Tumor Microenvironment Characterization in Glioblastoma Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Characterization of tumor microenvironment in glioblastoma multiforme identifies ITGB2 as a key immune and stromal related regulator in glial cell types

Glioblastoma multiforme (GBM) is the most aggressive form of brain tumor characterized by inter and intra-tumor heterogeneity and complex tumor microenvironment. To uncover the molecular targets in this milieu, we systematically identified immune and stromal interactions at the glial cell type level that leverages on RNA-sequencing data of GBM patients from The Cancer Genome Atlas. The perturbed genes between the high vs low immune and stromal scored patients were subjected to weighted gene co-expression network analysis to identify the glial cell type specific networks in immune and stromal infiltrated patients. The intramodular connectivity analysis identified the highly connected genes in each module. Combining it with univariable and multivariable prognostic analysis revealed common vital gene ITGB2, between the immune and stromal infiltrated patients enriched in microglia and newly formed oligodendrocytes. We found following unique hub genes in immune infiltrated patients; COL6A3 (microglia), ITGAM (oligodendrocyte precursor cells), TNFSF9 (microglia), and in stromal infiltrated patients, SERPINE1 (microglia) and THBS1 (newly formed oligodendrocytes, oligodendrocyte precursor cells). To validate these hub genes, we used external GBM patient single cell RNA-sequencing dataset and this identified ITGB2 to be significantly enriched in microglia, newly formed oligodendrocytes, T-cells, macrophages and adipocyte cell types in both immune and stromal datasets. The tumor infiltration analysis of ITGB2 showed that it is correlated with myeloid dendritic cells, macrophages, monocytes, neutrophils, B-cells, fibroblasts and adipocytes. Overall, the systematic screening of tumor microenvironment components at glial cell types uncovered ITGB2 as a potential target in primary GBM.

Radiomics for characterization of the glioma immune microenvironment

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

Extracellular vesicles carry miR-27a-3p to promote drug resistance of glioblastoma to temozolomide by targeting BTG2

OBJECTIVE

Glioblastoma (GBM) is the most common central nervous system tumor. Temozolomide (TMZ) is a commonly used drug for GBM management. This study explored the mechanism of extracellular vesicles (EVs) regulating TMZ-resistance in GBM.

METHODS

LN229 cells were inducted into TMZ-resistant LN229r strain by stepwise induction. After the intervention of miR-27a-3p expression, cell viability of GBM cells treated with different concentrations of TMZ was detected by MTT and IC50 value was calculated. Cell proliferation and apoptosis were detected by colony formation and flow cytometry. EVs extracted from LN18 cells were identified and the internalization of EVs by LN229r cells was evaluated. The 100 μmol/L TMZ-treated LN229r cells were treated with EVs or EVs with downregulated miR-27a-3p to verify the effect of EVs-carried miR-27a-3p on TMZ resistance. The binding relation between BTG2 and miR-27a-3p was verified. miR-27a-3p and BTG2 expressions in GBM cells and EVs were detected by RT-qPCR. The BTG2 effect on TMZ-resistance in GBM was verified. The xenograft tumor nude mouse model was established by injecting LN229r cells and treated with EVs and 100 μmol/L TMZ.

RESULTS

miR-27a-3p was highly expressed in LN229r cells. IC50 value and proliferation of LN229r cells with silenced miR-27a-3p were decreased and apoptosis was increased, indicating that miR-27a-3p silencing reduced the drug-resistant cell LN229r resistance to TMZ. LN18-derived EVs could be internalized by LN229r cells, and release its encapsulated miR-27a-3p into LN229r cells and increase miR-27a-3p expression. EV treatment increased LN229r cell proliferation and reduced apoptosis, while EVs with silenced miR-27a-3p showed the opposite trend. miR-27a-3p targeted BTG2. BTG2 overexpression reduced LN229r cell resistance to TMZ. In vivo, after EVs treatment, tumor volume and weight, Ki67-positive rate, and miR-27a-3p were increased, while BTG2 expression was decreased.

CONCLUSION

GBM-derived EVs were internalized by GBM cells, released miR-27a-3p into GBM cells, upregulated miR-27a-3p expression, and targeted BTG2, thus promoting TMZ resistance.

Tumor Microenvironment Characteristics of Pancreatic Cancer to Determine Prognosis and Immune-Related Gene Signatures

Background: Pancreatic cancer (PC) is one of the most lethal types of cancer with extremely poor diagnosis and prognosis, and the tumor microenvironment plays a pivotal role during PC progression. Poor prognosis is closely associated with the unsatisfactory results of currently available treatments, which are largely due to the unique pancreatic tumor microenvironment (TME). Methods: In this study, a total of 177 patients with PC from The Cancer Genome Atlas (TCGA) cohort and 65 patients with PC from the GSE62452 cohort in Gene Expression Omnibus (GEO) were included. Based on the proportions of 22 types of infiltrated immune cell subpopulations calculated by cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. A combination of the elbow method and the gap statistic was used to explore the likely number of distinct clusters in the data. The ConsensusClusterPlus package was utilized to identify radiomics clusters, and the samples were divided into two subtypes. Result: Survival analysis showed that the patients with TMEscore-high phenotype had better prognosis. In addition, the TMEscore-high had better inhibitory effect on the immune checkpoint. A total of 10 miRNAs, 311 DEGs, and 68 methylation sites related to survival were obtained, which could be biomarkers to evaluate the prognosis of patients with pancreatic cancer. Conclusions: Therefore, a comprehensive description of TME characteristics of pancreatic cancer can help explain the response of pancreatic cancer to immunotherapy and provide a new strategy for cancer treatment.

An Immune-Related Gene Pairs Signature for Predicting Survival in Glioblastoma

Background: Glioblastoma (GBM) is the frequently occurring and most aggressive form of brain tumors. In the study, we constructed an immune-related gene pairs (IRGPs) signature to predict overall survival (OS) in patients with GBM.

Methods: We established IRGPs with immune-related gene (IRG) matrix from The Cancer Genome Atlas (TCGA) database (Training cohort). After screened by the univariate regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis, IRGPs were subjected to the multivariable Cox regression to develop an IRGP signature. Then, the predicting accuracy of the signature was assessed with the area under the receiver operating characteristic curve (AUC) and validated the result using the Chinese Glioma Genome Atlas (CGGA) database (Validation cohorts 1 and 2).

Results: A 10-IRGP signature was established for predicting the OS of patients with GBM. The AUC for predicting 1-, 3-, and 5-year OS in Training cohort was 0.801, 0.901, and 0.964, respectively, in line with the AUC of Validation cohorts 1 and 2 [Validation cohort 1 (1 year: 0.763; 3 years: 0.786; and 5 years: 0.884); Validation cohort 2 (1 year: 0.745; 3 years: 0.989; and 5 years: 0.987)]. Moreover, survival analysis in three cohorts suggested that patients with low-risk GBM had better clinical outcomes than patients with high-risk GBM. The univariate and multivariable Cox regression demonstrated that the IRGPs signature was an independent prognostic factor.

Conclusions: We developed a novel IRGPs signature for predicting OS in patients with GBM.

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Glioblastoma (GBM) is associated with an increasing mortality and morbidity and is considered as an aggressive brain tumor. Recently, extensive studies have been carried out to examine the molecular biology of GBM, and the progression of GBM has been suggested to be correlated with the tumor immunophenotype in a variety of studies. Samples in the current study were extracted from the ImmPort and TCGA databases to identify immune-related genes affecting GBM prognosis. A total of 92 immune-related genes displaying a significant correlation with prognosis were mined, and a shrinkage estimate was conducted on them. Among them, the 14 most representative genes showed a marked correlation with patient prognosis, and LASSO and stepwise regression analysis was carried out to further identify the genes for the construction of a predictive GBM prognosis model. Then, samples in training and test cohorts were incorporated into the model and divided to evaluate the efficiency, stability, and accuracy of the model to predict and classify the prognosis of patients and to identify the relevant immune features according to the median value of RiskScore (namely, Risk-H and Risk-L). In addition, the constructed model was able to instruct clinicians in diagnosis and prognosis prediction for various immunophenotypes.