The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease

Chronic liver disease (CLD) is a major and growing healthcare burden but is asymptomatic until liver fibrosis is advanced. Antiviral therapy can cure 50% with chronic hepatitis C virus (HCV) infection. Accurate staging of fibrosis requires invasive liver biopsy; predicting response to antivirals pri...

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Main Author: Cowan, Matthew Lawson
Published: St George's, University of London 2011
Subjects:
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568724
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spelling ndltd-bl.uk-oai-ethos.bl.uk-5687242015-03-20T05:10:03ZThe application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver diseaseCowan, Matthew Lawson2011Chronic liver disease (CLD) is a major and growing healthcare burden but is asymptomatic until liver fibrosis is advanced. Antiviral therapy can cure 50% with chronic hepatitis C virus (HCV) infection. Accurate staging of fibrosis requires invasive liver biopsy; predicting response to antivirals prior to the start of treatment is not possible. Clinical details, serum, plasma and liver tissue for 443 subjects with CLD were collected in a purpose-built database. Patients undergoing staging liver biopsy or HCV antiviral therapy were recruited prospectively; HCV patients with gold standard liver biopsies, or completed antiviral therapy and available serum were identified retrospectively. Surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI) is a high-throughput proteomic technique for profiling proteins. A protocol for SELDI profiling of ascites was optimised and the differences between the proteome of ascites and paired serum samples assessed. Comparison of the proteome of clinical subjects demonstrated that interindividual differences in the blood proteome may be more important in translational studies than the choice of blood collection tube. Long-term storage of serum did not adversely affect the quality of proteomic spectra allowing validation of individual biomarkers and multivariable diagnostic algorithms from stored samples. Accurate predictive models for the classification and staging of liver fibrosis and prediction of response to antiviral therapy were built from clinical and SELDI i proteomic data. The accuracy of traditional linear regression and complex non- linear machine learning diagnostic algorithms was explored. Proteomic biomarkers and all diagnostic models were validated in independent testing sets. The composite fibrosis index was accurate in subjects with liver disease of varying aetiology and showed ability to discriminate subjects with decompensated from compensated cirrhosis.616.362075St George's, University of Londonhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568724Electronic Thesis or Dissertation
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spellingShingle 616.362075
Cowan, Matthew Lawson
The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
description Chronic liver disease (CLD) is a major and growing healthcare burden but is asymptomatic until liver fibrosis is advanced. Antiviral therapy can cure 50% with chronic hepatitis C virus (HCV) infection. Accurate staging of fibrosis requires invasive liver biopsy; predicting response to antivirals prior to the start of treatment is not possible. Clinical details, serum, plasma and liver tissue for 443 subjects with CLD were collected in a purpose-built database. Patients undergoing staging liver biopsy or HCV antiviral therapy were recruited prospectively; HCV patients with gold standard liver biopsies, or completed antiviral therapy and available serum were identified retrospectively. Surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI) is a high-throughput proteomic technique for profiling proteins. A protocol for SELDI profiling of ascites was optimised and the differences between the proteome of ascites and paired serum samples assessed. Comparison of the proteome of clinical subjects demonstrated that interindividual differences in the blood proteome may be more important in translational studies than the choice of blood collection tube. Long-term storage of serum did not adversely affect the quality of proteomic spectra allowing validation of individual biomarkers and multivariable diagnostic algorithms from stored samples. Accurate predictive models for the classification and staging of liver fibrosis and prediction of response to antiviral therapy were built from clinical and SELDI i proteomic data. The accuracy of traditional linear regression and complex non- linear machine learning diagnostic algorithms was explored. Proteomic biomarkers and all diagnostic models were validated in independent testing sets. The composite fibrosis index was accurate in subjects with liver disease of varying aetiology and showed ability to discriminate subjects with decompensated from compensated cirrhosis.
author Cowan, Matthew Lawson
author_facet Cowan, Matthew Lawson
author_sort Cowan, Matthew Lawson
title The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
title_short The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
title_full The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
title_fullStr The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
title_full_unstemmed The application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
title_sort application of proteomics to the discovery of biomarkers of diseases severity and prognosis in chronic liver disease
publisher St George's, University of London
publishDate 2011
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568724
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