Hormonal biomarkers remain prognostically relevant within the molecular subgroups in endometrial cancer
Bibliographic record
Abstract
OBJECTIVE: The prognostic relevance of hormonal biomarkers in endometrial cancer (EC) has been well-established. A refined three-tiered risk model for estrogen receptor (ER)/progesterone receptor (PR) expression was shown to improve prognostication. This has not been evaluated in relation to the molecular subgroups. This study aimed to evaluate the ER/PR expression within the molecular subgroups in EC. METHODS: A retrospective multicenter cohort study was performed and data from the European Network for Individualized Treatment centers and Vancouver, Canada were used. ER/PR immunohistochemical expression was grouped as: ER/PR 0-10 %, 20-80 % or 90-100 %. Molecular subgroups were determined with full next-generation sequencing or combined with immunohistochemistry: POLEmut, mismatch repair deficient (MMRd), p53mut and no-specific molecular profile (NSMP). RESULTS: A total of 739 patients were included (median follow-up 5.0 years). Tumors were classified as POLEmut in 9.1 %(N = 67), MMRd in 27.6 %(N = 204), p53mut in 20.8 %(N = 154) and NSMP in 42.5 %(N = 314). Among all molecular subgroups, patients with ER/PR 90-100 % expression revealed the best disease-specific survival (DSS). Within p53mut, PR 90-100 % expression showed a 5-year DSS of 100.0 %. ER expression is prognostic more relevant in MMRd and NSMP tumors while PR expression in p53mut and NSMP tumors. Across all molecular subgroups, PR 0-10 %, p53mut, lympho-vascular space invasion and FIGO stage III-IV remained independently prognostic for reduced DSS Whereas PR 90-100 % and POLEmut remained independently prognostic for improved DSS. CONCLUSION: We demonstrated that ER/PR expression remain prognostically relevant within the molecular subgroups, and that a three-tiered cutoff refines prognostication. These data support incorporating routine evaluation of ER/PR expression in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".