Abstract A012: Targeting the dysregulation of transcription and splicing in Ewing sarcoma
Bibliographic record
Abstract
Abstract The goals of this study are to investigate how EWS-FLI1 disrupts regulation of transcription and splicing in Ewing sarcoma, and to evaluate this dysregulation as a potential therapeutic target. Ewing Sarcoma is an aggressive pediatric bone and soft tissue cancer driven primarily by the EWS-FLI1 fusion oncogene. EWS-FLI1 acts as an oncogenic transcription factor and also interferes with normal regulation of transcription and transcription-associated RNA processing. Chemotherapy has substantially improved overall survival, but the prognosis is still very poor for those with metastatic or recurrent disease, and toxicity is a major concern in pediatric patients. Consequently, effective, less toxic, targeted treatment strategies are much needed. We performed a genome-wide RNAi screen in the Ewing sarcoma cell line TC32 and found that one of the top pathways required for cell survival was RNA processing/splicing. Previous studies have reported increased amounts of alternative splicing events in Ewing sarcoma. RNA processing, including splicing, is functionally coupled with transcription. We recently reported that Ewing sarcoma cells have high levels of phosphorylated RNA polymerase II and increased transcription activity, accumulate R-loops, and fail to regulate transcription appropriately in response to DNA damage. We proposed that EWS-FLI1 driven hyper-activation of transcription causes a targetable dependence on RNA splicing in Ewing sarcoma. Ewing sarcoma cells showed increased sensitivity to depletion of key splicing factors compared to mesenchymal stem cell controls; this was partially rescued by depleting EWS-FLI1 or by overexpressing RNASEH1, which degrades R-loops. Ewing sarcoma cells were extremely sensitive to splicing inhibition, which showed synergy with multiple chemotherapeutic agents. Splicing inhibition in Ewing sarcoma cells caused cell cycle arrest, induced apoptosis, and altered global gene expression, alternative splicing, and R-loop profiles as seen by RNA-seq and DRIP-seq. This work provides novel insight into transcription regulation and its dysregulation by EWS-FLI1. In addition, our results point to RNA splicing as a potential new therapeutic target in Ewing sarcoma, which has the potential to benefit all Ewing sarcoma patients, especially those with chemo-resistant disease. Citation Format: Liesl A. Lawrence, Henry Miller, Alexander Bishop. Targeting the dysregulation of transcription and splicing in Ewing sarcoma [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 A012.
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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) | 0.003 | 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".