Bcl-2 Is a Prognostic Marker in Breast Cancer Independently of the Nottingham Prognostic Index
Bibliographic record
Abstract
PURPOSE: Prognostication of breast cancer using clinicopathologic variables, although useful, remains imperfect. Many reports suggest that gene expression profiling can refine the current approach. Alternatively, it has been shown that panels of proteins assessed by immunohistochemistry might also be useful in this regard. We evaluate the prognostic potential of a panel of markers by immunohistochemistry in a large case series to establish if either a single marker or a panel could improve the prognostic power of the Nottingham Prognostic Index (NPI). We validated the results in an independent series. EXPERIMENTAL DESIGN AND RESULTS: The expression of 13 biomarkers was evaluated in 930 breast cancers on a tissue microarray. Eight markers [estrogen receptor (ER), progesterone receptor (PR), Bcl-2, cyclin E, p53, MIB-1, cytokeratin 5/6, and HER2] showed a significant association with survival at 10 years on univariate analysis. On multivariate analysis that included these eight markers and the NPI, only the NPI [hazard ratio (HR), 1.35; 95% confidence interval (95% CI), 1.16-1.56; P = 0.0005], ER (HR, 0.59; 95% CI, 0.39-0.88; P = 0.011), and Bcl-2 (HR, 0.68; 95% CI, 0.46-0.99; P = 0.055) were significant. In a subsequent multivariate analysis that included the NPI, ER, and Bcl-2, only Bcl-2 (HR, 0.62; 95% CI, 0.44-0.87; P = 0.006) remained independent of NPI (HR, 1.50; 95% CI, 1.16-1.56; P = 0.004). In addition, Bcl-2, used as a single marker, was more powerful than the use of a panel of markers. Based on these results, an independent series was used to validate the prognostic significance of Bcl-2. ER and PR were also evaluated in this validation series. Bcl-2 (HR, 0.83; 95% CI, 0.71-0.96; P = 0.018) retained prognostic significance independent of the NPI (HR, 2.04; 95% CI, 1.67-2.51; P < 0.001) with an effect that was maximal in the first 5 years. CONCLUSION: Bcl-2 is an independent predictor of breast cancer outcome and seems to be useful as a prognostic adjunct to the NPI, particularly in the first 5 years after diagnosis.
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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 | 0.003 |
| 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".