Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Abstract

Technological advances in mass spectrometry 14 (MS), liquid chromatography (LC) separations, nuclear 15 magnetic resonance (NMR) spectroscopy, and big data 16 analytics have made possible studying metabolism at an 17 “omics” or systems level. Here, we applied a multiplatform 18 (NMR + LC−MS) metabolomics approach to the study of 19 preoperative metabolic alterations associated with prostate 20 cancer recurrence. Thus far, predicting which patients will 21 recur even after radical prostatectomy has not been possible. 22 Correlation analysis on metabolite abundances detected on 23 serum samples collected prior to surgery from prostate cancer 24 patients (n = 40 remission vs n = 40 recurrence) showed 25 significant alterations in a number of pathways, including 26 amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. 27 Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included 28 triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and 29 ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample 30 that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation 31 conditions.