Folliculin (FLCN) in Thyroid Tumors: Incidence, Significance, and Role as a Driver Gene and Secondary Alteration
Bibliographic record
Abstract
Thyroid carcinomas are driven by diverse molecular alterations, but the tumor suppressor gene folliculin (FLCN), best known for its role in Birt–Hogg–Dubé (BHD) syndrome, has received limited attention in thyroid tumors. Here, we describe two thyroid tumors with pathogenic FLCN alterations—one germline and one somatic—and analyze the broader prevalence and significance of FLCN in thyroid carcinomas using multiple large sequencing datasets, including ORIEN-AVATAR. Patient 1, with a germline FLCN mutation and a history of BHD syndrome, presented with a well-circumscribed oncocytic adenoma. Molecular testing confirmed biallelic FLCN inactivation, but no additional mutations or aggressive features were observed, and the patient remained disease-free post-thyroidectomy. Patient 2 harbored a somatic FLCN mutation in an oncocytic poorly differentiated thyroid carcinoma, which exhibited extensive angioinvasion, high proliferative activity, and concurrent TP53 and RB1 mutations. The tumor progressed with metastatic disease despite multimodal treatment. Thyroid carcinomas revealed FLCN alterations in 1.1% of cases. Pathogenic mutations were rare but associated with oncocytic morphology, while homozygous deletions occurred more frequently in genomically unstable tumors, including anaplastic thyroid carcinoma. These findings suggest FLCN mutations may act as early oncogenic drivers in oncocytic thyroid neoplasms, while deletions represent secondary events in aggressive tumor evolution. The lack of FLCN coverage in standard thyroid molecular panels likely underestimates its clinical relevance. Including FLCN in genetic testing could improve tumor detection and characterization, particularly in BHD patients who may benefit from routine thyroid screening. Further studies are needed to clarify FLCN’s role in thyroid cancer pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".