Phenotyping of early stage ovarian cancer by mass spectrometry untargeted metabolomics

FACUNDO M. FERNÁNDEZ; DAVID A. GAUL; CHRISTINA M. JONES; MARÍA EUGENIA MONGE; MARTIN R. L. PAINE; LONG Q. TRAN ; JOHN F. MCDONALD

San Francisco

Conferencia; 11th International Conference of the Metabolomics Society; 2015

Metabolomics Society

INTRODUCTION: Lack of symptoms as well as the deficiency of highly specific biomarkers has resulted in only a quarter of ovarian cancer (OC) cases being diagnosed at stage I. Early detection combined with conventional therapies has resulted in 5-year survival rates up to 90%, while 5-year overall survival is less than 30% for women with advanced-stage OC. Investigation into characteristic metabolomic patterns for disease has the potential to detect changes in cells, tissues, and biofluids that can aid in early-stage diagnosis.METHODS: Serum samples were collected from early-stage papillary serous or endometrioid epithelial ovarian cancer (EOC) and normal patients, and analyzed using ultra performance liquid chromatography coupled with high resolution mass spectrometry (UPLC-MS) and tandem mass spectrometry (MS/MS). Metabolites were extracted from blood serum by precipitating proteins with methanol, lyophilization, and solvent reconstitution prior to MS analysis in negative electrospray ionization mode. Metabolic features were extracted with MZmine software. Untargeted multivariate statistical analysis employing support vector machine (SVM) learning methods and recursive feature elimination (RFE) selected a panel of metabolites that differentiates between the age-matched samples.RESULTS: Comparison of metabolic phenotypes of EOC with normal metabolic signatures revealed unique metabolite patterns for EOC in studies of two different patient cohorts. The first study compared early-stage papillary serous or endometrioid EOC (n=24) and normal patients (n=40). As papillary serous is the most commonly diagnosed histopathological subtype of EOC, a second study compared only early-stage papillary serous EOC (n=46) and normal patients (n=49). From multivariate statistical analysis, panels consisting of 16-22 metabolic features from serum samples were found to differentiate between early-stage EOC and normal with very high accuracy, sensitivity, and specificity. The dominant classes of metabolites in the panels were lipids and fatty acids; this correlated well with the literature in which the metabolomes of EOC patients exhibit disruption of lipid metabolism and profiles. Poor early diagnosis complicates collection of large patient cohorts for more detailed studies. Our preliminary work demonstrated that metabolites in serum samples are useful for detecting early-stage EOC and support conducting larger, more focused studies. NOVEL ASPECTS: First MS-based metabolomic study of early-stage EOC serum focused on biomarker discovery for early detection.