Are CYP17 Genotypes a Biomarker for Ovarian Cancer in Patients With Cancer History in Their Family?
Bibliographic record
Abstract
BRCA1 and BRCA2 genes are responsible for 5-10% of breast and ovarian cancer cases. However, the vast majority of ovarian and breast cancer cases do not display the hereditary form of the disease. Estrogen-metabolizing genes may also contribute to the predisposition of breast or ovarian cancer. Polymorphic variants of the estrogen-metabolizing gene, CYP17, have been associated with the risk of hormone-related cancers. In this study we investigated the CYP17 polymorphisms in ovarian cancer patients harboring mutations in the BRCA1 and BRCA2 genes, patients displaying familial characteristics but not carrying mutations and patients with sporadic ovarian cancer. Association between the allele frequencies, the CYP17 genotype and tumor characteristics or clinical parameters was evaluated. Our data suggest evidence for an association between ovarian cancer risk and the CYP17 genotype in the subgroup of patients with familial disease in whom no mutations in the BRCA genes are found. Although there were no statistically significant differences in the genotype distribution between the control group and the subgroup of patients with BRCA mutations, the frequency of the CYP17 A2 allele was significantly higher in the subgroup of patients without BRCA mutations. We found a four- to eightfold higher risk in ovarian cancer patients with family history but without BRCA mutations. Our data indicate that the CYP17 A2 allele polymorphism may confer an increased risk and can provide a biomarker for ovarian cancer patients in whom no mutations in the BRCA genes are observed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".