COMPREHENSIVE MULTIPLATFORM ANALYSIS OF CDKN2A ALTERATIONS IN MENINGIOMAS
Bibliographic record
Abstract
Abstract BTFC travel award recipient In meningiomas, CDKN2A/B deletions are associated with poor clinical outcomes but are exceeding rare in most cohorts (1-5% of cases). Large molecular datasets are therefore required to explore these deletions and their relationship to other CDKN2A alterations that may be more common, but also prognostic on a transcriptomic, epigenomic, and/or copy number level. METHODS: We used multidimensional molecular data of 560 meningioma samples from 5 independent cohorts to comprehensively interrogate the spectrum of CDKN2A alterations through DNA methylation, copy number variation, transcriptomics, and proteomics using an integrated molecular approach, and utilized preclinical models to validate our findings. RESULTS: Meningiomas with either CDKN2A/B deletions (partial or homozygous loss) or an intact CDKN2A gene locus but elevated mRNA expression (CDKN2Ahigh) both had poor clinical outcomes. Increased CDKN2A mRNA expression was a poor prognostic factor independent of deletion status. CDKN2A expression and p16 protein also progressively increased with tumor grade and more aggressive molecular and methylation groups. CDKN2Ahigh meningiomas and meningiomas with CDKN2A deletions were enriched for similar cell cycling pathways but dysregulated at different checkpoints (G1/S vs G2/M). p16 immunohistochemistry was unreliable in differentiating between meningiomas with and without CDKN2A deletions, but increased positivity was associated with mRNA expression. CDKN2Ahigh meningiomas were associated with gene hypermethylation, Rb-deficiency, and lack of response to CDK inhibition. CONCLUSIONS: In meningiomas, CDKN2A mRNA expression consistently increases with biological aggressiveness, is prognostic independent of copy number loss, and may be used as an important prognostic biomarker with therapeutic implications for resistance to CDK4/6 inhibitors clinically.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".