Abstract A006 Targeting boundary cap cells leads to mouse <i>Smarcb1</i>-deficient peripheral nerve tumors recapitulating human peripheral rhabdoid tumors
Bibliographic record
Abstract
Abstract Malignant rhabdoid tumors (MRT) are rare aggressive tumors of infancy characterized by the biallelic inactivation of SMARCB1. Their lineage of origin remains uncertain, but increasing evidence suggest a neural-crest origin. By analyzing the transcriptome of MRT occurring in peripheral and cranial nerves, we observe that their expression profiling was not distinct from any other tumor localization. We therefore investigated whether the development of Smarcb1-deficient tumors emerging from embryonic precursors of peripheral nerves in mice could recapitulate human MRT. we generated conditional Smarcb1 knockout in the Prss56-Cre mouse strain thereby targeting boundary cap cells which are neural crest derived Schwann cell precursor. All Smarcb1Flox/Flox;Prss56Cre+/+ showed ataxia within a median of two months, due to the development of massive intracranial tumor in the ponto-cerebellum angle. Smarcb1Flox/Flox;Prss56Cre+/- mainly developed paraplegia related to nerve root tumors, within a median delay of 7 months. Interestingly, transcriptome profiling confirmed the good correlation between mouse peripheral nerve tumors and human MRT; intracranial tumors in this new model correlated with MYC-ATRTs but with some specific features not observed in their human counterparts. All tumors harbored a significant immune infiltration, with some clonal T-cell expansion. Altogether, the Smarcb1Flox/Flox;Prss56-Cre mouse tumors offer a new model for human MRT and promising perspectives for immune-based innovative treatments. Citation Format: Zhi-Yan HAN, Mamy Andrianteranagna, Stéphanie Fitte-Duval, Valeria Manriquez, Magali Frah, Rachida Bouarich, Sandrina Turcynski, Arnault Tauziède-Espariat, Kevin Beccaria, Julien Masliah-Planchon, Christine Bourneix, Delphine Guillemot, Stéphanie Reynaud, Gaelle Pierron, Maëva Veyssiere, Owen Hoare, Céline Chauvin, Grégory Thomson, Didier Surdez, Pascale Varlet, Christelle Dufour, Volodia Dangouloff-Ros, Joshua J. Waterfall, Eliane Piaggio, Olivier Delattre, Piotr Topilko, Franck Bourdeaut. Targeting boundary cap cells leads to mouse Smarcb1-deficient peripheral nerve tumors recapitulating human peripheral rhabdoid tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A006.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".