Prognosis Stratification Tools in Early-Stage Endometrial Cancer: Could We Improve Their Accuracy?

Abstract BackgroundThere are 3 prognostic stratification tools used in clinics for endometrial cancer: ESMO-ESGO-ESTRO 2016, ProMisE, and ESGO-ESTRO-ESP 2020. However, these methods are not sufficiently accurate to address the prognosis and adjuvant therapy. Some other previously explored biomarkers have also shown prognostic relevance. The aim of this study was to investigate whether the integration of molecular classification and other biomarkers could be used to refine the prognosis in early-stage endometrial cancer. MethodsThis was a retrospective single-institution cohort of patients with early-stage endometrial cancer to evaluate these stratification tools. Relapse-free survival (RFS) and overall survival of each classifier were analysed, and the c-index was employed to assess accuracy. Other biomarkers were explored to improve the precision of risk classifiers.ResultsWe analysed 294 patients: 88% had endometrioid histology, 69% stage Ia, and 15% had a relapse. A comparison between the 3 classifiers showed a slightly improved accuracy in ESGO-ESTRO-ESP 2020 when RFS was evaluated (c-index = 0.79), although we did not find differences between intermediate prognostic groups. However, the inclusion of CTNNB1 status to stratify patients of intermediate groups allowed a better discrimination between the intermediate prognostic groups, resulting in a c-index of 0.82. Therefore, we propose a novel classifier based on ESGO-ESTRO-ESP 2020 and CTNNB1, which achieved statistically significant and clinically relevant differences in 5-year RFS: 93.4% for low risk, 79.6% for intermediate merged group/CTNNB1 wild type, and 37.3% for high risk (including patients from the merged intermediate groups with CTNNB1 mutation).ConclusionsThe incorporation of molecular classification in risk stratification of endometrial cancer resulted in better discriminatory capability, which was improved even further with the addition of CTNNB1 mutational evaluation.