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

CHRISTINA M. JONES; MARÍA E. MONGE; JAEYEON KIM; MARTIN M. MATZUK; JOHN F. MCDONALD; FACUNDO M. FERNANDEZ

Atlanta

Conferencia; International Conference Frontiers in Systems and Synthetic Biology ’13 (FSSB’13); 2013

Ovarian cancer is the fifth deadliest cancer among women. Asymptomatic early stages combined with a lack of high specificity biomarkers contribute to late diagnosis, with a 5-year survival rate of 10%. A Dicer and Pten double knockout (DKO) mouse model was developed to study the progression of ovarian cancer from early to late stages. Given the molecular similarities and upregulation of common genes between Dicer-Pten DKO mice tumors and those from humans, metabolomic investigation of this mouse model can lead to new strategies for early detection of ovarian cancer. In this work, metabolome profiles of healthy (H), early stage (ES) and late stage (LS) cancerous blood sera from Dicer-Pten DKO mice were acquired using Ultra Performance Liquid Chromatography mass spectrometry (UPLC®-MS). Extracted metabolic features were analyzed by orthogonal partial least squares discriminant analysis. 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 the metabolites found in our panel may have also been reported as related to human gynecological cancer proliferation. For example, lysophosphatidylcholine levels have been shown to decrease in human ovarian cancer patients, in agreement with our findings. Continuing work involves identity confirmation of these discriminating metabolites utilizing UPLC®-MS/MS, thereby providing insight into the metabolic alterations related to ovarian cancer proliferation.