Bibliographic record
Abstract
Abstract Synovial sarcoma (SS) is an aggressive soft-tissue malignancy that can arise in any area of the body, and most frequently affects adolescents and young adults. The 10-year survival rate has been estimated at 50%. SS is characterized by a pathognomonic t(X;18)(p11.2;q11.2) translocation which produces a fusion oncogene named SS18-SSX. In mouse models, expression of SS18-SSX is sufficient to drive the development of SS-like tumors. Despite recent advancements in our understanding of SS biology, the cell of origin remains undefined. In most instances, it is likely mesenchymal in nature. We previously generated a mesenchymal progenitor (MP) specific Cre line that has enabled us to identify MPs within the embryo [MOU1], and characterize and define their lineage trajectories through development into the adult. This line has been used to generate a unique mouse model of SS where the human SS18-SSX fusion oncogene with an EGFP marker is conditionally expressed in adult MPs. This model shows 100% penetrant SS tumor formation with a median latency period of 16 weeks. Histologic analyses of tumors reveal characteristic cellular morphology and the expression of common markers of SS. Single-cell (sc) RNA-seq gene expression profiles of murine tumors recapitulate those observed in human SS samples and highlight genetic programs involved in sarcomagenesis. Using this model, we can identify and isolate neoplastic lesions prior to the development of grossly visible tumors and trace these cells through the complete transformation continuum. By comparing tumor cells to normal MPs, we aim to better understand the mechanisms underlying the SS18-SSX induced transformation of MPs. Together, this model identifies a cell of origin for SS and provides us with a unique opportunity to address the molecular and cellular drivers of SS. Citation Format: T. Michael Underhill. Mesenchymal progenitors and sarcomagenesis [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 IA004.
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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 | 0.001 | 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".