# Delaware INBRE

> **NIH NIH P20** · UNIVERSITY OF DELAWARE · 2024 · $279,134

## Abstract

Project Summary/Abstract
 Ovarian cancer (OVC) is the deadliest gynecologic cancer and the fifth leading cause of cancer-related
death among US women. A critical limitation in fighting OVC is the inability to detect OVC early (stages I & II).
Detection at early stages improves five-year survival to ~95% compared to ~57% overall. The five-year survival
further drops to ~36% for black women. Currently, no reliable diagnostic markers are available that identify
OVC early and accurately. We utilize female chickens (hens) as a model to identify early and accurate
diagnostic biomarkers for OVC. Chickens (unlike mice) develop OVC spontaneously, just like humans.
Moreover, disease progression and disease staging parallels human OVC. Further, total number of ovulation
events (a risk factor, if high) and age of onset (menopause) are also similar across both species. The
overarching long-term goal of this research is to design and validate a diagnostic test similar to a mammogram
or a pap smear that can be routinely performed to identify OVC before it can be clinically identified. Based on
our preliminary data, this proposal seeks to identify and validate the potential role of lipid metabolites as
potential diagnostic biomarkers of OVC.
 In this proposal, we will utilize specific-pathogen free hens as an animal model. These hens develop
OVC spontaneously (similar to humans) with ~10-30% penetrance by 3.5 years of age. Based on our pilot
studies, we will perform non-biased metabolomic analysis on longitudinally harvested plasma samples using
the Sciex 6500+ quadropule ion trap (QTrap) in conjunction with the C18 liquid chromatography-tandem mass
spectrometry (LC-MS/MS) Multiple Reaction Monitoring (MRM) method for detecting ~550+ annotated
metabolites. Further, we will perform pre-clinical metabolomic validation on plasma samples obtained directly
from untreated patients, patients with benign disease presentation, and age-matched controls. Such
comprehensive metabolite profiling especially for lipid metabolites as early and accurate diagnostic biomarkers
in human ovarian cancer lays the groundwork for large-scale validation in patients, helping fulfill a critical need
in the ovarian cancer precision prevention landscape.

## Key facts

- **NIH application ID:** 10953619
- **Project number:** 3P20GM103446-23S9
- **Recipient organization:** UNIVERSITY OF DELAWARE
- **Principal Investigator:** MELINDA K DUNCAN
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $279,134
- **Award type:** 3
- **Project period:** 2001-09-30 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10953619

## Citation

> US National Institutes of Health, RePORTER application 10953619, Delaware INBRE (3P20GM103446-23S9). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10953619. Licensed CC0.

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