# Identification of a prognostic and predictive glucocorticoid receptor signature for ovarian cancer

> **NIH NIH R21** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $208,085

## Abstract

PROJECT ABSTRACT
The glucocorticoid receptor (GR) is a cortisol-activated transcription factor and chromatin remodeling protein
that is variably expressed in ovarian cancer (OvCa). GR-mediated gene expression encoding anti-apoptotic
pathway proteins is associated with chemotherapy-resistance in OvCa, while pre-treatment with a selective GR
antagonist enhances chemotherapy sensitivity. Our group also recently discovered that amongst a range of
OvCa histologic subtypes, both higher GR (NR3C1) mRNA and higher GR protein levels are associated with
overall worse clinical outcome. We now propose to develop a “GR transcriptional activity signature” (GRSig) for
OvCa to better assess GR activity in OvCa with the goal of identifying patients most likely to benefit from
addition of GR antagonism to chemotherapy. Using cell line models of GR+ OvCa representing a variety of
histologies (Aim 1), we will identify genes whose expression is either significantly upregulated or repressed
following GR activation by physiological glucocorticoid concentrations as well as significantly reversed in
expression by GR antagonism. GR ChIP-seq will be employed to determine which of these genes are putative
direct (rather than indirect) GR target genes with promoter or enhancer region GR chromatin association,
further refining the GRSig to represent canonical OvCa GR activity. The resulting GRSig will then be used to
assess relative GR activity in existing OvCa samples from a pooled public GEO dataset (Discovery set)
containing normalized RNA expression and outcome (PFS and OS). A GRSig score cutoff will be calculated
that identifies patients with worse prognosis in the GEO dataset. A second, well-annotated Mayo Clinic OvCa
cohort (Validation set) will then be used to validate the cutoff. We will also test the hypothesis that higher
GRSig scores (representing higher GR transcriptional activity) will associate with PFS and OS more strongly
than GR (NR3C1) mRNA expression alone. In Aim 2, we will examine the GRSig in N=131 OvCa PDX models
with chemotherapy response data to test the hypothesis that higher GRSig score associates with relative
chemotherapy resistance. Furthermore, we will explore the relationship of GRSig score to GR protein
expression by performing GR IHC in the PDX tumors, and then evaluating the strength of association of the
GRSig score relative to GR IHC score with respect to chemotherapy response. In an exploratory analysis,
GRSig-scored PDX models will be treated with chemotherapy +/- a GR antagonist (GRA) to test the hypothesis
that pre-treatment with a GRA improves tumor shrinkage and/or lengthens time to tumor regrowth in GRSig
high PDX models. Completion of this project will identify a GR transcriptional signature designed to identify
women with OvCa who have a worse prognosis and to for whom addition of a selective GR antagonist to
chemotherapy is expected to improve outcome compared to standard treatment with chemotherapy alone.

## Key facts

- **NIH application ID:** 9851835
- **Project number:** 7R21CA223426-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Suzanne Daniela Conzen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $208,085
- **Award type:** 7
- **Project period:** 2019-01-18 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9851835

## Citation

> US National Institutes of Health, RePORTER application 9851835, Identification of a prognostic and predictive glucocorticoid receptor signature for ovarian cancer (7R21CA223426-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9851835. Licensed CC0.

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