Metabolomic Analysis of Early-Stage Ovarian Cancer in a Dicer-Pten Double Knockout Mouse Model

CHRISTINA M. JONES; MARÍA EUGENIA MONGE; JAEYEON KIM; MARTIN M. MATZUK; FACUNDO M. FERNÁNDEZ

Seattle

Simposio; 10th Biennial Ovarian Cancer Research Symposium; 2014

Ovarian cancer is the leading cause of death among gynecologic cancers. Early disease stages with non-specific symptoms, combined with a lack of high-specificity biomarkers, contribute to late diagnosis with a 5-year survival rate as low as 18%. Closely modeling high-grade serous carcinoma (HGSC), a subtype causing 90% of ovarian cancer deaths, Dicer-Pten double knockout (DKO) mice present a unique opportunity to identify biomarkers for early detection of HGSC. During the last decade, metabolomics has emerged as a powerful tool to investigate characteristic metabolic patterns of disease, with the ultimate goal of discovering diagnostic biomarkers. Using ultra performance liquid chromatography-mass spectrometry (UPLC-MS), serum samples from DKO mice with early-stage tumors and control mice were analyzed to screen metabolites specific to early-stage HGSCs. Metabolites were extracted from blood serum using methanol in a 3:1 (v/v) dilution ratio to serum. Metabolite extracts were lyophilized and reconstituted in the initial composition of the chromatographic mobile phase. High resolution mass spectra were acquired in negative electrospray ionization mode for m/z 50-1200. Metabolic features were extracted using MZmine 2.10 software. With only 18 metabolites selected from these profiles, orthogonal projection to latent structures-discriminant analysis (oPLS-DA) discriminated early-stage DKO mice from control mice with 100% accuracy, sensitivity, and specificity. Of these 18 metabolites, 14 were tentatively identified using metabolomic databases. Previous reports suggest that some of these metabolites be related to human gynecological cancer progression and proliferation. For example, lysophosphatidylethanolamine has been shown to increase intracellular calcium concentrations and stimulate an unknown membrane receptor, causing chemotactic migration and cell invasion in the SK-OV3 ovarian cancer cell line. Continuing work focuses on confirming the identity of these discriminating features with UPLC-MS/MS. This study will provide new insights into the metabolic alterations unique to the early progression of high-grade serous ovarian cancer.