Abstract 3767: Highly effective inhibitory agent identified by high-throughput screening of genetically characterized anaplastic thyroid cancer cell lines
Bibliographic record
Abstract
Abstract Introduction While most types of thyroid cancer are treatable, anaplastic thyroid cancer (ATC) is the most lethal human malignancy. Current treatment strategies are largely ineffective with only 10% of patients surviving longer than one year. As such, new treatments are desperately needed to improve patient survival in this aggressive cancer. Objective Identify highly effective drugs using high-throughput screening methods with a large panel of kinase inhibitors that can be used in the treatment of ATC. Methods High-throughput screening (HTS) of 13 genetically characterized ATC cell lines was carried out over a 6-dose range using 320 kinase inhibitors. Dose-response curves were generated and drugs with high potency were identified. Drugs with the highest activity were selected for further evaluation including investigating mechanism of action and cell death, and assays for migration and invasion. Results Based on HTS studies, Lestaurtinib was identified as a highly effective agent with submicromolar mean inhibitory concentrations in all 13 ATC cell lines. Lestaurtinib was found to inhibit STAT5 phosphorylation, as well as cell line migration and invasion. The anti-proliferative effect of Lestaurtinib did not cause apoptosis, autophagy or cell senescence. Conclusions With the use of HTS, we have identified many (>20) kinase inhibitors as highly effective agents in the control of ATC. The kinase inhibitor Lestaurtinib was effective at controlling ATC cell proliferation, invasion and migration. Future in vivo analysis of Lestaurtinib with patient-derived xenografts are planned to continue preclinical development of this drug with the ultimate goal of improving outcomes for patients suffering with ATC. Citation Format: Nicole C. Pinto, Morgan Black, Kara Ruicci, John Yoo, Danielle MacNeil, Kevin Fung, Alessandro Datti, Stephenie Prokopec, Paul Boutros, John Barrett, Anthony Nichols. Highly effective inhibitory agent identified by high-throughput screening of genetically characterized anaplastic thyroid cancer cell lines. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3767.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".