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Background: Our group and others have recently identified four molecular groups of meningioma, with unique underlying biology and outcomes. The relevance of group-specific metabolite profiles (particularly among hypermetabolic tumours), has not been explored., Methods: We performed untargeted metabolic profiling of meningiomas representing each molecular group and WHO grade. Prognostic biochemicals were identified using Cox regression and their biological importance was explored using RNA and protein-based pathway analyses. Validation was performed using targeted high performance liquid chromatography-mass spectrometry (HPLC-MS/MS)., Results: Global metabolic profiling identified 560 unique biochemicals. We identified a 21-metabolite outcome signatures which is strongly predictive of outcome after adjusting for WHO grade, extent of resection, and receipt of adjuvant radiotherapy (HR 326.49, 95%CI 16.72-6375.48, p < 0.0001). The abundance of N6-trimethyllysine was associated with earlier time to recurrence on our whole cohort (log-rank p = 0.009) and within hypermetabolic and WHO grade 2 tumours specifically; this was validated using targeted HPLC-MS/MS on two cohorts. Consensus RNA and protein expression analysis demonstrated as association between N6-trimethyllysine abundance and activation of oxidative phosphorylation pathways, which portended worse outcomes in the hypermetabolic subgroup but, interestingly, better outcomes in the proliferative subgroup. By contrast, upregulated pyruvate and lactate transporters were associated with worse outcomes in proliferative meningiomas specifically., Conclusions: This is the first study to demonstrate a subgroup-specific prognostic role of N6-trimethyllysine in hypermetabolic meningiomas, offering increasingly granular outcome predictions using a widely accessible technique (HPLC-MS/MS). We also suggest fundamental differences in preferred energy utilization between and a potential need for subgroup-specific therapies., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Background: Meningiomas exhibit considerable clinical and biological heterogeneity. We previously identified four distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, proliferative) that address much of this heterogeneity. Despite the utility of these groups, the stochasticity of clustering methods and the use of multi-omics data for discovery limits the potential for classifying prospective cases. We sought to address this with a dedicated classifier., Methods: Using an international cohort of 1698 meningiomas, we constructed and rigorously validated a machine learning-based molecular classifier using only DNA methylation data as input. Original and newly-predicted molecular groups were compared using DNA methylation, RNA sequencing, copy number profiles, whole exome sequencing, and clinical outcomes., Results: We show that group-specific outcomes in the validation cohort are nearly identical to those originally described, with median PFS of 7.4 (4.9-Inf) years in hypermetabolic tumors and 2.5 (2.3-5.3) years in proliferative tumors (not reached in the other groups). Tumors classified as NF2-wildtype had no NF2 mutations, and 51.4% had canonical mutations previously described in this group. RNA pathway analysis revealed upregulation of immune-related pathways in the immunogenic group, metabolic pathways in the hypermetabolic group and cell-cycle programs in the proliferative group. Bulk deconvolution similarly revealed enrichment of macrophages in immunogenic tumours and neoplastic cells in hypermetabolic and proliferative tumours with similar proportions to those originally described., Conclusions: Our DNA methylation-based classifier, which is publicly available for immediate clinical use, recapitulates the biology and outcomes of the original molecular groups as assessed using multiple metrics/platforms that were not used in its training., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Brain metastases (BMs) are the most common and among the deadliest brain tumors. Currently, there are no reliable predictors of BM development from primary cancer, which limits early intervention. Lung adenocarcinoma (LUAD) is the most common BM source and here we obtained 402 tumor and plasma samples from a large cohort of patients with LUAD with or without BM (n = 346). LUAD DNA methylation signatures were evaluated to build and validate an accurate model predicting BM development from LUAD, which was integrated with clinical factors to provide comprehensive patient-specific BM risk probabilities in a nomogram. Additionally, immune and cell interaction gene sets were differentially methylated at promoters in BM versus paired primary LUAD and had aligning dysregulation in the proteome. Immune cells were differentially abundant in BM versus LUAD. Finally, liquid biomarkers identified from methylated cell-free DNA sequenced in plasma were used to generate and validate accurate classifiers for early BM detection. Overall, LUAD methylomes can be leveraged to predict and noninvasively identify BM, moving toward improved patient outcomes with personalized treatment., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)