Glioblastoma Dataset Research Articles

IMMU-36. MULTI-OMIC CHARACTERIZATION OF A SYNGENEIC MOUSE CANCER STEM CELL MODEL OF GBM FOR PRECLINICAL APPLICATIONS

Abstract Glioblastoma (GBM) is resistant to many therapies including immunotherapies. There is an urgent need to explore the role of the microenvironment in resistance to therapy with tailored pre-clinical models. Using multi-omic approaches we describe a syngeneic cancer stem cell mouse model of GBM, with a spontaneous amplification of Igf2. We investigate whether Igf2 influences tumour and microenvironment cells to promote immunosuppression in GBM. Three cell lines were previously established from spontaneous brain tumours in C57Bl/6 Trp53+/-/Nf1+/- mice and maintained under neural stem cell culture conditions. Whole genome sequencing (WGS) was used to determine single nucleotide, copy number, and structural variation, revealing loss of both copies of Trp53 and Nf1 in all three cell lines. However only one cell line, mBT0309, developed tumors when orthotopically allografted. Analysis of genomic alterations and transcriptomics revealed that mBT309 exhibits amplification of the Igf2 loci and overexpression was confirmed at the RNA and protein level. Spatial and single cell transcriptomics showed that Igf2 is overexpressed in mBT0309 allografted tumours and correlated with specific transcriptomic programs. A high-parameter Imaging Mass Cytometry (IMCTM) panel was used for spatial proteomic analysis, to monitor the development of tumours in a time-course experiment. In vitro growth characteristics and stem marker expression in both transcriptomic and spatial IMC data suggest that high levels of Igf2 may regulate GBM stemness features. Analysis of human GBM datasets revealed that Igf2 amplified tumours have reduced CXCL11 expression. These findings suggest that high levels of Igf2 may influence immunosuppression by reducing T-cell recruitment. This syngeneic immunocompetent GBM stem cell model harbouring Igf2 amplification adds to the suite of models to study the GBM microenvironment and its role in immunosuppression and resistance to therapy.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation

AbstractPrecise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post‐surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time‐consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over‐segmentation or under‐segmentation of tumour regions. Introducing an interactive deep‐learning tool would empower radiologists to rectify these inaccuracies by adjusting the over‐segmented and under‐segmented voxels as needed. This paper proposes a network named Atten‐SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi‐scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten‐SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post‐operative glioblastoma dataset. The methodology outperformed state‐of‐the‐art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance‐95 got is 7.78 mm for the Uppsala University dataset.

NKG2C/KLRC2 tumor cell expression enhances immunotherapeutic efficacy against glioblastoma

BackgroundActivating and inhibitory receptors of natural killer (NK) cells such as NKp, NKG2, or CLEC are highly relevant to cold tumors including glioblastoma (GBM). Here, we aimed to characterize the...

AB047. Searching for factors relating to long-term survivors of glioblastoma.

There remains controversy in the observed survival of gliomas worldwide, especially for glioblastoma (GBM). The 5-year survival rate ranged wildly, but comparable higher in several Asian countries, such as China showed almost 18% from CONCORD-3 data. Are there any special factors relating to long-term survivors (LTSs)? We reviewed our single center real-world data for the last 20 years, of 536 GBM [World Health Organization (WHO) grade 4] patients revealed 5-year overall survival (OS) of 19.1%. We analyzed our GBM patients and searched for possible factors relating to LTSs. We collected tumor samples of 13 LTSs (OS >60 months) and 19 short-term survivors (OS <24 months), and performed whole exome sequencing and transcriptome sequencing. From treatment setting, besides surgical resection, post-operational adjutant treatment (radiotherapy plus chemotherapy) are the most important factor contributing to long-term survival. Whole exome sequencing analysis revealed a higher proportion of mutation signature 19 was associated with LTSs. Analysis of copy number variation (CNV) showed that the LTSs had higher copy number variants at the chromosomal level (P=0.049). At the arm level, the proportion of 19p amplification in the LTS was significantly higher than in the short-term survivors (P=0.001). And in The Cancer Genome Atlas (TCGA) GBM dataset, GBM patients with 19p amplification also had a better prognosis (log-rank P=0.04). Based on RNA sequencing (RNAseq) and differential expression analysis, the differentially expressed genes were enriched in hypoxia-related processes, apoptosis, and immune-related processes. From our single-institution data, the factors relating to GBM LTSs should be both clinical management and genomic alternation which could be potential novel targets be applied to future clinical practice.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma.

To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions. The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

CXCR7 activation evokes the anti-PD-L1 antibody against glioblastoma by remodeling CXCL12-mediated immunity

The interaction between glioblastoma cells and glioblastoma-associated macrophages (GAMs) influences the immunosuppressive tumor microenvironment, leading to ineffective immunotherapies. We hypothesized that disrupting the communication between tumors and macrophages would enhance the efficacy of immunotherapies. Transcriptomic analysis of recurrent glioblastoma specimens indicated an enhanced neuroinflammatory pathway, with CXCL12 emerging as the top-ranked gene in secretory molecules. Single-cell transcriptome profiling of naïve glioblastoma specimens revealed CXCL12 expression in tumor and myeloid clusters. An analysis of public glioblastoma datasets has confirmed the association of CXCL12 with disease and PD-L1 expression. In vitro studies have demonstrated that exogenous CXCL12 induces pro-tumorigenic characteristics in macrophage-like cells and upregulated PD-L1 expression through NF-κB signaling. We identified CXCR7, an atypical receptor for CXCL12 predominantly present in tumor cells, as a negative regulator of CXCL12 expression by interfering with extracellular signal-regulated kinase activation. CXCR7 knockdown in a glioblastoma mouse model resulted in worse survival outcomes, increased PD-L1 expression in GAMs, and reduced CD8+ T-cell infiltration compared with the control group. Ex vivo T-cell experiments demonstrated enhanced cytotoxicity against tumor cells with a selective CXCR7 agonist, VUF11207, reversing GAM-induced immunosuppression in a glioblastoma cell-macrophage-T-cell co-culture system. Notably, VUF11207 prolonged survival and potentiated the anti-tumor effect of the anti-PD-L1 antibody in glioblastoma-bearing mice. This effect was mitigated by an anti-CD8β antibody, indicating the synergistic effect of VUF11207. In conclusion, CXCL12 conferred immunosuppression mediated by pro-tumorigenic and PD-L1-expressing GAMs in glioblastoma. Targeted activation of glioblastoma-derived CXCR7 inhibits CXCL12, thereby eliciting anti-tumor immunity and enhancing the efficacy of anti-PD-L1 antibodies.

Immunotherapy drives mesenchymal tumor cell state shift and TME immune response in glioblastoma patients.

Glioblastoma is a highly aggressive type of brain tumor for which there is no curative treatment available. Immunotherapies have shown limited responses in unselected patients, and there is an urgent need to identify mechanisms of treatment resistance to design novel therapy strategies. Here we investigated the phenotypic and transcriptional dynamics at single-cell resolution during nivolumab immune checkpoint treatment of glioblastoma patients. We present the integrative paired single-cell RNA-seq analysis of 76 tumor samples from patients in a clinical trial of the PD-1 inhibitor nivolumab and untreated patients. We identify a distinct aggressive phenotypic signature in both tumor cells and the tumor microenvironment in response to nivolumab. Moreover, nivolumab-treatment was associated with an increased transition to mesenchymal stem-like tumor cells, and an increase in TAMs and exhausted and proliferative T cells. We verify and extend our findings in large external glioblastoma dataset (n = 298), develop a latent immune signature and find 18% of primary glioblastoma samples to be latent immune, associated with mesenchymal tumor cell state and TME immune response. Finally, we show that latent immune glioblastoma patients are associated with shorter overall survival following immune checkpoint treatment (P = .0041). We find a resistance mechanism signature in one fifth of glioblastoma patients associated with a tumor-cell transition to a more aggressive mesenchymal-like state, increase in TAMs and proliferative and exhausted T cells in response to immunotherapy. These patients may instead benefit from neuro-oncology therapies targeting mesenchymal tumor cells.

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells.

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

AbstractWith the increasing availability of biomedical data from multiple platforms of the same patients in clinical research, such as epigenomics, gene expression, and clinical features, there is a growing need for statistical methods that can jointly analyze data from different platforms to provide complementary information for clinical studies. In this paper, we propose a two‐stage hierarchical Bayesian model that integrates high‐dimensional biomedical data from diverse platforms to select biomarkers associated with clinical outcomes of interest. In the first stage, we use Expectation Maximization‐based approach to learn the regulating mechanism between epigenomics (e.g., gene methylation) and gene expression while considering functional gene annotations. In the second stage, we group genes based on the regulating mechanism learned in the first stage. Then, we apply a group‐wise penalty to select genes significantly associated with clinical outcomes while incorporating clinical features. Simulation studies suggest that our model‐based data integration method shows lower false positives in selecting predictive variables compared with existing method. Moreover, real data analysis based on a glioblastoma (GBM) dataset reveals our method's potential to detect genes associated with GBM survival with higher accuracy than the existing method. Moreover, most of the selected biomarkers are crucial in GBM prognosis as confirmed by existing literature.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis. Several nano-based drug delivery studies are focused on for tumor diagnosis. But, the nano-based drug delivery efficiency was not increased for tumor diagnosis. This work proposes a method called point biserial correlation symbiotic organism search nanoengineering-based drug delivery (PBC-SOSN). The objective and aim of the PBC-SOSN method is to achieve higher drug delivery efficiency and lesser drug delivery time for tumor diagnosis. The contribution of the PBC-SOSN is to optimized nanonengineering-based drug delivery with higher r drug delivery detection rate and smaller drug delivery error detection rate. Initially, raw data acquired from the nano-tumor dataset, and nano-drugs for glioblastoma dataset, overhead improved preprocessed samples are evolved using nano variational model decomposition-based preprocessing. After that, the preprocessed samples as input are subjected to variance analysis and point biserial correlation-based feature selection model. Finally, the preprocessed samples and features selected are subjected to symbiotic organism search nanoengineering (SOSN) to corroborate the objective. Based on these findings, point biserial correlation-based feature selection and a symbiotic organism search nanoengineering were tested for their modeling performance with a nano-tumor dataset and nano-drugs for glioblastoma dataset, finding the latter the better algorithm. Incorporated into the method is the potential to adjust the drug delivery detection rate and drug delivery error detection rate of the learned method based on selected features determined by nano variational model decomposition for efficient drug delivery.

A novel prognostic risk-scoring system based on m5C methylation regulator-mediated patterns for glioma patients

N5-methylcytosine (m5C) methylation modification plays a crucial role in the epigenetic mechanisms underlying tumorigenesis, aggressiveness, and malignancy in diffuse glioma. Our study aimed to develop a novel prognostic risk-scoring system to assess the impact of m5C modification in glioma patients. Initially, we identified two distinct m5C clusters based on the expression level of m5C regulators in The Cancer Genome Atlas glioblastoma (TCGA-GBM) dataset. Differentially expressed genes (DEGs) between the two m5C cluster groups were determined. Utilizing these m5C regulation-related DEGs, we classified glioma patients into three gene cluster groups: A, B, and C. Subsequently, an m5C scoring system was developed through a univariate Cox regression model, quantifying the m5C modification patterns utilizing six DEGs associated with disease prognosis. The resulting scoring system allowed us to categorize patients into high- or low-risk groups based on their m5C scores. In test (TCGA-GBM) and validation (Chinese Glioma Genome Atlas [CGGA]-1018 and CGGA-301) datasets, glioma patients with a higher m5C score consistently exhibited shorter survival durations, fewer isocitrate dehydrogenase (IDH) mutations, less 1p/19q codeletion and higher World Health Organization (WHO) grades. Additionally, distinct immune cell infiltration characteristics were observed among different m5C cluster groups and risk groups. Our study developed a novel prognostic scoring system based on m5C modification patterns for glioma patients, complementing existing molecular classifications and providing valuable insights into prognosis for glioma patients.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

EPID-15. SEX-SPECIFIC DIFFERENCES IN GLIOBLASTOMA IN THE RESPOND CONSORTIUM

Abstract AIM The goal of this study was to understand sex-specific differences in the molecular, clinical and radiological tumor parameters and survival outcomes of Glioblastoma (GBM) patients within the international GBM dataset, known as the ReSPOND (Radiomic Signatures for PrecisiON Diagnostics) consortium. METHODS Sex-based differences were retrospectively studied in 1922 GBM patients from the ReSPOND consortium which includes information from over 14 institutions across 3 continents. The parameters include age, Methylguanine-DNA Methyltransferase (MGMT) promoter methylation status, isocitrate dehydrogenase 1 (IDH1) mutation status, Karnofsky performance status (KPS), extent of resection (EOR), tumor epicenter, volumes, laterality and spatial extent. Non-parametric tests, log-rank test and cox-proportional hazard analysis were performed to understand sex-based differences in tumor parameters, survival rates and hazard ratios. Spatial atlases were generated to understand radiological parameters such as tumor spatial extent. RESULTS GBM in was diagnosed at a median age of 62.6 years in females compared to 61 years in males (p = 0.001). Additionally, 44% females compared to 37% males (p = 0.04) had methylated MGMT and 79% females compared to 73% males (p = 0.004) had IDH1 wildtype. The tumor volumes were smaller in females (necrotic core, edema, and enhancing tumor) compared to males. Females exhibited a higher prevalence of right hemisphere (39.6%) and right temporal lobe tumors (19.7%), while males showed a higher prevalence of left hemisphere (40.3%) left temporal lobe tumors (23.7%). No significant sex-based differences in OS and PFS was observed in overall sample, although longer PFS was observed in elderly (above 60 years) female patients. CONCLUSION This is a first international large cohort study looking at sex-based differences in GBM patients using the ReSPOND consortium data. Several sex-specific differences in the distribution of various tumor phenotypes were noted, however sex was not a contributing factor in OS and PFS.

CSIG-09. RADIOTHERAPY WITH CONCURRENT INHIBITION OF CHEK2 ACTIVATES STING IN GLIOMAS

Abstract Using an in vivo CRISPR screen approach, we demonstrated the contribution of glioma cell intrinsic checkpoint kinase 2 (Chek2) in the evasion of tumor cells from CD8+ T cell recognition. Genetic depletion or pharmacological inhibition of Chek2 with blood-brain-barrier permeable drugs that are being evaluated in clinical trials, in combination with standard of care radiotherapy and PD-1/PD-L1 blockade, led to survival benefit in multiple preclinical glioma models. Moreover, we observed that Chek2 inhibition or depletion is associated with activation of cytosolic DNA-sensing STING pathway in the glioma cells. A positive correlation was identified between STING pathway activity and T cell infiltration in the TCGA glioblastoma dataset. STING activation has been shown to promote robust immune response in glioblastoma. Building upon these results, we evaluated the impact of radiotherapy on Chek2 and STING signaling. After applying 2 Gy radiotherapy, a 20 fold increase of Chek2 activation was observed within the first hour in the in vitro grown glioblastoma patient derived cells, specifically GBM6 and GBM43 cells. Chek2 activation levels returned to the baseline within 6 hours in these cells. However, STING activation was moderate to absent at different time intervals within the first 48 hours post 2 Gy radiotherapy. Interestingly, when the in vitro-grown GBM6 cells were pretreated with Chek1/2 inhibitor prexasertib for 48 hours prior to the 2 Gy radiotherapy exposure, there was 10 fold reduction in the activation of Chek2 as compared to the radiotherapy alone group. Concomitantly, a 2-fold increase in the activation of STING was noted for up to 48 h in the pretreated group, as compared to the radiotherapy alone group. Our in vitro findings suggest an inverse relationship between the activation of Chek2 and STING. In conclusion, radiotherapy with concurrent inhibition of Chek2 offers a novel therapeutic combination to modulate STING activation in gliomas.

Identification of circulating miRNA as early diagnostic molecular markers in malignant glioblastoma base on decision tree joint scoring algorithm.

The lack of clinical markers prevents early diagnosis of glioblastoma (GBM). Many studies have found that circulating microRNAs (miRNAs) can be used as early diagnostic markers of malignant tumours. Therefore, the identification of novel circulating miRNA biomolecular markers could be beneficial to clinicians in the early diagnosis of GBM. We developed a decision tree joint scoring algorithm (DTSA), systematically integrating significance analysis of microarray (SAM), Pearson hierarchical clustering, T test, Decision tree and Entropy weight score algorithm, to screen out circulating miRNA molecular markers with high sensitivity and accuracy for early diagnosis of GBM. DTSA was developed and applied for GBM datasets and three circulating miRNA molecular markers were identified, namely, hsa-miR-2278, hsa-miR-555 and hsa-miR-892b. We have found that hsa-miR-2278 and hsa-miR-892b regulate the GBM pathway through target genes, promoting the development of GBM and affecting the survival of patients. DTSA has better classification effect in all data sets than other classification algorithms, and identified miRNAs are better than existing markers of GBM. These results suggest that DTSA can effectively identify circulating miRNA, thus contributing to the early diagnosis and personalised treatment of GBM.

Epigenetic Activation of TUSC3 Sensitizes Glioblastoma to Temozolomide Independent of MGMT Promoter Methylation Status.

Temozolomide (TMZ) is an important first-line treatment for glioblastoma (GBM), but there are limitations to TMZ response in terms of durability and dependence on the promoter methylation status of the DNA repair gene O6-methylguanine DNA methyltransferase (MGMT). MGMT-promoter-hypermethylated (MGMT-M) GBMs are more sensitive to TMZ than MGMT-promoter-hypomethylated (MGMT-UM) GBMs. Moreover, TMZ resistance is inevitable even in TMZ-sensitive MGMT-M GBMs. Hence, epigenetic reprogramming strategies are desperately needed in order to enhance TMZ response in both MGMT-M and MGMT-UM GBMs. In this study, we present novel evidence that the epigenetic reactivation of Tumor Suppressor Candidate 3 (TUSC3) can reprogram sensitivity of GBM stem cells (GSCs) to TMZ irrespective of MGMT promoter methylation status. Interrogation of TCGA patient GBM datasets confirmed TUSC3 promoter regulation of TUSC3 expression and also revealed a strong positive correlation between TUSC3 expression and GBM patient survival. Using a combination of loss-of-function, gain-of-function and rescue studies, we demonstrate that TUSC3 reactivation is associated with enhanced TMZ response in both MGMT-M and MGMT-UM GSCs. Further, we provide novel evidence that the demethylating agent 5-Azacitidine (5-Aza) reactivates TUSC3 expression in MGMT-M GSCs, whereas the combination of 5-Aza and MGMT inhibitor Lomeguatrib is necessary for TUSC3 reactivation in MGMT-UM GSCs. Lastly, we propose a pharmacological epigenetic reactivation strategy involving TUSC3 that leads to significantly prolonged survival in MGMT-M and MGMT-UM orthotopic GSCs models. Collectively, our findings provide a framework and rationale to further explore TUSC3-mediated epigenetic reprogramming strategies that could enhance TMZ sensitivity and outcomes in GBM. Mechanistic and translational evidence gained from such studies could contribute towards optimal design of impactful trials for MGMT-UM GBMs that currently do not have good treatment options.

The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.

Development and Validation of CXCR4 Nomogram-Based Immune Infiltration/Tumor Inflammation in Primary Glioblastoma.

Glioblastoma (GBM) is a highly malignant and aggressive tumor with poor prognosis. Therefore, the discovery of new prognostic molecular markers is of great significance for clinical prognosis. The CXC chemokine receptor (CXCR) members play a key regulatory role in many cancers. In this study, we explore the clinical value and application of the CXCR members in primary glioblastoma. Two GBM datasets from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases were used to explore the relationship between differential expression of CXCRs and GBM subtypes as well as immune infiltration. C-X-C motif chemokine receptor 4 (CXCR4) was screened as an independent prognostic factor, and a nomogram and risk prediction model were developed and tested in the CGGA database using the TCGA database. Receiver operating curve (ROC) and decision curve analysis (DCA) found good accuracy and net benefit of the models. The correlation of CXCR4 with immune infiltration and tumor was analyzed using CancerSEA and TIMER. In in vitro experiments, we found that CXCR4 was significantly overexpressed in glioblastoma and was closely related to the inflammatory response of U251/U87 cells. CXCR4 is an excellent independent prognostic factor for glioblastoma and positively correlates with tumor inflammation.

Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BackgroundHomeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated.MethodsHOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan–Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan–Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts.ResultsHOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts.ConclusionHOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients.