Impact of germline <i>BRCA1</i> mutations and overexpression of p53 on prognosis and response to treatment following breast carcinoma
Bibliographic record
Abstract
BACKGROUND: Overexpression of p53 has been associated with poor survival following breast carcinoma. BRCA1 interacts biochemically with p53 and may also contribute to poor outcome when constitutionally mutated. The joint effect of both abnormalities has not been studied. The primary objective of this study was to assess the impact of germline BRCA1 mutations and p53 overexpression on survival after 10 years of follow-up. METHODS: A historical cohort of Ashkenazi Jewish women 65 years or younger with invasive breast carcinoma was tested for BRCA1 founder mutations. p53 overexpression was assessed by immunohistochemistry. Clinicopathologic information was obtained by chart review. RESULTS: In total, 278 women were analyzed. On univariate analysis, p53 overexpression (n = 63) was prognostic for worse overall survival (relative risk [RR] 2.6, P = 0.001) whereas BRCA1 germline mutations (n = 30) were of borderline significance (RR 1.9, P = 0.052). In the lymph node-negative subpopulation, BRCA1 mutation status conferred a higher mortality on univariate (RR 5.6, P < 0.001) and multivariate (RR 3.5, P = 0.03) analyses. There was a trend in favor of a worse prognosis for women who carried a germline BRCA1 mutation and whose tumor overexpressed p53. When compared with noncarriers, BRCA1 mutation carriers had a worse overall survival if they did not receive adjuvant chemotherapy (RR 3.3, P= 0.01) or adjuvant hormonal therapy (RR 2.3, P = 0.02). CONCLUSIONS: Germline BRCA1 mutations and p53 overexpression carry a negative prognosis that is not additive to known prognostic factors. Given the experimental sensitivity of BRCA1-mutated cells to chemotherapy, the worse survival among BRCA1 mutation-carrying lymph node-negative breast carcinoma patients may be partly explained by the significantly lower proportion of lymph node-negative patients who received adjuvant chemotherapy (P < 0.001).
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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 | 0.001 | 0.004 |
| 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".