The prevalence of BRCA1 mutations among young women with triple-negative breast cancer
Bibliographic record
Abstract
BACKGROUND: Molecular screening for BRCA1 and BRCA2 mutations is now an established component of risk evaluation and management of familial breast cancer. Features of hereditary breast cancer include an early age-of-onset and over-representation of the 'triple-negative' phenotype (negative for estrogen-receptor, progesterone-receptor and HER2). The decision to offer genetic testing to a breast cancer patient is usually based on her family history, but in the absence of a family history of cancer, some women may qualify for testing based on the age-of-onset and/or the pathologic features of the breast cancer. METHODS: We studied 54 women who were diagnosed with high-grade, triple-negative invasive breast cancer at or before age 40. These women were selected for study because they had little or no family history of breast or ovarian cancer and they did not qualify for genetic testing using conventional family history criteria. BRCA1 screening was performed using a combination of fluorescent multiplexed-PCR analysis, BRCA1 exon-13 6 kb duplication screening, the protein truncation test (PTT) and fluorescent multiplexed denaturing gradient gel electrophoresis (DGGE). All coding exons of BRCA1 were screened. The two large exons of BRCA2 were also screened using PTT. All mutations were confirmed with direct sequencing. RESULTS: Five deleterious BRCA1 mutations and one deleterious BRCA2 mutation were identified in the 54 patients with early-onset, triple-negative breast cancer (11%). CONCLUSION: Women with early-onset triple-negative breast cancer are candidates for genetic testing for BRCA1, even in the absence of a family history of breast or ovarian cancer.
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 | 0.002 |
| 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 | 0.001 | 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".