Genetic polymorphisms in interferon pathway and response to interferon treatment in hepatitis B patients: A pilot study

Genetic Polymorphisms in Interferon Pathway and Response to Interferon Treatment in Hepatitis B Patients: A Pilot Study Jennifer K. King,1 Shiou-Hwei Yeh,2,3 Ming-Wei Lin,4,5 Chun-Jen Liu,6,7 Ming-Yang Lai,6,7 Jia-Horng Kao,6,7 Ding-Shinn Chen,2,6 and Pei-Jer Chen2,7 Interferon alfa (IFN-␣) therapy remains a mainstay of treatment in active hepatitis B. However, sustained remission rates remain relatively low, and the search for factors important for response to therapy continues. Our study aimed to identify the host single nucleotide polymorphisms (SNPs) that predict IFN response in hepatitis B patients. We selected genes in the IFN pathway involved in antiviral and signaling activities and sequenced 22 SNPs for each of our 82 patients. Our results identified 2 SNPs in the antiviral pathway that may influence IFN response. One SNP in the regulatory region of the eIF-2␣ gene revealed A/G alleles. The rate of A/G heterozygotes is 22% in nonresponders (NR) and 2% in sustained responders (R), with an odds ratio (OR) of 12.82 (95% CI: 1.52-107.85, P ⴝ .009). After adjustment for age, sex, and HBV DNA level, the OR reaches 14.94 (95% CI: 1.45-153.71, P ⴝ .023). This marker revealed greater significance than HBV DNA levels (OR: 5, 95% CI: 1.01-2.43, P ⴝ .033) as a marker for IFN response, suggesting its potential advantage over conventional predictors. In addition, borderline significance for the SNP in MxA gene promoter at nt ⴚ88 revealed G/T alleles, with the G/T heterozygote rate being 19% in nonresponders and 43% in sustained R (P ⴝ .061), concurring with a previous study involving hepatitis C patients. In conclusion, this pilot identified SNPs as potential markers that could predict hepatitis B patient response. These observations may help guide future large-scale studies in examining host SNPs for their clinical utility in predicting IFN response. (HEPATOLOGY 2002;36:1416-1424.)

Abbreviations: HBeAg, hepatitis B virus e antigen; HBV, hepatitis B virus; ALT, alanine aminotransferase; IFN, interferon; SNPs, single nucleotide polymorphisms; OAS, oligoadenylate synthase; ADAR, adenosine deaminase; HBsAg, hepatitis B virus surface antigen; R, response/responders; NR, nonresponse/nonresponders; eIF2␣, eukaryotic translation initiation factor 2, subunit 1; GBP, guanylate-binding protein; JAK, Janus kinase; PCR, polymerase chain reaction. From the 1Harvard Medical School, Boston, MA; 2Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan; 3Division of Molecular and Genomic Medicine, National Health Research Institutes, Taipei, Taiwan; 4Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan; 5Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan; 6Department of Internal Medicine College of Medicine, National Taiwan University, Taipei, Taiwan; and 7Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan. Received May 31, 2002; accepted September 26, 2002. Supported by grants from the Center of Disease Control and National Health Research Institute, Department of Health and from National Science Council, Executive Yuan, Taiwan. Address requests reprints to: Pei-Jer Chen, M.D., Ph.D., Professor and Director, Hepatitis Research Center, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei 100, Taiwan. E-mail: [email protected]; fax: (886) 2-2331-7624. Copyright © 2002 by the American Association for the Study of Liver Diseases. 0270-9139/02/3606-0017$35.00/0 doi:10.1053/jhep.2002.37198 1416

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epatitis B is a worldwide disease and remains a significant etiology of chronic hepatitis, cirrhosis, and hepatocellular carcinoma. It is estimated to affect over 350 million people worldwide, with a mortality rate of over 1.2 million deaths per year because of acute or chronic hepatitis B infection.1 For active hepatitis B patients with detectable hepatitis B virus e antigen (HBeAg) or hepatitis B virus (HBV) DNA and elevated alanine aminotransferase (ALT) serum levels, treatment is often recommended. A 4- to 6-month course of interferon alfa (IFN-␣) therapy has been shown to induce a long-term sustained remission in 25% to 40% of chronic hepatitis B patients.2-4 Furthermore, a 1-year course of lamivudine yields similar remission responses.3,4 However, the question remains unresolved as to why only a certain percentage of patients respond to therapy. Hence, the search for predictive factors determining therapeutic responses continues. In HBeAg positive chronic HBV patients, the most important predictors of good response to IFN-␣ treatment include high ALT levels (⬎200 U/L), low serum

HEPATOLOGY, Vol. 36, No. 6, 2002

HBV DNA (⬍100 pg/mL), female gender, and active fibrosis on liver biopsy specimens.5,6 In addition, extensive studies on viral genetics have shown correlations between viral factors and antiviral response. For example, certain HBV genomes with “YMDD” motif variants in the polymerase gene have been associated with a greater propensity toward lamivudine resistance.7-9 In addition, recently, viral genotype study suggested that HBV genotype C is associated with more severe liver disease and lower response rates to IFN treatment than genotype B.10-13 Also, higher rates of IFN-induced seroconversion have been demonstrated in patients with genoype A than genoype D.14,15 Yet, despite these studies of viral factors affecting treatment response, the role of the host genetic background was less well studied. In focusing on the host genetic background, the role of single nucleotide polymorphisms (SNPs) in relation to disease and treatment response has become increasingly supported in a variety of illnesses. In particular to hepatitis B disease, MHC I and MHC II class polymorphisms, as well as TNF-␣ and mannose-binding protein (an opsonizing protein), the SNPs have been suggested to affect host immune and antiviral responses and, thus, are associated with variable disease progression (i.e., resistance and chronic infection) and treatment response.16-19 Hence, looking into such a topic may lead to important predictions of treatment response for HBV patients, especially for IFN therapy, given the many displeasing side effects associated with this medical regimen. Therefore, our study focuses on the role of host, genetic SNPs in affecting response to IFN treatment. Fortunately, advances in elucidating the IFN pathway have provided significant clues to possible key genes for our SNP selection. Specifically, we examined 4 pathways leading to IFNinduced cellular proteins with antiviral activities. These include the (1) PKR kinase, which inhibits translational initiation through the phosphorylation of protein synthesis initiation factor eIF-2␣; (2) 2⬘-5⬘ oligoadenylate synthase (2⬘-5⬘OAS) family and Rnase L nuclease, which mediate the degradation of both viral and cellular RNAs; (3) RNA-specific adenosine deaminase (ADAR), which edits dsRNA by deamination of adenosine to yield inosine; and (4) the family of Mx protein GTPase, which appears to target viral nucleocapsids and inhibit RNA synthesis.20 In addition, we also selected SNP in several key genes of the IFN-mediated signaling and transcriptional activation pathways in the context of the Janus kinase (JAK)-Stat pathway.21 Our hypothesis suggests that variations of these genes may account, in part, for responders (R) and nonresponders (NR) to INF therapy. The aim of this pilot study was to identify polymorphisms of the IFN pathway genes that show an association with

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IFN responsiveness or nonresponsiveness in patients with HBV infection.

Patients and Methods Study Subjects. We retrospectively enrolled 82 Chinese Han patients with chronic hepatitis B from our outpatient clinics at National Taiwan University Hospital. There were 36 NR to IFN treatment with a mean age of 34 years and 46 R with a mean age of 31 years. Males outnumbered females (M:F/59:23). All patients’ blood samples were hepatitis B virus surface antigen (HBsAg) positive (⫹) and HBeAg(⫹) and with an elevated ALT of at least 2-fold higher than the upper limits of normal for 6 months. Patients were excluded from receiving IFN therapy if they had any of the following criteria: neutrophil count ⬍1,500 cells/mm3, Hgb ⬍12g/dL in women or 13 g/dL in men, or platelet count ⬍90,000 cells/mm3, history of poorly controlled thyroid disease, and serum creatinine level ⬎1.5 times the upper limit of normal at screening. Approximately 30 patients underwent liver biopsy before treatment to document active hepatitis or to exclude severe cirrhosis. Eligible patients received IFN-␣ (2a or 2b) at a dosage of 5 to 10 mouse units (MU) 3 times per week for 4 to 6 months and were subsequently followed for treatment response via clinical, biochemical, and serologic markers for more than 1 year. The definition of sustained R to IFN treatment for chronic hepatitis B disease included patients with HBeAg(⫹) to HBeAg(⫺) conversion after treatment for at least 1 year after follow-up. NR were those with persistent or relapsed HBeAg(⫹) during the follow-up period. Patients with concurrent hepatitis C or D infection were excluded from the study. Our study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki as reflected by approval from our institutional review committee. Serologic Testing. Ten milliliters of whole blood samples were collected from each patient, and the serum were stored at ⫺20°C. Serologies for HBsAg, anti-HBs, anti-HBc Igs, HBeAg, and anti-HBeAg were tested by commercially available kits (Ausab, Ausria II, Murex HBeAg/anti-HBe; Abbott Laboratories, North Chicago, IL). Serum HBV DNA was detected by branched chain DNA method (QUANTIPLEX HBV DNA Assay; Chiron Corporation, Emeryville, CA). Patients positive for HIV or HCV or HDV antibodies were excluded from the study. Gene Selection and Polymorphisms. The JSNP, a database of common gene variations in the Japanese population (http://snp.ims.u-tokyo.ac.jp/index.html) was used to identify potential SNPs in our population of Asian study subjects. We included all the available SNPs

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Table 1. IFN-Induced Signaling and Antiviral Gene Polymorphisms Studied Genes Selected for Analysis

Antiviral genes PKR exon 01 (IMS-JST038184) elF-2␣ reg 1 (IMS-IST113191) elF-2␣ reg 2 (IMS-JST113192) MxA promoter ⫺88 (IMS-JST006892) MxA promoter ⫺123 ADAR exon 15-1 (IMS-JST006568) ADAR exon 15-2 (IMS-IST006569) ADAR exon 15-3 (IMS-JST006570) OAS1 exon 03 (IMS-JST093062) OAS2 exon 02 (IMS-JST008020) OAS3 exon 06-1 (IMS-JST036304) OAS3 exon 06-2 (IMS-JST036305) OAS3 exon 08 (IMS-JST008018) OAS3 exon 12 (IMS-JST011660) OAS3 exon 16-1 (IMS-JST008013) OAS3 exon 16-2 (IMS-JST008014) OAS3 exon 16-3 (IMS-JST008015) OAS3 exon 16-4 (IMS-JST008016) Signaling genes Jak1 promoter (IMS-JST115978) Stat1 exon 02 (IMS-JST066727) GBP2 exon 05-1 (IMS-JST051403) GBP2 exon 05-2

PCR Primer Sequences (Direction 5ⴕ to 3ⴕ)

Annealing Temperature (°C)

PCR Size (bp)

Accession No.

Location of SNP

Allele

F tggtccaggcaggcttaatg R aggcggaggagagctcaag* F gtgacttgtacagaactttgc R gcaatgaacaggaaaggtgg* F tgcttgctagtttgtttcccac3⬘ R gccatgtacatcacaggtttactg3⬘* F acacacccgtttccaccctggagaggccag R tgcgcagtgctggagtgcggcctccgctct* Same as MxA promoter ⫺88 F gaatattgccaagcttccagc* R ggctcgtttccagcagatg Same as ADAR exon 15-1

66

1,077

AC007899.3

#42057

T/C

60

595

AL139785.5

#50385

A/C

64

563

AL139785.5

#51056

A/G

65

599

X55639.1

#528

G/T

65 66

599 1,471

X55639.1 U75503.1

#493 #1275

C/A T/C

66

1,471

U75503.1

#1501

A/C

Same as ADAR exon 15-1 for PCR

66

1,471

U75503.1

#2049

G/A

66

511

AC004551.1

#129167

T/C

64

1,214

AC004551.1

#53144

G/C

60

501

AC004551.1

#91087

G/A

60

501

AC004551.1

#91258

C/G

64

337

AC004551.1

#79118

T/C

60

720

AC004551.1

#74353

G/A

S1 gtgttggtcatgactccaagagc* F ccttctgttgcaggctcctc R ggatcaggaatggacctcaag* F attaaaccagaagcctggtaagc R tcaggccctgggacagtaag* F caacctcagctccattgctg* R agaaggcattgggttgatgc Same as OAS3 exon 06-1 F caaaggaggggagatgtggg* R atgggttggtaaatgctgctg F caaagctgggaatgtgcaag R ctaactcacagtccagaacc* F ttcctttctttgtttcctgttcc* R tattgggtccctgttttctctc Same as OAS3 exon 16-1

64

650

AC004551.1

#68777

G/A

64

650

AC004551.1

#68861

C/T

Same as OAS3 exon 16-1

64

650

AC004551.1

#69040

C/G

F ttcctttctttgtttcctgttcc R tattgggtccctgttttctctc*

64

650

AC004551.1

#69163

T/C

F ggagtcatggaacttctcttagcc* R tgaagccattacacaaatgctg F tcggtgtcagtattccgtaacttg R acaacctgatgctaggaaggc* F atttgataaagagggagctgagaag R agctgacagatcgaatcaaggc R2 tcgattggcccgctcctaag* Same as GBP2 exon 05-1

62

539

AC093427.2

#7098

G/A

64

500

AC067945.1

#86937

A/G

1,048

AL161639.6

#41556

A/G

1,048

AL161639.6

#41544

G/C

TD 64, 42

NOTE. The JSNP ID number for each SNP selected from the JSNP database is listed under each gene. Abbreviations: reg, regulatory region; TD, touchdown for first 10 cycles, then for 25 cycles. *Primers used for sequencing reaction.

located in the promoter region, regulatory region, and exons of selected genes, which line in the JAK-Stat signaling and antiviral IFN pathways for the following SNP typing and comparison. Furthermore, to cover each gene more thoroughly by SNP for this study, we also included some SNPs listed in the SNP index from the National Cancer Institute’s (NCI) Cancer Genome Anatomy Project (CGAP; http://cgap.nci.nih.gov/). The genes and the corresponding SNPs are listed in Table 1.

Determination of SNPs. The SNPs were determined by direct sequencing of polymerase chain reaction (PCR)amplified DNA fragments. PCR amplification was performed via Biometra T3 Thermocycler (Biometra, Whatman Corp., Gottingen, Germany) with 15 to 30 ng genomic DNA, 30 ng per primer, PCR buffer of 20 mmol/L Tris, 50 mmol/L KCl, 1.66 mmol/L MgCl2, 200 ␮mol/L dNTP, and 0.4 U Platinum Taq polymerase (Invitrogen, Carlsbad, CA) in a total of 15 ␮L mixture.

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The following PCR cycling conditions were used: 95°C 5 minutes; 35 cycles of denaturing temperature 95°C 40 seconds, annealing temperature (see Table 1) 40 seconds, and extension temperature 72°C 90 seconds, which is then followed by postextension 72°C 10 minutes. The PCR products were then checked by DNA agarose gel electrophoresis, processed for sequencing reaction by ABI PRISM Big-dye kits (Applied Biosystems, Foster City, CA), and analyzed via ABI 3100 Genetics Analyzer. The primers used for PCR amplification and sequencing reactions for each SNP containing amplicon are listed in Table 1. Statistical Analysis. Genotype frequencies of each SNP between R and NR were compared by using the ␹2 test or Fisher exact test. Multiple logistic regression was performed to evaluate whether there was a difference in response effect for each SNP after adjustment for age, sex, and HBV DNA level. To evaluate the combined genotypes of 2 SNPs in the same genes or pathway, the ␹2 test or Fisher exact test was also performed. The SNPHAP program (Cambridge Institute for Medical Research, CA) was used for estimating frequencies of haplotypes of SNPs.22 The CLUMP program (London Statistical Genetics Group, United Kingdom) was used to assess the difference of haplotype frequencies between the responder and nonresponder groups.23 All statistical tests were 2-tailed. P values less than .05 were considered statistically significant. The analyses were performed using the SPSS statistical package version 10.

Results PCR amplicons containing each selected SNP were successfully amplified and sequenced for each individual. The results of genotype frequencies for each SNP polymorphism are listed in Table 2. The genotypes for each SNP are classified as homozygous and heterozygous and were categorized according to the IFN response, R or NR. We first conducted statistical calculations for each single SNP locus by using ␹2 test or Fisher exact test (Table 2). Among these SNPs, the eIF-2␣ reg2 SNP located at the intron 1 regulatory region of eIF-2␣ gene was shown to have a significant difference in the genotypic frequency distribution between R and NR patients. In addition, the SNP MxA nt ⫺88, which is located at the promoter region of MxA gene, was found to be of borderline significance for its genotype distribution between R and NR patients. Both genes might play a role in the host antiviral activities, although found in parallel pathways. For the SNP eIF-2␣ reg2, our study yielded an allele frequency of 95% “A” and 5% “G” in the 82 patients, which was similar to the JSNP allele frequency in the sample size of 1,482 study subjects: A, 96.5% versus G,

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3.5%. Eighty-nine percent (73 of 82) of patients were of the A/A homozygous genotype, and 11% (9 of 82) were of the A/G heterozygous genotype. There were no individuals homozygous for the “G” SNP allele at all. Notably, 8 of the 9 patients (89%) who were heterozygotes with the A/G genotype were NR to IFN therapy, and only 1 patient with A/G belongs to the R group. The rate of the A/G genotype in NR was 22% (8 of 36) versus 2% (1 of 46) in sustained R, showing the significant difference of genotype distribution with P ⫽ .009 (OR: 12.82, 95% CI: 1.52-107.85). However, the significance would disappear after correction of multiple testing by the Bonferroni method if we consider 22 SNPs conducted in this study. Another interesting marker is the SNP located at MxA gene promoter ⫺88, which gave a borderline significance (see Table 2). The genotype frequencies for the total 82 patients are as follows: 61% G/G (50 of 82), 33% G/T (27 of 82), and 9% T/T (5 of 82). The difference of genotype distribution between R and NR patients showed borderline significance with P ⫽ .061, mainly attributed by the heterozygous genotype of G/T. The rate of G/T heterozygotes in the NR group is 19% (7 of 36), whereas the rate in the sustained response group is 43% (20 of 46). Our results suggest the possible association of the G/T genotype with increased responsiveness to IFN, although further study using a larger sample size would be required to confirm this significance. Furthermore, we performed the haplotype analysis for evaluating the haplotype frequencies of SNPs located nearby at the same chromosome regions of the same antiviral pathway, trying to derive haplotypes specifically correlated with IFN response. The results are summarized in Table 3. The haplotypes of the eIF-2␣ reg1 and reg2 show a significant different distribution between the R and NR group (P ⫽ .015). In addition, a borderline significance for the haplotype composed of OAS1 and OAS2 has been noted to be differently distributed between R and NR (P ⫽ .075), indicating that patients with CG haplotype respond less to IFN treatment. We also tried to evaluate the effect of the combined genotypes of 2 SNPs in the same gene or pathway by the ␹2 test or Fisher exact test, which may presumably reveal the possible interaction or synergistic effect between the SNPs or genes in the same antiviral pathways. The results of the combined genotype analysis for 2 SNPs of eIF-2␣ reg1 versus reg2 (P ⫽ .041) and PKR versus eIF-2␣ reg2 (P ⫽ .042) both produced significantly different distributions between the R and the NR groups, respectively. Such significance may be mainly contributed by the effect of SNP eIF-2␣ reg2 and further confirm the significant effect of the eIF-2␣ reg2 polymorphism. Besides, the

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Table 2. Genotype Frequencies of SNPs in Different Pathways Between Responder and Nonresponder Patients Pathway

Anti viral PKR

Gene

PKR exon 01 eIF-2␣ reg 1 eIF-2␣ reg 2

Anti viral MxA

MxA promoter ⫺88 MxA promoter ⫺123

Anti viral ADAR

ADAR exon 15-1

ADAR exon 15-2

ADAR exon 15-3

Anti viral OAS/Rnase L

OAS1 exon

OAS2 exon 02

OAS3 exon 06-1

OAS3 exon 06-2

OAS3 exon 8

OAS3 exon 12

OAS3 exon 16-1

OAS3 exon 16-2

OAS3 exon 16-3

OAS3 exon 16-4

Jak-Stat signaling

Jak promoter

Stat1 exon 02

GBP2 exon 05-1

GBP2 exon 05-2

*Statistically significant P value.

Genotypes

Nonresponder

Responder

C/C C/T T/T A/A A/C C/C A/A A/G G/G G/G G/T T/T C/C C/A A/A T/T T/C C/C A/A A/C C/C G/G G/A A/A T/T T/C C/C C/C C/G G/G G/G G/A A/A C/C C/G G/G C/C C/T T/T A/A A/G G/G A/A A/G G/G C/C C/T T/T C/C C/G G/G C/C C/T T/T G/G G/A A/A A/A A/G G/G A/A A/G G/G C/C C/G G/G

2 13 22 13 16 7 28 8 0 26 7 3 31 5 0 22 12 2 23 12 1 20 15 1 19 10 7 0 5 31 30 0 6 7 1 28 4 13 19 0 8 28 17 16 3 22 12 2 14 16 6 0 8 28 21 13 2 31 4 1 32 4 0 22 13 1

1 22 23 12 22 12 45 1 0 24 20 2 35 11 0 20 23 3 20 23 3 20 22 4 19 12 15 0 2 44 41 0 5 8 0 38 3 15 28 1 7 38 20 20 6 33 13 0 15 23 8 1 7 38 23 20 3 42 3 1 40 6 0 29 15 2

P Value

.445

.577

.009*

.061

.256 .304

.172

.397

.387

.231

.523

.661

.629

.661

.857

.234

.834

.661

.823

.847

1.00

.923

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Table 3. Haplotype Analysis of SNPs of Genes on Adjacent Chromosome Regions Genes

Haplotypes

eIF-2␣ reg 1, reg 2

MxA promoter ⫺88, ⫺123

AA CA AG CG GC

TC TA ADAR exon 15-1, -2, -3 CCA TAG CCG TCA TCG OAS1 exon 03, OAS2 TG exon 02 CG TC CC OAS3 exon 06-1, -2, 8, GGTGACGT 12, 16-1, -2, -3, -4 GGTGGTCT GCCAACCC GGTGGCCT GGCGACCT AGCGACCT AGTGACCT GCTGACGT Others (including 9 haplotypes) GBP2 exon 05-1, 05-2 AC AG GG

Nonresponders Responders (n ⴝ 92) (n ⴝ 72)

45 45 1 1 68

34 30 8 0 59

13 11 62 29 1 0 0 50 40 0 2 34 10 9 16 6 6 0 4 7

8 5 54 14 2 1 1 44 23 4 1 21 12 8 6 5 3 3 3 11

73 13 6

57 11 4

P Value

.015*

.435

.441

.075

.219

1421

our sample collection. HBV DNA level ⬍100 pg/mL showed significantly good response to IFN treatment (OR: 5, 95% CI: 1.01-2.43, P ⫽ .033); however, elevated ALT levels did not show significant effects on IFN treatment (OR: 1.29, 95% CI: 0.508-3.32, P ⫽ .59). We then performed multiple logistic regression analysis. After adjustment for age, sex, and HBV DNA titers, the results revealed that the most predictive risk factor for IFN treatment was the genotypes of the SNP eIF-2␣ reg2 (OR: 14.93, 95% CI: 1.45-153.71, P ⫽ .023). If we include MxA promoter ⫺88 genotype into the above multiple logistic regression to evaluate the relative risks attributed by the various variables, the result suggested that patients of male gender, over 40 years of age, with HBV DNA level ⱖ100 pg/mL, and with eIF-2␣ reg2 A/G heterozygous genotype will respond poorly to IFN treatment. However, patients with the MxA promoter ⫺88 G/T genotype will respond well to the IFN treatment (Table 4).

Discussion

.952

combined genotype of 2 SNPs located in the JAK1 promoter and GBP2 exon 05-1 (P ⫽ .052) yielded a borderline significant result, mainly attributed by genotype combination of G/G A/G or G/A A/A for JAK1 and GBP2. Although individually, these respective SNPs did not show any significance, P ⫽ 1.0 for GBP2 exon03 and P ⫽ .823 for JAK1 promoter. It is possible that the combination of the specific genotype confers a greater propensity towards sustained response. To compare with other clinical factors, the logistic regression was used to evaluate the factors of HBV DNA level ⬍100 pg/mL and ALT ⬎200 pg/mL for their role as known predictors of good response to IFN treatment in

Our pilot study in limited cases suggested the SNP of 2 individual candidate genes in the IFN antiviral pathway, as well as suggesting the role of a haplotype or combined genotype of genes in the IFN signaling, that might influence host response to IFN treatment. For the SNP of eIF-2␣ reg2 located in the first intron regulatory region, both the single locus analysis and the logistical regression analysis adjusted for age, sex, and HBV DNA level yielded significant P values of .009 and .023, respectively. The analysis suggests that patients with the rare “G” SNP allele (thereby yielding the A/G heterozygote genotype) are more likely to be nonresponsive to IFN treatment. Moreover, with an odds ratio of 14.94 (95% CI: 1.45-153.71), the logistical regression analysis yielded even greater significance for the eIF-2␣ reg2 “G” allele in predicting nonresponse to medical treatment over HBV DNA levels alone (OR: 5, 95% CI: 1.01-2.43, P ⫽ .033) as a potentially predicting marker for patient response to IFN. Such results suggest the potential clinical application of host SNPs over conventional predictors of

Table 4. Results of Logistic Regression Analysis for Factors Predicting the Relative Treatment Success 95% CI for OR Variable

Reference

Odds Ratio

Lower

Upper

P Value

EIF-2␣ reg 2 AG Sex Age HBV MxA promoter-88 GT MxA promoter-88 TT

EIF-2␣ reg 2 AA Female ⱕ40 yr HBV ⬍100 pg/mL MxA promoter-88GG MxA promoter-88GG

16.11 3.02 6.65 12.27 0.225 4.91

1.51 0.79 1.11 1.55 0.06 0.33

172.1 11.47 39.85 97.39 0.83 73.08

.021 .105 .038 .018 .025 .249

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treatment response (i.e., HBV DNA levels, ALT, and others). Although the significance of the SNPs would disappear after correction of multiple testing by Bonferroni method, the biologic plausibility does exist between the SNPs of the genes and IFN response. The protein synthesis factor eIF-2 is an INF-induced protein in the protein kinase PKR antiviral pathway. The presence of IFN activates PKR, which then acts to phosphorylate the initiation factor eIF-2␣ at Ser-51.24,25 Subsequently, this phosphorylation precipitates the inhibition of messenger RNA (mRNA) translation via down-regulation of the eIF-2Bcatalyzed guanine nucleotide exchange reaction.20 Such inhibition of mRNA translation is believed to contribute to defending the host cell against viral replication. In addition to IFN, a variety of other physiologic states and other viral infection may stimulate eIF-2 ␣ phosphorylation and subsequent mRNA inhibition.26,27 In this study, we chose all potential SNP polymorphisms that fit in the promoter, regulatory, or exon categories of each gene. Thus, although the SNP eIF-2␣ reg2 lies in the intron 1 regulatory region of the untranslated leader sequence (see Table 1 for accession number), previous papers have identified numerous consensus binding sites both upstream and downstream of the promoter region and extending into the first intron region.28-30 In addition, Silverman et al.31 demonstrated the functional role of the first intron via deletion/mutations, showing that such intron sequences play a critical role in the regulation of promoter-driven transcription.31 Intriguingly, a regulatory element downstream of the cap-site cluster was identified, which coincided with the Inr consensus elements and was oriented in the opposite direction to the eIF-2␣ promoter.30 The Inr element directs the synthesis of an antisense transcript and, thus, will regulate the transcription level of the eIF-2␣.30 The SNP identified lies in the intron 1 of eIF-2␣ gene, which is 1.6-kb upstream of the Inr element. The feature thus raises an interesting question as to such nucleotide variation attenuating the antivirus activity of IFN-␣. Thus, it is reasonable to hypothesize that the eIF-2␣ reg2 SNP may indeed affect eIF-2␣ transcriptional activity, mRNA stability, and, consequently, protein response to IFN, although further functional studies would be needed to validate such a hypothesis. In addition, the borderline significance of the MxA nt ⫺88 promoter SNP should also be noted. The rate of G/T heterozygotes in the nonresponder group is 19% (7 of 36), whereas the rate in the sustained response group is 43% (20 of 46) (P ⫽ .061), suggesting a possible association of the G/T genotype with increased responsiveness to IFN. This promoter SNP locates within an IFN-stim-

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ulated response element-like region and has been shown to play a role in regulation of a downstream reporter gene by a luciferase reporter assay.32 The MxA protein has been shown via various animal models to contain intrinsic antiviral activity.20 The ability of the IFN-induced GTPase Mx proteins to inhibit viral replication occurs via the mechanisms of targeting of viral nucleocapsids and blocking RNA synthesis.33,34 The MxA protein has been shown in a variety of studies to be highly associated with resistance to certain viruses, including influenza A and C virus and Thogoto virus.35-38 Recently, Hijikata et al.39 have reported that hepatitis C patients with the same MxA ⫺88 promoter SNP is associated with predicted response to IFN. Their study showed significance for wild-type G/G genotype in corresponding to a larger proportion of NR, while also showing significance for the G/T genotype in correlating with a greater number of sustained R. Our pilot study (although using hepatitis B patients) agreed with the observation that those with the G/T genotype were associated with a greater response to IFN. Thus, although our study showed borderline significance, it is likely that a largescale study is needed to verify the true effect of this polymorphism. Although the sample size was small, we still conducted haplotype and combined genotype analysis in this pilot study. A borderline significance comes from haplotype analysis of OAS1 and OAS2 antiviral pathway, suggesting a possible association of specific haplotype with IFN response. In addition, a borderline significance (P ⫽ .052) of the combined genotype analysis between the signaling genes of the JAK1 promoter and GBP2 exon 05-1 suggests a correlation of the combined genotype may be associated with IFN responsiveness. The results might provide valuable clues to future studies in looking in detail at interactions of related genes in affecting clinical treatment response. Finally, the results of multiple logistic regression analysis indicate that patients of male gender, over 40 years of age, with HBV DNA level ⱖ100 pg/mL, and with eIF-2␣ reg2 genotype A/G will respond poorly to IFN treatment. However, patients with G/T genotype at MxA promoter ⫺88 who will respond well to IFN treatment. Most significantly, after adjustment of the sex, age, HBV titer, and MxA promoter ⫺88 SNP, an OR of 16.11 for eIF-2␣ reg2 genotype A/G has been shown to be correlated with the NR group. In summary, we emphasized the above observations as the result of a screening, pilot study from a retrospectively collected patient group that might not be representative. For example, it was noted that there was a higher percentage (51%) of R in this series than from other prospective

HEPATOLOGY, Vol. 36, No. 6, 2002

studies.40 Large-scale studies and functional assays are needed to further confirm and examine the role of these preliminarily identified potential polymorphic markers that may predict hepatitis B patient treatment response to IFN. Nevertheless, the importance of identifying potential SNP markers in predicting clinical response should not be downplayed. Our results supported the potential clinical usefulness of host SNPs over conventional predictors of treatment response (i.e., HBV DNA levels, ALT, and others). In light of recent improvements in SNP detection methods and of the potential for efficient mass screening,41-43 the greater specificity in using SNPs combined with conventional predictors of treatment response may allow physicians to individualize the medical regimen for each patient. The multiple discoveries of associations between SNPs and a wide variety of disease states, in combination with advances in novel SNP detection technology that allows rapid screening of a large amount of patient samples,44 yield a powerful knowledge base and tools to predict and change the course of patient care and treatment in the future. Acknowledgment: The authors thank Shu-Fen Lu and Ya-Wen Chen for their invaluable experimental advice.

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