SMARCA5 is required for the development of granule cell neuron precursors and Sonic Hedgehog Medulloblastoma growth
Bibliographic record
Abstract
Abstract Medulloblastoma constitutes a molecularly diverse group of malignant embryonal brain tumors. Sonic hedgehog molecular group of medulloblastoma (SHH-MB) is a highly heterogeneous tumor entity, characterized by constitutive activation of the SHH signaling pathway. Due to lack of suitable cell line models, little is known about genetic dependencies in SHH-MB outside of the SHH pathway. By performing a CRISPR-Cas9 dropout screen in SMB21 cells derived from SHH-MB in Ptch +/− mice, we aimed to identify genetic vulnerabilities in SHH-MB. Among the top scored gene hits, members of the SNF2-family of ATP-dependent chromatin remodelers including Smarca5 emerged as genetic dependencies in SHH-MB, and we validate that Smarca5 knockout inhibits SHH pathway activation and SHH-MB cell proliferation. Additional genetic ablation experiments in vivo revealed that conditional deletion of Smarca5 in cerebellar granule cell neuron precursors (GCNPs), the cell origin of SHH-MB, significantly reduces the proliferative capacity of GCNPs and leads to cerebellar hypoplasia in mice. Furthermore, loss of Smarca5 in GCNPs in an established mouse model of SHH-MB results in prolonged survival of tumor bearing mice. Our data underline the critical role of SMARCA5 during the development of the cerebellum and the pathogenesis of SHH-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) | 0.002 | 0.001 |
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.
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".