HG-76SPATIAL AND TEMPORAL HOMOGENEITY OF DRIVER MUTATIONS IN DIFFUSE INTRINSIC PONTINE GLIOMA
Bibliographic record
Abstract
Diffuse Intrinsic Pontine Glioma (DIPG) is a deadly pediatric brain tumor where needle biopsies help guide diagnosis and targeted therapies. Up to 80% of DIPGs harbor a de novo lysine 27 to methionine mutation in histone 3 (H3.1-, H3.3-K27M). However, the homogeneity of H3K27M and its partner mutation(s) across tumor mass is unknown. Furthermore, our recent findings indicate frequent tumor spread within the brain; however, the molecular signature of extended tumors is unknown. To address these questions, we molecularly analyzed (WES, MiSeq, ddPCR, RNA-Seq, methylation) 134 samples from various neuroanatomical structures of whole autopsied brains from nine DIPG patients. Comparative molecular characterization of extended and primary lesions was completed to assess spatial and temporal hetero/homogeneity of driver mutations in DIPGs. Mutation based-evolutionary reconstruction indicated H3K27M mutations potentially arise first and are associated with obligate partner mutations throughout the tumor and its spread, from diagnosis to end-stage disease. This data supports the necessity of partner mutations for development of DIPG. We also identified a H3.2K27M mutant DIPG. H3K27M associated partner mutations include alterations in cell cycle (TP53/PPM1D) or specific growth factor pathways (ACVR1/PIK3R1). Later oncogenic alterations arise in sub-clones and often affect the PI3K pathway. Our findings are consistent with early tumor spread outside the brainstem. The spatial and temporal homogeneity of driver mutations in DIPG implies they will be captured by limited biopsies, and emphasizes the need to develop therapies specifically targeting obligate oncohistone partnerships.
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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 | 0.001 | 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) | 0.001 | 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".