Abstract IA025: Using genetically engineered mouse models to study sarcoma metastasis
Bibliographic record
Abstract
Abstract Despite aggressive conventional therapy, many patients with high-risk soft-tissue sarcoma develop metastatic disease. To investigate mechanism(s) of sarcoma metastasis, our lab has utilized genetically engineered mouse models. For example, we injected an adenovirus expressing Cre recombinase (adeno-Cre) into the gastrocnemius muscle of LSL-KrasG12D; p53Flox/Flox (KP) mice to initiate high grade undifferentiated pleomorphic sarcomas, and after amputation approximately 40% of the mice develop lung metastasis. Using a genetic approach, we found that miR-182, NEAT-1, and HIF-1a regulate metastasis to the lung. We also performed lineage tracing with complementary fluorescent proteins and CRISPR-generated bar codes to find that lung metastases from KP sarcomas arise from clones with specific gene expression profiles. Although the KP sarcoma model is useful for studying metastasis, one limitation of this model is that there are few non-synonymous mutations to engage the immune system. Therefore, we generated a high mutational load primary mouse model of soft tissue sarcoma by injecting adeno-Cre into the gastrocnemius muscle of p53Flox/Flox mice to delete p53 and also injected 3-methylcholanthrene (MCA) to generate primary p53/MCA undifferentiated pleomorphic sarcomas. In this p53/MCA model, the overall rate of lung metastasis after amputation was surprisingly low (~12%). We hypothesized that the immune system suppressed lung metastasis in this model. However, when we generated p53/MCA sarcomas in Rag2 −/− mice that lack mature B and T cells, we still observed a low rate of lung metastasis after amputation. These data suggest that mutations caused by MCA may have disabled tumor intrinsic factors needed to drive sarcoma metastasis. We are currently performing genome-wide screens in the p53/MCA model to search for genes required for sarcoma metastasis. Citation Format: David G. Kirsch, Rutulkumar Patel, Sophie R. Finkelstein, Joy Ban, Yuning J Tang, Jianguo Huang, Benjamin A. Alman, Yvonne M. Mowery. Using genetically engineered mouse models to study sarcoma metastasis [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 IA025.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
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".