Prognosis of early breast cancer by immunohistochemistry defined intrinsic sub-types in patients treated with adjuvant chemotherapy in the NEAT/BR9601 trial
Bibliographic record
Abstract
Breast cancer can be classified into molecular sub-types that have distinct survival patterns. We evaluated the prognostic significance of breast cancer sub-types in a cohort of women taking part in the NEAT and BR9601 clinical trials comparing cyclophosphamide, methotrexate and fluorouracil (CMF) with ECMF (epirubicin and CMF). Furthermore, we evaluated whether the sub-types were predictive of the added benefit of epirubicin in these trials. Tumour tissue microarrays were stained and scored for ER, PR, HER2, EGFR and CK5/6. These were used to classify the tumours into six intrinsic sub-types. We used Cox regression to compare overall survival (OS), breast cancer-specific survival (BCSS) and relapse-free survival (RFS) in the different sub-groups. We also compared the effect of ECMF with CMF by sub-group. Immunohistochemistry data were available for 1,725 cases of whom 805 were luminal 1-basal negative. Median follow-up time was 7 years. The luminal 1-basal negative tumours were associated with the best prognosis in five years after surgery and the HER2-like tumours were associated with the poorest prognosis. There was little evidence for significant heterogeneity of this effect by tumour sub-type (OS p = 0.40, BCSS p = 0.53 RFS p = 0.50) - the largest additional benefit of epirubicin was in women with tumours of the 5-negative phenotype (OS HR = 0.39 95% CI: 0.21-0.73) and the smallest was in Luminal 1-basal negative tumours (OS HR = 0.86 95% CI: 0.64-1.16). We confirmed that breast cancer sub-types show distinct behaviour with differences in short- and long-term survival. The benefit of ECMF over CMF was statistically similar in all disease sub-types.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".