胶质瘤
髓样
髓系细胞
生物
癌症研究
计算生物学
作者
Tyler E. Miller,Chadi A. El Farran,Charles Couturier,Zeyu Chen,Joshua P. D’Antonio,Julia A. Verga,Martin Arreola Villanueva,L. Nicolas Gonzalez Castro,Yuzhou Evelyn Tong,Tariq Dhiyab Al-Saadi,Andrew N. Chiocca,Yuanyuan Zhang,David S. Fischer,Dieter Henrik Heiland,Jennifer L. Guerriero,Kevin Petrecca,Mario L. Suvà,Alex K. Shalek,B Bernstein
出处
期刊:Nature
[Nature Portfolio]
日期:2025-02-26
卷期号:640 (8060): 1072-1082
被引量:187
标识
DOI:10.1038/s41586-025-08633-8
摘要
Gliomas are incurable malignancies notable for having an immunosuppressive microenvironment with abundant myeloid cells, the immunomodulatory phenotypes of which remain poorly defined1. Here we systematically investigate these phenotypes by integrating single-cell RNA sequencing, chromatin accessibility, spatial transcriptomics and glioma organoid explant systems. We discovered four immunomodulatory expression programs: microglial inflammatory and scavenger immunosuppressive programs, which are both unique to primary brain tumours, and systemic inflammatory and complement immunosuppressive programs, which are also expressed by non-brain tumours. The programs are not contingent on myeloid cell type, developmental origin or tumour mutational state, but instead are driven by microenvironmental cues, including tumour hypoxia, interleukin-1β, TGFβ and standard-of-care dexamethasone treatment. Their relative expression can predict immunotherapy response and overall survival. By associating the respective programs with mediating genomic elements, transcription factors and signalling pathways, we uncover strategies for manipulating myeloid-cell phenotypes. Our study provides a framework to understand immunomodulation by myeloid cells in glioma and a foundation for the development of more-effective immunotherapies. A study of myeloid cells in gliomas, a type of brain tumour, used a factor-based computational framework to reveal four immunomodulatory gene-expression programs that are expressed across myeloid cell types, driven by microenvironmental cues and predictive of therapeutic response.
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