Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of 13C-labelled glucose metabolism

Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and have different therapeutic vulnerabilities1-3. Here we identified three metabolic subtypes: glycolytic, oxidative and a mix of glycolytic and oxidative, using mass spectrometry imaging of rapidly excised tumour sections from two patients with GB who were infused with [U-13C]glucose and from spatial transcriptomic analysis of contiguous sections. The phenotypes are not correlated with microenvironmental features, including proliferation rate, immune cell infiltration and vascularization, are retained when patient-derived cells are grown in vitro or as orthotopically implanted xenografts and are robust to changes in oxygen concentration, demonstrating their cell-intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes is large enough to be detected using clinically applicable metabolic imaging techniques. A limitation of the study is that it is based on only two patient tumours, albeit on multiple sections, and therefore represents a proof-of-concept study.

Neurodevelopmental hijacking of oligodendrocyte lineage programs drives glioblastoma infiltration

Glioblastoma (GBM) is an aggressive brain tumor with a highly invasive nature. Despite the clinical relevance of this behavior, the molecular underpinnings of infiltrating GBM cells in the peritumoral zone remain underexplored in patients. Here, we show that peritumoral progenitor-like GBM cells activate transcriptional programs associated with increased invasivity, synaptic activity, and NOTCH signaling. These cells spatially colocalize with neurons and exhibit an increased propensity for neuronal crosstalk. The epigenetic encoding of these infiltrative cells mirrors that of uncommitted oligodendrocyte progenitor cells (OPCs) in the developing human brain, a neurodevelopmental state marked by increased synaptic and migratory potential. Functional perturbation of a nominated regulatory factor, ZEB1, confirmed its role in maintaining the invasive and uncommitted developmental potential of infiltrative GBM cells. Our findings provide insights into the neurodevelopmental hijacking that drives GBM infiltration in patients, rationalizing further investigation into targeting differentiation potential as a therapeutic strategy.

Glioma grade and mortality in relation to sequence variation in the mitochondrial genome

PURPOSE

Glioma arises from glial cells and comprises ∼80 % of malignant adult brain tumors. The polymorphic mitochondrial genome plays a key role in maintaining redox homeostasis and generation of reactive oxygen species (ROS). ROS have a well-established role in glial tumors. We investigated associations between germline mtDNA variants and haplogroups with glioma grade and glioblastoma (GBM) survival.

METHODS

We conducted germline mtDNA sequencing for 388 patients (300 Caucasians, 88 African Americans [AA]) with incident glioma (105 non-GBM, 283 GBM). Across all patients we identified 1431 homoplasmic mtDNA variants, including 692 variants observed only in Caucasians, 474 only in AAs, and 265 in both groups. We estimated Odds Ratios (OR) and 95 % Confidence Intervals (CI) for mtDNA common variants, haplogroups, and gene variant burden in relation to glioma grade and tertiles of survival in GBM patients. Bonferroni and Benjamini-Hochberg correction were applied for multiple comparisons.

RESULTS

No mtDNA haplogroup was associated with glioma grade or patient survival in GBM. Common variants m.3010G>A, m.195T>C, and m.16189T>C were linked to lower-grade glioma risk. For GBM survival, m.1719G>A, m.14766T>C, m.16129G>A, and m.204T>C were associated with a poorer prognosis while variant m.73A>G was associated with an improved prognosis. A higher variant burden in MT-ND1 and MT-ND5 was associated with a better prognosis. No results remained statistically significant after correction.

CONCLUSION

This is the first comprehensive study of germline mtDNA sequence variation in relation to glioma grade at diagnosis and gliobastoma patient survival. Results warrant further study in larger populations and investigation of biologic mechanisms linking mtDNA polymorphism to these endpoints.

Repurposing trifluoperazine for glioblastoma treatment

Glioblastoma (GBM) remains a therapeutic challenge due to its heterogeneity and plasticity, which drive treatment resistance, especially when compounded by interactions with the brain microenvironment. Recent preclinical evidence indicates that trifluoperazine (TFP) inhibits treatment-induced malignant reprogramming of tumour cells, potentially helping to reduce tumour plasticity. TFP targets calmodulin, dopamine receptors, and stress-responsive proteins (nuclear protein 1, NUPR1). Through these mechanisms, TFP has been shown to reduce tumour growth, sensitise tumours to chemoradiotherapy, and prolong survival in xenograft animal models. The clinical safety profile of TFP is well known from its use as an antipsychotic, and recent preclinical evidence further indicates that TFP has low toxicity to healthy neurons and glia despite transient functional effects on dopamine receptors. This Opinion explores TFP mechanisms of action and clinical activity to assess its suitability as a repurposed therapeutic option for GBM.

Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics

The evolution of isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) after standard-of-care therapy remains poorly understood. Here we analyzed matched primary and recurrent GBMs from 59 patients using single-nucleus RNA sequencing and bulk DNA sequencing, assessing the longitudinal evolution of the GBM ecosystem across layers of cellular and molecular heterogeneity. The most consistent change was a lower malignant cell fraction at recurrence and a reciprocal increase in glial and neuronal cell types in the tumor microenvironment (TME). The predominant malignant cell state differed between most matched pairs, but no states were exclusive or highly enriched in either time point, nor was there a consistent longitudinal trajectory across the cohort. Nevertheless, specific trajectories were enriched in subsets of patients. Changes in malignant state abundances mirrored changes in TME composition and baseline profiles, reflecting the co-evolution of the GBM ecosystem. Our study provides a blueprint of GBM's diverse longitudinal trajectories and highlights the treatment and TME modifiers that shape them.

The multilayered transcriptional architecture of glioblastoma ecosystems

In isocitrate dehydrogenase wildtype glioblastoma (GBM), cellular heterogeneity across and within tumors may drive therapeutic resistance. Here we analyzed 121 primary and recurrent GBM samples from 59 patients using single-nucleus RNA sequencing and bulk tumor DNA sequencing to characterize GBM transcriptional heterogeneity. First, GBMs can be classified by their broad cellular composition, encompassing malignant and nonmalignant cell types. Second, in each cell type we describe the diversity of cellular states and their pathway activation, particularly an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like and cilia-like. Third, the remaining variation between GBMs highlights three baseline gene expression programs. These three layers of heterogeneity are interrelated and partially associated with specific genetic aberrations, thereby defining three stereotypic GBM ecosystems. This work provides an unparalleled view of the multilayered transcriptional architecture of GBM. How this architecture evolves during disease progression is addressed in the companion manuscript by Spitzer et al.

Investigative needle core biopsies support multimodal deep-data generation in glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer with few effective therapies. Stereotactic needle biopsies are routinely used for diagnosis; however, the feasibility and utility of investigative biopsies to monitor treatment response remains ill-defined. Here, we demonstrate the depth of data generation possible from routine stereotactic needle core biopsies and perform highly resolved multi-omics analyses, including single-cell RNA sequencing, spatial transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics on standard biopsy tissue obtained intra-operatively. We also examine biopsies taken from different locations and provide a framework for measuring spatial and genomic heterogeneity. Finally, we investigate the utility of stereotactic biopsies as a method for generating patient-derived xenograft (PDX) models. Multimodal dataset integration highlights spatially mapped immune cell-associated metabolic pathways and validates inferred cell-cell ligand-receptor interactions. In conclusion, investigative biopsies provide data-rich insight into disease processes and may be useful in evaluating treatment responses.

Screening of patient-derived organoids identifies mitophagy as a cell-intrinsic vulnerability in colorectal cancer during statin treatment

Statins, commonly used to lower cholesterol, are associated with improved prognosis in colorectal cancer (CRC), though their effectiveness varies. This study investigates the anti-cancer effects of atorvastatin in CRC using patient-derived organoids (PDOs) and PDO-derived xenograft (PDOX) models. Our findings reveal that atorvastatin induces mitochondrial dysfunction, leading to apoptosis in cancer cells. In response, cancer cells induce mitophagy to clear damaged mitochondria, enhancing survival and reducing statin efficacy. Analysis of a clinical cohort confirms mitophagy's role in diminishing statin effectiveness. Importantly, inhibiting mitophagy significantly enhances the anti-cancer effects of atorvastatin in CRC PDOs, xenograft models, and azoxymethane (AOM)-dextran sulfate sodium (DSS) mouse models. These findings identify mitophagy as a critical pro-survival mechanism in CRC during statin treatment, providing insights into the variable responses observed in epidemiological studies. Targeting this vulnerability through combination therapy can elicit potent therapeutic responses.

A coregulatory influence map of glioblastoma heterogeneity and plasticity

We present GBM-cRegMap, an online resource providing a comprehensive coregulatory influence network perspective on glioblastoma (GBM) heterogeneity and plasticity. Using representation learning algorithms, we derived two components of this resource: GBM-CoRegNet, a highly specific coregulatory network of tumor cells, and GBM-CoRegMap, a unified network influence map based on 1612 tumors from 16 studies. As a widely applicable closed-loop system connecting cellular models and tumors, GBM-cRegMap will provide the GBM research community with an easy-to-use web tool ( https://gbm.cregmap.com ) that maps any existing or newly generated transcriptomic "query" data to a reference coregulatory network and a large-scale manifold of disease heterogeneity. Using GBM-cRegMap, we demonstrated the synergy between the two components by refining the molecular classification of GBM, identifying potential key regulators, and aligning the transcriptional profiles of tumors and in vitro models. Through the amalgamation of a vast dataset, we validated the proneural (PN)-mesenchymal (MES) axis and identified three subclasses of classical (CL) tumors: astrocyte-like (CL-A), epithelial basal-like (CL-B), and cilium-rich (CL-C). We revealed the CL-C subclass, an intermediate state demonstrating the plasticity of GBM cells along the PN-MES axis under chemotherapy. We identified key regulators, such as PAX8, and NKX2.5, potentially involved in temozolomide (TMZ) resistance. Notably, NKX2.5, more expressed in higher-grade gliomas, negatively impacts patient survival, and regulates genes involved in glucose metabolism.

Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities

Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adult glioma, identifying three subtypes: MOFS1 (proneural) with favorable prognosis, elevated neurodevelopmental activity, and abundant neurocyte infiltration; MOFS2 (proliferative) with the worst prognosis, superior proliferative activity, and genome instability; MOFS3 (TME-rich) with intermediate prognosis, abundant immune and stromal components, and sensitive to anti-PD-1 immunotherapy. STRAP emerges as a prognostic biomarker and potential therapeutic target for MOFS2, associated with its proliferative phenotype. Stromal infiltration in MOFS3 serves as a crucial prognostic indicator, allowing for further prognostic stratification. Additionally, we develop a deep neural network (DNN) classifier based on radiological features to further enhance the clinical translatability, providing a non-invasive tool for predicting MOFS subtypes. Overall, these findings highlight the potential of multimodal fusion in improving the classification, prognostic accuracy, and precision therapy of IDH-wildtype glioma, offering an avenue for personalized management.

The development and potent antitumor efficacy of CD44/CD133 dual-targeting IL7Rα-armored CAR-T cells against glioblastoma

Tumor heterogeneity and an immunosuppressive microenvironment pose significant challenges for immunotherapy against solid tumors, particularly glioblastoma multiforme (GBM). Recent studies have highlighted the crucial role of glioma stem cells (GSCs) in tumor recurrence and therapeutic resistance. In this context, we developed a tandem chimeric antigen receptor (CAR)-T cell targeting CD44 and CD133 (PROM1), containing a truncated IL-7 receptor alpha intracellular domain (Δ7R) between the CD28 costimulatory receptor and the CD3ζ signaling chain (Tanζ-T28-Δ7R). Our target identification and validation were carried out using GSCs, samples from GBM patients, and the corresponding sequencing data. The antitumor efficacy of CAR-T cells was evaluated in patient-derived GSCs, intracranial xenograft models, patient-derived xenograft models, and glioblastoma organoids (GBOs). Single-cell RNA sequencing and mass cytometry were used to determine the immune phenotypes of CAR-T cells. We showed that locoregionally administered Tanζ-T28-Δ7R CAR-T cells induced long-term tumor regression with the desired safety outcomes. Patient-derived autologous Tanζ-T28-Δ7R CAR-T cells showed robust antitumor activity against GBOs. Our pre-clinical data has demonstrated the translational potential of Tanζ-T28-Δ7R CAR-T cell against GBM.

Programs, origins and immunomodulatory functions of myeloid cells in glioma

Gliomas are incurable malignancies notable for having an immunosuppressive microenvironment with abundant myeloid cells, the immunomodulatory phenotypes of which remain poorly defined1. Here we systematically investigate these phenotypes by integrating single-cell RNA sequencing, chromatin accessibility, spatial transcriptomics and glioma organoid explant systems. We discovered four immunomodulatory expression programs: microglial inflammatory and scavenger immunosuppressive programs, which are both unique to primary brain tumours, and systemic inflammatory and complement immunosuppressive programs, which are also expressed by non-brain tumours. The programs are not contingent on myeloid cell type, developmental origin or tumour mutational state, but instead are driven by microenvironmental cues, including tumour hypoxia, interleukin-1β, TGFβ and standard-of-care dexamethasone treatment. Their relative expression can predict immunotherapy response and overall survival. By associating the respective programs with mediating genomic elements, transcription factors and signalling pathways, we uncover strategies for manipulating myeloid-cell phenotypes. Our study provides a framework to understand immunomodulation by myeloid cells in glioma and a foundation for the development of more-effective immunotherapies.

Current Applications of Single-Cell RNA Sequencing in Glioblastoma: A Scoping Review

Background and Objective: The discovery of novel molecular biomarkers via next-generation sequencing technologies has revolutionized how glioblastomas (GBMs) are classified nowadays. This has resulted in more precise diagnostic, prognostic, and therapeutic approaches to address this malignancy. The present work examines the applications of single-cell RNA sequencing (scRNA-seq) in GBM, focusing on its potential to address tumor complexity and therapeutic resistance and improve patient outcomes. Methods: A scoping review of original studies published between 2009 and 2024 was conducted using the PUBMED and EMBASE databases. Studies in English or Spanish related to single-cell analysis and GBM were included. Key Findings: The database search yielded 453 publications. Themes related to scRNA-seq applied for the diagnosis, prognosis, treatment, and understanding of the cancer biology of GBM were used as criteria for article selection. Of the 24 studies that were included in the review, 11 focused on the tumor microenvironment and cell subpopulations in GBM samples, 5 investigated the use of sequencing to elucidate the GBM cancer biology, 3 examined disease prognosis using sequencing models, 3 applied translational research through scRNA-seq, and 2 addressed treatment-related problems in GBM elucidated by scRNA-seq. Conclusions: This scoping review explored the various clinical applications of scRNA-seq technologies in approaching GBM. The findings highlight the utility of this technology in unraveling the complex cellular and immune landscapes of GBM, paving the way for improved diagnosis and personalized treatments. This cutting-edge approach might strengthen treatment strategies against tumor progression and recurrence, setting the stage for multi-targeted interventions that could significantly improve outcomes for patients with aggressive, treatment-resistant GBMs.

Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics

Glioblastomas are aggressive brain tumors with dismal prognosis. One of the main bottlenecks for developing more effective therapies for glioblastoma stems from their histologic and molecular heterogeneity, leading to distinct tumor microenvironments and disease phenotypes. Effectively characterizing these features would improve the clinical management of glioblastoma. Glucose flux rates through glycolysis and mitochondrial oxidation have been recently shown to quantitatively depict glioblastoma proliferation in mouse models (GL261 and CT2A tumors) using dynamic glucose-enhanced (DGE) deuterium spectroscopy. However, the spatial features of tumor microenvironment phenotypes remain hitherto unresolved. Here, we develop a DGE Deuterium Metabolic Imaging (DMI) approach for profiling tumor microenvironments through glucose conversion kinetics. Using a multimodal combination of tumor mouse models, novel strategies for spectroscopic imaging and noise attenuation, and histopathological correlations, we show that tumor lactate turnover mirrors phenotype differences between GL261 and CT2A mouse glioblastoma, whereas recycling of the peritumoral glutamate-glutamine pool is a potential marker of invasion capacity in pooled cohorts, linked to secondary brain lesions. These findings were validated by histopathological characterization of each tumor, including cell density and proliferation, peritumoral invasion and distant migration, and immune cell infiltration. Our study bodes well for precision neuro-oncology, highlighting the importance of mapping glucose flux rates to better understand the metabolic heterogeneity of glioblastoma and its links to disease phenotypes.

Metabolic imaging distinguishes ovarian cancer subtypes and detects their early and variable responses to treatment

High grade serous ovarian cancer displays two metabolic subtypes; a high OXPHOS subtype that shows increased expression of genes encoding electron transport chain components, increased oxygen consumption, and increased chemosensitivity, and a low OXPHOS subtype that exhibits glycolytic metabolism and is more drug resistant. We show here in patient-derived organoids and in the xenografts obtained by their subcutaneous implantation that the low OXPHOS subtype shows higher lactate dehydrogenase activity and monocarboxylate transporter 4 expression than the high OXPHOS subtype and increased lactate labeling in 13C magnetic resonance spectroscopy (MRS) measurements of hyperpolarized [1-13C]pyruvate metabolism. There was no difference between the subtypes in PET measurements of 2-deoxy-2-[fluorine-18]fluoro-D-glucose ([18F]FDG) uptake. Both metabolic imaging techniques could detect the early response to Carboplatin treatment in drug-sensitive high OXPHOS xenografts and no response in drug-resistant in low OXPHOS xenografts. 13C magnetic resonance spectroscopic imaging of hyperpolarized [1-13C]pyruvate metabolism has the potential to be used clinically to distinguish low OXPHOS and high OXPHOS tumor deposits in HGSOC patients and to detect their differential responses to treatment.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Brain tumour histopathology through the lens of deep learning: A systematic review

PROBLEM

Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's complex pathogenesis.

AIM

Conduct a systematic review to investigate how ML/DL techniques have influenced the progression of brain tumour histopathological research, particularly in GBM.

METHODS

54 eligible studies were collected from the PubMed and ScienceDirect databases, and their information about the types of brain tumour/s used, types of -omics data used with histopathological data, origins of the data, types of ML/DL and its training and evaluation methodologies, and the ML/DL task it was set to perform in the study were extracted to inform us of trends in GBM-related ML/DL-based research.

RESULTS

Only 8 GBM-related studies in the eligible utilised ML/DL methodologies to gain deeper insights into GBM pathogenesis by contextualising histological data with -omics data. However, we report that these studies have been published more recently. The most popular ML/DL models used in GBM-related research are the SVM classifier and ResNet-based CNN architecture. Still, a considerable number of studies failed to state training and evaluative methodologies clearly.

CONCLUSION

There is a growing trend towards using ML/DL approaches to uncover relationships between biological and histopathological data to bring new insights into GBM, thus pushing GBM research forward. Much work still needs to be done to properly report the ML/DL methodologies to showcase the models' robustness and generalizability and ensure the models are reproducible.

Radiomics in glioma: emerging trends and challenges

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.

Exploiting metabolic vulnerability in glioblastoma using a brain-penetrant drug with a safe profile

Glioblastoma is one of the most treatment-resistant and lethal cancers, with a subset of self-renewing brain tumour stem cells (BTSCs), driving therapy resistance and relapse. Here, we report that mubritinib effectively impairs BTSC stemness and growth. Mechanistically, bioenergetic assays and rescue experiments showed that mubritinib targets complex I of the electron transport chain, thereby impairing BTSC self-renewal and proliferation. Gene expression profiling and Western blot analysis revealed that mubritinib disrupts the AMPK/p27Kip1 pathway, leading to cell-cycle impairment. By employing in vivo pharmacokinetic assays, we established that mubritinib crosses the blood-brain barrier. Using preclinical patient-derived and syngeneic models, we demonstrated that mubritinib delays glioblastoma progression and extends animal survival. Moreover, combining mubritinib with radiotherapy or chemotherapy offers survival advantage to animals. Notably, we showed that mubritinib alleviates hypoxia, thereby enhancing ROS generation, DNA damage, and apoptosis in tumours when combined with radiotherapy. Encouragingly, toxicological and behavioural studies revealed that mubritinib is well tolerated and spares normal cells. Our findings underscore the promising therapeutic potential of mubritinib, warranting its further exploration in clinic for glioblastoma therapy.

Proteogenomic characterisation of primary oral cancer unveils extracellular matrix remodelling and immunosuppressive microenvironment linked to lymph node metastasis

Oral squamous cell carcinoma (OSCC) is an increasingly prevalent malignancy worldwide. This study aims to understand molecular alterations associated with lymph node metastasis of OSCC in order to improve treatment strategies. We analysed a cohort of 46 patients with primary OSCC, including 10 with lymph node metastasis and 36 without. Using a comprehensive multi-omics approach - encompassing genomic, transcriptomic, proteomic, epigenetic, single-cell, and spatial analyses - we integrated data to delineate the molecular landscape of OSCC in the context of lymph node metastasis. Our genomic analysis identified significant mutations in key genes within the MAPK, TGF-β and WNT signalling pathways, which are essential for tumour development. The proteogenomic analysis highlighted pathways critical for lymph node dissemination and factors contributing to an immunosuppressive tumour microenvironment. Elevated levels of POSTN were found to reorganise the extracellular matrix (ECM), interact with TGF-β, disrupt cell cycle regulation and suppress the immune response by reducing VCAM1 activity. Integrated analyses of single-cell and spatial transcriptome data revealed that cancer-associated fibroblasts (CAFs) secrete TGF-β1/2, promoting cancer cell metastasis through epithelial-mesenchymal transition (EMT). Our integrated multi-omics analysis provides a detailed understanding of molecular mechanisms driving lymph node metastasis of OSCC. These insights could lead to more precise diagnostics and targeted treatments. This article is protected by copyright. All rights reserved.

Glioblastoma multiforme: insights into pathogenesis, key signaling pathways, and therapeutic strategies

Glioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumor in adults, characterized by a poor prognosis and significant resistance to existing treatments. Despite progress in therapeutic strategies, the median overall survival remains approximately 15 months. A hallmark of GBM is its intricate molecular profile, driven by disruptions in multiple signaling pathways, including PI3K/AKT/mTOR, Wnt, NF-κB, and TGF-β, critical to tumor growth, invasion, and treatment resistance. This review examines the epidemiology, molecular mechanisms, and therapeutic prospects of targeting these pathways in GBM, highlighting recent insights into pathway interactions and discovering new therapeutic targets to improve patient outcomes.

An integrated perspective on single-cell and spatial transcriptomic signatures in high-grade gliomas

High-grade gliomas (HGG) are incurable brain malignancies in children and adults. Breakthrough advances in transcriptomic technologies unveiled the intricate diversity of cellular states and their spatial organization within HGGs. We qualitatively integrated 55 neoplastic transcriptomic signatures described in 17 single-cell and spatial RNA sequencing-based studies. Our review delineates a spectrum of cellular states, represented by the expression of specific genes, which can be conceptualized along a "reactive-developmental programs" axis. Additionally, we discussed the potential cues influencing these cellular states, including how spatial organization may impact transcriptomic dynamics. Leveraging these insightful discoveries, we discussed a novel, evolutive way to integrate the different transcriptomic signatures in two or three dimensions, incorporating developmental states, their proliferative capacity, and their possible transition towards reactive states. This integrated analysis illuminates the diverse cellular landscape of HGGs and provides a valuable resource for further elucidation of malignant mechanisms, and for the design of therapeutic endeavors.

Spatial biology - unravelling complexity within the glioblastoma microenvironment

The advent and refinement of state-of-the-art spatial biology technologies have facilitated analysis that combines the advantages of high-throughput single cell analysis with techniques that preserve tissue architecture. This combination of cellular phenotyping with retained spatial context provides a much greater understanding of cellular interactions within the tumour microenvironment (TME). For glioblastoma, with its significant intra-tumoural heterogeneity, cellular plasticity, and complex TME, appreciating and understanding these spatial patterns may prove key to improving patient outcomes. This review examines the advances in spatial biology techniques, discusses how these methodologies are being applied to study glioblastoma, and explores how spatial information improves understanding of the TME. Ultimately, it is this spatial context that will accelerate the identification of more effective treatments for glioblastoma.

Glioblastoma Cortical Organoids Recapitulate Cell-State Heterogeneity and Intercellular Transfer

Glioblastoma (GBM) is characterized by heterogeneous malignant cells that are functionally integrated within the neuroglial microenvironment. In this study, we model this ecosystem by growing GBM into long-term cultured human cortical organoids that contain the major neuroglial cell types found in the cerebral cortex. Single-cell RNA sequencing analysis suggests that, compared with matched gliomasphere models, GBM cortical organoids more faithfully recapitulate the diversity and expression programs of malignant cell states found in patient tumors. Additionally, we observe widespread transfer of GBM transcripts and GFP to nonmalignant cells in the organoids. Mechanistically, this transfer involves extracellular vesicles and is biased toward defined GBM cell states and astroglia cell types. These results extend previous GBM organoid modeling efforts and suggest widespread intercellular transfer in the GBM neuroglial microenvironment. Significance: Models that recapitulate intercellular communications in GBM are limited. In this study, we leverage GBM cortical organoids to characterize widespread mRNA and GFP transfer from malignant to nonmalignant cells in the GBM neuroglial microenvironment. This transfer involves extracellular vesicles, may contribute to reprogramming the microenvironment, and may extend to other cancer types. See related commentary by Shakya et al., p. 261.

Morphoregulatory ADD3 underlies glioblastoma growth and formation of tumor-tumor connections

Glioblastoma is a major unmet clinical need characterized by striking inter- and intra-tumoral heterogeneity and a population of glioblastoma stem cells (GSCs), conferring aggressiveness and therapy resistance. GSCs communicate through a network of tumor-tumor connections (TTCs), including nanotubes and microtubes, promoting tumor progression. However, very little is known about the mechanisms underlying TTC formation and overall GSC morphology. As GSCs closely resemble neural progenitor cells during neurodevelopment, we hypothesized that GSCs' morphological features affect tumor progression. We identified GSC morphology as a new layer of tumoral heterogeneity with important consequences on GSC proliferation. Strikingly, we showed that the neurodevelopmental morphoregulator ADD3 is sufficient and necessary for maintaining proper GSC morphology, TTC abundance, cell cycle progression, and chemoresistance, as well as required for cell survival. Remarkably, both the effects on cell morphology and proliferation depend on the stability of actin cytoskeleton. Hence, cell morphology and its regulators play a key role in tumor progression by mediating cell-cell communication. We thus propose that GSC morphological heterogeneity holds the potential to identify new therapeutic targets and diagnostic markers.

Network analyses of brain tumor multiomic data reveal pharmacological opportunities to alter cell state transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e., phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing

Glioblastomas are invasive brain tumors with high therapeutic resistance. Neuron-to-glioma synapses have been shown to promote glioblastoma progression. However, a characterization of tumor-connected neurons has been hampered by a lack of technologies. Here, we adapted retrograde tracing using rabies viruses to investigate and manipulate neuron-tumor networks. Glioblastoma rapidly integrated into neural circuits across the brain, engaging in widespread functional communication, with cholinergic neurons driving glioblastoma invasion. We uncovered patient-specific and tumor-cell-state-dependent differences in synaptogenic gene expression associated with neuron-tumor connectivity and subsequent invasiveness. Importantly, radiotherapy enhanced neuron-tumor connectivity by increased neuronal activity. In turn, simultaneous neuronal activity inhibition and radiotherapy showed increased therapeutic effects, indicative of a role for neuron-to-glioma synapses in contributing to therapeutic resistance. Lastly, rabies-mediated genetic ablation of tumor-connected neurons halted glioblastoma progression, offering a viral strategy to tackle glioblastoma. Together, this study provides a framework to comprehensively characterize neuron-tumor networks and target glioblastoma.

Cancer Neuroscience of Brain Tumors: From Multicellular Networks to Neuroscience-Instructed Cancer Therapies

Deepening our understanding of neuro-cancer interactions can innovate brain tumor treatment. This mini review unfolds the most relevant and recent insights into the neural mechanisms contributing to brain tumor initiation, progression, and resistance, including synaptic connections between neurons and cancer cells, paracrine neuro-cancer signaling, and cancer cells' intrinsic neural properties. We explain the basic and clinical-translational relevance of these findings, identify unresolved questions and particularly interesting future research avenues, such as central nervous system neuro-immunooncology, and discuss the potential transferability to extracranial cancers. Lastly, we conceptualize ways toward clinical trials and develop a roadmap toward neuroscience-instructed brain tumor therapies. Significance: Neural influences on brain tumors drive their growth and invasion. Herein, we develop a roadmap to use these fundamentally new insights into brain tumor biology for improved outcomes.

Turning attention to tumor-host interface and focus on the peritumoral heterogeneity of glioblastoma

Approximately 90% of glioblastoma recurrences occur in the peritumoral brain zone (PBZ), while the spatial heterogeneity of the PBZ is not well studied. In this study, two PBZ tissues and one tumor tissue sample are obtained from each patient via preoperative imaging. We assess the microenvironment and the characteristics of infiltrating immune/tumor cells using various techniques. Our data indicate there are one or more regions with higher cerebral blood flow in PBZ, which we collectively name the "higher cerebral blood flow interface" (HBI). The HBI exhibited more neovascularization than the "lower cerebral blood flow interfaces" (LBI). The HBI tend to have increased infiltration of macrophages and T lymphocytes infiltration compared with that in LBI. There are more tumor cells in the HBI than in LBI, with substantial differences in the gene expression profiles of these tumor cells. HBI may be the key area of PBZ-targeting therapy after surgical resection.

Resistance to spindle inhibitors in glioblastoma depends on STAT3 and therapy induced senescence

While mitotic spindle inhibitors specifically kill proliferating tumor cells without the toxicities of microtubule poisons, resistance has limited their clinical utility. Treating glioblastomas with the spindle inhibitors ispinesib, alisertib, or volasertib creates a subpopulation of therapy induced senescent cells that resist these drugs by relying upon the anti-apoptotic and metabolic effects of activated STAT3. Furthermore, these senescent cells expand the repertoire of cells resistant to these drugs by secreting an array of factors, including TGFβ, which induce proliferating cells to exit mitosis and become quiescent-a state that also resists spindle inhibitors. Targeting STAT3 restores sensitivity to each of these drugs by depleting the senescent subpopulation and inducing quiescent cells to enter the mitotic cycle. These results support a therapeutic strategy of targeting STAT3-dependent therapy-induced senescence to enhance the efficacy of spindle inhibitors for the treatment of glioblastoma.

Glioblastoma functional heterogeneity and enrichment of cancer stem cells with tumor recurrence

Glioblastoma (GBM) is an incurable disease with high intratumoral heterogeneity. Bioinformatic studies have examined transcriptional heterogeneity with differing conclusions. Here, we characterize GBM heterogeneity and highlight critical phenotypic and hierarchical roles for quiescent cancer stem cells (qCSCs). Unsupervised single-cell transcriptomic analysis of patient-derived xenografts (PDXs) delineates six GBM transcriptional states with unique tumor exclusive gene signatures, five of which display congruence with central nervous system (CNS) cell lineages. We employ a surrogate tumor evolution assay by serial xenograft transplantation to demonstrate faithful preservation of somatic mutations, transcriptome, and qCSCs. PDX chemotherapy results in CSC resistance and expansion, also seen in recurrent patient GBM. In aggregate, these novel GBM transcriptional signatures exclusively identify tumor cells and define the hierarchical landscape as stable biologically discernible cell types that allow capture of their evolution upon recurrence, emphasizing the importance of CSCs and demonstrating general relevance to all GBM.

An ILK/STAT3 pathway controls glioblastoma stem cell plasticity

Glioblastoma (GBM) is driven by malignant neural stem-like cells that display extensive heterogeneity and phenotypic plasticity, which drive tumor progression and therapeutic resistance. Here, we show that the extracellular matrix-cell adhesion protein integrin-linked kinase (ILK) stimulates phenotypic plasticity and mesenchymal-like, invasive behavior in a murine GBM stem cell model. ILK is required for the interconversion of GBM stem cells between malignancy-associated glial-like states, and its loss produces cells that are unresponsive to multiple cell state transition cues. We further show that an ILK/STAT3 signaling pathway controls the plasticity that enables transition of GBM stem cells to an astrocyte-like state in vitro and in vivo. Finally, we find that ILK expression correlates with expression of STAT3-regulated proteins and protein signatures describing astrocyte-like and mesenchymal states in patient tumors. This work identifies ILK as a pivotal regulator of multiple malignancy-associated GBM phenotypes, including phenotypic plasticity and mesenchymal state.

Immune landscape of isocitrate dehydrogenase-stratified primary and recurrent human gliomas

BACKGROUND

Human gliomas are classified using isocitrate dehydrogenase (IDH) status as a prognosticator; however, the influence of genetic differences and treatment effects on ensuing immunity remains unclear.

METHODS

In this study, we used sequential single-cell transcriptomics on 144 678 and spectral cytometry on over 2 million immune cells encompassing 48 human gliomas to decipher their immune landscape.

RESULTS

We identified 22 distinct immune cell types that contribute to glioma immunity. Specifically, brain-resident microglia (MG) were reduced with a concomitant increase in CD8+ T lymphocytes during glioma recurrence independent of IDH status. In contrast, IDH-wild type-associated patterns, such as an abundance of antigen-presenting cell-like MG and cytotoxic CD8+ T cells, were observed. Beyond elucidating the differences in IDH, relapse, and treatment-associated immunity, we discovered novel inflammatory MG subpopulations expressing granulysin, a cytotoxic peptide that is otherwise expressed in lymphocytes only. Furthermore, we provide a robust genomic framework for defining macrophage polarization beyond M1/M2 paradigm and reference signatures of glioma-specific tumor immune microenvironment (termed GlioTIME-36) for deconvoluting transcriptomic datasets.

CONCLUSIONS

This study provides advanced optics of the human pan-glioma immune contexture as a valuable guide for translational and clinical applications.

Artificial intelligence and omics in malignant gliomas

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

Differential gene expression analysis supports dysregulation of mitochondrial activity as a new perspective for glioblastoma's aggressiveness

Brain cancer is considered one of the most aggressive and lethal types of cancer, including primary tumors, being subdivided into milder forms such as low-grade gliomas and glioblastoma, considered the most aggressive form with higher invasion. Among the hallmarks of glioblastoma, the deregulation of mitochondrial metabolism has not yet been fully elucidated. Therefore, the search for mitochondrial biomarkers that can be used as indicators of the progression of this type of cancer is necessary. The aim of this study was to investigate the difference in gene expression between astrocytoma-type gliomas and glioblastomas, and how genes involved in mitochondrial metabolism can influence the proliferative cascade and be associated with tumor invasion. From the differential analysis of glioblastoma expression when compared to the milder form, 11 differentially expressed genes (DEGs) were found in our study, six of which were upregulated (ATP5MGL, C15orf48, MCUB, TERT, AGXT and CYP27B1) and four downregulated (SLC2A4, GK2, SLC25A48, ETNPPL and HMGCS2). To validate the findings, we used other independent bulk RNA-seq datasets and evaluated the number of normalized counts of the DEGs founded. Among these genes, we highlight that none of them had been reported in glioblastoma until this research, and we suggest these genes as possible biomarkers to be further explored, since they are associated with essential pathways for the tumor, such as glucose metabolization, gluconeogenesis, calcium and vitamin D metabolism, tumor progression and activation of the invasion cascade.

Targeting Mitochondria in Glioma: New Hopes for a Cure

Drugs targeting mitochondrial energy metabolism are emerging as promising antitumor therapeutics. Glioma treatment is extremely challenging due to the high complexity of the tumor and the high cellular heterogeneity. From a metabolic perspective, glioma cancer cells can be classified into the oxidative metabolic phenotype (mainly depending on mitochondrial respiration for energy production) and glycolytic phenotype or "Warburg effect" (mainly depending on glycolysis). Herein, we reviewed the function of novel bio-active molecules targeting oxidative phosphorylation (OXPHOS), mitochondrial membrane potential and mitochondrial dynamics. These molecules exhibit intriguing preclinical and clinical results and have been proven to be promising candidates to be further developed for glioma therapy. However, despite these initial encouraging results, it is imperative to rigorously assess the side effects of these metabolic drugs, which have a non-negligible toxicity profile.

Single-cell multi-omics sequencing uncovers region-specific plasticity of glioblastoma for complementary therapeutic targeting

Glioblastoma (GBM) cells are highly heterogeneous and invasive, leading to treatment resistance and relapse. However, the molecular regulation in and distal to tumors remains elusive. Here, we collected paired tissues from the tumor core (TC) and peritumoral brain (PTB) for integrated snRNA-seq and snATAC-seq analyses. Tumor cells infiltrating PTB from TC behave more like oligodendrocyte progenitor cells than astrocytes at the transcriptome level. Dual-omics analyses further suggest that the distal regulatory regions in the tumor genome and specific transcription factors are potential determinants of regional heterogeneity. Notably, while activator protein 1 (AP-1) is active in all GBM states, its activity declines from TC to PTB, with another transcription factor, BACH1, showing the opposite trend. Combined inhibition of AP-1 and BACH1 more efficiently attenuates the tumor progression in mice and prolongs survival than either single-target treatment. Together, our work reveals marked molecular alterations of infiltrated GBM cells and a synergy of combination therapy targeting intratumor heterogeneity in and distal to GBM.

[Glioblastoma and its interaction with neurogenesis]

Glioblastoma (GBM) is the most frequent and aggressive malignant primary tumor of the central nervous system in adults, with an incidence of 3.23 per 100,000 people. Despite the existence of various therapeutic approaches, the absence of a cure and the unfavorable prognosis persist for this neoplasm, with a median survival of approximately 8-15 months and a 5-year survival rate of 6.9%. In this review, we address the epidemiology, histopathology, molecular characteristics, and treatment of GBM. We highlight the relationship of GBM with the microenvironment in the lateral ventricle wall and the cerebrospinal fluid. The location of GBM in this region results in more aggressive tumors and shorter life expectancy for patients. Understanding the malignancy mechanisms that hinder remission, treatment, and positive prognosis opens the possibility of improving diagnostic and therapeutic interventions against GBM.

SOX10 mediates glioblastoma cell-state plasticity

Phenotypic plasticity is a cause of glioblastoma therapy failure. We previously showed that suppressing the oligodendrocyte-lineage regulator SOX10 promotes glioblastoma progression. Here, we analyze SOX10-mediated phenotypic plasticity and exploit it for glioblastoma therapy design. We show that low SOX10 expression is linked to neural stem-cell (NSC)-like glioblastoma cell states and is a consequence of temozolomide treatment in animal and cell line models. Single-cell transcriptome profiling of Sox10-KD tumors indicates that Sox10 suppression is sufficient to induce tumor progression to an aggressive NSC/developmental-like phenotype, including a quiescent NSC-like cell population. The quiescent NSC state is induced by temozolomide and Sox10-KD and reduced by Notch pathway inhibition in cell line models. Combination treatment using Notch and HDAC/PI3K inhibitors extends the survival of mice carrying Sox10-KD tumors, validating our experimental therapy approach. In summary, SOX10 suppression mediates glioblastoma progression through NSC/developmental cell-state transition, including the induction of a targetable quiescent NSC state. This work provides a rationale for the design of tumor therapies based on single-cell phenotypic plasticity analysis.

High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity

Glioblastoma, the most aggressive primary brain cancer, has a dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we systematically screened repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling more than 2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug-target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of more than 1 million compounds with high patient validation accuracy. Deep multimodal profiling confirmed Ca2+-driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the anti-depressant vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology.

Amino acid metabolism in glioma: in vivo MR-spectroscopic detection of alanine as a potential biomarker of poor survival in glioma patients

PURPOSE

Reprogramming of amino acid metabolism is relevant for initiating and fueling tumor formation and growth. Therefore, there has been growing interest in anticancer therapies targeting amino acid metabolism. While developing personalized therapeutic approaches to glioma, in vivo proton magnetic resonance spectroscopy (MRS) is a valuable tool for non-invasive monitoring of tumor metabolism. Here, we evaluated MRS-detected brain amino acids and myo-inositol as potential diagnostic and prognostic biomarkers in glioma.

METHOD

We measured alanine, glycine, glutamate, glutamine, and myo-inositol in 38 patients with MRI-suspected glioma using short and long echo-time single-voxel PRESS MRS sequences. The detectability of alanine, glycine, and myo-inositol and the (glutamate + glutamine)/total creatine ratio were evaluated against the patients' IDH mutation status, CNS WHO grade, and overall survival.

RESULTS

While the detection of alanine and non-detection of myo-inositol significantly correlated with IDH wildtype (p = 0.0008, p = 0.007, respectively) and WHO grade 4 (p = 0.01, p = 0.04, respectively), glycine detection was not significantly associated with either. The ratio of (glutamate + glutamine)/total creatine was significantly higher in WHO grade 4 than in 2 and 3. We found that the overall survival was significantly shorter in glioma patients with alanine detection (p = 0.00002).

CONCLUSION

Focusing on amino acids in MRS can improve its diagnostic and prognostic value in glioma. Alanine, which is visible at long TE even in the presence of lipids, could be a relevant indicator for overall survival.

Metabolic dysregulation of tricarboxylic acid cycle and oxidative phosphorylation in glioblastoma

Glioblastoma multiforme (GBM) exhibits genetic alterations that induce the deregulation of oncogenic pathways, thus promoting metabolic adaptation. The modulation of metabolic enzyme activities is necessary to generate nucleotides, amino acids, and fatty acids, which provide energy and metabolic intermediates essential for fulfilling the biosynthetic needs of glioma cells. Moreover, the TCA cycle produces intermediates that play important roles in the metabolism of glucose, fatty acids, or non-essential amino acids, and act as signaling molecules associated with the activation of oncogenic pathways, transcriptional changes, and epigenetic modifications. In this review, we aim to explore how dysregulated metabolic enzymes from the TCA cycle and oxidative phosphorylation, along with their metabolites, modulate both catabolic and anabolic metabolic pathways, as well as pro-oncogenic signaling pathways, transcriptional changes, and epigenetic modifications in GBM cells, contributing to the formation, survival, growth, and invasion of glioma cells. Additionally, we discuss promising therapeutic strategies targeting key players in metabolic regulation. Therefore, understanding metabolic reprogramming is necessary to fully comprehend the biology of malignant gliomas and significantly improve patient survival.

Modeling Glioma Intratumoral Heterogeneity with Primary Human Neural Stem and Progenitor Cells

Glioblastoma multiforme (GBM) is a deadly form of glioma notable for its significant intratumoral heterogeneity, which is believed to drive therapy resistance. GBM has been observed to mimic a neural stem cell hierarchy reminiscent of normal brain development. However, it is still unclear how cell-of-origin shapes intratumoral heterogeneity. Here, we develop a model of glioma initiation using neural stem and progenitor cells (NSPCs) purified from fetal human brain tissue. We previously described a method to prospectively isolate and culture tripotent neural stem cells (NSCs), bipotent glial progenitor cells (GPCs), and unipotent oligodendrocyte precursor cells (OPCs). We transduced these isogenic lines with dominant-negative TP53R175H and NF1 knockdown, a commonly-used genetic model of GBM in mice. These reprogrammed lines robustly engrafted when transplanted into the brains of immunodeficient mice, and showed significant expansion over time. Engrafted cells were reextracted from the mouse brain for single cell RNA sequencing (scRNA-seq), in order to quantify how the cell-of-origin modulates the cellular subtypes found in the resulting tumor. This result revealed the strong influence the cell-of-origin plays in glioma heterogeneity. Our platform is highly adaptable and allows for modular and systematic interrogation of how cell-of-origin shape the tumor landscape.

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges to identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens and by performing genetic and pharmacological experiments. Our analysis uncovers synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

SUMOylation modification of HNRNPK at the K422 site promotes invasion in glioblastoma

Glioblastoma multiforme (GBM) is a highly heterogeneous brain tumor with limited treatment options. Recent studies revealed cellular heterogeneity and the potential for interconversion between distinct cell types on the basis of RNA sequencing and single-cell analyses. The ability of different cell types to adapt to their surrounding environment and undergo transformation significantly complicates the study and treatment of GBM. In this study, we reveal that HNRNPK-SUMO1 expression is predominantly found in the GBM infiltration area. SUMOylation of the K422 residue of HNRNPK interferes with its DNA binding ability, thereby disrupting downstream transcription, and ultimately leading to transitions between different states of glioblastoma stem cells. Although the proneural subtype is considered to have a better prognosis, transitioning towards this state promotes tumor invasion. These findings serve as a reminder to exercise caution when considering treatments targeting specific cellular subtypes.

Matrix Protein of Vesicular Stomatitis Virus Targets the Mitochondria, Reprograms Glucose Metabolism, and Sensitizes to 2-Deoxyglucose in Glioblastoma

A potential therapeutic approach for cancer treatment is target oxidative phosphorylation and glycolysis simultaneously. The matrix protein of vesicular stomatitis virus (VSV MP) can target the surface of mitochondria, causing morphological changes that may be associated with mitochondrial dysfunction and oxidative phosphorylation inhibition. Previous research has shown that mitochondrial abnormalities can direct glucose metabolism toward glycolysis. Thus, after treatment with VSV MP, glycolysis inhibition is necessary to completely block glucose metabolism and eradicate cancer. Here, to inhibit glycolysis, the 2-deoxy-D-glucose (2-DG), a synthetic glucose analog was used to combine with VSV MP to treat cancer. This study aims to determine how VSV MP affects the glucose bioenergetic metabolism of cancer cells and to evaluate the synergistic effect of 2-DG when combined with VSV. Our results indicated that in U87 and C6 glioblastoma cell lines, VSV MP caused mitochondrial membrane potential loss, cytochrome c release, and glucose bioenergetics metabolism reprogramming. When combined with 2-DG, VSV MP synergistically aggravated cell viability, apoptosis, and G2/M phase arrest. Meanwhile, the combination therapy exacerbated ATP depletion, activated AMPK, and inhibited mammalian target of rapamycin signaling pathways. In addition, 2-DG treatment alone induced autophagy in glioblastoma cells; however, VSV MP inhibited the autophagy induced by 2-DG in combined treatment and finally contributed to the enhanced cytotoxic effect of the combination strategy in U87 and C6 cancer cells. In the orthotopic U87 glioblastoma model and subcutaneous C6 glioblastoma model, the combined treatment led to significant tumor regression and prolonged survival. A potent therapeutic approach for treating glioblastoma may be found in the combination of VSV MP and glycolytic inhibitors.

A cell state-specific metabolic vulnerability to GPX4-dependent ferroptosis in glioblastoma

Glioma cells hijack developmental programs to control cell state. Here, we uncover a glioma cell state-specific metabolic liability that can be therapeutically targeted. To model cell conditions at brain tumor inception, we generated genetically engineered murine gliomas, with deletion of p53 alone (p53) or with constitutively active Notch signaling (N1IC), a pathway critical in controlling astrocyte differentiation during brain development. N1IC tumors harbored quiescent astrocyte-like transformed cell populations while p53 tumors were predominantly comprised of proliferating progenitor-like cell states. Further, N1IC transformed cells exhibited increased mitochondrial lipid peroxidation, high ROS production and depletion of reduced glutathione. This altered mitochondrial phenotype rendered the astrocyte-like, quiescent populations more sensitive to pharmacologic or genetic inhibition of the lipid hydroperoxidase GPX4 and induction of ferroptosis. Treatment of patient-derived early-passage cell lines and glioma slice cultures generated from surgical samples with a GPX4 inhibitor induced selective depletion of quiescent astrocyte-like glioma cell populations with similar metabolic profiles. Collectively, these findings reveal a specific therapeutic vulnerability to ferroptosis linked to mitochondrial redox imbalance in a subpopulation of quiescent astrocyte-like glioma cells resistant to standard forms of treatment.

Macrophage-mediated myelin recycling fuels brain cancer malignancy

Tumors growing in metabolically challenged environments, such as glioblastoma in the brain, are particularly reliant on crosstalk with their tumor microenvironment (TME) to satisfy their high energetic needs. To study the intricacies of this metabolic interplay, we interrogated the heterogeneity of the glioblastoma TME using single-cell and multi-omics analyses and identified metabolically rewired tumor-associated macrophage (TAM) subpopulations with pro-tumorigenic properties. These TAM subsets, termed lipid-laden macrophages (LLMs) to reflect their cholesterol accumulation, are epigenetically rewired, display immunosuppressive features, and are enriched in the aggressive mesenchymal glioblastoma subtype. Engulfment of cholesterol-rich myelin debris endows subsets of TAMs to acquire an LLM phenotype. Subsequently, LLMs directly transfer myelin-derived lipids to cancer cells in an LXR/Abca1-dependent manner, thereby fueling the heightened metabolic demands of mesenchymal glioblastoma. Our work provides an in-depth understanding of the immune-metabolic interplay during glioblastoma progression, thereby laying a framework to unveil targetable metabolic vulnerabilities in glioblastoma.

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.

Unveiling the role of TAGLN2 in glioblastoma: From proneural-mesenchymal transition to Temozolomide resistance

Glioblastoma (GBM) presents a daunting challenge due to its resistance to temozolomide (TMZ), a hurdle exacerbated by the proneural-to-mesenchymal transition (PMT) from a proneural (PN) to a mesenchymal (MES) phenotype. TAGLN2 is prominently expressed in GBM, particularly in the MES subtype compared to low-grade glioma (LGG) and the PN subtype. Our research reveals TAGLN2's involvement in PMT and TMZ resistance through a series of in vitro and in vivo experiments. TAGLN2 knockdown can restrain proliferation and invasion, trigger DNA damage and apoptosis, and heighten TMZ sensitivity in GBM cells. Conversely, elevating TAGLN2 levels amplifies resistance to TMZ in cellular and intracranial xenograft mouse models. We demonstrate the interaction relationship between TAGLN2 and ERK1/2 through co-immunoprecipitation (Co-IP) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectrometry analysis. Knockdown of TAGLN2 results in a decrease in the expression of p-ERK1/2, whereas overexpression of TAGLN2 leads to an increase in p-ERK1/2 expression within the nucleus. Subsequently, the regulatory role of TAGLN2 in the expression and control of MGMT has been demonstrated. Finally, the regulation of TAGLN2 by NF-κB has been validated through chromatin immunoprecipitation and ChIP-PCR assays. In conclusion, our results confirm that TAGLN2 exerts its biological functions by interacting with the ERK/MGMT axis and being regulated by NF-κB, thereby facilitating the acquisition of promoting PMT and increased resistance to TMZ therapy in glioblastoma. These results provide valuable insights for the advancement of targeted therapeutic approaches to overcome TMZ resistance in clinical treatments.