SEARCH:   Advanced

Blood, 15 June 2007, Vol. 109, No. 12, pp. 5075-5076. This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Rights and Permissions Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via Google Scholar Google Scholar Articles by Thongboonkerd, V. Search for Related Content PubMed Articles by Thongboonkerd, V. Related Collections Related Article in Blood Online Social Bookmarking                   What's this?  Previous Article  |  Table of Contents  |  Next Article  TRANSPLANTATION Comment on Weissinger et al, page 5511 Clinical proteomics: towards diagnostics and prognostics Visith Thongboonkerd 1 SIRIRAJ HOSPITAL AT MAHIDOL UNIVERSITY Clinical proteomics has been applied to define a specific polypeptide profile for acute graft-versus-host disease (aGvHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). This profile can then be used for accurate prediction of the occurrence and recurrence of aGvHD. A biomarker has been recently defined by the Biomarkers Definitions Working Group as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathological processes, or pharmacological responses to a therapeutic intervention."1, pg91 Biomarkers, therefore, have potential values in monitoring health status. They can be used as a diagnostic tool for disease detection and staging, as well as a tool for disease prognosis and prediction of therapeutic outcome or response to an intervention. Additionally, the ideal biomarkers should also be able to predict recurrence or relapse of the disease after treatment. After the completion of the Human Genome Project, the post-genomic research has been developing rapidly and has focused largely on translating the genomic information to clinical applications. Clinical proteomics has become one of the most interesting fields with an ultimate goal of biomarker discovery for earlier and more accurate diagnosis, and for more accurate prognosis of the disease. Although the main focuses are diagnostics and biomarker discovery, clinical proteomics also covers identification of new therapeutic targets, drugs, and vaccines for better therapeutic outcomes and successful disease prevention. Recently, a group of proteomists, clinicians, biochemists, chemists, bioinformaticians, and statisticians from more than 25 institutions worldwide have discussed and begun to define the field and to set adequate standards for clinical proteomics.2 Through this collaborative effort, clinical proteomics has been defined as "the application of proteomic analysis with the aim of solving a specific clinical problem within the context of a clinical study."2,pg149 A clinical proteomic study should begin with a well-framed clinical question or problem, followed by selection of the appropriate study populations, samples to be analyzed, and technology to analyze the samples.2 View larger version (50K): [in this window] [in a new window]   Analysis of urinary polypeptide patterns of aGvHD and non-aGvHD using CE-MS. See the complete figure in the article beginning on page5511.   Although some single biomarkers have been identified for particular diseases, it has been suggested that a single ideal biomarker may not exist for every disease. Evaluation of a panel of multiple potential disease-specific biomarkers is, therefore, crucially required; proteomics serves as an important tool for such purpose to examine the molecular signature of proteins in a biological sample (proteome profiling). One of the proteomic methodologies that is suitable for proteome profiling is capillary electrophoresis coupled to mass spectrometry (CE-MS).3 Advantages of CE-MS include automation, high-throughput capability, high sensitivity, and less demand of sample volume. In this issue of Blood, Weissinger and colleagues have applied state-of-the-art proteomic technology using CE-MS to evaluate urinary polypeptide profiles (patterns) of 141 allo-HSCT patients from 5 centers in Germany and the United States. Using this technique for analyzing a training set of 13 urine samples from patients with aGvHD and 50 samples from patients without aGvHD, they defined the aGvHD-specific urinary polypeptide pattern. A model of multiple potential biomarkers containing 31 polypeptides allowed accurate classification of urine samples in the training set with a sensitivity of 100% and specificity of 98%. This panel of multiple potential biomarkers was then used for classifying 599 urine samples in the validation set and provided satisfactory sensitivity and specificity (83.1% and 75.6%, respectively). In addition, urine samples from healthy individuals and patients with other diseases were also analyzed as the quality-control set. Moreover, their findings, particularly the classification factor or support vector machine (SVM) score, could be used for prediction of the occurrence and recurrence of GvHD. This study is an excellent model to underscore the applicability of clinical proteomics to diagnostics and prognostics. However, a limitation of CE-MS should be also noted. Any proteins with molecular masses greater than 20 kDa are not suitable for CE-MS analysis and, thus, require other complementary methods. Footnotes Conflict-of-interest disclosure: The author declares no competing financial interests. REFERENCES Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69:89–95.[CrossRef][Medline] [Order article via Infotrieve] Mischak H, Apweiler R, Banks RE, et al. Clinical proteomics: a need to define the field and to begin to set adequate standards. Proteomics Clin Appl 2007; 1:148–156.[CrossRef] Weissinger EM, Hertenstein B, Mischak H, Ganser A. Online coupling of capillary electrophoresis with mass spectrometry for the identification of biomarkers for clinical diagnosis. Expert Rev Proteomics 2005; 2:639–647.[CrossRef][Medline] [Order article via Infotrieve] CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this? Related Article in Blood Online: Proteomic patterns predict acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation Eva M. Weissinger, Eric Schiffer, Bernd Hertenstein, James L. Ferrara, Ernst Holler, Michael Stadler, Hans-Jochem Kolb, Axel Zander, Petra Zürbig, Markus Kellmann, and Arnold Ganser Blood 2007 109: 5511-5519. [Abstract] [Full Text] [PDF] This article has been cited by other articles: F. Fournier, E. M. Gardner, D. A. Kedra, P. M. Donaldson, R. Guo, S. A. Butcher, I. R. Gould, K. R. Willison, and D. R. Klug Protein identification and quantification by two-dimensional infrared spectroscopy: Implications for an all-optical proteomic platform PNAS, October 7, 2008; 105(40): 15352 - 15357. [Abstract] [Full Text] [PDF] This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Rights and Permissions Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via Google Scholar Google Scholar Articles by Thongboonkerd, V. Search for Related Content PubMed Articles by Thongboonkerd, V. Related Collections Related Article in Blood Online Social Bookmarking                   What's this?

	Copyright © 2007 by American Society of Hematology         Online ISSN: 1528-0020