Mutational Profile of Advanced Primary and Metastatic Radioactive Iodine-Refractory Thyroid Cancers Reveals Distinct Pathogenetic Roles for <i>BRAF, PIK3CA</i> , and <i>AKT1</i>
Bibliographic record
Abstract
Patients with poorly differentiated thyroid cancers (PDTC), anaplastic thyroid cancers (ATC), and radioactive iodine-refractory (RAIR) differentiated thyroid cancers have a high mortality, particularly if positive on [(18)F]fluorodeoxyglucose (FDG)-positron emission tomography (PET). To obtain comprehensive genetic information on advanced thyroid cancers, we designed an assay panel for mass spectrometry genotyping encompassing the most significant oncogenes in this disease: 111 mutations in RET, BRAF, NRAS, HRAS, KRAS, PIK3CA, AKT1, and other related genes were surveyed in 31 cell lines, 52 primary tumors (34 PDTC and 18 ATC), and 55 RAIR, FDG-PET-positive recurrences and metastases (nodal and distant) from 42 patients. RAS mutations were more prevalent than BRAF (44 versus 12%; P = 0.002) in primary PDTC, whereas BRAF was more common than RAS (39 versus 13%; P = 0.04) in PET-positive metastatic PDTC. BRAF mutations were highly prevalent in ATC (44%) and in metastatic tumors from RAIR PTC patients (95%). Among patients with multiple metastases, 9 of 10 showed between-sample concordance for BRAF or RAS mutations. By contrast, 5 of 6 patients were discordant for mutations of PIK3CA or AKT1. AKT1_G49A was found in 9 specimens, exclusively in metastases. This is the first documentation of AKT1 mutation in thyroid cancer. Thus, RAIR, FDG-PET-positive metastases are enriched for BRAF mutations. If BRAF is mutated in the primary, it is likely that the metastases will harbor the defect. By contrast, absence of PIK3CA/AKT1 mutations in one specimen may not reflect the status at other sites because these mutations arise during progression, an important consideration for therapies directed at phosphoinositide 3-kinase effectors.
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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".