PATH-41. INTEGRATED MOLECULAR ANALYSIS REVEALS HYPERMETHYLATION AND OVEREXPRESSION OF HOX GENES TO BE POOR PROGNOSTICATORS IN IDH MUTANT GLIOMA
Bibliographic record
Abstract
Abstract BACKGROUND Diffuse gliomas represent over 80% of malignant brain tumors ranging from low-grade to aggressive high-grade lesions. Within IDH-mutant gliomas there is a high variability in survival and a need to more accurately predict outcome. METHODS To identify and characterize a predictive signature of outcome in gliomas, we utilized an integrative molecular analysis (using methylation, mRNA, copy number variation (CNV) and mutation data), analyzing a total of 729 IDH-mutant samples including a test set of 99 from University Health Network (UHN) and two validation cohorts including the German Cancer Research Center (DKFZ) and The Cancer Genome Atlas (TCGA). RESULTS Cox regression analysis of methylation data from the UHN cohort identified CpG-based signatures that split the glioma cohort into two prognostic groups strongly predicting survival that were validated using two independent cohorts from TCGA and DKFZ (all p-values< 0.0001). The methylation signatures that predicted poor outcome also exhibited high CNV instability and hypermethylation of HOX gene probes. Integrated multi-platform analyses using mRNA and methylation (iRM) showed that parallel HOX gene overexpression and simultaneous hypermethylation were significantly associated with increased mutational load, high aneuploidy and worse survival (p-value< 0.0001). A 7-HOX gene signature was developed and validated using the most significantly associated HOX genes with patient outcome in both 1p/19q codeleted and non-codeleted IDHmut gliomas. CONCLUSIONS HOX gene methylation and expression provide important prognostic information in IDH-mutant gliomas that are not captured by current molecular diagnostics. A 7-HOX gene signature of outcome shows significant survival differences in both 1p/19q codeleted and non-codeleted IDH-mutant gliomas.
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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 | 0.000 |
| 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".