STEM-21. INVESTIGATING DOT1L AS AN EPIGENETIC VULNERABILITY IN BRAIN TUMOR STEM CELLS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM), the most common and aggressive primary adult brain cancer, is thought to be driven by a small subpopulation of brain tumor stem cells (BTSCs). BTSCs exhibit shared properties with normal stem cells such as self-renewal and multilineage differentiation. These stem cell properties have been proposed to underlie GBM tumorigenicity, treatment evasion and contribute to tumor heterogeneity. To investigate the biology underlying the stem cell properties of GBM, we compared gene essentiality profiles for a panel of BTSCs, fetal neural stem cells and non-GBM cell lines using a CRISPR Cas9 knockout library. Interestingly, from these screens, we identified the histone methyltransferase disrupter of telomeric silencing-1-like (DOT1L) as an essential gene for the growth of BTSCs and fetal neural stem cells but not for non-GBM cell lines. DOT1L is the only known histone methyltransferase responsible for histone 3 lysine 79 methylation, an epigenetic mark associated with active gene transcription. The role of this epigenetic regulator in BTSCs was investigated in depth using EPZ-5676, a clinically relevant small molecule inhibitor. Short-term treatment with EPZ-5676 in BTSCs showed minimal effects on cell viability but led to striking morphological changes, increased neuronal and astrocytic differentiation and a reduction in self-renewal. Longer treatment periods with EPZ-5676 led to a decrease in BTSC proliferation and an increase in apoptosis. Furthermore, BTSCs pretreated with EPZ-5676 led to slowed orthotopic tumor growth and improved overall survival in a SCID mouse model. Overall, these findings suggest DOT1L epigenetically regulates GBM stem cell properties and tumor growth. We are further investigating the mechanisms underlying DOT1L regulation of gene expression in BTSCs with the goal of improving the field’s understanding of epigenetics and the therapeutic implications of targeting epigenetic processes in GBM.
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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 | 0.001 |
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".