Monge, ME and Zang, X and Jones, CM and Tran, Quoc Long and Zhou, Manshui and Walker L., DeEtte and Mezencev, Roman and Gray, Alexander and McDonald, John F. and Fernandez, Facundo (2016) High Accuracy Prostate Cancer Detection Using Human Blood Serum Metabolomic Profiling. In: 2nd Latin American Metabolic Profiling Symposium, October 2016.
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Abstract
Prostate cancer (PCa) represents the second leading cause of cancer mortality in many western countries. Although the Prostate-Specific Antigen (PSA) test is widely used to screen for PCa, 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 metabolite profiling to discover new differential biomarkers that could improve the specificity of PCa diagnosis.1 In this work,2 untargeted metabolomic profiling of age-matched serum samples from PCa patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry and machine learning methods. PCa was detected in serum samples with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy by means of a metabolite-based in vitro diagnostic multivariate index assay, which outperformed the prevalent PSA test. Within the panel of 40 metabolic spectral features that was found to be differential, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed 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 metabolite alterations associated with the disease. Our current work involves the prospective analysis of a larger sample cohort (n=500) that includes samples from PCa patients before and after undergoing surgery, patients with benign prostatitis, and healthy individuals with measured PSA values, in order to evaluate the influence of ethnicity and cohort size on the robustness of the metabolic biomarker panel that we have previously found, and to discover biomarkers useful for follow-up care.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Subjects: | Information Technology (IT) |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Long Trần Quốc |
| Date Deposited: | 17 Jan 2017 02:10 |
| Last Modified: | 17 Jan 2017 02:10 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/2405 |
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