Characterizing the three‐dimensional organization of telomeres in papillary thyroid carcinoma cells
Bibliographic record
Abstract
Abstract The relationship between the three‐dimensional (3D) nuclear telomere architecture and specific genetic alterations in papillary thyroid carcinoma (PTC), in particular in cancer stem‐like cells (CSLCs), has not yet been investigated. We isolated thyrospheres containing CSLCs from B‐CPAP, K1, and TPC‐1 PTC‐derived cell lines, representative of tumors with different genetic backgrounds within the newly identified BRAFV600E‐like PTC subgroup, and used immortalized normal human thyrocytes (Nthy‐ori 3.1) as control. We performed quantitative fluorescence in situ hybridization, 3D imaging, and 3D telomere analysis using TeloView software to examine telomere dysfunction in both parental and thyrosphere cells. Among the 3D telomere profile, a wide heterogeneity was observed, except for telomere intensity. Our findings indicate that CSLCs of each cell line had longer telomeres than parental cells, according to telomere intensity values, which correlate with telomere length. Indeed, the thyrosphere cells had lower numbers of lower‐intensity telomeres (≤5,000 arbitrary fluorescent units, a.u.), compared with parental cancer cells, as well as parental control cells, (p < 0.0001). The B‐CPAP thyrospheres showed a decreased number of higher intensity telomeres (>17,000 a.u.) than K1 and TPC‐1 cells, as well as control cells (p < 0.0001). By selecting PTC‐derived cell lines with different genetic backgrounds characteristic of BRAFV600E‐like PTC subgroups, we demonstrate that thyrosphere cells with BRAFV600E and TP53 mutations show shorter telomeres than those harboring RET/PTC or BRAFV600Eand wild‐type TP53. Hence, our data reveal a trend towards a decrease in telomere shortening in CSLCs, representing the early cancer‐promoting subpopulation, as opposed to parental cells representing the tumor bulk cells.
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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".