Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population

BMC Med Genomics. 2019 May 3;12(1):59. doi: 10.1186/s12920-019-0504-9.


Background: Endometrial cancer (EMCA) is the fifth most common cancer among women in the world. Identification of potentially pathogenic germline variants from individuals with EMCA will help characterize genetic features that underlie the disease and potentially predispose individuals to its pathogenesis.

Methods: The Geisinger Health System's (GHS) DiscovEHR cohort includes exome sequencing on over 50,000 consenting patients, 297 of whom have evidence of an EMCA diagnosis in their electronic health record. Here, rare variants were annotated as potentially pathogenic.

Results: Eight genes were identified as having increased burden in the EMCA cohort relative to the non-cancer control cohort. None of the eight genes had an increased burden in the other hormone related cancer cohort from GHS, suggesting they can help characterize the underlying genetic variation that gives rise to EMCA. Comparing GHS to the cancer genome atlas (TCGA) EMCA germline data illustrated 34 genes with potentially pathogenic variation and eight unique potentially pathogenic variants that were present in both studies. Thus, similar germline variation among genes can be observed in unique EMCA cohorts and could help prioritize genes to investigate for future work.

Conclusion: In summary, this systematic characterization of potentially pathogenic germline variants describes the genetic underpinnings of EMCA through the use of data from a single hospital system.

Keywords: DiscovEHR; Endometrial Cancer; Germline variants; TCGA; Uterine Cancer; Whole exome sequencing.

Publication types

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

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Electronic Health Records*
  • Endometrial Neoplasms / genetics*
  • Endometrial Neoplasms / pathology
  • Female
  • Germ-Line Mutation*
  • Humans
  • Middle Aged
  • Whole Exome Sequencing