Dual Inhibition of <i>RET</i> and <i>FGFR4</i> Restrains Medullary Thyroid Cancer Cell Growth
Bibliographic record
Abstract
Medullary thyroid cancer is frequently an aggressive form of carcinoma for which there are currently no effective forms of systemic therapy. These carcinomas arise as a result of activating mutations in the RET proto-oncogene transmembrane tyrosine kinase receptor. We, therefore, examined the potential efficacy of the tyrosine kinase inhibitor STI571 on the growth of human TT medullary cancer cells in vitro and in xenografted severe combined immunodeficiency mice. Treatment with STI571 resulted in inhibition of RET phosphorylation, cell proliferation, tumor growth and invasiveness. Based on the profile of expression of fibroblast growth factor receptors (FGFR), we examined the effects of FGFR tyrosine kinase inhibition using the small molecule FGFR inhibitor PD173074. This inhibitor resulted in abrogation of fibroblast growth factor-1-mediated FGFR4 phosphorylation in TT cells, an effect that was accompanied by significant arrest of cell proliferation and tumor growth in vivo. Moreover, the combination of STI571 and PD173074 resulted in greater suppression of cell proliferation in vitro and tumor control in vivo than that achieved with either agent alone. These data highlight RET and FGFR4 as therapeutic targets and suggest a potential role for the combined use of tyrosine kinase inhibitors in the management of inoperable medullary thyroid cancers.
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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) | 0.001 | 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 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".