Metabolomics of Disease Progression in an Ovarian Cancer Dicer-Pten Double Knockout Mouse Model

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

Atlanta

Conferencia; Georgia Tech Research and Innovation Conference; 2013

Ovarian cancer is the fifth deadliest cancer among women and the leading cause of death among gynecologic cancers. Its high fatality rate results from patients not being diagnosed until later stages where the 5-year survival rate is as low as 10%. Asymptomatic early stages combined with a lack of high specificity biomarkers contribute to late diagnosis. Recent blood sera and tissue analysis has demonstrated the impact that metabolomics research can have on ovarian cancer detection. Alterations in metabolite levels associated with glycolysis and β-oxidation of fatty acids as well as phenylalanine catabolism and histamine metabolism have been found. A Dicer and Pten double knockout (DKO) mouse model has been developed to study the progression of ovarian cancer from early stage to late stage where mortality occurs. 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 mouse model can lead to the identification of metabolic biomarkers for early detection of ovarian cancer. In this work, metabolic profiles of healthy and early and late stage cancerous blood sera from Dicer-Pten DKO mice were acquired using Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). Metabolomic features extracted from these profiles were analyzed by orthogonal partial least squares discriminant analysis (OPLSDA). Early stage samples were successfully discriminated from late stage and healthy samples with high accuracy, 98% and 95% respectively. Current ongoing work involves identifying these discriminating metabolites, thereby providing beneficial insights into the metabolic alterations of ovarian cancer proliferation.