macroH2A2 shapes chromatin accessibility at enhancer elements in glioblastoma to modulate a targetable self-renewal epigenetic network
Bibliographic record
Abstract
SUMMARY Self-renewal is a crucial property of glioblastoma cells and is enabled by the choreographed function of chromatin regulators and transcription factors. Identifying targetable epigenetic mechanisms of self-renewal could represent an important step toward developing new and effective treatments for this universally lethal cancer. Here we uncover a targetable epigenetic axis of self-renewal mediated by the histone variant macroH2A2. Using patient-derived in vitro and in vivo models, we show that macroH2A2 has a direct role in shaping chromatin accessibility at enhancer elements to antagonize transcriptional programs of self-renewal. Pharmaceutical inhibition of the chromatin remodeler Menin increased macroH2A2 levels and repressed self- renewal. Our results reveal a targetable epigenetic mechanism of self-renewal controlled by macroH2A2 and suggest new treatment approaches for glioblastoma patients. SIGNIFICANCE Glioblastoma is an incurable brain cancer. Malignant self-renewing cells have been shown to drive tumor growth, to be refractory to current treatment approaches and to seed relapses, which ultimately prove lethal. Identifying new and targetable mechanisms associated with self-renewal could be a fundamental first step in designing effective therapies that slow or prevent glioblastoma relapses. Using patient-derived models of glioblastoma, we deployed epigenomic approaches and functional assays to define the role of the histone variant macroH2A2 in repressing self-renewal. We identified compounds that increase macroH2A2 levels and repress self-renewal, including a Menin inhibitor. As Menin inhibitors are being tested in clinical trials, these compounds could be used in new therapeutic paradigms to target self-renewing cell populations in glioblastoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".