Widespread resetting of DNA methylation in glioblastoma-initiating cells suppresses malignant cellular behavior in a lineage-dependent manner
Bibliographic record
Abstract
Epigenetic changes are frequently observed in cancer. However, their role in establishing or sustaining the malignant state has been difficult to determine due to the lack of experimental tools that enable resetting of epigenetic abnormalities. To address this, we applied induced pluripotent stem cell (iPSC) reprogramming techniques to invoke widespread epigenetic resetting of glioblastoma (GBM)-derived neural stem (GNS) cells. GBM iPSCs (GiPSCs) were subsequently redifferentiated to the neural lineage to assess the impact of cancer-specific epigenetic abnormalities on tumorigenicity. GiPSCs and their differentiating derivatives display widespread resetting of common GBM-associated changes, such as DNA hypermethylation of promoter regions of the cell motility regulator TES (testis-derived transcript), the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C; p57KIP2), and many polycomb-repressive complex 2 (PRC2) target genes (e.g., SFRP2). Surprisingly, despite such global epigenetic reconfiguration, GiPSC-derived neural progenitors remained highly malignant upon xenotransplantation. Only when GiPSCs were directed to nonneural cell types did we observe sustained expression of reactivated tumor suppressors and reduced infiltrative behavior. These data suggest that imposing an epigenome associated with an alternative developmental lineage can suppress malignant behavior. However, in the context of the neural lineage, widespread resetting of GBM-associated epigenetic abnormalities is not sufficient to override the cancer genome.
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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".