Emerging Medical Treatments for Meningioma in the Molecular Era
Bibliographic record
Abstract
Meningiomas are the most common type of primary central nervous system tumors. Approximately, 80% of meningiomas are classified by the World Health Organization (WHO) as grade I, and 20% of these tumors are grade II and III, considered high-grade meningiomas (HGMs). Clinical control of HGMs, as well as meningiomas that relapse after surgery, and radiation therapy is difficult, and novel therapeutic approaches are necessary. However, traditional chemotherapies, interferons, hormonal therapies, and other targeted therapies have so far failed to provide clinical benefit. During the last several years, next generation sequencing has dissected the genetic heterogeneity of meningioma and enriched our knowledge about distinct oncogenic pathways driving different subtypes of meningiomas, opening up a door to new personalized targeted therapies. Molecular classification of meningioma allows a new design of clinical trials that assign patients to corresponding targeted agents based on the tumor genetic subtypes. In this review, we will shed light on emerging medical treatments of meningiomas with a particular focus on the new targets identified with genomic sequencing that have led to clinical trials testing novel compounds. Moreover, we present recent development of patient-derived preclinical models that provide platforms for assessing targeted therapies as well as strategies with novel mechanism of action such as oncolytic viruses.
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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 | 0.000 | 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) | 0.003 | 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".