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The Evolution of Estrogen Receptor Signaling in the Progression of Endometriosis to Endometriosis-Associated Ovarian Cancer

  • Courtney L. Andersen
  • Michelle M. Boisen
  • Matthew J. Sikora
  • Tianzhou Ma
  • George Tseng
  • Swati Suryawanshi
  • Anda Vlad
  • Esther Elishaev
  • Robert P. Edwards
  • Steffi Oesterreich
Original Paper

Abstract

To investigate changes in estrogen receptor alpha (ERα) signaling during progression of endometriosis to endometriosis-associated ovarian cancer (EAOC) as a driver of malignant transformation. We procured tissue samples of normal endometrium, endometriosis (benign, atypical, concurrent with EAOC), and EAOC. We evaluated expression of a 236-gene signature of estrogen signaling. ANOVA and unsupervised clustering were used to identify gene expression profiles across disease states. These profiles were compared to profiles of estrogen regulation in cancer models from the Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA) was performed to determine whether gene expression in EAOC was consistent with ERα activity. ANOVA revealed 158 differentially expressed genes (q < 0.05) and unsupervised clustering identified five distinct gene clusters. The estrogen signaling profile of EAOC was not consistent with activated ERα in pre-clinical models. Gene set enrichment analysis did not identify signatures of activated ERα in EAOC but instead identified expression patterns consistent with loss of ERα function and development of endocrine resistance. Gene expression data suggest that ERα signaling becomes inactivated throughout the progression of endometriosis to EAOC. The gene expression pattern in EAOC is more consistent with profiles of endocrine resistance.

Keywords

Endometriosis Estrogen receptor alpha Human Estrogens Ovarian neoplasms Transcriptome 

Notes

Funding

This study was supported by the UPMC Research Fund (to R. Edwards and A.M. Vlad). C. L. Andersen was supported by F31CA186376, T32GM008424, and the ARCS Foundation. M.J. Sikora was supported by K99CA193734.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

12672_2018_350_MOESM1_ESM.xlsx (131 kb)
Table S1 Raw Nanostring Data. (XLSX 130 kb)
12672_2018_350_MOESM2_ESM.pptx (128 kb)
Table S2 E2-Regulated genes grouped by cluster. Genes within each cluster as identified by ANOVA. (PPTX 128 kb)
12672_2018_350_MOESM3_ESM.pptx (255 kb)
Figure S1 Comparison of the E2sig between pre- and post-menopausal patients. Heat map showing expression if E2sig in normal tumors. (PPTX 255 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Courtney L. Andersen
    • 1
    • 2
    • 3
  • Michelle M. Boisen
    • 4
    • 5
  • Matthew J. Sikora
    • 1
    • 3
  • Tianzhou Ma
    • 6
  • George Tseng
    • 6
  • Swati Suryawanshi
    • 4
  • Anda Vlad
    • 4
  • Esther Elishaev
    • 7
  • Robert P. Edwards
    • 3
    • 4
  • Steffi Oesterreich
    • 1
    • 3
  1. 1.Deptartment of Pharmacology & Chemical BiologyUniversity of PittsburghPittsburghUSA
  2. 2.Molecular Pharmacology Training ProgramUniversity of PittsburghPittsburghUSA
  3. 3.Women’s Cancer Research CenterUniversity of Pittsburgh Cancer InstitutePittsburghUSA
  4. 4.Deptartment of Obstetrics, Gynecology, & Reproductive SciencesUniversity of PittsburghPittsburghUSA
  5. 5.Division of Gynecologic OncologyMagee-Womens Hospital of the University of Pittsburgh Medical CenterPittsburghUSA
  6. 6.Deptartment of BiostatisticsUniversity of PittsburghPittsburghUSA
  7. 7.Deptartment of PathologyMagee-Womens Hospital of UPMCPittsburghUSA

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