Founder effect and a high prevalence of BRCA1 mutations among young Mexican triple-negative breast cancer (TNBC) patients.
Bibliographic record
Abstract
1522 Background: Previous studies have shown that the prevalence of BRCA mutations among young TNBC patients is elevated. Current guidelines recommend that women ≤60 years with TNBC be referred for genetic counseling. Different studies in Mexico have shown an early age of onset of BC and a high prevalence of TNBC, which suggests that BRCA mutations may account for a higher proportion of breast cancers in this population. However, there is limited information regarding BRCA mutation prevalence mainly due to lack of access to clinical BRCA gene analyses in Mexico. Methods: The purpose of this study is to analyze BRCA mutation frequency in a cohort of young Mexican TNBC patients using a panel assay of 114 recurrent BRCA mutations found in women of Hispanic ancestry (HISPANEL) on the Sequenom platform. Mexican women diagnosed with TNBC at or before age 50 were prospectively recruited from the National Cancer Institute in Mexico City. Patients were screened by HISPANEL and by PCR for the Mexican founder BRCA1 ex9-12del large rearrangement. Results: Among 190 consecutive TNBC cases, the median age of diagnosis was 42 years old and 69% were younger than 45 years. The majority of patients presented with locally advanced disease (69%). A BRCA mutation was detected in 43/190 (23%) of patients (42-BRCA1, 1-BRCA2), and BRCA1 ex9-12del accounted for 42% of the mutations. Only 45% of the BRCA mutation carriers had a family history of breast and/or ovarian cancer. Samples were processed in a two-week period, with a total cost of $4,000 USD. Conclusions: There is a remarkable prevalence of BRCA1 mutations among young TNBC patients in our population. The first documented Mexican founder mutation, BRCA1 ex9-12del, was the most frequent BRCA mutation and is likely responsible for a significant burden of disease in women from Southern Mexico. The HISPANEL can be completed within 72 hours from sample collection, at a modest cost of $20 USD per sample, and implementation among women of Mexican ancestry could reduce overall genotyping cost and increase access to cancer prevention among underserved women in Mexico and the U.S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.003 | 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".