Department of Medical Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
Anticancer Drugs. 2024 Nov 1;35(10):952-968. doi: 10.1097/CAD.0000000000001651. Epub 2024 Aug 5.
Factors that determine nonresponse to immune checkpoint inhibitor (ICI) remain unclear. The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for cancer treatment. There is, however, a lack of CAF-related signature in predicting response to immunotherapy in gastric cancer (GC). Single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data of GC immunotherapy were downloaded from the Gene Expression Omnibus database. Bulk RNA-seq data were obtained from The Cancer Genome Atlas. The R package 'Seurat' was used for scRNA-seq data processing. Cellular infiltration, receptor-ligand interactions, and evolutionary trajectory analysis were further explored. Differentially expressed genes affecting overall survival were obtained using the limma package. Weighted Gene Correlation Network Analysis was used to identify key modules of immunotherapy nonresponder. Prognostic model was constructed by univariate Cox and least absolute contraction and selection operator analysis using the intersection of activated fibroblast genes (AFGs) with key module genes. The differences in clinicopathological features, immune microenvironment, immunotherapy prediction, and sensitivity to small molecule agents between the high- and low-risk groups were further investigated. Based on scRNA-seq, we finally identified 20 AFGs associations with the prognosis of GC patients. AFGs' high expression levels were correlated with both poor prognosis and tumor progression. Three genes ( FRZB , SPARC , and FKBP10 ) were identified as immunotherapy nonresponse-related fibroblast genes and used to construct the prognostic signature. This signature is an independent significant risk factor affecting the clinical outcomes of GC patients. Remarkably, there were more CD4 memory T cells, resting mast cells, and M2 macrophages infiltrating in the high-risk group, which was characterized by higher tumor immune exclusion. Moreover, patients with higher risk scores were more prone to not respond to immunotherapy but were more sensitive to various small molecule agents, such as memantine. In conclusion, this study constructed a fibroblast-associated ICI nonresponse gene signature, which could predict the response to immunotherapy. This study potentially revealed a novel way to overcome immune resistance in GC.
影响免疫检查点抑制剂（ICI）无应答的因素尚不清楚。癌症相关成纤维细胞（CAFs）的促肿瘤活性表明它们是癌症治疗的潜在治疗靶点。然而，在胃癌（GC）中，缺乏与 CAF 相关的特征来预测免疫治疗反应。从基因表达综合数据库（GEO）下载 GC 免疫治疗的单细胞 RNA 测序（scRNA-seq）和 RNA 测序（RNA-seq）数据。从癌症基因组图谱（TCGA）获得批量 RNA-seq 数据。使用 R 包 'Seurat' 处理 scRNA-seq 数据。进一步探索细胞浸润、受体-配体相互作用和进化轨迹分析。使用 limma 包获得影响总生存期的差异表达基因。使用加权基因相关网络分析（WGCNA）识别免疫治疗无应答的关键模块。使用激活成纤维细胞基因（AFGs）与关键模块基因的交集，通过单变量 Cox 和最小绝对值收缩和选择算子分析构建预后模型。进一步研究高风险和低风险组之间的临床病理特征、免疫微环境、免疫治疗预测和小分子药物敏感性的差异。基于 scRNA-seq，我们最终确定了 20 个与 GC 患者预后相关的 AFGs 关联。AFGs 的高表达水平与预后不良和肿瘤进展相关。确定了三个与免疫治疗无反应相关的成纤维细胞基因（FRZB、SPARC 和 FKBP10），并用于构建预后特征。该特征是影响 GC 患者临床结局的独立显著危险因素。值得注意的是，高风险组中有更多的 CD4 记忆 T 细胞、静止肥大细胞和 M2 巨噬细胞浸润，其特点是肿瘤免疫排斥更高。此外，风险评分较高的患者更倾向于对免疫治疗无反应，但对各种小分子药物（如美金刚）更敏感。总之，本研究构建了一个与成纤维细胞相关的 ICI 无应答基因特征，可以预测免疫治疗的反应。本研究可能揭示了克服 GC 免疫抵抗的新途径。
