Data from Genetic Variation in <i>TYMS</i> in the One-Carbon Transfer Pathway Is Associated with Ovarian Carcinoma Types in the Ovarian Cancer Association Consortium
Bibliographic record
Abstract
Abstract Background: We previously reported the risks of ovarian carcinoma for common polymorphisms in one-carbon transfer genes. We sought to replicate associations for DPYD rs1801265, DNMT3A rs13420827, MTHFD1 rs1950902, MTHFS rs17284990, and TYMS rs495139 with risk of ovarian carcinoma overall and to use the large sample of assembled cases to investigate associations by histologic type. Methods: Associations were evaluated in the Ovarian Cancer Association Consortium, including 16 studies of 5,593 epithelial ovarian carcinoma cases and 9,962 controls of white non-Hispanic origin. Odds ratios (OR) and 95% confidence intervals (CI) were adjusted for age and study site. Results: The five polymorphisms were not associated with ovarian carcinoma overall (Ptrend > 0.13); however, associations for the minor allele at TYMS rs495139 were observed for carcinomas of mucinous type (OR, 1.19; 95% CI, 1.03-1.39; P = 0.02), clear cell type (OR, 0.86; 95% CI, 0.75-0.99; P = 0.04), and endometrioid type (OR, 0.90; 95% CI, 0.81-0.99; P = 0.04; Pheterogeneity = 0.001). Restriction to low-grade mucinous carcinomas further strengthened the association for the mucinous type (OR, 1.32; 95% CI, 1.07-1.62; P = 0.01). TYMS rs495139 was not associated with serous type (OR, 1.06; 95% CI, 1.00-1.13; P = 0.05). Conclusions: TYMS rs495139 may be associated with a differential risk of ovarian carcinoma types, indicating the importance of accurate histopathologic classification. Impact: Biomarkers that distinguish ovarian carcinoma types are few, and TYMS rs495139 may provide a novel clue to type etiology. Cancer Epidemiol Biomarkers Prev; 19(7); 1822–30. ©2010 AACR.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".