Germline BRCA1 Mutations and a Basal Epithelial Phenotype in Breast Cancer
Bibliographic record
Abstract
A basal epithelial phenotype is found in not more than 15% of all invasive breast cancers. Microarray studies have shown that this phenotype is associated with breast cancers that express neither estrogen receptor (ER) nor erbB-2 (HER2/neu) (i.e., ER/erbB-2-negative tumors). The ER/erbB-2- negative phenotype is also found in breast cancers occurring in BRCA1 mutation carriers (i.e., BRCA1-related breast cancers). We tested the hypothesis that BRCA1-related breast cancers are more likely than non-BRCA1/ 2-related breast cancer to express a basal epithelial phenotype. Among 292 breast cancer specimens previously analyzed for ER, erbB-2, p53, and germline mutations in BRCA1 and BRCA2, we identified 76 that did not overexpress ER or erbB-2. Of the 72 specimens with sufficient material for testing, 40 expressed stratified epithelial cytokeratin 5 and/or 6 (5/6). In univariate analysis, the expression of cytokeratin 5/6 was statistically significantly associated with BRCA1-related breast cancers (odds ratio = 9.0, 95% confidence interval = 1.9 to 43; P =.002, two-sided Fisher's exact test). Thus, germline BRCA1 mutations appear to be associated with a distinctive breast cancer phenotype.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".