| Summary: | 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.
|
|---|
