OS4 - 161 Activated Wnt Signaling for the Therapeutic Targeting of Treatment-Refractory Medulloblastoma Stem Cells
Bibliographic record
Abstract
Brain tumours represent the leading cause of childhood cancer mortality, of which medulloblastoma (MB) is the most frequent malignant pediatric brain tumour. Current molecular Nsubgroups of MB recognize distinct disease entities of which activated Wnt signaling (monosomy 6, exon 3 mutations in CTNNB1, and Wnt gene signature) is associated with a distinct subgroup and the best overall outcome. In contrast, only non-Wnt MBs are characterized by metastatic disease, increased rate of recurrence, and poor overall survivorship. Given the excellent clinical outcome in patients with Wnt-driven MB, we aimed to convert treatment-resistant MB subgroups into an ostensibly benign tumour through selective targeting by small molecule Wnt agonists (Wnt3A), GSK3 inhibitors (CHIR99021), and transgenic lines containing a stabilized beta-catenin mutant. Activated Wnt signaling resulted in decreased in vitro self-renewal and promoted differentiation within primary human MB stem cells. The clinical relevance of these findings were demonstrated with an in vivo survival advantage in mice containing orthotopic injections of cells containing a stabilized beta-catenin mutant representative of constitutively active Wnt signaling. Xenografts generated from Wnt-activated tumours were much smaller in size, maintained a much lower rate of proliferation, and reduction in key MB stem cell self-renewal genes (Bmi1, Sox2, Msi1, FoxG1). Our work establishes activated Wnt signaling as a novel treatment paradigm in childhood MB, while providing evidence for the context-specific tumour suppressive function of the canonical Wnt pathway.
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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".