呈一致的正相关。此外，T 特征与免疫检查点和刺激基因以及 NSCLC 患者的预后密切相关。一个基于 T 的机器学习模型被用来预测患者的生存情况，并且进一步开发了一个 18 个基因的风险评分，有效地将患者分为低风险和高风险类别，其中高风险评分的患者的总生存率明显低于低风险评分的患者。风险评分的预后价值通过癌症基因组图谱计划（TCGA）数据集和多个独立的 NSCLC 患者数据集进行了独立验证。值得注意的是，具有较高 T 浸润的低风险 NSCLC 患者表现出增强的 T 细胞免疫、自然杀伤细胞激活和其他 TIME 免疫反应相关途径，表明更活跃的免疫特征受益于免疫治疗。然而，与腺癌相比，T 特征揭示了低风险 NSCLC 患者 T 细胞丰度较低且与预后无关，这表明这两种 NSCLC 亚型由不同的 TIME 驱动。总的来说，这项研究提供了关于 T 和 TIME 之间复杂相互作用及其对 NSCLC 患者预后影响的有价值的见解。18 个基因风险评分的开发为风险分层提供了一个实用的预后标志物。
