C.4 Epigenetic drivers of malignant transformation in diffuse gliomas
Bibliographic record
Abstract
Background: Despite treatment, gliomas often exhibit disease progression, leading to recurrent glioblastoma (GBM) or malignant transformation of low-grade gliomas (LGG) associated with treatment resistance and poor prognosis. To date, the molecular factors driving glioma recurrence are poorly understood. Methods: We analyzed a cohort of 324 glioma samples from our institution including a unique cohort of 81 patients with matched primary and recurrent tumour pairs. We performed a paired, integrated multi-platform analysis consisting of DNA methylation profiling on all 324 samples, gene expression on 87 samples, and matched plasma cell-free DNA methylome analysis on 82 samples. Results: LGG that undergo malignant transformation are associated with DNA hypomethylation at recurrence, including decreased tumour purity and increased copy number variation. Integrated pathway analyses identified IL-6, associated with multiple pro-oncogenic pathways, and CCR2, associated with the recruitment of tumour-associated macrophages, as top genes involved in malignant transformation. Matched plasma methylation demonstrated a shift in the methylation signature at recurrence that can prove valuable as a non-invasive biomarker for early detection of malignant progression. Conclusions: We provide the first detailed description of the epigenetic evolution of gliomas and identify epigenetic drivers of malignant transformation including non-invasive biomarkers for early detection of malignant progression.
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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 | 0.000 |
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".