A Quantitative Immunofluorescence Assay (AQUA) Suggests Significant Misclassification (15%) of Estrogen Receptor Status in Breast Cancer.
Bibliographic record
Abstract
Abstract Introduction: Estrogen Receptor (ER) is arguably the most powerful predictive marker in breast cancer. However, a recent incident in Canada revealed a strikingly high false-negative rate and raised awareness of the current limitations in our measurement of ER. The current clinical standard is both subjective and qualitative. Even though guidelines are about to be issued to standardize this assay, there is very little data on the misclassification rate in current practice in the US.Hypothesis: Our hypothesis is that the use of a quantitative assay for ER on a series of retrospective collections may reveal both the level and significance of the misclassification rate.Method: Cell lines with a range of ER expression levels were analyzed by quantitative western blotting in parallel with IF/AQUA analysis (r2 = 0.865), in order to create standard curves for assessment of absolute ER protein concentration in tissue. The optimized assay was then used to quantify ER protein expression in a large cohort of archival breast cancer samples from Yale (1962-1982, n =617).Results: Using a set of standard curves with recombinant ER and cell line controls, we developed a standardized method for quantifying ER as an absolute concentration (pg ER per μg total protein) in formalin-fixed tissue on TMAs. This ER AQUA assay has a range in sensitivity from 50 pg/μg to 1500pg/μg total protein. Quantification of ER protein expression on the Yale archival cohort revealed a unimodal distribution with 49.8% of cases above the 50pg/ug threshold and thus defined as positive. When compared to pathologist performed conventional ER classification, we found a false negative rate of 6.65% and a false positive rate of 10.2%, for a total misclassification rate of 16.6%. Although no response data is available on that cohort, data is under analysis on 4 other cohorts with endocrine therapy treatment information, including an independent Yale cohort, SWOG 9313, NSABP B14, and the TEAM trial.Conclusion: We have developed a quantitative method to measure absolute levels of ER in breast tissue. Use of this assay on a series of cohorts suggests a misclassification rate in the 15% range. The significance of this level of misclassification with respect to response to endocrine therapy is currently under study. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4068.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".