DIPG-35. IDENTIFYING DRIVER-SPECIFIC VULNERABILITIES IN PAEDIATRIC HIGH GRADE GLIOMA SUBTYPES
Bibliographic record
Abstract
Abstract Paediatric high-grade gliomas (pHGGs) are incurable malignant brain tumours and a leading cause of cancer-related death in children. The majority of pHGGs carry lysine-to-methionine (K27M) or glycine-to-arginine/valine (G34R/V) mutations in histone variants H3.1 or H3.3. Moreover, histone mutations associate with different anatomical locations and co-segregating mutations defining distinct tumour subtypes within pHGG. However whether these co-occuring mutations can act as drivers to modify tumour phenotypes and drug sensitivities is currently unknown. In order to functionally evaluate the role of partner alterations and to identify new therapeutic targets, we developed in vivo tumour models of pHGG subtypes using in utero electroporation (IUE) in combination with piggyBac transposon and CRISPR technology. We also established ex vivo glioma stem cell (GSCs) lines from mouse models with different co-segregating mutations, able to engraft in syngeneic immune-competent mice. Then, we performed transcriptome analysis and drug screening identifying selective pharmacological vulnerabilities. Our approach represents a preclinical platform to evaluate subtype-specific precision therapies identifying new pathways involved in brain tumour initiation, progression and maintenance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".