Short NK- and Naïve T-Cell Telomere Length Is Associated with Thyroid Cancer in Childhood Cancer Survivors: A Report from the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Survivors of childhood cancer are at risk for therapy-related subsequent malignant neoplasms (SMN), including thyroid SMN. Telomere length (TL) is associated with cancer risk, but the relationship between TL and SMN risk among survivors is less clear. METHODS: We conducted a nested, matched case-control study of radiation-exposed 15-year+ adult survivors of childhood cancer with thyroid SMN (cases) and without SMN (controls). Forty-six cases were matched to 46 controls by primary diagnosis, chemotherapy (yes/no), radiation field, and follow-up duration. Lymphocyte TL (LTL) was measured by telomere flow-FISH cytometry using blood samples banked at a mean of 38.9 years (cases), 39.2 years (controls). Genetic variation in telomere genes was assessed by whole genome sequencing. Point estimates for LTL <10th percentile were determined for cases and controls. RESULTS: = 0.01), and 2.8-fold more likely to have naïve T-cell LTL <10th percentile than controls (CI, 1.07-8.78). Five out of 15 cases with a rare indel or missense variant had naïve T-cell LTL <10th percentile, compared with one out of eight controls. CONCLUSIONS: Long-term survivors have shorter than expected LTL, a finding that is more pronounced among survivors with thyroid SMN. IMPACT: The long-term impact of childhood cancer treatment on immune function is poorly understood. Our findings support immune function studies in larger survivor cohorts to assess long-term deficits in adaptive and innate immunity that may underlie SMN risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".