Clinical Application of Next-Generation Sequencing for Molecular Classification in the Management of Endometrial...

Endometrial cancer (EC) is the most common malignancy of the female genital tract. In 2013, The Cancer Genome Atlas analyzed the molecular profile of endometrial tumors identifying four risk classes (POLE ultramutated, mismatch repair-deficient, copy-number low-microsatellite stable, and copy-number high-serous-like. This classification is reshaping the current understanding of EC, enabling more refined risk stratification and uncovering potential therapeutic targets tailored to specific molecular subgroups. In the context of these four categories, it is possible to identify different molecular alterations that correlate with different prognoses.

We retrospectively analyzed tissue samples from eighty-five EC patients, performing multigene profiling using a 50-gene next-generation sequencing (NGS) panel to categorize them into distinct molecular subtypes; we observed the following distribution: 5.9% POLE, 25.8% mismatch repair-deficient/microsatellite instability (MMRd/MSI), 11.8% p53abn/TP53mut, and 56.5% NSMP. A favorable concordance (97.6%) was shown in MSI NGS-based analysis and MMR IHC results, and the agreement rate of p53 IHC and TP53 mutation was 92.3%. When we analyzed the correlation between molecular subtypes and clinicopathological features, we found that molecular subtypes significantly differentiated by grade, FIGO stage, and lymphovascular invasion (LVSI). These findings seem to support the effectiveness of our NGS-based classifier and its reliability in distinguishing both MSI and TP53 mutated cancers. This study also explored mutations in PIK3CA, PTEN, KRAS, ERBB2, and ESR1 genes, noting their potential as targets for treatments. PIK3CA mutations were linked to favorable features, such as early disease stage and absence of LVSI.

Our study highlights the potential of a medium-complexity NGS panel for supporting the molecular classification of endometrial cancer, complementing the existing diagnostic algorithms. By identifying additional biomarkers, we provided valuable insights into the genomic landscape of EC. However, further exploration of the molecular profiles is needed to validate these findings and improve the identification of patients at a higher risk of unfavorable outcomes.