J Gynecol Oncol.  2022 May;33(3):e29. 10.3802/jgo.2022.33.e29.

Genomic landscape of advanced endometrial cancer analyzed by targeted next-generation sequencing and the cancer genome atlas (TCGA) dataset

Affiliations
  • 1Department of Obstetrics and Gynecology, Guro Hospital, Korea University College of Medicine, Seoul, Korea
  • 2Department of Obstetrics and Gynecology, School of Medicine, Kangwon National University, Chuncheon, Korea
  • 3Department of Pathology, Guro Hospital, Korea University College of Medicine, Seoul, Korea
  • 4Medical Science Research Center, Guro Hospital, Korea University College of Medicine, Seoul, Korea

Abstract


Objective
Recent studies have detailed the genomic landscape of endometrial cancer (EC); however, no study has focused on genetic alterations in advanced EC. We performed genomic profiling of patients with advanced EC using targeted next-generation sequencing (NGS).
Methods
Archival tissue samples from 21 patients diagnosed with stage III and IV EC were obtained and subjected to NGS. Our data and the cancer genome atlas dataset were combined, and somatic mutation patterns were analyzed and compared according to the stage and histological type. Additionally, survival effects of specific mutated genes were analyzed.
Results
Somatic mutation patterns of 38 genes were identified in 263 EC samples, and the most commonly mutated genes were PTEN and PIK3CA. PTEN was the most common in endometrioid histology, while PPP2R1A was the most commonly mutated gene in serous histology. The mutation rates of PPP2R1A and TP53 were significantly higher in advanced EC sample than in stage I samples (22.5% vs. 4.3% [p<0.001] and 8.4% vs. 1.4% [p=0.021], respectively). Survival analysis of the total population and endometrioid subgroup revealed that patients with PPP2R1A mutations had significantly shorter survival than did those without mutations (p=0.005 and p<0.001, respectively).
Conclusion
PPP2R1A mutations might have a role in dismal prognosis of advanced EC.

Keyword

Endometrial Cancer; Survival Analysis; PPP2R1A; TP53
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