Abstract B018: A robust system to study human soft-tissue sarcoma lung metastasis
Bibliographic record
Abstract
Abstract Soft-tissue sarcoma (STS) is a rare connective tissue cancer that encompasses over 50 distinct subtypes including liposarcoma, undifferentiated pleomorphic sarcoma (UPS) and pleomorphic rhabdomyosarcoma. While these subtypes are histologically and genetically different, all are capable of metastasizing into the lungs. However, the mechanisms of metastasis are poorly understood, and patients with metastatic disease have low survival rates. Current statistics show that these patients have a 15% 5-year relative survival rate. It is crucial to understand the mechanisms of metastasis to improve patient therapeutics and survival. Thus, we developed a robust system to enrich and study spontaneous metastatic cells in STS. To do this, we started by screening multiple cell lines for metastatic activity in-vivo. First, immunocompromised mice were injected in the thigh (intramuscularly) with tumor cells. The liposarcoma cell line SW872 gave rise to metastatic tumors in the lung although with limited penetrance and a long latency. Therefore, we identified a mouse with a high metastatic burden and generated a cell line from the primary tumor that was highly metastatic. These serially passaged cells were injected intramuscularly into the thigh of immunocompromised mice and allowed to spontaneously metastasize. This resulted in earlier detection of tumors, and a decreased latency to metastatic disease. For downstream analysis, we enriched the metastatic population resulting from the serially passaged tumor. This was accomplished by first digesting lungs, then depleting mouse cells with magnetic beads. We further enriched the population by sorting HLA-APC positive (human) cells using fluorescence-activated cell sorting (FACS). This process was necessary to reduce mouse cell contamination, and ensure we solely analyzed human cells. Afterwards, metastatic cells can be compared to the primary tumor using RNA, ATAC or Whole-Exome sequencing. We hypothesize that a comparison between the primary and metastatic tumors will identify pathways that allow cells to metastasize. These pathways can be targeted in future therapeutics to improve patient outcomes. This system can also be applied to other STS subtypes. Overall, our robust system allows us to study spontaneous lung metastases, is reflective of the human disease and will provide insight into the mechanisms of metastasis in STS. Citation Format: Maria Muñoz, Janai R. Carr-Ascher. A robust system to study human soft-tissue sarcoma lung 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 B018.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".