Predicting the probable outcome of treatment in HCV patients

Hepatitis C virus (HCV) is a major cause of chronic liver disease infecting more than 170 million people worldwide. HCV produces a wide gamut of manifestations varying from mild self-limiting disease to cirrhosis and hepatocellular carcinoma. A variety of viral, environmental and host genetic factors contribute to the clinical spectrum of patients infected with HCV and influence response to interferon (IFN) therapy. Predicting the probable outcome of treatment in patients with HCV infection has always been a challenging task. Treatment of HCV by pegylated interferon (peg-IFN) plus ribavirin eradicates the virus in approximately 60% of patients - HCV genotype 1 (42-51% response rates) and genotypes 2 and 3 (76-84% response rates); however, a significant number of patients do not respond to therapy or relapse following discontinuation of treatment or have significant side effects that preclude further treatment. Accurately predicting the patients who will respond to therapy is becoming increasingly important, both from the point of patient care and also with respect to the healthcare cost as clinicians need to continue treatment in patients who will respond and stop treatment in patients who are unlikely to respond. Viral RNA measurements and genotyping are used to optimize treatment as a low viral load and nongenotype 1 is more likely to be associated with sustained virological response (SVR). Rapid virological response (RVR) defined by undetectable HCV RNA at 4 weeks of treatment is increasingly being recognized as a powerful tool for predicting treatment response. A variety of host factors including single nuclear polymorphisms (SNPs) of IFN response genes, insulin resistance, obesity, ethnicity, human leukocyte antigens and difference in T-cell immune response has been found to modulate the response to antiviral treatment. The presence of severe fibrosis/cirrhosis on pretreatment liver biopsy predicts a poor response to treatment. Recent studies on gene expression profiling and characterization of the liver and serum proteome provide options to accurately predict the outcome of patients infected with HCV in the future. Future studies on the factors that predict treatment response and tailoring treatment based on this is required if we are to conquer this disease.