Utility of molecular subtypes and genetic alterations for evaluating clinical outcomes in 1029 patients with endometrial cancer

Br J Cancer. 2023 Apr;128(8):1582-1591. doi: 10.1038/s41416-023-02203-3. Epub 2023 Feb 16.

Abstract

Background: We investigated the utility of a molecular classifier tool and genetic alterations for predicting prognosis in Japanese patients with endometrial cancer.

Methods: A total of 1029 patients with endometrial cancer from two independent cohorts were classified into four molecular subtype groups. The primary and secondary endpoints were relapse-free survival (RFS) and overall survival (OS), respectively.

Results: Among the 265 patients who underwent initial surgery, classified according to immunohistochemistry, patients with DNA polymerase epsilon exonuclease domain mutation had an excellent prognosis (RFS and OS), patients with no specific molecular profile (NSMP) and mismatch repair protein deficiency had an intermediate prognosis, and those with protein 53 abnormal expression (p53abn) had the worst prognosis (P < 0.001). In the NSMP group, mutant KRAS and wild-type ARID1A were associated with significantly poorer 5-year RFS (41.2%) than other genomic characteristics (P < 0.001). The distribution of the subtypes differed significantly between patients with recurrence/progression and classified by sequencing (n = 764) and patients who underwent initial surgery (P < 0.001). Among patients with recurrence/progression, 51.4% had the opportunity to receive molecular targeted therapy.

Conclusions: A molecular classifier is a useful tool for determining prognosis and eligibility for molecularly targeted therapy in patients with endometrial cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor* / genetics
  • Biomarkers, Tumor* / metabolism
  • Endometrial Neoplasms* / genetics
  • Endometrial Neoplasms* / metabolism
  • Endometrial Neoplasms* / surgery
  • Female
  • Humans
  • Mutation
  • Neoplasm Recurrence, Local
  • Prognosis

Substances

  • Biomarkers, Tumor