Citations

PURPOSE Age ≥40 years is regarded as a high-risk feature and an indication for adjuvant chemoradiotherapy for patients with lower-grade glioma in clinical practice guidelines. It is unclear whether age remains a relevant prognostic factor for contemporary definitions of lower-grade gliomas in the molecular era. METHODS The Prospective Gliomas Research (PROGRES) database contains individual patient-level data from 11 prospective clinical trials or observational registries of histologically defined lower-grade 2-3 oligodendroglioma or astrocytoma. We determined the association of age (18-39 years v ≥40 years) with progression-free survival (PFS) stratified by isocitrate dehydrogenase 1 or 2 ( IDH1/2 ) status, using log-rank tests and Cox regression models. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] database). RESULTS We identified 1,619 and 1,292 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH -wildtype tumors were more common in patients 40 years and older (38% v 5%, odds ratio: 11.3 [95% CI, 6.5 to 19.7]). Age was associated with PFS in IDH -wildtype (5-year PFS for ≥40 v 18-39 years: 6% v 24%, hazard ratio [HR], 1.74 [95% CI, 1.21 to 2.50]) but not in IDH -mutant glioma (60% v 59%, HR, 0.89 [95% CI, 0.76 to 1.05], P interaction < .001). In IDH -wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% v 28%), EGFR amplification (41% v 15%), and chromosome +7/–10 alteration (57% v 25%). In a pooled analysis of four clinical trials, age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone for IDH -mutant glioma. CONCLUSION In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral of adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

Abstract Cerebrospinal fluid (CSF) is the biofluid most proximal to the central nervous system (CNS), yet its proteomic organization across neurological disease remains unresolved. We assembled a meta-atlas of 35 CSF proteomic datasets (2016–2026) comprising 10,560 quality-controlled samples spanning neurodegenerative, neuroinflammatory, neuro-oncological, infectious and structural-injury contexts. Meta-analysis of cross-cohort detection defined a 747-protein core mass-spectrometry-accessible CSF proteome that generalizes to held-out cohorts. We derived thirteen consensus meta-programs that transfer across independent cohorts and span the major neural, barrier, immune and plasma compartments. Unexpectedly, these programs are dominated by a single axis of variation opposing complement- and plasma-associated inflammation against neuronal-synaptic homeostasis. This injury–homeostasis axis is diagnosis-spanning and tracks clinical severity and disease activity across diverse neurological conditions. We show that a two-protein C5–NRCAM model can approximate the injury–homeostasis axis, bridging deep proteomic profiling toward bedside assessment.

Abstract Introduction Leptomeningeal disease (LMD) is the seeding of tumor cells in the leptomeninges and the cerebrospinal fluid (CSF) and carries a poor prognosis of 2-4 months. CSF sampling is required to study LMD, but genomic analysis may not be able to identify non-genomic and post-translational modifications. Proteomics can address this gap but is limited by reproducibility and disease heterogeneity. Integrating proteomic with genomic data may prioritize driver genes that promote LMD. Methods 23 patients with breast cancer-associated LMD with matched CSF proteomic and genomic data were studied. Variant allele frequencies (VAFs) for genes TP53, NFE2L2, ERBB2 and PIK3CA were derived, and proteomic profiling was performed on matched CSF. Spearman correlations between driver-gene VAF and protein expression were computed, restricting to genes with VAF&gt;0 in ≥ 3 samples, and Benjamini-Hochberg correction was applied across gene-protein pairs. Proteins significantly associated with VAF were analyzed using over representation analysis (ORA) and gene segment enrichment analysis (GSEA) with KEGG, Gene Ontology, and MSigDB. Results TP53 VAF was associated with upregulation of proteins involved in glycolysis and cell proliferation, with enrichment of hallmark glycolysis (FDR=2.6x10-8), mTORC1 signalling (FDR=8.5x10-8), and reactive oxygen species pathways (FDR=2.7x10-3), and downregulation of complement (FDR=6.8 x 10-6) and coagulation cascades (FDR=0.011). NFE2L2 VAF was associated with epithelial-mesenchymal transition by both GSEA (FDR=2.3x10-9) and ORA (FDR=4.6x10-3), TGF-β signalling (FDR=0.015) and glycoprotein metabolism (FDR=1.6x10-7). ERBB2 VAF was associated with an upregulation of cell-cycle regulation (FDR=6.4x10-10), mTORC1 pathway (FDR=3.2x10-4), and a downregulation of proteins involved in cell-cell adhesion (FDR=4.0x10-9), axon guidance (GSEA FDR=0.025), and developmental growth (GOBP FDR=3.4x10-4). Conclusion Distinct CSF proteomic signatures were associated with specific driver mutations, implicating metabolic and oxidative stress pathways (TP53), stromal remodelling (NFE2L2), and disruption of CNS structural pathways (ERBB2). Validation in prospective LMD cohorts is needed to confirm these mutation-specific signatures and inform diagnostic and therapeutic strategies.

Abstract Background Glioblastoma (GB) exhibits marked tumor microenvironmental heterogeneity, contributing to both therapy resistance and poor survival outcomes. Intercellular adhesion molecule 1 (ICAM1) is an inflammation-associated adhesion molecule implicated in immune-stromal interactions, but its clinical and biological significance in GB remains incompletely defined. Methods We performed ICAM1 immunohistochemistry, bulk RNA sequencing, DNA methylation analysis, single-nucleus RNA sequencing, and preoperative MRI-derived morphologic feature analysis across multi-institutional cohorts of treatment-naïve IDH-wildtype GB. Results High ICAM1 expression at both transcript and protein levels is associated with shorter overall survival, particularly within the mesenchymal transcriptional subtype. ICAM1 expression is inversely associated with promoter methylation. Single-nucleus RNA-sequencing analysis shows that ICAM1-high tumors are enriched for mesenchymal, hypoxia, stress-associated, and immunosuppressive myeloid programs, suggesting that ICAM1 reflects a multicompartment inflammatory tumor ecosystem. ICAM1 expression, tumor surface irregularity, and patient age are independently prognostic and minimally correlated, capturing complementary clinical, imaging, and molecular features of GB biology. When combined, our Clinical-Imaging-Molecular (CIM) framework refines outcome prediction across independent GB cohorts using routinely available clinical data, conventional imaging, and standard immunohistochemistry. Conclusions ICAM1 characterizes an immune-remodeled, stress-adapted GB ecosystem associated with poor clinical outcomes. Integrating patient age, surface irregularity, and ICAM1 expression yields a scalable and clinically accessible prognostic framework for GB, supporting tailored outcome prediction, particularly in low- and middle-resource settings without routine access to advanced molecular profiling.

Abstract Background Glioblastoma (GBM) is the most common malignant primary brain tumor and is associated with a poor prognosis. Repurposed posaconazole (PCZ) has demonstrated efficacy in vitro by targeting key metabolic pathways. The aim of this study was to determine the neuropharmacological profile of PCZ in patients with GBM through a Phase 0 first-in-human trial for this indication (NCT04825275). Methods We conducted an open-label, non-randomized, parallel-arm trial in participants with primary or recurrent GBM. Patients in the treatment arm received oral PCZ for 7-10 days presurgically to achieve steady-state concentrations. Microdialysis catheters were implanted to measure interstitial drug levels. Postoperative tissue and fluid samples were analyzed to evaluate PCZ pharmacokinetics (mass spectrometry) and pharmacodynamics, including vascular density, apoptosis, and metabolite concentrations (lactate and pyruvate) compared to controls. Results The trial was closed early due to slow accrual, enrolling 2 participants in the PCZ arm and three in the control arm. No drug-related safety issues were observed. Although PCZ was undetectable in microdialysate fluid, it accumulated in tumor tissue, with concentrations showing an association with CD31-measured endothelial content. PCZ-treated tumors exhibited trends toward lower BCL2 expression indicative of increased apoptosis, and significantly lower pyruvate levels in the tumor periphery compared to controls. Plasma lactate levels in treated patients normalized to control levels over 24 h. Conclusions Despite limited accrual, this trial provides the first clinical evidence that oral PCZ accumulates in human GBM tissue and shows preliminary signals of metabolic modulation. These findings provide biological rationale warranting further clinical investigation, potentially utilizing direct tissue sampling rather than microdialysis.

Abstract Purpose: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities. Methods: This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE). Results: Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5-200, p<0.001 and 2.85 [1.92-27.6], p=0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: (https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/) Conclusion: Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of simple but clinically meaningful definitions of dysfunction. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials.

Abstract Background DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed. Methods We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade. Results We first demonstrated that all risk groups benefit from gross total resection. All “high-risk, RT sensitive” tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (P = .003); after subtotal resection (STR), from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (P = .004). “Low-risk, RT sensitive cases” (n = 1207, 61.8%) also benefited from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, P = .03), suggesting that RT be considered in these patients. Neither “low-risk RT resistant” (n = 84, 4.3%) nor “high-risk RT resistant” (n = 356, 18.2%) cases benefited from RT, and the latter group was associated with universally poor outcomes. Conclusions We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.

Abstract Glioblastoma (GBM) is a highly aggressive brain tumor with poor survival outcomes and limited tools for accurate prediction of individual patient prognosis. While many prognostic models rely on advanced molecular testing, such methods are not routinely accessible across most clinical settings. In this study, we identify a practical and clinically applicable approach to estimate prognosis for GBM patients using three objective and readily accessible features: the patient’s age, tumor surface irregularity determined from MRI scans, and the expression of ICAM1 measured by standard immunohistochemistry. We show that higher ICAM1 levels are linked to worse survival and a more inflamed but immunosuppressive tumor microenvironment. These findings are functionally validated in ICAM1-wildtype and knockout mouse models using single-cell proteomics via cytometry by time-of-flight, and an immune-comprehensive panel of antibodies. By combining age, ICAM1, and MRI-derived shape features into a single Clinical-Imaging-Molecular (CIM) score, we developed a model that can reliably separate patients with a GBM into higher- and lower-risk groups. This score was validated across multiple international institutions and patient populations and does not require genomic sequencing or specialized technologies. The CIM model could serve as a supplement for care providers to better tailor treatment plans and follow-up strategies for individual patients, making personalized care more achievable even in resource-limited settings.

Abstract IDH-wildtype diffuse astrocytic gliomas with lower-grade histological features alongside specific genomic alterations (e.g., TERT promoter mutation, EGFR amplification, gain Chr7/loss Chr10) are presently diagnosed as glioblastoma, IDH-wildtype (GBM) under WHO 2021 criteria. Previous analyses categorized these into “early/evolving” or “surgically undersampled” GBMs. However, it remains unclear whether these tumors share identical clinical and prognostic features with histologic GBM. Data from IDH-wt molecular GBMs across six institutions were aggregated. Astrocytic IDH-wt tumors of any grade that lacked necrosis or microvascular proliferation on initial histopathology yet exhibited molecular alterations consistent with WHO 2021 GBM diagnosis, were included. Recursive partitioning analysis (RPA) was used to categorize patients into risk groups based on overall survival (OS). Among the 264 patients included, the median age was 60 years (IQR: 51-67), with 162 (61%) being male. Additionally, 210 (80%) were diagnosed since 2016, and 216 (82%) received chemoradiation plus adjuvant temozolomide (GBM-SOC). The median follow-up period was 3.9 years (95% CI: 3.4, 4.6). Identified univariate risk factors for worse survival included older age, biopsy-only status, EGFR amplification, absence of initial adjuvant treatment, low post-operative KPS, and tumor histologic grade of 3. RPA divided patients into four distinct survival groups (p&lt;0.0001). The “best” risk group comprised lower grade (&lt;=2) tumors (n=58, median OS (mOS): 40 months (95% CI: 33, NA)). The “second best” group included Grade 3 patients receiving GBM-SOC without steroids during initial RT (n=139, mOS: 25 months (95% CI: 22, 28)). The “second worst” group encompassed Grade 3 patients who were on steroids during initial RT while receiving GBM-SOC (n=38, mOS: 15 months (95% CI: 13, 20)). The “worst” group contained Grade 3 tumors that had not undergone SOC (n=29, mOS: 9 months (95% CI: 4, 26)). These data show that histologic features and treatment are strong indicators of patient outcomes.