Abstract B44: Role of EMT transcription factors in the metastatic potential of osteosarcoma
Bibliographic record
Abstract
Abstract Objective: The main culprit for mortality in osteosarcoma (OS) is metastatic pulmonary disease, which originates from a highly selected group of tumor cells that acquire a heightened ability to migrate from the primary tumor, intravasate, and survive in the bloodstream while circulating to remote sites. The early steps of this process are thought to be regulated by epithelial-to-mesenchymal translation (EMT)-related transcription factors (EMT-TFs), which can surprisingly occur in nonepithelial malignancies such as glioma, leukemia, and sarcoma. To determine how EMT-TFs and other mediators of stemness contribute to the early steps of tumor metastasis, drug resistance, and ultimately to the shedding of circulating tumor cells (CTC), we explored the presence and role of these EMT-TFs such as SNAIL, ZEB1, TWIST, and AXL in OS. Methods: The acellularized rat lung (ACL) model used in our research provided a unique ability to isolate and characterize thousands of lab-derived circulating tumor cells (dCTCs) and compare them to the primary tumors formed in rat lung by injecting OS-D OS cell line. Expression of EMT-TFs (SNAIL, ZEB1, TWIST, AXL) in cells cultured in 2D monolayer, primary tumors (PT) formed in acellularized lung, and the derived CTCs were evaluated by immune-fluorescence (IF) staining using confocal microscopy and quantified by IMARIS software. We also compared the level of expression of EMT-TFs in parental OS cell line MG63, and its derivative metastatic cell lines MG63.2 and MG63.3 by IF and Western blotting. Circulating tumor cells were also isolated from OS patients for IF analysis of EMT-TFs and compared with their paired primary tumor. Results: The dCTCs collected from ACL OS model showed substantially higher expression of SNAIL, TWIST, and AXL, as well as considerably higher expression of ZEB1 compared with cells from 2D culture and PTs. AXL showed significantly increased expression in metastatic cell lines MG63.2 and MG63.3 compared with the parental cell line MG63. ZEB1, TWIST, or SNAIL did not replicate this difference in expression, likely due to the phenotypic changes that cells undergo when grown on the 2D monolayer. Results from the ACL experiments were validated by comparing the expression of AXL, TWIST, and ZEB1 in CTCs collected from patients with a limited set of paired primary patient tumors. Conclusion: An ACL model of the lung microenvironment yielded an opportunity to characterize lab-derived CTCs, using techniques that cannot readily be achieved from clinical specimens. Surprisingly, EMT-TFs were enriched in dCTCs, an important finding that suggests a small subset of OS cells—which are already high grade—can become even more stem-like as they navigate the initial steps required of early metastasis. Though we have just begun to validate this result using paired tumor/CTC clinical samples, early evidence confirms our lab findings. The identification of EMT-TF in OS CTCs suggests that antagonists of AXL, ZEB, or TWIST might impede the earliest steps in the metastatic cascade. Citation Format: Sana Mohiuddin, Salah-Eddine Lamhamedi-Cherradi, Dhruva K. Mishra, Kristi Pence, Sandhya Krishnan, Brian A. Mnegaz, Alejandra Ruiz Velasco, Danh Dinh Troung, Branko Cuglievan, Amelia Vetter, Eric R. Molina, Min P. Kim, Joseph A. Ludwig. Role of EMT transcription factors in the metastatic potential of osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B44.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".