Polygenic risk of subsequent thyroid cancer after childhood cancer: A report from St. Jude lifetime cohort (SJLIFE) and Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10060 Background: Subsequent thyroid cancer (STC) is among the most common malignancies in childhood cancer survivors, especially those with thyroid exposure to radiotherapy (RT). Identification of genetic risk factors may inform screening practices. Methods: Twelve SNPs were previously identified as thyroid cancer risk loci in the general population of European ancestry. A polygenic risk score (PRS) was calculated as a sum of risk alleles carried by a survivor, weighted by the natural logarithm of the published per-allele odds ratios (range: 1.2-1.8). With piecewise exponential models, associations of STC rates with PRS were assessed, both overall and stratified by neck RT exposure. Models were adjusted for sex, age at primary diagnosis, attained age, neck RT dose, epipodophyllotoxin therapy, and eigenvectors within survivors of European ancestry from SJLIFE with whole-genome sequencing data and CCSS with SNP data imputed to Haplotype Reference Consortium. Results: Among 2,324 SJLIFE survivors, 61 (43 with, 18 without neck RT) developed STC. The rate of STC was increased by 5.3-fold (95% confidence interval (CI), 2.2-12.6) and 3.1-fold (CI, 1.3-7.7) for survivors in the third and second PRS tertiles, respectively, compared to those in the first tertile, with corresponding cumulative incidence at age of 40 years of 5.3% (CI, 3.3-7.3%), 2.5% (CI, 1.1-3.9%), and 1.0% (CI, 0.005-2.0%), respectively. Stratified by neck RT, the corresponding rate increases were 7.6 (CI, 2.3-25.3) and 3.8 (CI, 1.1-13.4), respectively, among survivors exposed to neck RT; however, no association was observed among survivors without neck RT (only 18 STC cases). Replication was performed among 4,302 CCSS survivors, 100 (61 with, 39 without neck RT) developed STC. The rates of STC were increased by 2.3-fold (CI, 1.4-3.9) and 1.7-fold (CI, 1.0-2.9) for survivors in the third and the second PRS tertiles, compared to those in the first tertile. The similar significant associations were observed in survivors with and without neck RT ( Ptrend = 0.04 and 0.02, respectively). Conclusions: High PRS conferring STC risk can inform screening practices and help personalize and improve survivorship care.
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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 | 0.000 | 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 | 0.000 |
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".