Progesterone receptor variant increases ovarian cancer risk in BRCA1 and BRCA2 mutation carriers who were never exposed to oral contraceptives
Bibliographic record
Abstract
Oral contraceptives have been shown to be protective against hereditary ovarian cancer. The variant progesterone receptor allele named PROGINS is characterized by an Alu insertion into intron G and two additional mutations in exons 4 and 5. The PROGINS allele codes for a progesterone receptor with increased stability and increased hormone-induced transcriptional activity. We studied the role of the PROGINS allele as a modifying gene in hereditary breast and ovarian cancer. The study included 195 BRCA1 and BRCA2 carriers with a prior diagnosis of ovarian cancer, 392 carriers with a diagnosis of breast cancer and 249 carriers with neither cancer. Fifty-eight women had both forms of cancer. Five hundred and ninety-five women had a BRCA1 mutation and 183 women had a BRCA2 mutation. Overall, there was no association between disease status and the presence of the PROGINS allele. Information on oral contraception use was available for 663 of the 778 carriers of BRCA1 or BRCA2 mutations. Among the 449 subjects with a history of oral contraceptive use (74 cases and 365 controls), no modifying effect of PROGINS was observed [odds ratio (OR) 0.8; 95% confidence interval (CI) 0.5-1.3]. Among the 214 carriers with no past exposure to oral contraceptives, the presence of one or more PROGINS alleles was associated with an OR of 2.4 for ovarian cancer, compared to women without ovarian cancer and with no PROGINS allele (P = 0.004; 95% CI 1.4-4.3). The association was present after adjustment for ethnic group and for year of birth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".