Prostate Cancer Susceptibility Polymorphism rs2660753 Is Not Associated with Invasive Ovarian Cancer
Bibliographic record
Abstract
BACKGROUND: We previously reported an association between rs2660753, a prostate cancer susceptibility polymorphism, and invasive epithelial ovarian cancer (EOC; OR = 1.2, 95% CI=1.0-1.4, P(trend) = 0.01) that showed a stronger association with the serous histological subtype (OR = 1.3, 95% CI = 1.1-1.5, P(trend) = 0.003). METHODS: We sought to replicate this association in 12 other studies comprising 4,482 cases and 6,894 controls of white non-Hispanic ancestry in the Ovarian Cancer Association Consortium. RESULTS: No evidence for an association with all cancers or serous cancers was observed in a combined analysis of data from the replication studies (all: OR = 1.0, 95% CI = 0.9-1.1, P(trend) = 0.61; serous: OR = 1.0, 95% CI = 0.9-1.1, P(trend) = 0.85) or from the combined analysis of discovery and replication studies (all: OR = 1.0, 95% CI = 1.0-1.1, P(trend) = 0.28; serous: OR = 1.1, 95% CI = 1.0-1.2, P(trend) = 0.11). There was no evidence for statistical heterogeneity in ORs across the studies. CONCLUSIONS: Although rs2660753 is a strong prostate cancer susceptibility polymorphism, the association with another hormonally related cancer, invasive EOC, is not supported by this replication study. IMPACT: Our findings, based on a larger sample size, emphasize the importance of replicating potentially promising genetic risk associations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".