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

• Autores: Zang, Xiaoling; Jones, Christina M.; Long, Tran Q.; Monge, Maria Eugenia; Zhou, Manshui; DeEtte Walker, L.; Mezencev, Roman; Gray, Alexander; McDonald, John F.; Fernandez, Facundo M.
• Año de publicación: 2014
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Prostate Cancer; Prostate Cancer Detection; Untargeted Metabolomics; Oncometabolomics; Ultraperformance Liquid Chromatography; Mass Spectrometry; Machine Learning Methods; Support Vector Machines; In Vitro Diagnostic Multivariate Index Assay; Ivdmia
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Zang, Xiaoling. Georgia Institute of Techology; Estados Unidos
Fil: Jones, Christina M.. Georgia Institute of Techology; Estados Unidos
Fil: Long, Tran Q.. Georgia Institute of Techology; Estados Unidos
Fil: Monge, Maria Eugenia. Georgia Institute of Techology; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zhou, Manshui. Georgia Institute of Techology; Estados Unidos
Fil: DeEtte Walker, L.. Georgia Institute of Techology; Estados Unidos
Fil: Mezencev, Roman. Georgia Institute of Techology; Estados Unidos
Fil: Gray, Alexander. Georgia Institute of Techology; Estados Unidos
Fil: McDonald, John F.. Georgia Institute of Techology; Estados Unidos
Fil: Fernandez, Facundo M.. Georgia Institute of Techology; Estados Unidos