The EWS/FLI Oncogene Drives Changes in Cellular Morphology, Adhesion, and Migration in Ewing Sarcoma
Bibliographic record
Abstract
Ewing sarcoma is a tumor of the bone and soft tissue caused by the expression of a translocation-derived oncogenic transcription factor, EWS/FLI. Overt metastases are associated with a poor prognosis in Ewing sarcoma, but patients without overt metastases frequently harbor micrometastatic disease at presentation. This suggests that the metastatic potential of Ewing sarcoma exists at an early stage during tumor development. We have therefore explored whether the inciting oncogenic event in Ewing sarcoma, EWS/FLI, directly modulates tumor cell features that support metastasis, such as cell adhesion, cell migration, and cytoarchitecture. We used an RNAi-based approach in patient-derived Ewing sarcoma cell lines. Although we hypothesized that EWS/FLI might induce classic metastatic features, such as increased cell adhesion, migration, and invasion (similar to the phenotypes observed when epithelial malignancies undergo an epithelial-to-mesenchymal transition during the process of metastasis), surprisingly, we found the opposite. Thus, EWS/FLI expression inhibited the adhesion of isolated cells in culture and prevented adhesion in an in vivo mouse lung assay. Cell migration was similarly inhibited by EWS/FLI expression. Furthermore, EWS/FLI expression caused a striking loss of organized actin stress fibers and focal adhesions and a concomitant loss of cell spreading, suggesting that EWS/FLI disrupts the mesenchymal phenotype of a putative tumor cell-of-origin. These data suggest a new paradigm for the dissemination and metastasis of mesenchymally derived tumors: these tumors may disseminate via a "passive/stochastic" model rather than via an "active" epithelial-to-mesenchymal type transition. In the case of Ewing sarcoma, it appears that the loss of cell adhesion needed to promote tumor cell dissemination might be induced by the EWS/FLI oncogene itself rather than via an accumulation of stepwise mutations.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".