Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development

EGFLAM exhibits oncogenic activity and shows promise as a prognostic biomarker and therapeutic target in glioblastoma

Glioblastoma (GBM) remains the most lethal primary brain tumor, characterized by dismal survival rates. Novel molecular targets are urgently required to enhance therapeutic outcomes. A combination of bioinformatics analysis and experimental validation was employed to investigate the role of EGFLAM in GBM. The Chinese Glioma Genome Atlas provided a platform for gene expression profiling, while siRNA-mediated knockdown and overexpression assays in GBM cell lines, alongside in vivo tumorigenesis models, facilitated functional validation. EGFLAM was found to be significantly overexpressed in GBM tissues, correlating with adverse prognostic factors and higher tumor grades, particularly in patients over the age of 41. Functional assays indicated that EGFLAM is vital for maintaining GBM cell proliferation, viability, and invasiveness. Knockdown of EGFLAM expression led to a marked decrease in tumorigenic capabilities. Proteomic interactions involving EGFLAM, such as with NUP205, were implicated in cell cycle regulation, providing insight into its oncogenic mechanism. In vivo studies further demonstrated that silencing EGFLAM expression could inhibit tumor growth, underscoring its therapeutic potential. The study identifies EGFLAM as a pivotal oncogenic factor in GBM, serving as both a prognostic biomarker and a viable therapeutic target. These findings lay the groundwork for future research into EGFLAM-targeted therapies, aiming to improve clinical outcomes for GBM patients.

[Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma]

OBJECTIVE

To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients.

METHODS

Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets.

RESULTS

The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients.

CONCLUSION

This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma

The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumors - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.

Therapeutically targeting the unique disease landscape of pediatric high-grade gliomas

Pediatric high-grade gliomas (pHGG) are a rare yet devastating malignancy of the central nervous system's glial support cells, affecting children, adolescents, and young adults. Tumors of the central nervous system account for the leading cause of pediatric mortality of which high-grade gliomas present a significantly grim prognosis. While the past few decades have seen many pediatric cancers experiencing significant improvements in overall survival, the prospect of survival for patients diagnosed with pHGGs has conversely remained unchanged. This can be attributed in part to tumor heterogeneity and the existence of the blood-brain barrier. Advances in discovery research have substantiated the existence of unique subgroups of pHGGs displaying alternate responses to different therapeutics and varying degrees of overall survival. This highlights a necessity to approach discovery research and clinical management of the disease in an alternative subtype-dependent manner. This review covers traditional approaches to the therapeutic management of pHGGs, limitations of such methods and emerging alternatives. Novel mutations which predominate the pHGG landscape are highlighted and the therapeutic potential of targeting them in a subtype specific manner discussed. Collectively, this provides an insight into issues in need of transformative progress which arise during the management of pHGGs.

mRNA-based precision targeting of neoantigens and tumor-associated antigens in malignant brain tumors

BACKGROUND

Despite advancements in the successful use of immunotherapy in treating a variety of solid tumors, applications in treating brain tumors have lagged considerably. This is due, at least in part, to the lack of well-characterized antigens expressed within brain tumors that can mediate tumor rejection; the low mutational burden of these tumors that limits the abundance of targetable neoantigens; and the immunologically "cold" tumor microenvironment that hampers the generation of sustained and productive immunologic responses. The field of mRNA-based therapeutics has experienced a boon following the universal approval of COVID-19 mRNA vaccines. mRNA-based immunotherapeutics have also garnered widespread interest for their potential to revolutionize cancer treatment. In this study, we developed a novel and scalable approach for the production of personalized mRNA-based therapeutics that target multiple tumor rejection antigens in a single therapy for the treatment of refractory brain tumors.

METHODS

Tumor-specific neoantigens and aberrantly overexpressed tumor-associated antigens were identified for glioblastoma and medulloblastoma tumors using our cancer immunogenomics pipeline called Open Reading Frame Antigen Network (O.R.A.N). Personalized tumor antigen-specific mRNA vaccine was developed for each individual tumor model using selective gene capture and enrichment strategy. The immunogenicity and efficacy of the personalized mRNA vaccines was evaluated in combination with anti-PD-1 immune checkpoint blockade therapy or adoptive cellular therapy with ex vivo expanded tumor antigen-specific lymphocytes in highly aggressive murine GBM models.

RESULTS

Our results demonstrate the effectiveness of the antigen-specific mRNA vaccines in eliciting robust anti-tumor immune responses in GBM hosts. Our findings substantiate an increase in tumor-infiltrating lymphocytes characterized by enhanced effector function, both intratumorally and systemically, after antigen-specific mRNA-directed immunotherapy, resulting in a favorable shift in the tumor microenvironment from immunologically cold to hot. Capacity to generate personalized mRNA vaccines targeting human GBM antigens was also demonstrated.

CONCLUSIONS

We have established a personalized and customizable mRNA-therapeutic approach that effectively targets a plurality of tumor antigens and demonstrated potent anti-tumor response in preclinical brain tumor models. This platform mRNA technology uniquely addresses the challenge of tumor heterogeneity and low antigen burden, two key deficiencies in targeting the classically immunotherapy-resistant CNS malignancies, and possibly other cold tumor types.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BACKGROUND

Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.

METHODS

RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.

RESULTS

This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.

CONCLUSION

The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma

Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.

Roles of extracellular vesicles in glioblastoma: foes, friends and informers

Glioblastoma (GB) tumors are one of the most insidious cancers which take over the brain and defy therapy. Over time and in response to treatment the tumor and the brain cells in the tumor microenvironment (TME) undergo many genetic/epigenetic driven changes in their phenotypes and this is reflected in the cellular contents within the extracellular vesicles (EVs) they produce. With the result that some EVs try to subdue the tumor (friends of the brain), while others participate in the glioblastoma takeover (foes of the brain) in a dynamic and ever changing process. Monitoring the contents of these EVs in biofluids can inform decisions based on GB status to guide therapeutic intervention. This review covers primarily recent research describing the different cell types in the brain, as well as the tumor cells, which participate in this EV deluge. This includes EVs produced by the tumor which manipulate the transcriptome of normal cells in their environment in support of tumor growth (foes), as well as responses of normal cells which try to restrict tumor growth and invasion, including traveling to cervical lymph nodes to present tumor neo-antigens to dendritic cells (DCs). In addition EVs released by tumors into biofluids can report on the status of living tumor cells via their cargo and thus serving as biomarkers. However, EVs released by tumor cells and their influence on normal cells in the tumor microenvironment is a major factor in immune suppression and coercion of normal brain cells to join the GB "band wagon". Efforts are being made to deploy EVs as therapeutic vehicles for drugs and small inhibitory RNAs. Increasing knowledge about EVs in the TME is being utilized to track tumor progression and response to therapy and even to weaponize EVs to fight the tumor.

Advancing personalized medicine in brain cancer: exploring the role of mRNA vaccines

Advancing personalized medicine in brain cancer relies on innovative strategies, with mRNA vaccines emerging as a promising avenue. While the initial use of mRNA vaccines was in oncology, their stunning success in COVID-19 resulted in widespread attention, both positive and negative. Regardless of politically biased opinions, which relate more to the antigenic source than form of delivery, we feel it is important to objectively review this modality as relates to brain cancer. This class of vaccines trigger robust immune responses through MHC-I and MHC-II pathways, in both prophylactic and therapeutic settings. The mRNA platform offers advantages of rapid development, high potency, cost-effectiveness, and safety. This review provides an overview of mRNA vaccine delivery technologies, tumor antigen identification, combination therapies, and recent therapeutic outcomes, with a particular focus on brain cancer. Combinatorial approaches are vital to maximizing mRNA cancer vaccine efficacy, with ongoing clinical trials exploring combinations with adjuvants and checkpoint inhibitors and even adoptive cell therapy. Efficient delivery, neoantigen identification, preclinical studies, and clinical trial results are highlighted, underscoring mRNA vaccines' potential in advancing personalized medicine for brain cancer. Synergistic combinatorial therapies play a crucial role, emphasizing the need for continued research and collaboration in this area.

Methods behind oncolytic virus-based DC vaccines in cancer: Toward a multiphase combined treatment strategy for Glioblastoma (GBM) patients

Glioblastoma (GBM) remains an orphan cancer disease with poor outcome. Novel treatment strategies are needed. Immunotherapy has several modes of action. The addition of active specific immunotherapy with dendritic cell vaccines resulted in improved overall survival of patients. Integration of DC vaccination within the first-line combined treatment became a challenge, and immunogenic cell death immunotherapy during chemotherapy was introduced. We used a retrospective analysis using real world data to evaluate the complex combined treatment, which included individualized multimodal immunotherapy during and after standard of care, and which required adaptations during treatment, and found a further improvement of overall survival. We also discuss the use of real world data as evidence. Novel strategies to move the field of individualized multimodal immunotherapy forward for GBM patients are reviewed.

Dendritic cell vaccine trials in gliomas: Untangling the lines

Glioblastoma is a deadly brain tumor without any significantly successful treatments to date. Tumor antigen-targeted immunotherapy platforms including peptide and dendritic cell (DC) vaccines, have extended survival in hematologic malignancies. The relatively "cold" tumor immune microenvironment and heterogenous nature of glioblastoma have proven to be major limitations to translational application and efficacy of DC vaccines. Furthermore, many DC vaccine trials in glioblastoma are difficult to interpret due to a lack of contemporaneous controls, absence of any control comparison, or inconsistent patient populations. Here we review glioblastoma immunobiology aspects that are relevant to DC vaccines, review the clinical experience with DC vaccines targeting glioblastoma, discuss challenges in clinical trial design, and summarize conclusions and directions for future research for the development of effective DC vaccines for patients.

The role of immune subtyping in glioma mRNA vaccine development

Studies on the development of mRNA vaccines for central nervous system tumors have used gene expression profiles, clinical data and RNA sequencing from sources such as The Cancer Genome Atlas and Chinese Glioma Genome Atlas to identify effective antigens. These studies revealed several immune subtypes of glioma, each one linked to unique prognoses and genetic/immune-modulatory changes. Potential antigens include ARPC1B, BRCA2, COL6A1, ITGB3, IDH1, LILRB2, TP53 and KDR, among others. Patients with immune-active and immune-suppressive phenotypes were found to respond better to mRNA vaccines. While these findings indicate the potential of mRNA vaccines in cancer therapy, further research is required to optimize administration and adjuvant selection, and precisely identify target antigens.

Identification and validation of a novel HOX-related classifier signature for predicting prognosis and immune microenvironment in pediatric gliomas

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients' prognosis and immune infiltrate characteristics in PGs. Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort. Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis. Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.

Insight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma

Introduction

The exploration of lipid metabolism dysregulation may provide novel perspectives for retroperitoneal liposarcoma (RPLS). In our study, we aimed to investigate potential targets and facilitate further understanding of immune landscape in RPLS, through lipid metabolism-associated genes (LMAGs) based prognostic model.

Methods

Gene expression profiles and corresponding clinical information of 234 cases were enrolled from two public databases and the largest retroperitoneal tumor research center of East China, including cohort-TCGA (n=58), cohort-GSE30929 (n=92), cohort-FD (n=50), cohort-scRNA-seq (n=4) and cohort-validation (n=30). Consensus clustering analysis was performed to identify lipid metabolism-associated molecular subtypes (LMSs). A prognostic risk model containing 13 LMAGs was established using LASSO algorithm and multivariate Cox analysis in cohort-TCGA. ESTIMATE, CIBERSORT, XCELL and MCP analyses were performed to visualize the immune landscape. WGCNA was used to identify three hub genes among the 13 model LMAGs, and preliminarily validated in both cohort-GSE30929 and cohort-FD. Moreover, TIMER was used to visualize the correlation between antigen-presenting cells and potential targets. Finally, single-cell RNA-sequencing (scRNA-seq) analysis of four RPLS and multiplexed immunohistochemistry (mIHC) were performed in cohort-validation to validate the discoveries of bioinformatics analysis.

Results

LMS1 and LMS2 were characterized as immune-infiltrated and -excluded tumors, with significant differences in molecular features and clinical prognosis, respectively. Elongation of very long chain fatty acids protein 2 (ELOVL2), the enzyme that catalyzed the elongation of long chain fatty acids, involved in the maintenance of lipid metabolism and cellular homeostasis in normal cells, was identified and negatively correlated with antigen-presenting cells and identified as a potential target in RPLS. Furthermore, ELOVL2 was enriched in LMS2 with significantly lower immunoscore and unfavorable prognosis. Finally, a high-resolution dissection through scRNA-seq was performed in four RPLS, revealing the entire tumor ecosystem and validated previous findings.

Discussion

The LMS subgroups and risk model based on LMAGs proposed in our study were both promising prognostic classifications for RPLS. ELOVL2 is a potential target linking lipid metabolism to immune regulations against RPLS, specifically for patients with LMS2 tumors.

A tumor microenvironment-based prognostic index for osteosarcoma

BACKGROUND

The tumor microenvironment (TME) has a central role in the oncogenesis of osteosarcomas. The composition of the TME is essential for the interaction between tumor and immune cells. The aim of this study was to establish a prognostic index (TMEindex) for osteosarcoma based on the TME, from which estimates about patient survival and individual response to immune checkpoint inhibitor (ICI) therapy can be deduced.

METHODS

Based on osteosarcoma samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, the ESTIMATE algorithm was used to estimate ImmuneScore and StromalScore. Combined differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression and stepwise regression to construct the TMEindex. The prognostic role of TMEindex was validated in three independent datasets. The molecular and immune characteristics of TMEindex and the impact on immunotherapy were then comprehensively investigated. The expression of TMEindex genes in different cell types and its effects on osteosarcoma cells were explored by scRNA-Seq analysis and molecular biology experiments.

RESULTS

Fundamental is the expression of MYC, P4HA1, RAMP1 and TAC4. Patients with high TMEindex had worse overall survival, recurrence-free survival, and metastasis-free survival. TMEindex is an independent prognostic factor in osteosarcoma. TMEindex genes were mainly expressed in malignant cells. The knockdown of MYC and P4HA1 significantly inhibited the proliferation, invasion and migration of osteosarcoma cells. A high TME index is related to the MYC, mTOR, and DNA replication-related pathways. In contrast, a low TME index is related to immune-related signaling pathways such as the inflammatory response. The TMEindex was negatively correlated with ImmuneScore, StromalScore, immune cell infiltration, and various immune-related signature scores. Patients with a higher TMEindex had an immune-cold TME and higher invasiveness. Patients with a low TME index were more likely to respond to ICI therapy and achieve clinical benefit. In addition, the TME index correlated with response to 29 oncologic drugs.

CONCLUSIONS

The TMEindex is a promising biomarker to predict the prognosis of patients with osteosarcoma and their response to ICI therapy, and to distinguish the molecular and immune characteristics.

mRNA-From COVID-19 Treatment to Cancer Immunotherapy

This review provides an overview covering mRNA from its use in the COVID-19 pandemic to cancer immunotherapy, starting from the selection of appropriate antigens, tumor-associated and tumor-specific antigens, neoantigens, the basics of optimizing the mRNA molecule in terms of stability, efficacy, and tolerability, choosing the best formulation and the optimal route of administration, to summarizing current clinical trials of mRNA vaccines in tumor therapy.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

Introduction

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Methods and results

In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.

Conclusion

Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Glioma diagnosis and therapy: Current challenges and nanomaterial-based solutions

Glioma is often referred to as one of the most dreadful central nervous system (CNS)-specific tumors with rapidly-proliferating cancerous glial cells, accounting for nearly half of the brain tumors at an annual incidence rate of 30-80 per a million population. Although glioma treatment remains a significant challenge for researchers and clinicians, the rapid development of nanomedicine provides tremendous opportunities for long-term glioma therapy. However, several obstacles impede the development of novel therapeutics, such as the very tight blood-brain barrier (BBB), undesirable hypoxia, and complex tumor microenvironment (TME). Several efforts have been dedicated to exploring various nanoformulations for improving BBB permeation and precise tumor ablation to address these challenges. Initially, this article briefly introduces glioma classification and various pathogenic factors. Further, currently available therapeutic approaches are illustrated in detail, including traditional chemotherapy, radiotherapy, and surgical practices. Then, different innovative treatment strategies, such as tumor-treating fields, gene therapy, immunotherapy, and phototherapy, are emphasized. In conclusion, we summarize the article with interesting perspectives, providing suggestions for future glioma diagnosis and therapy improvement.

Recent Advances in Glioma Cancer Treatment: Conventional and Epigenetic Realms

Glioblastoma (GBM) is the most typical and aggressive form of primary brain tumor in adults, with a poor prognosis. Successful glioma treatment is hampered by ineffective medication distribution across the blood-brain barrier (BBB) and the emergence of drug resistance. Although a few FDA-approved multimodal treatments are available for glioblastoma, most patients still have poor prognoses. Targeting epigenetic variables, immunotherapy, gene therapy, and different vaccine- and peptide-based treatments are some innovative approaches to improve anti-glioma treatment efficacy. Following the identification of lymphatics in the central nervous system, immunotherapy offers a potential method with the potency to permeate the blood-brain barrier. This review will discuss the rationale, tactics, benefits, and drawbacks of current glioma therapy options in clinical and preclinical investigations.