<i>BRCA1</i> and <i>BRCA2</i> mutations among breast cancer patients from the Philippines
Bibliographic record
Abstract
Age-adjusted incidence rates of breast cancer vary more than 10-fold worldwide, with the highest rates reported in North America and Europe. The highest breast cancer incidence rates in Southeast Asia have been reported for the Manila Cancer Registry in the Philippines, with an age-standardized rate of 47.7 per 100,000 per year. The possible contribution of hereditary factors to these elevated rates has not been investigated. We conducted a case-control study of 294 unselected incident breast cancer cases and 346 female controls from Manila, Philippines. Cases and controls were selected from women below the age of 65 undergoing evaluation at the PGH in Manila because of a suspicious breast mass. Molecular analysis identified 12 BRCA2 mutations and 3 BRCA1 mutations. We estimate the prevalence of BRCA mutations among unselected breast cancer cases in the Philippines to be 5.1% (95% CI: 2.6-7.6%), with a prevalence of 4.1% (95% CI: 1.8-6.4%) for BRCA2 mutations alone. The BRCA2 4265delCT and 4859delA mutations were found in 2 and 4 unrelated cases, respectively; haplotype analysis confirmed that these, and the BRCA1 5454delC mutation, are founder mutations. BRCA2 mutations were also found in 2 of 346 controls (0.6%; 95% CI: 0.2-1.4%). Compared with non-carrier cases, the cumulative risk of breast cancer for first-degree relatives of mutation carriers was 24.3% to age 50, compared with <4% for first-degree relatives of non-carrier cases (RR = 6.6; 95% CI: 2.6-17.2; p= 7.5 x 10(-6)). Our data suggest that penetrance of BRCA mutations is not reduced in the Philippines. Germline mutations in the BRCA2 gene contribute more than mutations BRCA1 to breast cancer in the Philippines, due in large part to the presence of 2 common founder mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".