Abstract P3-05-06: A better clinical cutpoint for progesterone receptor expression in tamoxifen treated breast cancer
Bibliographic record
Abstract
Abstract INTRODUCTION: Hormone receptors are routinely measured by immunohistochemistry (IHC) to classify breast cancers (BC) and guide treatment decisions. Both estrogen receptor (ER) and progesterone receptor (PR) are measured yet “the precise role of PR in patient management has not been strongly established” (ASCO/CAP; JCO 2010 28(16)). PR expression is under the transcriptional control of ER, and its been suggested that BC with high PR expression are more estrogen-dependent and thus more sensitive to endocrine therapy. We hypothesized that using a higher PR threshold would be prognostic in hormone therapy (HT) treated ER+/HER2- BC patients compared to the current PR cutpoint (Allred Score≥3). METHODS: We analyzed PR expression using the Calgary Tamoxifen BC Cohort (Cal-TBCC), a retrospective database that has a clinically annotated tissue microarray (TMA) series of 532 BC patients treated with HT. The NCI Stage I BC TMA set (N = 590) was obtained for validation. ER was visualized by staining with Dako ER pharmDx, and HER2 with Dako HercepTest. We visualized PR expression using ready-to-use PR assays from 3 commercial IHC vendors (Dako, Leica, Ventana) for the Cal-TBCC. The NCI validation set was stained only using the Dako assay. Allred scores were generated, capturing intensity and percentage coverage of PR expression. Only patients receiving HT that were ER+/HER2- by IHC were included for study. RESULTS: We found that ER+/HER2- patients from the Cal-TBCC stratified using a newly defined PR Allred cutpoint to capture only PR-High BC had significantly better disease free survival (DFS) compared to the current cutpoint (PR+≥3), regardless of platform used (Table 1). Multivariate analysis - adjusting for tumor grade, size and lymph node status - confirmed the improved prognostic significance of the new PR cutpoint (Table 2A). We next validated the prognostic power of the newly defined cutpoint using the NCI series. This set also has a group of ER+/HER2- BC patients who were not treated with HT, allowing us to evaluate the ability of ER+/HER2-/PR-High to predict DFS in patients treated with or without HT. We found that in patients who weren't treated with HT, PR was not prognostic using either cutpoint (Logrank p = 0.952; p = 0.611); but in the HT group, the new cutpoint was predictive of treatment response (p = 0.343; p = 0.047) (Cox Table 2B). Table 1 Current CutpointNew CutpointDako PRp = 0.024p = 0.0002Leica PRp = 0.102p = 0.0002Ventana PRp = 0.058p = 0.0001 CONCLUSIONS: We have identified a new cutpoint for PR expression in BC that is prognostic within a tamoxifen treated BC cohort, and that maintained significance across 3 commercial PR assays. Moreover, this new cutpoint seems to be predictive of HT, as the untreated NCI controls did worse than the HT treated group. We intend to continue to evaluate the clinical role for the new PR cutpoint by re-analyzing PR data from completed clinical trials. Table 2 Current Cutpoint New Cutpoint 2A: Cal-TBCCHR95% CIp-valueHR95% CIp-valueDako PR0.7020.295-1.6700.420.6390.369-1.1060.11Leica PR0.7590.301-1.9130.560.5020.283-0.8880.018Ventana PR0.4480.201-0.9980.0490.4620.253-0.8440.0122B: NCI No HT0.9690.349-2.6910.9520.8530.461-1.5770.61HT0.5980.204-1.7510.3490.4340.185-1.0150.054 Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P3-05-06.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".