Citations

The classification and management of central nervous system (CNS) tumours have undergone substantial transformation over the past two decades, with the recognition of an expanding set of molecularly defined disease entities. Genome-wide DNA methylation profiling has emerged as an important complement to histopathology and targeted molecular testing, providing an epigenetic framework that captures tumour lineage, biological state and large-scale genomic alterations within a single assay. Accumulating evidence demonstrates that methylation-based approaches improve diagnostic precision, resolve biologically heterogeneous entities and support clinically meaningful risk stratification across a broad spectrum of CNS tumours. Methylation profiling has also increasingly been applied to outcome prediction, liquid biopsy-based analyses and the resolution of diagnostically challenging or misclassified cases. In addition, advances in multi-omic integration, computational inference from routinely acquired clinical data and evolving implementation frameworks are extending the clinical applicability of epigenetic profiling. This review synthesises the current state of DNA methylation profiling within the diagnostic and clinical framework of CNS tumours and considers how emerging technologies and practice models are shaping its ongoing integration into neuro-oncological care.

Abstract Background Approximately 12 000 adolescents and young adults (ages 15-39, AYAs) are diagnosed with a primary central nervous system (CNS) tumor each year. DNA methylation profiling has transformed CNS tumor classification by refining diagnostic accuracy and identifying biologically distinct subtypes, but its application has not been systematically evaluated in the AYA population. In this study, we examined the spectrum of CNS tumor types across age groups, with a focus on the AYA population. Methods We assembled a large dataset of CNS tumor samples with age annotations and methylation profiles matching with high confidence to a CNS tumor type using the NCI/Bethesda classifier. Prevalence of tumor type, methylation class and DNA copy number aberrations were compared across age strata (pediatric, AYA, and adult). Results The cohort of 21 712 CNS tumors included 5351 tumors from AYAs (25%), which showed a mixed pattern, with tumors typical of childhood (eg, medulloblastoma) as well as those common in older adults (eg, glioblastoma). Several tumor types were specifically enriched in the AYA, including IDH-mutant astrocytoma, pleomorphic xanthoastrocytoma, posterior fossa group B ependymoma, and diffuse hemispheric glioma, H3 G34-mutant, among others. Distinct methylation subclasses of multiple tumor types were observed in the AYA, and patterns of genomic aberrations showed age-specific distributions. Conclusion Large-scale methylation profiling revealed unique classification patterns of CNS tumors in AYAs, with specific tumor types, subclasses, and genomic alterations enriched in this population. These data may serve as a valuable reference resource for better understanding the spectrum of CNS tumors affecting AYA patients.

Abstract Purpose: Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor, with a median survival of only 15 months. Effective therapeutic advances remain limited, with few clinical trial patients exhibiting robust responses to experimental therapies. This has led to efforts to identify molecular biomarker signatures capable of stratifying patient responses either prognostically (pre-treatment) or diagnostically (post-treatment). This study aims to assess the robustness and reproducibility of published transcriptional GBM biomarker signatures for predicting clinical outcomes in GBM trials. Methods: We analyzed published RNA-seq data from 4 clinical trials involving oncolytic virotherapy and immune checkpoint inhibitors, as well as multiple cohorts of patients treated via the Stupp protocol. The performance of reported immune gene signatures was evaluated across all datasets using both grouped and continuous survival modeling. Intra-patient signature variation between different biopsy sites and timepoints was assessed using longitudinal, multi-site sampling collected by the Break Through Cancer Accelerating GBM Therapies TeamLab. LASSO, multivariate Cox regression, and risk scoring with FDR adjustment were used to identify novel signatures with cross-cohort utility. Key Findings: No single published signature consistently predicted survival across all analyzed trials. A post-treatment ssGSEA antitumor cytokine signature that was associated with survival in the CAN-3110 OV trial (R = 0.74, p < 0.01) was also significantly associated with improved survival in an adjuvant anti-PD1 trial (R = 0.75, p = 0.03) and neoadjuvant anti-PD1 trial (R = 0.30, p = 0.05), but not in standard-of-care controls. PAM clustering of MCP immune signatures, as reported for the DNX-2401 OV+Anti-PD1 trial, also stratified post-treatment survival in the CAN-3110 OV trial (p < 0.01), with the coldest TME subtype consistently predicting worse survival. However, the prognostic value of the most immune enriched TME subtype was inconsistent and did not extend to the Anti-PD1 trials. Furthermore, multi-site, longitudinal biopsy samples revealed marked heterogeneity in biomarker signatures between biopsies from individual patients, even when assessed at the same timepoint. We’ve identified several candidate transcriptomic signatures that show promise for generalizable prognostic value in GBM patient cohorts and are characterizing their spatiotemporal variability. Conclusions: While no individual immune signature is universally predictive in GBM immunotherapy, certain post-treatment cytokine signatures and TME stratifications are significant in multiple immunotherapy contexts, though not in standard-of-care patient cohorts. Marked spatiotemporal heterogeneity in these signatures underscores the need for composite prognostic markers resilient to sampling variance. Citation Format: Christopher M. Jannotta, C Zoe Linke, Charles A. Whittaker, Vikas Patil, Accelerating GBM Therapies TeamLab, Farshad Nassiri, Gelareh Zadeh, E Antonio Chiocca, Alexander L. Ling. Assessing biomarker reproducibility for glioblastoma patient response stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1055.

Abstract Neurosurgical oncology is a rapidly advancing field, driven by interdisciplinary collaboration, technological innovation, and a growing understanding of tumor biology. As we celebrate the 40th anniversary of the AANS/CNS Section on Tumors, this editorial highlights the collaborative spirit that defines the Section and explores the many ways it intersects with other neurosurgical subspecialties and AANS/CNS Sections to optimize patient care. This special edition features a series of articles showcasing these intersections across functional, cerebrovascular, spine, trauma, pain, and pediatric neurosurgery. For example, insights into neuron–glioma interactions—enhanced by tools such as optogenetics and advanced brain mapping—are opening doors to more personalized treatment strategies. Innovations in preoperative embolization for skull base meningiomas are improving surgical precision and outcomes. Similarly, advances in managing vertebral metastatic disease with surgical techniques and stereotactic body radiation therapy (SBRT) are reshaping standards of care. Neuro-intensive care has evolved to support personalized postoperative recovery, with a focus on neurological function and cognitive rehabilitation. In pain management, neuromodulation, including intrathecal drug delivery and spinal cord stimulation, is offering new relief to tumor patients. Adolescents and young adults (AYA) with gliomas remain an underserved population, requiring age-specific treatment frameworks and better integration into clinical trials. This edition celebrates the diverse and collaborative workforce propelling neurosurgical oncology forward.