Genomic and transcriptomic analysis of high-grade endometrial carcinoma reveals biological heterogeneity and molecular classification challenges

High-grade endometrial carcinoma (EC) exhibits marked histological diversity, yet its molecular basis and the potential contribution of transcriptomic phenotyping to molecular classification remain incompletely understood. To address this, we performed whole-exome and RNA sequencing on 81 high-grade ECs, including serous, clear cell, grade 3 endometrioid, and carcinosarcoma. Tumors were assigned to molecular subtypes (POLE-ultramutated, microsatellite-instability high (MSI-H), TP53 mutated (TP53-mut), and no specific molecular profile (NSMP), based on The Cancer Genome Atlas (TCGA) and ProMisE frameworks. Transcriptomic phenotypes were identified by unsupervised clustering of gene expression and analyzed in relation to histology, molecular subtypes, immune-related gene expression, and clinical outcomes. In this context, substantial discordance was observed among TP53 mutation status, p53 immunohistochemistry, and copy-number-based classification in non-POLE/non-MSI-H tumors. Transcriptomic clustering identified three phenotypic groups linked to cell differentiation status: glandular/luminal, ciliated, and epithelial-mesenchymal-transition-like (EMT-like). These phenotypes transcended molecular subtype boundaries. For example, TP53-mut tumors were distributed across both glandular/luminal and EMT-like phenotypes. The glandular/luminal phenotype was associated with elevated antigen presentation (e.g., HLA expression) and immune-related signaling, whereas the EMT-like phenotype, frequently observed in carcinosarcoma, was linked to stemness and metastatic potential. TP53-mut and NSMP were associated with poor prognosis in high-grade EC, whereas the glandular/luminal phenotype was associated with better outcomes than the EMT-like phenotype, an effect largely influenced by carcinosarcoma prevalence. Transcriptomic phenotypes complement molecular subtypes in high-grade EC, enhancing biological resolution and capturing clinically relevant heterogeneity. These results underscore persistent challenges of current molecular classification approaches, supporting the need for integrative strategies in high-grade disease.