# Deep Ovarian Cancer Metabolomics

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2022 · $402,223

## Abstract

PROJECT SUMMARY
 Ovarian cancer (OC) is the 5th leading cause of cancer-related deaths for U.S. women and the
deadliest gynecological disease. Lack of symptoms in addition to the deficiency of highly specific biomarkers
for detection typically result in only 25% of OC cases being diagnosed at FIGO stage I. High-grade serous
carcinoma (HGSC) is the most prevalent form of OC, but three rarer histological subtypes also exist—
endometrioid, clear cell, and mucinous. An effective screening strategy for early diagnosis would be particularly
advantageous since 5-year OC survival rates can be as high as 90%. Unfortunately, protein biomarkers such
as CA-125 do not have sufficient positive predictive value to be useful from a clinical perspective. We
hypothesize that useful information regarding early stage HGSC and other ovarian cancers can be found in the
serum metabolome. Our pilot studies in both humans and OC models, such as the double-knockout Dicer-Pten
mouse recently developed by our team members, show great promise in this regard— average sensitivity and
specificity for early detection have reached 97.8% and 99.0% in banked human serum samples, and up
to100% in mice. These results have prompted us to perform a much deeper investigation of metabolome
alterations associated with early stage ovarian cancers in larger serum sample sets, and over time. We will
perform metabolomics experiments in mice and banked de-identified human serum samples with much higher
coverage than before by “data fusing” various modes of ultraperformance liquid chromatography-mass
spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR), coupled with pathway-centric data analysis.
We also propose supplementing serum-level metabolomics experiments with deep-coverage tissue mass
spectrometry imaging (MSI) in both 2-D and 3-D, using a combination of matrix-assisted laser
desorption/ionization (MALDI) and desorption electrospray ionization (DESI), which have complementary
ionization mechanisms. Furthermore, we propose to depart from the commonly used approach of tentatively
identifying spectral features by only using accurate masses, and implement a “deep metabolite annotation”
approach that uses both “fused” high-resolution techniques (high field Orbitrap MS, MS/MS, 2-D NMR) and a
new technology based on collisional cross section predictions for both travelling wave and drift tube ion
mobility-MS.

## Key facts

- **NIH application ID:** 10480837
- **Project number:** 5R01CA218664-05
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Facundo Martin Fernandez
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $402,223
- **Award type:** 5
- **Project period:** 2018-09-20 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10480837

## Citation

> US National Institutes of Health, RePORTER application 10480837, Deep Ovarian Cancer Metabolomics (5R01CA218664-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10480837. Licensed CC0.

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