Metabolomic Study of Early-Stage High-Grade Serous Ovarian Cancer in a Dicer-PTEN Double Knockout Mouse Model

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

Los Cocos

Congreso; Segundo Congreso Argentino de Espectrometría de Mas; 2014

SAEM

Ovarian cancer is the leading cause of death among gynecologic cancers. The unavailability of reliable screening tests in clinical practice and the asymptomatic course through early stages of the disease contribute to late diagnosis and low five-year survival rates. In particular, high-grade serous carcinoma (HGSC) is the subtype with the highest occurrence and mortality, causing 90% of all ovarian cancer deaths. However, its origin and early progression are still poorly understood.1-2 Recently, a mouse model of HGSC was developed by conditionally disabling two critical genes, Dicer and Pten, showing that HGSCs arise from the fallopian tube.3 As there are molecular similarities and up-regulation of common genes between Dicer-Pten double knockout (DKO) mice tumors and those from humans, this mouse model provides a unique opportunity to study cancer progression and 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. In this work, the metabolome of this serous ovarian cancer mouse model was investigated through an untargeted approach using ultra performance liquid chromatography-mass spectrometry (UPLC-MS). Serum samples from DKO mice with early-stage tumors and control mice were analyzed to discover metabolic differentiating features of early-stage HGSC. Metabolites were extracted from blood serum using methanol in a 3:1 (v/v) dilution ratio to serum. 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. After down-selection of spectral features with maximum discriminatory power, only 18 metabolites discriminated early-stage DKO mice from control mice with 100% accuracy, sensitivity, and specificity by means of orthogonal projection to latent structures-discriminant analysis (oPLS-DA) modelling. Eleven of the 18 metabolic features were identified by MS and Q-TOF MS/MS experiments using metabolomic databases, while 4 were further confirmed chromatographically by chemical standards. Altered metabolic pathways include those of fatty acids, bile acids and alcohols, glycerophospholipids, peptides, and phytochemicals. These alterations show alterations on cellular energy storage and membrane stability, as well as defenses against oxidative stress. This study is a first step toward understanding the underlying metabolic alterations resulting from the initial stages of HGSC originating in the fallopian tubes. References: 1- Cho, K. R.; Shih Ie, M.; Annu. Rev. Pathol. 2009, 4, 287-313. 2- Bast, R. C., Jr.; Hennessy, B.; Mills, G. B.; Nat. Rev. Cancer 2009, 9, 415-28. 3- Kim, J.; Coffey, D. M.; Creighton, C. J.; Yu, Z.; Hawkins, S. M.; Matzuk, M. M.; Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 3921-3926.