The PAX8 / PPARγ Fusion Oncogene as a Potential Therapeutic Target in Follicular Thyroid Carcinoma
Bibliographic record
Abstract
Follicular thyroid carcinoma (FTC) accounts for approximately 20% of all thyroid cancers, and up to 40% of the deaths associated with this disease. Current treatment approaches include surgery, followed by radioactive iodine therapy. However, a significant proportion of locally advanced and metastatic FTC fails to concentrate iodine. Because traditional chemotherapeutic agents have not been shown to alter outcomes in this disease, novel therapeutic strategies are needed for advanced disease. Recently, a genomic rearrangement has been identified in up to 50% of FTC, involving a translocation event between chromosome regions 3p25 and 2q13. This translocation fuses the thyroid-specific transcription factor PAX8 gene with the PPARgamma gene, a ubiquitously expressed transcription factor. We have confirmed that this Pax8/PPARgamma fusion gene (designated PPFP) is an oncogene, which accelerates cell growth, reduces rates of apoptosis and permits anchorage independent and contact uninhibited growth of a thyroid cell line. The action of PPFP arises, at least in part, through its activity as a dominant-negative inhibitor of the wild-type PPARgamma transcription factor. Although the mechanism by which PPFP impairs PPARgamma activity remains unknown at this time, it is likely to be mediated by competition for the genomic PPARgamma response elements, the endogenous ligand, or various cofactors, including the Retinoid X Receptor (RXR). Consequently, modulation of PPFP activity might be possible through the use of PPARgamma agonists, RXR-agonists, or specific modulators of PPFP itself. Alternatively, modulation of several down-stream regulatory pathways may become possible, as the consequences of PPARgamma inhibition become better known. PPFP represents a potential novel target for the management of advanced FTC.
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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".