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Abstract

Objectives

Endometrioid ovarian carcinoma (ENOC) is generally associated with a more favorable prognosis compared to other ovarian carcinoma histotypes. Nonetheless, patients are still treated according to a “one size fits all” approach. While tumor staging offers some stratification, the development of personalized treatment concepts remain elusive. Our group has recently validated the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), to distinguish clinically relevant prognostic groups. ENOC shares risk factors, genomics, and histology with it’s endometrial counterpart. The aim of our study was to apply and investigate ProMisE in ovarian endometrioid carcinoma.

Methods

ProMisE was applied to n=509 ENOC after biomarker-assisted review of endometrioid histotype. Cases were aligned into four groups: low risk POLE mutant (POLE); moderate risk mismatch repair deficient (MMRd); high-risk p53 abnormal (p53abn); and a final moderate risk category lacking these biomarkers (p53wt). Kaplan-Meier and multivariable survival analyses were performed.

Results

4% of cases were POLE, 16% MMRd, 10% p53abn and 71% p53wt. Groups showed distinct progression-free and overall survival (p<0.001), near-identical to profiles of endometrial carcinoma. 5-year PFS was 54% in p53abn, 81% in MMRd, 84% in p53wt, and 100% in POLE cases. ProMisE classes of ENOC were independent of stage and residual disease in multivariable analysis.

Conclusions

ProMisE risk classification provides additional prognostic information in a large cohort of ENOC. Our findings support the introduction of ProMisE-stratified treatment algorithms to ultimately improve endometrioid ovarian carcinoma patient care. Further, ENOC may benefit from parallel efforts under investigation in endometrial carcinoma.
Reference Key
openalex_W2331051435 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors E. S. Holden
Journal american journal of science
Year 1888
DOI
10.2475/ajs.s3-35.210.427
URL
https://ajsonline.org/article/62881.pdf
Keywords Keywords not found

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