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Immune landscapes associated with different glioblastoma molecular subtypes.

ABSTRACT: Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68+ and CD163+ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163+ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.

SUBMITTER: Martinez-Lage M

PROVIDER: S-EPMC6902522 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Immune landscapes associated with different glioblastoma molecular subtypes.

Martinez-Lage Maria M Lynch Timothy M TM Bi Yingtao Y Cocito Carolina C Way Gregory P GP Pal Sharmistha S Haller Josephine J Yan Rachel E RE Ziober Amy A Nguyen Aivi A Kandpal Manoj M O'Rourke Donald M DM Greenfield Jeffrey P JP Greene Casey S CS Davuluri Ramana V RV Dahmane Nadia N

Acta neuropathologica communications 20191129 1

Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-ba ...[more]

PMID: 31815646

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Project description:BackgroundGlioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.MethodsUnivariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.ResultsA total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.ConclusionConstruction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

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Immune landscapes associated with different glioblastoma molecular subtypes.

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Immune landscapes associated with different glioblastoma molecular subtypes.

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