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Athymic mice reveal a requirement for T-cell-microglia interactions in establishing a microenvironment supportive of Nf1 low-grade glioma growth.

ABSTRACT: Pediatric low-grade gliomas (LGGs) frequently do not engraft in immunocompromised mice, limiting their use as an experimental platform. In contrast, murine Neurofibromatosis-1 (Nf1) optic LGG stem cells (o-GSCs) form glioma-like lesions in wild-type, but not athymic, mice following transplantation. Here, we show that the inability of athymic mice to support o-GSC engraftment results from impaired microglia/macrophage function, including reduced expression of Ccr2 and Ccl5, both of which are required for o-GSC engraftment and Nf1 optic glioma growth. Impaired Ccr2 and Ccl5 expression in athymic microglia/macrophages was restored by T-cell exposure, establishing T-cell-microglia/macrophage interactions as critical stromal determinants that support NF1 LGG growth.

SUBMITTER: Pan Y

PROVIDER: S-EPMC5959233 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Athymic mice reveal a requirement for T-cell-microglia interactions in establishing a microenvironment supportive of <i>Nf1</i> low-grade glioma growth.

Pan Yuan Y Xiong Min M Chen Ran R Ma Yu Y Corman Courtney C Maricos Meron M Kindler Urs U Semtner Marcus M Chen Yi-Hsien YH Dahiya Sonika S Gutmann David H DH

Genes & development 20180409 7-8

Pediatric low-grade gliomas (LGGs) frequently do not engraft in immunocompromised mice, limiting their use as an experimental platform. In contrast, murine Neurofibromatosis-1 (<i>Nf1</i>) optic LGG stem cells (o-GSCs) form glioma-like lesions in wild-type, but not athymic, mice following transplantation. Here, we show that the inability of athymic mice to support o-GSC engraftment results from impaired microglia/macrophage function, including reduced expression of Ccr2 and Ccl5, both of which a ...[more]

PMID: 29632086

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Project description:PurposeThis study aimed to: determine the supportive care available for Australian patients with High Grade Glioma (HGG) and their carers; identify service gaps; and inform changes needed to implement guidelines and Optimal Care Pathways.MethodsThis cross-sectional online survey recruited multidisciplinary health professionals (HPs) who were members of the Cooperative Trials Group for Neuro-Oncology involved in management of patients diagnosed with HGG in Australian hospitals. Descriptive statistics were calculated. Fisher's exact test was used to explore differences between groups.Results42 complete responses were received. A majority of MDT meetings were attended by a: neurosurgeon, radiation oncologist, medical oncologist, radiologist, and care coordinator. Less than 10% reported attendance by a palliative care nurse; physiotherapist; neuropsychologist; or speech therapist. Most could access referral pathways to a cancer care coordinator (76%), neuropsychologist (78%), radiation oncology nurse (77%), or psycho-oncologist (73%), palliative care (93-100%) and mental health professionals (60-85%). However, few routinely referred to an exercise physiologist (10%), rehabilitation physician (22%), dietitian (22%) or speech therapist (28%). Similarly, routine referrals to specialist mental health services were not standard practice. Nearly all HPs (94%) reported HGG patients were advised to present to their GP for pre-existing conditions/comorbidities; however, most HPs took responsibility (≤ 36% referred to GP) for social issues, mental health, symptoms, cancer complications, and treatment side-effects.ConclusionsWhile certain services are accessible to HGG patients nationally, improvements are needed. Psychosocial support, specialist allied health, and primary care providers are not yet routinely integrated into the care of HGG patients and their carers despite these services being considered essential in clinical practice guidelines and optimal care pathways.

Project description:Background: The natural history of patients with low-grade glioma (LGG) varies widely, but most patients eventually deteriorate, leading to poor prognostic outcomes. We aim to develop biological models that can accurately predict the outcome of LGG prognosis. Methods: Prognostic genes for glutamine metabolism were searched by univariate Cox regression, and molecular typing was constructed. Functional enrichment analysis was done to evaluate potential prognostic-related pathways by analyzing differential genes in different subtypes. Enrichment scores of specific gene sets in different subtypes were measured by gene set enrichment analysis. Different immune infiltration levels among subtypes were calculated using algorithms such as CIBERSORT and ESTIMATE. Gene expression levels of prognostic-related gene signatures of glutamine metabolism phenotypes were used to construct a RiskScore model. Receiver operating characteristic curve, decision curve and calibration curve analyses were used to evaluate the reliability and validity of the risk model. The decision tree model was used to determine the best predictor variable ultimately. Results: We found that C1 had the worst prognosis and the highest level of immune infiltration, among which the highest macrophage infiltration can be found in the M2 stage. Moreover, most of the pathways associated with tumor development, such as MYC_TARGETS_V1 and EPITHELIAL_MESENCHYMAL_TRANSITION, were significantly enriched in C1. The wild-type IDH and MGMT hypermethylation were the most abundant in C1. A five-gene risk model related to glutamine metabolism phenotype was established with good performance in both training and validation datasets. The final decision tree demonstrated the RiskScore model as the most significant predictor of prognostic outcomes in individuals with LGG. Conclusion: The RiskScore model related to glutamine metabolism can be an exceedingly accurate predictor for LGG patients, providing valuable suggestions for personalized treatment.

Project description:BackgroundTo identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.MethodsWe downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.ResultsGene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.ConclusionsWe identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Dataset Information

Athymic mice reveal a requirement for T-cell-microglia interactions in establishing a microenvironment supportive of Nf1 low-grade glioma growth.

Publications

Athymic mice reveal a requirement for T-cell-microglia interactions in establishing a microenvironment supportive of <i>Nf1</i> low-grade glioma growth.

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