Immunohistochemical Detection Using the New Rabbit Monoclonal Antibody SP1 of Estrogen Receptor in Breast Cancer Is Superior to Mouse Monoclonal Antibody 1D5 in Predicting Survival
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Bibliographic record
Abstract
PURPOSE: Estrogen receptor (ER) expression predicts improved breast cancer-specific survival and reduced risk of recurrence and is targeted in breast cancer therapy. A high-quality antibody to identify ER-positive patients plays an important role in clinical decision making for women with breast cancer. This study evaluates immunohistochemistry using two anti-ER antibodies, a new rabbit monoclonal antibody (SP1) and the mouse monoclonal antibody (1D5), in relation to biochemical ER assay results and clinical data on survival and adjuvant systemic therapy. PATIENTS AND METHODS: A population-based tissue microarray series of 4,150 invasive breast cancers was constructed. All patients had staging, pathology, treatment, and follow-up information. The median follow-up was 12.4 years and the median age at diagnosis 60 years. Survival analysis and log-rank tests were used to evaluate the prognostic value of ER status and correlations with clinical data. RESULTS: Among the 4,105 samples interpretable for both antibodies, SP1 detected ER positivity in 69.5% and 1D5 in 63.1% of cases. Both monoclonal antibodies are demonstrated to be good prognostic indictors for breast cancer-specific and relapse-free survival. In multivariate analysis, including age, tumor size, grade, and lymphovascular and nodal status, SP1 was a better independent prognostic factor than 1D5. Among patients with discrepant ER results, the 8% of patients who were SP1 positive/1D5 negative showed good outcomes, and the 2% SP1-negative/1D5 positive had poor outcomes. Maintaining the same 92% specificity and 98% positive predictive value, SP1 is 8% more sensitive than 1D5 using biochemical assay as gold standard. CONCLUSION: SP1 represents an improved standard for ER immunohistochemistry assessment in breast cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it