Therapeutic targeting of prenatal pontine ID1 signaling in diffuse midline glioma
Bibliographic record
Abstract
BACKGROUND: Diffuse midline gliomas (DMG) are highly invasive brain tumors with rare survival beyond two years past diagnosis and limited understanding of the mechanism behind tumor invasion. Previous reports demonstrate upregulation of the protein ID1 with H3K27M and ACVR1 mutations in DMG, but this has not been confirmed in human tumors or therapeutically targeted. METHODS: Whole exome, RNA, and ChIP-sequencing was performed on the ID1 locus in DMG tissue. Scratch-assay migration and transwell invasion assays of cultured cells were performed following shRNA-mediated ID1-knockdown. In vitro and in vivo genetic and pharmacologic [cannabidiol (CBD)] inhibition of ID1 on DMG tumor growth was assessed. Patient-reported CBD dosing information was collected. RESULTS: Increased ID1 expression in human DMG and in utero electroporation (IUE) murine tumors is associated with H3K27M mutation and brainstem location. ChIP-sequencing indicates ID1 regulatory regions are epigenetically active in human H3K27M-DMG tumors and prenatal pontine cells. Higher ID1-expressing astrocyte-like DMG cells share a transcriptional program with oligo/astrocyte-precursor cells (OAPCs) from the developing human brain and demonstrate upregulation of the migration regulatory protein SPARCL1. Genetic and pharmacologic (CBD) suppression of ID1 decreases tumor cell invasion/migration and tumor growth in H3.3/H3.1K27M PPK-IUE and human DIPGXIIIP* in vivo models of pHGG. The effect of CBD on cell proliferation appears to be non-ID1 mediated. Finally, we collected patient-reported CBD treatment data, finding that a clinical trial to standardize dosing may be beneficial. CONCLUSIONS: H3K27M-mediated re-activation of ID1 in DMG results in a SPARCL1+ migratory transcriptional program that is therapeutically targetable with CBD.
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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 | 0.000 | 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".