Abstract IA014: Stem cell and developmental hierarchies in rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood and is comprised of at least two major molecular subtypes. Despite sharing features with skeletal muscle, the conservation of underlying cellular hierarchy with human muscle development and the identification of molecularly-defined tumor-propagating cells have not been reported. Using single-cell RNA sequencing of patient-derived RMS, DNA-barcode cell fate mapping, antibody enrichment and functional stem cell assays, and mouse xenograft modeling, we have uncovered tumor cell hierarchies in Fusion-negative (FN-) RMS that are shared with normal human muscle development. We also identified common developmental stages at which tumor cells become arrested. FN-RMS resemble early muscle found in embryonic and larval development, while fusion-positive (FP-) RMS express a highly specific developmental gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. FP-RMS also have neural-pathway enriched cell states, suggesting less-rigid adherence to muscle development hierarchies in this disease. Finally, we identify a new molecularly-defined tumor-propagating cell in FN-RMS that shares remarkable similarity to the newly described bi-potent, muscle mesenchyme stem/progenitor cell that makes both muscle and osteogenic cells. Citation Format: David M. Langenau, Yun Wei, Qian Qin, Luca Pinello. Stem cell and developmental hierarchies in rhabdomyosarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA014.
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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 | 0.001 | 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".