MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma

Design and Computational Analysis of an MMP9 Inhibitor in Hypoxia-Induced Glioblastoma Multiforme

The main therapeutic difficulties in treating hypoxia-induced glioblastoma multiforme (GBM) are toxicity of current treatments and the resistance brought on by the microenvironment. More effective therapeutic alternatives are urgently needed to reduce tumor lethality. Hence, we screened plant-based natural product panels intending to identify novel drugs without elevating drug resistance. We explored GEO for the hypoxia GBM model and compared hypoxic genes to non-neoplastic brain cells. A total of 2429 differentially expressed genes expressed exclusively in hypoxia were identified. The functional enrichment analysis demonstrated genes associated with GBM, further PPI network was constructed, and biological pathways associated with them were explored. Seven webtools, including GEPIA2.0, TIMER2.0, TCGA-GBM, and GlioVis, were used to validate 32 hub genes discovered using Cytoscape tool in GBM patient samples. Four GBM-specific hypoxic hub genes, LYN, MMP9, PSMB9, and TIMP1, were connected to the tumor microenvironment using TIMER analysis. 11 promising hits demonstrated positive drug-likeness with nontoxic characteristics and successfully crossed blood-brain barrier and ADMET analyses. Top-ranking hits have stable intermolecular interactions with the MMP9 protein according to molecular docking, MD simulation, MM-PBSA, PCA, and DCCM analyses. Herein, we have reported flavonoids, 7,4'-dihydroxyflavan, (3R)-3-(4-hydroxybenzyl)-6-hydroxy-8-methoxy-3,4-dihydro-2H-1-benzopyran, and 4'-hydroxy-7-methoxyflavan, to inhibit MMP9, a novel hypoxia gene signature that could serve as a promising predictor in various clinical applications, including GBM diagnosis, prognosis, and targeted therapy.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Histologic Definition of Enhancing Core and FLAIR Hyperintensity Region of Glioblastoma, IDH-Wild Type: A Clinico-Pathologic Study on a Single-Institution Series

The extent of resection beyond the enhancing core (EC) in glioblastoma IDH-wild type (GBM, IDHwt) is one of the most debated topics in neuro-oncology. Indeed, it has been demonstrated that local disease recurrence often arises in peritumoral areas and that radiologically-defined FLAIR hyperintensity areas of GBM IDHwt are often visible beyond the conventional EC. Therefore, the need to extend the surgical resection also to the FLAIR hyperintensity areas is a matter of debate. Since little is known about the histological composition of FLAIR hyperintensity regions, in this study we aimed to provide a comprehensive description of the histological features of EC and FLAIR hyperintensity regions sampled intraoperatively using neuronavigation and 5-aminolevulinic acid (5-ALA) fluorescence, in 33 patients with GBM, IDHwt. Assessing a total 109 histological samples, we found that FLAIR areas consisted in: (i) fragments of white matter focally to diffusely infiltrated by tumor cells in 76% of cases; (ii) a mixture of white matter with reactive astrogliosis and grey matter with perineuronal satellitosis in 15% and (iii) tumor tissue in 9%. A deeper knowledge of the histology of FLAIR hyperintensity areas in GBM, IDH-wt may serve to better guide neurosurgeons on the choice of the most appropriate surgical approach in patients with this neoplasm.

Multi-Omics Integration Analysis of TK1 in Glioma: A Potential Biomarker for Predictive, Preventive, and Personalized Medical Approaches

Multi-omics expression datasets obtained from multiple public databases were used to elucidate the biological function of TK1 and its effects on clinical outcomes. The Kaplan-Meier curve, a predictive nomogram mode, and the time-dependent receiver operating characteristic (ROC) curve were established to assess the role of TK1 expression in glioma prognosis. TK1 was overexpressed in glioma compared with normal samples, and patients with elevated expression of TK1 had poor overall survival. The ROC curves indicated a high diagnostic value of TK1 expression in patients of glioma; the areas under the ROC curve (AUC) were 0.682, 0.735, and 0.758 for 1 year, 3 years, and 5 years of glioma survival, respectively. For a model based on TK1 expression and other clinical characteristics, the values of AUC were 0.864, 0.896, and 0.898 for 1 year, 3 years, and 5 years, respectively. Additionally, the calibration curve indicated that the predicted and observed areas at 1 year, 3 years, and 5 years of survival were in excellent agreement. Three types of TK1 alterations-missense mutations, splice mutations, and amplifications-were identified in 25 of 2706 glioma samples. The TK1-altered group had better overall survival than the unaltered group. Single-cell function analysis showed that TK1 was positively associated with proliferation, the cell cycle, DNA repair, DNA damage, and epithelial-mesenchymal transition in glioma. Immunoinfiltration analysis indicated that TK1 expression might play different roles in low-grade glioma and glioblastoma multiforme tumor microenvironments, but TK1 expression was positively associated with activated CD4 and Th2, regardless of tumor grade. In summary, our findings identified TK1 as a novel marker for predicting clinical outcomes and a potential target for glioma.

RUNX1/CD44 axis regulates the proliferation, migration, and immunotherapy of gliomas: A single-cell sequencing analysis

BACKGROUND

Glioma is one of the most common, primary, and lethal adult brain tumors because of its extreme aggressiveness and poor prognosis. Several recent studies relevant to the immune function of CD44, a transmembrane glycoprotein as a significant hyaluronic acid receptor, have achieved great success, revealing the critical role of CD44 in immune infiltration in gliomas. The overexpression of CD44 has been verified to correlate with cancer aggressiveness and migration, while the clinical and immune features of CD44 expression have not yet been thoroughly characterized in gliomas.

METHODS

Molecular and clinical data of glioma collected from publicly available genomic databases were analyzed.

RESULTS

CD44 was up-expressed in malignant gliomas, notably in the 1p/19q non-codeletion cases, isocitrate dehydrogenase (IDH) wild-type, and mesenchymal subtypes in GBM samples. CD44 expression level strongly correlates with stromal and immune cells, mainly infiltrating the glioma microenvironment by single-cell sequencing analysis. Meanwhile, CD44 can be a promising biomarker in predicting immunotherapy responses and mediating the expression of PD-L1. Finally, RUNX1/CD44 axis could promote the proliferation and migration of gliomas.

CONCLUSIONS

Therefore, CD44 was responsible for glioma growth and progression. It could potentially lead to a novel target for glioma immunotherapy or a prognostic biomarker.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

NSD1 Mutations and Pediatric High-Grade Gliomas: A Comparative Genomic Study in Primary and Recurrent Tumors

Pediatric high-grade gliomas represent a heterogeneous group of tumors with a wide variety of molecular features. We performed whole exome sequencing and methylation profiling on matched primary and recurrent tumors from four pediatric patients with hemispheric high-grade gliomas. Genetic analysis showed the presence of some variants shared between primary and recurrent tumors, along with other variants exclusive of primary or recurrent tumors. NSD1 variants, all novel and not previously reported, were present at high frequency in our series (100%) and were all shared between the samples, independently of primary or recurrence. For every variant, in silico prediction tools estimated a high probability of altering protein function. The novel NSD1 variant (c.5924T > A; p.Leu1975His) was present in one in four cases at recurrence, and in two in four cases at primary. The novel NSD1 variant (c.5993T > A; p.Met1998Lys) was present in one in four cases both at primary and recurrence, and in one in four cases only at primary. The presence of NSD1 mutations only at recurrence may suggest that they can be sub-clonal, while the presence in both primary and recurrence implies that they can also represent early and stable events. Furthermore, their presence only in primary, but not in recurrent tumors, suggest that NSD1 mutations may also be influenced by treatment.

Glioblastoma Molecular Classification Tool Based on mRNA Analysis: From Wet-Lab to Subtype

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.

Meta-Analysis of RNA-Seq Datasets Identifies Novel Players in Glioblastoma

Glioblastoma is a devastating grade IV glioma with poor prognosis. Identification of predictive molecular biomarkers of disease progression would substantially contribute to better disease management. In the current study, we performed a meta-analysis of different RNA-seq datasets to identify differentially expressed protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs). This meta-analysis aimed to improve power and reproducibility of the individual studies while identifying overlapping disease-relevant pathways. We supplemented the meta-analysis with small RNA-seq on glioblastoma tissue samples to provide an overall transcriptomic view of glioblastoma. Co-expression correlation of filtered differentially expressed PCGs and lncRNAs identified a functionally relevant sub-cluster containing DANCR and SNHG6, with two novel lncRNAs and two novel PCGs. Small RNA-seq of glioblastoma tissues identified five differentially expressed microRNAs of which three interacted with the functionally relevant sub-cluster. Pathway analysis of this sub-cluster identified several glioblastoma-linked pathways, which were also previously associated with the novel cell death pathway, ferroptosis. In conclusion, the current meta-analysis strengthens evidence of an overarching involvement of ferroptosis in glioblastoma pathogenesis and also suggests some candidates for further analyses.

Single unit analysis and wide-field imaging reveal alterations in excitatory and inhibitory neurons in glioma

While several studies have attributed the development of tumour-associated seizures to an excitatory-inhibitory imbalance, we have yet to resolve the spatiotemporal interplay between different types of neuron in glioma-infiltrated cortex. Herein, we combined methods for single unit analysis of microelectrode array recordings with wide-field optical mapping of Thy1-GCaMP pyramidal cells in an ex vivo acute slice model of diffusely infiltrating glioma. This enabled simultaneous tracking of individual neurons from both excitatory and inhibitory populations throughout seizure-like events. Moreover, our approach allowed for observation of how the crosstalk between these neurons varied spatially, as we recorded across an extended region of glioma-infiltrated cortex. In tumour-bearing slices, we observed marked alterations in single units classified as putative fast-spiking interneurons, including reduced firing, activity concentrated within excitatory bursts and deficits in local inhibition. These results were correlated with increases in overall excitability. Mechanistic perturbation of this system with the mTOR inhibitor AZD8055 revealed increased firing of putative fast-spiking interneurons and restoration of local inhibition, with concomitant decreases in overall excitability. Altogether, our findings suggest that diffusely infiltrating glioma affect the interplay between excitatory and inhibitory neuronal populations in a reversible manner, highlighting a prominent role for functional mechanisms linked to mTOR activation.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

Cancer cell heterogeneity & plasticity in glioblastoma and brain tumors

Brain tumors remain one of the most difficult tumors to treat and, depending on the diagnosis, have a poor prognosis. Of brain tumors, glioblastoma (GBM) is the most common malignant glioma and has a dismal prognosis, with only about 5% of patients alive five years after diagnosis. While advances in targeted therapies and immunotherapies are rapidly improving outcomes in a variety of other cancers, the standard of care for GBM has largely remained unaltered since 2005. There are many well-studied challenges that are either unique to brain tumors (i.e., blood-brain barrier and immunosuppressive environment) or amplified within GBM (i.e., tumor heterogeneity at the cellular and molecular levels, plasticity, and cancer stem cells) that make this disease particularly difficult to treat. While we touch on all these concepts, the focus of this review is to discuss the immense inter- and intra-tumoral heterogeneity and advances in our understanding of tumor cell plasticity and epigenetics in GBM. With each improvement in technology, our understanding of the complexity of tumoral heterogeneity and plasticity improves and we gain more clarity on the causes underlying previous therapeutic failures. However, these advances are unlocking new therapeutic opportunities that scientists and physicians are currently exploiting and have the potential for new breakthroughs.

Iron commensalism of mesenchymal glioblastoma promotes ferroptosis susceptibility upon dopamine treatment

The heterogeneity of glioblastoma multiforme (GBM) leads to poor patient prognosis. Here, we aim to investigate the mechanism through which GBM heterogeneity is coordinated to promote tumor progression. We find that proneural (PN)-GBM stem cells (GSCs) secreted dopamine (DA) and transferrin (TF), inducing the proliferation of mesenchymal (MES)-GSCs and enhancing their susceptibility toward ferroptosis. PN-GSC-derived TF stimulates MES-GSC proliferation in an iron-dependent manner. DA acts in an autocrine on PN-GSC growth in a DA receptor D1-dependent manner, while in a paracrine it induces TF receptor 1 expression in MES-GSCs to assist iron uptake and thus enhance ferroptotic vulnerability. Analysis of public datasets reveals worse prognosis of patients with heterogeneous GBM with high iron uptake than those with other GBM subtypes. Collectively, the findings here provide evidence of commensalism symbiosis that causes MES-GSCs to become iron-addicted, which in turn provides a rationale for targeting ferroptosis to treat resistant MES GBM.

Glioblastoma stem-cell derived mesenchymal cells become reliant on iron but vulnerable to ferroptosis and within patients of heterogeneous glioblastoma multiforme prognosis for those with high iron uptake is poorer than other subtypes.

Integrative multi-omics approach to targeted therapy for glioblastoma

This review describes recent technological advances applied to glioblastoma (GBM), a brain tumor with dismal prognosis. International consortial efforts suggest the presence of molecular subtypes within histologically identical GBM tumors. This emphasizes that future treatment decisions should no longer be made based solely on morphological analyses, but must now take into consideration such molecular and cellular heterogeneity. The use of single-cell technologies has advanced our understanding and assignation of functional subtypes revealing therapeutic vulnerabilities. Our team has developed stratification approaches in the past few years, and we have been able to identify patient cohorts enriched for various signaling pathways. Importantly, our Glioportal brain tumor resource has been established under the National Neuroscience Institute Tissue Bank in 2021. This resource offers preclinical capability to validate working hypotheses established from patient clinical datasets. This review highlights recent developments with the ultimate goal of assigning functional meaning to molecular subtypes, revealing therapeutic vulnerabilities.

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.

Glioblastoma Stem-like Cell Detection Using Perfusion and Diffusion MRI

Simple Summary

Glioblastoma stem-like cells (GSCs) are known to be aggressive and radio-resistant and proliferate heterogeneously in preferred environments. Additionally, quantitative diffusion and perfusion MRI biomarkers provide insight into the tissue micro-environment. This study assessed the sensitivity of these imaging biomarkers to GSCs in the hyperintensities-FLAIR region, where relapses may occur. A total of 16 patients underwent an MRI session and biopsies were extracted to study the GSCs. In vivo and in vitro biomarkers were compared and both Apparent Diffusion Coefficient (ADC) and relative Cerebral Blood Volume (rCBV) MRI metrics were found to be good predictors of GSCs presence and aggressiveness.

Abstract

Purpose: With current gold standard treatment, which associates maximum safe surgery and chemo-radiation, the large majority of glioblastoma patients relapse within a year in the peritumoral non contrast-enhanced region (NCE). A subpopulation of glioblastoma stem-like cells (GSC) are known to be particularly radio-resistant and aggressive, and are thus suspected to be the cause of these relapses. Previous studies have shown that their distribution is heterogeneous in the NCE compartment, but no study exists on the sensitivity of medical imaging for localizing these cells. In this work, we propose to study the magnetic resonance (MR) signature of these infiltrative cells. Methods: In the context of a clinical trial on 16 glioblastoma patients, relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC) were measured in a preoperative diffusion and perfusion MRI examination. During surgery, two biopsies were extracted using image-guidance in the hyperintensities-FLAIR region. GSC subpopulation was quantified within the biopsies and then cultivated in selective conditions to determine their density and aggressiveness. Results: Low ADC was found to be a good predictor of the time to GSC neurospheres formation in vitro. In addition, GSCs were found in higher concentrations in areas with high rCBV. Conclusions: This study confirms that GSCs have a critical role for glioblastoma aggressiveness and supports the idea that peritumoral sites with low ADC or high rCBV should be preferably removed when possible during surgery and targeted by radiotherapy.

Alarm Signal S100-Related Signature Is Correlated with Tumor Microenvironment and Predicts Prognosis in Glioma

Glioma are the most common malignant central nervous system tumor and are characterized by uncontrolled proliferation and resistance to therapy. Dysregulation of S100 proteins may augment tumor initiation, proliferation, and metastasis by modulating immune response. However, the comprehensive function and prognostic value of S100 proteins in glioma remain unclear. Here, we explored the expression profiles of 17 S100 family genes and constructed a high-efficient prediction model for glioma based on CGGA and TCGA datasets. Immune landscape analysis displayed that the distribution of immune scores, ESTIMATE scores, and stromal scores, as well as infiltrating immune cells (macrophages M0/M1/M2, T cell CD4+ naïve, Tregs, monocyte, neutrophil, and NK activated), were significant different between risk-score subgroups. Overall, we demonstrated the value of S100 protein-related signature in the prediction of glioma patients’ prognosis and determined its relationship with the tumor microenvironment (TME) in glioma.

FLAIRectomy: Resecting beyond the Contrast Margin for Glioblastoma

The standard of care for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is maximal resection followed by chemotherapy and radiation. Studies investigating the resection of GBM have primarily focused on the contrast enhancing portion of the tumor on magnetic resonance imaging. Histopathological studies, however, have demonstrated tumor infiltration within peri-tumoral fluid-attenuated inversion recovery (FLAIR) abnormalities, which is often not resected. The histopathology of FLAIR and local recurrence patterns of GBM have prompted interest in the resection of peri-tumoral FLAIR, or FLAIRectomy. To this point, recent studies have suggested a significant survival benefit associated with safe peri-tumoral FLAIR resection. In this review, we discuss the evidence surrounding the composition of peri-tumoral FLAIR, outcomes associated with FLAIRectomy, future directions of the field, and potential implications for patients.

Intraoperative MR Imaging during Glioma Resection

One of the major issues in the surgical treatment of gliomas is the concern about maximizing the extent of resection while minimizing neurological impairment. Thus, surgical planning by carefully observing the relationship between the glioma infiltration area and eloquent area of the connecting fibers is crucial. Neurosurgeons usually detect an eloquent area by functional MRI and identify a connecting fiber by diffusion tensor imaging. However, during surgery, the accuracy of neuronavigation can be decreased due to brain shift, but the positional information may be updated by intraoperative MRI and the next steps can be planned accordingly. In addition, various intraoperative modalities may be used to guide surgery, including neurophysiological monitoring that provides real-time information (e.g., awake surgery, motor-evoked potentials, and sensory evoked potential); photodynamic diagnosis, which can identify high-grade glioma cells; and other imaging techniques that provide anatomical information during the surgery. In this review, we present the historical and current context of the intraoperative MRI and some related approaches for an audience active in the technical, clinical, and research areas of radiology, as well as mention important aspects regarding safety and types of devices.

Adapt to Persist: Glioblastoma Microenvironment and Epigenetic Regulation on Cell Plasticity

Glioblastoma (GBM) is the most frequent and aggressive brain tumor, characterized by great resistance to treatments, as well as inter- and intra-tumoral heterogeneity. GBM exhibits infiltration, vascularization and hypoxia-associated necrosis, characteristics that shape a unique microenvironment in which diverse cell types are integrated. A subpopulation of cells denominated GBM stem-like cells (GSCs) exhibits multipotency and self-renewal capacity. GSCs are considered the conductors of tumor progression due to their high tumorigenic capacity, enhanced proliferation, invasion and therapeutic resistance compared to non-GSCs cells. GSCs have been classified into two molecular subtypes: proneural and mesenchymal, the latter showing a more aggressive phenotype. Tumor microenvironment and therapy can induce a proneural-to-mesenchymal transition, as a mechanism of adaptation and resistance to treatments. In addition, GSCs can transition between quiescent and proliferative substates, allowing them to persist in different niches and adapt to different stages of tumor progression. Three niches have been described for GSCs: hypoxic/necrotic, invasive and perivascular, enhancing metabolic changes and cellular interactions shaping GSCs phenotype through metabolic changes and cellular interactions that favor their stemness. The phenotypic flexibility of GSCs to adapt to each niche is modulated by dynamic epigenetic modifications. Methylases, demethylases and histone deacetylase are deregulated in GSCs, allowing them to unlock transcriptional programs that are necessary for cell survival and plasticity. In this review, we described the effects of GSCs plasticity on GBM progression, discussing the role of GSCs niches on modulating their phenotype. Finally, we described epigenetic alterations in GSCs that are important for stemness, cell fate and therapeutic resistance.

Construction of Molecular Subtypes and Related Prognostic and Immune Response Models Based on M2 Macrophages in Glioblastoma

OBJECTIVES

To identify the molecular subtypes of glioblastoma multiforme (GBM) related to M2 macrophage-based prognostic genes, then to preliminarily explore their biological functions and construct immunotherapy response gene models.

MATERIAL AND METHODS

We used R language to analyze GBM microarray data, and other tools, including xCell and CIBERSORTx, to identify subtypes of GBM that related to M2 macrophages. The process started with the exploration of biological functions of the two subtypes by pathway analyses and GSEA, and continued with a combined procedure of constructing an M2 macrophage-related prognostic gene model and exploring the immune treatment response for GBM.

RESULTS

A high abundance of M2 macrophages in GBM was associated with poor prognosis. According to M2 macrophage-related prognostic genes, GBM was divided into two subtypes (cluster A and cluster B). The differential gene enrichment analysis of the two clusters showed that cluster A was less enriched in M2 macrophages and had immunopotential. The M2score, which was constructed based on M2 macrophage-related prognostic genes, was not only related to the survival and prognosis of patients with GBM, but also predictive of the effectiveness of immunotherapy in these patients. This result has been effectively verified in an external data set.

CONCLUSION

GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.

Detecting molecular subtypes from multi-omics datasets using SUMO

We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.

Deep learning identified glioblastoma subtypes based on internal genomic expression ranks

Background

Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles which were standardized with various procedures from different studies and to manually classify a given GBM sample into a subtype.

Methods

An algorithm was developed to unify the genomic profiles of GBM samples into a standardized normal distribution (SND), based on their internal expression ranks. Deep neural networks (DNN) and convolutional DNN (CDNN) models were trained on original and SND data. In addition, expanded SND data by combining various The Cancer Genome Atlas (TCGA) datasets were used to improve the robustness and generalization capacity of the CDNN models.

Results

The SND data kept unimodal distribution similar to their original data, and also kept the internal expression ranks of all genes for each sample. CDNN models trained on the SND data showed significantly higher accuracy compared to DNN and CDNN models trained on primary expression data. Interestingly, the CDNN models classified the NE subtype with the lowest accuracy in the GBM datasets, expanded datasets and in IDH wide type GBMs, consistent with the recent studies that NE subtype should be excluded. Furthermore, the CDNN models also recognized independent GBM datasets, even with small set of genomic expressions.

Conclusions

The GBM expression profiles can be transformed into unified SND data, which can be used to train CDNN models with high accuracy and generalization capacity. These models suggested NE subtype may be not compatible with the 4 subtypes classification system.

Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing

Glioblastoma (GBM) is the most invasive and deadliest brain cancer in adults. Its inherent heterogeneity has been designated as the main cause of treatment failure. Thus, a deeper understanding of the diversity that shapes GBM pathobiology is of utmost importance. Single-cell RNA sequencing (scRNA-seq) technologies have begun to uncover the hidden composition of complex tumor ecosystems. Herein, a semi-systematic search of reference literature databases provided all existing publications using scRNA-seq for the investigation of human GBM. We compared and discussed findings from these works to build a more robust and unified knowledge base. All aspects ranging from inter-patient heterogeneity to intra-tumoral organization, cancer stem cell diversity, clonal mosaicism, and the tumor microenvironment (TME) are comprehensively covered in this report. Tumor composition not only differs across patients but also involves a great extent of heterogeneity within itself. Spatial and cellular heterogeneity can reveal tumor evolution dynamics. In addition, the discovery of distinct cell phenotypes might lead to the development of targeted treatment approaches. In conclusion, scRNA-seq expands our knowledge of GBM heterogeneity and helps to unravel putative therapeutic targets.

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.

Proteo-transcriptomics meta-analysis identifies SUMO2 as a promising target in glioblastoma multiforme therapeutics

Background

Glioblastoma multiforme (GBM) is a deadly brain tumour with minimal survival rates due to the ever-expanding heterogeneity, chemo and radioresistance. Kinases are known to crucially drive GBM pathology; however, a rationale therapeutic combination that can simultaneously inhibit multiple kinases has not yet emerged successfully.

Results

Here, we analyzed the GBM patient data from several publicly available repositories and deduced hub GBM kinases, most of which were identified to be SUMOylated by SUMO2/3 isoforms. Not only the hub kinases but a significant proportion of GBM upregulated genes involved in proliferation, metastasis, invasion, epithelial-mesenchymal transition, stemness, DNA repair, stromal and macrophages maintenance were also identified to be the targets of SUMO2 isoform. Correlatively, high expression of SUMO2 isoform was found to be significantly associated with poor patient survival.

Conclusions

Although many natural products and drugs are evidenced to target general SUMOylation, however, our meta-analysis strongly calls for the need to design SUMO2/3 or even better SUMO2 specific inhibitors and also explore the SUMO2 transcription inhibitors for universally potential, physiologically non-toxic anti-GBM drug therapy.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02279-y.

The major highlights of this study are as follows:

Key upregulated hub kinases and coding genes in GBM are found to be targets of SUMO2 conjugation.

SUMO2 is significantly expressed in adult primary and recurrent GBMs as well as in pediatric GBM tumours.

Orthotropic xenografts from adult and pediatric GBMs confirm high expression of SUMO2 in GBM tumour samples.

SUMO2 is significantly associated with patient survival plot and pan-cancer cell fitness.

Rationale design of SUMO2 inhibitors or search for its transcriptional inhibitors is urgently required through industry-academia collaboration for an anti-GBM and potentially pan-cancer therapeutics.

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Background

The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative.

Methods

In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets.

Results

The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort).

Conclusions

Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03083-y.

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

A systematic review of extracellular vesicles as non-invasive biomarkers in glioma diagnosis, prognosis, and treatment response monitoring

The present systematic review was done to investigate the possible application of Extracellular vesicles (EVs) in the diagnosis, prognosis, and treatment response monitoring of gliomas using available literature to wrap up the final applicable conclusion in this regard. we searched PubMed/MEDLINE, Scopus, and ISI Web of Science databases. Authors evaluated the quality of the included studies by the QUADAS-2 tool. In total, 2037 published datasets were retrieved through systematic search. Upon screening for eligibility, 35 datasets were determined as eligible. Exosome was the EV-subtype described in the majority of studies, and most datasets used serum as the primary EVs isolation source. EVs isolation was primarily conducted by ultracentrifugation. 31 datasets reported that EVs hold considerable potential for being used in diagnostics, with the majority reporting different types of miRNAs as biomarkers. Besides, 8 datasets reported that EVs could be a potential source of prognostic biomarkers. And finally, 3 datasets reported that EVs might be a reliable strategy for monitoring therapy response in glioma patients. According to the findings of the current systematic review, it seems that miR-301, miR-21, and HOTAIR had the highest diagnostic accuracy. However, heterogeneous and limited evidence regarding prognosis and treatment response monitoring precludes us from drawing a practical conclusion regarding EVs.

Commentary: "Zooming in" on Glioblastoma: Understanding Tumor Heterogeneity and Its Clinical Implications in the Era of Single-Cell Ribonucleic Acid Sequencing

This article has been withdrawn due to an error that caused the article to be duplicated. The definitive version of this article is published under DOI 10.1093/neuros/nyab288.

The tweety Gene Family: From Embryo to Disease

The tweety genes encode gated chloride channels that are found in animals, plants, and even simple eukaryotes, signifying their deep evolutionary origin. In vertebrates, the tweety gene family is highly conserved and consists of three members—ttyh1, ttyh2, and ttyh3—that are important for the regulation of cell volume. While research has elucidated potential physiological functions of ttyh1 in neural stem cell maintenance, proliferation, and filopodia formation during neural development, the roles of ttyh2 and ttyh3 are less characterized, though their expression patterns during embryonic and fetal development suggest potential roles in the development of a wide range of tissues including a role in the immune system in response to pathogen-associated molecules. Additionally, members of the tweety gene family have been implicated in various pathologies including cancers, particularly pediatric brain tumors, and neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Here, we review the current state of research using information from published articles and open-source databases on the tweety gene family with regard to its structure, evolution, expression during development and adulthood, biochemical and cellular functions, and role in human disease. We also identify promising areas for further research to advance our understanding of this important, yet still understudied, family of genes.

RelB and Neuroinflammation

Neuroinflammation within the central nervous system involves multiple cell types that coordinate their responses by secreting and responding to a plethora of inflammatory mediators. These factors activate multiple signaling cascades to orchestrate initial inflammatory response and subsequent resolution. Activation of NF-κB pathways in several cell types is critical during neuroinflammation. In contrast to the well-studied role of p65 NF-κB during neuroinflammation, the mechanisms of RelB activation in specific cell types and its roles during neuroinflammatory response are less understood. In this review, we summarize the mechanisms of RelB activation in specific cell types of the CNS and the specialized effects this transcription factor exerts during neuroinflammation.

Tumor-associated hematopoietic stem and progenitor cells positively linked to glioblastoma progression

Brain tumors are typically immunosuppressive and refractory to immunotherapies for reasons that remain poorly understood. The unbiased profiling of immune cell types in the tumor microenvironment may reveal immunologic networks affecting therapy and course of disease. Here we identify and validate the presence of hematopoietic stem and progenitor cells (HSPCs) within glioblastoma tissues. Furthermore, we demonstrate a positive link of tumor-associated HSPCs with malignant and immunosuppressive phenotypes. Compared to the medullary hematopoietic compartment, tumor-associated HSPCs contain a higher fraction of immunophenotypically and transcriptomically immature, CD38- cells, such as hematopoietic stem cells and multipotent progenitors, express genes related to glioblastoma progression and display signatures of active cell cycle phases. When cultured ex vivo, tumor-associated HSPCs form myeloid colonies, suggesting potential in situ myelopoiesis. In experimental models, HSPCs promote tumor cell proliferation, expression of the immune checkpoint PD-L1 and secretion of tumor promoting cytokines such as IL-6, IL-8 and CCL2, indicating concomitant support of both malignancy and immunosuppression. In patients, the amount of tumor-associated HSPCs in tumor tissues is prognostic for patient survival and correlates with immunosuppressive phenotypes. These findings identify an important element in the complex landscape of glioblastoma that may serve as a target for brain tumor immunotherapies.

A deeper knowledge of the immune cell profile within the brain cancer tumor microenvironment (TM) could identify targets to improve immunotherapy efficacy. Here, in glioblastoma, the authors find haematopoietic stem and progenitor cells in the TM, which are associated with poor prognosis and increased immunosuppression.

Rationale and Clinical Implications of Fluorescein-Guided Supramarginal Resection in Newly Diagnosed High-Grade Glioma

Current standard of care for glioblastoma is surgical resection followed by temozolomide chemotherapy and radiation. Recent studies have demonstrated that >95% extent of resection is associated with better outcomes, including prolonged progression-free and overall survival. The diffusely infiltrative pattern of growth in gliomas results in microscopic extension of tumor cells into surrounding brain parenchyma that makes complete resection unattainable. The historical goal of surgical management has therefore been maximal safe resection, traditionally guided by MRI and defined as removal of all contrast-enhancing tumor. Optimization of surgical resection has led to the concept of supramarginal resection, or removal beyond the contrast-enhancing region on MRI. This strategy of extending the cytoreductive goal targets a tumor region thought to be important in the recurrence or progression of disease as well as resistance to systemic and local treatment. This approach must be balanced against the risk of impacting eloquent regions of brain and causing permanent neurologic deficit, an important factor affecting overall survival. Over the years, fluorescent agents such as fluorescein sodium have been explored as a means of more reliably delineating the boundary between tumor core, tumor-infiltrated brain, and surrounding cortex. Here we examine the rationale behind extending resection into the infiltrative tumor margins, review the current literature surrounding the use of fluorescein in supramarginal resection of gliomas, discuss the experience of our own institution in utilizing fluorescein to maximize glioma extent of resection, and assess the clinical implications of this treatment strategy.

Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq

BACKGROUND

Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity.

METHODS

We combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses in individual patients. We applied this approach to drug perturbations on slices derived from six glioblastoma (GBM) resections to identify conserved drug responses and to one additional GBM resection to identify patient-specific responses.

RESULTS

We used scRNA-seq to demonstrate that acute slice cultures recapitulate the cellular and molecular features of the originating tumor tissue and the feasibility of drug screening from an individual tumor. Detailed investigation of etoposide, a topoisomerase poison, and the histone deacetylase (HDAC) inhibitor panobinostat in acute slice cultures revealed cell type-specific responses across multiple patients. Etoposide has a conserved impact on proliferating tumor cells, while panobinostat treatment affects both tumor and non-tumor populations, including unexpected effects on the immune microenvironment.

CONCLUSIONS

Acute slice cultures recapitulate the major cellular and molecular features of GBM at the single-cell level. In combination with scRNA-seq, this approach enables cell type-specific analysis of sensitivity to multiple drugs in individual tumors. We anticipate that this approach will facilitate pre-clinical studies that identify effective therapies for solid tumors.

"Zooming in" on Glioblastoma: Understanding Tumor Heterogeneity and its Clinical Implications in the Era of Single-Cell Ribonucleic Acid Sequencing

Glioblastoma (GBM) is the most common primary brain malignancy in adults and one of the most aggressive of all human cancers. It is highly recurrent and treatment-resistant, in large part due to its infiltrative nature and inter- and intratumoral heterogeneity. This heterogeneity entails varying genomic landscapes and cell types within and between tumors and the tumor microenvironment (TME). In GBM, heterogeneity is a driver of treatment resistance, recurrence, and poor prognosis, representing a substantial impediment to personalized medicine. Over the last decade, sequencing technologies have facilitated deeper understanding of GBM heterogeneity by "zooming in" progressively further on tumor genomics and transcriptomics. Initial efforts employed bulk ribonucleic acid (RNA) sequencing, which examines composite gene expression of whole tumor specimens. While groundbreaking at the time, this bulk RNAseq masks the crucial contributions of distinct tumor subpopulations to overall gene expression. This work progressed to the use of bulk RNA sequencing in anatomically and spatially distinct tumor subsections, which demonstrated previously underappreciated genomic complexity of GBM. A revolutionary next step forward has been the advent of single-cell RNA sequencing (scRNAseq), which examines gene expression at the single-cell level. scRNAseq has enabled us to understand GBM heterogeneity in unprecedented detail. We review seminal studies in our progression of understanding GBM heterogeneity, with a focus on scRNAseq and the insights that it has provided into understanding the GBM tumor mass, peritumoral space, and TME. We highlight preclinical and clinical implications of this work and consider its potential to impact neuro-oncology and to improve patient outcomes via personalized medicine.

The evolution of the cancer stem cell state in glioblastoma: emerging insights into the next generation of functional interactions

Cellular heterogeneity is a hallmark of advanced cancers and has been ascribed in part to a population of self-renewing, therapeutically resistant cancer stem cells (CSCs). Glioblastoma (GBM), the most common primary malignant brain tumor, has served as a platform for the study of CSCs. In addition to illustrating the complexities of CSC biology, these investigations have led to a deeper understanding of GBM pathogenesis, revealed novel therapeutic targets, and driven innovation towards the development of next-generation therapies. While there continues to be an expansion in our knowledge of how CSCs contribute to GBM progression, opportunities have emerged to revisit this conceptual framework. In this review, we will summarize the current state of CSCs in GBM using key concepts of evolution as a paradigm (variation, inheritance, selection, and time) to describe how the CSC state is subject to alterations of cell intrinsic and extrinsic interactions that shape their evolutionarily trajectory. We identify emerging areas for future consideration, including appreciating CSCs as a cell state that is subject to plasticity, as opposed to a discrete population. These future considerations will not only have an impact on our understanding of this ever-expanding field but will also provide an opportunity to inform future therapies to effectively treat this complex and devastating disease.

Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases

PURPOSE

Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM.

PROCEDURES

Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol.

RESULTS

Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040).

CONCLUSION

Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.

Decoding Cancer Biology One Cell at a Time

Human tumors are composed of diverse malignant and nonmalignant cells, generating a complex ecosystem that governs tumor biology and response to treatments. Recent technological advances have enabled the characterization of tumors at single-cell resolution, providing a compelling strategy to dissect their intricate biology. Here we describe recent developments in single-cell expression profiling and the studies applying them in clinical settings. We highlight some of the powerful insights gleaned from these studies for tumor classification, stem cell programs, tumor microenvironment, metastasis, and response to targeted and immune therapies. SIGNIFICANCE: Intratumor heterogeneity (ITH) has been a major barrier to our understanding of cancer. Single-cell genomics is leading a revolution in our ability to systematically dissect ITH. In this review, we focus on single-cell expression profiling and lessons learned in key aspects of human tumor biology.

Residual Disease in Glioma Recurrence: A Dangerous Liaison with Senescence

With a dismally low median survival of less than two years after diagnosis, Glioblastoma (GBM) is the most lethal type of brain cancer. The standard-of-care of surgical resection, followed by DNA-damaging chemo-/radiotherapy, is often non-curative. In part, this is because individual cells close to the resection border remain alive and eventually undergo renewed proliferation. These residual, therapy-resistant cells lead to rapid recurrence, against which no effective treatment exists to date. Thus, new experimental approaches need to be developed against residual disease to prevent GBM survival and recurrence. Cellular senescence is an attractive area for the development of such new approaches. Senescence can occur in healthy cells when they are irreparably damaged. Senescent cells develop a chronic secretory phenotype that is generally considered pro-tumorigenic and pro-migratory. Age is a negative prognostic factor for GBM stage, and, with age, senescence steadily increases. Moreover, chemo-/radiotherapy can provide an additional increase in senescence close to the tumor. In light of this, we will review the importance of senescence in the tumor-supportive brain parenchyma, focusing on the invasion and growth of GBM in residual disease. We will propose a future direction on the application of anti-senescence therapies against recurrent GBM.

Aberrations of Genomic Imprinting in Glioblastoma Formation

In human glioblastoma (GBM), the presence of a small population of cells with stem cell characteristics, the glioma stem cells (GSCs), has been described. These cells have GBM potential and are responsible for the origin of the tumors. However, whether GSCs originate from normal neural stem cells (NSCs) as a consequence of genetic and epigenetic changes and/or dedifferentiation from somatic cells remains to be investigated. Genomic imprinting is an epigenetic marking process that causes genes to be expressed depending on their parental origin. The dysregulation of the imprinting pattern or the loss of genomic imprinting (LOI) have been described in different tumors including GBM, being one of the earliest and most common events that occurs in human cancers. Here we have gathered the current knowledge of the role of imprinted genes in normal NSCs function and how the imprinting process is altered in human GBM. We also review the changes at particular imprinted loci that might be involved in the development of the tumor. Understanding the mechanistic similarities in the regulation of genomic imprinting between normal NSCs and GBM cells will be helpful to identify molecular players that might be involved in the development of human GBM.

A distinct microglial subset at the tumor-stroma interface of glioma

The characterization of the tumor microenvironment (TME) in high grade gliomas (HGG) has generated significant interest in an effort to understand how neoplastic lesions in the central nervous system (CNS) are supported and to devise novel therapeutic targets. The TME of the CNS contains unique and specialized cells, including the resident myeloid cells, microglia. Myeloid involvement in HGG, such as glioblastoma, is associated with poor outcomes. Glioma-associated microglia and infiltrating monocytes/macrophages (GAM) accumulate within the neoplastic lesion where they facilitate tumor growth and drive immunosuppression. However, it has been difficult to differentiate whether microglia and macrophages have similar or distinct roles in pathology, and if the spatial organization of these cells informs outcomes. Here, we characterize the tumor-stroma border and identify peritumoral GAM (PGAM) as a unique subpopulation of GAM. Using data mining and analyses of samples derived from both murine and human sources we show that PGAM exhibit a pro-inflammatory and chemotactic phenotype that is associated with peripheral monocyte recruitment, and decreased overall survival. PGAM act as a unique subset of GAM at the tumor-stroma interface. We define a novel gene signature to identify these cells and suggest that PGAM constitute a cellular target of the TME.

The Role of Network Science in Glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

Diffuse Glioma Heterogeneity and Its Therapeutic Implications

Diffuse gliomas represent a heterogeneous group of universally lethal brain tumors characterized by minimally effective genotype-targeted therapies. Recent advances have revealed that a remarkable level of genetic, epigenetic, and environmental heterogeneity exists within each individual glioma. Together, these interconnected layers of intratumoral heterogeneity result in extreme phenotypic heterogeneity at the cellular level, providing for multiple mechanisms of therapeutic resistance and forming a highly adaptable and resilient disease. In this review, we discuss how glioma intratumoral heterogeneity and malignant cellular state plasticity drive resistance to existing therapies and look to a future in which these challenges may be overcome. SIGNIFICANCE: Glioma intratumoral heterogeneity and malignant cell state plasticity represent formidable hurdles to the development of novel targeted therapies. However, the convergence of genotypically diverse glioma cells into a limited set of epigenetically encoded transcriptional cell states may present an opportunity for a novel therapeutic strategy we call "State Selective Lethality." In this approach, cellular states (as opposed to genetic perturbations/mutations) are the subject of therapeutic targeting, and plasticity-mediated resistance is minimized through the design of cell state "trapping agents."

Differential gene expression in peritumoral brain zone of glioblastoma: role of SERPINA3 in promoting invasion, stemness and radioresistance of glioma cells and association with poor patient prognosis and recurrence

PURPOSE

Glioblastoma (GBM) is a highly invasive tumor. Despite advances in treatment modalities, tumor recurrence is common, seen mainly in the peritumoral brain zone (PBZ). We aimed to molecularly characterize PBZ, to understand the pathobiology of tumor recurrence.

METHODS/PATIENTS

We selected eight differentially regulated genes from our previous transcriptome profiling study on tumor core and PBZ. Expression of selected genes were validated in GBM (tumor core and PBZ, n = 37) and control (n = 22) samples by real time quantitative polymerase chain reaction (qPCR). Serine protease inhibitor clade A, member 3 (SERPINA3) was selected for further functional characterization in vitro by gene knockdown approach in glioma cells. Its protein expression by immunohistochemistry (IHC) was correlated with other clinically relevant GBM markers, patient prognosis and tumor recurrence.

RESULTS

The mRNA expression of selected genes from the microarray data validated in tumor core and PBZ and was similar to publicly available databases. SERPINA3 knock down in vitro showed decreased tumor cell proliferation, invasion, migration, transition to mesenchymal phenotype, stemness and radioresistance. SERPINA3 protein expression was higher in PBZ compared to tumor core and also was higher in older patients, IDH wild type and recurrent tumors. Finally, its expression showed positive correlation with poor patient prognosis.

CONCLUSIONS

SERPINA3 expression contributes to aggressive GBM phenotype by regulating pro-tumorigenic actions in vitro and is associated with adverse clinical outcome.

PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.

Personalizing Oncolytic Virotherapy for Glioblastoma: In Search of Biomarkers for Response

Simple Summary

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor. Despite multimodal treatment, the prognosis of GBM patients remains very poor. Oncolytic virotherapy is being evaluated as novel treatment for this patient group and clinical trials testing oncolytic viruses have shown impressive responses, albeit in a small subset of GBM patients. Obtaining insight into specific tumor- or patient-related characteristics of the responding patients, may in the future improve response rates. In this review we discuss factors related to oncolytic activity of the most widely applied oncolytic virus strains as well as potential biomarkers and future assays that may allow us to predict response to these agents. Such biomarkers and tools may in the future enable personalizing oncolytic virotherapy for GBM patients.

Abstract

Oncolytic virus (OV) treatment may offer a new treatment option for the aggressive brain tumor glioblastoma. Clinical trials testing oncolytic viruses in this patient group have shown promising results, with patients achieving impressive long-term clinical responses. However, the number of responders to each OV remains low. This is thought to arise from the large heterogeneity of these tumors, both in terms of molecular make-up and their immune-suppressive microenvironment, leading to variability in responses. An approach that may improve response rates is the personalized utilization of oncolytic viruses against Glioblastoma (GBM), based on specific tumor- or patient-related characteristics. In this review, we discuss potential biomarkers for response to different OVs as well as emerging ex vivo assays that in the future may enable selection of optimal OV for a specific patient and design of stratified clinical OV trials for GBM.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.