Abstract 3822: Development and characterization of mouse models of epithelioid sarcoma
Bibliographic record
Abstract
Abstract Epithelioid sarcoma (EPS) is a soft-tissue tumor that arises in the extremities (classical type) or the trunk (proximal type). Genetically, EPS is characterized by the loss of SMARCB1, a core subunit of the SWI/SNF (BAF) chromatin remodeling complex. Although rare, EPS predominantly affects young adults and is considered one of the most aggressive types of sarcomas due to its highly metastatic nature and few effective treatment options. Research on EPS has been hindered by limited patient samples and models that allow investigation of mechanisms of tumor initiation, inter-patient heterogeneity, and therapeutic targets. Here, we report a systematic study aimed to develop mouse models of EPS. We deleted Smarcb1 in genetically engineered mouse models (GEMMs) in a site-specific or lineage-specific manner. For site-specific deletion, we injected Adeno-CMV-Cre (Ad-Cre) and TAT-Cre into the gastrocnemius muscle of Smarcb1fl/fl (S) and/or Smarcb1fl/fl;p53fl/fl (SP) mice. As an alternative approach, we utilized mice with constitutive Cas9 expression and delivered sgRNAs for Smarcb1 (sgSmarcb1) and p53 (sgTrp53) via electroporation. Lineage-specific deletion of Smarcb1 was performed in CreERT2-expressing mouse lines with distinct lineage-specific promoters after crossing with S and SP mice. CreERT2 was activated by injecting 4-hydroxy tamoxifen (4OHT) into the gastrocnemius muscle. Ad-Cre injection generated tumors at the injection site in both S and SP mice with higher efficiency in SP mice (incidence rate: 40% vs 90%, median time to tumor: 440 days vs 228 days). TAT-Cre injection generated tumors in SP mice with 100% penetrance and a median latency of 68 days. CRISPR/Cas9-mediated Smarcb1 inactivation effectively induced tumors when p53 was concomitantly deleted. Among the CreERT2 lines tested, Pax7-CreERT2 was the most efficient, inducing tumors in 11 out of 20 SP mice within 2-6 months after 4OHT injection. Prrx1-CreERT2 induced two on-target tumors out of 20 mice after a longer latency. The Prrx1 model features off-target tumors in 12 mice, mostly on the face. Principal component analysis (PCA) of bulk tumor RNA-seq revealed several distinct tumor clusters. These included clusters characterized by a predominant contribution of the Pax7 model, expression of fibro-adipogenic progenitor marker Pdgfra, or expression of tenocyte genes such as Prg4 and Tnmd. In this study, we developed highly efficient conditional mouse models of EPS, providing a robust platform for preclinical testing of novel therapeutic approaches. Our results support a model where multiple muscle-resident cell types can give rise to EPS following SMARCB1 loss. Further study using EPS mouse models with distinct cellular lineages or additional gene inactivation, as well as their derived cell lines, will help elucidate how different mechanisms drive EPS developments and impact the response to therapies. Citation Format: Ryo Miyamoto, Jiyeon Park, Benigno Aquino, David Kirsch. Development and characterization of mouse models of epithelioid sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3822.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".