PDGFRα Regulates Follicular Cell Differentiation Driving Treatment Resistance and Disease Recurrence in Papillary Thyroid Cancer
Bibliographic record
Abstract
Dedifferentiation of follicular cells is a central event in resistance to radioactive iodine and patient mortality in papillary thyroid carcinoma (PTC). We reveal that platelet derived growth factor receptor alpha (PDGFRα) specifically drives dedifferentiation in PTC by disrupting the transcriptional activity of thyroid transcription factor-1 (TTF1). PDGFRα activation dephosphorylates TTF1 consequently shifting the localization of this transcription factor from the nucleus to the cytoplasm. TTF1 is required for follicular cell development and disrupting its function abrogates thyroglobulin production and sodium iodide transport. PDGFRα also promotes a more invasive and migratory cell phenotype with a dramatic increase in xenograft tumor formation. In patient tumors we confirm that nuclear TTF1 expression is inversely proportional to PDGFRα levels. Patients exhibiting PDGFRα at time of diagnosis are three times more likely to exhibit nodal metastases and are 18 times more likely to recur within 5years than those patients lacking PDGFRα expression. Moreover, high levels of PDGFRα and low levels of nuclear TTF1 predict resistance to radioactive iodine therapy. We demonstrate in SCID xenografts that focused PDGFRα blockade restores iodide transport and decreases tumor burden by >50%. Focused PDGFRα inhibitors, combined with radioactive iodine, represent an additional avenue for treating patients with aggressive variants of PTC.
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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 | 0.000 |
| 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".