Feasibility of Detecting Prostate Cancer by Ultra­performance Liquid Chromatography–Mass Spectrometry Serum Metabolomics

• Published in: Journal of proteome research 2014-07, Vol.13 (7), p.3444-3454
• Main Authors: Zang, Xiaoling, Jones, Christina M, Long, Tran Q, Monge, María Eugenia, Zhou, Manshui, Walker, L. DeEtte, Mezencev, Roman, Gray, Alexander, McDonald, John F, Fernández, Facundo M
• Format: Article
• Language: English
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/pr500409q

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, over­diagnosis, and over­treatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultra­performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multi­variate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromato­graphically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lyso­phospho­lipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.