Glioblastoma (GBM) and solitary brain metastasis (SBM) exhibit similar radiomics features on magnetic resonance imaging (MRI), yet their treatment strategies and prognoses significantly differ. Therefore, accurate differentiation between these two types of tumors is crucial for clinical decision-making. This study aims to establish and validate an efficient diagnostic model based on the radiomic features of the T1-weighted contrast-enhanced (T1CE) sequence in the 10 mm brain-tumor interface region to achieve precise differentiation between GBM and SBM.

This study retrospectively collected contrast-enhanced T1-weighted imaging data from 226 GBM patients and 206 SBM patients at three centers between January 2010 and October 2024. Samples from centers 1 and 2 were used as the training set, while samples from center 3 were used as the test set. Two observers manually delineated the tumor edges on the T1CE images layer by layer to obtain the Region of Interest (ROI) covering the entire tumor volume. A 10 mm brain-to-tumor interface (BTI) was extracted using Python code. Radiomic features were extracted from the 10 mm BTI region, followed by feature selection and model construction. Finally, SHAP (SHapley Additive exPlanations) was used to visualize the model. Three radiologists with 2, 6, and 18 years of diagnostic experience independently evaluated the test set samples without knowing the patient information or pathology results, establishing three diagnostic models. The DeLong test was used to compare these models with the radiomic model.

Ultimately, ten radiomic features were used for modeling. The model established using the logistic regression (LR) algorithm had an AUC of 0.893 on the training set and 0.808 on the test set. The AUCs of the three radiologists with different diagnostic experiences on the test set were 0.699, 0.740, and 0.789, respectively, all lower than that of the radiomic model. The DeLong test showed that ModelBTI performed significantly better than Doctor 1 (p<0.05) in the test set, but there was no statistically significant difference in performance between ModelBTI and Doctors 2 and 3.

The radiomic model constructed based on the 10 mm brain-tumor interface can effectively differentiate between GBM and SBM, capturing tumor heterogeneity from a new perspective, thereby significantly improving diagnostic performance and providing assistance for clinical diagnosis.

The original contributions presented in the study are included in the article/Supplemental material, further inquiries can be directed to the corresponding authors.

Glioblastoma (GBM) is recognized as the most lethal primary malignant tumor of the central nervous system. Although traditional treatments can somewhat prolong patient survival, the overall prognosis remains grim. Immunotherapy has become an effective method for GBM treatment. Oncolytic virus, checkpoint inhibitors, CAR T cells and tumor vaccines have all been applied in this field. Moreover, the combining of immunotherapy with traditional radiotherapy, chemotherapy, or gene therapy can further improve the treatment outcome. This review systematically summarizes the features of GBM, the recent progress of immunotherapy in overcoming GBM.

Glioblastoma (GBM) is a highly aggressive and treatment-resistant brain tumor. Recent advancements have highlighted the crucial role of long noncoding RNAs (lncRNAs) in GBM's molecular biology. Unlike protein-coding RNAs, lncRNAs regulate gene expression through transcription, post-transcriptional modifications, and chromatin remodeling. Some lncRNAs, like HOTAIR, CCAT2, CRNDE, and MALAT1, promote GBM development by affecting tumor suppressors and various signaling pathways like PI3K/Akt, mTOR, EGFR, NF-κB, and Wnt/β-catenin. Conversely, certain lncRNAs such as TUG1, MEG3, and GAS8-AS1 act as tumor suppressors and are associated with better prognosis. The study presented in the manuscript aims to explore the involvement of lncRNAs in GBM, focusing on their roles in tumor progression, proliferation, invasion, and potential implications for early detection and immunotherapy. The research seeks to elucidate the mechanisms by which specific lncRNAs influence GBM characteristics and highlight their potential as therapeutic targets or biomarkers in managing this aggressive form of brain cancer.

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

We retrospectively collected enhanced T1-weighted imaging data from 97 patients with glioblastoma (GBM) and 90 patients with single brain metastasis (SBM) between 2010 and 2024. Machine learning is used to establish multiple diagnostic models for discriminating GBM and SBM based on texture features of the entire tumor and 10 mm tumor-to-brain interface regions. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis(KW) and Logistic Regression(LR), the AUC was highest using the "one-standard error" rule. '10mm_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. The best models in the training set, test set, and validation set were not the same. In the test set, the KW1LR model had the highest AUC of 0.880 and an accuracy of 0.824.

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

Energy is necessary for tumor cell viability and growth. Aerobic glucose-driven lactic acid fermentation is a common metabolic phenotype seen in most cancers including malignant gliomas. This metabolic phenotype is linked to abnormalities in mitochondrial structure and function. A luciferin-luciferase bioluminescence ATP assay was used to measure the influence of amino acids, glucose, and oxygen on ATP content and viability in mouse (VM-M3 and CT-2A) and human (U-87MG) glioma cells that differed in cell biology, genetic background, and species origin. Oxygen consumption was measured using the Resipher system. Extracellular lactate and succinate were measured as end products of the glycolysis and glutaminolysis pathways, respectively. The results showed that: (1) glutamine was a source of ATP content irrespective of oxygen. No other amino acid could replace glutamine in sustaining ATP content and viability; (2) ATP content persisted in the absence of glucose and under hypoxia, ruling out substantial contribution through either glycolysis or oxidative phosphorylation (OxPhos) under these conditions; (3) Mitochondrial complex IV inhibition showed that oxygen consumption was not an accurate measure for ATP production through OxPhos. The glutaminase inhibitor, 6-diazo-5-oxo-L-norleucine (DON), reduced ATP content and succinate export in cells grown in glutamine. The data suggests that mitochondrial substrate level phosphorylation in the glutamine-driven glutaminolysis pathway contributes to ATP content in these glioma cells. A new model is presented highlighting the synergistic interaction between the high-throughput glycolysis and glutaminolysis pathways that drive malignant glioma growth and maintain ATP content through the aerobic fermentation of both glucose and glutamine.

Glioblastoma (GBM) is the most aggressive and lethal central nervous system (CNS) tumor. The treatment strategy is mainly surgery and/or radiation therapy, both combined with adjuvant temozolomide (TMZ) chemotherapy. Historically, methylation of MGMT gene promoter is used as the major biomarker predicting individual tumor response to TMZ.

This research aimed to analyze genes and molecular pathways of DNA repair as biomarkers for sensitivity to TMZ treatment in GBM using updated The Cancer Genome Atlas (TCGA) data and validate the results on experimental datasets.

Survival analysis of GBM patients under TMZ therapy and hazard ratio (HR) calculation were used to assess all putative biomarkers on World Health Organization CNS5 reclassified TCGA project collection of molecular profiles and experimental multicenter GBM patient cohort. Pathway activation levels were calculated for 38 DNA repair pathways. TMZ sensitivity pathway was reconstructed using a human interactome model built using pairwise interactions extracted from 51,672 human molecular pathways.

We found that expression/activation levels of seven and six emerging gene/pathway biomarkers served as high-quality positive (HR < 0.61) and negative (HR > 1.63), respectively, patient survival biomarkers performing better than MGMT methylation. Positive survival biomarkers were enriched in the processes of ATM-dependent checkpoint activation and cell cycle arrest whereas negative-in excision DNA repair. We also built and characterized gene pathways which were informative for GBM patient survival following TMZ administration (HR 0.18-0.44, p < 0.0009; area under the curve 0.68-0.9).

In this study, a comprehensive analysis of the expression of 361 DNA repair genes and activation levels of 38 DNA repair pathways revealed 13 potential survival biomarkers with increased prognostic potential compared to MGMT methylation. We algorithmically reconstructed the TMZ sensitivity pathway with strong predictive capacity in GBM.

Background/Objectives: Glioblastomas (GBMs) are dreadful brain tumors with abysmal survival outcomes. GBM extracellular vesicles (EVs) dramatically affect normal brain cells (largely astrocytes) constituting the tumor microenvironment (TME). We asked if EVs from different GBM patient-derived spheroid lines would differentially alter recipient brain cell phenotypes. This turned out to be the case, with the net outcome of treatment with GBM EVs nonetheless converging on increased tumorigenicity. Methods: GBM spheroids and brain slices were derived from neurosurgical patient tissues following informed consent. Astrocytes were commercially obtained. EVs were isolated from conditioned culture media by ultrafiltration, concentration, and ultracentrifugation. EVs were characterized by nanoparticle tracking analysis, electron microscopy, biochemical markers, and proteomics. Astrocytes/brain tissues were treated with GBM EVs before downstream analyses. Results: EVs from different GBMs induced brain cells to alter secretomes with pro-inflammatory or TME-modifying (proteolytic) effects. Astrocyte responses ranged from anti-viral gene/protein expression and cytokine release to altered extracellular signal-regulated protein kinase (ERK1/2) signaling pathways, and conditioned media from EV-treated cells increased GBM cell proliferation. Conclusions: Astrocytes/brain slices treated with different GBM EVs underwent non-identical changes in various omics readouts and other assays, indicating "personalized" tumor-specific GBM EV effects on the TME. This raises concern regarding reliance on "model" systems as a sole basis for translational direction. Nonetheless, net downstream impacts from differential cellular and TME effects still led to increased tumorigenic capacities for the different GBMs.

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. The UPENN-GBM dataset was used for model development (70%) and internal validation (30%), while the UCSF-PDGM dataset served as an external test set. The NGBoost Survival model was developed to generate continuous probability estimates as well as predictions for 6-, 12-, 18-, and 24-month OS. (3) Results: The NGBoost Survival model successfully predicted survival, achieving a C-index of 0.801 on internal validation and 0.725 on external validation. For 6-month OS, the model attained an AUROC of 0.791 (95% CI: 0.742-0.832) and 0.708 (95% CI: 0.654-0.748) for internal and external validation, respectively. (4) Conclusions: The radiomics-based ML model demonstrates potential to improve the prediction of OS for patients with GBM.

The emergence of programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) immunosuppressants provides new therapeutic directions for various advanced malignant cancers. At present, PD-1/PD-L1 immunosuppressants have made significant progress in clinical trials of some gliomas, but PD-1/PD-L1 inhibitors have not yet shown convincing clinical efficacy in gliomas. This article summarizes the research progress of the PD-1 /PD-L1 pathway in gliomas through the following three aspects. It mainly includes the complex expression levels and regulatory mechanisms of PD-1/PD-L1 in the glioma microenvironment, the immune infiltration in glioma immunosuppressive microenvironment, and research progress on the application of PD-1/PD-L1 immunosuppressants in clinical treatment trials for gliomas. This will help to understand the current treatment progress and future research directions better.

Brain tumors have a poor prognosis. Early, accurate diagnosis and treatment are crucial. Although brain surgical biopsy can provide an accurate diagnosis, it is highly invasive and risky and is not suitable for follow-up examination. Blood-based liquid biopsies have a low detection rate of tumor biomarkers and limited evaluation ability due to the existence of the blood-brain barrier (BBB). The BBB is composed of brain capillary endothelial cells through tight junctions, which prevents the release of brain tumor markers to the human peripheral circulation, making it more difficult to diagnose, predict prognosis, and evaluate therapeutic response through brain tumor markers than other tumors. Focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) is an emerging technique using FUS to promote the release of tumor markers into the circulatory system and cerebrospinal fluid, thus facilitating tumor detection. The feasibility and safety data from both animal models and clinical trials support sonobiopsy as a great potential in the diagnosis of brain diseases.

Inhibiting epigenetic modulators can transcriptionally reactivate transposable elements (TEs). These TE transcripts often generate unique peptides that can serve as immunogenic antigens for immunotherapy. Here, we ask whether TEs activated by epigenetic therapy could appreciably increase the antigen repertoire in glioblastoma, an aggressive brain cancer with low mutation and neoantigen burden. We treated patient-derived primary glioblastoma stem cell lines, an astrocyte cell line and primary fibroblast cell lines with epigenetic drugs, and identified treatment-induced, TE-derived transcripts that are preferentially expressed in cancer cells. We verified that these transcripts could produce human leukocyte antigen class I-presented antigens using liquid chromatography with tandem mass spectrometry pulldown experiments. Importantly, many TEs were also transcribed, even in proliferating nontumor cell lines, after epigenetic therapy, which suggests that targeted strategies like CRISPR-mediated activation could minimize potential side effects of activating unwanted genomic regions. The results highlight both the need for caution and the promise of future translational efforts in harnessing treatment-induced TE-derived antigens for targeted immunotherapy.

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

Recent clinical trials have highlighted the limited efficacy of T cell-based immunotherapy in patients with glioblastoma (GBM). To better understand the characteristics of tumor-infiltrating lymphocytes (TIL) in GBM, we performed cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing with paired V(D)J sequencing, respectively, on TILs from two cohorts of patients totaling 15 patients with high-grade glioma, including GBM or astrocytoma, IDH-mutant, grade 4 (G4A). Analysis of the CD8+ TIL landscape reveals an enrichment of clonally expanded GZMK+ effector T cells in the tumor compared with matched blood, which was validated at the protein level. Furthermore, integration with other cancer types highlights the lack of a canonically exhausted CD8+ T-cell population in GBM TIL. These data suggest that GZMK+ effector T cells represent an important T-cell subset within the GBM microenvironment and may harbor potential therapeutic implications.

To understand the limited efficacy of immune-checkpoint blockade in GBM, we applied a multiomics approach to understand the TIL landscape. By highlighting the enrichment of GZMK+ effector T cells and the lack of exhausted T cells, we provide a new potential mechanism of resistance to immunotherapy in GBM. This article is featured in Selected Articles from This Issue, p. 897.

Maximum safe surgical resection followed by adjuvant chemoradiation and temozolomide chemotherapy is the current standard of care in the management of newly diagnosed high grade glioma. However, there are controversies about the optimal number of adjuvant temozolomide cycles. This study aimed to compare the survival benefits of 12 cycles against 6 cycles of adjuvant temozolomide adults with newly diagnosed high grade gliomas.

Adult patients with newly diagnosed high grade gliomas, and a Karnofsky performance status>60%, were randomized to receive either 6 cycles or 12 cycles of adjuvant temozolomide. Patients were followed-up for assessment of overall survival (OS) and progression-free survival (PFS) by brain MRI every 3 months within the first year after treatment and then every six months.

A total of 100 patients (6 cycles, 50; 12 cycles, 50) were entered. The rate of treatment completion in 6 cycles and 12 cycles groups were 91.3% and 55.1%, respectively. With a median follow-up of 26 months, the 12-, 24-, 36-, and 48-month OS rates in 6 cycles and 12 cycles groups were 81.3% vs 78.8%, 58.3% vs 49.8%, 47.6% vs 34.1%, and 47.6% vs 31.5%, respectively (p-value=.19). Median OS of 6 cycles and 12 cycles groups were 35 months (95% confidence interval (CI), 11.0 to 58.9) and 23 months (95%CI, 16.9 to 29.0). The 12-, 24-, 36-, and 48- month PFS rates in 6 cycles and 12 cycles groups were 70.8% vs 56.9%, 39.5% and 32.7%, 27.1% vs 28.8%, and 21.1% vs 28.8%, respectively (p=.88). The Median PFS of 6 cycles and 12 cycles groups was 18 months (95% CI, 14.8 to 21.1) and 16 (95% CI, 11.0 to 20.9) months.

Patients with newly diagnosed high grade gliomas treated with adjuvant temozolomide after maximum safe surgical resection and adjuvant chemoradiation do not benefit from extended adjuvant temozolomide beyond 6 cycles.

Prospectively registered with the Iranian Registry of Clinical Trials: IRCT20160706028815N3. Date registered: 18/03/14.

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood.

To address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue.

Through the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs.

This study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.

This study aimed to investigate the effects of tetrahydrolipstatin (orlistat) on heterotopic glioblastoma in mice by applying MRI and correlating the results with histopathology and immunochemistry.

Human glioblastoma cells were injected subcutaneously into the groins of immunodeficient mice. After tumor growth of >150 mm3, the animals were assigned into a treatment group (n = 6), which received daily intraperitoneal injections of orlistat, and a control group (n = 7). MRI was performed at the time of randomization and before euthanizing the animals. Tumor volumes were calculated, and signal intensities were analyzed. The internal tumor structure was evaluated visually and with texture analysis. Western blotting and protein expression analysis were performed.

At histology, all tumors showed high mitotic and proliferative activity (Ki67 ≥ 10%). Reduced fatty acid synthetase expression was measured in the orlistat group (p < 0.05). Based on the results of morphologic MRI-based analysis, tumor growth remained concentric in the control group and changed to eccentric in the treatment group (p < 0.05). The largest area under the receiver operating curve of the predictors derived from the texture analysis of T2w images was for wavelet transform parameters WavEnHL_s3 and WavEnLH_s4 at 0.96 and 1.00, respectively.

Orlistat showed effects on heterotopically implanted glioblastoma multiforme in MRI studies of mice based on morphologic and texture analysis.

In the dynamic landscape of glioblastoma, the 2021 World Health Organization Classification of Central Nervous System tumours endeavoured to establish biological homogeneity, yet isocitrate dehydrogenase-wild-type (IDH-wt) glioblastoma persists as a tapestry of clinical and molecular diversity. Intertumoural heterogeneity in IDH-wt glioblastoma presents a formidable challenge in treatment strategies. Recent strides in genetics and molecular biology have enhanced diagnostic precision, revealing distinct subtypes and invasive patterns that influence survival in patients with IDH-wt glioblastoma. Genetic and molecular biomarkers, such as the overexpression of neurofibromin 1, phosphatase and tensin homolog and/or cyclin-dependent kinase inhibitor 2A, along with specific immune cell abundance and neurotransmitters, correlate with favourable outcomes. Conversely, increased expression of epidermal growth factor receptor tyrosine kinase, platelet-derived growth factor receptor alpha and/or vascular endothelial growth factor receptor, coupled with the prevalence of glioma stem cells, tumour-associated myeloid cells, regulatory T cells and exhausted effector cells, signifies an unfavourable prognosis. The methylation status of O6-methylguanine-DNA methyltransferase and the influence of microenvironmental factors and neurotransmitters further shape treatment responses. Understanding intertumoural heterogeneity is complemented by insights into intratumoural dynamics and cellular interactions within the tumour microenvironment. Glioma stem cells and immune cell composition significantly impact progression and outcomes, emphasizing the need for personalized therapies targeting pro-tumoural signalling pathways and resistance mechanisms. A successful glioblastoma management demands biomarker identification, combination therapies and a nuanced approach considering intratumoural variability. These advancements herald a transformative era in glioblastoma comprehension and treatment.

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

Severe tumor heterogeneity drives the aggressive and treatment refractory nature of glioblastomas (GBMs). While limiting GBM heterogeneity offers promising therapeutic potential, the underlying mechanisms that regulate GBM plasticity remain poorly understood. We utilized 14 patient-derived and four commercially available cell lines to uncover miR-194-3p as a key epigenetic determinant of stemness and transcriptional subtype in GBM. We demonstrate that miR-194-3p degrades TAB2, an important mediator of NF-κB activity, decreasing NF-κB transcriptional activity. The loss in NF-κB activity following miR-194-3p overexpression or TAB2 silencing decreased expression of induced pluripotent stem cell (iPSC) genes, inhibited the oncogenic IL-6/STAT3 signaling axis, suppressed the mesenchymal transcriptional subtype in relation to the proneural subtype, and induced differentiation from the glioma stem cell (GSC) to monolayer (ML) phenotype. miR-194-3p/TAB2/NF-κB signaling axis acts as an epigenetic switch that regulates GBM plasticity and targeting this signaling axis represents a potential strategy to limit transcriptional heterogeneity in GBMs.

The fusion of multi-modal data, e.g., pathology slides and genomic profiles, can provide complementary information and benefit glioma grading. However, genomic profiles are difficult to obtain due to the high costs and technical challenges, thus limiting the clinical applications of multi-modal diagnosis. In this work, we investigate the realistic problem where paired pathology-genomic data are available during training, while only pathology slides are accessible for inference. To solve this problem, a comprehensive learning and adaptive teaching framework is proposed to improve the performance of pathological grading models by transferring the privileged knowledge from the multi-modal teacher to the pathology student. For comprehensive learning of the multi-modal teacher, we propose a novel Saliency-Aware Masking (SA-Mask) strategy to explore richer disease-related features from both modalities by masking the most salient features. For adaptive teaching of the pathology student, we first devise a Local Topology Preserving and Discrepancy Eliminating Contrastive Distillation (TDC-Distill) module to align the feature distributions of the teacher and student models. Furthermore, considering the multi-modal teacher may include incorrect information, we propose a Gradient-guided Knowledge Refinement (GK-Refine) module that builds a knowledge bank and adaptively absorbs the reliable knowledge according to their agreement in the gradient space. Experiments on the TCGA GBM-LGG dataset show that our proposed distillation framework improves the pathological glioma grading and outperforms other KD methods. Notably, with the sole pathology slides, our method achieves comparable performance with existing multi-modal methods. The code is available at https://github.com/CUHK-AIM-Group/MultiModal-learning.

This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)-based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients.

We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles.

TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 - 0.923; p = 0.025).

MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

The presence of circulating Hsp70 levels and their influence on the immunophenotype of circulating lymphocyte subsets were examined as diagnostic/prognostic biomarkers for the overall survival (OS) in patients with IDH-mutant WHO grade 3 oligodendroglioma, astrocytoma, and IDH-wildtype grade 4 glioblastoma (GBM). Vesicular and free Hsp70 in the plasma/serum was measured using the Hsp70-exo and R&D Systems DuoSet® Hsp70 ELISAs. The immunophenotype and membrane Hsp70 status was determined by multiparameter flow cytometry on peripheral blood lymphocytes and single-cell suspensions of tumor specimens and cultured cells. Compared to healthy controls, circulating vesicular Hsp70 levels were significantly increased in patients with GBM, concomitant with a significant decrease in the proportion of CD3+/CD4+ helper T cells, whereas the frequency of NK cells was most prominently increased in patients with grade 3 gliomas. Elevated circulating Hsp70 levels and a higher prevalence of activated CD3-/CD56+/CD94+/CD69+ NK cells were associated with an improved OS in grade 3 gliomas, whereas high Hsp70 levels and low CD3+/CD4+ frequencies were associated with an adverse OS in GBM. It is assumed that a reduced membrane Hsp70 density on grade 4 versus grade 3 primary glioma cells and reduced CD3+/CD4+ T cell counts in GBM might drive an immunosuppressive tumor microenvironment.

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.

Glioblastoma (GBM) remains an orphan cancer disease with poor outcome. Novel treatment strategies are needed. Immunotherapy has several modes of action. The addition of active specific immunotherapy with dendritic cell vaccines resulted in improved overall survival of patients. Integration of DC vaccination within the first-line combined treatment became a challenge, and immunogenic cell death immunotherapy during chemotherapy was introduced. We used a retrospective analysis using real world data to evaluate the complex combined treatment, which included individualized multimodal immunotherapy during and after standard of care, and which required adaptations during treatment, and found a further improvement of overall survival. We also discuss the use of real world data as evidence. Novel strategies to move the field of individualized multimodal immunotherapy forward for GBM patients are reviewed.

Glioblastomas (GBs) are incurable brain tumors. The persistence of aggressive stem-like tumor cells after cytotoxic treatments compromises therapeutic efficacy, leading to GBM recurrence. Forcing the GBM cells to irreversibly abandon their aggressive stem-like phenotype may offer an alternative to conventional cytotoxic treatments. Here, we show that the RNA binding protein CELF2 is strongly expressed in mitotic and OLIG2-positive GBM cells, while it is downregulated in differentiated and non-mitotic cells by miR-199a-3p, exemplifying GBM intra-tumor heterogeneity. Using patient-derived cells and human GBM samples, we demonstrate that CELF2 plays a key role in maintaining the proliferative/OLIG2 cell phenotype with clonal and tumorigenic properties. Indeed, we show that CELF2 deficiency in patient-derived GSCs drastically reduced tumor growth in the brains of nude mice. We further show that CELF2 promotes TRIM28 and G9a expression, which drive a H3K9me3 epigenetic profile responsible for the silencing of the SOX3 gene. Thus, CELF2, which is positively correlated with OLIG2 and Ki67 expression in human GBM samples, is inversely correlated with SOX3 and miR-199a-3p. Accordingly, the invalidation of SOX3 in CELF2-deficient patient-derived cells rescued proliferation and OLIG2 expression. Finally, patients expressing SOX3 above the median level of expression tend to have a longer life expectancy. CELF2 is therefore a crucial target for the malignant potential of GBM and warrants attention when developing novel anticancer strategies.

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Before a tumor is diagnosed and surgically removed, it has been growing for many months or even years [...].

The neuro-oncology team faces a unique challenge when assessing treatment response in patients diagnosed with glioblastoma. Magnetic resonance imaging (MRI) remains the standard imaging modality for measuring therapeutic response in both clinical practice and clinical trials. However, even for the neuroradiologist, MRI interpretations are not straightforward because of tumor heterogeneity, as evidenced by varying degrees of enhancement, infiltrating tumor patterns, cellular densities, and vasogenic edema. The situation is even more perplexing following therapy since treatment-related changes can mimic viable tumor. Additionally, antiangiogenic therapies can dramatically decrease contrast enhancement giving the false impression of decreasing tumor burden. Over the past few decades, several approaches have emerged to augment and improve visual interpretation of glioblastoma response to therapeutics. Herein, we summarize the state of the art for evaluating the response of glioblastoma to standard therapies and investigational agents as well as challenges and future directions for assessing treatment response in neuro-oncology.

Monitoring glioblastoma responses to standard therapy and novel agents has been fraught with many challenges and limitations over the past decade. Excitingly, new promising methods are emerging to help address these challenges. Recently, the Response Assessment in Neuro-Oncology (RANO) working group proposed an updated response criteria (RANO 2.0) for the evaluation of all grades of glial tumors regardless of IDH status or therapies being evaluated. In addition, advanced neuroimaging techniques, such as histogram analysis, parametric response maps, morphometric segmentation, radio pharmacodynamics approaches, and the integrating of amino acid radiotracers in the tumor evaluation algorithm may help resolve equivocal lesion interpretations without operative intervention. Moreover, the introduction of other techniques, such as liquid biopsy and artificial intelligence could complement conventional visual assessment of glioblastoma response to therapies. Neuro-oncology has evolved over the past decade and has achieved significant milestones, including the establishment of new standards of care, emerging therapeutic options, and novel clinical, translational, and basic research. More recently, the integration of histopathology with molecular features for tumor classification has marked an important paradigm shift in brain tumor diagnosis. In a similar manner, treatment response monitoring in neuro-oncology has made considerable progress. While most techniques are still in their inception, there is an emerging body of evidence for clinical application. Further research will be critically important for the development of impactful breakthroughs in this area of the field.

Despite the emergence of cutting-edge therapeutic strategies and tremendous progress in research, a complete cure of glioma remains elusive. The heterogenous nature of tumor, immunosuppressive state and presence of blood brain barrier are few of the major obstacles in this regard. Long-acting depot formulations such as injectables and implantables are gaining attention for drug delivery to brain owing to their ease in administration and ability to elute drug locally for extended durations in a controlled manner with minimal toxicity. Hybrid matrices fabricated by incorporating nanoparticulates within such systems help to enhance pharmaceutical advantages. Utilization of long-acting depots as monotherapy or in conjunction with existing strategies rendered significant survival benefits in many preclinical studies and some clinical trials. The discovery of novel targets, immunotherapeutic strategies and alternative drug administration routes are now coupled with several long-acting systems with an ultimate aim to enhance patient survival and prevent glioma recurrences.

Improved treatment of glioblastoma (GBM) needs to address tumor invasion, a hallmark of the disease that remains poorly understood. In this study, we profiled GBM invasion through integrative analysis of histological and single-cell RNA sequencing (scRNA-seq) data from 10 patients.

Human histology samples, patient-derived xenograft mouse histology samples, and scRNA-seq data were collected from 10 GBM patients. Tumor invasion was characterized and quantified at the phenotypic level using hematoxylin and eosin and Ki-67 histology stains. Crystallin alpha B (CRYAB) and CD44 were identified as regulators of tumor invasion from scRNA-seq transcriptomic data and validated in vitro, in vivo, and in a mouse GBM resection model.

At the cellular level, we found that invasive GBM are less dense and proliferative than their non-invasive counterparts. At the molecular level, we identified unique transcriptomic features that significantly contribute to GBM invasion. Specifically, we found that CRYAB significantly contributes to postoperative recurrence and is highly co-expressed with CD44 in invasive GBM samples.

Collectively, our analysis identifies differentially expressed features between invasive and nodular GBM, and describes a novel relationship between CRYAB and CD44 that contributes to tumor invasiveness, establishing a cellular and molecular landscape of GBM invasion.