The Role of <i>KRAS</i> rs61764370 in Invasive Epithelial Ovarian Cancer: Implications for Clinical Testing
Bibliographic record
Abstract
PURPOSE: An assay for the single-nucleotide polymorphism (SNP), rs61764370, has recently been commercially marketed as a clinical test to aid ovarian cancer risk evaluation in women with family histories of the disease. rs67164370 is in a 3'-UTR miRNA binding site of the KRAS oncogene and is a candidate for epithelial ovarian cancer (EOC) susceptibility. However, only one published article, analyzing fewer than 1,000 subjects in total, has examined this association. EXPERIMENTAL DESIGN: Risk association was evaluated in 8,669 cases of invasive EOC and 10,012 controls from 19 studies participating in the Ovarian Cancer Association Consortium, and in 683 cases and 2,044 controls carrying BRCA1 mutations from studies in the Consortium of Investigators of Modifiers of BRCA1/2. Prognosis association was also examined in a subset of five studies with progression-free survival (PFS) data and 18 studies with all-cause mortality data. RESULTS: No evidence of association was observed between genotype and risk of unselected EOC (OR = 1.02, 95% CI: 0.95-1.10), serous EOC (OR = 1.08, 95% CI: 0.98-1.18), familial EOC (OR = 1.09, 95% CI: 0.78-1.54), or among women carrying deleterious mutations in BRCA1 (OR = 1.09, 95% CI: 0.88-1.36). There was little evidence for association with survival time among unselected cases (HR = 1.10, 95% CI: 0.99-1.22), among serous cases (HR = 1.12, 95% CI = 0.99-1.28), or with PFS in 540 cases treated with carboplatin and paclitaxel (HR = 1.18, 95% CI: 0.93-1.52). CONCLUSIONS: These data exclude the possibility of an association between rs61764370 and a clinically significant risk of ovarian cancer or of familial ovarian cancer. Use of this SNP for ovarian cancer clinical risk prediction, therefore, seems unwarranted.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".