STEM-04. LONGITUDINAL DNA METHYLATION CHANGE FUELS DISEASE PROGRESSION THROUGH ALTERED CELL STATE DIFFERENTIATION IN IDH-MUTANT GLIOMA
Bibliographic record
Abstract
Abstract IDH-mutant glioma (IDH-G) is initially slow-growing but invariably progresses to fatal disease. While IDH-G is generally characterized by global hypermethylation at CpG islands - known as the glioma CpG islands methylator phenotype (G-CIMP), recent bulk profiling studies have shown that progressed IDH-G loses the G-CIMP status (shift to G-CIMP-low). However, the impact of G-CIMP loss on glioma transcriptional cell states and their inheritance has not been elucidated. To address the question, we leveraged recent advances in single-cell multi-omics technologies to profile a longitudinal cohort of 35 IDH-G tumor samples (15 pairs), co-capturing full-length transcriptional (by Smart-Seq2) and DNA methylation (by extended-representation bisulfite sequencing (XRBS)), within the same single-nuclei. Compared to reduced-representation bisulfite sequencing (RRBS) used for single-cell DNA methylation profiling in our prior multi-omic single-cell study (Nat Genet., 2021), XRBS provided higher coverage of CpG islands (mean of 378,888 vs 198,345.1, P = 2.2 x 10-16) in this study. Transcriptional and epigenetic comparisons of cell states during IDH-G progression revealed increased stem-like states, decreased differentiation and identified potential cell states regulators. Single-cell DNA methylation analysis demonstrated methylation loss in progressed tumors, however the degree of global DNA methylation was similar between different malignant cells states within individual tumors. This suggests that lower DNA methylation is not due to a change in cell state composition, but rather that it may alter cell state architecture. To address whether decreased methylation in progressed tumors affects cellular dynamics, we applied a quantitative framework to directly measure cell state heritability and plasticity based on transcriptional annotation of high-resolution phylogenetic trees in clinical samples. The analysis suggested that decreased methylation reshapes cellular hierarchy to increased heritability of stem-like states and decreased differentiation. This study provides insight into the impact of DNA methylation on glioma progression, integrating cell states transition dynamics with epigenetic evolution.
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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) | 0.002 | 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".