PATH-49. COMPREHENSIVE RNA-SEQ ANALYSIS REVEALS NOVEL SUBTYPES OF MENINGIOMAS AND PREDICTS PATIENT OUTCOME.
Bibliographic record
Abstract
Abstract OBJECTIVE Meningiomas are the most common intracranial tumor in humans. While most tumors are benign, some are malignant and ultimately lethal. Current grading systems based on WHO grade do not provide adequate risk stratification, therefore, better characterizations of the biology of aggressive meningiomas are needed. METHOD: We obtained 12 datasets from 9 institutions and 5 countries in North America, Europe and Asia and combined them with ~300 tumors sequenced from the University of Washington to create a set of ~1300 meningioma tumors. Raw sequencing data was aligned to hg38, batch corrected and then dimension reduction of normalized counts was performed to create a UMAP landscape. Differential expression analysis and gene set enrichment analysis were performed to elucidate the underlying biology of meningioma subtypes. RESULTS The analysis revealed nine distinct meningioma subtypes through unsupervised clustering. Clinical metadata, DNA sequencing, copy number alterations, and gene-fusion data effectively correlated with these identified clusters. These subtypes exhibited specific biological signatures. Notably, regional distribution of time to recurrence identified subtypes as well as intra-cluster differences of meningiomas with varying patient outcomes. The most aggressive subtype, characterized by higher WHO grades, frequent tumor recurrences, and shorter time to recurrence, exhibited elevated proliferation rates and RNA expression resembling embryonic limb development. To facilitate clinical applications, we developed a cross-validated nearest-neighbors-based algorithm that accurately mapped new patients onto this UMAP landscape, achieving a remarkable 95% accuracy. CONCLUSION Our study highlights the utility of RNA sequencing in discerning meningioma heterogeneity and provides a valuable tool in predicting tumor biology and patient prognosis. The integration of our UMAP landscape with patient data offers an effective approach for personalized treatment strategies.
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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 | 0.001 | 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 | 0.001 | 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 | 0.001 |
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".