Tumor-propagating side population cells are a dynamic subpopulation in undifferentiated pleomorphic sarcoma
Bibliographic record
Abstract
Sarcomas contain a subpopulation of tumor-propagating cells (TPCs) with enhanced tumor-initiating and self-renewal properties. However, it is unclear whether the TPC phenotype in sarcomas is stable or a dynamic cell state that can derive from non-TPCs. In this study, we utilized a mouse model of undifferentiated pleomorphic sarcoma (UPS) to trace the lineage relationship between sarcoma side population (SP) cells that are enriched for TPCs and non-SP cells. By cotransplanting SP and non-SP cells expressing different endogenous fluorescent reporters, we show that non-SP cells can give rise to SP cells with enhanced tumor-propagating potential in vivo. Lineage trajectory analysis using single-cell RNA sequencing from SP and non-SP cells supports the notion that non-SP cells can assume the SP cell phenotype de novo. To test the effect of eradicating SP cells on tumor growth and self-renewal, we generated mouse sarcomas in which the diphtheria toxin receptor is expressed in the SP cells and their progeny. Ablation of the SP population using diphtheria toxin did not impede tumor growth or self-renewal. Altogether, we show that the sarcoma SP represent a dynamic cell state and targeting TPCs alone is insufficient to eliminate tumor progression.
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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".