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Record W4416141534 · doi:10.1093/neuonc/noaf201.0334

STEM-27. TRACING THE ORIGINS OF H3.3K27M MUTANT DIFFUSE MIDLINE GLIOMA

2025· article· en· W4416141534 on OpenAlexaff
Evan Lubanszky, Quang M. Trinh, Luiza Lopes Pontual, Jannine Forst, Cynthia Hawkins, Peter B. Dirks

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsForebrainHindbrainGliomaCorticogenesisNeurogenesisFate mappingMutantMutation

Abstract

fetched live from OpenAlex

Abstract Diffuse midline gliomas (DMGs) are universally fatal pediatric brain tumours defined by their midline localization, early onset, and distinct mutational landscape. The H3.3K27M mutation is found in approximately 80% of DMGs, marking it as a key initiating event. However, H3.3K27M alone does not induce brain tumour formation in experimental models, suggesting that its oncogenic potential may be linked to specific developmental windows and susceptible precursor populations. Identifying the cellular states that are vulnerable to transformation is critical to understanding DMG initiation. To establish a reference framework for these vulnerable states, we generated a single-cell RNA sequencing atlas of normal hindbrain development. Using a Sox2-eGFP reporter mouse and FACS-based enrichment of Sox2+ precursors, we profiled over 120,000 cells across ten developmental stages, capturing critical transitions in neurogenesis and gliogenesis. To reveal regional differences, we integrated the atlas with a similarly generated forebrain atlas, providing a comprehensive map of neurodevelopmental trajectories where DMG mutations might exert their effects. Building on this developmental reference, we utilized inducible mouse models to introduce H3.3K27M mutations in precursors and employ lineage tracing to track their fate over time. When combined with loss of Trp53 and overexpression of PDGFRa, alterations prevalent in DMG, mice develop tumours that resemble the human disease. H3.3K27M-driven tumours consistently localize to the pons, in contrast to H3 wild-type tumours, which occur more variably across the forebrain and hindbrain. Tracking these pontine tumours using serial MRI revealed that tumours arise well before symptom onset and exhibit growth dynamics that vary by location and timing. Earlier-arising tumours are more aggressive and tend to occur in posterior locations. Together, our integrated approach combining lineage tracing, tumour modelling, and a developmental atlas provides a powerful platform to dissect the early events in DMG pathogenesis and identify developmental vulnerabilities that may be targeted for early intervention.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.310
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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