Evaluation of the false-negative rate of standardized and quantitative measurement of estrogen receptor (ER) in tissue using AQUA technology
Bibliographic record
Abstract
567 Background: The discovery of an astoundingly high false negative rate for estrogen receptor (ER) testing in Canada has raised questions about the accuracy and reproducibility of ER testing. One solution would be the introduction of a standardized and reproducible diagnostic test that is easily adaptable in the clinical setting. Here, we have tested the AQUA method of quantitative immunofluorescence for the standardized and reproducible quantification of ER protein expression in tissue. Methods: Quantitative Western blotting was used in conjunction with AQUA analysis to create standard curves for assessment of absolute ER protein concentration in tissue (n = 118). We used standard scoring methods and AQUA analysis to quantify ER protein expression in a large cohort of breast cancer samples (n =669). Results: Using a series of standard curves, we determined that the range of the ER AQUA assay is between 100 pg/μg and 1500 pg/μg total protein. ER protein concentration in breast cancer samples showed an expected unimodal distribution for quantitative assessment of ER. Reproducibility studies of AQUA analysis demonstrated significant instrument-to-instrument (laboratory-to-laboratory) reproducibility for 3 instruments across the range of AQUA scores (average %CV = 1.34; R2>0.99; ANOVA p = 0.67). The same cases were then read and classified by 3 pathologists using the Allred scoring system. Although their concordance is similar to that seen in the literature (Kappa = 0.81, 0.88, and 0.89), pathologist concordance rate is lower than for that observed with AQUA analysis (Kappa = 0.95, 0.96, and 0.97). Importantly, 9.0% of cases showed a change of diagnosis (positive/negative) across 3 pathologists whereas only 2.8% of cases changed classification using AQUA analysis. Additionally, misclassified cases occurred across the entire range of Allred scores, but were restricted to a narrow region defined by the cut-point for AQUA scoring. Conclusions: We have demonstrated that AQUA technology can provide for the standardized and reproducible quantification of ER with a 3 fold reduction in misclassification. This approach has the potential to decrease the problem of false negative tests for ER. [Table: see text]
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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 | 0.057 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".