High Accuracy Prostate Cancer Detection Using Human Blood Sera Metabolomic Profiling

FACUNDO M. FERNANDEZ; XIAOLING ZANG; CHRISTINA M. JONES; TRAN Q. LONG; MARÍA E. MONGE; MANSHUI ZHOU; L. DEETTE WALKER; ALEXANDER GRAY; NIKHIL SHAH; RAJESH LAUNGANI; JOHN F. MCDONALD

Simposio; The 9th Annual Symposium on Prostate; 2013

Prostate cancer is the sixth deadliest cancer among men. Although the Prostate-Specific Antigen (PSA) test has been widely used to screen for prostate cancer, certain advisory groups recommend against its use because it suffers from false positive results and over-treatment. These drawbacks have led to the increased interest of using metabolomic profiling to discover new differential metabolic biomarkers that could improve the specificity of prostate cancer diagnosis. However, global metabolomic profiling of prostate cancer patients is still at an early stage. In this study, liquid chromatography coupled to mass spectrometry and machine learning methods were used to profile and identify metabolites in blood sera samples from prostate cancer patients that discriminate them from healthy individuals. Age-matched serum samples from prostate cancer patients (n=64, age range: 49-65, = 59 ± 4) and healthy individuals (n=53, age range: 45-76, = 58 ± 8) were profiled by UPLC®-MS. Metabolites were extracted from serum using a mixture of acetone, acetonitrile and methanol (1:1:1 v/v). High resolution mass spectra were acquired for the non-lipid fraction in negative ionization mode for m/z 50-2000. Metabolomic features were obtained using MarkerLynx 4.1 software. Support vector machines (SVMs) were used for sample classification. Serum samples were successfully classified as cancerous or healthy based on their metabolic features (retention time, m/z pairs) with 92% accuracy, 98% sensitivity, and 88% specificity. Of the 51 metabolic features found to best discriminate prostate cancer patients from healthy individuals, 40 were tentatively associated with metabolites in the Human Metabolome Database (HMDB). UPLC®-MS/MS experiments are being performed to confirm the identities of the metabolites responsible for classification, providing further insights into the metabolic alterations and related biological pathways .associated with prostate cancer.