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Utility of Serum Biomarker Profiles in Predicting the Treatment Outcome in Chronic Hepatitis C Virus Patients
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Reported by Jules Levin
DDW May 17-22, 2008, San Diego
Krishna S Kasturi1, John R Petersen3, Audrey N Nguyen1, Ned Snyder2
1. Internal Medicine, University of Texas Medical Branch, Galveston, TX, USA, 2. Gastroenterology & Hepatology, University of Texas Medical Branch, Galveston, TX, USA, 3. Pathology, University of Texas Medical Branch, Galveston, TX, USA
Background & Aim: ProteinChip technology (SELDI-TOF) has been used as a profiling technique to identify potential biomarkers which may represent proteins, protease-cleavage products, or novel peptides. The purpose of this study was to use this technology to investigate the differences in the pre-treatment serum profiles of patients who were later classified as HCV treatment responders (R1) and non-responders/ relapsers (R2).
Materials and Methods: 59 patients that had completed HCV combination therapy were identified and classified as R1 or R2. Blood samples prior to treatment were prefractionated using Equalizer® bead protein biomarker discovery technology and SELDI ProteinChip® array profiling. The fractions were analyzed on the PCS 4000 time of flight mass spectrometer using CM10, IMAC (pretreated with Cu) and Q10 chips. Pooled normal human plasma was analyzed concurrently as controls and to establish criteria for peak magnitude significance. Data of the proteomic spectra was analyzed by Ciphergen Express Data Manager software with Pattern Track and Two-way Hierarchical Clustering algorithm. Advanced statistical analyses (logistic regression, correlations, and case summaries) were done using SPSS v.11.0 for Windows.
Results:
Sample characteristics: Males=39, Females=12; Caucasian=31, Blacks=9, Hispanics=10, Asian=1; Mean age 45 years (range 28 - 58); Stage of fibrosis 0 (n=4), 1 (n=11), 2 (n=16), 3 (n=12), and 4 (n=8); R1 (n=27), R2 (n=24).
Biomarker panel: 83 peaks of <20 KDa significantly differentiated (p<0.05) R1 from R2. Upon further analysis (logistic regression), we were able to identify 4 peaks of < 20 KDa (panel of 4 potential biomarkers) that allowed the separation of R1 and R2 (sensitivity 81.5%, specificity 90.6%, PPV 88%, NPV 85%). Inclusion of other routinely obtained biochemical variables did not have a favorable influence on this panel. A predictive model was also created using age, gender, race, HCVRNA and genotype of the study subjects (sensitivity 81.5%, specificity 81.3%, PPV 78.5%, NPV 83.8%).
Conclusion: A predictive model using a biomarker panel was superior in differentiating the serum profiles of treatment responders from non-responders/ relapsers. Additional work to identify these putative biomarkers along with the development of assays to validate this study, and the application of this research data in a larger sample will be necessary to evaluate the usefulness of serum biomarker profiles in predicting response to HCV combination therapy. We would like to acknowledge the technical support from Ciphergen Biosystems, Inc., Fremont, CA, for this project.
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