Estrogen Receptor Status in <b> <i>BRCA1</i> </b>- and <b> <i>BRCA2</i> </b>-Related Breast Cancer
Bibliographic record
Abstract
PURPOSE: BRCA1-related breast cancers are more frequently estrogen receptor (ER) negative than are either BRCA2-related or nonhereditary breast cancers. The relationship between ER status and other clinical features of hereditary breast cancers has not been well studied. EXPERIMENTAL DESIGN: ER status, grade, and histological tumor type were evaluated in 1131 women with invasive breast cancer, ascertained at 10 centers in North America. There were 208 BRCA1 mutation carriers, 88 BRCA2 carriers, and 804 women without a known mutation. We stratified the patients by mutation status, grade, age, and histological type and calculated the percentage of ER-positive tumors within each stratum. RESULTS: BRCA1 mutation carriers were more likely to have ER-negative breast cancers than were women in other groups, after adjustment for age, grade, and histological subtype (P < 0.001). Only 3.9% of BRCA1-related breast cancers were ER-positive cancers occurring in women in their postmenopausal years. The direction and magnitude of the change in ER status with increasing age at diagnosis in BRCA1 carriers was significantly different from in BRCA2 carriers (P(intercept) = 0.0002, P(slope) = 0.04). Notably, changes in ER status with age at diagnosis for BRCA1 carriers and noncarriers were almost identical (P(slope) = 0.98). CONCLUSIONS: The strong relationship between the presence of a BRCA1 mutation and the ER-negative status of the breast cancers is neither a consequence of the young age at onset nor the high grade but is an intrinsic property of BRCA1-related cancers. The ER-negative status of these cancers may reflect the cell of origin of BRCA1-related cancers.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".