HGG-01. A novel genetically engineered H3.3G34R model reveals cooperation with ATRX loss in upregulation of PRC2 target genes and promotion of the NOTCH pathway
Bibliographic record
Abstract
Abstract BACKGROUND: Pediatric high-grade gliomas (pHGGs) are an aggressive CNS tumor which are often characterized by mutations in H3F3A, the gene that encodes Histone H3.3 (H3.3). A substitution of the Glycine at position 34 of H3.3 with either Arginine or Valine (H3.3G34R/V), was recently described in a large cohort of pHGG samples and has been characterized as occurring in anywhere between 5-20% of pHGGs. Attempts to study the mechanisms of H3.3G34R have proven difficult due to the developmental nature of the disease and the requirement of co-occurring mutations for model development. METHODS: We utilized the RCAS system to develop a genetically engineered mouse model (GEMM) that incorporates PDGF-A activation, TP53 loss and the H3.3G34R mutation both in the context of ATRX loss and ATRX presence in nestin expressing progenitors. RESULTS: We show that in H3.3G34R expressing mice, ATRX loss significantly increased tumor latency from 90 days to 143 days (p < 0.01, Log rank test) and decreased tumor incidence from 81% to 57% (p < 0.01, Fisher’s exact test). By contrast, H3.3G34R did not significantly impact tumor latency in either our ATRX loss (163 days to 143 days, p = 0.178, Log-rank test) or our ATRX expressing (95 days to 90 days, p = 0.415, Log-rank test) models. Transcriptomic analysis revealed that ATRX loss in the context of H3.3G34R upregulates the PRC2 associated genes Hoxa2, Hoxa3, Hoxa5, and Hoxa7 (p < 0.05, unpaired t-test). GSEA analysis and RT-qPCR data suggest that ATRX loss works synergistically with H3.3G34R to promote NOTCH pathway activation through upregulation of the NOTCH ligand Dll3 (p < 0.01, unpaired t-test). CONCLUSIONS: Our study proposes a model in which ATRX loss is the major contributor to transcriptomic changes in the majority of H3.3G34R pHGGs. Broadly, our work highlights the importance of studying mechanisms of co-occurring genetic events separately and in combination.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".