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Since completion of the human genome and HapMap projects it is likely that an increasing number of studies dealing with genetic associations in specific diseases will be published in medical journals. Hepatitis C and especially its response to antiviral treatment does not constitute an exception to this rule. It has been known for quite a long time that viral factors such as genotype and viral load have a major influence on the outcome of antiviral treatment.1 Nevertheless, researchers have become increasingly aware that host genetic factors including ethnicity, human leucocyte antigens, cytokine production and differences in T cell immune responses can modulate the response to antiviral treatment and viral clearance.2 3 In this issue of Gut (see page 10.1136/gut.2007.129478), Persico et al report on single nucleotide polymorphisms (SNPs) of suppressor of cytokine signalling 3 (SOCS3) being positively and negatively associated with response to antiviral therapy in hepatitis C virus (HCV) genotype-1-infected patients.4 Interestingly, these data were generated from peripheral mononuclear blood cells (PMBCs), a material that would be easily available for large-scale studies in the future. The concept of SOCS3 being involved in modulating antiviral response mechanisms is appealing to the scientific community. SOCS3 acts as a negative regulator of the cytokine-induced JAK–STAT (Janus kinase–signal transducer and activator of transcription) pathway. As a consequence of a classical negative feedback circuit, SOCS3 inhibits the cytokine receptor-associated JAK tyrosine kinase through either direct interaction with JAKs or indirect inhibition by binding to specific sites on the receptor.5 6 Inhibition of JAKs leads in turn to deficient STAT phosphorylation and dimerisation required for nuclear translocation of STATs and ultimately to blockage of transcriptional activity of STAT-responsive genes involved in regulation of immune responses and maintaining immunological homeostasis.6 Interferons (IFNs) are naturally occurring proteins that induce an antiviral state in cells by activating the JAK–STAT pathway.7 Type I IFN, mainly IFNα, and the type II IFNγ act synergistically and induce the expression of a large number of interferon-stimulated genes, setting up an antiviral, antiproliferative and immunoregulatory state in the host cells. Both IFNs have been shown to inhibit HCV replication.8–11 However, HCV seems to have developed strategies that interfere with the IFN effector pathways attenuating their antiviral efficacy.12 13 In addition, it has been shown that the HCV core protein is able to induce SOCS3 and to suppress JAK–STAT signalling in cell culture.14 15 From this point of view, the results of Persico et al pointing to SOCS3 and its gene polymorphisms as a modulator of antiviral responses, in particular by downregulating IFN-dependent responses, in vivo in humans is important since they open up new perspectives for future research. A second paper in this issue of the journal (see page 516) also supports the view that HCV evades the immune system by interfering with IFN-stimulated genes. Asselah et al report that the expression of three genes (IFI-6-16, IFI27 and ISG15) coding for IFN-inducible proteins is upregulated in non-responders to antiviral therapy.16 The authors further show that a two-gene signature including one of these three genes (IFI27) predicts treatment outcome reasonably well.
Although the association of SNPs of SOCS3 with antiviral responses seems to be robust in the study of Persico et al,4 one has to keep in mind that such an analysis is demanding from the statistical point of view and bears a high risk of false-positive results principally due to constraints in terms of number of samples.17 In particular, the reader should interpret the findings of Asselah et al with caution as the number of patients included in each subgroup analysis was rather low, generating p values at the lower end of the spectrum of significance.16 In addition, the sustained viral responder and non-responder subgroups display substantial heterogeneity concerning viral genotypes and fibrosis scores, both of which are known to influence response to antiviral treatment. The paper also included a responder–relapser subgroup. However, this group of patients did not separate out as an independent subgroup as their gene expression profiles were distinguishable neither from those of sustained responders nor from those of non-responders. Moreover, the two-gene signature analysis predicted 73% of them to be sustained responders despite the fact that the final result is failure of antiviral treatment. In principle, relapsers should be assimilated into the group of non-responders if one considers treatment failure defined as HCV-RNA positivity 6 months after cessation of antiviral treatment as the primary end point. As a consequence, it is likely that the overall predictive accuracy of treatment response lies below the 79% claimed by Asselah et al if both subgroups are pooled together into one treatment failure group. Considering all these different aspects, it is therefore mandatory that the consistency and strength of associations are verified in large-scale replication studies in different sets of patients by independent research groups.
Although these studies tell us that a particular gene might be important in the pathogenesis of a given disease, they do not tell us anything about the links between these associations and disease biology. Gene products are subjected to several levels of regulation starting with its mRNA and extending to elaboration of the final protein that might suppress, attenuate or amplify the functional consequences of a given polymorphism. It is encumbent on the researchers to explain how variation in the function of genes leads to clinical disease and to unravel the mechanism that is responsible for the clinical picture of a disease. In an attempt to address this issue, Persico et al showed higher levels of basal SOCS3 mRNA and protein expression in PMBCs in non-responders to IFN-based antiviral therapy compared with responders. These results have to be treated with caution because the analysis has been performed in a relatively low number of patients (as few as six patients per group in some experiments). In addition, it is not clear from the study whether the results obtained in PMBCs can be extrapolated to the target organ—that is, the liver, and what the basal expression of SOCS3 would be in a “normal” liver or in liver disease that is not related to HCV. High SOCS3 levels might be related to liver injury and repair mechanisms independent of viral infection. Showing that SOCS3 expression is low in normal livers and subsequently increases in HCV-infected non-responders as well as the absence of an association of SOCS3 with liver diseases other than HCV would substantially reinforce the link between SOCS3 and non-response to treatment. Asselah et al did not address the functional consequences of their findings, although it is striking that expression of IFN-stimulated genes is induced in non-responders.16 These observations imply that, in their study, the functional integrity of IFN signalling pathways is preserved in non-responders, which is in contradiction to the data reported in the study of Persico et al.4 A more thorough analysis of how the three upregulated genes coding for the IFNα-inducible proteins 2, 3 and 27 hamper IFN-based antiviral therapy would not only considerably strengthen the data presented by Asselah et al, but would also shed light on the multiple facets by which HCV finally gets around antiviral defence mechanisms.
Some support for the findings reported in the study by Persico et al comes, however, from two recent studies reporting SOCS3 overexpression in the livers of HCV-infected, obese patients not responding to antiviral therapy and in livers of chimpanzees infected with HCV that are resistant to type I and type II IFNs.18 19 Given the complexity of gene regulation and the number of genes involved in fine-tuning the IFN response to viral infection, it is likely that other SNPs, such as, for example, those described in the IFNγ promoter or SNPs that modulate T lymphocyte responses, will be found to be associated with treatment response in HCV patients.3 20
In addition, interfering with the JAK–STAT pathway might not only impact on antiviral defence mechanisms that principally involve STAT1 but may also disturb liver damage and repair mechanisms. STAT3, a member of the JAK–STAT family, is also closely regulated by SOCS3, and disruption of STAT3-mediated intracellular signal transduction pathways could lead to disturbed liver regeneration and repair.21–24 Data in the literature suggest that HCV modulates STAT3 signalling,25 26 and in vivo studies in humans add further evidence that deficient STAT3 signalling does contribute to liver fibrosis progression in HCV-infected patients.27 28 It is conceivable that SNPs like those described by Persico et al in the SOCS3 gene lead to a wider range of functional consequences integrating blunted antiviral defence and inadequate liver repair in response to injury.
Both studies fall short of suggesting answers to two well-known phenomena. First, it is not clear how SNPs in the SOCS3 gene or upregulation of IFN-inducible genes could explain the different response rates to IFN-based therapies in patients with advanced fibrosis or cirrhosis compared with their non-fibrotic counterparts regardless of the genotype these patients are infected with. Even though the model proposed by Asselah et al seems to predict reasonably well the treatment outcome in patients with advanced fibrosis, the suggested gene signature profile does not explain the differences in response rates at baseline.
It is likely that both patient groups carry a similar distribution of SNPs or gene expression profiles before starting antiviral therapy, but they do not show the same response rates, these being lower in patients with advanced fibrosis and/or cirrhosis. Genes or factors other than those described in the two papers should account for the different response rates to IFN-based therapy encountered in these groups. Secondly, similar considerations could be applied to patients infected with genotype 2 and 3 viruses. They respond extremely well to IFN therapy although the proportion of patients carrying SNPs or gene expression profiles supposed to influence antiviral responses negatively is likely to be similar at baseline compared with genotype 1-infected patients. Detailed studies comparing responders with non-responders in both groups, the difficult to treat fibrotic patients and the easy to treat genotypes 2 and 3, are warranted as they might help to identify those genes that are associated with non-response independently from the genotype and the fibrosis status of a given patient.
Genome-wide association studies are likely to define new directions of research in the future that will help to better target antiviral therapy to individual patients, with a maximum benefit for those who are currently considered as belonging to the difficult to treat patient groups. The challenge for researchers will be to determine what SNPs in a given gene or what combination of SNPs or expression profiles in a panel of genes finally leads to functional consequences in vivo in humans and ultimately best predicts response and/or non-response to antiviral therapy. Regarding the question of whether the time has come to apply these findings to daily clinical practice, the answer should be—not yet. Much basic science and clinical research is still needed before genetics and genetic associations will enter clinical decision making. However, studies such as that of Persico et al constitute a step forward towards, for example, the elaboration of SNP microarrays which might help to better stratify patients into groups having a high, intermediate or low likelihood for responding favourably to IFN-based antiviral therapy.
Competing interests: None.
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