“Homogenous subgroups of atypical meningiomas defined using oncogenic signatures: basis for a new grading system?”
Bibliographic record
Abstract
Abstract Meningiomas are the most common brain tumor with a prevalence of 3% in the population. Histological grading of meningiomas (1 through 3) has a major role in determining treatment choice and predicting outcome. While largely indolent grade 1 and the highly aggressive grade 3 meningiomas as considered mostly homogenous in clinical behavior, atypical or grade 2 meningiomas have highly diverse biological properties. Our aim was to identify homogenous subgroups of atypical meningiomas with the working hypothesis that these subgroups would share features with grade 1 and grade 3 counterparts. We carried out systems level analysis by gene module discovery using co-expression networks on the transcriptomics of 212 meningiomas. The newly identified subgroups were characterized in terms of recurrence rate and overlapping biological processes in gene ontology. We were able to reclassify 33 of 46 atypical meningiomas (72%) into a benign “grade 1-like” (14/46) and malignant “grade 3-like” (19/46) subgroup based on oncogenic signatures. Recurrence rates of “Grade 1-like” and “grade 3-like” tumors was 0% and 72% respectively. These two new subgroups showed similar recurrence rates and concordant biological processes with the respected grades. Our findings help resolve the heterogeneity/uncertainty around atypical meningioma biology and identify subgroups more homogenous than in prior studies. These results may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials. The findings demonstrate the conceptual advantage of systems biology approaches and underpin the utility of molecular signatures as complements to the current histological grading system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".