LGG-10. EPIGENETIC/GENETIC/MORPHOLOGIC ANALYSES REVEAL CLINICAL/PROGNOSTIC INSIGHT OF PEDIATRIC LOW GRADE GLIOMAS
Bibliographic record
Abstract
Methylation analysis provides insight into the diagnosis and prognosis of pediatric brain tumors. However, the role of the methylome on pediatric low grade gliomas (PLGG) is still unclear. We performed methylome analysis using the Illumina EPIC array combined with pathologic, molecular and outcome data on 153 well annotated PLGG from both St-Jude and SickKids. For BRAFV600E gliomas, high grade gliomas were also included. Hierarchical clustering and t-Distributed Stochastic Neighbor Embedding (tSNE) plots uncovered multiple factors that influence methylation-based clustering of PLGG. Importantly, tumor location and lymphocyte infiltration influence the cluster more than molecular status or pathology. Methylation data results in helpful information to change the clinical management in 2.2% of tumors but classified tumors incorrectly 4.3%. For BRAF-V600E gliomas (n=81), all tumors with CDKN2A deletion were included in a PXA cluster regardless of the pathology. Gene-ontology analysis shows that the genes with highly methylated promoter regions in the PXA cluster are relevant to central nervous system/cell/tissue differentiation/development. Tumors clustering as PXA (DKFZ classifier) had 78% 5-year overall survival (OS). However, this group could be further stratified into 100% OS for those with low grade histology versus 30% for HGG (p<0.003). The PLGG methylome is affected by multiple non-neoplastic factors and provides information on rare subtypes. Combined molecular and pathological and methylation classification is required to implement this analysis in a clinical setting.
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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 | 0.001 |
| 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) | 0.002 | 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".