Integration of Clinical and Molecular Features into Prediction Models for Outcomes in Endometrial Cancer

Clin Obstet Gynecol. 2020 Mar;63(1):40-47. doi: 10.1097/GRF.0000000000000498.

Abstract

Endometrial cancer recurrence carries a poor prognosis. The rising incidence of endometrial cancer calls for improvements in treatment of advanced and recurrent diseases. Efforts have been made to molecularly characterize endometrial cancer with the goal of improving therapies. The study presented here describes the utilization of molecular features of endometrial cancer tumors that are likely to recur, along with clinical characteristics utilized together to predict recurrence. This work further studies recurrent endometrial cancers to group them into "clusters" based on the tumor's molecular makeups with the ultimate aim to focus therapy on the molecular pathways potentially leading to recurrence.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Big Data
  • Biomarkers, Tumor / genetics
  • Cluster Analysis
  • Endometrial Neoplasms / genetics*
  • Endometrial Neoplasms / therapy
  • Female
  • Genomics / methods
  • Humans
  • Neoplasm Recurrence, Local / diagnosis*
  • Neoplasm Recurrence, Local / genetics
  • Predictive Value of Tests
  • ROC Curve

Substances

  • Biomarkers, Tumor