The multiomic landscape of meningiomas: a review and update
Bibliographic record
Abstract
PURPOSE: Meningiomas are the most common primary brain tumor in adults. Traditionally they have been understudied compared to other central nervous system (CNS) tumors. However over the last decade, there has been renewed interest in uncovering the molecular topography of these tumors, with landmark studies identifying key driver alterations contributing to meningioma development and progression. Recent work from several independent research groups have integrated different genomic and epigenomic platforms to develop a molecular-based classification scheme for meningiomas that could supersede histopathological grading in terms of diagnostic accuracy, biological relevance, and outcome prediction, keeping pace with contemporary grading schemes for other CNS tumors including gliomas and medulloblastomas. METHODS: Here we summarize the studies that have uncovered key alterations in meningiomas which builds towards the discovery of consensus molecular groups in meningiomas by integrating these findings. These groups supersede WHO grade and other clinical factors in being able to accurately predict tumor biology and clinical outcomes following surgery. RESULTS: Despite differences in the nomenclature of recently uncovered molecular groups across different studies, the biological similarities between these groups enables us to likely reconciliate these groups into four consensus molecular groups: two benign groups largely dichotomized by NF2-status, and two clinically aggressive groups defined by their hypermetabolic transcriptome, and by their preponderance of proliferative, cell-cycling pathways respectively. CONCLUSION: Future work, including by our group and others are underway to validate these molecular groups and harmonize the nomenclature for routine clinical use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".