High-throughput Chemical Screening Identifies Focal Adhesion Kinase and Aurora Kinase B Inhibition as a Synergistic Treatment Combination in Ewing Sarcoma
Bibliographic record
Abstract
Abstract Purpose: Ewing sarcoma is an aggressive solid tumor malignancy of childhood. Although current treatment regimens cure approximately 70% of patients with localized disease, they are ineffective for most patients with metastases or relapse. New treatment combinations are necessary for these patients. Experimental Design: Ewing sarcoma cells are dependent on focal adhesion kinase (FAK) for growth. To identify candidate treatment combinations for Ewing sarcoma, we performed a small-molecule library screen to identify compounds synergistic with FAK inhibitors in impairing Ewing cell growth. The activity of a top-scoring class of compounds was then validated across multiple Ewing cell lines in vitro and in multiple xenograft models of Ewing sarcoma. Results: Numerous Aurora kinase inhibitors scored as synergistic with FAK inhibition in this screen. We found that Aurora kinase B inhibitors were synergistic across a larger range of concentrations than Aurora kinase A inhibitors when combined with FAK inhibitors in multiple Ewing cell lines. The combination of AZD-1152, an Aurora kinase B–selective inhibitor, and PF-562271 or VS-4718, FAK-selective inhibitors, induced apoptosis in Ewing sarcoma cells at concentrations that had minimal effects on survival when cells were treated with either drug alone. We also found that the combination significantly impaired tumor progression in multiple xenograft models of Ewing sarcoma. Conclusions: FAK and Aurora kinase B inhibitors synergistically impair Ewing sarcoma cell viability and significantly inhibit tumor progression. This study provides preclinical support for the consideration of a clinical trial testing the safety and efficacy of this combination for patients with Ewing sarcoma.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".