Metabolic profiling of meningioma reveals novel subgroup-specific biologic insights and outcome dependencies
Bibliographic record
Abstract
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 tumors), has not been explored. METHODS: We performed untargeted metabolic profiling of meningiomas representing each molecular group and World Health Organization (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 signature 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 < .0001). The abundance of N6-trimethyllysine was associated with earlier time to recurrence on our whole cohort (log-rank P = .009) and within hypermetabolic and WHO grade 2 tumors 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".