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Published on: February 16, 2024
Clinicopathological Features and Risk Stratification of Multiple-Classifier Endometrial Cancers: A Multicenter Study
Wiktor Szatkowski1, Małgorzata Nowak-Jastrząb1, Tomasz Kluz2
1Department of Gynaecologic Oncology Maria Sklodowska-Curie National Research Institute of Oncology Krakow Branch, 31-315 Krakow, Poland.
Overlapping molecular features in endometrial cancer (EC) are common. These multiple-classifier ECs show more aggressive traits, challenging current risk assessment models.
Area of Science:
- Oncology
- Molecular Pathology
- Gynecologic Oncology
Background:
- The ProMisE molecular classification aids endometrial cancer (EC) risk stratification.
- A subset of EC cases (3-11%) presents with overlapping molecular features, posing diagnostic challenges.
Purpose of the Study:
- To determine the prevalence and clinicopathological characteristics of multiple-classifier endometrial cancers.
- To evaluate the impact of co-occurring molecular features on EC aggressiveness and risk stratification.
Main Methods:
- Retrospective analysis of 1075 EC cases (2022-2025) from four Polish institutions.
- Molecular classification using MMR and p53 immunohistochemistry, and POLE exon sequencing.
- Definition of multiple-classifier ECs as tumors with ≥2 molecular features (e.g., MMRd-p53abn, POLEmut-p53abn).
Main Results:
- Multiple-classifier ECs accounted for 6.9% of cases, with MMRd-p53abn being the most frequent subtype (3.9%).
- These tumors displayed significantly higher rates of Grade 3, non-endometrioid histology, and high-intermediate/high-risk groups compared to MMRd-only ECs.
- POLEmut-p53abn and POLEmut-MMRd-p53abn subtypes were associated with advanced FIGO stages (III-IV) and increased nodal metastasis rates.
Conclusions:
- The co-occurrence of molecular classifiers in EC, including triple-classifier tumors, is linked to more adverse clinicopathological profiles.
- Current molecular risk stratification paradigms may be insufficient for ECs with overlapping molecular features.
- Further research is needed to refine molecular models for accurate risk assessment in EC, especially in cases with complex molecular profiles.
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