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Departments of Microbiology and Molecular Cell Biology [B-L. A., M. D. W., M. A. C., L. H. C., O. J. S., G. L. W.] and Urology [P. F. S., G. L. W.] and Virginia Prostate Center [B-L. A., J. W. D., M. D. W., M. A. C., L. H. C., O. J. S., P. F. S., G. L. W.], Eastern Virginia Medical School, Norfolk, Virginia, 23501; Sentara Cancer Institute, Norfolk, Virginia 23501 [B-L. A., J. W. D., M. D. W., M. A. C., L. H. C., O. J. S., P. F. S., G. L. W.]; Department of Urology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030 [J. W. D.]; and Fred Hutchinson Cancer Research Center, Seattle, Washington 98109 [Y. Q., Y. Y., Z. F.]
The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.
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D. Sidransky, R. Irizarry, J. A. Califano, X. Li, H. Ren, N. Benoit, and L. Mao Serum Protein MALDI Profiling to Distinguish Upper Aerodigestive Tract Cancer Patients From Control Subjects J Natl Cancer Inst, November 19, 2003; 95(22): 1711 - 1717. [Abstract] [Full Text] [PDF] |
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M. A. Rogers, P. Clarke, J. Noble, N. P. Munro, A. Paul, P. J. Selby, and R. E. Banks Proteomic Profiling of Urinary Proteins in Renal Cancer by Surface Enhanced Laser Desorption Ionization and Neural-Network Analysis: Identification of Key Issues Affecting Potential Clinical Utility Cancer Res., October 15, 2003; 63(20): 6971 - 6983. [Abstract] [Full Text] [PDF] |
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E. P. Diamandis Proteomic Patterns in Biological Fluids: Do They Represent the Future of Cancer Diagnostics? Clin. Chem., August 1, 2003; 49(8): 1272 - 1275. [Full Text] [PDF] |
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E. Petricoin III and L. A. Liotta The Vision for a New Diagnostic Paradigm Clin. Chem., August 1, 2003; 49(8): 1276 - 1278. [Full Text] [PDF] |
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P. Stattin, M. Hakama, G. L. Wright Jr., and P. F. Schellhammer Correspondence re: B-L. Adam et al., Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men. Cancer Res., 62: 3609-3614, 2002 Cancer Res., May 15, 2003; 63(10): 2701 - 2702. [Full Text] [PDF] |
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D. R. Rhodes, M. G. Sanda, A. P. Otte, A. M. Chinnaiyan, and M. A. Rubin Multiplex Biomarker Approach for Determining Risk of Prostate-Specific Antigen-Defined Recurrence of Prostate Cancer J Natl Cancer Inst, May 7, 2003; 95(9): 661 - 668. [Abstract] [Full Text] [PDF] |
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T. C.W. Poon, T.-T. Yip, A. T.C. Chan, C. Yip, V. Yip, T. S.K. Mok, C. C.Y. Lee, T. W.T. Leung, S. K.W. Ho, and P. J. Johnson Comprehensive Proteomic Profiling Identifies Serum Proteomic Signatures for Detection of Hepatocellular Carcinoma and Its Subtypes Clin. Chem., May 1, 2003; 49(5): 752 - 760. [Abstract] [Full Text] [PDF] |
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E. F. Petricoin and L. A. Liotta Mass Spectrometry-based Diagnostics: The Upcoming Revolution in Disease Detection Clin. Chem., April 1, 2003; 49(4): 533 - 534. [Full Text] [PDF] |
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B. J. Duggan, J. J. McKnight, K. E. Williamson, M. Loughrey, D. O'Rourke, P. W. Hamilton, S. R. Johnston, C. C. Schulman, and A. R. Zlotta The Need to Embrace Molecular Profiling of Tumor Cells in Prostate and Bladder Cancer Clin. Cancer Res., April 1, 2003; 9(4): 1240 - 1247. [Abstract] [Full Text] [PDF] |
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E. F. Petricoin III and L. A. Liotta RESPONSE: Re: Serum Proteomic Patterns for Detection of Prostate Cancer J Natl Cancer Inst, March 19, 2003; 95(6): 490 - 491. [Full Text] [PDF] |
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Y. Qu, B.-L. Adam, Y. Yasui, M. D. Ward, L. H. Cazares, P. F. Schellhammer, Z. Feng, O. J. Semmes, and G. L. Wright Jr. Boosted Decision Tree Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients Clin. Chem., October 1, 2002; 48(10): 1835 - 1843. [Abstract] [Full Text] [PDF] |
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