Androgen Receptor Expression and Outcomes in Early Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The androgen receptor (AR) is expressed frequently in breast cancer, but its prognostic significance is unclear. Preclinical data suggest that expression of AR may modify clinical outcomes in early breast cancer with improved prognosis in estrogen receptor (ER)-positive disease and poorer prognosis in ER-negative disease. METHODS: A systematic review of electronic databases was conducted to identify studies published between 1946 and July 2012 and to explore the association between AR expression and overall survival (OS) and disease-free survival (DFS) in women diagnosed with early breast cancer. The odds ratios (OR) for OS and DFS at 3 and 5 years were calculated and then weighted and pooled in a meta-analysis with Mantel-Haenszel random-effect modeling. All statistical tests were two-sided. RESULTS: Nineteen studies with a total of 7693 women were included. AR expression was documented in 60.5% of patients. ER-positive tumors were more likely to express AR- than ER-negative tumors (74.8% vs 31.8%, χ(2) P < .001). Compared with tumors without AR expression, those expressing AR were associated with improved OS at both 3 and 5 years (OR = 0.47, 95% confidence interval [CI] = 0.39 to 0.58, P < .001; and OR = 0.40, 95% CI = 0.29 to 0.56, P < .001). The absolute differences in the probability of OS at 3 and 5 years were 6.7% (95% CI = 3.5% to 9.8%) and 13.5% (95% CI = 7.5% to 19.6%), respectively. Results for 3- and 5-year DFS were similar. Coexpression of the ER did not influence OS at 3 or at 5 years. CONCLUSIONS: Expression of AR in women with breast cancer is associated with better OS and DFS irrespective of coexpression of ER.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".