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Remodeling of the tumor microenvironment in muscle-invasive bladder cancer: insights from single-cell and spatial transcriptomics after neoadjuvant immunochemotherapy

肿瘤微环境 生物 转录组 癌症研究 膀胱肿瘤 膀胱癌 新辅助治疗 内科学 基因表达谱 肿瘤免疫学
作者
Guozhi Zhao,Yuanyuan Bai,Xin Chen,Junwu Li,Hai Chang,Zhu Y,Haoyu Xu,Yiqing You,Gautam Sethi,Deyu Zuo,W Tang,Lei Yang,Weiyang He
出处
期刊:Molecular Cancer [BioMed Central]
标识
DOI:10.1186/s12943-026-02738-y
摘要

BACKGROUND: Neoadjuvant immunochemotherapy (nICT), defined in this study as PD-L1 blockade combined with cisplatin-based chemotherapy, provides substantial clinical benefits in muscle-invasive bladder cancer (MIBC). However, the tumor immune microenvironment (TIME) is highly heterogeneous, resulting in variable patient responses and persistent therapy resistance. This study investigates key immune cell subsets, intercellular communication networks, and spatial distributions within the TIME of MIBC, with a focus on cellular and spatial features associated with nICT response. METHODS: Patients were classified into responder and non-responder groups according to RECIST 1.1-based radiological evaluation. Responders were defined as patients achieving complete or partial response, whereas non-responders were defined as patients with stable or progressive disease after adequate nICT exposure and evaluable imaging. Representative computed tomography images and fresh tissue samples were collected from four no-treatment controls (NT), three nICT responders (R), and three nICT non-responders (NR). Samples underwent single-cell RNA sequencing and spatial transcriptomics sequencing. Cell populations were annotated to assess infiltration abundance, cell-cell communication networks, and pseudotime trajectories. Resistance-associated cell subsets and their spatial niches were identified. RESULTS: Following quality control, the tumor microenvironment (TME) was classified into ten major cell subsets based on canonical markers. Cross-sectional group comparative analyses showed significant remodeling of the TME in the NR and R groups. B-cell infiltration was elevated in the NR group, whereas macrophages, fibroblasts, and mast cells were enriched in the R group. Functional alterations in activated CD4 + T cells and impaired differentiation of naive CD8 + T cells were identified as key drivers of acquired therapy resistance. Accumulation of macrophage-derived CXCL8 was also observed as a resistance driver. Cancer-associated fibroblasts (CAFs) served as a physical barrier, contributing to the establishment of an immunosuppressive TME. Notably, strong spatial co-localization between macrophage-derived CXCL8 and CAFs was observed in NR tumors, suggesting a cooperative role in mediating resistance to nICT. Further cell-cell communication analysis using spatial transcriptomics indicated that SPP1 signaling from macrophages (CXCL8) to CAFs contributes to TME reprogramming and resistance to nICT. CONCLUSION: The integrated analysis identifies distinct immunological features that underlie differential nICT responses in MIBC. These findings provide a theoretical basis for optimizing personalized neoadjuvant therapy strategies.
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