Journal
CLINICAL BIOCHEMISTRY
Volume 37, Issue 9, Pages 772-779Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.clinbiochem.2004.04.002
Keywords
biomarker; decision tree; transitional cell carcinoma; surface-enhanced laser desorption/ionization (SELDI); mass spectrometry
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Background: Recent advances in proteomic profiling technologies, such as surface-enhanced laser desorption/ionization mass spectrometry (SELDI), have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic Computational models. We developed a bioinformatics tool and used it to identify proteomic patterns in urine that distinguish transitional cell carcinoma (TCC) from noncancer. Methods: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary training set of spectra derived from analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that identified a fine-protein mass pattern that discriminated cancer from noncancer effectively. A blinded test set, including 38 new cases, was used to determine the sensitivity and specificity of the classification system. Results: The algorithm identified a cluster pattern that, in the training set, segregated cancer from noncancer with sensitivity of 84.8% and specificity of 91.7%. The discriminatory pattern correctly identified. A sensitivity of 93.3% and a specificity of 87.0% for the blinded test were obtained when comparing the TCC vs. noncancer. Conclusions: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for bladder cancer in high-risk and general populations. (C) 2004 The Canadian Society of Clinical Chemists. All rights reserved.
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