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Macrophage-mediated myelin recycling fuels brain cancer malignancy (RNA-Seq in vitro)

ABSTRACT: Macrophage-mediated myelin recycling fuels brain cancer malignancy (RNA-Seq in vitro)

PROVIDER: PRJNA1105981 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28839364_1.fastq.gz	Fastqsanger.gz
	SRR28839364_2.fastq.gz	Fastqsanger.gz
	SRR28839365_1.fastq.gz	Fastqsanger.gz
	SRR28839365_2.fastq.gz	Fastqsanger.gz
	SRR28839366_1.fastq.gz	Fastqsanger.gz

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Project description:Tumors growing in metabolically-challenged environments, such as glioblastoma in the brain, are particularly reliant on cross-talk with their tumor microenvironment (TME) to satisfy their high energetic needs. However, the intricacies of this metabolic interplay and the consequences on immune cell subset diversity and function remain largely unexplored. We interrogated the heterogeneity of the glioblastoma TME using single cell multi-omics analyses in preclinical glioblastoma mouse models and patient samples, and identified metabolically-rewired tumor-associated macrophage (TAM) subpopulations that fuel glioblastoma malignancy. These TAM subsets, termed lipid-laden macrophages (LLMs) to reflect their increased lipid metabolism activity and cholesterol storage, are epigenetically rewired, display immunosuppressive features and are enriched in the aggressive mesenchymal glioblastoma subtype. In response to TME-derived cues triggering liver-X-receptor (LXR) expression, macrophages increase engulfment of cholesterol-rich myelin debris and acquire an LLM phenotype. Subsequently, LLMs directly transfer myelin-derived lipids to cancer cells in an LXR/Abca1-dependent manner, thereby fueling the heightened metabolic demands of mesenchymal glioblastoma. Furthermore, LLM content predicts clinical outcomes and immune checkpoint blockade response in glioblastoma patients and other cancer types. Our work provides an in-depth understanding of the immune-metabolic interplay during glioblastoma progression in a subtype- and microanatomical niche-dependent manner, thereby laying a framework for the discovery of targetable metabolic vulnerabilities in glioblastoma.

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