MODL-12. A NOVEL GENETICALLY ENGINEERED H3.3G34R MODEL REVEALS COOPERATION WITH ATRX LOSS IN UPREGULATION OF HOXA CLUSTER GENES AND PROMOTION OF NEURONAL LINEAGE
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/tv-a 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 progenitor cells. RESULTS Transcriptomic analysis revealed that ATRX loss in the context of H3.3G34R upregulates the Hoxa cluster genes Hoxa2, Hoxa3, Hoxa5, and Hoxa7 (p < 0.05, unpaired t-test). H&E staining and EMA immune-staining indicate that ATRX loss is correlated with a higher incidence of ependymal differentiation in H3.3G34R expressing samples (p < 0.05, unpaired t-test). GSEA and RT-qPCR analysis demonstrate that H3.3G34R expression in the context of ATRX loss promotes increased expression in genes associated with neuronal lineage (FWER pval < 1.0) and exhibits upregulation of the neurofilament polypeptides Nefl, Nefm (p < 0.05, unpaired t-test) as well as the neuronal differentiation marker Stmn2 (p < 0.001, unpaired t-test). CONCLUSION Our study proposes a model in which cooperation between ATRX loss and H3.3G34R expression mediate major transcriptomic and histopathological changes in H3.3G34R pHGGs. Broadly, our work highlights the importance to study 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 | 0.000 |
| 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".