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Background: Molecular subtype classification is essential for staging endometrial cancer, planning adjuvant treatments, and stratifying prognosis. However, the correlation between mutational signature analysis using tumor-only targeted next-generation sequencing (NGS) data and molecular subtypes, as well as the interpretive value at the individual case level, remains inadequately characterized. Methods: We performed mutational signature analysis on tumor-only targeted NGS data from 72 endometrial cancer cases using the SATS R package. Molecular classification into four subtypes (POLE-mutated, mismatch repair deficient [MMRd], TP53-mutant, and no specific molecular profile [NSMP]) was established through a standard stepwise algorithm that integrated NGS and immunohistochemistry. Results: Out of the 72 endometrial cancers, 12 were classified as POLE-mutated, 10 as MMRd, six as TP53-mutant, and 44 as NSMP. All POLE-mutated tumors exhibited POLE-associated signatures (SBS10a/SBS10b), including cases with non-hotspot POLE exonuclease variants and tumor mutation burdens below the conventional ultramutation threshold. MMRd-associated signatures showed a broader distribution across molecular subtypes; while the summed MMRd-associated signatures were typically higher in MMRd tumors, low-level contributions were also noted in subsets of NSMP and TP53-mutant tumors. Within the POLE-mutated group, MMRd-associated signatures were more pronounced in cases displaying subclonal MMR protein loss as determined by immunohistochemistry. Conclusions: In this small, clinically selected cohort, targeted NGS-based mutational signatures provided exploratory, complementary information regarding POLE-associated mutagenesis and mixed MMR-related patterns. Overlapping signature distributions, dependence on the analytical pipeline, and the use of tumor-only targeted sequencing limit case-level interpretation. Mutational signature analysis should be interpreted in conjunction with established molecular tests and should not be used as a stand-alone classifier.
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Kim et al. (2026) studied this question.
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