Implementation of the 2021 molecular ESGO/ESTRO/ESP risk groups in endometrial cancer.

Gynecol Oncol

Department of Oncology-Pathology, Karolinska Institutet, Department of Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden. Electronic address:

Published: August 2021

Introduction: In 2021, a joint ESGO/ESTRO/ESP committee updated their evidence-based guidelines for endometrial cancer, recommending a new risk grouping incorporating both clinicopathologic and molecular parameters. We applied the new risk grouping and compared the results to those of the prior 2016 clinicopathologic system.

Materials And Methods: We classified molecularly a cohort of 604 women diagnosed with endometrial cancer using immunohistochemistry for TP53 and MMR proteins on a tissue microarray, as well as Sanger sequencing for POLE mutations. These results, combined with clinicopathologic data, allowed the patients to be risk grouped using both the new 2021 molecular/clinicopathologic parameters and the prior 2016 clinicopathologic system.

Results: The application of the 2021 molecular markers shows Kaplan-Meier curves with a significant difference between the groups for all survival. Molecular classification under the 2021 guidelines revealed a total of 39 patients (39/594, 7%) with a change in risk group in relation to the 2016 classification system: the shift was alone due to either P53abn or POLEmut molecular marker. In order to ensure correct 2021 molecular risk classification, not all patients with endometrial cancer need a molecular diagnostic: 433 (72.9%) cases would need to be analyzed by TP53 IHC, only 46 (7.7%) by MMR IHC and 286 (48.1%) POLE sequencing reactions.

Conclusion: Application of the 2021 molecular risk groups is feasible and shows significant differences in survival. IHC for TP53 and MMR and applying POLE sequencing is only needed in selected cases and leads to shifting risk groups both upward and downward for a sizeable number of patients. It is possible to significantly reduce the number of analyses required to implement the classification if resources are limited.

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Source
http://dx.doi.org/10.1016/j.ygyno.2021.05.026DOI Listing

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