Chronic hepatitis C (CHC) infection is a leading cause of end-stage liver disease. Current standard-of-care (SOC) interferon-based therapy results in sustained virological response (SVR) in only one-half of patients, and is associated with significant side-effects. Accurate host predictors of virologic response are needed to individualize treatment regimens. We applied a label-free LC-MS-based proteomics discovery platform to pre-treatment sera from a well-characterized and matched training cohort of 55 CHC patients, and an independent validation set of 41 CHC genotype 1 patients with characterized IL28B genotype. Accurate mass and retention time methods aligned samples to generate quantitative peptide data, with predictive modeling using Bayesian sparse latent factor regression.

We identified 105 proteins of interest with two or more peptides, and a total of 3768 peptides. Regression modeling selected three identified metaproteins, vitamin D binding protein, alpha 2 HS glycoprotein, and Complement C5, with a high predictive AUROC of 0.90 for SVR in the training cohort. A model averaging approach for identified peptides resulted in AUROC of 0.86 in the validation cohort, and correctly identified virologic response in 71% of patients without the favorable IL28B 'responder' genotype. CONCLUSION: Our preliminary data indicates that a serum-based protein signature can accurately predict treatment response to current SOC in most CHC patients.