Germline BRCA1/2 Mutations and p27<sup>Kip1</sup> Protein Levels Independently Predict Outcome After Breast Cancer
Bibliographic record
Abstract
PURPOSE: Decreased levels of the cyclin-dependent kinase inhibitor p27(Kip1) in breast cancer are associated with a poor outcome. The prognostic significance of BRCA1/2 mutations is less clear, and the relationship between BRCA1/2 mutation status, p27(Kip1) protein levels, and outcome has not been studied. PATIENTS AND METHODS: Pathology blocks from 202 consecutive Ashkenazi Jewish women with primary invasive breast cancer were studied. Tumor DNA was tested for the three common BRCA1/2 founder mutations present in Ashkenazi Jews, and p27(Kip1) expression was evaluated by immunohistochemistry. The median follow-up was 6.4 years. RESULTS: Thirty-two tumors (16%) were positive for a BRCA1/2 mutation. Low p27(Kip1) expression was seen in 110 tumors (63%) and was significantly associated with BRCA1/2 mutations (odds ratio, 4.0; 95% confidence interval [CI], 1.4 to 11.1; P =.009). BRCA1/2 mutation carriers had a significantly worse 5-year distant disease-free survival (DDFS) compared with women without BRCA1/2 mutations (58% v 82%; P =.003). Similar results were seen for women whose tumors expressed low levels of p27(Kip1), compared with those with high levels (5-year DDFS, 68% v 93%; P<.0001). In a multivariate analysis, both BRCA1/2 mutation and low p27(Kip1) expression were associated with a shorter DDFS (relative risk [RR], 2.1; 95% CI, 1.0 to 4.3; P =.05; and RR, 3.9; 95% CI, 1.4 to 11.1; P =.01, respectively). CONCLUSION: In this study, we showed that BRCA1/2 mutations were associated with low levels of p27(Kip1) in breast cancer. Both BRCA1/2 and p27(Kip1) status were identified as independent prognostic factors.
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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) | 0.002 | 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".