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• W2105764241 abstract "Background & Aims: Pegylated interferon (IFN)-α plus ribavirin is the most effective treatment of chronic hepatitis C but has unpleasant side effects and high costs. A large proportion of patients do not respond to therapy for reasons that are unclear. We used gene expression profiling to investigate the molecular basis for treatment failure. Methods: Expression profiling was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene expression levels were compared among 15 nonresponder, 16 responder, and 20 normal liver biopsy specimens. Differential gene expression was confirmed using real-time polymerase chain reaction. Results: We identified 18 genes whose expression differed significantly between all responders and all nonresponders (P < .005). Many of these 18 genes are IFN sensitive and 2 (ISG15/USP18) are linked in a novel IFN-regulatory pathway, suggesting a possible rationale for treatment resistance. Using a number of independent classifier analyses, an 8-gene subset accurately predicted treatment response for 30 of 31 patients. The classifier analyses were applicable to patients with genotype 1 infection and were not correlated with viral load, disease activity, or fibrosis. Conclusions: Hepatic gene expression profiling identified consistent differences in patients who subsequently fail treatment with pegylated IFN-α plus ribavirin: up-regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy. These data may be of use in predicting clinical responses to treatment. Background & Aims: Pegylated interferon (IFN)-α plus ribavirin is the most effective treatment of chronic hepatitis C but has unpleasant side effects and high costs. A large proportion of patients do not respond to therapy for reasons that are unclear. We used gene expression profiling to investigate the molecular basis for treatment failure. Methods: Expression profiling was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene expression levels were compared among 15 nonresponder, 16 responder, and 20 normal liver biopsy specimens. Differential gene expression was confirmed using real-time polymerase chain reaction. Results: We identified 18 genes whose expression differed significantly between all responders and all nonresponders (P < .005). Many of these 18 genes are IFN sensitive and 2 (ISG15/USP18) are linked in a novel IFN-regulatory pathway, suggesting a possible rationale for treatment resistance. Using a number of independent classifier analyses, an 8-gene subset accurately predicted treatment response for 30 of 31 patients. The classifier analyses were applicable to patients with genotype 1 infection and were not correlated with viral load, disease activity, or fibrosis. Conclusions: Hepatic gene expression profiling identified consistent differences in patients who subsequently fail treatment with pegylated IFN-α plus ribavirin: up-regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy. These data may be of use in predicting clinical responses to treatment. More than 170 million people worldwide are infected chronically with hepatitic C virus (HCV).1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar, 2Poynard T. Yuen M.F. Ratziu V. Lai C.L. Viral hepatitis C.Lancet. 2003; 362: 2095-2100Abstract Full Text Full Text PDF PubMed Scopus (746) Google Scholar Currently there is no vaccine or small molecule therapy for this disease, which can lead to liver failure and cancer. The most effective treatment is pegylated interferon (IFN)-α plus ribavirin, which has morbid side effects, variable cure rates, and high costs.1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar Although the interaction of the virus with hepatic microenvironments creates a cellular state that is nonresponsive to treatment,3Girard S. Shalhoub P. Lescure P. Sabile A. Misek D.E. Hanash S. Brechot C. Beretta L. An altered cellular response to interferon and up-regulation of interleukin-8 induced by the hepatitis C viral protein NS5A uncovered by microarray analysis.Virology. 2002; 295: 272-283Crossref PubMed Scopus (68) Google Scholar, 4Ghosh A.K. Majumder M. Steele R. Ray R. Ray R.B. Modulation of interferon expression by hepatitis C virus NS5A protein and human homeodomain protein PTX1.Virology. 2003; 306: 51-59Crossref PubMed Scopus (19) Google Scholar, 5Naganuma A. Nozaki A. Tanaka T. Sugiyama K. Takagi H. Mori M. Shimotohno K. Kato N. Activation of the interferon-inducible 2′-5′-oligoadenylate synthetase gene by hepatitis C virus core protein.J Virol. 2000; 74: 8744-8750Crossref PubMed Scopus (72) Google Scholar the underlying molecular mechanisms are unknown and it is not possible to predict treatment outcomes before initiating therapy. Viral and host factors both play a role; for example, infection with HCV genotypes 1 or 4 is associated with at best a 60% response rate, and increasing degrees of hepatic fibrosis can decrease response rates.1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar Mutations in viral (NS5A, NS5B) and host (MxA, OAS, PKR) proteins can enhance (NS5A, NS5B) or partially inhibit (MxA) the response to IFN-based treatment.6Nishiguchi S. Ueda T. Itoh T. Enomoto M. Tanaka M. Tatsumi N. Fukuda K. Tamori A. Habu D. Takeda T. Otani S. Shiomi S. Method to detect substitutions in the interferon-sensitivity-determining region of hepatitis C virus 1b for prediction of response to interferon therapy.Hepatology. 2001; 33: 241-247Crossref PubMed Scopus (16) Google Scholar, 7Watanabe H. Enomoto N. Nagayama K. Izumi N. Marumo F. Sato C. Watanabe M. Number and position of mutations in the interferon (IFN) sensitivity-determining region of the gene for nonstructural protein 5A correlate with IFN efficacy in hepatitis C virus genotype 1b infection.J Infect Dis. 2001; 183: 1195-1203Crossref PubMed Scopus (64) Google Scholar, 8Murashima S. Kumashiro R. Ide T. Miyajima I. Hino T. Koga Y. Ishii K. Ueno T. Sakisaka S. Sata M. Effect of interferon treatment on serum 2′-5′-oligoadenylate synthetase levels in hepatitis C-infected patients.J Med Virol. 2000; 62: 185-190Crossref PubMed Scopus (25) Google Scholar, 9Knapp S. Yee L.J. Frodsham A.J. Hennig B.J. Hellier S. Zhang L. Wright M. Chiaramonte M. Graves M. Thomas H.C. Hill A.V. Thursz M.R. Polymorphisms in interferon-induced genes and the outcome of hepatitis C virus infection roles of MxA, OAS-1 and PKR.Genes Immun. 2003; 4: 411-419Crossref PubMed Scopus (192) Google Scholar, 10Suzuki F. Arase Y. Suzuki Y. Tsubota A. Akuta N. Hosaka T. Someya T. Kobayashi M. Saitoh S. Ikeda K. Kobayashi M. Matsuda M. Takagi K. Satoh J. Kumada H. Single nucleotide polymorphism of the MxA gene promoter influences the response to interferon monotherapy in patients with hepatitis C viral infection.J Viral Hepat. 2004; 11: 271-276Crossref PubMed Scopus (67) Google Scholar Increased hepatic MxA protein expression is associated with poorer treatment responses.11MacQuillan G.C. de Boer W.B. Platten M.A. McCaul K.A. Reed W.D. Jeffrey G.P. Allan J.E. Intrahepatic MxA and PKR protein expression in chronic hepatitis C virus infection.J Med Virol. 2002; 68: 197-205Crossref PubMed Scopus (38) Google Scholar While these results are intriguing, the heterogeneity of viral and host phenotypes makes it unlikely that any single factor will accurately predict the cellular response to treatment. The ultimate response to treatment can only be gauged after treatment with pegylated IFN-α plus ribavirin has been initiated. Patients undergo at least a 12-week course of combination therapy and then are assessed for an antiviral response. An early viral response (2-log decrease in baseline HCV RNA titers) suggests the eventual outcome, although only with 60%–90% accuracy.1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar However, this 3-month regimen is associated with maximum morbid side effects and is expensive.1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar, 12Fried M.W. Side effects of therapy of hepatitis C and their management.Hepatology. 2002; 36: S237-S244Crossref PubMed Google Scholar We hypothesized that pretreatment nonresponder (NR) and responder (R) liver tissue would show consistent differences in gene expression levels and that these differences could be used to predict treatment outcomes. Thirty-one patients with chronic HCV (23 genotype 1, 4 genotype 2, 3 genotype 3, and 1 genotype 6) were treated at University Health Network from October 2001 to May 2004. All treatment-naive patients considering treatment with IFN/ribavirin underwent percutaneous liver biopsy (via a 15-gauge needle) and had baseline viral loads determined. Treatment consisted of pegylated IFN-α2a/2b 180 μg weekly by subcutaneous injection and oral ribavirin 800–1200 mg daily (depending on genotype and weight) for 24 (genotype 2/3) or 48 (genotype 1/6) weeks. Quantitative HCV RNA was determined at completion of therapy and 6 months after. Patients were designated as NRs if HCV RNA was detectable at the end of therapy, as relapsers if HCV RNA was undetectable at the end of treatment but was detectable at the 6-month follow-up, and as having a sustained viral response if HCV RNA was undetectable at both the end of therapy and the 6-month follow-up. Compliance was excellent (30 of 31 patients completed therapy). For the purposes of this study, patients were designated as Rs if the initial post-treatment HCV RNA titer was negative. Biopsies were performed on normal (HCV-negative) liver tissue as the first step of 20 right hepatectomy operations performed on living transplant donors. All patients gave informed consent for the research protocol, which was approved by the hospital research ethics board. A portion of each liver biopsy specimen (0.5–1.0 cm if percutaneous 15-g core) was immersed in RNAlater (Qiagen, Mississauga, Ontario, Canada). Total RNA was extracted,13Chen L. Goryachev A. Sun J. Kim P. Zhang H. Phillips M.J. Macgregor P. Lebel S. Edwards A.M. Cao Q. Furuya K.N. Altered expression of genes involved in hepatic morphogenesis and fibrogenesis are identified by cDNA microarray analysis in biliary atresia.Hepatology. 2003; 38: 567-576Crossref PubMed Scopus (52) Google Scholar and 2 μg of total RNA from each biopsy specimen or from Universal Human Reference RNA (Stratagene, La Jolla, CA) was amplified using the MessageAmp aRNA kit (Ambion, Austin, TX). Gene expression profiles from amplified RNA were highly correlated to those developed from nonamplified RNA (correlation coefficient ≥0.85, data not shown). Human single spot microarrays comprising 19,000 human clones were used (UHN Microarray Center; http://www.microarrays.ca/support/glists.html). For each array, 5 μg of liver amplified antisense RNA was compared with 5 μg of reference amplified antisense RNA. After reverse transcription, liver complementary DNA was labeled with Cy5 and reference RNA with Cy3.13Chen L. Goryachev A. Sun J. Kim P. Zhang H. Phillips M.J. Macgregor P. Lebel S. Edwards A.M. Cao Q. Furuya K.N. Altered expression of genes involved in hepatic morphogenesis and fibrogenesis are identified by cDNA microarray analysis in biliary atresia.Hepatology. 2003; 38: 567-576Crossref PubMed Scopus (52) Google Scholar Hybridization was performed overnight at 37°C (DIGEasy; Roche Diagnostics, GmbH, Mannheim, Germany). Arrays were read with a GenePix 4000A laser scanner and quantified with GenePix Pro software (Axon Instruments, Union City, CA). Microarray data were normalized using an R-based, intensity-dependent LOWESS scatter plot smoother (http://142.150.56.35/∼LiverArrayProject/home.html).14Becker R.A. Chambers J.M. Wilks A.R. The new S language. Wadsworth and Brooks/Cole, 1988Google Scholar, 15Cleveland W.S. Robust locally weighted regression and smoothing scatterplots.J Am Stat Assoc. 1979; 74: 829-836Crossref Scopus (7386) Google Scholar, 16Cleveland W.S. LOWESS a program for smoothing scatterplots by robust locally weighted regression.Am Stat. 1981; 35: 54Crossref Google Scholar Two-step real-time polymerase chain reaction (PCR) was performed after reverse transcription of 5 μg of amplified antisense RNA with 5 μg pd(N)6-random hexamer primer (Amersham, Oakville, Ontario, Canada). The resulting complementary DNA was used as a template for real-time PCR quantification with the QuantiTect SYBR PCR Kit (Qiagen), and real-time PCR (normalized to β-actin) was performed using the DNA Engine Opticon 2 cycler (MJ Research, Reno, NV). For primers, see Table 1.Table 1Real-Time PCR PrimersClone IDForward primerReverse primerPCR product length (base pairs)Gene name229295CAGACCCTGACAATCCACCTAGCTCATACTGCCCTCCAGA164Ubiquitin-specific protease 18Anderberg M.R. Cluster analysis for applications. Academic Press, New York, NY1973Google Scholar37942GATTGCTGGAGGGAATCAAATTGGATTTCCCTTTTTGTGC160Cyclin E binding protein 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar149319CGCAGATCACCCAGAAGATTGCCCTTGTTATTCCTCACCA185IFN-α–inducible protein 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar136508TCAGCGAGGCCAGTAATCTTGCAGGACATTCCAAGATGGT1542′,5′-oligo adenylate synthetase 2Poynard T. Yuen M.F. Ratziu V. Lai C.L. Viral hepatitis C.Lancet. 2003; 362: 2095-2100Abstract Full Text Full Text PDF PubMed Scopus (746) Google Scholar324912CTCGCTGATGAGCTGGTCTATACTTGTGGGTGGCGTAGC148IFN-α–inducible protein (clone IFI-6-16)324284GTCAAACCCAAGCCACAAGTGGGCGAATGTTCACAAAGTT1102′,5′-oligoadenylate synthetase 3, 100 kilodaltons5474956GCTGTAGCCGTCTCTGCTGAAAAAGGCCAAATCCCATGT135Ribosomal protein, large P2325364GCAGCCAAGTTTTACCGAAGGCCCTATCTGGTGATGCAGT109IFN-induced protein with tetratricopeptide repeats 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar120600CTTTTGCTGGGAAGCTCTTGCAGCTGCTGCTTTCTCCTCT131Viperin176650CCGTGTGCAGCCTATCAAGTTTACATTGCGGATGATGGA129RPS285745506CTGCAGAGAGCTTTCCATCCGTCTCTGGCTCATCGTCACA134Phosphoinositide-3-kinase adaptor protein 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar325130GTGCATTGCAGAAGGTCAGACTGGTGATAGGCCATCAGGT140Myxovirus (influenza virus) resistance 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar52905CCAACCATTTTGAGGGTCACACCCTTCCTCCAGCATTCTT130Dual specificity phosphatase 1National Institutes of HealthNational Institutes of Health Consensus Development Conference Statement management of hepatitis.Hepatology. 2002; 36: S3-S20PubMed Google Scholar127270AGCCCCCTGTCTTGGATACTCGAGAAGGTTGAGGTGGAGA133Activating transcription factor 5Naganuma A. Nozaki A. Tanaka T. Sugiyama K. Takagi H. Mori M. Shimotohno K. Kato N. Activation of the interferon-inducible 2′-5′-oligoadenylate synthetase gene by hepatitis C virus core protein.J Virol. 2000; 74: 8744-8750Crossref PubMed Scopus (72) Google Scholar487534GGTGCCATGGATGTAGCTTTAGAGAGGCATCCTCCAGACA124Leucine aminopeptidase 3Girard S. Shalhoub P. Lescure P. Sabile A. Misek D.E. Hanash S. Brechot C. Beretta L. An altered cellular response to interferon and up-regulation of interleukin-8 induced by the hepatitis C viral protein NS5A uncovered by microarray analysis.Virology. 2002; 295: 272-283Crossref PubMed Scopus (68) Google Scholar207669GCAGGAAGACAGTGGAGAGCGAGCCAGCACTTCTGGGTAG125D11lgp1e-like3930678AGCGGAAGGAGGAGAAAAAGGTACTCTTGGGCAGGTGAGC121Eukaryotic translation elongation factor 1 γ231624GTTCATCTGATGGGCTTCGTTTTGTTGTGGTGGTTCTCCA132Syntaxin binding protein 5 (tomosyn) Open table in a new tab Comparisons between continuous variables were performed using the 2-sample Welch t statistic with the multtest package, which includes an estimation of adjusted P values by permutation.17Dudoit S. Shaffer J.P. Boldrick J.C. Multiple hypothesis testing in microarray experiments.Stat Sci. 2003; 18: 71-103Crossref Scopus (721) Google Scholar Unsupervised hierarchical clustering and unsupervised principal components analyses (PCA) were performed using the R mva package.18Anderberg M.R. Cluster analysis for applications. Academic Press, New York, NY1973Google Scholar, 19Gordon A.D. Classification. 2nd ed. Chapman and Hall CRC, London, England1999Google Scholar Nearest neighbor classifier analyses (KNN) were performed using the R class package, and linear discriminant analyses (LDA) were performed with the R MASS package.20Ripley B.D. Pattern recognition and neural networks. Cambridge University Press, Cambridge, UK1996Google Scholar, 21Venables W.N. Ripley B.D. Modern applied statistics with S. 4th ed. Springer, 2002Crossref Google Scholar Details can be found at http://142.150.56.35/∼LiverArrayProject/home.html. The patients in this study were well matched for most clinical variables with the exception of viral genotype and sex (Table 2). There were no significant differences between R and NR patients when compared for age, baseline viral load, disease activity, hepatic fibrosis, compliance to therapy, or dose reduction. Patients with genotype 1 infection had the highest failure rate with therapy and accounted for all NR patients in our cohort.Table 2Patient Characteristics: All PatientsVariableNRRPNo.1516Age (y)46.4 ± 1448.3 ± 10.6896Sex (no. male)7/1513/16.0443aP < .05Genotype 1 infection15/158/16.0015aP < .05Viral load (IU/mL)2.4 × 106 ± 3.7 × 1063.8 × 106 ± 4.3 × 106.3529Activity1.63 ± 0.441.81 ± 0.51.3049Fibrosis2.50 ± 0.842.65 ± 0.94.6305Completed course of treatment14/1516/16.72Pegylated IFN-α plus ribavirin dose >80%14/1512/16.69Alcohol (10 drinks/week)2/122/13.66Smoking (1 pack/day)5/94/8.74Race (no. black)3/150/16.083NOTE. All patient characteristics were recorded in a prospectively maintained database. In general, data are presented as mean ± SD. Where data are presented in fractions, the denominator represents the number of patients for whom full data were available. Statistics are either Welch t test or χ2 analysis. The number of patients who received at least 80% of the dose of pegylated IFN-α plus ribavirin for at least 80% of the time was recorded over the entire course of therapy.a P < .05 Open table in a new tab NOTE. All patient characteristics were recorded in a prospectively maintained database. In general, data are presented as mean ± SD. Where data are presented in fractions, the denominator represents the number of patients for whom full data were available. Statistics are either Welch t test or χ2 analysis. The number of patients who received at least 80% of the dose of pegylated IFN-α plus ribavirin for at least 80% of the time was recorded over the entire course of therapy. To define which genes discriminate between HCV infection of Rs and NRs, we compared gene expression levels from 15 NR, 16 R, and 20 normal liver biopsy specimens. We determined that the levels of 18 genes differed between R and NR groups with P < .005 (Table 3) and verified these differences using real-time PCR (Figure 1). Within these 18 genes, most of the difference between NR and sustained virologic response samples was a relative up-regulation in NR tissue; R gene expression profiles actually cocluster with normal liver (Figure 2). Hierarchical cluster analysis clearly segregated all NR samples in one family, with all but 2 R samples and all normal liver samples segregated in another cluster (Figure 2). Thus, there is a consistent difference in the NR response to HCV reflected in the expression of 18 genes.Table 3Eighteen Genes That Differ Between NR and R Hepatic Gene Expression ProfilesClone IDNameSymbolNR/RP (NR vs R)NR/normalP (NR vs normal)R/normalP (R vs normal)149319aUp-regulated in NR.IFN-α–inducible protein (clone IFI-15K)cIFN-sensitive gene.G1P2/ISG15/IFI154.37.00019.69.00012.22.0001136508aUp-regulated in NR.2′, 5′-oligoadenylate synthetase 2cIFN-sensitive gene.OAS23.80.00016.58.00011.73.0009324912aUp-regulated in NR.IFN-α–inducible protein (clone IFI-6-16)cIFN-sensitive gene.G1P3/IFI6162.83.00014.72.00011.67.0002324284aUp-regulated in NR.2′, 5′-oligoadenylate synthetase 3cIFN-sensitive gene.OAS32.54.00013.42.00011.35.0055474956aUp-regulated in NR.Ribosomal protein, large P2RPLP22.53.00013.70.00011.46.000237942aUp-regulated in NR.Cyclin E binding protein 1cIFN-sensitive gene.CEB12.15.00012.55.00011.19.0777325364aUp-regulated in NR.IFN-induced protein with tetratricopeptide repeatscIFN-sensitive gene.IFIT12.14.00012.83.00011.32.0127120600aUp-regulated in NR.ViperincIFN-sensitive gene.VIPERIN/cig51.82.00021.78.00010.98.8031176650aUp-regulated in NR.40S ribosomal protein S28RPS281.75.00042.38.00011.35.00025745506aUp-regulated in NR.Phosphoinositide-3-kinase adaptor protein 1PI3KAP11.60.0051.66.00221.04.8283325130aUp-regulated in NR.Myxovirus (influenza virus) resistance 1, IFN-inducible protein p78cIFN-sensitive gene.MX11.58.00131.98.00011.25.039452905aUp-regulated in NR.Dual specificity phosphatase 1DUSP11.56.00030.59.0020.38.0001127270aUp-regulated in NR.Activating transcription factor 5ATF51.56.00460.96.69840.62.0024487534aUp-regulated in NR.Leucine aminopeptidase 3LAP31.56.00032.10.00011.35.0067229295aUp-regulated in NR.Ubiquitin-specific protease 18USP18/UBP431.52.00011.72.00011.13.0791207669aUp-regulated in NR.D11lgp1e-likeLGP11.51.00141.38.00940.92.13513930678bDown-regulated in NR.Eukaryotic translation elongation factor 1 γETEF10.65.00320.75.00091.15.7341231624bDown-regulated in NR.Syntaxin binding protein 5 (tomosyn)STXBP50.65.00340.96.71561.47.0126NOTE. Gene expression ratios were compared among NR, R, and normal liver gene expression values. Statistics are calculated using the Welch t test.a Up-regulated in NR.b Down-regulated in NR.c IFN-sensitive gene. Open table in a new tab Figure 2Hierarchical cluster analysis using the 20 genes present in all 31 samples. Unsupervised hierarchical cluster analysis was performed as described in Materials and Methods, restricting the analysis to the 18 genes in Table 2. Red denotes an increase and green a decrease when compared with the reference RNA pool. An asterisk denotes the patients who experienced a relapse following treatment with pegylated IFN-α plus ribavirin. Note that normal liver tissue coclusters with patients who respond to treatment, while all NR samples form part of a discrete cluster.View Large Image Figure ViewerDownload (PPT) NOTE. Gene expression ratios were compared among NR, R, and normal liver gene expression values. Statistics are calculated using the Welch t test. Unsupervised cluster analyses do not assign predictive end points (in this case, response to treatment). To determine whether the genes that differed between R and NR tissue could be used to predict treatment response, we used 2 supervised classifier analyses (KNN and LDA) and collaborated these results with a further unsupervised cluster analysis (PCA). Because different gene combinations will have different predictive abilities, we randomly drew 50,000 combinations of 6, 8, 10, 12, and 14 genes from Table 2 and assessed their individual ability to correctly classify the 31 NR and R samples. We determined a subset of 8 genes with the most consistent ability to correctly classify NR and R samples, comprising GIP2/ IFI15/ISG15, ATF5, IFIT1, MX1, USP18/UBP43, DUSP1, CEB1, and RPS28. Using this predictive gene subset, unsupervised hierarchical cluster analysis identified 2 clusters: one comprised of all NR samples but one, and the other of all R samples but one (Figure 3A). Both KNN and LDA accurately identified 30 of 31 samples, while PCA clearly separated R and NR samples into 2 distinct groups (Figure 3B). For comparison, if all 18 genes are used, the KNN prediction rate decreases to 28 of 31. Because patients with genotype 1 infection are the least likely to respond to treatment, and because classifier analyses are influenced by the numbers and characteristics of the samples in a teaching set, we examined whether the predictive gene subset was valid within the 23 patients with genotype 1 infection in our cohort. As shown in Table 4, there were no significant clinical differences in these patients. The predictive gene subset correctly classified 21 of 23 samples using KNN and LDA, while PCA and unsupervised hierarchical clustering again clearly created 2 distinct clusters (Figure 4). Together, our results argue that a gene subset can predict NR and R status independent of genotype.Table 4Patient Characteristics: Patients With Genotype 1 Infection OnlyVariableNRRPNo.158Age (y)50.2 ± 5.143.9 ± 9.0.1032Sex (no. male)7/156/8.1917Viral load (IU/mL)2.40 × 106 ± 3.7 × 1064.87 × 106 ± 5.1 × 106.2597Activity1.63 ± 0.441.75 ± 0.46.5681Fibrosis2.50 ± 0.842.56 ± 0.98.881Completed course of treatment13/147/7.85Pegylated IFN-α plus ribavirin dose >80%14/157/8.72Alcohol (10 drinks/wk)2/122/5.41Smoking (1 pack/day)5/93/4.76NOTE. Patient characteristics restricted to those patients infected with genotype 1 HCV. Again, data are presented as mean ± SD. Where data are presented in fractions, the denominator represents the number of patients for whom full data were available. Statistics are either Welch t test or χ2 analysis. Open table in a new tab NOTE. Patient characteristics restricted to those patients infected with genotype 1 HCV. Again, data are presented as mean ± SD. Where data are presented in fractions, the denominator represents the number of patients for whom full data were available. Statistics are either Welch t test or χ2 analysis. Our study compared hepatic gene expression profiles from liver biopsy specimens taken from 31 patients before treatment with pegylated IFN-α plus ribavirin. We identified 18 genes, confirmed by real-time PCR, with expression levels that differed consistently between NR and R liver tissue and were not correlated to any obvious clinical parameter. The raw data set can be accessed at http://142.150.56.35/∼LiverArrayProject/home.html. Levels for these 18 genes in R liver were closer to uninfected tissue than to NR liver, with a general up-regulation of gene expression in NR liver. Interestingly, many of these genes are IFN responsive, suggesting that the NR patients have adopted a different, yet characteristic, equilibrium in their host-virus immune response. Although this study examined a relatively small set of patients (31), 3 arguments suggest that the results are broadly applicable. First, although the discriminatory genes were identified based solely on mathematical grounds, several have been previously linked either to HCV infection or to the response to viral infection. For example, polymorphisms of OAS have been weakly linked to self-limited HCV infection and polymorphisms of Mx1 have been weakly linked to response status.9Knapp S. Yee L.J. Frodsham A.J. Hennig B.J. Hellier S. Zhang L. Wright M. Chiaramonte M. Graves M. Thomas H.C. Hill A.V. Thursz M.R. Polymorphisms in interferon-induced genes and the outcome of hepatitis C virus infection roles of MxA, OAS-1 and PKR.Genes Immun. 2003; 4: 411-419Crossref PubMed Scopus (192) Google Scholar Hepatic messenger RNA levels for OAS, Mx1, and GIP2 are increased in chronic HCV, but none alone have been linked to treatment outcome.11MacQuillan G.C. de Boer W.B. Platten M.A. McCaul K.A. Reed W.D. Jeffrey G.P. Allan J.E. Intrahepatic MxA and PKR protein expression in chronic hepatitis C virus infection.J Med Virol. 2002; 68: 197-205Crossref PubMed Scopus (38) Google Scholar, 22MacQuillan G.C. Mamotte C. Reed W.D. Jeffrey G.P. Allan J.E. Upregulation of endogenous intrahepatic interferon stimulated genes during chronic hepatitis C virus infection.J Med Virol. 2003; 70: 219-227Crossref PubMed Scopus (58) Google Scholar Many of the others are IFN-sensitive genes with antiviral activity and are consistent with an alteration in IFN responsiveness being linked to treatment nonresponse. The genes that are not directly IFN responsive may play roles in cellular pathways important for IFN responses (PI3AP1, DUSP1)23Rani M.R. Hibbert L. Sizemore N. Stark G.R. Ransohoff R.M. Requirement of phosphoinositide 3-kinase and Akt for interferon-beta-mediated induction of the beta-R1 (SCYB11) gene.J Biol Chem. 2002; 277: 38456-38461Crossref PubMed Scopus (39) Google Scholar, 24Duong F.H. Filipowicz M. Tripodi M. La Monica N. Heim M.H. Hepatitis C virus inhibits interferon signaling through up-regulation of protein phosphatase 2A.Gastroenterology. 2004; 126: 263-277Abstract Full Text Full Text PDF PubMed Scopus (195) Google Scholar and are involved in inflammatory cell activation and maturation.25Beninga J. Rock K.L. Goldberg A.L. Interferon-gamma can stimulate post-proteasomal trimming of the N terminus of an antigenic peptide by inducing leucine aminopeptidase.J Biol Chem. 1998; 273: 18734-18742Crossref PubMed Scopus (251) Google Scholar, 26Verhoeckx K.C. Bijlsma S. de Groene E.M. Witkamp R.F. van der Greef J. Rodenburg R.J. A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.Proteomics. 2004; 4: 1014-1028Crossref PubMed Scopus (116) Google Scholar Second, the predictive subset of 8 genes performed well across all 4 statistical analyses (hierarchical clustering, KNN, LDA, and PCA). Third, the composition of the classifier set was unrelated to confounding clinical factors, such as viral load, degree of fibrosis, and age. In multivariate analyses, USP18 expression was significantly affected by degree of fibrosis (data not shown), but none of the other 17 genes were linked to any of the clinical factors. Two genes in the classifier gene set, ISG15/IFI15 and USP18/UBP43, are noteworthy for belonging to a novel IFN-regulatory pathway. In our study, NR and R patients were distinguished by up-regulated expression of ISG15 and USP18 in their pretreatment liver biopsy tissue. ISG15 is a ubiquitin-like protein that is believed to be important to innate immune functions.27Kim K.I. Zhang D.E. ISG15, not just another ubiquitin-like protein.Biochem Biophys Res Commun. 2003; 307: 431-434Crossref PubMed Scopus (63) Google Scholar The USP18/UBP43 protease specifically removes ISG15 from ISG15-modified proteins28Malakhov M.P. Malakhova O.A. Kim K.I. Ritchie K.J. Zhang D.E. UBP43 (USP18) specifically removes ISG15 from conjugated proteins.J Biol Chem. 2002; 277: 9976-9981Crossref PubMed Scopus (382) Google Scholar; loss of USP18 in mice leads to IFN hypersensitivity.29Malakhova O.A. Yan M. Malakhov M.P. Yuan Y. Ritchie K.J. Kim K.I. Peterson L.F. Shuai K. Zhang D.E. Protein ISGylation modulates the JAK-STAT signaling pathway.Genes Dev. 2003; 17: 455-460Crossref PubMed Scopus (265) Google Scholar The USP18-ISG15 pathway is important in innate immunity against viral infection; a recent, elegant study showed that USP18 knockout mice are resistant and wild-type mice are susceptible to fatal intracerebral infection by lymphocytic choriomeningitis virus or vesicular stomatitis virus, concurrent with decreased viral replication and increased protein ISGylation in the knockout mice.30Ritchie K.J. Hahn C.S. Kim K.I. Yan M. Rosario D. Li L. de la Torre J.C. Zhang D.E. Role of ISG15 protease UBP43 (USP18) in innate immunity to viral infection.Nat Med. 2004; 10: 1374-1378Crossref PubMed Scopus (222) Google Scholar Although the investigators suggest that the pathway may be relevant to human disease, ours is the first demonstration of the potential relevance of the USP18-ISG15 pathway in human viral infection. In our study, USP18 up-regulation was one of the factors predicting a lack of response to treatment with IFN, consistent with a role for USP18 in modifying the antiviral IFN response. In conclusion, our study shows that NR and R patients differ fundamentally in their innate IFN response to HCV infection. These differences suggest novel aspects of HCV pathogenesis and form the basis for a predictive subset of genes that can predict treatment responses before initiation of pegylated IFN-α plus ribavirin therapy.22MacQuillan G.C. Mamotte C. Reed W.D. Jeffrey G.P. Allan J.E. Upregulation of endogenous intrahepatic interferon stimulated genes during chronic hepatitis C virus infection.J Med Virol. 2003; 70: 219-227Crossref PubMed Scopus (58) Google Scholar The authors thank the physicians and surgeons of the Toronto Multi-Organ Toronto Transplant Program for their support and interest, particularly Drs David Grant, Mark Cattral, Paul Greig, and Gary Levy, and thank Drs Elizabeth Edwards and Kaiguo Mo for assistance with the real-time polymerase chain reaction studies. A.M.E. is the Banbury Chair of Medical Research. The authors also thank the CIHR National Research Training Program–HCV for its interest in this study. This Month in GastroenterologyGastroenterologyVol. 128Issue 5PreviewThe risks associated with nonsteroidal anti-inflammatory drugs (NSAIDs) have received a great amount of press in recent months. Gastric complications associated with NSAID use have been well described in previous studies. In other studies, NSAIDs have been shown to increase intestinal permeability within 12 hours of administration. Until recently our ability to assess mucosal small bowel injury resulting from NSAID use has been limited. The development of wireless capsule enteroscopy now allows investigators to examine the effects of NSAIDs on the small bowel. Full-Text PDF Evaluation of Microarray Analysis for Predicting Treatment Responsiveness in Patients With Chronic Hepatitis C Viral InfectionGastroenterologyVol. 129Issue 5PreviewWe recently read a paper entitled “Hepatic gene expression discriminates responders and nonresponders in treatment of chronic hepatitis C viral infection” published in Gastroenterology with interest.1 It reported that the response of patients with chronic hepatitis C viral (HCV) infection to treatment with pegylated interferon-α plus ribavirin could be predicted by gene expression profiling. They collected pretreatment liver biopsy specimens from 15 NR and 16 R patients and used microarray analysis to compare them with 20 normal liver specimens. Full-Text PDF" @default.
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