Development of a Serum Metabolic In Vitro Diagnostic Multivariate Index Assay for Prostate Cancer Detection

XIAOLING ZANG; CHRISTINA M. JONES; TRAN Q. LONG; MARÍA EUGENIA MONGE; MANSHUI ZHOU; L. DEETTE WALKER; ROMAN MEZENCEV; ALEXANDER GRAY; JOHN F. MCDONALD; FACUNDO M. FERNANDEZ

Congreso; SciX 2014; 2014

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men worldwide. Although the Prostate Specific Antigen (PSA) screening test is the prevalent PCa diagnosis method, it suffers from low specificity and over-diagnosis. These shortcomings have led to an increased interest in using untargeted metabolite profiling to discover new metabolic biomarkers that could improve the specificity of PCa diagnosis. Current research has shown that several metabolic alterations are associated with PCa proliferation, and sarcosine has been suggested as a biomarker for aggressive PCa. However, global untargeted metabolic profiling of PCa patients is still in a premature phase, and there is no biomarker panel presently used for clinical diagnosis. In this study, ultra high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-MS/MS) was used to profile blood sera from 64 PCa patients and 50 healthy individuals. Support vector machines were utilized to identify metabolites in blood sera and to develop an in vitro diagnostic multivariate index assay (IVDMIA) which discriminates the PCa patients from the healthy individuals. A total of 480 metabolic features (retention time, m/z pairs) were quantitatively detected in serum metabolome profiles from the entire cohort. The developed IVDMIA successfully predicted the presence of PCa with 93.0% accuracy, 94.3% specificity, and 92.1% sensitivity using only a panel of 40 metabolic spectral features. Our IVDMIA proved to have a better detection performance than the prevalent PSA test for PCa patients in the cohort, as 33% of the PCa patients had PSA values lower than the commonly used cutoff point of 4.0 ng mL-1. These results highlight that a combination of multiple discriminant metabolic features yields higher predictive power for PCa diagnosis than univariate analysis of a single biomarker. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by chemical standards. The identified metabolites are involved in fatty acid, amino acid, lysophospholipid, bile acid and steroid hormone metabolisms. The most relevant finding is that many metabolites in our IVDMIA panel belong to the steroid hormone biosynthesis pathway, which supplies androgens to support the growth of androgen-dependent PCa.