Development and validation of a RNAseq signature for prognostic stratification in endometrial cancer
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. 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). 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). RNA-seq profiling can provide an additional prognostic information to established classification systems, and warrants validation for potential RNAseq-based therapeutic strategies in EC.