An interferon response gene signature is associated with the therapeutic response of hepatitis C patients.

Infection with the hepatitis C virus (HCV) is a major cause of chronic liver diseases and hepatocellular carcinoma worldwide, and thus represents a significant public health problem. The type I interferon (IFN), IFNα, has been successful in treating HCV-infected patients, but current IFN-based treatment regimens for HCV have suboptimal efficacy, and relatively little is known about why IFN therapy eliminates the virus in some patients but not in others. Therefore, it is critical to understand the basic mechanisms that underlie the therapeutic resistance to IFN action in HCV-infected individuals, and there is an urgent need to identify those patients most likely to respond to IFN therapy for HCV. To characterize the response of HCV-infected patients to treatment with IFNα, the expression of an IFN-response gene signature comprised of IFN-stimulated genes and genes that play an important role in the innate immune response was examined in liver biopsies from HCV-infected patients enrolled in a clinical trial. In the present study we found that the expression of a subset of IFN-response genes was dysregulated in liver biopsy samples from nonresponsive hepatitis C patients as compared with virologic responders. Based on these findings, a statistical model was developed to help predict the response of patients to IFN therapy, and compared to results obtained to the IL28 mutation model, which is highly predictive of the response to IFN-based therapy in HCV-infected patients. We found that a model incorporating gene expression data can improve predictions of IFN responsiveness compared to IL28 mutation status alone

Logistic regression modeling of patient data.

<p>*OR for gene expression is for a change of 10 units.</p

IL28 Genotype analysis (112/130 patients).

<p>IL28 Genotype analysis (112/130 patients).</p

Genes in IRF activation by cytosolic pattern recognition receptors are associated with IFN responsiveness.

<p>The gene network was generated using Ingenuity Pathway Analysis software. Genes upregulated in nonresponders (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104202#pone-0104202-g001" target="_blank">Figure 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104202#pone-0104202-g002" target="_blank">2</a>) were highlighted.</p

Patient Demographics.

<p>Patient Demographics.</p

The distribution of predicted probability of therapeutic response (with relapsers included).

<p>We found that those who actually responded to IFN-ribavirin treatment had higher predicted probabilities of response in the model.</p

Enrolled patient information.

1<p>Weight is in US pounds.</p>2<p>Likelihood ratio = 24.72, <i>p = 4.3×10⁻⁶</i>. <i>Il28B</i> rs12979860 SNP genotypes were determined for 107 AA and 60 C.</p

Differential expression of an IFN-regulated signature geneset in responders versus nonresponders to therapy.

<p>Expression of an IFN-regulated signature geneset was determined in RNA extracted from FFPE liver biopsies by nCounter analysis. Boxplots of genes found to be statistically differentially expressed by nonparametric Mann-Whitney analysis (p<0.05).</p

Signaling networks of genes that are associated with IFN responsiveness.

<p>The gene network was generated using Ingenuity Pathway Analysis software.</p

Differential expression of an IFN-regulated signature geneset in relapsers versus responders to IFN-ribavirin therapy to therapy.

<p>Expression of an IFN signature geneset was determined in RNA extracted from FFPE liver biopsies by nCounter analysis. Boxplots of genes found to be statistically differentially expressed by nonparametric Mann-Whitney analysis (p<0.05).</p