Recurrent <i>BRCA1</i> and <i>BRCA2</i> Mutations in Mexican Women with Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Germline mutations in the BRCA1 and BRCA2 genes confer an estimated 58% to 80% lifetime risk of breast cancer. In general, screening is done for cancer patients if a relative has been diagnosed with breast or ovarian cancer. There are few data on the prevalence of mutations in these genes in Mexican women with breast cancer and this hampers efforts to develop screening policies in Mexico. METHODS: We screened 810 unselected women with breast cancer from three cities in Mexico (Mexico City, Veracruz, and Monterrey) for mutations in BRCA1 and BRCA2, including a panel of 26 previously reported mutations. RESULTS: Thirty-five mutations were identified in 34 women (4.3% of total) including 20 BRCA1 mutations and 15 BRCA2 mutations. Twenty-two of the 35 mutations were recurrent mutations (62.8%). Only five of the 34 mutation carriers had a first-degree relative with breast cancer (three with BRCA1 and two with BRCA2 mutations). CONCLUSION: These results support the rationale for a strategy of screening for recurrent mutations in all women with breast cancer in Mexico, as opposed to restricting screening to those with a sister or mother with breast or ovarian cancer. IMPACT: These results will impact cancer genetic testing in Mexico and the identification of at-risk individuals who will benefit from increased surveillance. Cancer Epidemiol Biomarkers Prev; 24(3); 498-505. ©2014 AACR.
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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 | 0.002 |
| 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 | 0.000 |
| 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".