Abstract A009: Synthetic lethality in the context of STAG2-mutant Ewing sarcoma
Bibliographic record
Abstract
Abstract Introduction: Ewing sarcoma is an aggressive type of pediatric bone cancer, characterized by a fusion transcription factor (EWSR1-FLI1 in 85% of cases1,2). Recurrent secondary mutations are limited to alterations in TP53, CDKN2A and most frequently STAG2 (15-22%3-6). STAG2-loss-of-function (LOF) mutations correlate with poor prognosis, metastasis and relapse3,5,6, and are shown to induce a metastatic phenotype in preclinical studies7,8. Despite intensive multimodal treatment (surgery, radiation and chemotherapy), the estimated 5-year survival is a mere 65-75% for patients with localized disease, and below 30% for those presenting with metastasis or relapse9. Targeted treatment is currently not available. Our current work aims at identifying synthetic lethal (SL) interactions with STAG2-LOF mutations in the context of Ewing sarcoma, which could represent potential avenues for targeted therapy. Methods: In order to identify STAG2-SL interactions, we have performed genome-wide and subsequent targeted CRISPR knockout (KO) screening in Ewing sarcoma cell lines and their isogenic CRISPR-engineered STAG2-KO clones. To capture Ewing sarcoma inter- and intra-tumoral heterogeneity, we engineered additional bulk STAG2-KO populations across a wide range of cell lines and primary samples, using a lentiviral approach. These isogenic (STAG2 +/-) models are used for high throughput drug screening, validation of screening results and investigation of underlying SL mechanisms. Results: The quality of our genetic screening is validated by the recurrent emergence of (untargetable) paralog STAG1 as the top STAG2-SL hit. Additional preliminary results include a targetable candidate that emerged from both CRISPR- and drug-based screens in most of our tested models. Validation experiments across a broader panel of isogenic cell lines and primary models are currently ongoing. Conclusion: In this work, we employ genetic and drug-based screening approaches across a panel of isogenic (STAG2+/-) Ewing sarcoma cell lines, aiming to identify STAG2-SL interactions. These approaches have yielded potential hits that are currently being investigated across a broader panel of Ewing cell lines and primary models. Ultimately, this work aims to identify novel targeted therapeutic strategies for this aggressive subtype of Ewing sarcoma. References: 1. Grünewald, T. et al. Nat. Rev. Dis. Prim. (2018). 2. Delattre, O. et al. Nature (1992). 3. Crompton, B. D. et al. Cancer Discov. (2014). 4. Brohl, A. S. et al. PLoS Genet. (2014). 5. Tirode, F. et al. Cancer Discov. (2014). 6. Shulman, D. S. et al. Br. J. Cancer (2022). 7. Surdez, D. et al. Cancer Cell (2021). 8. Adane, B. et al. Cancer Cell (2021). 9. Gaspar, N. et al. J. Clin. Oncol. (2015). Citation Format: Lieke Mous, Michelle Hofer, Dorita Zabėlė, Ingrid Bechtold, Sakina Zaidi, Camille Fouassier, Pierre Gestraud, Aurélien Boré, Raphaël Margueron, Marco Wachtel, Beat Schäfer, Olivier Delattre, Didier Surdez. Synthetic lethality in the context of STAG2-mutant Ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A009.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".