Background: Despite recent advances in endometrial carcinoma (EC) molecular characterization, its prognostication remains challenging. We aimed to assess whether RNAseq could stratify EC patient prognosis beyond current classification systems.
Methods: A prognostic signature was identified using a LASSO-penalized Cox model trained on TCGA (N = 543 patients). A clinically applicable polyA-RNAseq-based work-flow was developed for validation of the signature in a cohort of stage I-IV patients treated in two Hospitals [2010-2017]. Model performances were evaluated using time-dependent ROC curves (prediction of disease-specific-survival (DSS)). The additional value of the RNAseq signature was evaluated by multivariable Cox model, adjusted on high-risk prognostic group (2021 ESGO-ESTRO-ESP guidelines: non-endometrioid histology or stage III-IVA orTP53-mutated molecular subgroup).
Results: Among 209 patients included in the external validation cohort, 61 (30%), 10 (5%), 52 (25%), and 82 (40%), had mismatch repair-deficient, POLE-mutated, TP53-mutated tumors, and tumors with no specific molecular profile, respectively. The 38-genes signature accurately predicted DSS (AUC = 0.80). Most disease-related deaths occurred in high-risk patients (5-years DSS = 78% (95% CI = [68%-89%]) versus 99% [97%-100%] in patients without high-risk). A composite classifier accounting for the TP53-mutated subgroup and the RNAseq signature identified three classes independently associated with DSS: RNAseq-good prognosis (reference, 5-years DSS = 99%), non-TP53 tumors but with RNAseq-poor prognosis (adjusted-hazard ratio (aHR) = 5.75, 95% CI[1.14-29.0]), and TP53-mutated subgroup (aHR = 5.64 [1.12-28.3]). The model accounting for the high-risk group and the composite classifier predicted DSS with AUC = 0.84, versus AUC = 0.76 without (p = 0.01).
Conclusion: RNA-seq profiling can provide an additional prognostic information to established classification systems, and warrants validation for potential RNAseq-based therapeutic strategies in EC.
Keywords: Endometrial carcinoma; Molecular characterization; Personalized medicine; Prognostic stratification; Transcriptome analysis.
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