Novel Prognostic Immunohistochemical Biomarker Panel for Estrogen Receptor–Positive Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Patients with breast cancer experience progression and respond to treatment in diverse ways, but prognostic and predictive tools for the oncologist are limited. We have used gene expression data to guide the production of hundreds of novel antibody reagents to discover novel diagnostic tools for stratifying carcinoma patients. PATIENTS AND METHODS: One hundred forty novel and 23 commercial antisera, selected on their ability to differentially stain tumor samples, were used to stain paraffin blocks from a retrospective breast cancer cohort. Cox proportional hazards and regression tree analysis identified minimal panels of reagents able to predict risk of recurrence. We tested the prognostic association of these prospectively defined algorithms in two independent cohorts. RESULTS: In both validation cohorts, the Kaplan-Meier estimates of recurrence confirmed that both the Cox model using five reagents (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C) and the regression tree model using six reagents (p53, PR, Ki67, NAT1, SLC7A5, and HTF9C) distinguished estrogen receptor (ER)-positive patients with poor outcomes. The Cox model was superior and distinguished patients with poor outcomes from patients with good or moderate outcomes with a hazard ratio of 2.21 (P = .0008) in validation cohort 1 and 1.88 (P = .004) in cohort 2. In multivariable analysis, the calculated risk of recurrence was independent of stage, grade, and lymph node status. A model proposed for ER-negative patients failed validation in the independent cohorts. CONCLUSION: A panel of five antibodies can significantly improve on traditional prognosticators in predicting outcome for ER-positive breast cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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