Immune Landscape in PTEN-Related Glioma Microenvironment: A Bioinformatic Analysis

Radiomics Multifactorial in Silico Model for Spatial Prediction of Glioblastoma Progression and Recurrence: A Proof-of-Concept

BACKGROUND

Radiomics-based prediction of glioblastoma spatial progression and recurrence may improve personalized strategies. However, most prototypes are based on limited monofactorial Gompertzian models of tumor growth. The present study consists of a proof of concept on the accuracy of a radiomics multifactorial in silico model in predicting short-term spatial growth and recurrence of glioblastoma.

METHODS

A radiomics-based biomathematical multifactorial in silico model was developed using magnetic resonance imaging (MRI) data from a 53-year-old patient with newly diagnosed glioblastoma of the right supramarginal gyrus. Raw and optimized models were derived from the MRI at diagnosis and matched to the preoperative MRI obtained 28 days after diagnosis to test the accuracy in predicting the short-term spatial growth of the tumor. An additional optimized model was derived from the early postoperative MRI and matched to the MRI documenting tumor recurrence to test spatial accuracy in predicting the location of recurrence. The spatial prediction accuracy of the model was reported as an average Jaccard index.

RESULTS

Optimized models yielded an average Jaccard index of 0.69 and 0.26 for short-term tumor growth and long-term recurrence site, respectively.

CONCLUSIONS

The present radiomics-based multifactorial in silico model was feasible, reliable, and accurate for short-term spatial prediction of glioblastoma progression. The predictive value for the spatial location of recurrence was still low, and refinements in the description of tissue reorganization in the peritumoral and resected areas may be critical to optimize accuracy further.

Investigating the effects of PTEN mutations on cGAS-STING pathway in glioblastoma tumours

BACKGROUND

PTEN is a tumour suppressor gene and well-known for being frequently mutated in several cancer types. Loss of immunogenicity can also be attributed to PTEN loss, because of its role in establishing the tumour microenvironment. Therefore, this study aimed to represent the link between PTEN and cGAS-STING activity, a key mediator of inflammation, in tumour samples of glioblastoma patients.

METHODS

Tumour samples of 36 glioblastoma patients were collected. After DNA isolation, all coding regions of PTEN were sequenced and analysed. PTEN expression status was also evaluated by qRT-PCR, western blot, and immunohistochemical methods. Interferon-stimulated gene expressions, cGAMP activity, CD8 infiltration, and Granzyme B expression levels were determined especially for the evaluation of cGAS-STING activity and immunogenicity.

RESULTS

Mutant PTEN patients had significantly lower PTEN expression, both at mRNA and protein levels. Decreased STING, IRF3, NF-KB1, and RELA mRNA expressions were also found in patients with mutant PTEN. Immunohistochemistry staining of PTEN displayed expressional loss in 38.1% of the patients. Besides, patients with PTEN loss had considerably lower amounts of IFNB and IFIT2 mRNA expressions. Furthermore, CD8 infiltration, cGAMP, and Granzyme B levels were reduced in the PTEN loss group.

CONCLUSION

This study reveals the immunosuppressive effects of PTEN loss in glioblastoma tumours via the cGAS-STING pathway. Therefore, determining the PTEN status in tumours is of great importance, like in situations when considering the treatment of glioblastoma patients with immunotherapeutic agents.

Investigating the Association between the Autophagy Markers LC3B, SQSTM1/p62, and DRAM and Autophagy-Related Genes in Glioma

High-grade gliomas are extremely fatal tumors, marked by severe hypoxia and therapeutic resistance. Autophagy is a cellular degradative process that can be activated by hypoxia, ultimately resulting in tumor advancement and chemo-resistance. Our study aimed to examine the link between autophagy markers' expression in low-grade gliomas (LGGs) and high-grade gliomas (HGGs). In 39 glioma cases, we assessed the protein expression of autophagy markers LC3B, SQSTM1/p62, and DRAM by immunohistochemistry (IHC) and the mRNA expression of the autophagy genes PTEN, PI3K, AKT, mTOR, ULK1, ULK2, UVRAG, Beclin 1, and VPS34 using RT-qPCR. LC3B, SQSTM1/p62, and DRAM expression were positive in 64.1%, 51.3%, and 28.2% of glioma cases, respectively. The expression of LC3B and SQSTM1/p62 was notably higher in HGGs compared to LGGs. VPS34 exhibited a significant differential expression, displaying increased fold change in HGGs compared to LGGs. Additionally, it exhibited robust positive associations with Beclin1 (rs = 0.768), UVRAG (rs = 0.802), and ULK2 (rs = 0.786) in HGGs. This underscores a potential association between autophagy and the progression of gliomas. We provide preliminary data for the functional analysis of autophagy using a cell culture model and to identify potential targets for therapeutic interventions.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

The global change of gene expression pattern caused by PTEN mutation affects the prognosis of glioblastoma

Glioblastoma (GBM), an aggressive primary tumor, is common in humans, accounting for 12–15% of all intracranial tumors, and has median survival of fewer than 15 months. Since a growing body of evidence suggests that conventional drugs are ineffective against GBM, our goal is to find emerging therapies that play a role in its treatment. This research constructs a risk model to predict the prognosis of GBM patients. A set of genes associated with GBM was taken from a GBM gene data bank, and clinical information on patients with GBM was retrieved from the Cancer Genome Atlas (TCGA) data bank. One-way Cox and Kaplan–Meier analyses were performed to identify genes in relation to prognosis. Groups were classified into high and low expression level of PTEN expression. Prognosis-related genes were further identified, and multi-factor Cox regression analysis was used to build risk score equations for the prognostic model to construct a survival prognostic model. The area under the ROC curve suggested that the pattern had high accuracy. When combined with nomogram analysis, GJB2 was considered an independent predictor of GBM prognosis. This study provides a potential prognostic predictive biological marker for GBM patients and confirms that GJB2 is a key gene for GBM progression.

Emerging immune-based technologies for high-grade gliomas

INTRODUCTION

The selection of a tailored and successful strategy for high-grade gliomas (HGGs) treatment is still a concern. The abundance of aberrant mutations within the heterogenic genetic landscape of glioblastoma strongly influences cell expansion, proliferation, and therapeutic resistance. Identification of immune evasion pathways opens the way to novel immune-based strategies. This review intends to explore the emerging immunotherapies for HGGs. The immunosuppressive mechanisms related to the tumor microenvironment and future perspectives to overcome glioma immunity barriers are also debated.

AREAS COVERED

An extensive literature review was performed on the PubMed/Medline and ClinicalTrials.gov databases. Only highly relevant articles in English and published in the last 20 years were selected. Data about immunotherapies coming from preclinical and clinical trials were summarized.

EXPERT OPINION

The overall level of evidence about the efficacy and safety of immunotherapies for HGGs is noteworthy. Monoclonal antibodies have been approved as second-line treatment, while peptide vaccines, viral gene strategies, and adoptive technologies proved to boost a vivid antitumor immunization. Malignant brain tumor-treating fields are ever-changing in the upcoming years. Constant refinements and development of new routes of drug administration will permit to design of novel immune-based treatment algorithms thus improving the overall survival.