Dataset Information

Immune microenvironments differ in immune characteristics and outcome of glioblastoma multiforme.

ABSTRACT: Understanding the interactions between tumors and the host immune system holds great promise to uncover biomarkers for targeted therapies, predict the prognosis of patients and guide clinical treatment. However, the immune signatures of glioblastoma multiforme (GBM) remain largely unstudied in terms of systematic analyses. We aimed to classify GBM samples according to immune-related genes and complement the existing immunotherapy system knowledge. In this study, we designed a strategy to identify 3 immune subtypes representing 3 different immune microenvironments (M1-M3) and associated with prognosis. The 3 subtypes were significantly different in terms of specific immune characteristics (immune cell subpopulations, immune responses, immune cells, and checkpoint gene interactions). In additional, copy number variations and methylation changes were identified that correlated with genes related to a worse prognosis subtype in the microenvironment. More importantly, in M3 (worst prognosis subtype) and M2 (best prognosis subtype), the interaction between immune cells and checkpoint genes was different, which had an important effect on the prognosis. Finally, we used risk scores of immune cells and checkpoint genes to evaluate the prognosis of GBM patients and validated the results with 3 independent datasets. Disordered interactions between immune cells and checkpoint genes result in a change in the immune microenvironment and affects the prognosis of patients. We propose that a better understanding of the immune microenvironment of advanced cancers may provide new insights into immunotherapy.

SUBMITTER: Zhang B

PROVIDER: S-EPMC6558448 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Immune microenvironments differ in immune characteristics and outcome of glioblastoma multiforme.

Zhang Botao B Shen Rongfang R Cheng Shujun S Feng Lin L

Cancer medicine 20190430 6

Understanding the interactions between tumors and the host immune system holds great promise to uncover biomarkers for targeted therapies, predict the prognosis of patients and guide clinical treatment. However, the immune signatures of glioblastoma multiforme (GBM) remain largely unstudied in terms of systematic analyses. We aimed to classify GBM samples according to immune-related genes and complement the existing immunotherapy system knowledge. In this study, we designed a strategy to identif ...[more]

PMID: 31038851

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Project description:Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.

Dataset Information

Immune microenvironments differ in immune characteristics and outcome of glioblastoma multiforme.

Publications

Immune microenvironments differ in immune characteristics and outcome of glioblastoma multiforme.

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