OLIG2 mediates a rare targetable stem cell fate transition in sonic hedgehog medulloblastoma
Bibliographic record
Abstract
Functional cellular heterogeneity in tumours often underlies incomplete response to therapy and relapse. Previously, we demonstrated that the growth of the paediatric brain malignancy, sonic hedgehog subgroup medulloblastoma, is rooted in a dysregulated developmental hierarchy, the apex of which is defined by characteristically quiescent SOX2+ stem-like cells. Integrating gene expression and chromatin accessibility patterns in distinct cellular compartments, we identify the transcription factor Olig2 as regulating the stem cell fate transition from quiescence to activation, driving the generation of downstream neoplastic progenitors. Inactivation of Olig2 blocks stem cell activation and tumour output. Targeting this rare OLIG2-driven proliferative programme with a small molecule inhibitor, CT-179, dramatically attenuates early tumour formation and tumour regrowth post-therapy, and significantly increases median survival in vivo. We demonstrate that targeting transition from quiescence to proliferation at the level of the tumorigenic cell could be a pivotal medulloblastoma treatment strategy. Previous work shows that a small population of quiescent SOX2+ medulloblastoma (MB) stem cells can drive tumour growth in early tumorigenesis and relapse. Here, the authors identify OLIG2 as a transcriptional mediator of the transition from quiescent to rapidly proliferating progenitor states and therapeutically target this axis in preclinical models of MB.
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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".