Citations

Abstract Background Adolescent and young adult (AYA) patients remain underrepresented in neuro-oncology research. Despite being the second most common primary brain tumor in this population, meningiomas have not been studied using age-specific molecular analyses. DNA methylation-based classification and prognostic tools have transformed meningioma care. This study aimed to evaluate the performance of these tools across age groups. Methods We analyzed 1,568 meningiomas with DNA methylation and clinical data, including 18 pediatric patients (<15 years), 195 AYA patients (15-39 years), and 1,355 adult patients (>39 years). Pediatric and AYA (P/AYA) tumors were combined and compared with adult tumors. The performance of established molecular classifiers and recurrence predictors, as well as differences in chromosomal copy number alterations were compared across age groups. Results While histologic grading was comparable between cohorts, P/AYA tumors displayed significantly fewer aggressive molecular groups and lower frequencies of chromosomal arm losses, including 1p, 6q, and 14q. The adult-trained recurrence predictor failed in the P/AYA population (AUC 0.57), despite similar score distributions. Retraining the model on an age-specific cohort using an identical analytic framework improved performance (AUC 0.79) and enabled effective stratification of progression-free survival (P = 0.00054). Importantly, 1p loss retained prognostic significance within the P/AYA group, supporting its clinical utility. Conclusions Molecular tools developed in adult-dominant cohorts do not generalize to younger patients due to both biological divergence and exclusion from model development. These findings underscore the need for age-specific molecular frameworks and highlight the imperative of including P/AYA populations in precision neuro-oncology research to ensure lifespan-equitable care.

Abstract BACKGROUND IDH-mutant gliomas are diffusely infiltrating, slow-growing brain tumors that inevitably undergo malignant progression to grade IV astrocytoma - a highly aggressive and uniformly fatal disease with dismal prognosis. Despite extensive research, the molecular mechanisms underlying this transformation remain poorly understood. While alterations in canonical driver genes such as CDKN2A, PTEN, and PDGFRA have been implicated, the broader landscape of genetic drivers involved in IDH-mutant glioma progression is largely uncharacterized. In contrast to many other malignancies, IDH-mutant gliomas acquire relatively few somatic point mutations during progression - typically in the range of 30 to 40 - suggesting that alternative mechanisms may be driving tumor evolution. Notably, these tumors exhibit widespread chromosomal instability, resulting in a heterogeneous and highly prevalent pattern of copy number alterations (CNAs). This observation raises the possibility that gene dosage changes due to CNAs play a critical role in tumor progression. MATERIAL AND METHODS To investigate this hypothesis, we performed integrative analysis of IDH-mutant glioma cohorts from the Mayo Clinic and TCGA datasets (~1,500 patients), identifying twelve highly recurrent CNAs significantly associated with patient outcomes. We refer to these as malignant IDH-associated CNAs (mIDH CNAs). We hypothesized that these recurrent CNAs harbor driver genes that confer selective growth advantages, thereby contributing to their recurrence and prognostic relevance. To systematically assess the functional impact of genes within these regions, we developed a novel in vivo CRISPR-based screening platform, termed CRISPR-Knock Out and Activation Linked Assay (CRISPR-KOALA). This approach utilizes stereotaxic delivery of lentiviral gRNA libraries - targeting mouse orthologs of ~1,800 genes within recurrently deleted regions and ~1,200 genes within amplified regions - into the brains of a genetically engineered mouse model of IDH-mutant glioma (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022). RESULTS Mice injected with mIDH CNA-targeting libraries developed tumors significantly more rapidly than control cohorts. Deconvolution of the CRISPR-KOALA screen identified several putative driver genes, including regulators of the NOTCH signaling pathway, lipid metabolism modulators such as THEM6, and the NMDA receptor modulator GRINA - implicating dysregulation of metabolism, immune response, and glutamatergic signaling in the malignant transformation of IDH-mutant gliomas. CONCLUSION Together, these findings provide critical insights into the genetic mechanisms driving the progression of IDH-mutant gliomas and identify new candidate genes and pathways for potential therapeutic targeting.

Abstract BACKGROUND Glioblastoma (GBM) is a highly aggressive brain tumour with limited prognostic tools that are broadly implementable in routine clinical settings. Current models often depend on molecular platforms not universally accessible. We aimed to develop and validate a clinically practical prognostic model for GBM that integrates ICAM1 protein expression, imaging-derived tumor morphometrics, and clinical features. MATERIAL AND METHODS We analyzed ICAM1 expression in GBM at the DNA methylation, RNA, and protein levels using public datasets and institutional cohorts, confirming its association with tumor grade and poor survival. ICAM1 protein levels were then quantified via digital pathology in a multi-institutional cohort of 197 newly diagnosed, treatment-naïve GBM patients. Tumor surface irregularity (SIR) was calculated from pre-operative contrast-enhanced MRI using open-source imaging tools. Age at diagnosis was also recorded. These three variables—ICAM1 expression, SIR, and age—were integrated into a Clinical-Imaging-Molecular (CIM) model using multivariable Cox regression. The model was validated in an independent external GBM cohort (n = 20). Survival prediction was assessed using Kaplan-Meier curves, ROC analysis, and concordance index (c-index). RESULTS ICAM1 expression was associated with DNA hypomethylation and significantly correlated with reduced survival at transcriptomic and proteomic levels. Pathway analysis identified associations with immune activation and angiogenesis. In the clinical cohort, high ICAM1 expression, elevated tumor SIR, and older age independently predicted worse survival. The CIM model combining these factors stratified patients into distinct prognostic groups, achieving strong performance (c-index = 0.779, AUC = 0.778). External validation confirmed the model’s predictive utility (median survival difference: 9.1 months, P = 0.009). CONCLUSION We present a validated and clinically applicable prognostic model for GBM that integrates ICAM1 expression, imaging features, and age. The CIM model is scalable, interpretable, and feasible for implementation across academic and community settings, providing a robust tool for patient stratification, treatment planning, and trial design.

Key Points Among US preemptively waitlisted patients with CKD, deceased donor transplants increased for Black and Hispanic patients, comparable with White patients. Dialysis initiation was higher for waitlisted Asian, Black, and Hispanic patients compared with White patients, mirroring lower living donor transplants. Organ Procurement and Transplantation Network wait time credit and Increasing Organ Transplant Access metric could expand preemptive transplantation. Background Preemptive kidney transplantation (before dialysis initiation) is the optimal therapy for most transplant-eligible advanced patients with CKD. Although prior research has documented racial and ethnic disparities in preemptive referrals, less is known about distinct outcomes after preemptive waitlisting. This study examines the association between race and ethnicity and outcomes for preemptively waitlisted patients. Methods We conducted a secondary analysis of a prospectively maintained US cohort using data from the Scientific Registry of Transplant Recipients. The study population included 98,863 adult first-time kidney transplant candidates who were preemptively waitlisted from January 1, 2009, to December 31, 2020, and followed through December 31, 2024. The exposure of interest was race and ethnicity. The primary outcome was 3-year preemptive deceased donor kidney transplantation, living donor kidney transplantation, or dialysis initiation. We used competing risk models to estimate adjusted subdistribution hazard ratios (aSHRs). Data were stratified into 3-year intervals (2009–2011, 2012–2014, 2015–2017, and 2018–2020). Results Black and Hispanic patients received preemptive deceased donor kidney transplants as often as White patients (era 2018–2020, aSHRs [95% confidence interval (CI)]: 0.99 [95% CI, 0.92 to 1.07] and 1.02 [95% CI, 0.93 to 1.12], respectively), while Asian patients had lower rates (0.78 [95% CI, 0.69 to 0.88]). However, Asian, Black, and Hispanic patients were more likely to initiate dialysis (aSHRs: 1.50 [95% CI, 1.39 to 1.61]; 1.41 [95% CI, 1.34 to 1.49]; and 1.21 [95% CI, 1.14 to 1.29], respectively) and were less likely to receive preemptive living donor kidney transplants than White patients (era 2018–2020, aSHRs: 0.49 [95% CI, 0.44 to 0.54]; 0.31 [95% CI, 0.29 to 0.34]; and 0.61 [95% CI, 0.56 to 0.66], respectively). Conclusions Among waitlisted patients with CKD in the United States between 2009 and 2020, rates of preemptive deceased donor kidney transplantation improved for Black and Hispanic patients and became comparable with White patients. However, Asian, Black, and Hispanic patients experienced higher rates of dialysis initiation and lower rates of preemptive living donor kidney transplantation.

Abstract Glioblastoma is the most common type of malignant primary brain tumor and a major cause of morbidity and mortality. In 2021, the World Health Organization updated the classification of Central Nervous System (CNS) tumors to restrict glioblastomas to isocitrate dehydrogenase-wildtype (IDHwt) tumors, improving understanding of the prognosis and optimal therapy for these tumors. This revision also enables more homogeneous populations of patients to be enrolled in clinical trials, facilitating the evaluation of novel therapies. In this updated consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of patients with glioblastoma is discussed. In addition, novel therapies such as immunotherapies, viral therapies, targeted molecular therapies, theranostics, and antibody–drug conjugates will be reviewed, as well as the current challenges and future directions for research.

2030 Background: Brain metastases (BM) are common and arise in 30% of lung adenocarcinoma (LUAD) patients. Patients with LUAD that develop BM have significantly poorer outcomes, with a 10-16 month median overall survival. Unfortunately, current clinical practice for BM prediction is limited and so BM are typically detected after they develop and grow to cause neurological symptoms. Once BM are detected, currently neurosurgical tumor biopsies are performed to enable BM diagnosis via neuropathological evaluation. The aims of this study were to develop DNA methylation-based models that predict LUAD BM and non-invasively detect BM in blood to enable early diagnosis and treatment. Methods: DNA methylomes were acquired from 402 tumor tissue and plasma samples in a cohort of 346 LUAD and BM patients. Machine learning models were built using DNA methylation signatures that stratify BM risk in tissue and detect BM in plasma. Models were evaluated in independent validation datasets. A predictive nomogram was developed using the BM prediction model together with clinical factors to provide composite patient-specific scores reflecting BM risk. Results: The methylation-based BM predictor accurately stratified BM risk in a univariable Cox model using validation set data (HR = 5.65, 95%CI 1.85–17.2, p = 0.0023). Model utility was independent of the predictive value of clinical factors in a multivariable Cox model using validation set data (Table 1: HR = 8.92, 95%CI 1.97–40.5, p = 0.0046). The 5-year model accuracy was 0.81 and significantly higher than a similarly built cancer stage-based model (0.65), demonstrating utility over current practice. The combinatorial clinical-methylomic predictive nomogram had enhanced utility with an accuracy of 0.82 univariable Cox HR of 17.2 (95%CI 4.13–71.3, p < 0.0001), demonstrating comprehensive patient-specificity. The plasma-based model accurately classified BM from gliomas and lymphomas (AUROC=0.80), as typical clinical differential diagnoses, in validation set data. The models were validated further in additional external data. Conclusions: DNA methylation-based modeling of BM can accurately predict LUAD patients at risk for BM development and can non-invasively detect BM that develop. Future treatment approaches may tailor initial LUAD treatment and ongoing cancer surveillance to a patient’s BM risk, allowing for the potential to prevent and treat BM early. DNA methylation-based BM prediction is independent of clinical factors in a multivariable Cox proportional hazards model. Variable HR 95% CI p Methylome risk score 8.92 1.97–40.5 0.005 Age Years 0.96 0.92–1.02 0.177 Smoking Pack-years 0.99 0.95–1.03 0.496 EGFR Mutant vs wildtype 0.92 0.25–3.34 0.895 T T2 vs T1 1.58 0.41–6.04 0.505 T3−4 vs T1 1.49 0.28–7.98 0.642 N N1 vs N0 1.05 0.31–3.58 0.943 N2−3 vs N0 1.00 0.27–3.69 0.995 M M1 vs M0 145 12.2–1730 <0.001

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.

Abstract Cerebrospinal fluid (CSF) has emerged as a valuable liquid biopsy source for glioma biomarker discovery and validation. CSF produced within the ventricles circulates through the subarachnoid space, where the composition of glioma-derived analytes is influenced by the proximity and anatomical location of sampling relative to tumor, in addition to underlying tumor biology. The substantial gradients observed between lumbar and intracranial CSF compartments for tumor-derived analytes underscore the importance of sampling site selection. Moreover, radiographic features, such as tumor-CSF contact and blood-brain barrier disruption, are critical covariates that may affect biomarker detection and the abundance of plasma-derived analytes in CSF, respectively. Longitudinal intracranial CSF sampling, enabled by access devices like Ommaya reservoirs, may offer a window into treatment response and disease progression, though variability in analyte yield, sample volumes, and the dynamic effects of surgical resection pose challenges. This review critically evaluates the anatomic, radiographic, and longitudinal factors, or “time-space continuum,” that impact glioma CSF biomarker abundance. Practical considerations for longitudinal CSF biobanking, including access device placement and collection, are also reviewed. Key takeaways and recommendations for CSF glioma biomarker discovery and validation are provided as a “hitchhiker’s guide” based on our collective experience, along with resources for investigators aiming to develop CSF biobanking at their institutions.