Metabolomic Signatures in Sera from Early Stage Ovarian Cancer Patients

DAVID A. GAUL; CHRISTINA M. JONES; MARÍA EUGENIA MONGE; TRAN QUOC LONG; MARTIN M. MATZUK; JOHN F. MCDONALD; FACUNDO M. FERNÁNDEZ

Seattle

Simposio; 10th Biennial Ovarian Cancer Research Symposium; 2014

PURPOSE: Lack of symptoms as well as the deficiency of highly specific biomarkers has resulted in only a quarter of ovarian cancer cases being diagnosed in 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 ovarian cancer. An effective screening strategy for early diagnosis would be particularly advantageous for ovarian cancer patients. Investigation into characteristic metabolomic patterns for disease has the potential to detect changes in cells, tissues, and biofluids that can aid in diagnosis at an early stage. METHODS: Serum samples were collected from early-stage serous papillary or endometrioid ovarian cancer (n=26) and normal patients (n=40) and analyzed using ultra high 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 both positive and negative electrospray ionization modes. Metabolic features were extracted with MZmine software. Untargeted multivariate statistical analysis employing support vector machine (SVM) learning methods, genetic algorithms and recursive feature elimination (RFE) selected a panel of metabolites that differentiates between the age-matched samples with better than 92% sensitivity and 91% specificity in all cases tested. RESULTS: Comparison of metabolic phenotypes of ovarian cancer with normal metabolic signatures revealed unique metabolite patterns for ovarian cancer. From multivariate statistical analysis, panels consisting of 17-35 metabolic features from serum samples were found to differentiate between early stage cancer and normal with very high accuracy, sensitivity, and specificity. CONCLUSIONS: Poor early diagnosis complicates collection of large patient cohorts for more detailed studies. Our preliminary work demonstrated that metabolites in serum samples may be useful for detecting early-stage ovarian cancer and support conducting larger, more focused studies.