Three-Dimensional Telomere Dynamics in Follicular Thyroid Cancer
Bibliographic record
Abstract
BACKGROUND: Over the last decade, annual incidence rates for thyroid cancer have been among the highest of all cancers in the Western world. However, the genomic mechanisms impacting thyroid carcinogenesis remain elusive. METHODS: We employed an established mouse model of follicular thyroid cancer (FTC) with a homozygous proline to valine mutation (Thrb(PV/PV)) in the thyroid receptor β1 (TRβ1) and applied quantitative three-dimensional (3D) telomere analysis to determine 3D telomeric profiles in Thrb(PV)(/PV), Thrb(PV/)(+), and Thrb(+/+) mouse thyrocytes before and after histological presentation of FTC. RESULTS: Using quantitative fluorescent in situ hybridization (Q-FISH) and TeloView™ image analysis, we found altered telomeric signatures specifically in mutant mouse thyrocytes. As early as 1 month of age, Thrb(PV/PV) mouse thyrocytes showed more telomeres than normal and heterozygous age-matched counterparts. Importantly, at the very early age of 1 month, 3D telomeric profiles of Thrb(PV/PV) thyrocyte nuclei reveal genetic heterogeneity with several nuclei populations exhibiting different telomere numbers, suggestive of various degrees of aneuploidy within the same animal. This was detected exclusively in Thrb(PV/PV) mice well before the presentation of histological signs of thyroid carcinoma. CONCLUSIONS: We identified quantitative 3D telomere analysis as a novel tool for early detection and monitoring of thyrocyte chromosomal (in)stability. This technique has the potential to identify human patients at risk for developing thyroid carcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".