Neuro-Oncology Advances

Abstract and KeywordsO_ST_ABSBackground and ObjectivesC_ST_ABSPreoperative prediction of functional outcomes in contrast-enhancing glioma could support surgical decision-making and patient counseling, yet most existing models incorporate histopathological or postoperative variables unavailable before surgery. Our objectives were to develop a preoperative-only prediction model for one-year functional status and evaluate the added value of MRI-based tumor characteristics beyond clinical predictors. MethodsWe conducted a retrospective cohort study of consecutive adults ([≥] 18 years old) undergoing first resection of supratentorial contrast-enhancing glioma (WHO grade [≥] 2, histopathologically confirmed postoperatively) at a single center, with one-year follow-up. The primary outcome was functional status classified as mortality (Karnofsky Performance Score (KPS) = 0), functional dependence (KPS 10-60), or functional independence (KPS [≥] 70). In addition to clinical variables (age, sex, preoperative KPS, preoperative seizures), a deep learning tool was used to extract structural MRI-based tumor characteristics as predictors. A machine-learning model was developed and conformal prediction was applied to stratify patients by prediction confidence level. Results552 patients were included (median age: 60 years, range: 18-84; median contrast-enhancing volume: 24 mL, IQR: 10-43; median preoperative KPS: 80, range: 30-100; retrospectively confirmed 88% glioblastoma). Most MRI-based predictors did not improve performance as the best-performing model included three predictors: age at diagnosis, contrast-enhancing volume, preoperative KPS. Bootstrapped areas under the curves were 0.77 (95% confidence interval 0.70-0.84) for mortality, 0.64 (0.52-0.77) for functional dependence, and 0.71 (0.63-0.79) for functional independence. F1 scores per class were 0.65, 0.24, 0.65, respectively. Conformal prediction provided reliable predictions for 18% patients, moderate uncertainty for 57%, and identified 25% with genuinely unpredictable outcomes. DiscussionOur preoperative machine-learning model predicted one-year functional status in contrast-enhancing glioma with functional independence being the most reliably classified outcome (ROC-AUC = 0.77, F1 score = 0.65) and functional dependence the most challenging to predict (ROC-AUC = 0.64, F1 score = 0.24). A small set of three preoperative predictors drove model performance, supporting generalizability to broader patient populations. Our open-source model enables individualized risk stratification and may help clinicians identify patients with uncertain prognoses warranting more intensive preoperative counseling or follow-up planning.

BackgroundGlioblastoma is an aggressive and incurable brain tumor. Clinical trials of immune checkpoint inhibitors showed no clinical benefit in glioblastoma when given after surgery. However, a clinical trial in which PD1 inhibition was given prior to second surgery did show pharmacodynamic evidence for activity. This suggests the possibility that immune checkpoint inhibitors may be more effective in a setting where large tumors are present. Here we have studied immune responses to large tumors in an autochthonous mouse model of glioblastoma. MethodsGlioblastoma was induced by transfection with oncogenic plasmids injected directly into the lateral ventricle of neonatal mice. Immune responses were assessed using a combination of spectral flow cytometry and immunohistochemistry. ResultsThere was a marked immune response to large tumors, with significant increases in CD4 T cells and dendritic cells. T cell changes occurred primarily at leptomeningeal/perivascular border sites. A large proportion of CD4 T cells expressed PD1 and half of these were regulatory T cells. NK cells were also increased in mice with large tumors, but were predominantly in immature states. The mouse model accurately recapitulates the formation of palisading necroses. These contain apoptotic cells and avidly recruit myeloid cells that are induced to express large amounts of TGF{beta}. ConclusionsLarge glioblastoma tumors generate a border site population of PD1 positive T cells that may explain the pharmacodynamic response in neoadjuvant trials, and a palisading necrosis-driven immunosuppressive mechanism that may explain why responses are insufficient to provide a significant clinical benefit. KEY POINTSThe SB mouse model accurately recapitulates immune features of human glioblastoma Large tumors induce a significant border site immune response Palisading necroses in large tumors counter this with a strong immunosuppressive response IMPORTANCE OF STUDYImmune checkpoint inhibitors have not shown efficacy in glioblastoma when used post-surgery, but do show pharmacodynamic activity when used in patients prior to second surgery (i.e. neoadjuvant). This suggest the possibility that immune checkpoint inhibition is more effective when large tumors are present. Using a clinically-relevant autochthonous mouse model, we show here that large tumors induce an immune response that is evident in leptomeningeal border sites. Large tumors in this mouse model also generate palisading necroses, a well-known diagnostic feature in glioblastoma tumors. These palisading necroses generate large amounts of TGF{beta}, providing a mechanism by which large tumors can suppress border site immune responses. This further supports the concept that palisading necroses are drivers of glioblastoma malignancy and suggests novel strategies to enhance responses to immune checkpoint inhibition in this cancer.

Background: Extent of resection remains central to meningioma management, yet Simpson grading is subjective and may not reflect measurable postoperative residual disease. We compared surgeon-reported Simpson grade, report-derived radiological grading, and residual tumour volumetry across a multicentre cohort. Methods: We performed a retrospective study across two tertiary neurosciences centres comprising four hospitals, including patients undergoing primary cranial meningioma resection from 2006 to 2025. Postoperative magnetic resonance imaging (MRI) reports were harmonised using weakly supervised natural language processing based on term frequency-inverse document frequency (TF-IDF) and a linear support vector machine classifier. Residual tumour volume was segmented from contrast-enhanced postoperative MRI and log-transformed. Concordance between Simpson and radiological gross-total/subtotal resection classification was assessed using absolute agreement and prevalence-adjusted bias-adjusted kappa (PABAK). Cox models assessed recurrence-free survival, with bootstrap validation and anatomical and scan-timing sensitivity analyses. Results: Among 912 patients, recurrence or residual progression occurred in 281. Surgical-radiological agreement was substantial but imperfect (absolute agreement 74%; PABAK 0.61), with lower agreement in skull-base and parafalcine-parasagittal tumours. In adjusted models, recurrence hazard increased with Simpson grade (hazard ratio 1.54, 95% confidence interval 1.37-1.72), radiological grade (1.92, 1.68-2.20), and log-transformed residual volume (1.20, 1.16-1.24; all p<0.0005). Optimism corrected concordance increased from Simpson grade to radiological grade and log-volumetry (0.692, 0.733, and 0.748), with this ranking preserved across sensitivity analyses. Conclusions: Imaging-based postoperative residual disease measures outperformed Simpson grade. TF-IDF-assisted report-derived grading provides a scalable bridge to volumetry, while quantitative residual volume offers the strongest prognostic representation.

Background and ObjectivesIntramedullary spinal cord tumors (IMSCTs) are rare, and the extent of surgical resection may influence overall survival (OS). Gross total resection (GTR) may offer superior outcomes compared to subtotal resection (STR) or biopsy. Our study seeks to quantify the benefits of resection extent on OS in patients with spinal gliomas (SGs). MethodsA systematic review was conducted using the following databases: Scopus, Embase, and PubMed. Studies reporting OS in patients who underwent GTR, STR, or biopsy for low- or high-grade SG. We used a random-effects model to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs); this was performed separately for low-grade (WHO grade I-II) and high-grade (III-IV) SGs. Subgroup analysis was performed for radiotherapy. I2 statistic and Cochrans Q tests evaluated study heterogeneity, Eggers and funnel plot asymmetry tests assessed publication bias, and Risk Of Bias In Non-randomized Studies of Exposure (ROBINS-E) evaluated individual study bias. ResultsIn a pooled analysis of 5 studies, GTR was not associated with improvement in OS compared to STR or biopsy in high grade SGs (HR=0.48, 95% CI: 0.19 -1.26). However, low-grade SGs revealed significant benefit in overall survival with GTR (HR=0.27, 95% CI: 0.15-0.46). Patients treated with radiotherapy were associated with worse outcomes following GTR in low-grade SGs (HR=1.48, 95% CI: 1.30-1.69) but no survival differences in high-grade SGs (HR=1.21, 95% CI: 0.52-2.83). ROBINS-E determined only 1 study with high risk of bias. ConclusionGTR for intramedullary spinal gliomas may not confer a significant benefit in overall survival for high-grade lesions but may provide benefit in lower grades. Radiotherapy confers a worse survival in lower-grade tumors, potentially due to their infiltrative nature. Future studies should stratify outcomes based on tumor biology, as well as follow functional outcomes overtime.

IntroductionCraniopharyngioma (CP) comprises two distinct histological subtypes, adamantinomatous craniopharyngioma (ACP) and papillary craniopharyngioma (PCP), which are often challenging to distinguish preoperatively. Approximately 95% of PCP harbor the BRAF V600E mutation, whereas ACP lacks this alteration, making PCP uniquely sensitive to BRAF and MEK inhibition. However, in the absence of a reliable preoperative classification strategy, targeted therapy has been limited to recurrent disease or to cases with histological confirmation. This study aims to describe and prospectively evaluate a pragmatic preoperative classification strategy and short-course neoadjuvant BRAF and MEK inhibition followed by surgery in newly diagnosed, preoperatively classified PCP. Methods and analysisThis is a prospective, single-arm, open-label study. Patients with newly diagnosed craniopharyngioma will be screened using an integrated preoperative strategy combining imaging-based prediction and selective cerebrospinal fluid (CSF) cell-free DNA testing for BRAF V600E in indeterminate cases. Twelve participants preoperatively predicted as PCP and BRAF V600E positive will receive dabrafenib 150 mg twice daily plus trametinib 2 mg once daily for up to three 28-day cycles, followed by transnasal endoscopic surgery. Assessments are scheduled at days 7, 14, 28, 56, and 84 until surgery. The primary endpoint is objective response rate, assessed by contrast-enhanced MRI using RANO 2.0 criteria. Secondary outcomes include progression-free survival, local disease control, endocrine outcomes of the hypothalamic-pituitary-adrenal and hypothalamic-pituitary-thyroid axes, visual and cognitive outcomes, postoperative diabetes insipidus, surgical complexity, and concordance between the preoperative classification strategy and postoperative pathology and BRAF V600E status. Exploratory analyses will evaluate treatment-related changes in tumor vascularity, tissue characteristics, and post-treatment molecular alterations in tumor tissue. Ethics and disseminationThis protocol has been approved by the Ethics Committee of Huashan Hospital, Fudan University (KY2024-028). Written informed consent will be obtained from all participants. Results will be disseminated through peer-reviewed publications and scientific conferences. Trial registration numberChiCTR2400081636 STRENGTHS AND LIMITATIONS OF THIS STUDYO_ST_ABSStrengthC_ST_ABS[tpltrtarr] This study proposes an integrated, clinically applicable preoperative strategy that combines imaging-based prediction with selective cerebrospinal fluid cell-free DNA analysis to identify papillary craniopharyngioma (PCP) prior to surgery. [tpltrtarr]It prospectively evaluates short-course neoadjuvant BRAF and MEK inhibition in newly diagnosed PCP, addressing a clinically relevant gap in current management. [tpltrtarr]Standardized, multidimensional assessments are performed across the neoadjuvant, perioperative, and early postoperative periods, capturing radiographic, surgical, endocrine, visual, and cognitive outcomes. Limitation[tpltrtarr] The single-arm, open-label design without a surgical control group limits direct comparison with upfront surgery. [tpltrtarr]Despite the integrated prediction strategy, preoperative misclassification cannot be excluded entirely.

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.

Importance: Glioblastoma (GBM) cells integrate into neuronal circuits, and preclinical work implicates multiple neurotransmitter (NT) networks as key drivers of invasion and treatment resistance. Whether the integration of GBM within NT-defined large-scale brain networks conveys prognostic information for overall survival (OS) is unknown. Objective: To determine whether NT-specific network involvement of GBM is associated with OS in patients with newly diagnosed Isocitrate dehydrogenase (IDH)-wildtype(wt) GBM. Design, Setting, and Participants: In this observational multicenter cohort study, we analyzed two independent cohorts of adults with histopathologically confirmed IDH-wt GBM. Cohort 1 included 153 patients treated at the University Medical Center Hamburg-Eppendorf, Germany (2012-2024), and cohort 2 comprised 264 patients from the University of Pennsylvania Health System, USA (2006-2018). Preoperative contrast-enhanced MRI was used to derive individual tumor masks, which were spatially mapped onto normative NT-informed structural connectomes spanning 19 receptor and transporter systems. Exposures: Preoperative contrast-enhancing GBM lesions, quantified as patient-specific involvement scores (0-1) within each NT-defined brain network. Statistics: We used partial least-squares regression for variable selection and multivariable Cox proportional-hazards models alongside regularized logistic regression with out-of-sample prediction, adjusted for age, methylguanine methyltransferase (MGMT) promoter methylation, and extent of resection, to test associations between NT-specific GBM network involvement and OS. Results: Across 417 patients in two cohorts, greater GBM involvement within cholinergic networks, defined by normative vesicular acetylcholine transporter (VAChT)-weighted as well as dopaminergic D2 receptor involvement, was consistently associated with reduced OS, independent of age, MGMT status, and resection extent. Further, cholinergic network involvement showed the strongest contribution to the prediction models. Other NT networks did not show reproducible prognostic effects across cohorts. Tumor-intrinsic hypomethylation of acetylcholine receptor-associated regions correlated with imaging-based cholinergic network involvement and mirrored its prognostic relevance. Conclusion and Relevance: Tumor integration into neurotransmitter-specific brain networks is an independent predictor of poorer survival in GBM. By combining routine clinical MRI with normative NT-informed connectome data, this approach delineates a novel systems-level marker of tumor aggressiveness and supports cholinergic inhibition as a putative therapeutic target in GBM.

BackgroundGliomas are heterogeneous tumors with poor outcomes following current therapies, including immunotherapy. The tumor microenvironment (TME) is a critical determinant of therapeutic response in gliomas. We have classified the immune TME of gliomas by multiplex immunofluorescence (mIF). MethodsTissue taken at initial resection from 354 patients with newly-diagnosed glioma grades 2-4 were analyzed using three mIF panels of markers for T, B, and myeloid cells. Tumor cores were characterized by the relative abundances of: (i) 15 primary immune phenotypes, (ii) 96 secondary immune phenotypes, and, (iii) 29 biologically meaningful multi-marker immune phenotypes. ResultsUsing unsupervised cluster analysis of WHO grade 4 gliomas we identified four subtypes , {beta}, {gamma}, and {delta} that were internally reproducible. Immune subtype was characterized by high abundance of antigen-presenting cells (APCs) and low levels of MHC II- monocytes. Subtype {beta} was high in regulatory T cells and myeloid cells, but low in lymphocytes with effector functions. Subtype {gamma} displayed high abundance of immune cell phenotypes, particularly lymphocytes with effector or helper functions. Subtype {delta} was low in lymphoid and myeloid immune phenotypes and APCs, with poorer outcomes. Grade 3 tumors could also be classified into , {beta}, {gamma}, and {delta} subtypes, indicating generalizability of these immune TME subtypes across high grade gliomas. ConclusionsWe have identified internally reproducible criteria for classifying gliomas according to the immune microenvironment, findings that could aid our understanding of the natural progression of low- and high-grade gliomas and inform the rational application of immune-oncologic therapeutic interventions.

Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric assessment increasingly guides surveillance and treatment decisions. Automated segmentation could enable standardized volumetry but requires robust validation. Purpose: To develop a fully automated three-dimensional deep learning model for meningioma segmentation on multiparametric MRI, and to evaluate segmentation accuracy, external generalizability, failure modes, radiologist-rated clinical plausibility, and workflow feasibility. Methods: From 2024 to 2026, this retrospective study trained a custom 3D nnU-Net residual encoder model. Expert segmentations covered enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Dice similarity coefficient (DSC) was the primary metric. External validation used an independent single-institution dataset (n = 310 intracranial cases) with incomplete MRI protocols. Failure modes, model equity, and inference time were assessed. A blinded multi-rater study (10 radiologists; 510 cases) rated TC segmentations using a 0-10 Likert scale, analyzed with linear mixed-effects models. Results: Model training used the BraTS Meningioma 2023 dataset (n = 1000; mean age 60.2 {+/-} 14.5; 705 female). In cross-validation, mean DSC was 0.939 for ET, 0.937 for TC, and 0.921 for WT. In external validation, mean DSC was 0.872 for TC and 0.842 for WT, despite heterogeneous protocols and incomplete sequences. Predicted TC volumes correlated strongly with reference volumes in cross-validation (r = 0.995) and external validation (r = 0.971). Most common failure modes were skull base and intraosseous tumors with performance equitable across demographic subgroups. Mean inference time was 1.2 seconds. In blinded evaluation (1120 ratings), model segmentations received higher scores than reference annotations (+0.32 BraTS; +1.38 external validation). Conclusion: A fully automated deep-learning model achieved high meningioma segmentation accuracy across multi-institutional training data and external clinical imaging. In a blinded study, model segmentation quality exceeded reference annotations, and 1.2-second inference supported workflow integration. Prospective evaluation is warranted before routine deployment.

BackgroundThe Netrin-1 dependence receptor pathway plays critical roles in neural development, but its expression landscape and prognostic significance in glioblastoma (GBM) remain poorly characterized. MethodsSingle-cell RNA-seq data from 148,019 cells across 34 tumors (Neftel et al., 2019) were analyzed to map Netrin-1 pathway gene expression across GBM cellular states. Differential gene expression and pathway enrichment analyses were performed on NEO1-defined subpopulations. Bulk RNA-seq survival analysis was conducted across three independent GBM cohorts TCGA (n=106), CGGA mRNAseq_325 (n=137), and CGGA mRNAseq_693 (n=237), totaling 480 patients. Primary analysis used continuous Cox regression (per-SD hazard ratios); meta-analysis employed fixed-effects inverse-variance weighting. ResultsIn GBM single-cell data, Netrin-1 pathway genes showed state-specific enrichment --NEO1, DCC, NTN1, and RGMB were predominantly expressed in oligodendrocyte-precursor (OPC) and neural-progenitor (NPC) states. Cells positive for NEO1 were enriched for neural differentiation programs (nervous system development, p=9.6x10-; Axon Guidance, p=2.8x10-), whereas NEO1-negative cells were dominated by ribosomal/translational and immune activation programs. In the 3-cohort survival meta-analysis, NTN1 (Netrin-1 ligand) emerged as the sole gene reaching meta-analytic significance as a risk factor (Meta HR=1.163 per SD, 95% CI 1.056-1.281, p=0.0021, I{superscript 2}=0%, 3/3 cohorts concordant), while DCC and RGMB showed directionally consistent protective trends (DCC: Meta HR=0.938, 95% CI 0.858-1.025, p=0.156; RGMB: Meta HR=0.979, 95% CI 0.881-1.087, p=0.686; both 3/3 cohorts concordant). NEO1 itself did not independently predict survival (Meta HR=1.008, 95% CI 0.885-1.147, p=0.910). After Bonferroni correction for 10 genes tested (threshold p<0.005), only NTN1 met strict significance. In exploratory sex-stratified analysis of a single cohort (CGGA 693, n=237), NEO1 and NTN1 exhibited female-specific risk enhancement (NEO1: HR=1.417, p=0.014; NTN1: HR=1.249, p=0.019), with minimal effects in males. UNC5B showed context-dependent risk in MGMT-unmethylated tumors (HR=1.331, p=0.037). These sex-dimorphic findings require independent validation. ConclusionsThe Netrin-1 pathway exhibits divergent prognostic trends in GBM, with NTN1 as a risk factor and DCC trending toward protection--consistent with the dependence receptor model. These findings, which should be interpreted as hypothesis-generating, nominate NTN1 as a candidate therapeutic target and highlight the potential importance of sex-stratified evaluation in future Netrin-1-directed trials. Independent replication in larger cohorts is warranted.

Atypical teratoid rhabdoid tumor (ATRT) is a malignant brain tumor of children that has an overall survival of less than 40 percent even with aggressive therapy. We identified upregulation of the mitogen activated protein (MAP) kinase pathway in ATRT. The novel, brain-penetrant MEK inhibitor mirdametinib inhibited the growth of ATRT cell lines in culture at nanomolar concentrations. Mirdametinib suppressed proliferation as measured by BrdU incorporation and induced apoptosis as measured by cPARP and Annexin V staining. Monotherapy with mirdametinib extended the life of mice bearing orthotopic xenografts. Combination therapy with the brain-penetrant cyclin dependent kinase 4/6 inhibitor abemaciclib further suppressed growth and BrdU incorporation in ATRT cell lines representing all molecular subgroups. Mirdametinib and abemaciclib combined to extend survival of mice bearing orthotopic ATRT xenografts. In conclusion, mirdametinib has single agent activity against ATRT and combines with abemaciclib to decrease proliferation and extend survival in orthotopic xenograft models of ATRT.

Oligodendroglioma is a primary central nervous system tumor classified by the presence of isocitrate dehydrogenase (IDH) mutations and codeletion of 1p/19q. Here we describe the generation of an IDH-mutant 1p/19q-codeleted oligodendroglioma mouse model using in utero electroporation. We identified IDH1R132H, PIK3CAE545K, CicKO, Fubp1KO and Cdkn2aKO as the optimal combination (termed OligoCdkn2a) to drive fully penetrant tumors that histologically resemble human grade II/III IDH-mutant, 1p/19q-codeleted oligodendroglioma. Replacing Cdkn2a with Trp53 loss in this mouse model shifted tumor histology towards high grade astrocytoma. OligoCdkn2a tumors displayed metabolic and transcriptional changes associated with IDH and CIC mutations, and single cell sequencing identified a bias towards oligodendrocyte differentiation compared to an IDH wild-type glioblastoma mouse model. OligoCdkn2a tumors represent the first mouse model system to recapitulate the genetic, histological and transcriptional features of human IDH-mutant 1p/19q-codeleted oligodendrogliomas, offering a platform to further dissect tumor biology and test new therapeutic strategies.

PurposeGlioblastoma (GBM) is characterized by poor clinical outcomes and marked resistance to radiotherapy. Because effective radiosensitizing strategies for GBM remain limited, we investigated whether inhibition of KRAS/RAS signaling could enhance radiation response in GBM. In particular, we evaluated the radiosensitizing potential of RMC-6236, an RAS(ON) multiselective inhibitor that suppresses active RAS signaling across multiple RAS-dependent states. Experimental DesignHuman GBM cell lines (U251, LN-18, ACPK1, and OSU61) were treated with radiation, with or without genetic or pharmacological KRAS inhibition. KRAS signaling was suppressed by siRNA-mediated knockdown or RMC-6236 treatment. Radiation-induced KRAS activation and downstream MAPK signaling were assessed by Raf-RBD pull-down assays and immunoblotting. Radiosensitivity was evaluated using clonogenic survival assay. DNA damage persistence, cell cycle distribution, and mitotic catastrophe were analyzed by {gamma}H2AX immunofluorescence, flow cytometry, and nuclear morphology assessment, respectively. In vivo therapeutic efficacy was examined in an orthotopic U251 xenograft model. ResultsRadiation-induced transient activation and increased KRAS protein expression of KRAS, accompanied by activation of ERK, JNK, and p38 signaling in GBM cells. siKRAS suppressed radiation-induced KRAS and MAPK activation, and significantly enhanced radiosensitivity in all four GBM cell lines. Similarly, RMC-6236 inhibited radiation-induced KRAS activation and attenuated downstream MAPK signaling without reducing the total KRAS protein expression. RMC-6236 significantly increased the radiosensitivity across all GBM cell lines, with dose enhancement factors ranging from 1.33 1.46. Mechanistically, combined treatment with RMC-6236 and radiation increased persistent {gamma}H2AX foci and enhanced mitotic catastrophe without producing consistent redistribution of cells into radiosensitive cell cycle phases. In an orthotopic GBM model, the combination of RMC-6236 and radiation significantly prolonged survival compared to that of the control and radiation alone. ConclusionsThese findings indicate that radiation-induced KRAS signaling is a functionally important mediator of radioresistance in GBM and demonstrate that inhibition of KRAS/RAS signaling enhances the radiation response in vitro and in vivo. RMC-6236 may represent a promising radiosensitizing strategy for GBM by suppressing adaptive RAS/MAPK signaling and promoting persistent DNA damage and mitotic catastrophe following irradiation. However, clinical trials of this combination are warranted.

BackgroundPatient-derived tumor organoids are widely used in cancer research, yet the biological impact of tissue processing during model generation remains unclear. Fragment-based and dissociation-based approaches are commonly assumed to trade fidelity for uniformity, but their molecular consequences remain incompletely defined. MethodsWe performed a proteome-wide comparison of fragment-based (CUT) and dissociation-based (DIS) glioblastoma organoid protocols using quantitative mass spectrometry. Organoids from multiple patient tumors were cultured under growth factor-free or growth factor-supplemented conditions and compared with matched primary tissue. ResultsBoth protocols produced technically robust glioblastoma organoids when maintained in their native media. However, CUT organoids matched the reproducibility of DIS cultures while preserving a broader extracellular matrix repertoire and networks linked to collagen assembly, vascular support, and cell-matrix signaling. DIS cultures were biased toward exogenous basement membrane components and proliferative, growth factor-responsive states. Across tumors, CUT organoids consistently showed greater proteomic similarity to matched primary tissue, retaining neural, glial, stromal, and extracellular features largely absent from DIS models. ConclusionsFragment-based glioblastoma organoids can be both reproducible and biologically faithful. Tissue dissociation acts as a major perturbation that reshapes extracellular matrix organization, cellular states, and tumor identity, making protocol choice a critical determinant of model fidelity and translational relevance.

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.

BackgroundGlioblastoma (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. MethodWe 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. ResultsThe 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}B signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. ConclusionsClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}B pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation. Key PointsO_LIClonoScreen3D-CRISPRi combines gene knockdown with 3D clonogenic survival assays C_LIO_LIWe identified NFKB2, RELB, and CDK9 as modifiers of radioresistance in two GBM cell lines C_LIO_LIValidation experiments show ClonoScreen3D-CRISPRi reliably identifies radiosensitizers in GBM C_LI Importance of the studyGlioblastoma (GBM) remains one of the most lethal human cancers, with radioresistance representing a central barrier to improved patient outcomes. While CRISPR-based screens have begun to illuminate genetic drivers of GBM biology, prior approaches using human models have largely relied on two-dimensional culture systems and short-term viability readouts that inadequately model the disease. This study introduces ClonoScreen3D-CRISPRi, a novel platform that integrates CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays in patient-derived GBM cells -- more faithfully recapitulating the long-term clonogenic potential that underlies post-radiotherapy recurrence. Using this platform, we identified NFKB2, RELB, and CDK9 as potent genetic modifiers of radioresistance, with sensitizer enhancement ratios exceeding those of established clinical radiosensitizers such as PARP and ATM inhibitors. Pharmacological validation of the non-canonical NF-{kappa}B pathway demonstrates direct translational relevance, providing a rationale for targeting this axis in combination with radiotherapy to improve GBM treatment.

Background: Reirradiation (reRT) is increasingly offered following progression in diffuse intrinsic pontine glioma (DIPG) and diffuse midline glioma (DMG), though optimal patient selection remains a challenge. This study evaluated clinical outcomes after reRT in a contemporary cohort of patients with DIPG/DMG. Methods: Patients <26 years old with DMG/DIPG treated with radiation therapy between 2011-2025 were retrospectively reviewed. Primary endpoints included overall survival (OS2) and progression-free survival (PFS2), measured from first progression, and change in neurologic symptoms after reRT. Survival was estimated using Kaplan Meier methods, with Cox proportional hazards modeling for prognostic factors. Results: Fifty eight patients were included; 37 (63.8%) underwent reRT. Tumors were predominantly pontine (74.1%). ReRT was associated with improvement in motor function (51.4% vs. 9.5%, p=0.002), cranial nerve function (29.7% vs. 4.8%, p=0.044), and gait ataxia (35.1% vs. 9.5%, p=0.059). Median OS2 and PFS2 were improved with reRT (OS2: 9.67 vs. 2.57 months, p<0.001; PFS2: 5.63 vs. 1.57 months, p<0.001). OS2 was independently associated with reRT (HR 0.27, p<0.0001), pontine location (HR 2.94, p=0.004), and steroid use at progression (HR 4.12, p=0.001). PFS2 was independently associated with reRT (HR 0.23, p < .0001) and distant pattern of failure (HR 2.83, p=.037). Among reRT patients, non-pontine location was associated with improved OS2 (p=0.02), and local failure was associated with improved PFS2 (p=0.003). Conclusion: ReRT was associated with neurologic improvement and prolonged survival. Patients with non-pontine tumors or local-only failure might derive the greatest benefit. Prospective studies are warranted to define optimal dose/fractionation and refine patient selection.

BackgroundGlioblastoma (GB) is a highly aggressive brain tumor with a median survival of approximately 14 months, primarily due to its ability to infiltrate healthy brain tissue both as single cells and in collectives. A deeper understanding of GB cell motility, both individual and collective, is crucial for developing patient-specific therapies. We aimed to characterize migration in patient-derived GB cells using advanced modeling to identify stratification markers and therapeutic vulnerabilities. MethodsWe developed Single-Cell Behavior Live Imaging (ScBLI), an approach integrating live imaging with computational analysis, applied to 30 GB primary cell cultures. Trajectories and morphological features were tracked and analyzed. Diffusion Entropy Analysis (DEA) was applied to classify trajectories based on the Delta Scaling parameter ({delta} scaling). We evaluated functional responses correlating all findings with clinical outcomes and transcriptomic profiles. ResultsWe analyzed 4,279 cell trajectories. Based on {delta} scaling (range 0.28-0.837), we defined three distinct motility groups: Low (L, {delta} scaling [&le;]0.5), Medium (M, 0.5 < {delta} scaling [&le;] 0.7), and High (H, {delta} scaling >0.7). Functional assays demonstrated that Group H cells are more performant in both positive and negative chemotaxis. Clinically, the three groups showed a clear linear progression with patient survival: High {delta} scaling correlated with the shortest survival (poorer prognosis), while Low {delta} correlated with the longest survival, suggesting that structured motility drives invasiveness. Integrative multi-omic analysis, encompassing both exome and transcriptome profiling, demonstrated that these groups are defined by distinct molecular landscapes rather than poor behavioral traits. Moreover, exome data revealed that Group H is significantly enriched in PTEN alterations (75% vs. 8% in Group L), with PTEN gain-of-function (GoF) mutations exclusively restricted to this group (100% vs 0% in Group L). Notably, within our extended cohort (n=51) currently characterized by whole-exome sequencing, we observed that specific PTEN GoF mutations were associated with a significantly shorter survival compared to PTEN wild-type cases (median OS 6.4 vs 16.6 months; p=0.02), which typically harbor the canonical loss of chromosome 10q. A similar clinical trend was observed when comparing directly GoF carriers to patients with truncating (Ter) alterations (median OS 6.4 vs 14.3 months; p=0.09). Conversely, no survival difference was found between truncating (Ter) mutations and wild-type cases. ConclusionOur findings demonstrate for the first time that migratory efficiency, quantified through DEA, represents a powerful predictor of glioblastoma aggressiveness. Tumor cells adopting highly efficient exploration strategies are strongly associated with poor clinical outcomes and are characterized by distinct molecular signatures, notably PTEN gain-of-function alterations. Statement of significanceOur multi-scale computational framework elucidates emergent behavioral phenotypes as pivotal drivers of glioblastoma progression. By demonstrating a correlation between enhanced migratory efficiency, PTEN gain-of-function, and significantly reduced overall survival, we establish a foundational paradigm for deciphering the emergent complexity governing tumor invasiveness.

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.

BackgroundPrevious machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such as DeepGlioma, have achieved high performance (91.5% accuracy) on curated datasets. However, when used intraoperatively, DeepGlioma (162M parameters) runs slowly on current SRH hardware and underperforms due to its lack of an image rejection mechanism and its validation on curated images. Here, we introduce SRH-Informed Glioma classificatioN with Attention Learning (SIGNAL) (27M parameters), a lighter model with a built-in attention-based rejection mechanism that outperforms DeepGlioma on uncurated clinical datasets. MethodsSIGNAL was developed using 1.56 million SRH fields-of-view from 967 adult diffuse glioma patients collected between December 2017 and July 2025. We used 412 patients from NYU for training and internal validation and a multi-institutional, international cohort of 555 patients for testing. SIGNAL uses a ResNet50 backbone pretrained using a hierarchical contrastive loss function followed by a multi-head multi-layer perceptron (MLP). Using a patch-based attention threshold of 0.6, a final MLP was trained to predict glioma subtypes: glioblastoma, oligodendroglioma, or astrocytoma. ResultsSIGNAL outperformed DeepGlioma, achieving greater overall accuracy (90.10% vs. 72.59%) while running faster (16.0 vs. 6.7 patches/s). SIGNAL also outperformed DeepGlioma on all three molecular classification tasks, including IDH mutation (accuracy: 93.51% vs. 79.22%), 1p19q codeletion (93.51% vs. 88.31%), and ATRX loss (89.61% vs. 83.98%). SIGNALs attention mechanism had a strong positive linear correlation with mean patch cellularity (r=0.96, p<0.001) and a strong negative correlation with patch blood coverage (r=-0.99,p<0.001). Finally, subtype and molecular accuracy between tumor core and margin samples were equivalent despite significantly lower patch retention in tumor margins (44.5% vs 60.2%, p<0.0001). ConclusionSIGNAL is a lightweight model for intraoperative molecular classification of gliomas using SRH imaging. Its attention-based image quality filter allows for excellent performance, quick processing, and highly interpretable outputs critical for reliable use in intraoperative workflows. Brief 1-2 Sentence DescriptionWe present SIGNAL, a lightweight machine learning model for intraoperative molecular classification of diffuse gliomas using stimulated Raman histology, whose core innovation is a learned attention mechanism that filters diagnostically uninformative tissue, such as blood and acellular regions, before classification, enabling robust real-world generalizability. Validated on 555 patients across four international centers, SIGNAL outperforms the previous state-of-the-art model DeepGlioma on glioma subtype classification (90.10% vs. 72.59% accuracy) while running 2.4 times faster on intraoperative hardware.