High-throughput drug library screening identifies colchicine as a thyroid cancer inhibitor
Bibliographic record
Abstract
// Le Zhang 1, 2 , Zhaoying Yang 1, 2 , Letizia Granieri 3 , Adrian Pasculescu 3 , Alessandro Datti 3, 4 , Sylvia L. Asa 2, 5 , Zheli Xu 1, 2 , Shereen Ezzat 2, 6 1 Department of Breast Surgery, China-Japan Union Hospital, Jilin University, Changchun, Jilin, P.R. China 2 Ontario Cancer Institute and The Endocrine Oncology Site Group, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada 3 SMART Laboratory for High-Throughput Screening Programs, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada 4 Department of Agricultural, Food, and Environmental Sciences, University of Perugia, Perugia, Italy 5 Department of Laboratory Medicine and Pathobiology, Toronto, ON, Canada 6 Department of Medicine, University of Toronto, Toronto, ON, Canada Correspondence to: Shereen Ezzat, e-mail: shereen.ezzat@utoronto.ca Zheli Xu, e-mail: zhelixujlu@163.com Keywords: thyroid cancer, colchicine, BRAF, BRAF resistance, high throughput drug screening Received: November 20, 2015 Accepted: February 09, 2016 Published: March 03, 2016 ABSTRACT We employed a high-throughput drug library screening platform to identify novel agents affecting thyroid cancer cells. We used human thyroid cancer cell lines to screen a collection of approximately 5200 small molecules with biological and/or pharmacologial properties. Parallel primary screens yielded a number of hits differentially active between thyroid and melanoma cells. Amongst compounds specifically targeting thyroid cancer cells, colchicine emerged as an effective candidate. Colchicine inhibited cell growth which correlated with G2 cell cycle arrest and apoptosis. These effects were hampered through inhibition of MEK1/2 and JNK. In contrast, inhibition of p38-MAPK had little effect, and AKT had no impact on colchicine action. Systemic colchicine inhibited thyroid cancer progression in xenografted mice. These findings demonstrate that our screening platform is an effective vehicle for drug reposition and show that colchicine warrants further attention in well-defined clinical niches such as thyroid cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 teacher head, 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".