Can we identify a group of breast cancer patients with a good prognosis despite four or more positive (4+) axillary nodes using a tissue microarray (TMA)?
Bibliographic record
Abstract
10582 Background: Although breast cancer with 4+ axillary lymph nodes generally carries a poor prognosis, we hypothesized that a good prognostic subgroup of such patients would be identifiable by immunohistochemical (IHC) biomarkers. Methods: Patients with primary breast cancer with 4+ axillary nodes and no metastatic disease at diagnosis were identified from a large clinically annotated TMA of formalin-fixed paraffin-embedded archival breast cancers and analyzed for eight IHC based biomarkers: estrogen receptor, HER2, carbonic anhydrase IX, EGFR, CK 5/6, progesterone receptor, p53 and Ki67. Expression of each biomarker was scored 0 or 1 to indicate good or bad prognosis based on univariate analysis of relapse free survival (RFS). Patients were banded as having a total score of 0 (i.e. each biomarker predicted a good outcome), 1–4 or 5–8. Kaplan Meier and Cox regression analysis of RFS outcomes was performed. 10 year RFS for each band was compared to the mean of predicted outcomes based on the prognostic tool Adjuvant! ( www.adjuvantonline.com ). Results: 313 eligible patients were identified and complete data were available for 228. The subset of 228 was similar to the larger group of 313 with respect to RFS and conventional prognostic factors. 10 year RFS for the 228 patients was 39.5% (standard error, SE 3.4%). The subgroup of 37 (16%) scoring zero for all 8 biomarkers had a mean 10 year RFS of 77.6% (SE 7.0). Mean 10 year RFS for the bands scoring 1–4 (154 patients, 68%) and 5–8 (37 patients, 16%) were 34.9% (SE 4.1) and 19.0% (SE 6.9) respectively. Mean 10 year RFS predictions by Adjuvant! were 35.9% (SE 2.6), 34.5% (SE 1.2) and 34.3% (SE 2.3) respectively. In multivariate analysis with conventional prognostic factors, the banded biomarker score retained statistical significance for predicting RFS (p=0.0007) along with estrogen receptor status (p=0.03) and tumour size (p=0.01). Conclusions: This TMA biomarker panel identified a breast cancer subgroup with good prognosis despite extensive axillary node involvement. Long term outcome was markedly better than that predicted by conventional prognostic factors. If validated, treatment decisions and clinical trial stratification might be modified using this new score. No significant financial relationships to disclose.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".