MOMC-3. Hypermethylation and overexpression of HOX genes are poor prognosticators in Lower-Grade Glioma
Bibliographic record
Abstract
Abstract Diffuse gliomas represent over 80% of malignant brain tumors ranging from low-grade to aggressive high-grade lesions. Molecular characterization of these tumors led to the development of new classification system comprising specific glioma subtypes. While this provides novel molecular insight into gliomas it does not fully explain the variability in patient outcome. To identify and characterize a predictive signature of outcome in diffuse gliomas, we utilized an integrative molecular analysis (methylation, mRNA, copy number variation (CNV) and mutation data) using multiple molecular platforms, including a total of 310 IDH mutant glioma samples from University Health Network (UHN) and German Cancer Research Center (DKFZ) together with 419 samples from The Cancer Genome Atlas (TCGA). 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 (p-value < 0.0001). The CpG-based signatures were reliably validated using two independent datasets from TCGA and DKFZ cohorts (both p-values < 0.0001). The results show that the methylation signatures that predict poor outcome also correlated with G-CIMP low status, elevated CNV instability and hypermethylation of a set of HOX gene probes. Further study in diffuse lower-grade glioma (LGG) using integrated mRNA and methylation (iRM) analyses showed that parallel HOX gene overexpression and hypermethylation in the same direction were significantly associated with increased mutational load, high aneuploidy and worse survival (p-value < 0.0001). Furthermore, this iRM high group was characterized by a 7-HOX gene signature showed significant survival differences not only in IDH mutant LGG but also in IDH wildtype LGG. These results demonstrate the importance of HOX genes in predicting the outcome of diffuse gliomas to identify relevant molecular subtyping independent of histology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".