A Ki67/BCL2 index based on immunohistochemistry is highly prognostic in ER‐positive breast cancer
Bibliographic record
Abstract
There is an urgent need to improve prognostic classifiers in breast cancer. Ki67 and B-cell lymphoma 2 protein (BCL2) are established prognostic markers which have traditionally been assessed separately, in a dichotomous manner. This study was conducted to test the hypothesis that combinatorial assessment of these markers would provide superior prognostic information and improve their clinical utility. Tissue microarrays were used to assess the expression of Ki67 and BCL2 in 2749 cases of invasive breast cancer. We devised a Ki67/BCL2 index representing the relative expression of each protein and assessed its association with breast cancer-specific survival (BCSS) using a Cox proportional-hazards model. Based on our findings, an independent cohort of 3992 cases was used to validate the prognostic significance of the Ki67/BCL2 index. All survival analyses were conducted on complete data as well as data where missing values were resolved using multiple imputation. This study complied with reporting recommendations for tumour marker prognostic studies (REMARK) criteria. The Ki67/BCL2 index showed a significant association with BCSS at 10 years in estrogen receptor (ER)-positive disease. In multivariate analysis, adjusting for major clinical and molecular markers, the Ki67/BCL2 index retained prognostic significance, robustly classifying cases into three risk groups [intermediate- versus low-risk hazard ratio (HR), 1.5; 95% confidence interval (95% CI), 1.0-2.0; p = 0.031; high- versus low-risk HR, 2.6; 95% CI, 1.3-5.0; p = 0.005]. This finding was validated in an independent cohort of 3992 tumours containing 2761 ER-positive tumours (intermediate- versus low-risk HR, 1.7; 95% CI, 1.3-2.1; p < 0.001; high- versus low-risk HR, 2.0; 95% CI, 1.4-2.9; p < 0.001). Ki67 and BCL2 can be effectively combined to produce an index which is an independent predictor of BCSS in ER-positive breast cancer, enhancing their potential prognostic utility.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".