Abstract PR007: A single cysteine in PAX3-FOXO1 is relevant for transactivation and survival of rhabdomyosarcoma cells
Bibliographic record
Abstract
Abstract The fusion transcription factor PAX3-FOXO1 (P3F) is the major driver of alveolar rhabdomyosarcoma. Since these tumors are characterized by a mostly quiet mutational landscape and a paucity of druggable oncogenes, the fusion protein itself remains the most important drug target. Transcription factors are challenging proteins for drug development since they lack enzymatic activities and are, apart from DNA binding domains, largely intrinsically disordered. Identification of defined and possibly druggable structures in such proteins is therefore of great clinical interest. Towards this aim, we performed a CRISPR/Cas9-based domain screen in P3F-positive rhabdomyosarcoma cells and identified a small, structured and highly important domain within the transactivation domain of P3F. Further, we demonstrate that cysteine C793 located in this region is indispensable for target gene activation by P3F. Its mutation significantly reduced cell proliferation and induced differentiation of RMS cells. Mechanistically, we identified p300/CBP proteins as important co-factors and interactors of C793 that co-regulate a majority of P3F target genes. Their inhibition/degradation by small molecules efficiently reduces rhabdomyosarcoma cell survival. These data suggest that mainly one single amino acid drives oncogenicity of P3F and identify a potentially targetable structure within the fusion protein. Citation Format: Beat W. Schäfer, Katharina Benischke, Jakob Wurth, Dominik Laubscher, Quy A. Ngo, Sara Danielli, Marco Wachtel. A single cysteine in PAX3-FOXO1 is relevant for transactivation and survival of rhabdomyosarcoma cells [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 PR007.
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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) | 0.007 | 0.002 |
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".