Metabolomics Investigation of Ovarian Cancer Progression in a Dicer-Pten Double Knockout Mouse Model

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

Minneapolis

Conferencia; 61st ASMS Conference on Mass Spectrometry & Allied Topic; 2013

American Society for Mass Spectrometry

Introduction Ovarian cancer is the fifth deadliest cancer among women and the leading cause of death among gynecologic cancers. Asymptomatic early stages combined with a lack of high specificity biomarkers contribute to late diagnosis when the 5-year survival rate is as low as 10%. A Dicer and Pten double knockout (DKO) mouse model of high-grade serous ovarian cancer, the subtype causing 70% of ovarian cancer deaths, has been developed to study disease progression. As there are molecular similarities and upregulation of common genes between Dicer-Pten DKO mice tumors and those from humans, investigating the metabolome of this first serous ovarian cancer model can lead to the identification of metabolic biomarkers for early detection of this disease. Methods Serum samples from healthy and the Dicer-Pten DKO mice were analyzed by UPLC®-MS, using a Waters ACQUITY H Class system fitted with a Waters ACQUITY UPLC® BEH C8 column (2.1 × 100 mm, 1.7 µm) and coupled to a Xevo G2 TOF Mass Spectrometer (Waters Corporation). Metabolites were extracted 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 both positive and negative ESI modes for m/z 50-1200. Metabolomic features were obtained using MarkerLynx 4.1 software and analyzed by orthogonal partial least squares discriminant analysis (oPLS-DA). Preliminary Results In this work, metabolic profiles of healthy (H), early stage (ES) and late stage (LS) cancerous blood sera from Dicer-Pten DKO mice were acquired using UPLC®-MS. Metabolic features extracted from these profiles were analyzed by oPLS-DA. Samples from ES cancerous blood sera were successfully discriminated from LS and H with high accuracy, 98% and 95% respectively. Of the 8 features responsible for discrimination between H and ES mice, 6 metabolites were tentatively identified using metabolomic databases. Based on reported literature, some of these metabolites may also be related to human gynecological cancer progression and proliferation. For example, a lysophosphatidylcholine has been tentatively identified as a downregulated metabolite in ES mice. In agreement with our findings, lysophosphatidylcholine levels have been shown to decrease in human ovarian cancer patients. Moreover, oleamide was tentatively identified as an upregulated metabolite in ES mice. It has been demonstrated that oleamide and its derivatives can inhibit the functions of the connexin 26 and 32 proteins which act as endometrial cancer tumor suppressors. , Continuing work involves identity confirmation of these discriminating features utilizing UPLC®-MS/MS, thereby providing new insights into the metabolic alterations associated with ovarian cancer proliferation. Novel Aspect This is the first metabolomic study of disease progression in in a mouse model of the deadliest ovarian cancer subtype.