Introduction The conventional molecular classifier of endometrial carcinoma (EC) based on POLE mutations, TP53 mutations, and microsatellite instability (MSI) status is widely utilized for personalized risk stratification. However, it relies on multiple testing platforms, leading to high cost and interobserver variance. Therefore, there is a need for a more accurate classifier to improve EC patient stratification.

Methods A retrospective analysis of 142 EC patients from Peking University Cancer Hospital between 2011 and 2020 was conducted. Genomic profiling of tumor tissues was performed using targeted sequencing with a 520-gene panel and the homologous recombination deficiency (HRD) score was calculated. HRD score was integrated with the conventional classifier to construct a new classifier, termed Endometrial Cancer Molecular Risk Classifier Optimized by Homologous Recombination Deficiency (ENERGY). Its performance was evaluated using The Cancer Genome Atlas (TCGA)-EC dataset.

Results ENERGY delineated four subtypes: POLE-mutant (7%), MSI-high (31%), TP53-mutant with HRD-low or TP53-wide type with HRD-middle/-low (50%, TH-L), and TP53-mutant with HRD-high/middle or TP53-wide type with HRD-high (12%, TH-H). The POLE-mutant subgroup exhibited superior median DFS and OS (both for not reached). Conversely, TH-H patients had the worst median DFS and OS (25 and 44 months). ENERGY was an independent prognostic factor for DFS and OS (both for p<0.001). Harrell’s C-index analyses demonstrated discriminative power of ENERGY improved in predicting DFS (0.74~0.89) and OS (0.84~0.91). In the TCGA-EC cohort, ENERGY also showed greater advantages for both DFS (0.66~0.69) and OS prediction (0.65~0.77).

Conclusion/Implications ENERGY provides a more accessible and accurate approach to stratifying EC risk and informing prognosis.