Abstract B008: Investigating the evolution of undifferentiated soft tissue sarcomas in a genetically engineered mouse model
Bibliographic record
Abstract
Abstract Undifferentiated soft tissue sarcomas (USTS) are an aggressive class of sarcomas with few effective treatment options. Because of the rarity of these sarcomas and the paucity of representative disease models, little is known about how they develop. We used single cell transcriptomics to study the initiation and evolution of an USTS in an autochthonous genetically engineered mouse model. We profiled multiple stages of tumor development and identified intermediary cell states that define sarcomagenesis. Interestingly, we identify two discrete evolutionary pathways that arise in parallel in the mouse model. To determine the human relevance of our findings, we are developing machine learning approaches to compare cell states between the mouse model with single-cell transcriptomes that we generated from patient-derived xenografts. This cross-species comparison will uncover core USTS cell states and define their ancestral state transitions. Our ultimate goal is to identify essential molecular features of USTS, which may represent entry points for future therapeutic strategies. Citation Format: Jason E. Chan, Jonathan Rub, Carleigh Sussman, Olivera Grbovic-huezo, William D. Tap, Cristina Antonescu, Sam Singer, Doron Betel, Tuomas Tammela. Investigating the evolution of undifferentiated soft tissue sarcomas in a genetically engineered mouse model [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 B008.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".