YAP confers resistance to vandetanib in medullary thyroid cancer
Bibliographic record
Abstract
Medullary thyroid cancer (MTC) is the third most common thyroid cancer. RET (Rearranged in Transformation) gene mutations are considered as one of the major drivers of MTC. Vandetanib suppresses RET activity, and has shown promise in clinical trials. Unfortunately, acquired resistance to vandetanib has been observed in MTC, although the mechanism was largely unknown. We investigated the critical role of YAP (Yes-Associated Protein) on vandetanib resistance in MTC. For this, TT cells (medullary thyroid cancer cells) were treated with vandetanib for 3 months to generate a vandetanib-resistant cell line (TT-R). We investigated the role of YAP on vandetanib-resistance in TT-R cells by performing cell proliferation and colony formation assays, and examined the antitumor effects of YAP inhibitor and vandetanib in a mouse model of xenografted MTC. The TT-R cells displayed 6-fold higher IC50 to vandetanib than the TT cells. Overexpression of YAP resulted in resistance to vandetanib, whereas knockdown of YAP re-sensitized the TT-R cells to vandetanib. The YAP inhibitor synergized with vandetanib on tumor inhibition. Our results suggest that YAP plays an important role in acquired resistance to vandetanib in MTC, providing basis for combating MTC with YAP inhibitor and vandetanib.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".