CD8+ T-cell-Mediated Immunoediting Influences Genomic Evolution and Immune Evasion in Murine Gliomas

ERK1/2 Phosphorylation Predicts Survival in Recurrent Glioblastoma Following Intracerebral and Adjuvant PD-1/CTLA-4 Immunotherapy: A REMARK-guided Analysis

PURPOSE

Evidence suggests that MAPK pathway activation, as measured by ERK1/2 phosphorylation (p-ERK), predicts overall survival (OS) in patients with recurrent glioblastoma receiving anti-PD-1 therapy. We aimed to validate these findings in independent cohorts.

EXPERIMENTAL DESIGN

In a 24-patient clinical trial on recurrent glioblastoma and high-grade gliomas, we examined the link between p-ERK levels and OS. Patients received intravenous nivolumab, followed by maximal safe resection and an intracerebral injection of either ipilimumab alone or combined with nivolumab. Biweekly adjuvant nivolumab was then administered up to five times (NCT03233152). Using REporting recommendations for tumor MARKER prognostic studies (REMARK) criteria, we conducted independent analyses for p-ERK quantification and statistical evaluations. Additional comparative analysis included prior cohorts, totaling 65 patients. Cox proportional hazards models and meta-analysis were employed to assess p-ERK as a predictive biomarker after immunotherapy.

RESULTS

Lower median p-ERK+ cell density was observed compared with prior studies, likely due to variable tissue processing across cohorts. Nonetheless, high p-ERK was associated with prolonged OS, particularly in isocitrate dehydrogenase wild-type glioblastomas (P = 0.036). Median OS for high and low p-ERK patients were 55.6 and 30 weeks, respectively. Multivariable analysis reinforced p-ERK's significance in survival prediction (P = 0.011). Upon p-ERK normalization across cohorts (n = 65), meta-analysis supported the survival benefit of elevated tumor p-ERK levels (P = 0.0424).

CONCLUSIONS

This study strengthens the role of p-ERK as a predictive biomarker for OS in patients with glioblastoma on immune checkpoint blockade. Future research should focus on further validation in prospective trials and the standardization of preanalytical variables influencing p-ERK quantification.

Clonal Extinction Drives Tumorigenesis

Before a tumor is diagnosed and surgically removed, it has been growing for many months or even years [...].

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma

Background

Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms.

Methods

Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets.

Results

We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors.

Conclusion

In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

Advanced immunotherapies for glioblastoma: tumor neoantigen vaccines in combination with immunomodulators

Glial-origin brain tumors, including glioblastomas (GBM), have one of the worst prognoses due to their rapid and fatal progression. From an oncological point of view, advances in complete surgical resection fail to eliminate the entire tumor and the remaining cells allow a rapid recurrence, which does not respond to traditional therapeutic treatments. Here, we have reviewed new immunotherapy strategies in association with the knowledge of the immune micro-environment. To understand the best lines for the future, we address the advances in the design of neoantigen vaccines and possible new immune modulators. Recently, the efficacy and availability of vaccine development with different formulations, especially liposome plus mRNA vaccines, has been observed. We believe that the application of new strategies used with mRNA vaccines in combination with personalized medicine (guided by different omic's strategies) could give good results in glioma therapy. In addition, a large part of the possible advances in new immunotherapy strategies focused on GBM may be key improving current therapies of immune checkpoint inhibitors (ICI), given the fact that this type of tumor has been highly refractory to ICI.

Construction and validation of a novel prognostic signature for cutaneous melanoma based on ferroptosis-related genes

Ferroptosis, a recently uncovered iron-dependent, non-apoptotic cell death process, has been increasingly linked to cancer development. In this study, our objective was to develop a prognostic model centered on ferroptosis-related genes (FRGs) and assess its efficacy as an overall survival (OS) prediction biomarker. We conducted a systematic analysis of cutaneous melanoma (CM) and devised a novel ferroptosis-related prognostic signature (FRGSig) using the TCGA database. An independent dataset from GSE65904 was employed to corroborate the validity of the FRGSig. Both univariate and multivariate Cox proportional hazard regression analyses were utilized to construct a FRGSig composed of five FRGs. mRNA expression and immunohistochemistry (IHC) analysis demonstrated that the expression of FRGSig genes varied between tumor and normal tissues. According to Kaplan-Meier analysis, patients with elevated FRGsig scores faced a worse prognosis. The predictive accuracy of FRGSig was evaluated using the time-dependent receiver operating characteristic curve (ROC), with the area under the curve (AUC) values for 1, 3, and 5 OS at 0.682, 0.711, 0.735 in the TCGA cohort, and 0.662, 0.695, 0.712 in the validation dataset, respectively. Univariate and multivariate Cox regression analyses demonstrated that FRGSig served as an independent prognostic factor. Further analysis revealed a significant relationship between FRGSig and Tumor Mutational Burden (TMB) as well as immune infiltration levels. Gene set enrichment analysis (GSEA) disclosed functional disparities between high- and low-risk groups, suggesting that immune checkpoint-related pathways could be instrumental in the improved prognosis of the low-risk group. Taken together, the FRGSig has potential guidance for prognosis prediction and clinical treatment of CM.

Identification of INSRR as an immune-related gene in the tumor microenvironment of glioblastoma by integrated bioinformatics analysis

Gliomas are the most common malignant tumors in the central nervous system. The tumor microenvironment (TME) plays a crucial role in tumor proliferation, invasion, angiogenesis, and immune escape. However, little is known about TME in gliomas. The purpose of this study was to explore the biomarkers associated with TME in glioblastoma (GBM) to predict immunotherapy effectiveness and prognosis in patients. Based on RNA-seq transcriptome data and clinical features of 1222 samples (113 normal samples and 1109 tumor samples) in The Cancer Genome Atlas (TCGA) database, the ImmuneScore, StromalScore, and ESTIMATEScore were calculated by ESTIMATE algorithm. The differentially expressed genes (DEGs) and differentially mutated genes (DMGs) were determined in the TCGA GBM cohort. Furthermore, gene set enrichment analysis (GSEA) was used to investigate the enrichment pathways of INSRR genes with abnormal expression. The proportion of tumor-infiltrating immune cells (TIICs) was evaluated by CIBERSORT. Frequent mutations of TP53, EGFR, and PTEN occurred in high and low immune scores. The cross-analysis of DEGs and DMGs revealed that INSRR was an immune-related biomarker in the TCGA GBM cohort. According to GSEA, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway with INSRR abnormal expression were IgA-produced intestinal immune network and Alzheimer's disease, oxidative phosphorylation, and Parkinson's disease, respectively. Additionally, INSRR expression was correlated with dendritic cells activated, dendritic cells resting, T cells CD8, and T cell gamma delta. INSRR is associated with the immune microenvironment in GBM and is used as a biomarker to predict immune invasion.

Novel strategies for cancer immunotherapy: counter-immunoediting therapy

The advent of immunotherapy has made an indelible mark on the field of cancer therapy, especially the application of immune checkpoint inhibitors in clinical practice. Although immunotherapy has proven its efficacy and safety in some tumors, many patients still have innate or acquired resistance to immunotherapy. The emergence of this phenomenon is closely related to the highly heterogeneous immune microenvironment formed by tumor cells after undergoing cancer immunoediting. The process of cancer immunoediting refers to the cooperative interaction between tumor cells and the immune system that involves three phases: elimination, equilibrium, and escape. During these phases, conflicting interactions between the immune system and tumor cells result in the formation of a complex immune microenvironment, which contributes to the acquisition of different levels of immunotherapy resistance in tumor cells. In this review, we summarize the characteristics of different phases of cancer immunoediting and the corresponding therapeutic tools, and we propose normalized therapeutic strategies based on immunophenotyping. The process of cancer immunoediting is retrograded through targeted interventions in different phases of cancer immunoediting, making immunotherapy in the context of precision therapy the most promising therapy to cure cancer.

Immune checkpoint blockade in glioblastoma: from tumor heterogeneity to personalized treatment

Immune checkpoint blockade (ICB) has revolutionized modern cancer therapy, arousing great interest in the neuro-oncology community. While several reports show that subsets of patients with glioma exhibit durable responses to immunotherapy, the efficacy of this treatment has not been observed for unselected patient populations, preventing its broad clinical implementation for gliomas and glioblastoma (GBM). To exploit the maximum therapeutic potential of ICB for patients with glioma, understanding the different aspects of glioma-related tumor immune responses is of critical importance. In this Review, we discuss contributing factors that distinguish subsets of patients with glioma who may benefit from ICB. Specifically, we discuss (a) the complex interaction between the tumor immune microenvironment and glioma cells as a potential influence on immunotherapy responses; (b) promising biomarkers for responses to immune checkpoint inhibitors; and (c) the potential contributions of peripheral immune cells to therapeutic responses.

Prognosis and immune features of pyroptosis-related RNA patterns in low-grade glioma

Purpose

Low-grade gliomas (LGG), which are malignant primary brain tumors, are more prevalent in young adults. Pyroptosis, an inflammatory form of programmed cell death, has been shown in recent years to be directly associated with tumor growth and tumor microenvironment (TME). However, the correlation between LGG and pyroptosis remained to be explored. In this research, we explored pyroptosis-related gene expression patterns and their prognostic significance based on transcriptome profiles and clinical data in LGG.

Methods

We identified 31 pyroptosis-related genes differentially expressed at the mRNA level between the data of LGG patients from TCGA and the data of normal brain tissues from GTEx. Univariate Cox regression analysis was used to screen 16 differentially expressed genes (DEGs) based on survival data. Next, the prognostic model was established using LASSO Cox regression, which divided LGG patients into high- and low- risk subgroups and showed an independent prognostic value for overall survival (OS) combined with clinical factors in the CGGA test cohort. Pyroptosis and immune cells were correlated through the CIBERSORT R package and the TIMER database.

Results

Based on the analyses of 523 LGG and 1152 normal tissues, nine significant differential genes were identified. The AUC remained at about 0.74 when combined with the risk score and clinical factors. Enrichment analyses revealed that DEGs were mainly enriched in cytokine-cytokine receptor interactions, immune response and chemokine signaling pathways. Immune cell enrichment analysis demonstrated that scores for most immune cell types differed significantly between the high-and low-risk groups, and further infiltrating analysis showed obvious differences between these two risk subgroups.

Conclusion

Pyroptosis-related genes play a pivotal role in LGG and are associated with tumor immunity, which may be beneficial to the prognosis and immunotherapy of LGG.

A novel risk score model based on fourteen chromatin regulators-based genes for predicting overall survival of patients with lower-grade gliomas

Background: Low grade gliomas(LGGs) present vexatious management issues for neurosurgeons. Chromatin regulators (CRs) are emerging as a focus of tumor research due to their pivotal role in tumorigenesis and progression. Hence, the goal of the current work was to unveil the function and value of CRs in patients with LGGs.

Methods: RNA-Sequencing and corresponding clinical data were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. A single-cell RNA-seq dataset was sourced from the Gene Expression Omnibus (GEO) database. Altogether 870 CRs were retrieved from the published articles in top academic journals. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were applied to construct the prognostic risk model. Patients were then assigned into high- and low-risk groups based on the median risk score. The Kaplan–Meier (K-M) survival curve and receiver operating characteristic curve (ROC) were performed to assess the prognostic value. Sequentially, functional enrichment, tumor immune microenvironment, tumor mutation burden, drug prediction, single cell analysis and so on were analyzed to further explore the value of CR-based signature. Finally, the expression of signature genes were validated by immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR).

Results: We successfully constructed and validated a 14 CRs-based model for predicting the prognosis of patients with LGGs. Moreover, we also found 14 CRs-based model was an independent prognostic factor. Functional analysis revealed that the differentially expressed genes were mainly enriched in tumor and immune related pathways. Subsequently, our research uncovered that LGGs patients with higher risk scores exhibited a higher TMB and were less likely to be responsive to immunotherapy. Meanwhile, the results of drug analysis offered several potential drug candidates. Furthermore, tSNE plots highlighting the magnitude of expression of the genes of interest in the cells from the scRNA-seq assay. Ultimately, transcription expression of six representative signature genes at the mRNA level was consistent with their protein expression changes.

Conclusion: Our findings provided a reliable biomarker for predicting the prognosis, which is expected to offer new insight into LGGs management and would hopefully become a promising target for future research.

Next Steps for Immunotherapy in Glioblastoma

Outcomes for glioblastoma (GBM) patients undergoing standard of care treatment remain poor. Here we discuss the portfolio of previously investigated immunotherapies for glioblastoma, including vaccine therapy and checkpoint inhibitors, as well as novel emerging therapeutic approaches. In addition, we explore the factors that potentially influence response to immunotherapy, which should be considered in future research aimed at improving immunotherapy efficacy.

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Background

Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.

Methods

In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).

Results

Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.

Conclusion

Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

Translational landscape of glioblastoma immunotherapy for physicians: guiding clinical practice with basic scientific evidence

Despite recent advances in cancer therapeutics, glioblastoma (GBM) remains one of the most difficult cancers to treat in both the primary and recurrent settings. GBM presents a unique therapeutic challenge given the immune-privileged environment of the brain and the aggressive nature of the disease. Furthermore, it can change phenotypes throughout the course of disease—switching between mesenchymal, neural, and classic gene signatures, each with specific markers and mechanisms of resistance. Recent advancements in the field of immunotherapy—which utilizes strategies to reenergize or alter the immune system to target cancer—have shown striking results in patients with many types of malignancy. Immune checkpoint inhibitors, adoptive cellular therapy, cellular and peptide vaccines, and other technologies provide clinicians with a vast array of tools to design highly individualized treatment and potential for combination strategies. There are currently over 80 active clinical trials evaluating immunotherapies for GBM, often in combination with standard secondary treatment options including re-resection and anti-angiogenic agents, such as bevacizumab. This review will provide a clinically focused overview of the immune environment present in GBM, which is frequently immunosuppressive and characterized by M2 macrophages, T cell exhaustion, enhanced transforming growth factor-β signaling, and others. We will also outline existing immunotherapeutic strategies, with a special focus on immune checkpoint inhibitors, chimeric antigen receptor therapy, and dendritic cell vaccines. Finally, we will summarize key discoveries in the field and discuss currently active clinical trials, including combination strategies, burgeoning technology like nucleic acid and nanoparticle therapy, and novel anticancer vaccines. This review aims to provide the most updated summary of the field of immunotherapy for GBM and offer both historical perspective and future directions to help inform clinical practice.

Glioma progression is shaped by genetic evolution and microenvironment interactions

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

Potential of IDH mutations as immunotherapeutic targets in gliomas: a review and meta-analysis

INTRODUCTION

Gliomas are stratified by the presence of a hotspot mutation in the enzyme isocitrate dehydrogenase genes (IDH1/2). While mutated IDH (mIDH) correlates with better prognosis, the role of this mutation in antitumor immunity and the response to immunotherapy is not completely understood. Understanding the relationship between the genetic features of these tumors and the tumor immune microenvironment (TIME) may help to develop appropriate therapeutic strategies.

AREAS COVERED

In this review we discussed the available literature related to the potential role of IDH mutations as an immunotherapeutic target in gliomas and profiled the immune transcriptome of glioma biopsies. We aimed to shed light on the role of mIDH on the immunological landscape of the different subtypes of gliomas, taking into account the most recent WHO classification of tumors of the central nervous system (CNS). We also discussed different immunotherapeutic approaches to target mIDH tumors and to overcome their immunosuppressive microenvironment.

EXPERT OPINION

Data presented here indicates that the TIME not only differs in association with IDH mutation status, but also within glioma subtypes, suggesting that the cellular context affects the overall effect of this genetic lesion. Thus, specific therapeutic combinations may help patients diagnosed with different glioma subtypes.

ERK1/2 phosphorylation predicts survival following anti-PD-1 immunotherapy in recurrent glioblastoma

Only a subset of recurrent glioblastoma (rGBM) responds to anti-PD-1 immunotherapy. Previously, we reported enrichment of BRAF/PTPN11 mutations in 30% of rGBM that responded to PD-1 blockade. Given that BRAF and PTPN11 promote MAPK/ERK signaling, we investigated whether activation of this pathway is associated with response to PD-1 inhibitors in rGBM, including patients that do not harbor BRAF/PTPN11 mutations. Here we show that immunohistochemistry for ERK1/2 phosphorylation (p-ERK), a marker of MAPK/ERK pathway activation, is predictive of overall survival following adjuvant PD-1 blockade in two independent rGBM patient cohorts. Single-cell RNA-sequencing and multiplex immunofluorescence analyses revealed that p-ERK was mainly localized in tumor cells and that high-p-ERK GBMs contained tumor-infiltrating myeloid cells and microglia with elevated expression of MHC class II and associated genes. These findings indicate that ERK1/2 activation in rGBM is predictive of response to PD-1 blockade and is associated with a distinct myeloid cell phenotype.

Prognostic Model and Nomogram Construction Based on a Novel Ferroptosis-Related Gene Signature in Lower-Grade Glioma

Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years.

Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals’ one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell’s concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature.

Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan–Meier (K−M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG.

Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

Tipping cancer cells over the edge: the context-dependent cost of high ploidy

Tetraploidy is an aneuploidy-permissive condition that can fuel tumorgenesis. The tip-over hypothesis of cytotoxic therapy-sensitivity proposes that therapy is effective if it pushes a cell's aneuploidy above a viable tipping point. But elevated aneuploidy alone may not account for this tipping point. Tissue micro-environments (TMEs) that lack sufficient resources to support tetraploid cells can explain the fitness cost of aneuploidy. Raw materials needed to generate deoxynucleotides, the building blocks of DNA, are candidate rate-limiting factors for the evolution of high-ploidy cancer cells. Understanding the resource cost of high ploidy is key to uncover its therapeutic vulnerabilities across tissue sites with versatile energy supplies.

Prognosis and Immunotherapy Significances of a Cancer-Associated Fibroblasts-Related Gene Signature in Gliomas

As a cold tumor, malignant glioma has strong immunosuppression and immune escape characteristics. The tumor microenvironment (TME) provides the "soil" for the survival of malignant tumors, and cancer-associated fibroblasts (CAFs) are the architects of matrix remodeling in TME. Therefore, CAFs have potent regulatory effects on the recruitment and functional differentiation of immune cells, whereby they synthesize and secrete numerous collagens, cytokines, chemokines, and other soluble factors whose interaction with tumor cells creates an immunosuppressive TME. This consequently facilitates the immune escape of tumor cells. Targeting CAFs would improve the TME and enhance the efficacy of immunotherapy. Thus, regulation of CAFs and CAFs-related genes holds promise as effective immunotherapies for gliomas. Here, by analyzing the Chinese Glioma Genome Atlas and the Cancer Genome Atlas database, the proportion of CAFs in the tumor was revealed to be associated with clinical and immune characteristics of gliomas. Moreover, a risk model based on the expression of CAFs-related six-gene for the assessment of glioma patients was constructed using the least absolute shrinkage and selection operator and the results showed that a high-risk group had a higher expression of the CAFs-related six-genes and lower overall survival rates compared with those in the low-risk group. Additionally, patients in the high-risk group exhibited older age, high tumor grade, isocitrate dehydrogenase wildtype, 1p/19q non-codeletion, O-6-methylguanine-DNA methyltransferase promoter unmethylation and poor prognosis. The high-risk subtype had a high proportion CAFs in the TME of glioma, and a high expression of immune checkpoint genes. Analysis of the Submap algorithm indicated that the high-risk patients could show potent response to anti-PD-1 therapy. The established risk prediction model based on the expression of six CAFs-related genes has application prospects as an independent prognostic indicator and a predictor of the response of patients to immunotherapy.

TOP2B Enzymatic Activity on Promoters and Introns Modulates Multiple Oncogenes in Human Gliomas

PURPOSE

The epigenetic mechanisms involved in transcriptional regulation leading to malignant phenotype in gliomas remains poorly understood. Topoisomerase IIB (TOP2B), an enzyme that decoils and releases torsional forces in DNA, is overexpressed in a subset of gliomas. Therefore, we investigated its role in epigenetic regulation in these tumors.

EXPERIMENTAL DESIGN

To investigate the role of TOP2B in epigenetic regulation in gliomas, we performed paired chromatin immunoprecipitation sequencing for TOP2B and RNA-sequencing analysis of glioma cell lines with and without TOP2B inhibition and in human glioma specimens. These experiments were complemented with assay for transposase-accessible chromatin using sequencing, gene silencing, and mouse xenograft experiments to investigate the function of TOP2B and its role in glioma phenotypes.

RESULTS

We discovered that TOP2B modulates transcription of multiple oncogenes in human gliomas. TOP2B regulated transcription only at sites where it was enzymatically active, but not at all native binding sites. In particular, TOP2B activity localized in enhancers, promoters, and introns of PDGFRA and MYC, facilitating their expression. TOP2B levels and genomic localization was associated with PDGFRA and MYC expression across glioma specimens, which was not seen in nontumoral human brain tissue. In vivo, TOP2B knockdown of human glioma intracranial implants prolonged survival and downregulated PDGFRA.

CONCLUSIONS

Our results indicate that TOP2B activity exerts a pleiotropic role in transcriptional regulation of oncogenes in a subset of gliomas promoting a proliferative phenotype.

The CXCL Family Contributes to Immunosuppressive Microenvironment in Gliomas and Assists in Gliomas Chemotherapy

Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient's clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas' potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis.

Emerging immunotherapies for malignant glioma: from immunogenomics to cell therapy

As immunotherapy assumes a central role in the management of many cancers, ongoing work is directed at understanding whether immune-based treatments will be successful in patients with glioblastoma (GBM). Despite several large studies conducted in the last several years, there remain no FDA-approved immunotherapies in this patient population. Nevertheless, there are a range of exciting new approaches being applied to GBM, all of which may not only allow us to develop new treatments but also help us understand fundamental features of the immune response in the central nervous system. In this review, we summarize new developments in the application of immune checkpoint blockade, from biomarker-driven patient selection to the timing of treatment. Moreover, we summarize novel work in personalized immune-oncology by reviewing work in cancer immunogenomics-driven neoantigen vaccine studies. Finally, we discuss cell therapy efforts by reviewing the current state of chimeric antigen receptor T-cell therapy.

Immune biology of glioma-associated macrophages and microglia: functional and therapeutic implications

CNS immune defenses are marshaled and dominated by brain resident macrophages and microglia, which are the innate immune sentinels and frontline host immune barriers against various pathogenic insults. These myeloid lineage cells are the predominant immune population in gliomas and can constitute up to 30-50% of the total cellular composition. Parenchymal microglial cells and recruited monocyte-derived macrophages from the periphery exhibit disease-specific phenotypic characteristics with spatial and temporal distinctions and are heterogeneous subpopulations based on their molecular signatures. A preponderance of myeloid over lymphoid lineage cells during CNS inflammation, including gliomas, is a contrasting feature of brain immunity relative to peripheral immunity. Herein we discuss glioma-associated macrophage and microglia immune biology in the context of their identity, molecular drivers of recruitment, nomenclature and functional paradoxes, therapeutic reprogramming and polarization strategies, relevant challenges, and our perspectives on therapeutic modulation.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

Objective

In the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.

Methods

Stromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.

Results

We obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.

Conclusions

The PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Anti-PD-1 Induces M1 Polarization in the Glioma Microenvironment and Exerts Therapeutic Efficacy in the Absence of CD8 Cytotoxic T Cells

PURPOSE

Anti-programmed cell death protein 1 (PD-1) therapy has demonstrated inconsistent therapeutic results in patients with glioblastoma (GBM) including those with profound impairments in CD8 T-cell effector responses.

EXPERIMENTAL DESIGN

We ablated the CD8α gene in BL6 mice and intercrossed them with Ntv-a mice to determine how CD8 T cells affect malignant progression in forming endogenous gliomas. Tumor-bearing mice were treated with PD-1 to determine the efficacy of this treatment in the absence of T cells. The tumor microenvironment of treated and control mice was analyzed by IHC and FACS.

RESULTS

We observed a survival benefit in immunocompetent mice with endogenously arising intracranial glioblastomas after intravenous administration of anti-PD-1. The therapeutic effect of PD-1 administration persisted in mice even after genetic ablation of the CD8 gene (CD8^-/-). CD11b⁺ and Iba1⁺ monocytes and macrophages were enriched in the glioma microenvironment of the CD8^-/- mice. The macrophages and microglia assumed a proinflammatory M1 response signature in the setting of anti-PD-1 blockade through the elimination of PD-1-expressing macrophages and microglia in the tumor microenvironment. Anti-PD-1 can inhibit the proliferation of and induce apoptosis of microglia through antibody-dependent cellular cytotoxicity, as fluorescently labeled anti-PD-1 was shown to gain direct access to the glioma microenvironment.

CONCLUSIONS

Our results show that the therapeutic effect of anti-PD-1 blockade in GBM may be mediated by the innate immune system, rather than by CD8 T cells. Anti-PD-1 immunologically modulates innate immunity in the glioma microenvironment-likely a key mode of activity.