Multiple Faces of the Glioblastoma Microenvironment

Oncolytic virotherapy improves immunotherapies targeting cancer stemness in glioblastoma

Despite advances in cancer therapies, glioblastoma (GBM) remains the most resistant and recurrent tumor in the central nervous system. GBM tumor microenvironment (TME) is a highly dynamic landscape consistent with alteration in tumor infiltration cells, playing a critical role in tumor progression and invasion. In addition, glioma stem cells (GSCs) with self-renewal capability promote tumor recurrence and induce therapy resistance, which all have complicated eradication of GBM with existing therapies. Oncolytic virotherapy is a promising field of therapy that can kill tumor cells in a targeted manner. Manipulated oncolytic viruses (OVs) improve cancer immunotherapy by directly lysis tumor cells, infiltrating antitumor cells, inducing immunogenic cell death, and sensitizing immune-resistant TME to an immune-responsive hot state. Importantly, OVs can target stemness-driven GBM progression. In this review, we will discuss how OVs as a therapeutic option target GBM, especially the GSC subpopulation, and induce immunogenicity to remodel the TME, which subsequently enhances immunotherapies' efficiency.

Texture feature differentiation of glioblastoma and solitary brain metastases based on tumor and tumor-brain interface

RATIONALE AND OBJECTIVES

Texture features, derived from both the entire tumor area and the region of the tumor-to-brain interface, are crucial indicators for distinguishing tumor types and their degrees of malignancy. However, the discriminative value of texture features from both regions for identifying glioblastomas and metastatic tumors has not been thoroughly explored. The aim of this study is to develop and validate a diagnostic model that combines texture features from the entire tumor area and a 10 mm tumor-to-brain interface region, in an attempt to identify more stable and effective texture features.

METHOD

We retrospectively collected enhanced T1-weighted imaging data from 97 patients with glioblastoma(GBM) and single brain metastasis(SBM) between 2010 and 2024. Machine learning is used to establish multiple diagnostic models for discriminating GBM and SBM based on texture features of the entire tumor and 10 mm tumor-to-brain interface regions. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

RESULTS

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Relief and autoencoder (AE), the AUC was highest using the "one-standard error" rule. '10mm_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. The best models in the training set, test set, and validation set were not the same. In the test set, the Relief19AE model had the highest AUC of 0.869 and an accuracy of 0.857.

CONCLUSION

The texture feature model that combines the overall tumor and the tumor-brain interface is beneficial for distinguishing glioblastoma from solitary metastatic tumors, and the texture features of the tumor interface exhibit higher heterogeneity.

Coexisting Meningioma and Glioma in the Same Patient: A Case Report

The simultaneous occurrence of two distinct intracranial neoplasms within a single patient presents a unique and exceedingly rare clinical scenario. Such cases pose significant diagnostic and therapeutic challenges, complicating clinical decision-making and treatment planning. Understanding the intricacies of these cases is crucial for improving patient outcomes and advancing clinical knowledge. This report aims to provide valuable insights into the complexities surrounding the management of patients with coexisting different intracranial neoplasms. This case report seeks to enhance the understanding of healthcare professionals dealing with similar cases and contribute to the existing literature on this rare phenomenon. This case report details a patient harboring two different but coexisting intracranial neoplasms. The patient, a 73-year-old woman, presented with behavioral changes and bilateral leg weakness. Initial imaging studies, including CT and MRI scans, revealed the presence of two distinct intracranial masses located in the frontal lobes and the falx cerebri. Neurosurgical, chemotherapy, and radiotherapy were offered; however, the patient's family opted for palliative care. The concurrent presence of two distinct intracranial neoplasms in a single patient underscores the complexity of such cases. This report highlights the diagnostic and therapeutic challenges encountered and the importance of a multidisciplinary approach to managing these patients. By sharing this case, we hope to contribute to a broader understanding of such rare clinical scenarios and aid in developing more effective management strategies in the future.

High expression of BTN3A1 is associated with clinical and immunological characteristics and predicts a poor prognosis in advanced human gliomas

Introduction

Gliomas represent the most prevalent and aggressive tumors within the central nervous system. Despite the current standard treatments, the median survival time for glioblastoma patients remains dismal, hovering around 14 months. While attempts have been made to inhibit the PD-1/PD-L1 and CTLA-4/CD80-CD86 axes through immunotherapy, the outcomes have yet to demonstrate significant efficacy. The immune checkpoint Butyrophilin 3A1 (BTN3A1) can either be involved in advantageous or detrimental function depending on the cancer type.

Methods

In our study, we utilized a Moroccan cohort to delve into the role of BTN3A1 in gliomas. A transcriptomic analysis was conducted on 34 patients, which was then corroborated through a protein analysis in 27 patients and validated using the TCGA database (n = 667).

Results

Our results revealed an elevated expression of BTN3A1 in glioblastoma (grade 4), as evidenced in both the TCGA database and our cohort of Moroccan glioma patients. Within the TCGA cohort, BTN3A1 expression was notably higher in patients with wild-type IDH. We observed a positive correlation between BTN3A1 expression and immune infiltration of B cells, CD8+ T cells, naive CD4+ T cells, and M2 macrophages. Patients exhibiting increased BTN3A1 expression also presented elevated levels of TGF-β, IL-10, and TIM-3 compared to those with reduced BTN3A1 expression. Notably, patients with high BTN3A1 expression were associated with a poorer prognosis than their counterparts with lower expression.

Conclussion

Our findings suggest that BTN3A1 might promote the establishment of an immunosuppressive microenvironment. Consequently, targeting BTN3A1 could offer novel therapeutic avenues for the management of advanced gliomas.

Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach

BACKGROUND

Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection.

METHODS

Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.

FINDINGS

The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.

CONCLUSIONS

We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.

New Directions in the Therapy of Glioblastoma

Glioblastoma is the most common histologic type of all gliomas and contributes to 57.3% of all cases. Despite the standard management based on surgical resection and radiotherapy, it is related to poor outcome, with a 5-year relative survival rate below 6.9%. In order to improve the overall outcome for patients, the new therapeutic strategies are needed. Herein, we describe the current state of knowledge on novel targeted therapies in glioblastoma. Based on recent studies, we compared treatment efficacy measured by overall survival and progression-free survival in patients treated with selected potential antitumor drugs. The results of the application of the analyzed inhibitors are highly variable despite the encouraging conclusions of previous preclinical studies. This paper focused on drugs that target major glioblastoma kinases. As far, the results of some BRAF inhibitors are favorable. Vemurafenib demonstrated a long-term efficacy in clinical trials while the combination of dabrafenib and trametinib improves PFS compared with both vemurafenib and dabrafenib alone. There is no evidence that any MEK inhibitor is effective in monotherapy. According to the current state of knowledge, BRAF and MEK inhibition are more advantageous than BRAF inhibitor monotherapy. Moreover, mTOR inhibitors (especially paxalisib) may be considered a particularly important group. Everolimus demonstrated a partial response in a significant proportion of patients when combined with bevacizumab, however its actual role in the treatment is unclear. Neither nintedanib nor pemigatinib were efficient in treatment of GBM. Among the anti-VEGF drugs, bevacizumab monotherapy was a well-tolerated option, significantly associated with anti-GBM activity in patients with recurrent GBM. The efficacy of aflibercept and pazopanib in monotherapy has not been demonstrated. Apatinib has been proven to be effective and tolerable by a single clinical trial, but more research is needed. Lenvatinib is under trial. Finally, promising results from a study with regorafenib may be confirmed by the ongoing randomized AGILE trial. The studies conducted so far have provided a relatively wide range of drugs, which are at least well tolerated and demonstrated some efficacy in the randomized clinical trials. The comprehensive understanding of the molecular biology of gliomas promises to further improve the treatment outcomes of patients.

Inflammation-targeted nanomedicine against brain cancer: From design strategies to future developments

Brain cancer is an aggressive type of cancer with poor prognosis. While the immune system protects against cancer in the early stages, the tumor exploits the healing arm of inflammatory reactions to accelerate its growth and spread. Various immune cells penetrate the developing tumor region, establishing a pro-inflammatory tumor milieu. Additionally, tumor cells may release chemokines and cytokines to attract immune cells and promote cancer growth. Inflammation and its associated mechanisms in the progression of cancer have been extensively studied in the majority of solid tumors, especially brain tumors. However, treatment of the malignant brain cancer is hindered by several obstacles, such as the blood-brain barrier, transportation inside the brain interstitium, inflammatory mediators that promote tumor growth and invasiveness, complications in administering therapies to tumor cells specifically, the highly invasive nature of gliomas, and the resistance to drugs. To resolve these obstacles, nanomedicine could be a potential strategy that has facilitated advancements in diagnosing and treating brain cancer. Due to the numerous benefits provided by their small size and other features, nanoparticles have been a prominent focus of research in the drug-delivery field. The purpose of this article is to discuss the role of inflammatory mediators and signaling pathways in brain cancer as well as the recent advances in understanding the nano-carrier approaches for enhancing drug delivery to the brain in the treatment of brain cancer.

Potential Neurotoxic Effects of Glioblastoma-Derived Exosomes in Primary Cultures of Cerebellar Neurons via Oxidant Stress and Glutathione Depletion

High-grade gliomas are the most fatal brain tumors. Grade 4 gliomas are called glioblastoma multiforme (GBM), which are associated with the poorest survival and a 5-year survival rate of less than 4%. Many patients with GBM developed concomitant cognitive dysfunctions and epilepsy. Although the cognitive decline is well defined in glioblastomas, the neurotoxic factors underlying this pathology are not well understood in GBM patients. In this study, we aimed to investigate whether GBM-derived exosomes play a role in neuronal toxicity. For this purpose, exosomes obtained from T98G and U373 GBM cells were applied to primary neuron culture at different concentrations. Subsequently, MTT, LDH, GSH, TAS, and TOS tests were performed. Both GBM-derived exosomes induced a dose-dependent and statistically significant increase of LDH release in cerebellar neurons. MTT assay revealed as both T98G and U373 GBM-derived exosomes induced dose-dependent neurotoxic effects in cerebellar neurons. To the best of our knowledge, this study is the first study demonstrating the toxic potential of GBM-derived exosomes to primary neurons, which may explain the peritumoral edema and cognitive decline in GBM patients.

The Vascular Microenvironment in Glioblastoma: A Comprehensive Review

Glioblastoma multiforme, the deadliest primary brain tumor, is characterized by an excessive and aberrant neovascularization. The initial expectations raised by anti-angiogenic drugs were soon tempered due to their limited efficacy in improving the overall survival. Intrinsic resistance and escape mechanisms against anti-VEGF therapies evidenced that tumor angiogenesis is an intricate multifaceted phenomenon and that vessels not only support the tumor but exert indispensable interactions for resistance and spreading. This holistic review covers the essentials of the vascular microenvironment of glioblastoma, including the perivascular niche components, the vascular generation patterns and the implicated signaling pathways, the endothelial–tumor interrelation, and the interconnection between vessel aberrancies and immune disarrangement. The revised concepts provide novel insights into the preclinical models and the potential explanations for the failure of conventional anti-angiogenic therapies, leading to an era of new and combined anti-angiogenic-based approaches.

Glioblastoma Microenvironment and Cellular Interactions

Simple Summary

This paper summarizes the crosstalk between tumor/non-tumor cells and other elements of the glioblastoma (GB) microenvironment. In tumor pathology, glial cells result in the highest number of cancers, and GB is considered the most lethal tumor of the central nervous system (CNS). The tumor microenvironment (TME) is a complex peritumoral hallo composed of tumor cells and several non-tumor cells (e.g., nervous cells, stem cells, fibroblasts, vascular and immune cells), which might be a key factor for the ineffective treatment since the microenvironment modulates the biologic status of the tumor with the increase in its evasion capacity. A deeper understanding of cell–cell interactions in the TME and with the tumor cells could be the basis for a more efficient therapy.

Abstract

The central nervous system (CNS) represents a complex network of different cells, such as neurons, glial cells, and blood vessels. In tumor pathology, glial cells result in the highest number of cancers, and glioblastoma (GB) is considered the most lethal tumor in this region. The development of GB leads to the infiltration of healthy tissue through the interaction between all the elements of the brain network. This results in a GB microenvironment, a complex peritumoral hallo composed of tumor cells and several non-tumor cells (e.g., nervous cells, stem cells, fibroblasts, vascular and immune cells), which might be the principal factor for the ineffective treatment due to the fact that the microenvironment modulates the biologic status of the tumor with the increase in its evasion capacity. Crosstalk between glioma cells and the brain microenvironment finally inhibits the beneficial action of molecular pathways, favoring the development and invasion of the tumor and its increasing resistance to treatment. A deeper understanding of cell–cell interactions in the tumor microenvironment (TME) and with the tumor cells could be the basis for a more efficient therapy.