OS3.6 Development and validation of a DNA methylome-based predictor of meningioma recurrence and meningioma recurrence score
Bibliographic record
Abstract
The greatest clinical challenge faced in meningioma is the inability to predict recurrence for individual patients, limiting the ability to select patients who would benefit from adjuvant radiation therapy to prevent recurrence. In this multi-center cohort study, we utilized global epigenome DNA methylation profiles from human tumor samples to generate and validate a methylome-based predictor of recurrence-free survival (RFS) in individual patients to guide decision making regarding adjuvant treatment. Cox modeling of individual probes was used for feature selection in a training set (N=228 patients) which was then applied to two independent validation sets (N=54; N=140 patients). Gene-expression analysis was correlated to DNA methylation profiles using two published microarray datasets (GSE16581; GSE9438). Finally, penalized Cox modeling was used to generate a 5-year meningioma recurrence score based on a nomogram that integrated our validated methylome-based predictor with established clinical factors. The methlyome-based predictor was independently associated with RFS in each of the two validation sets, after adjusting for tumor grade and extent of resection (EOR; HR 4.0, 95%CI 1·4 - 11·5, P = 0·01 and HR 2·3, 95%CI 1·4 - 3.8, P = 0·002). Using a 5-year RFS metric, the methylome-based predictor performed favourably compared to a grade-based predictor in both validation cohorts (ΔAUC =15%; ΔAUC=12%). Functional annotation of the included probes implicated the homeobox gene family. A nomogram constructed using the validated methylome-predictor with WHO grade and EOR demonstrated greater predictive performance than a nomogram using clinical factors alone (ΔAUC = 7·7%) and resulted in two different risk groups with distinct recurrence patterns (P < 0·001). The methylome-based predictor and meningioma recurrence score developed and validated in this study provide important prognostic information not captured by established clinical factors. The meningioma recurrence score represent the first combined molecular and clinical prognostic tool that is individualized for patients with meningiomas, and hence an advance towards precision medicine in meningiomas
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".