Analyzing the Effects of Pretreatment Diversity on HCV Drug Treatment Responsiveness Using Bayesian Partition methods

Traditional therapies for Hepatitis C Virus (HCV) often yield unsatisfactory results. The reason for this may lie in the mechanism of drug resistance of the HCV virus. Despite doing a plain vanilla comparison between the treated and untreated groups, this paper takes a detour and investigates the drug resistance mechanism to interferon plus ribavirin combined therapy by comparing pretreatment sequence data between response and non-response patients in the NS5A region for genotype 1a HCV virus. We use Bayesian probabilistic models to detect single mutation or mutation combinations, and infer interaction structures between these mutations, to investigate the drug resistance combinations differences between those patients. We hope to decipher, at least partially, the reason behind the unsatisfactory results received from interferon plus ribavirin therapy.

HCV treatment results have been historically suboptimal^[1-3]. HCV drug resistance, which further hinders the treatment effects, is caused by mutations of viral proteins that disrupt the drugs' binding but do not affect the viral survival. Due to the high rate and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. M.J. Donlin et al indicate that pretreatment sequence diversity correlates with response effects^[15]. We incorporate this idea and use a Bayesian approach to look into the pretreatment sequences diversity of HCV virus between response and non-response groups, under a combined treatment of interferon and ribavirin.