Dataset Information

Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells.

ABSTRACT: Brain malignancies encompass a range of primary and metastatic cancers, including low-grade and high-grade gliomas and brain metastases (BrMs) originating from diverse extracranial tumors. Our understanding of the brain tumor microenvironment (TME) remains limited, and it is unknown whether it is sculpted differentially by primary versus metastatic disease. We therefore comprehensively analyzed the brain TME landscape via flow cytometry, RNA sequencing, protein arrays, culture assays, and spatial tissue characterization. This revealed disease-specific enrichment of immune cells with pronounced differences in proportional abundance of tissue-resident microglia, infiltrating monocyte-derived macrophages, neutrophils, and T cells. These integrated analyses also uncovered multifaceted immune cell activation within brain malignancies entailing converging transcriptional trajectories while maintaining disease- and cell-type-specific programs. Given the interest in developing TME-targeted therapies for brain malignancies, this comprehensive resource of the immune landscape offers insights into possible strategies to overcome tumor-supporting TME properties and instead harness the TME to fight cancer.

SUBMITTER: Klemm F

PROVIDER: S-EPMC8558904 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIncreasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated.MethodTCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package.ResultsWe identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven.ConclusionThis study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

Dataset Information

Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets