Spatial and temporal homogeneity of driver mutations in diffuse intrinsic pontine glioma
Bibliographic record
Abstract
Diffuse Intrinsic Pontine Gliomas (DIPGs) are deadly paediatric brain tumours where needle biopsies help guide diagnosis and targeted therapies. To address spatial heterogeneity, here we analyse 134 specimens from various neuroanatomical structures of whole autopsy brains from nine DIPG patients. Evolutionary reconstruction indicates histone 3 (H3) K27M--including H3.2K27M--mutations potentially arise first and are invariably associated with specific, high-fidelity obligate partners throughout the tumour and its spread, from diagnosis to end-stage disease, suggesting mutual need for tumorigenesis. These H3K27M ubiquitously-associated mutations involve alterations in TP53 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 tumour spread outside the brainstem including the cerebrum. The spatial and temporal homogeneity of main driver mutations in DIPG implies they will be captured by limited biopsies and emphasizes the need to develop therapies specifically targeting obligate oncohistone partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".