Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Department of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China.
Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China.
Int Immunopharmacol. 2020 Sep;86:106744. doi: 10.1016/j.intimp.2020.106744. Epub 2020 Jul 2.
Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.
了解肿瘤浸润免疫细胞（TIICs）在非小细胞肺癌（NSCLC）中的作用对于寻找新的预后生物标志物和改善预后评估至关重要。在此，我们旨在全面分析 NSCLC 中 TIIC 的肿瘤浸润模式，并建立一个与 TIIC 相关的、用于临床实践的风险分层预后模型。我们应用 CIBERSORT 和 ESTIMATE 计算方法分析了来自癌症基因组图谱（TCGA）的 852 例 NSCLC 患者的 RNA-seq 样本。通过单变量和多变量 Cox 回归分析确定总生存（OS）的预后因素。基于 TCGA 队列开发了一种新的模型来预测 NSCLC 的 1 年、3 年和 5 年 OS，通过外部验证队列（GSE31210、GSE37745）进行验证，并通过 C-指数和校准图进行评估。在各种 NSCLC 病理亚型和不同性别之间，TIIC 浸润模式存在显著的异质性。进一步的分析表明，幼稚 B 细胞（NBCs）、T 细胞和肥大细胞（MCs）的丰度与预后呈正相关。富含 T 细胞的肿瘤样本往往具有更高的免疫检查点基因（PD-1、PD-L1、CTLA-4）的表达水平。通过 5 个与 TIICs 丰度和预后密切相关的免疫相关差异表达基因（DEGs）（BTK、CCR2、CLEC10A、NCR3 和 PRKCB）构建了一个新的免疫基因相关指数（IGRI）。肿瘤分期、IGRI、NBCs、T 细胞、MCs 和 NK 细胞的丰度是显著的独立预后因素，并被纳入预测列线图。列线图的内部和外部校准图吻合度良好。这项研究表明，TIICs 与 NSCLC 的临床病理特征和预后显著相关，因此可以作为潜在的预后生物标志物或治疗靶点。TIICs 的显著异质性表明，在确定 NSCLC 患者的个体化免疫治疗策略时，还应考虑特定的 TIIC 浸润模式。
