F.2 Multiplatform molecular analysis of vestibular schwannoma reveals two robust subgroups with distinct microenvironment
Bibliographic record
Abstract
Background: Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease. Methods: We select all VS from our tumour bank with both methylation and RNA profiling. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings. Results: A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups (“immunogenic” and “proliferative”) with significant differences in immune, stroma, and tumour cell abundance. Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial-to-mesenchymal transition program with associated candidate drug targets, suggesting a need for subgroup-specific treatment/trial design in the future. Conclusions: We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".