Molecular Characterization and Clinical Implications of Endometrial Cancer
Bibliographic record
Abstract
The classification of endometrial cancer (EC) has diverged from traditional histologic features based on microscopic appearance to objective molecular characterization. Molecular characterization of EC is pivotal to inform prognosis and to guide therapeutic recommendations. First described by the Cancer Genome Atlas, molecular profiling was later revised by the Proactive Molecular Risk Classifier for Endometrial Cancer and TransPORTEC algorithms to create clinically applicable and relatively easy-to-implement molecular classification systems. Since 2020, the World Health Organization recommended molecular classification of EC into four distinct prognostic subtypes: ECs with polymerase ε (POLE) pathogenic mutations assessed by gene sequencing, mismatch repair deficiency determined by immunohistochemistry or microsatellite instability assay, and p53 abnormalities determined by immunohistochemistry or next-generation sequencing. The final molecular subtype without any of these defining features is called "no specific molecular profile" (NSMP). This is further stratified by estrogen receptor (ER) immunohistochemistry status. Patients with cancers identified as POLE pathogenic mutations have the best prognosis with almost no recurrence or death events, followed by those with strong ER-positive NSMP cancers. Mismatch repair deficiency ECs have intermediate prognosis, whereas p53 abnormalities and ER-negative NSMP have the worst prognosis. Other molecular and pathologic biomarkers of interest include tumor mutational burden, human epidermal growth factor receptor 2, L1 cell adhesion molecule, β-catenin ( CTNNB1 ), and lymph vascular space invasion, which may have prognostic and predictive implications. The current guidelines will continue to evolve; however, at minimum, it is recommended that all patients undergo testing for mismatch repair, p53, and ER, and POLE testing may be prioritized in select circumstances. Molecular classification provides the critical framework to deliver effective, personalized, high-quality care and informs clinical trial design. Molecular assessment ensures consistent diagnosis and provides prognostic information and predictive data to guide appropriate management.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".