Differential impact of clinicopathological risk factors within the 2 largest ProMisE molecular subgroups of endometrial carcinoma
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Published on: September 27, 2024
Annukka Pasanen1,2, Mikko Loukovaara3, Terhi Ahvenainen2,4
1Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Objective:
To assess whether the prognostic impact of conventional risk factors and ancillary biomarkers differs across the 2 largest ProMisE molecular subgroups of endometrial carcinoma (EC).
Methods:
Direct sequencing of POLE exonuclease domain hot spots and immunohistochemistry for MLH1, PMS2, MSH2, MSH6 and p53 were performed on 745 unselected endometrioid ECs to identify mismatch repair deficient (MMR-D, n = 264) and no specific molecular profile (NSMP, n = 206) ECs. Molecular group-specific survival analyses and interaction analyses were performed to determine the prognostic relevance of clinicopathological factors and various biomarkers (L1 cell adhesion molecule, estrogen and progesterone receptor, beta-catenin, p16, E-cadherin, KRAS) within the subgroups.
Results:
Molecular subgroup did not have an independent effect on disease-specific survival after adjustment for conventional risk factors (P = 0.101). High grade (G3) and p16 hyperexpression remained significant predictors of survival in NSMP. Stage II-IV, ≥50% myometrial invasion, lymphovascular space invasion and loss of E-cadherin were independent predictors in the MMR-D group. In the interaction analysis, molecular subclass significantly modified the prognostic effect of high grade and p16 hyperexpression, which showed a stronger negative effect on survival in NSMP as compared to MMR-D (P for interaction = 0.016 for grade and 0.033 for p16).
Conclusions:
Grade of differentiation and p16 hyperexpression appear to have a stronger prognostic impact in NSMP as compared to MMR-D EC. While these results need to be confirmed in a larger study population, they indicate that differential impact of risk factors needs to be taken into account when developing new molecular class-integrated risk stratification algorithms for EC.
