DIPG-06. Uncovering the FACTs in Diffuse Midline Glioma (DMG)
Bibliographic record
Abstract
Abstract Effective treatments are urgently needed for the incurable paediatric brainstem tumour Diffuse Midline Glioma (DMG). Most DMGs contain Histone H3 mutations (H3K27M), which produce extensive epigenetic dysregulation by inhibiting EZH2-mediated trimethylation of H3K27 (H3K27me3). Global depletion of the repressive H3K27me3 modification results in aberrantly open chromatin and is central to DMG tumorigenesis. Thus, targeting the epigenome is a promising avenue of treatment for DMG. We found that targeting the histone chaperone complex Facilitates Chromatin Transcription (FACT) with the curaxin drug CBL0137 to have potent pre-clinical efficacy against DMG, leading to a paediatric Phase I/II clinical trial for CBL0137 which includes a DMG/DIPG cohort (NCT04870944). In this project we aim to elucidate CBL0137’s molecular mechanism in DMG. FACT is critical for maintaining chromatin homeostasis during DNA replication, transcription, and repair. We therefore hypothesised that FACT maintains the aberrant epigenetic landscape resulting from H3K27M. Consistently, we found CBL0137 to be more cytotoxic against H3K27M-mutant, compared to H3-WT or isogenic DMG cells lacking the mutation. Furthermore, FACT directly interacts with H3K27M, and FACT inhibition increases both EZH2 catalytic activity and global H3K27me3 levels. We are now using ChIP-seq to discover the genome-wide distribution of FACT and H3K27M, and will interrogate the consequence of CBL0137 treatment on the epigenome and transcriptome. Preliminary results suggest that FACT is located at certain genes co-occupied by H3K27M, the active histone mark H3K27ac, and the BET protein BRD4. This implies that FACT is involved in maintaining open chromatin and perhaps transcription at these regions. Combining CBL0137 with other epigenetic drugs, such as BET inhibitors, could therefore represent a rational therapeutic opportunity for DMG. This work will ultimately inform mechanism-based targeted therapies to combine with CBL0137 to improve its efficacy and uncover valuable new insights into DMG epigenetics and pathobiology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".