Prevalence of <i>BRCA1</i> and <i>BRCA2</i> mutations in Pakistani breast and ovarian cancer patients
Bibliographic record
Abstract
Among Asian countries, Pakistan has the highest rates of breast and ovarian cancer. To assess the contribution of the BRCA1 and BRCA2 germ line mutations to these high rates, we conducted the first study of 176 Pakistani breast and ovarian cancer patients, selected on family history and on age of diagnosis. Comprehensive BRCA mutation screening was performed using a range of techniques, including denaturing high-pressure liquid chromatography, single strand conformational polymorphism analysis and protein truncation test, followed by DNA sequencing. Thirty deleterious germ-line mutations were identified in the 176 families (17.0%), including 23 in BRCA1 and 7 in BRCA2. Four mutations, 185delAG, 185insA, S1503X and R1835X, were recurrent; these accounted for 52% of all identified BRCA1 mutations. Haplotype analyses suggested founder effects for 3 of these. The prevalence of BRCA1 or BRCA2 mutations was 42.8% for families with multiple cases of breast cancer, and was 50.0% for the breast/ovarian cancer families. The prevalence of mutations was 11.9% for single cases of early-onset breast cancer (< or =30 years) and was 9.0% for single cases of early-onset ovarian cancer (< or =45 years). Our findings show that BRCA mutations account for a substantial proportion of hereditary breast/ovarian cancer and early-onset breast and ovarian cancer cases in Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".