EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS
Bibliographic record
Abstract
Abstract Machine learning-based molecular classifications, particularly those using DNA methylation data, have greatly advanced diagnostics for meningioma, the most common type of primary intracranial tumor. Meningiomas have historically been classified into NF2-mutant and NF2-wild-type groups, while additional mutations and copy-number variations associated with progression risk have been incorporated into WHO grading. Several genome-wide methylation-based classification systems have been proposed. The systems, such as the random forest Brain Tumour Classifier, have been incorporated into diagnostic guidelines. However, while a number of core archetypes are shared among the different classifications, discrepancies on the definition and granularity of subtypes remain an obstacle to their clinical application. Understanding the underlying heterogeneity driving these classifications is therefore crucial. Through an integrated analysis of single-nuclei and spatially resolved transcriptomic data, as well as DNA methylation array data from multiple meningioma cohorts, we identified cell types and epigenetic signatures that are associated with increased aggressiveness in meningiomas. The results demonstrated that incremental changes in the tumor microenvironment (TME), particularly shifts in compositions and epigenetic-transcriptomic signatures in tumor-associated monocytes/macrophages and microglia-like cells, have a decisive impact on epigenetic classifications alongside tumor cells, and significantly affect clinical outcome. Therefore, we refine the previously proposed distinct molecular subtypes with a TME-determined risk continuum model for NF2-mutant meningiomas. Based on these discoveries, we additionally designed an immunohistochemistry-based diagnostic approach, which also captures intra-tumoral heterogeneities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".