Abstract 1943: 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 Ewing sarcoma is the second most common bone malignancy of childhood. Current treatment employs chemotherapy, surgery, and radiation. Although this approach cures approximately 70% of patients with localized disease, treatments are largely ineffective for patients with metastases or relapse. Furthermore, these treatments are associated with an alarming rate of long-term toxicities. New treatment combinations are necessary to improve cures and lower toxicities for these patients. We recently found that Ewing sarcoma is dependent on focal adhesion kinase (FAK) for cell viability and tumor proliferation. In order to identify candidate treatment combinations for Ewing sarcoma, we performed a screen of 1912 compounds to identify those with synergistic anti-Ewing activity when combined with FAK inhibition. The A673 Ewing cell line was treated with PF-562271, a FAK-specific inhibitor, in combination with compounds from the Mechanism Interrogation PlatE (MIPE) 4.0 library. Cell viability was measured after 48 hours of treatment. Multiple computational metrics were utilized to identify and rank all compound combinations for synergistic impairment of cell viability. Multiple Aurora kinase inhibitors scored as synergistic with FAK inhibition in this screen. Aurora kinases are important in the regulation of mitosis and are highly expressed in Ewing sarcoma tumors and cell lines. We found that Aurora kinase B inhibitors were synergistic across a larger range of concentrations than Aurora kinase A inhibitors when combined with FAK inhibition in multiple Ewing cell lines. We found that AZD-1152, an Aurora kinase B-selective inhibitor, and PF-562271 when used in combination induced apoptosis in Ewing cells at concentrations that had minimal effect on cell survival when either drug was used alone. We also found that the combination significantly impaired tumor proliferation in zebrafish xenograft models of Ewing sarcoma and prolonged survival in murine xenografts compared to either single-agent treatment alone. Interestingly, treatment with AZD-1152 alone also significantly impaired tumor proliferation and prolonged survival compared to vehicle treatment in a mouse xenograft model of Ewing sarcoma. Our data demonstrate that FAK and Aurora kinase B inhibition synergistically impair Ewing sarcoma cell viability in vitro and significantly inhibit tumor proliferation in vivo. With multiple FAK and Aurora kinase inhibitors in early phase trials for adult malignancies, these results have the potential to be translated into clinical trials for patients with Ewing sarcoma. Previous studies have also suggested a dependency of Ewing sarcoma on Aurora kinase activity; our data further supports a role for Aurora kinase B inhibitors as therapeutic candidates in this disease. Citation Format: Brian Crompton, Sarah Wang, Elizabeth Hwang, Rajarshi Guha, Matthew Boxer, Crystal McKnight, Min Shen, Nicole Melong, Chansey Veinotte, Amy Conway, Jason Berman, Matthew Hall, Mindy Davis, Kimberly Stegmaier. High-throughput chemical screening identifies Focal Adhesion Kinase and Aurora Kinase B inhibition as a synergistic treatment combination in Ewing sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1943. doi:10.1158/1538-7445.AM2017-1943
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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) | 0.004 | 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".