Abstract A017 Dynamic modelling of EWS::FLI1 fluctuations reveals molecular determinants of phenotypic tumor plasticity and prognosis in Ewing sarcoma
Bibliographic record
Abstract
Abstract Ewing sarcoma is a highly aggressive pediatric cancer driven by the EWS::FLI1 (EF) fusion oncogene. Evidence suggests that EF expression levels dictate tumor cell plasticity contributing to treatment resistance and relapse. Our study aims to elucidate the transcriptional programs and phenotypes associated with distinct EF thresholds. To dynamically fine-tune EF levels, we engineered Ewing sarcoma cell line models by C-terminally tagging endogenous EF with a fluorescent (mNG) and a degron tag (dTAG). Using mNG as a quantitative proxy for EF levels, we confirmed that increasing concentrations of the dTAG ligand led to a gradual loss of the fusion protein. Already subtle modulation of EF thresholds significantly enhanced tumor cell migration. Furthermore, a 25% EF reduction was sufficient to significantly lower anchorage-independent growth in soft agar, which was completely abolished with further EF depletion. Supported by zebrafish xenograft experiments, these findings suggest that minor EF fluctuations enhance migratory properties while still retaining significant in vitro tumor-forming potential. To characterize acute transcriptional responses to variations in EF thresholds, we performed SLAM-sequencing after 3 hours of treatment with increasing dTAG concentrations. While minor reduction of the fusion protein to 76% led to immediate downregulation of EF-activated genes enriched in GGAA microsatellites, most EF-repressed targets were upregulated only when EF levels were reduced by 58% to 96%. These de-repressed targets were significantly enriched for epithelial-to-mesenchymal transition (EMT)-related pathways. Testing EF chromatin occupancy by CUT&RUN followed by motif enrichment analysis, identified several transcription factor binding motifs linked to EF-repressed genes. Promising candidates include SP1, EGR1, and TFAP2A, all of which have previously been implicated in the regulation of metastasis-related pathways. Long-term dTAG treatment (1 to 3 weeks), followed by EF rescue through dTAG ligand washout for 7 days revealed a set of metastasis-related genes that notably remained upregulated despite a full recovery of EF levels already within 24 hours of ligand washout. This suggests that transient oncogene fluctuations may cause a sustained increase in metastatic potential of Ewing sarcoma cells. Importantly, about thirty of these genes negatively correlated with overall survival in a cohort of 196 Ewing sarcoma patients and were anti-correlated with the GGAA microsatellite-driven transcriptional repressor BCL11B, which was highly sensitive to minor changes in EF thresholds. Collectively, our results highlight the critical role of EF oncogene fluctuations in Ewing sarcoma cell plasticity and patient prognosis providing new insights into the gene regulatory mechanisms involved. Further, our findings have important implications for therapeutic approaches aiming at directly targeting EF. Understanding EF dynamics in Ewing sarcoma serves a paradigmatic approach to the identification of therapeutic vulnerabilities in fusion oncogene-driven cancers. Citation Format: Veveeyan Suresh, Christoph Hafemeister, Sarah Grissenberger, Caterina Sturtzel, Florencia Cidre Aranaz, Thomas Grünewald, Ana Sastre, Javier Alonso, Martin Distel, Florian Halbritter, Heinrich Kovar, Valerie Fock. Dynamic modelling of EWS::FLI1 fluctuations reveals molecular determinants of phenotypic tumor plasticity and prognosis in Ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A017.
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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) | 0.001 | 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".