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
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".