Comprehensive exploration of tumor mutational burden and immune infiltration in diffuse glioma

The impact of gliomas on the normal brain microenvironment: a pilot study

Gliomas are malignant tumors of neuronal support cells within the central nervous system (CNS) and are characterized by poor overall prognoses and limited treatment options due to their infiltrative growth patterns. The neural tumor microenvironment, composed of benign neurons, neuroglia, endothelial cells, and intravascular white blood cells, is a target-rich site for potential chemotherapeutic agents. This study assessed cell proliferation rates, white blood cell components, and a limited number of nuclear, cytoplasmic, and membrane markers using immunohistochemistry (IHC) assays on formalin-fixed and paraffin-embedded benign and glial tumor tissue samples from the CNS. It was observed that glioma tissues had increased rates of glial cell proliferation and significant increases in the number of observed T-lymphocytes and granulocytes but decreased expression of markers Somatostatin receptor 2 (SSTR2), L1 cell adhesion molecule (L1CAM), and GATA binding protein 3 (GATA3) when compared to benign tissue samples. Understanding the lack of protein expression and population expansion potential of the glioma microenvironment in greater detail could help identify valuable therapeutic target combinations for future treatments.

Immune Resistance in Glioblastoma: Understanding the Barriers to ICI and CAR-T Cell Therapy

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor, with fewer than 5% of patients surviving five years after diagnosis. The introduction of immune checkpoint inhibitors (ICIs), followed by chimeric antigen receptor (CAR) T-cell therapy, marked major advancements in oncology. Despite demonstrating efficacy in other blood and solid cancers, these therapies have yielded limited success in clinical trials for both newly diagnosed and recurrent GBM. A deeper understanding of GBM's resistance to immunotherapy is essential for enhancing treatment responses and translating results seen in other cancer models.

OBJECTIVES

In this review, we examine clinical trial outcomes involving ICIs and CAR-T for GBM patients and explore the evasive mechanisms of GBM and the tumor microenvironment.

FINDINGS AND DISCUSSION

Multiple clinical trials investigating ICIs in GBM have shown poor outcomes, with no significant improvement in progression-free survival (PFS) or overall survival (OS). Results from smaller case studies with CAR-T therapy have warranted further investigation. However, no large-scale trials or robust studies have yet established these immunotherapeutic approaches as definitive treatment strategies. Future research should shift focus from addressing the scarcity of functional T cells to exploiting the abundant myeloid-derived cells within the tumor microenvironment.

CONCLUSIONS

Translating these therapies into effective treatments for glioblastoma in humans remains a significant challenge. The highly immunosuppressive nature of GBM and its tumor microenvironment continue to hinder the success of these innovative immunotherapeutic approaches. Targeting the myeloid-derived compartment may lead to more robust and sustained immune responses.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

Pathways to hypermutation in high-grade gliomas: Mechanisms, syndromes, and opportunities for immunotherapy

Despite rapid advances in the field of immunotherapy, including the success of immune checkpoint inhibition in treating multiple cancer types, clinical response in high-grade gliomas (HGGs) has been disappointing. This has been in part attributed to the low tumor mutational burden (TMB) of the majority of HGGs. Hypermutation is a recently characterized glioma signature that occurs in a small subset of cases, which may open an avenue to immunotherapy. The substantially elevated TMB of these tumors most commonly results from alterations in the DNA mismatch repair pathway in the setting of extensive exposure to temozolomide or, less frequently, from inherited cancer predisposition syndromes. In this review, we discuss the genetics and etiology of hypermutation in HGGs, with an emphasis on the resulting genomic signatures, and the state and future directions of immuno-oncology research in these patient populations.

Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning

Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients). TMB-related features were used for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent performance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response were enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways.

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.

Identification of natural killer cell-related characteristics to predict the clinical prognosis and immune microenvironment of patients with low-grade glioma

BACKGROUND

Individuals with low-grade glioma (LGG) have a dismal prognosis, and most patients will eventually progress to high-grade disease. Therefore, it is crucial to accurately determine their prognoses.

METHODS

Seventy-nine NK cell genes were downloaded from the LM22 database and univariate Cox regression analysis was utilized to detect NK cell-related genes affecting prognosis. Molecular types were established for LGG using the "ConsensusClusterPlus" R package. The results from a functional enrichment analysis and the immune microenvironment were intensively explored to determine molecular heterogeneity and immune characteristics across distinct subtypes. Furthermore, a RiskScore model was developed and verified using expression profiles of NK cells, and a nomogram consisting of the RiskScore model and clinical traits was constructed. Moreover, pan-cancer traits of NK cells were also investigated.

RESULTS

The C1 subtype included the greatest amount of immune infiltration and the poorest prognosis among well-established subtypes. The majority of enriched pathways were those involved in tumor progression, including epithelial-mesenchymal transition and cell cycle pathways. Differentially expressed genes among distinct subtypes were determined and used to develop a novel RiskScore model. This model was able to distinguish low-risk patients with LGG from those with high-risk disease. An accurate nomogram including the RiskScore, disease grade and patient's age was constructed to predict clinical outcomes of LGG patients. Finally, a pan-cancer analysis further highlighted the crucial roles of NK cell-related genes in the tumor microenvironment.

CONCLUSIONS

An NK cell-related RiskScore model can accurately predict the prognoses of patients with LGG and provide valuable insights into personalized medicine.

Comprehensive analysis of the prognostic and immunological signature of eight Tripartitemotif (TRIM) family molecules in human gliomas

BACKGROUND

TRIM family molecules have been identified as being involved in the tumor progression of various cancer types. Increasingly, experimental evidence indicates that some of TRIM family molecules are implicated in glioma tumorigenesis. However, the diverse genomic changes, prognostic values and immunological landscapes of TRIM family of molecules have yet to be fully determined in glioma.

METHODS

In our study, employing the comprehensive bioinformatics tools, we evaluated the unique functions of 8 TRIM members including TRIM5/17/21/22/24/28/34/47 in gliomas.

RESULTS

The expression levels of 7 TRIM members (TRIM5/21/22/24/28/34/47) were higher in glioma as well as its diverse cancer subtypes than in normal tissues, whereas the expression level of TRIM17 was the opposite, lower in the former than in the latter. In addition, survival analysis revealed that the high expression profiles of TRIM5/21/22/24/28/34/47 were associated with poor overall survival (OS), disease-specific survival (DSS) and progress-free interval (PFI) in glioma patients, whereas TRIM17 displayed adverse outcomes. Moreover, the 8 TRIM molecules expression as well as methylation profiles remarkably correlated with different WHO grades. And genetic alterations, including mutations and copy number alterations (CNAs), in the TRIM family were correlated with longer OS, DSS and progress-free survival (PFS) in glioma patients. Furthermore, through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis results of these 8 molecules and their related genes, we found that these molecules may change the immune infiltration of the tumor microenvironment and regulate the expression of immune checkpoint molecules (ICMs), affecting the occurrence and development of gliomas. The correlation analyses between the 8 TRIM molecules and TMB (tumor mutational burden)/MSI (microsatellite instability)/ICMs discovered that as the expression level of TRIM5/21/22/24/28/34/47 increased, the TMB score also increased significantly, while TRIM17 showed an opposite outcome. Further, a 6-gene signature (TRIM 5/17/21/28/34/47) for predicting overall survival (OS) in gliomas was built by using the least absolute shrinkage and selection operator (LASSO) regression, and the survival and time-dependent ROC analyses all were found to perform well in testing and validation cohorts. Results of multivariate COX regression analysis showed that TRIM5/28 are both expected to become independent risk predictors to guide clinical treatment.

CONCLUSION

In general, the results indicate that TRIM5/17/21/22/24/28/34/47 might exert a crucial influence on gliomas tumorigenesis and might be putative prognostic markers and therapeutic targets for glioma patients.

The role of costimulatory molecules in glioma biology and immune microenvironment

Background: Extensive research showed costimulatory molecules regulate tumor progression. Nevertheless, a small amount of literature has concentrated on the potential prognostic and therapeutic effects of costimulatory molecules in patients with glioma. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, Chinese Glioma Genome Atlas (CGGA) database, and Gene Expression Omnibus (GEO) database for bioinformatics analysis. R software was applied for statistical analysis. Using the FigureYa and Xiantao online tools (https://www.xiantao.love/) for mapping. Results: The Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were utilized to identify the signature consisting of five costimulatory molecules. Multivariate regression analysis revealed that the prognosis of glioma could be independently predicted by the riskscore. Furthermore, we explored clinical and genomic feature differences between the two groups. The level of tumor mutational burden (TMB) was higher in the high-risk group, while more mutation of IDH1 was observed in the low-risk group. Results of Tumor Immune Dysfunction and Exclusion (TIDE) analysis showed that high-risk patients were more prone to be responded to immunotherapy. In addition, subclass mapping analysis was performed to validate our findings and the results revealed that a significantly higher percentage of immunotherapy response rate was observed in the high-risk group. Conclusion: A novel signature with a good independent predictive capacity of prognosis was successfully identified. And our findings reveal that patients with high-risk scores were more likely to be responded to immunotherapy.

Effects of comprehensive nursing on postoperative complications, mental status and quality of life in patients with glioma

BACKGROUND

The complexity and refractory of brain glioma requires treatment that should involve a multidisciplinary approach to improve quality of care and fulfill patients’ needs.

AIM

To explore the effects of comprehensive nursing on postoperative complications, psychological state and quality of life in patients with brain glioma.

METHODS

A total of 106 patients with confirmed brain gliomas admitted to Nanchong Central Hospital between January 2019 and May 2021 were selected by random sampling. They were categorized into an observation group and a control group using a random number table with 53 patients in each group. Patients in the observation group were given comprehensive nursing in addition to conventional nursing and patients in the control group were given conventional nursing. The overall incidence of postoperative complications including limb dysfunction, high fever and epilepsy was compared between the two groups. The mental status was evaluated in the two groups before and after intervention using self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Quality of life was assessed and compared using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire between the two groups before and after the intervention.

RESULTS

After intervention, the overall incidence of postoperative complications was significantly lower in the observation group (7.55%) than that in the control group (20.75%) (P < 0.05). Before intervention, there was no significant difference in SAS and SDS scores between the two groups (P > 0.05). However, after intervention, scores of SAS and SDS decreased in the two groups compared with those before intervention, and the scores of SAS and SDS were lower in the observation group than in the control group (all P < 0.05). There was no significant difference in quality of life between the two groups before the intervention (P > 0.05). In contrast, quality of life increased in the two groups compared with those before intervention, and it was higher in the observation group than in the control group (P < 0.05).

CONCLUSION

Comprehensive nursing can reduce the incidence of postoperative complications, improve the psychological state of anxiety and depression and improve quality of life in patients with brain glioma.

Single-Cell Omics in Dissecting Immune Microenvironment of Malignant Gliomas-Challenges and Perspectives

Single-cell technologies allow precise identification of tumor composition at the single-cell level, providing high-resolution insights into the intratumoral heterogeneity and transcriptional activity of cells in the tumor microenvironment (TME) that previous approaches failed to capture. Malignant gliomas, the most common primary brain tumors in adults, are genetically heterogeneous and their TME consists of various stromal and immune cells playing an important role in tumor progression and responses to therapies. Previous gene expression or immunocytochemical studies of immune cells infiltrating TME of malignant gliomas failed to dissect their functional phenotypes. Single-cell RNA sequencing (scRNA-seq) and cytometry by time-of-flight (CyTOF) are powerful techniques allowing quantification of whole transcriptomes or >30 protein targets in individual cells. Both methods provide unprecedented resolution of TME. We summarize the findings from these studies and the current state of knowledge of a functional diversity of immune infiltrates in malignant gliomas with different genetic alterations. A precise definition of functional phenotypes of myeloid and lymphoid cells might be essential for designing effective immunotherapies. Single-cell omics studies have identified crucial cell subpopulations and signaling pathways that promote tumor progression, influence patient survival or make tumors vulnerable to immunotherapy. We anticipate that the widespread usage of single-cell omics would allow rational design of oncoimmunotherapeutics.