Abstract 2137: JAK2 as a novel therapeutic target in anaplastic thyroid cancer
Bibliographic record
Abstract
Abstract Introduction: Thyroid carcinoma is the most common endocrine malignancy. Anaplastic thyroid cancer (ATC) is rare (1.3%) and represents arguably the most lethal human malignancy with 1-year survival rates of only 10%. There are currently no effective treatments for the majority of patients, highlighting an urgent need for novel therapeutics to manage this disease. Objective: To validate the functional importance of JAK2 as a therapeutic target in ATC. Methods: In this study, we have used siRNA knockdown to interrogate the JAK2 signaling pathway as a therapeutic target in ATC. We investigated the mechanism of action and cell death, and assayed for migration and invasion, in vitro. The chick chorioallantoic membrane (CAM) model was also utilized for drug testing, whereby 1x106 Cal62 ATC cells were on-planted to the chick embryo membrane. Two days post on-plant, CAM models were treated with the vehicle (DMSO) or lestaurtinib to measure outcomes including tumor volume and vascularity. Results: We identified the JAK2 inhibitor lestaurtinib as an inhibitory agent controlling cell line proliferation at submicromolar mean inhibitory concentrations. Immunoblotting revealed the inhibition of phosphorylation of the downstream signaling molecule STAT5 in a dose-dependent manner. Treatment of Cal62 cells resulted in a decrease in cell migration using the scratch-wound assay. The anti-proliferative effective of lestaurtinib did not cause apoptosis, autophagy or cell senescence. CAM models treated with a 4 uM dose of lestaurtinib showed a significant decrease in both tumor volume and vascularity. Conclusions: Lestaurtinib was found to provide potent control of ATC cell proliferation and migration, and was also found to decrease tumor growth and vascularity in a CAM model. Knockout studies are underway to confirm that the anticancer effect of this drug is indeed mediated through JAK2 signaling. If validated, JAK2 represents a novel therapeutic target for the treatment of aggressive thyroid cancers. Citation Format: Nicole C. Pinto, Kara Ruicci, Stephenie Prokopec, Karlee Searle, Matthew Lowerison, John Yoo, Kevin Fung, Danielle MacNeil, Hon S. Leong, Alessandro Datti, Paul C. Boutros, John W. Barrett, Anthony C. Nichols. JAK2 as a novel therapeutic target in anaplastic thyroid cancer [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 2137. doi:10.1158/1538-7445.AM2017-2137
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".