The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

169 Disease Markers 19 (2003,2004) 169–183 IOS Press The use of urine proteomic and metabonomic patterns for the diagnosis of interstitial cystitis and bacterial cystitis Que N. Vana , John R. Klose a , David A. Lucas a, DaRue A. Prietoa , Brian Lukeb , Jack Collinsb , Stanley K. Burtb , Gwendolyn N. Chmurny a, Haleem J. Issaq a, Thomas P. Conrads a, Timothy D. Veenstraa and Susan K. Keay c,d,∗ a Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick, Inc., NCI Frederick, Frederick, MD, USA Advanced Biomedical Computer Center, SAIC-Frederick Inc., NCI-Frederick, Frederick, MD, USA c Division of Infectious Diseases, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA d Research Service, VA Maryland Health Care System, Baltimore, MD 21201, USA b Abstract. The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1 H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1 H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%. 1. Introduction With the rapid development of methods in the fields of genomics (DNA), transcriptomics (mRNA), proteomics (proteins), and metabonomics (low molecular weight metabolites) there is general enthusiasm towards revolutions in systems biology that will lead to more advanced approaches to diagnostics and thera∗ Corresponding authot: Dr. Susan Keay, VA Medical Center, Room 3B-184, 10 N. Greene Street, Baltimore, MD 21201, USA. TeL.: +1 410 605 7000 ext. 6450; Fax: +1 410 605 7837; E-mail: . peutics. Much of the effort in these areas focuses on comparing thousands of species between unaffected and diseased individuals with the hope that one, or a few, key differences in the two states may be identified. While ideally these differences would be recognized in readily obtainable biofluids such as urine, plasma, or serum, the inter-person variability of these samples makes the identification of unique, disease-reflective differences quite challenging. While unique biomarkers, such as HCG for pregnancy, are extremely effective, others such as Cancer Antigen 125 and prostate specific antigen possess poor positive-predictive value – particularly for early disease stage diagnosis. ISSN 0278-0240/03,04/$17.00  2003,2004 – IOS Press and the authors. All rights reserved 170 Q.N. Van et al. / The use of urine proteomic and metabonomic patterns for the diagnosis of interstitial cystitis Petricoin et al. have recently demonstrated that low molecular weight serum proteomic patterns from surface-enhanced laser desorption ionization time-offlight mass spectral (SELDI TOF-MS) data can distinguish neoplastic from non-neoplastic disease within the ovary [16]. A key aspect to their study was the application of a high-order self-organizing cluster analysis approach based on a genetic algorithm that was “trained” on SELDI-TOF MS spectra from serum derived from either healthy women or women with ovarian cancer. The “trained” algorithm was applied to a masked set of samples and resulted in a sensitivity of 100%, a specificity of 95% and a positive-predictive value of ovarian cancer of 94% [16]. The success of the use of proteomic patterns for the diagnosis of stage I ovarian cancer suggests that patterns generated from other biomolecules within biofluids may also provide a useful indicator of the early onset of a particular disease state. Since proteomic patterns of serum acquired using SELDI TOF-MS can be diagnostic of a particular disease state, it follows that spectral patterns of biofluids acquired using other types of analytical techniques may also be useful diagnostic tools. Nuclear magnetic resonance (NMR) spectroscopic analysis of bulk biofluids such as urine or plasma (e.g. metabonomics) has been utilized as a means to measure time-related biochemical responses resulting from physiological, pathological, or interventional genetic events [12–14]. High-field proton ( 1 H) NMR spectra of biofluids typically contain several hundred resolvable lines, potentially providing structural and quantitative information on hundreds of compounds in a single, nondestructive analysis that takes only a few minutes. The resulting spectrum provides a profile of the metabolic status of the organism. Recently, Brindle et al. showed the capability of discriminating serum samples acquired from patients with coronary heart disase from those with angiographically normal coronary arteries by analyzing the 1 H-NMR spectra of each sample using a supervised partial least squares discriminant algorithm [1]. This non-invasive method was shown to have a specificity of >90%. We studied the effectiveness of analyzing MS and 1 H-NMR data using a genetic algorithm combined with a self-organizing cluster analysis to correctly discriminate urine samples from individuals suffering from interstitial cystitis (IC) from those of healthy individuals. IC is a debilitating chronic bladder disease of unknown etiology that affects an estimated 750,000 women in the United States, with one-tenth as many men also diagnosed with this disease [2,15,17,18]. IC is currently diagnosed only by symptomatic criteria (urinary frequency plus pain and/or urgency) in the absence of specific identifiable causes, combined with cystoscopic findings (including petechial hemorrhages called “glomerulations” in approximately 90% of patients, or ulcers that extend into the lamina propria in approximately 10%) [3,6,20]. None of these symptoms, however, are specific for IC, and the specificity of glomerulations for this disorder has also been called into question [21], making it currently difficult to establish the (...truncated)