Molecular and translational advances in meningiomas
Bibliographic record
Abstract
Meningiomas are the most common primary intracranial neoplasm. The current World Health Organization (WHO) classification categorizes meningiomas based on histopathological features, but emerging molecular data demonstrate the importance of genomic and epigenomic factors in the clinical behavior of these tumors. Treatment options for symptomatic meningiomas are limited to surgical resection where possible and adjuvant radiation therapy for tumors with concerning histopathological features or recurrent disease. At present, alternative adjuvant treatment options are not available in part due to limited historical biological analysis and clinical trial investigation on meningiomas. With advances in molecular and genomic techniques in the last decade, we have witnessed a surge of interest in understanding the genomic and epigenomic landscape of meningiomas. The field is now at the stage to adopt this molecular knowledge to refine meningioma classification and introduce molecular algorithms that can guide prediction and therapeutics for this tumor type. Animal models that recapitulate meningiomas faithfully are in critical need to test new therapeutics to facilitate rapid-cycle translation to clinical trials. Here we review the most up-to-date knowledge of molecular alterations that provide insight into meningioma behavior and are ready for application to clinical trial investigation, and highlight the landscape of available preclinical models in meningiomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".