PO.CL01.02 · 临床研究
一种新型上皮肿瘤特征谱预测非小细胞肺癌的生存及对PD-1阻断治疗的应答
A novel epithelial tumor signature predicts survival and response to PD-1 blockade in non-small cell lung cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：免疫检查点抑制剂（ICI）是缺乏驱动改变的NSCLC的标准治疗，但许多患者未能获得临床获益。当前的生物标志物（如PD-L1）在预测应答方面并不完善。识别与免疫治疗应答相关的肿瘤上皮基因标志物可改善对患者结局的预测。
方法：分析了接受抗PD1治疗的NSCLC患者单细胞RNA（scRNA）数据集（GSE205335，n=12,975个细胞），以识别应答者（CR/PR）与非应答者（SD/PD）之间的DEGs。使用一个bulk RNA-seq队列（SU2C-MARK，n=142）构建预后模型。使用单变量Cox回归识别与总生存（OS）相关的基因，随后通过最小绝对收缩和选择算子（LASSO）及多变量Cox模型识别生存的独立预测因子。计算基于特征谱的风险评分，根据中位风险评分将患者分为高风险组和低风险组。使用Wilcoxon、Kaplan-Meier和log-rank检验评估与应答和生存的关联。进行多变量Cox分析以校正临床协变量。使用合并的、批次校正后的数据集（GSE13522、GSE126044、GSE190265、GSE274975；n=129）验证该特征谱。使用xCELL进行计算免疫解卷积。
结果：scRNA-seq分析揭示了816个与治疗应答相关的上皮基因。对bulk RNA-seq队列的单变量Cox回归识别出36个OS相关基因，经LASSO-Cox缩减为17个基因。多变量Cox回归得出由4个生存独立预测基因组成的特征谱：CXCL8（HR：1.27，p=0.008）、C11orf58（HR：0.59，p=0.010）、SERF2（HR：2.11，p=0.015）和MT-CO1（HR：1.27，p=0.045）。使用该4基因风险评分将患者分为高风险组（n=71）和低风险组（n=71）。高风险患者表现出更短的OS和PFS（HR=3.49，p=4×10⁻⁷；HR=2.029，p=0.0007），非应答者与更高的风险评分相关（p=0.0175）。在校正年龄、性别、分期、吸烟和PD-L1 TPS后，该4基因风险评分是OS（HR=2.785，p=0.0082）和PFS（HR=1.650，p=0.028）的独立预测因子。TME解卷积显示高风险组具有更高的巨噬细胞（M0、M1）和中性粒细胞，而低风险组具有更高的CD4+ T细胞和初始B细胞。在合并的验证数据集（n=129）中，高风险组的PFS较低风险组更差（HR=1.58，p=0.036）。
结论：我们提出了一种由4个肿瘤内在上皮基因（CXCL8、C11orf58、SERF2、MT-CO1）组成的新型特征谱，可预测接受抗PD-1治疗的NSCLC患者的应答和生存。这些基因与免疫微环境之间的关联凸显了一个关键的肿瘤-TME轴，并为转化生物标志物研究展现了良好的潜力。
查看英文原文 English abstract
Introduction: Immune checkpoint inhibitors (ICIs) are the standard of care for NSCLC lacking driver alterations, yet many patients do not derive clinical benefit. Current biomarkers, such as PD-L1, are imperfect in predicting response. Identifying tumor epithelial gene markers linked to immunotherapy response could improve prediction of patient outcomes.
Methods: A single-cell RNA (scRNA) dataset (GSE205335, n=12,975 cells) of NSCLC patients treated with anti-PD1 therapy was analysed to identify DEGs between responders (CR/PR) and non-responders (SD/PD). A bulk RNA-seq cohort (SU2C-MARK, n=142) was used to build a prognostic model. Genes associated with overall survival (OS) were identified using univariate Cox regression, followed by Least Absolute Shrinkage and Selection Operator (LASSO) and a multivariate Cox model to identify independent predictors of survival. A signature-based risk score was calculated, stratifying patients into High and Low-Risk groups based on median risk score. Associations with response and survival were assessed using Wilcoxon, Kaplan-Meier, and log-rank tests. Multivariate Cox analysis was performed to adjust for clinical covariates. The signature was validated using combined, batch-corrected datasets (GSE13522, GSE126044, GSE190265, GSE274975; n=129). xCELL was used to perform computational immune deconvolution.
Results: scRNA-seq analysis revealed 816 epithelial genes linked to treatment response. Univariate Cox regression of the bulk RNA-seq cohort identified 36 OS-associated genes, which were reduced to 17 genes by LASSO-Cox. Multivariate Cox regression resulted in a 4-gene signature of independent predictors of survival: CXCL8 (HR: 1.27, p=0.008), C11orf58 (HR: 0.59, p=0.010), SERF2 (HR: 2.11, p=0.015), and MT-CO1 (HR: 1.27, p=0.045). The 4-gene risk score was used to split patients into High (n=71) and Low (n=71) risk groups. High-Risk patients exhibited shorter OS and PFS (HR=3.49, p = 4×10⁻⁷; HR=2.029, p = 0.0007), and non-responders were associated with higher risk scores (p=0.0175). The 4-gene risk score was an independent predictor of OS (HR=2.785, p=0.0082) and PFS (HR=1.650, p=0.028) after adjusting for age, sex, stage, smoking, and PD-L1 TPS. TME deconvolution revealed that the High-Risk group featured higher Macrophages (M0, M1) and Neutrophils, while the Low-Risk group featured higher CD4+ T-cells and naive B-cells. In the combined validation dataset (n=129), the High-Risk group experienced worse PFS compared to the Low-Risk group (HR=1.58, p=0.036).
Conclusion: We present a novel signature of 4 tumor-intrinsic epithelial genes (CXCL8, C11orf58, SERF2, MT-CO1) that predict response and survival in NSCLC patients treated with anti-PD-1 therapy. The link between these genes and the immune microenvironment highlights a key tumor-TME axis, and represents promising potential for translational biomarker studies.
利益披露 Disclosure
M. Alsufi, None..
J. Yasin, None..
M. Alsufi, None..
O. Younis, None..
A. Assayed, None.