# Deep Ovarian Cancer Metabolomics > **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2024 · $476,813 ## Abstract Principal Investigator: Facundo M. Fernández – Deep Ovarian Cancer Metabolomics 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 ovarian cancer, also known as 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% with early- stage detection. Unfortunately, protein biomarkers such as CA-125 do not have sufficient positive predictive value to be useful from a clinical perspective. During the first cycle of this project, we have mapped the metabolic alterations in both serum and ovarian tissues for double- and triple-knockout mouse models of HGSC from early stage to animal death. This has led to several potential biomarker panels that could be useful in humans and a deeper understanding of metabolic rewiring that is observed at early stages of the disease. We have also found that, contrary to the perceived notion in the field, ovarian progesterone is a crucial endogenous factor that induces the development of primary tumors progressing to metastatic ovarian cancer in mouse models. Remarkably, blocking progesterone signaling effectively suppresses ovarian cancer development and its peritoneal metastases. Moreover, we have found that there are many unknown metabolites and lipids that are highly correlated with disease progression, but we have been unable to identify all of them with current structural annotation tools. These results have prompted us to propose the development of new tools to identify metabolites using triboelectric nanogenerators (TENG) coupled to energy-resolved collision-induced fingerprints (CIF) in Aim 1. Because the metabolic rewiring observed at the reproductive tissue level becomes diluted in serum, in Aim 2 we propose to look for OC biomarkers in closer proximity to the ovary by phenotyping “mock” Pap samples in collaboration with Prof. Skubitz from the University of Minnesota. Aim 3 focuses on understanding the progesterone-mediated mechanisms involved in suppression of ovarian cancer at a systems level applying Brca1 mouse models for high-risk BRCA1 carriers. In this aim, we will conduct serum metabolic phenotyping, mass spectrometry tissue imaging, and gene expression studies in robust Brca1 mouse models of ovarian and breast cancers in collaboration with Prof. Kim from Indiana University School of Medicine. We will investigate changes associated with treatment with progesterone or an antiprogestin, allowing us to understand differences with animals without the mutation. Overall, the successful completion of the... ## Key facts - **NIH application ID:** 10979871 - **Project number:** 2R01CA218664-06A1 - **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY - **Principal Investigator:** Facundo Martin Fernandez - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $476,813 - **Award type:** 2 - **Project period:** 2018-09-20 → 2029-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10979871 ## Citation > US National Institutes of Health, RePORTER application 10979871, Deep Ovarian Cancer Metabolomics (2R01CA218664-06A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10979871. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*