Atypical Teratoid Rhabdoid Tumours Are Susceptible to Panobinostat-Mediated Differentiation Therapy
Bibliographic record
Abstract
Atypical teratoid rhabdoid tumour (ATRT) is a rare but highly aggressive undifferentiated solid tumour arising in the central nervous system and predominantly affecting infants and young children. ATRT is exclusively characterized by the inactivation of SMARCB1, a member of the SWI/SNF chromatin remodelling complex that is essential for the regulation of large sets of genes required for normal development and differentiation. Histone deacetylase inhibitors (HDACi) are a promising anticancer therapy and are able to mimic the normal acetylation functions of SMARCB1 in SMARCB1-deficient cells and drive multilineage differentiation in extracranial rhabdoid tumours. However, the potential efficacy of HDACi in ATRT is unknown. Here, we show that human ATRT cells are highly responsive to the HDACi panobinostat and that sustained treatment leads to growth arrest, increased cell senescence, decreased clonogenicity and induction of a neurogenesis gene-expression profile. Furthermore, in an orthotopic ATRT xenograft model, continuous panobinostat treatment inhibits tumour growth, increases survival and drives neuronal differentiation as shown by the expression of the neuronal marker, TUJ1. Collectively, this preclinical study supports the therapeutic potential of panobinostat-mediated differentiation therapy for ATRT.
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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".