Abstract PD10-08: Quantitative and Standardized Measurement of Estrogen Receptor Predicts Response to Radiation Therapy in Breast Cancer
Bibliographic record
Abstract
Abstract Background: It has been repeatedly demonstrated that ER-positive patients have a significantly increased response to tamoxifen, however, continuous association between relative risk and quantitative ER expression has yet to be fully established. Additionally, although it has been hypothesized that estrogen receptor positivity and tamoxifen treatment may play a role in radiation therapeutic response, an assessment of whether quantitative levels of ER predict response to radiation therapy has yet to be determined. Materials and Methods: Fluorescence immunohistochemistry with AQUA® technology was used to quantitatively assess ER expression on a cohort (n = 568) of retrospectively collected breast cancer specimens from patients treated with tamoxifen (20mg) ± radiotherapy (RT). Staining, image acquisition and AQUA analysis was performed at two independent sites using two different standardized digital pathology platforms. AQUA technology is a completely standardized and objective platform with minimized operator interaction that provides tumor-specific, quantitative and continuous expression score data. Results: A comparison between completely independent sample staining and quantitative assessment at two sites showed highly significant correlation, with AQUA score values approaching unity (Pearson's R=0.94; linear slope = 0.999) and indistinguishable means (p=0.93). Assessment of the same tissue slides on two different instrument platforms (HistoRx PM2000 v. Aperio Scanscope FL) also showed highly significant correlation (Pearson's R=0.95; linear slope = 1.01; p = 0.89). Continuous ER AQUA scores showed a highly significant association with 5-year disease-free survival by itself (HR = 0.80 (95%CI: 0.70-0.91); p=0.001) and when put into a model with nodal status and tumor size (HR=0.80 (95%CI: 0.68 — 0.94); p=0.006). The cohort was then divided at the median AQUA score representing relative low and high ER expressing patients. The low ER expressing group showed significant benefit from RT for 5-year disease-free survival (HR = 0.56 (95%CI: 0.32-0.95); p=0.03) and maintained significance at the 10% level when nodal status and tumor size were adjusted for in the model (HR = 0.60 (95%CI: 0.32 — 1.10); p=0.097). In contrast, the high ER expressing group showed no benefit (HR = 0.81 (95%CI: 0.43-1.52); p=0.51) for radiation treatment. No benefit for either group was observed for 15-year overall survival. Discussion: Taken together, these data demonstrate that quantification of ER, beyond simple positive/negative characterization, could provide valuable predictive information for the treatment of breast cancer, specifically for predicting a group more likely to respond to radiation therapy and sparing patients from a potentially harmful treatment. These data will need further validation on an independent cohort designed to differentiate radiation response. Furthermore, these data indicate that true quantification of ER expression provides a continuous recurrence risk assessment for patients being treated with tamoxifen. Because these data are standardized across sites and imaging platforms, misclassification of patients is significantly reduced as compared to the current standard by which ER expression is determined. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD10-08.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".