Neuro-Oncology Advances

Background/ObjectivesIncreasing evidence indicates that gliomas co-opt mechanisms of excitatory synaptic transmission and plasticity to support tumor progression, yet these processes remain poorly characterized in lower-grade gliomas (LGGs). Here, we investigated whether genes associated with excitatory synaptic function are linked to patient prognosis in LGG. MethodsA curated panel of 36 synaptic genes was analyzed in LGG using RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Correlations among gene expression levels were analyzed using the Evergene platform. ResultsAmong the genes investigated, DLG2, DLG3, and DLG4, which encode the postsynaptic scaffolding proteins PSD-93, SAP-102, and PSD-95, respectively, showed strong associations with patient overall survival (OS). Higher expression of each gene was consistently associated with longer OS across both datasets. Expression of DLG2-DLG4 was higher in oligodendroglioma and IDH-mutant, 1p/19q co-deleted tumors, and lower in astrocytoma and IDH-wild-type tumors. Furthermore, expression of all three genes positively correlated with a broad gene signature related to excitatory synaptic transmission and synaptic plasticity, including multiple components of glutamatergic signaling and postsynaptic organization. ConclusionsThese findings suggest that elevated expression of DLG2-DLG4 is associated with a transcriptional program resembling differentiated neuronal-like features and favorable clinical outcome in LGG. Simple SummaryLower-grade gliomas are brain tumors with highly variable outcomes, and better markers are needed to predict how patients will fare. Recent research suggests that these tumors may use mechanisms normally involved in communication between brain cells, but this is not well understood in these cancer types. In this study, we analyzed large patient datasets to examine genes related to synaptic function. We found that higher expression of three genes involved in synaptic membrane organization, DLG2, DLG3, and DLG4 was consistently associated with longer patient survival. These genes were also linked to a broader pattern of gene expression suggestive of neural transmission and plasticity. Our findings suggest that some lower-grade gliomas may adopt characteristics of normal brain cells that are associated with less aggressive behavior. This work may help guide future research on prognostic markers and improve understanding of brain tumor biology.

Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.

Diffuse midline gliomas (DMGs) are a deadly class of pediatric high-grade brain cancers. Approximately 80% of pontine DMGs feature a dominant, somatic, heterozygous point mutation in the non-canonical histone H3.3-coding gene H3-3A. This dominant-negative mutation replaces lysine 27 with methionine (K27M) and prevents global K27 di- and tri-methylation of all wild-type histone H3 proteins. We aimed to target the H3.3K27M onco-histone pre-mRNA with splice-switching antisense oligonucleotides (ASOs) designed to promote skipping of H3-3A exon 2, as this constitutive exon comprises both the K27M mutation and the natural in-frame start codon of the gene. The lead ASO identified in a systematic screen specifically induced H3-3A exon 2 skipping, did not affect expression or splicing of the paralog gene H3-3B--which also encodes histone H3.3--and restored global H3K27me3 marks in patient-derived DMG cells grown as neurospheres. In a patient-derived orthotopic xenograft tumor mouse model, the lead ASO reduced proliferation and extended survival. Our results show the potential of exon-skipping ASOs targeting H3-3A exon 2 as a therapeutic option for H3.3K27M-altered DMG. More generally, they exemplify the strategy of using ASOs to induce skipping of a constitutive exon to effectively achieve gene downregulation.

Introduction: Glioblastoma multiforme (GBM) remains the most lethal primary brain tumor with median survival of 14-15 months. Current prognostic markers inadequately stratify patient outcomes. PINK1 (PTEN-induced putative kinase 1), a mitochondrial kinase regulating mitophagy and cellular stress responses, has emerged as a promising prognostic candidate. Our preliminary analysis of 20 GBM cases demonstrated significant PINK1 expression with correlation to aggressive phenotypes (Turizo Smith et al., 2025). This multicenter study aims to prospectively validate PINK1 as a prognostic biomarker for survival and functional outcomes in a Latin American cohort. Methods and analysis: PINK1-GBM Colombia is a multicenter, observational cohort study across four tertiary hospitals in Bogota, Colombia (Hospital de Kennedy, Hospital El Tunal, Hospital Santa Clara and Hospital Universitario de la Samaritana). We will enroll at least 26-50 adults (18+ years) with newly diagnosed IDH-wild type GBM undergoing surgical resection. PINK1 expression will be quantified by immunohistochemistry (IHC) on formalin-fixed paraffin embedded (FFPE) tissue using standardized protocols. Primary outcomes: overall survival (OS) and progression-free survival (PFS). Secondary outcomes: functional status trajectories (KPS/ECOG). Follow-up extends 24 months with clinical, imaging (RANO 2.0), and telephone assessments. Survival analyses will employ Kaplan-Meier methods, log-rank tests, and Cox proportional hazards models adjusted for established prognostic factors. Ethics and dissemination: Approved by Universidad Nacional de Colombia Ethics Committee (Acta 001, February 5, 2026; Ref: 2.FM.1.002-CE-002-26), Subred Sur Occidente (P-AP-19-2025, July 11, 2025), and Subred Centro Oriente (CEI 067/2025, October 24, 2025). Conducted per Declaration of Helsinki and Colombian Resolution 8430/1993. Results will be disseminated via peer-reviewed publication, international conferences, and thesis submission.

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Radioresistance, partly mediated by glioma stem-like cells, represents a major clinical challenge which could be overcome by the identification of the modulators of radioresistance. Existing CRISPR screens in human GBM models have largely used two-dimensional cultures with short-term viability readouts, failing to capture the long-term clonogenic behaviour underlying tumour recurrence after radiotherapy. Method: We developed ClonoScreen3D-CRISPRi, combining CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays. Two GBM cell lines (G7 and GBML20), differing in MGMT promoter methylation status, were engineered to express the KRAB-dCas9 editor. Nine candidate radiosensitivity modifiers, selected through transcriptomic analysis, pharmacological studies, and literature review, were examined in both lines. Target validation was performed using full radiation dose-response assays and a pharmacological inhibitor. Results: The majority of candidate genes significantly altered survival fraction following irradiation in both cell lines. Knockdown of NFKB2, RELB, and CDK9 produced the most potent radiosensitization, with sensitizer enhancement ratios of 1.39-1.70 in validation studies, exceeding those of established radiosensitizers including PARP and ATM inhibitors. Notably, knockdown of these genes induced no significant cytotoxicity in the absence of radiation. Pharmacological validation using an IKK inhibitor confirmed these findings, implicating non-canonical NF-{kappa}{beta} signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. Conclusions: ClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}{beta} pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation.

PURPOSE: Newly-diagnosed glioblastoma (nGBM) is a devastating tumor with median survival of only 14-18 months despite aggressive standard of care (SOC). Dendritic cell (DC) homologous antigenic double-loading provides a powerful pattern-based signal that initiates cDC1-like skewing of monocytic precursors, inducing downstream development of CD8+ memory effectors. Here we report phase I results for DOC1021 (dubodencel), a novel DC vaccine regimen integrated with SOC. METHODS: In this dose-escalating study, DC prepared from mobilized peripheral blood were doubly loaded with autologous tumor lysate and amplified tumor mRNA and administered bilaterally near the deep cervical node chains in three biweekly courses given with weekly peg-IFN after conclusion of chemoradiation. Four dose levels from 3.5x106 to 3.6x107 total cells were tested. Patients with subtotal resection or tumor progression prior to vaccination were not excluded. RESULTS: Eighteen patients (median age 61 years (range 47-73), 94% MGMT unmethylated, 25% subtotal/partial resected) completed vaccination (16 nGBM, 2 recurrent) with no dose-limiting toxicities. Attributable AE were mostly mild and flu-like or injection-site reactions. Twelve-month OS among the newly-diagnosed cohort was 88% compared to an expected ~60% for SOC alone. Patients who received observation rather than reoperation in response to worsening MRI contrast-enhancement demonstrated gradual lesional resolution and improved OS. Immunophenotyping revealed post-vaccination elevations in CD4 and CD8 memory T-cells in peripheral blood, and spatial transcriptomic analysis revealed foci of activated inflammatory complexes at the primary tumor site. CONCLUSIONS: DOC1021 was safe, feasibly integrated within SOC, and associated with more favorable outcomes in this challenging patient population. Patients who received observation rather than reoperation for worsening MRI contrast-enhancement exhibited superior survival, suggesting an immune-reactive tumor microenvironment manifesting as pseudo-progression. These data supported initiation of a randomized Phase II trial (NCT06805305) for nGBM.

Intratumor heterogeneity (ITH) is one of the main reasons for the lack of effective targeted therapies for glioblastoma (GBM). Imaging-guided surgical navigation allows for tumor-wide sampling to account for variation across distant regions of the tumor, but typical drug screening is performed on cell lines derived from a single biopsy and does not account for GBM heterogeneity. Here we profiled matching MRI-guided multi-region primary tumor biopsies from 6 GBM cases (n=40 biopsies) and corresponding neurosphere cultures (n=30) derived from these spatially distinct tumor samples. We found that in vitro cultures derived from distinct regions of the same tumor display divergent phenotypes, proliferative capacity and ability to accumulate 5-aminolevulinic acid, used to visualize cancer cells during surgery. The differential drug response of the multi-region neurospheres remains linked to the gene expression of the original tumor biopsies. Thus, studies with multiregion-derived neurospheres are essential to faithfully model GBM ITH for therapeutic testing. KEY POINTSO_LIMulti-region biopsy-derived neurospheres represent distinct spatial locations in the GBM tumor. C_LIO_LICultures derived from different regions of the tumor retain phenotypic diversity. C_LIO_LIParental biopsy phenotype predicts drug response better than to in vitro phenotype. C_LI IMPORTANCE OF THE STUDYCell lines developed from spatially distinct regions of glioblastoma capture its intratumor heterogeneity. We show that while the transcriptional output of these cell lines is not connected to their spatial origin, their drug response can be linked to it. Thus, spatial heterogeneity reflected in our neurosphere collection provides a new paradigm for drug screening in these highly heterogenous and difficult to treat tumors.

Abstract: Purpose: Recurrence in high grade glioma (HGG) predominantly occurs within the high dose radiation field, raising the question of whether treatment failure reflects limitations in radiation target delineation or is driven by intrinsic tumor biology. This study evaluated recurrence patterns following standard chemoradiotherapy and their treatment implications. Material and Methods: This retrospective single center study included 41 patients with histologically confirmed HGG treated with surgery followed by radiotherapy with concurrent and adjuvant temozolomide (TMZ). Patients were followed through August 2018; those with recurrence were included in the analysis. Recurrence patterns were classified based on their spatial relationship to the 60 Gy isodose line as central, infield, marginal, or distant. Survival outcomes were estimated using the Kaplan-Meier method and compared using the log rank test. Results: The most common pattern of recurrence was central (15 patients, 36.5%), followed by infield (11, 26.8%), distant (6, 14.6%), marginal (5, 12.1%), and multicentric (4, 9.8%). Central and in field recurrences (local failures) accounted for 26 patients (63%). Median overall survival (OS) was 27 months, and median progression-free survival (PFS) was 12 months. Survival differed significantly by recurrence pattern (log-rank p = 0.018), with marginal recurrence associated with more favorable outcomes. Conclusion: The predominance of central and infield recurrences within the high-dose region suggests that treatment failure in HGG is not solely explained by inadequate target delineation and may also be driven, in part, by intrinsic tumor biology, including radioresistant subpopulations and tumor heterogeneity. Future strategies may benefit from incorporating biologically guided approaches alongside optimization of radiation treatment parameters.

BackgroundGroup 3 (MYC-driven) medulloblastoma (MB) is a highly aggressive brain tumor with poor-prognosis and limited treatment options. We previously identified protein-arginine methyltransferase-5 (PRMT5) as a promising target in Group 3 MB with its control on MYC protein stability. In this follow up study, we further mechanistically investigated PRMT5 control on MYC transcription and targeted it pharmacologically for therapeutic proof-of-concept. MethodsUsing pharmacogenetic inhibition approaches against PRMT5 in MYC-amplified (Group 3) MB cell line and neurosphere models in vitro and in vivo, we investigated molecular mechanism(s) and anti-cancer efficacy of PRMT5 inhibition. ResultsOur experiments demonstrated that PRMT5 epigenetically regulates MYC transcription in MYC-amplified MB cells by binding to the proximal-promoter region of the MYC gene and contributing to the enriched symmetric-dimethylation of histone H4R3 in the same region. We further showed that PRMT5 is recruited to the MYC promoter by its interaction with BRD4, the major BET-protein responsible for MYC transcription. PRMT5 inhibition caused the suppression of MYC-induced transcriptional programs and target genes, with widespread disruption of splicing across the transcriptome, particularly affecting metabolism-related gene products. Pharmacologic inhibition of PRMT5 using a panel of selective small-molecule inhibitors demonstrates suppression of cell growth/survival in a MYC-dependent manner in MB cells. Moreover, our in vivo analyses of PRMT5 inhibition, in mice treated with one of the potent pharmacologic inhibitors, particularly a lipid-decorated form of it, demonstrated reduced cerebellar tumor growth with suppressed MYC expression and prolonged survival of mice with MYC-amplified MB xenografts. ConclusionsOur findings establish a functional link between PRMT5 and MYC-mediated transcriptional regulation, suggesting a promising therapeutic approach targeting the PRMT5-MYC axis for MYC-driven MB. Key PointsO_LIPRMT5 acts as an epigenetic regulator of MYC transcription, RNA splicing and associated energy metabolism in MYC-driven MB. C_LIO_LIPRMT5 inhibition selectively suppresses cell growth/survival in MYC-driven MB. C_LIO_LIPRMT5 inhibition reduces tumor burden and prolongs survival in a MYC-driven MB mouse model. C_LI Importance of the StudyGroup 3 medulloblastoma is a highly aggressive pediatric brain tumor marked by MYC amplification, malignant clinical behavior, and poor survival outcomes despite intensive multimodal therapy. Because MYC remains largely undruggable, there is an urgent need for effective and less toxic treatment options for affected children. This study identifies protein arginine methyltransferase 5 (PRMT5) as a key epigenetic regulator of MYC transcription and MYC-dependent oncogenic programs in Group 3 MB. We show that PRMT5 is recruited to the MYC promoter via BRD4, sustains MYC-driven transcription and RNA splicing networks associated with metabolism, and supports MB tumor growth. Importantly, pharmacologic inhibition of PRMT5 using a selective brain-penetrant inhibitor suppresses MYC expression, reduces cerebellar tumor burden, and prolongs survival in MYC-amplified MB models. These findings provide a strong translational rationale for PRMT5 inhibition as a targeted therapeutic strategy for high-risk MB, with the potential to improve outcomes while reducing treatment-related toxicity.

Background. Adult diffuse glioma is a representative class of primary brain tumors for which accurate MRI-based tumor segmentation is indispensable for treatment planning. Conventional automated segmentation methods have relied primarily on image information and spatial prompts, and auxiliary clinical information that is routinely acquired in clinical practice has not been sufficiently exploited as an input. Objective. Building on a dual-prompt-driven Segment Anything Model (SAM) extension framework that fuses visual and language reference prompts, we propose a method that integrates patient demographics, unsupervised molecular cluster variables derived from TCGA high-throughput profiling, and histopathological parameters as learnable prompt embeddings, and we evaluate its effect on the accuracy of lower-grade glioma (LGG) MRI segmentation. Methods. An auxiliary prompt encoder converts clinical metadata into high-dimensional embeddings that are fused with the prompt representations of Segment Anything Model (SAM) ViT-B through a cross-attention fusion mechanism. The TCGA-LGG MRI Segmentation dataset (Kaggle release by Buda et al.; n = 110 patients; WHO grade II-III) was split at the patient level (train/val/test = 71/17/22) using three different random seeds, and the three slices with the largest tumor area were extracted from each patient. To avoid pseudo-replication arising from multiple slices per patient and repeated measurements across seeds, our primary analysis aggregated Dice and 95th-percentile Hausdorff distance (HD95) to the patient x seed unit (n = 66); secondary analyses at the unique-patient level (n = 22) and at the per-slice level (n = 198) are also reported. Pairwise comparisons used paired t-tests with Bonferroni correction (k = 3) and Wilcoxon signed-rank tests, and a permutation test (K = 30) served as an auxiliary check of effective use of the auxiliary information. Results. At the patient x seed level (n = 66), Proposed (full clinical) achieved a Dice gain of +0.287 over the zero-shot SAM ViT-B baseline (paired-t p = 4.2 x 10^-15, Cohen's d_z = +1.25, Bonferroni-corrected p << 0.001; Wilcoxon p = 2.0 x 10^-10), and HD95 improved from 218.2 to 64.6. Because zero-shot SAM is not designed for domain-specific medical segmentation, the large absolute HD95 gap largely reflects the expected domain gap rather than a competitive baseline. The additional contribution of the full clinical configuration over the demographics-only configuration was Dice = +0.023 (paired-t p = 0.057, Bonferroni-corrected p = 0.172), which did not reach statistical significance at the patient level and is reported as a directional trend. The permutation test (K = 30, seed 2025) yielded real-metadata Dice = 0.819 versus a shuffled-metadata mean of 0.773, giving an empirical p = 0.032 = 1/(K + 1), which is at the resolution limit of this test and should therefore be interpreted as preliminary evidence. Conclusions. Integrating auxiliary clinical information as multimodal prompts produced a large improvement over the zero-shot SAM baseline on this LGG cohort. More importantly, a robustness analysis showed that Proposed (full clinical) outperformed the trained Base (no auxiliary information) under all tested spatial-prompt conditions, including perfect centroid (+0.014), and that the advantage was most pronounced in the prompt-free regime (+0.231, p = 0.039), where the base model collapsed but the proposed model maintained meaningful segmentation by leveraging clinical metadata alone. The additional contribution of molecular and histopathological information beyond demographics was not statistically resolved at the patient level (+0.023, n.s.). Establishing clinical utility will require external validation on larger multi-center cohorts and direct comparisons with established segmentation methods. Keywords: brain tumor segmentation; Segment Anything Model (SAM); vision-language prompt-driven segmentation; auxiliary clinical prompts; multimodal learning; TCGA-LGG; deep learning

Purpose: To develop a deep neural network-based, AIF-free, perfusion estimation method (QTMnet) for improved performance on glioma classification. Methods: A globally defined arterial input function (AIF) is needed to recover perfusion parameters in the two-compartment exchange model (2CXM). We have developed Quantitative Transport Mapping (QTM) to create an AIF-independent estimation method. QTM estimation can be formulated using deep neural networks trained on synthetic DCE-MRI data (QTMnet). Here, we provide a fluid mechanics-based DCE-MRI simulation with exchange between the capillaries and extravascular extracellular space. We implemented tumor ROI generation to morphologically characterize tissue perfusion. We compared our QTMnet implementation with 2CXM on 30 glioma human subjects, 15 of which had low-grade gliomas, and 15 with high-grade glioblastomas. Results: QTMnet outperforms (best AUC: 0.973) traditional 2CXM (best AUC: 0.911) in a glioma grading task. Conclusion: The AIF-independent QTMnet estimation provides a quantitative delineation between low-grade and high-grade gliomas.

We studied the effect of stimulating innate immune function in tumor-associated myeloid cells (TAMs) in medulloblastoma (MB) and diffuse midline glioma (DMG), using a polyoxazoline nanoparticle formulation of the TLR7/8 agonist resiquimod (ResiPOx). Children with MB and DMG need novel therapeutic strategies to improve outcomes and reduce recurrence. We investigated the effect of systemically administered ResiPOx on TAMs in MB and DMG using endogenous MB and DMG models in immune-competent mice and identified multiple mechanisms of anti-tumor effect. We packaged resiquimod into polyoxazoline micelles to generate ResiPOx. We studied ResiPOx efficacy as a single agent or paired with radiation therapy (RT). We determined ResiPOx pharmacokinetics (PK) using tritium-labeled resiquimod and mass spectroscopy imaging (MSI). We determined ResiPOx pharmacodynamics (PD) using flow cytometry immunohistochemistry, bulk and single-cell RNA-seq and immunoblotting. We then studied ResiPOx safety and PD in a non-human primate model using rhesus macaques. ResiPOx formulation improved the blood-brain barrier penetration and anti-tumor efficacy of resiquimod. ResiPOx treatment extended progression-free survival (PFS) in mice with MB and DMG. In both tumor types, ResiPOx expanded TAM populations and reprogrammed TAMs toward anti-tumoral states, characterized by activation of IFN{beta} and extrinsic apoptosis pathway signaling, antigen presentation, and T cell activation signatures. In rhesus macaques, systemic ResiPOx administration was well tolerated and induced brain transcriptional responses that resembled ResiPOx responses in DMG and MB mouse models, indicating common effects across species from mice to non-human primates, and highlighting potential for similar effects in patients. ResiPOx is a brain-penetrant immunomodulatory therapeutic that reshapes the immune-privileged brain tumor microenvironment. Systemic administration activates myeloid-driven anti-tumoral immunity mediated by microglial and macrophage TAMs, and improves survival in preclinical models of DMG and MB.

Glioblastoma (GBM) is the most prevalent primary brain cancer, with poor prognosis and limited therapeutic options available. The genetic and cellular heterogeneity characteristic of GBM contributes to poor response rates. Activating mutations of the epidermal growth factor receptor (EGFR) gene are among the most frequent alterations in GBM, occurring in roughly half of cases. Despite the prevalence of EGFR mutations, EGFR inhibition has shown limited success in GBM. The transcription factor C/EBP{beta} is a master regulator of the mesenchymal transformation in GBM, an aggressive state characterized by increased invasiveness and resistance to chemotherapy. Lucicebtide is a C/EBP{beta} antagonist peptide with demonstrated single agent activity in patients with recurrent GBM that is currently being evaluated in a clinical trial in combination with radiation and temozolomide in patients with newly-diagnosed GBM (NCT04478279), with emerging data supporting clinical activity in that setting. Here we show that in the TCGA-GBM dataset, patients with EGFR mutations display significant enrichment of a high C/EBP{beta} activity signature. Functionally, genetic inactivation of EGFR by CRISPR results in synthetic lethality in the presence of lucicebtide in GBM cell lines, and synergistic in vitro cytotoxicity and suppression of C/EBP{beta} target gene expression was observed in combination experiments with lucicebtide and EGFR inhibitors. Finally, enhanced anti-tumor activity was demonstrated in vivo in the combination setting, as combined subpharmacologic dose levels of lucicebtide and the EGFR inhibitor osimertinib potently suppressed GBM xenograft growth. These data identify EGFR and C/EBP{beta} dependencies in GBM and support lucicebtide combination with EGFR inhibitors as a potential therapeutic option for a sizable fraction of GBM patients.

Engineering macrophages with chimeric antigen receptors is emerging as a promising cancer therapeutic. Chimeric antigen receptor-expressing macrophages (CAR-Ms) engineered to recognize tumor-specific antigens have been shown to inhibit tumor growth and activate adaptive immune responses, leading to robust tumor control in animal studies. Based on this work, clinical trials have been initiated. While the trials have shown promise, challenges remain. The dynamic interactions between CAR-Ms and cancer cells and the exact mechanisms driving anti-tumor effects remain poorly defined. Defining the dynamic interactions between CAR-Ms and cancer cells will provide critical insights for optimizing future CAR-M design and improving therapeutic efficacy. We sought to directly visualize CAR-M interactions with glioblastoma cells at high-resolution and in real-time using CAR-Ms engineered to recognize Neural-Glial Antigen 2 (NG2), an antigen expressed on glioblastoma cells. Using patient-derived glioblastoma cells, we formed glioblastoma spheroids and embedded them in a 3D matrix together with CAR-Ms. Using time-lapse microscopy, as expected, we found that NG2-targeting CAR-Ms engulfed glioblastoma cells. However, excitingly, we found that NG2-targeting CAR-Ms blocked >85% of glioblastoma cell invasion in 3D. This inhibition of glioblastoma invasion was not due to a significant change in CAR-M polarization states. Together, these data suggest that NG2-targeting CAR-Ms both engulf glioblastoma cells and block glioblastoma invasive behavior.

Prehabilitation (PRH) is a preoperative process aimed at optimizing patients functional capacity to improve surgical outcomes and overall well-being. While its physical and cognitive benefits are increasingly documented, its emotional impact, particularly in neuro-oncology patients, remains less explored. This study assessed the psychological effects of a PRH program on 29 brain tumor patients. The primary outcome, emotional well-being, was measured using quality of life and emotional distress metrices. Secondary outcomes included perceived stress levels and control attitudes. Additionally, qualitative data from structured interviews provided further insights into the psychological effects of the intervention. The results indicated significant improvements in quality of life and reductions in emotional distress, particularly among women. While perceived stress levels remained stable, control attitudes showed an increase. Qualitative analysis further highlighted the positive changes in the control sense and identified additional factors, such as the importance of social support sources during the PRH process. Overall, these findings suggest that PRH interventions play a significant role in enhancing emotional well-being among neuro-oncological patients in the preoperative phase. These results underscore the importance of implementing comprehensive and personalized PRH approaches to optimize clinical status both before and after surgery, thereby promoting sustained psychological benefits in this population. This study is based on data collected at Institut Guttmann in Barcelona in the context of the Prehabilita project (ClinicalTrials.gov identifier: NCT05844605; registration date: 06/05/2023).

Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of early IC response and OS. MoEMIL employed multi-instance learning to process WSIs, PyRadiomics and a convolutional neural network (ResNet50) to extract MRI features, and fused multimodal features through a multi-gate mixture-of-experts architecture. Clustering-constrained attention multiple instance learning and gradient-weighted class activation mapping were applied for visualization and interpretation. MoEMIL effectively stratified patients into good and poor IC response groups, achieving areas under the curve of 0.917, 0.869, and 0.801 in the train, validation, and test sets, respectively, and outperformed the deep learning radiomics model, the pathomics model and TNM staging. The model also stratified patients into high- and low-risk OS groups (P < 0.05). MoEMIL shows promise as a decision-support tool for early IC response prediction and prognostication in LANPC. Author SummaryWe have developed a deep learning model that integrates two types of medical images, including magnetic resonance imaging (MRI) and digital pathological slices, to simultaneously predict response to induction chemotherapy and prognosis in patients with locally advanced nasopharyngeal carcinoma. Current treatment decisions primarily rely on traditional tumor staging (TNM), which often fails to comprehensively reflect the complexity of the disease. Our model, named MoEMIL, was trained and tested on data from 404 patients across two hospitals and consistently outperformed both single-model approaches and TNM staging methods. By identifying patients who exhibit poor response to induction chemotherapy or higher prognostic risk, our tool can assist clinicians in achieving personalized treatment, enabling intensified management for high-risk patients and avoiding unnecessary side effects for low-risk patients. Additionally, we visualize the models reasoning process through heat map generation, which highlights the image regions exerting the greatest influence on prediction outcomes. This work represents a step toward more precise treatment for nasopharyngeal carcinoma; however, larger-scale prospective studies are required before the model can be integrated into routine clinical practice.

BackgroundGangliogliomas (GGs) are low-grade glioneuronal tumors that frequently present with drug-resistant epilepsy. Although their indolent course contrasts with their high epileptogenic potential, the oncogenic mechanisms sustaining neuronal precursor-like populations within the tumor microenvironment remain poorly defined. MethodsWe performed spatial transcriptomic profiling on eight histologically confirmed GGs and matched healthy cortex to map the cellular and molecular architecture of the tumor microenvironment. Integrated analysis with weighted gene correlation network analysis (WGCNA) defined recurrent oncogenic programs and spatially resolved tumor-stroma interactions. ResultsEight conserved gene modules emerged, encompassing physiological cortical, reactive glial, and oncopathological programs. The latter captured extracellular matrix (ECM) remodeling, vascular-immune signaling, and persistence of immature, proliferative neuronal-like states. Spatial modeling revealed that these oncopathological programs form structured niches at the tumor-brain interface, where radial glia-derived neuronal-like tumor cells coexist with immune and stromal elements engaged in ECM turnover and cytokine signaling. ConclusionsGanglioglioma represents a hybrid glioneuronal neoplasm in which developmental neuronal programs are co-opted by tumor-associated stromal and immune cues. This convergence establishes a permissive oncogenic niche that sustains precursor-like tumor cells and provides a mechanistic basis for both the tumors benign growth and its intrinsic epileptogenicity. Key PointsO_LISpatial transcriptomics identifies reproducible transcriptional programs that define the ganglioglioma microenvironment. C_LIO_LITumor-associated regions show transcriptional programs consistent with immature neuronal states together with ECM remodelling and immune activity. C_LIO_LISingle-cell reference data indicate that immature neuronal programs in ganglioglioma resemble radial glia-derived developmental states. C_LI Importance of the StudyGanglioglioma is a low-grade glioneuronal tumor that combines benign growth with pronounced epileptogenicity, yet the molecular basis of this dual behavior remains poorly understood. Through spatial transcriptomics integrated with single-cell analysis, we reveal that ganglioglioma architecture is defined by two interacting transcriptional axes: a residual glioneuronal network and a tumoral niche enriched for extracellular-matrix, vascular, and immune programs. Within these niches, immature neuronal-like tumor cells persist in a developmentally arrested state maintained by ECM-immune signaling. This spatially organized interplay between physiological and pathological programs explains both the low oncologic aggressiveness and high excitability of these lesions. Our findings provide molecular signatures that may refine diagnostic classification within the LEAT spectrum, delineate epileptogenic zones, and identify candidate pathways for therapeutic modulation of the ganglioglioma microenvironment.

Glioma is the most common primary malignant tumor of the brain, and accumulating evidence indicates that neuronal activity plays a pivotal role in tumor progression. In this study, neuronal activity is modulated in vitro using potassium chloride (KCl)-induced depolarization and midazolam (MDZ)-mediated suppression. MDZ is a neuronal activity modulation medication, commonly used for sedation, anxiolysis, and amnesia in clinics. After treatment, conditioned media derived from these neuronal cultures are subsequently co-cultured with glioma cells. EdU incorporation assays demonstrate that MDZ significantly inhibits glioma cell proliferation in vitro. Furthermore, an orthotopic xenograft glioma model is established to assess the anti-tumor efficacy of MDZ in vivo, as evaluated by tumor volume and Ki-67 immunostaining. Mechanistically, insulin-like growth factor 1 (IGF1) is identified as the neuronal-activity-regulated factor that promotes glioma growth through activation of the PI3K/AKT signaling pathway. Moreover, transcriptomic profiling of brain tissues reveals that MDZ attenuates neuronal activity and downregulates neuron-derived growth factors in both glioma and non-tumor regions, thereby exerting anti-tumor effects in vivo. Collectively, these findings demonstrate that MDZ suppresses glioma progression by suppressing neuronal activity and inhibiting neuron-derived trophic factors, providing new insights into the development of therapeutic strategies for glioma.

AO_SCPLOWBSTRACTC_SCPLOWThe classification of pediatric brain tumors is investigated using deep learning on hematoxylin and eosin (H&E) and antigen Ki-67 (Ki-67) whole slide images (WSIs) from the Childrens Brain Tumor Network (CBTN) dataset. A total of 1,662 unregistered WSIs (1,047 H&E and 615 Ki-67 images) were analyzed, including low-grade glioma/astrocytoma (grades 1, 2) (LGG), high-grade glioma/astrocytoma (grades 3, 4) (HGG), medulloblastoma (MB), ependymoma (EP) and ganglioglioma. The The aim of this study was to effectively classify pediatric brain tumors using H&E and Ki-67 WSIs individually, and to investigate whether early, intermediate, and late fusion could improve the predictive performance. From each WSI, 224x 224 pixel patches were extracted, and the instance (patch)-level features were obtained using the histology foundation model CONCHv1_5. The instances were aggregated using clustering-constrained attention multiple instance learning (CLAM) for patient-level classification. Model interpretability and explainability was assessed through attention heatmaps, cell density and Ki-67 labelling index (LI) maps. In the binary grade classification between LGG and HGG, the intermediate concatenation fusion achieved the best performance with a balanced accuracy of 0.88 {+/-} 0.05, (p < 0.005) compared to the single-stain models (H&E: 0.84 {+/-} 0.05, Ki-67: 0.86 {+/-} 0.05). For the 5-class tumor type classification, the one-hidden layer late fusion learning model achieved the highest balanced accuracy of 0.83 {+/-} 0.04 (p < 0.005), outperforming the single-stain models (H&E: 0.77 {+/-} 0.05, Ki-67: 0.74 {+/-} 0.05). Overall, most of the fusion approaches outperformed the single-stain models in both classification tasks (p < 0.005). The Ki-67 attention maps demonstrated moderate to strong Spearman correlation ({rho} = 0.576 - 0.823) with the cell density and Ki-67 LI maps, suggesting that these features are associated with the models predictions, although additional features may contribute. The results show that H&E and Ki-67 images provide complementary information, and most of the multi-stain fusion approaches using deep learning improve pediatric brain tumor diagnosis.

BackgroundQuantitative cerebral blood flow (CBF) measured with [15O]water positron emission tomography (PET) is the reference standard for quantifying brain perfusion. However, clinical interpretation of individual CBF measurements is limited by the absence of large normative datasets accounting for physiological variability across the adult lifespan. Long-axial field-of-view PET enables high-sensitivity quantitative [15O]water perfusion imaging without arterial blood sampling, allowing normative characterization of cerebral perfusion at unprecedented scale. The aim of this study was to establish normative and covariate-adjusted models of cerebral blood flow across the adult lifespan using total-body [15O]water PET. MethodsQuantitative CBF measurements were obtained in 302 neurologically healthy adults (age 21-86 years) using total-body [15O]water PET. Linear mixed-effects models were used to evaluate the effects of age, sex, body mass index (BMI), and blood hemoglobin concentration on CBF and to generate normative prediction models across the adult lifespan. Between-subject and within-subject variability were estimated from repeated scans in a subset of participants (n=51). ResultsMean grey matter CBF was 46.1 mL/(min*dL), with substantial inter-individual variability but high within-subject reproducibility (intraclass correlation coefficients 0.78-0.89). Advancing age was associated with a decline in CBF of approximately 7% per decade (p_FDR < 10-12). Higher BMI was associated with lower CBF (approximately -6% per 10 kg/m2; p_FDR < 0.01). Women exhibited higher CBF than men (approximately 7.5%), but this difference was largely explained by lower blood hemoglobin concentration in women. Covariate-adjusted models were used to generate normative predictions and prediction intervals describing expected CBF across adulthood. ConclusionThis study establishes a normative database of quantitative cerebral blood flow across the adult lifespan using high-sensitivity [15O]water PET. Age, BMI, and hemoglobin are major determinants of inter-individual variability in CBF. The resulting generative models provide a quantitative reference framework for interpreting cerebral perfusion measurements and may enable automated detection of abnormal brain perfusion in clinical PET imaging.