GENE-40. CHARACTERIZING EPIGENETIC INTRATUMORAL HETEROGENEITY IN GLIOMA USING SINGLE-CELL BISULFITE SEQUENCING
Bibliographic record
Abstract
Abstract Genetic and epigenetic alterations contribute to the observed intratumoral heterogeneity in adult glioma. Current glioma classification, based on genotype (e.g., IDH1 mutations) and DNA methylation profiles (e.g., glioma CpG Island Methylator Phenotype), can provide clinically relevant tumor subgroups. However, traditional bulk sampling fails to adequately capture the full complement of epigenomic heterogeneity, and may mask deadly features present in less abundant glioma cells. To more precisely characterize the glioma epigenome, we separately profiled single-cell DNA methylation (Reduced Representation Bisulfite Sequencing, RRBS), single-cell RNA expression (10X genomics), and bulk whole genome sequencing in nine gliomas. The genomic regions profiled by scRRBS were primarily gene promoters, but adequate coverage was also reached for glioma-specific enhancer elements and binding sites of chromatin remodelers. Unsupervised clustering of single-cell DNA methylation data revealed intratumoral variability in epigenetic classification and these cell types were distinguished by regulatory element DNA methylation. We further integrated single-cell epigenetic, single-cell transcriptomic, and genomic features to better understand gene regulation and reconstruct each tumor’s lineage history. Together, our study aims to generate a glioma cellular hierarchy shaped by the epigenetic programs that drive tumor growth.
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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 | 0.000 | 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) | 0.001 | 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".