ATRT-16. MODELLING ATRT THROUGH SWI/SNF COMPLEX DEFICIENCY IN GENETICALLY-ENGINEERED MOUSE MODELS
Bibliographic record
Abstract
Abstract Atypical Teratoid/Rhabdoid Tumours (ATRT) are highly malignant neoplasms arising primarily in the CNS of children. They are defined by loss of function mutations in smarcb1, a gene serving a vital role in neurogenesis and differentiation. In order to recapitulate ATRT in the mouse, we used a Cre-Lox recombination system to conditionally knockout smarcb1 in specific cell compartments. Loss of smarcb1 in BLBP-expressing cells of the developing brain led to severe neurologic defects. Mice exhibited seizures, ataxia, and median 12-day survival. Histological analysis revealed severe thinning of the cerebral cortex and cerebellum. Temporally-targeted smarcb1 loss in BLBP/Nestin-expressing embryonic compartments did not result in tumour formation. Similarly, BLBP-expressing, smarcb1-deficient neural stem/progenitor cells (NSC/NPCs) were isolated and allografted but did not form tumours. These cells demonstrated decreased proliferation, higher apoptosis, and upregulation of p53, CDKN1A, and CDKN2A. In contrast, ubiquitous smarcb1 loss at the earlier embryonic day 6.5 produced widespread tumorigenicity in the forebrain, hindbrain, skullbase, and spine; each with unique phenotypes, survival, and morphology. We employed a clinically-relevant Nanostring gene-panel screen to stratify tumours into genetically distinct subgroups. Our findings indicate that smarcb1 plays an important role in CNS development. Loss of smarcb1 in NSC/NPCs is lethal, and its developmental context influences cell fate. Targeted smarcb1 loss likely plays a tumorigenic role at an earlier developmental stage than previously determined, in a diverse array of primitive stem cells. These data support the generation of a murine ATRT model capable of producing distinct tumour entities that recapitulate the human disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".