PO.CL12.01 · 临床研究

SPP1驱动的胶质母细胞瘤肿瘤相关中性粒细胞免疫代谢重编程

SPP1-driven immunometabolic reprogramming of tumor-associated neutrophils in glioblastoma

编号 3879 展板 12 时间 4/20 02:00–05:00 区域 Section 46 主讲 Matthew Abikenari
分会场 Molecular Classification and Tumor Biology in Cancer
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作者与单位 Authors & Affiliations

Matthew Alexander Abikenari1, John Hyunkuk Choi1, Ravi Medikonda1, Lily Kim1, Rohit Verma2, Justin Liu1, Adam Sjoholm1, George Nageeb1, Brandon Hwa-Lin Bergsneider2, Caren Yu-Ju Wu3, Kwang Bog Cho1, Andrew Tran1, David Bakalov4, Matei Banu1, Michael Lim1

1Neurosurgery, Stanford University School of Medicine, Stanford, CA,2Stanford University School of Medicine, Stanford, CA,3Taipei Chang Gung Memorial Hospital, Ghanghua, Taiwan,4Medical Scientist Training Program, University of Utah, Salt Lake City, UT

摘要 Abstract

中文摘要
引言:胶质母细胞瘤(GBM)中的肿瘤相关中性粒细胞(TANs)具有异质性,经典的N1/N2二分法难以充分刻画。我们相对于外周血中性粒细胞(PBNs)表征了TAN状态,并探讨是否存在一个离散的、可靶向的程序驱动其促肿瘤表型。 方法:对公开的scRNA-seq数据集进行重新分析(GSM8380727、GSM8380728:TANs 15,000个细胞;PBNs 10,000个细胞;36,601个基因)。应用标准Seurat工作流程(严格质控;SCTransform;整合;PCA/UMAP;共享最近邻聚类)。差异表达采用Wilcoxon秩和检验并进行Bonferroni FDR校正(校正p<0.05,|log2FC|>0.25)。模块评分考察了预先设定的程序:抗原提呈/共刺激、干扰素/细胞毒性、脂质/应激适应。功能富集和蛋白-蛋白网络通过GSEA和STRING评估。 结果:整合分析揭示了超越经典N1/N2范式的六种肿瘤相关中性粒细胞状态。其中,一个SPP1+(骨桥蛋白高表达)(平均log2FC约10.7;校正p≈0)群体在转录上尤其与脂质加工基因(APOE、APOC1、APOC2)及免疫细胞募集和代谢重编程的趋化因子主调控因子(CCL3、CCL4)相关。SPP1+ TANs表现为细胞毒性通路受抑及氧化和脂质代谢模块被诱导,提示其从抗菌活性向组织重塑和支持肿瘤活性的转变。网络分析揭示了两个大型枢纽:SPP1-APOE/APOC(脂质重塑)和CCL3/CCL4(免疫信号),二者通过代谢应激基因(CTSB、EIF1B)桥接为一个连贯的免疫代谢环路。 结论:单细胞分析表征了GBM TAN的异质性,并揭示了一个以SPP1为中心、APC样的中性粒细胞轴,桥接脂质调控(APOE/APOCs)与趋化因子信号(CCL3/CCL4)。这一骨桥蛋白驱动的轴为TAN介导的肿瘤支持提供了机制基础,并提名SPP1及其脂质-趋化因子网络作为重编程TANs以发挥抗肿瘤活性的可行靶点。本研究是首批界定促肿瘤中性粒细胞中骨桥蛋白驱动的免疫代谢轴的研究之一,为未来胶质母细胞瘤中针对中性粒细胞的免疫治疗建立了机制框架。
查看英文原文 English abstract
Introduction: Tumor-associated neutrophils (TANs) in glioblastoma (GBM) are heterogeneous and poorly captured by the classical N1/N2 dichotomy. We characterized TAN states relative to peripheral blood neutrophils (PBNs) and wondered if a discrete, targetable program drives their pro-tumoral phenotype. Methods: Public scRNA-seq datasets were reanalyzed (GSM8380727, GSM8380728: TANs 15,000 cells; PBNs 10,000 cells; 36,601 genes). Standard Seurat workflow was applied (stringent QC; SCTransform; integration; PCA/UMAP; shared-nearest-neighbor clustering). Differential expression used Wilcoxon rank-sum with Bonferroni FDR (adj. p<0.05, |log2FC|>0.25). Module scoring examined prespecified programs: antigen presentation/co-stimulation, interferon/cytotoxicity, lipid/stress adaptation. Functional enrichment and protein-protein networks were assessed via GSEA and STRING. Results: Integration revealed six tumor-associated Neutrophil states beyond the classical N1/N2 paradigm. Among them, an SPP1+(osteopontin-high) (avg log2FC ~10.7; adj. p≈0) population was particularly transcriptionally associated with lipid processing genes (APOE, APOC1, APOC2) and chemokine master regulators (CCL3, CCL4) of immune cell recruitment and metabolic reprogramming. SPP1+ TANs featured repression of cytotoxicity pathways and induction of oxidative and lipid metabolism modules, pointing toward a shift from antimicrobial to tissue-remodeling and tumor-supporting activities. Network analysis revealed two large hubs: SPP1-APOE/APOC (lipid remodeling) and CCL3/CCL4 (immune signaling) that are bridged by metabolic stress genes (CTSB, EIF1B) into a cohesive immunometabolic circuit. Conclusion: Single-cell analysis characterizes GBM TAN heterogeneity and implicates an SPP1-centered, APC-like neutrophil axis bridging lipid regulation (APOE/APOCs) and chemokine signaling (CCL3/CCL4). This osteopontin-driven axis establishes a mechanistic basis for TAN-mediated tumor support and nominates SPP1 and its lipid-chemokine network as actionable targets to reprogram TANs for anti-tumor activity.This is among the first studies to define an osteopontin-driven immunometabolic axis in pro-tumoral neutrophils, establishing a mechanistic framework for future neutrophil-targeted immunotherapies in glioblastoma.
利益披露 Disclosure
M. A. Abikenari, None.. J. H. Choi, None.. R. Medikonda, None.. L. Kim, None.. J. Liu, None.. A. Sjoholm, None.. G. Nageeb, None.. K. Cho, None.. A. Tran, None.. D. Bakalov, None.. M. Banu, None.. M. Lim, None.

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