EPCO-16. ONCOHISTONE INTERACTOME PROFILING UNCOVERS MECHANISMS OF CHROMATIN DISRUPTION AND IDENTIFIES POTENTIAL THERAPEUTIC TARGETS IN PEDIATRIC HIGH-GRADE GLIOMA
Bibliographic record
Abstract
Abstract Mutations in histone H3 at amino acids 27 (H3K27M) and 34 (H3G34R) occur with high-frequency in pediatric high-grade glioma. H3K27M mutations have been shown to lead to global disruption of H3K27me3 through dominant negative PRC2 inhibition with accompanying gains in H3K36me3, while H3G34R mutations lead to local losses of H3K36me3 through inhibition of SETD2. However, the mechanism of action of these mutants on the broader landscape of chromatin-associated proteins remains unknown. Importantly, proteins with differential associations with oncohistones could be targeted therapeutically. Here we profiled the interactomes of the H3.1K27M, H3.3K27M and H3.3G34R oncohistones using BioID to gain an unbiased measure of their interaction landscapes. Among the differential interactors all 3 mutants lost interaction with H3K9 methyltransferases, while H3G34R also had reduced interaction with DNA methyltransferases accompanied by genome-wide DNA hypomethylation. In contrast, H3K27M mutants had increased association with transcription factors, consistent with the activation of transcription induced by the global loss of H3K27me3. H3K9me3 was reduced in H3K27M-containing nucleosomes, and cis-H3K9 methylation was required for H3K27M to exert its effect on global H3K27me3. Depletion of H3K9 methyltransferases with shRNA or treatment with H3K9 methyltransferase inhibitors was lethal to H3.1K27M, H3.3K27M and H3.3G34R mutant pHGG cell lines, underscoring the importance of H3K9 methylation for oncohistone-mutant gliomas and suggesting it could make an attractive therapeutic target.
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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".