Widespread resetting of DNA methylation in glioblastoma-initiating cells suppresses malignant cellular behavior in a lineage-dependent manner
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it