Effect of estrogen and progesterone receptor expression on progression-free and overall survival outcomes in low-grade serous ovarian cancer.
Bibliographic record
Abstract
5560 Background: Research on ER/PR receptor function in low-grade serous ovarian cancer (LGSC) and the determinants of response to treatment are lacking. A recent study (Sehouli et al.,2018) described ER/PR immunohistochemistry (IHC) cut-points that distinguished PFS. Thus, we report on a group of patients with ER/PR expression by IHC in tumor samples of patients with LGSC and used this information to evaluate survival outcomes. Methods: Clinical information and FFPE sections were obtained from the Canadian Ovarian Experimental Unified Resource (COEUR). Tissue microarray (TMA) sections were stained for ER/PR using standard IHC techniques (MK). 50 stage 3 and 5 stage 4 patients were analyzed. ER/PR expression was scored using a simple scoring system ( < 1% cells staining, 1-50%, and ≥ 50%) and Allred scoring. We compared Kaplan-Meier (KM) survival (PFS and OS) curves using Log rank testing and Cox regression was used to model predictive/prognostic factors. A p-value of 0.05 was considered significant. Results: The mean age of the population was 49.5 years (SD;13.7). Ninety percent of patients were treated by surgery followed by platinum-based chemotherapy (PBC). Simple scoring did not discriminate outcomes as well for ER levels. PR Allred score ( < 2, vs 2- < 6 vs ≥6) clearly discriminated KM curves for PFS (p = 0.036) and OS (p = 0.01). For Allred ER score ( < 7 vs.7- < 8 vs 8) did not distinguish PFS (p = 0.4) but notably most patients received PBC after surgery. ER Allred score significantly distinguished OS (p = 0.008). Significant factors on Cox regression for PFS were residuum (p = 0.008;95%CI:1.2-3.1) and PR (p = 0.05;95%CI:0.39-0.99), whereas for OS ER(p = 0.01:95%CI:0.2-0.8) and residuum (p = 0.04;95%CI:1-2.8). Conclusions: ER/PR expression by Allred scoring was associated with PFS and OS. Patients will benefit from much needed research on ER/PR prediction/prognosis in LGSC. This work can inform clinical trials selection/stratification and patient selection for endocrine treatment.
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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 | 0.000 | 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) | 0.001 | 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 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".