Placental Cadherin and the Basal Epithelial Phenotype of <i>BRCA1</i>-Related Breast Cancer
Bibliographic record
Abstract
PURPOSE: BRCA1-related breast cancer frequently has a basal epithelial phenotype, and P-cadherin is a basal marker. We undertook a detailed evaluation of the relationship among P-cadherin, prognostic markers in breast cancer, and outcome. EXPERIMENTAL DESIGN: This study was restricted to 292 cases of first primary invasive breast cancer diagnosed in Ashkenazi Jewish women between 1980 and 1995. All available blocks were stained for P-cadherin, and 261 were included in the final statistical analyses, including 27 germ line BRCA1 mutation carriers and 8 BRCA2 mutation carriers. Descriptive analyses were done followed by survival analyses and a Poisson regression analysis. RESULTS: P-cadherin was present in 80 of the 261 breast cancers (31%) and was more frequently present in tumors that have a basal epithelial phenotype [i.e., high-grade, estrogen receptor- and KIP1 (p27(Kip1))-negative tumors, with expression of cytokeratin 5/6, cyclin E, TP53, and presence of BRCA1 mutations and vascular nests (all P < 0.001)]. In a univariate survival model, expression of P-cadherin was associated with a relative risk (RR) of death from breast cancer at a 10-year follow-up of 2.9 (95% confidence interval, 1.8-4.7; P < 0.0001) and was a predictor of poor univariate survival in both lymph node-negative and -positive breast cancers. In a multivariate analysis, the effect of P-cadherin levels was not independent of other basal-related markers. Multivariable interaction modeling showed that P-cadherin positivity was highly predictive of a poor prognosis in small, node-negative breast cancers (RR, 7.1; P = 0.006). CONCLUSIONS: P-cadherin is a marker for basal-like breast cancers and is strongly associated with the presence of a BRCA1 mutation. It is an adverse prognostic factor, particularly in small, node-negative breast 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 | 0.002 |
| 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".