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Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Background

The IL28B genotype in rs12979860 predicts success of peginterferon/ribavirin (PEG/RBV) therapy in patients with chronic hepatitis C (CHC). Recently, a dinucleotide frame shift variant in ss469415590 (TT or ΔG) was described, which generates the novel interferon lambda 4 protein (IFNL4). IFNL4 ss469415590 (ΔG) allele carriers have an impaired clearance of HCV infection and response to IFN-α therapy. In this study, we compared the role of IFNL4 polymorphism with the two commonly used IL28B SNPs rs12979860 and rs8099917 on response to PEG/RBV in patients with CHC.

Aim

To compare the role of IFNL4 polymorphism with the two commonly used IL28B SNPs rs12979860 and rs8099917 on response to PEG/RBV in patients with CHC.

Methods

A total of 754 PEG/RBV patients treated (male/female = 484/270; Caucasians: 98.8%; mean age: 42.8 [CI 95%: 42.0–43.6] y; genotype (GT)1: n = 435, GT2: n = 23, GT3: n = 185, GT4: n = 114) were investigated. Liver fibrosis was assessed by liver biopsy in 456 patients. Single nucleotide polymorphisms (SNPs) in ss469415590, rs12979860 and rs8099917 were analysed by RT-PCR system.

Results

Of the patients, 12.9% (n = 97) had the ss469415590 ΔG/ΔG genotype (IFNL4), 51.3% (n = 387) were heterozygous (TT/ΔG) and 35.8% (n = 270) had TT/TT. IFNL4 polymorphism was independently associated with SVR in GT1 (OR: 2.539, CI 95%: 1.629–3.021, P < 0.001) and GT4 (OR: 12.573, CI 95%: 3.427–46.133, P < 0.001), but not in GT3 (OR: 1.514, CI 95%: 0.933–2.458, P = 0.093).

IFNL4 correlated strongly with rs12979860 (ρ = 0.988, < 0.001), but only moderately with rs8099917 (ρ = 0.598, P < 0.001).

Conclusions

These findings underscore the role of IFNL4 for treatment response in patients with CHC genotypes 1 and 4. However, due to its strong correlation with rs12979860 in IL28B, there is no benefit in additional testing for IFNL4 for treatment prediction in Caucasian patients. By contrast, IFNL4 improves prediction of response to interferon-based therapies, if SNP rs8099917 is used.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Several single nucleotide polymorphisms (SNPs) in the region of the IL28B (rs12979860, rs8099917, rs12980275) gene are well-established predictors for treatment response with pegylated interferon-α (PEG) and ribavirin (RBV) in chronic hepatitis C (CHC) patients.[1-3] Patients infected with HCV genotype (GT) 1 homozygous for the C allele of rs12979860 or the T allele of rs8099917 have a two- to threefold higher chance of eradicating the virus under treatment with PEG/RBV than patients carrying the T or the G allele respectively. Further studies confirmed these data and extended their observations to patients with GT2, 3 and 4.[4-6] The SNP rs8099917 is commonly used in Asia, while in the USA and Europe, rs12979860 is determined to predict response to therapy. Furthermore, there is a strong association between beneficial IL28B genotype and spontaneous HCV clearance in acute hepatitis C.[7-9]

Both rs12979860 and rs8099917 reside on the short arm of chromosome 19 (19q13.13) in the vicinity of the IL28B gene. IL28B encodes for interferon-λ3 (IFNL3), which belongs – together with IFNL1 (IL29) and IFNL2 (IL28A) – to the family of the type III interferons that were first described in 2003.[10] The expression of type I (IFN-α and-β) and type III IFNs is induced by the same ligands and regulated by common mechanisms,11 but the IFNL3 signals through unique janus-kinases/signal transducers and activators of transcription (JAK/STAT) sharing common downstream signalling system with the type I IFNs.[12]

The molecular mechanisms, how IL28B influences treatment outcome are still not established. One possible answer might be an upregulation of interferon-stimulated genes (ISG) in patients carrying the unfavourable genotype. [13, 14] Recently, Prokunina-Olsson et al. described a dinucleotide variant upstream of IL28B in ss469415590 (TT or ΔG) that is in strong linkage disequilibrium with rs12979860.[15] The SNP ss469415590 (ΔG) results in a frame shift mutation leading to a novel gene product – designated as interferon-λ4 (IFNL4) – sharing 40.8% amino acid sequence similarity with IFNL3. IFNL4 triggers the expression of hepatic interferon stimulated genes, which is considered to be the reason for poor response to exogenous interferon. Loss of function of the new interferon IFNL4 may confer protection from hepatitis C.[16, 17] The ss469415590 was more strongly associated with HCV clearance in individuals of African ancestry when compared with Europeans and Asians.

The aim of our study was to evaluate the impact and clinical usefulness of IFNL4 genotype in relation to IL28B polymorphisms to predict response to PEG/RBV treatment in patients with chronic hepatitis C.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Study population

Overall, 754 consecutive subjects from several tertiary referral centres in Austria who underwent a complete treatment course with peginterferon-α-2a (PEG) and ribavirin (RBV) between 2001 and 2011 were included in the study; 467 patients participated in various randomised controlled trials[18-21] and 62 in the PROPHESYS trial.[22] The remaining 225 patients were treated according to the label (GT1/4: PEG 180 μg/week and RBV 1000–1200 mg/day weight-based for 24–72 weeks; GT2/3: PEG 180 μg/week and RBV 800 mg/day for 24 weeks). All patients were treatment-naïve and positive for anti-HCV and HCV-RNA for at least 6 months before treatment initiation. This retrospective non-interventional analysis was approved by the institutional review board of the Medical University of Vienna and all patients consented for genetic testing.

The primary efficacy outcome was sustained virological response (SVR) defined as undetectable HCV-RNA 24 weeks after treatment cessation. Secondary end points were the on-treatment virological responses at weeks 4 and 12. Rapid virological response (RVR) was defined as undetectable HCV-RNA at week 4 during treatment. Null response (NR) was defined as a decrease in viral load of less than 2 log10 at week 12 of treatment.

Testing for serum hepatitis C virus RNA

HCV-RNA was quantified in all patients at baseline and after 4, 12, 24, 48 and 72 weeks of treatment and 24 weeks after treatment-free follow-up by the COBAS Amplicor HCV Monitor Test [limit of quantification (LOQ): <50 IU/mL] and from 2007 onwards by the COBAS AmpliPrep/COBAS TaqMan HCV Test (Roche Diagnostics; Pleasanton, CA, USA; LOQ: <15 IU/mL). The HCV genotype was determined by using the VERSANT HCV Genotype 2.0 Assay (LiPA) (Siemens Medical Solutions Diagnostics; Tarrytown, NY, USA). All assays were performed according to the manufacturers' instructions.

Genetic testing

Single nucleotide polymorphisms from rs12979860, rs8099917 (IL28B) and ss469415590 (IFNL4) were analysed by using the StepOnePlus Real Time PCR Systems (Applied Biosystems; Foster City, CA, USA) with a TaqMan SNP Genotyping Assay developed together with Applied Biosystems using published sequences from the NCBI Entrez SNP Database (www.ncbi.nlm.nih.gov/sites/entrez).

Assessment of liver fibrosis

Liver fibrosis was assessed in 456 patients by liver biopsy. All liver biopsies were obtained by Menghini technique within 2 years prior to treatment initiation and read by board-certified pathologists at each centre, who were unaware of the clinical and laboratory data of the patients. Biopsy samples were routinely processed (formalin-fixed and paraffin-embedded) and stained with haematoxylin/eosin and chrome aniline blue for assessment of fibrosis, steatosis and inflammation.

Fibrosis was staged according to the METAVIR-Score: no fibrosis (F0), portal fibrosis without septa (F1), portal fibrosis with few septa (F2), numerous septa without cirrhosis (F3) and cirrhosis (F4).[23]

Statistical analysis

All statistical analyses were performed with commercially available software (SPSS Version 20; IBM Corp., Chicago, IL, USA).

Continuous variables were expressed as mean and 95% confidence interval (CI 95%), categorical variables were given as absolute and relative frequencies (in %). Categorical variables were analysed using the Pearson's χ²-test. Correlation analysis between SNPs in IL28B and IFNL4 was carried out using the Spearman's rank correlation test. For further analysis, a logistic regression analysis was performed to identify factors associated with SVR. Factors that were significantly associated in univariate analysis were included into a multivariate model to identify those parameters that were independently associated with SVR.

All tests performed were two-sided and used a P-value of 0.05 as level of significance.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Overall, 754 patients were studied. Mean age was 42.8 (CI 95%: 42.0–43.6) years and mean BMI was 25.1 (24.8–25.4) kg/m². 35.8% (n = 270) of the patients were female and 98.8% (n = 745) were Caucasians. Only six patients (0.8%) were Asians and three (0.4%) were Africans. 57.3% (n = 432) were infected with HCV GT1, 3.1% (n = 23) with GT2, 24.5% (n = 185) with GT3 and 15.1% (n = 114) with GT4. All GT4 patients were native Egyptians. Mean baseline viral load was 5.87 (5.82–5.93) log10 IU/mL. Liver biopsies prior to treatment initiation were performed in 456 patients (GT1: n = 358, GT3: n = 9, GT4: n = 89); overall, 33.8% (n = 154) had advanced fibrosis (F3-4).

Of the overall population, 12.9% (n = 97) had the ΔG/ΔG genotype in ss469415590 (IFNL4), 51.3% (n = 387) were heterozygous (TT/ΔG) and 35.8% (n = 270) had TT/TT. Corresponding figures for rs12979860 (IL28B) were: TT: 13.0% (n = 98), TC: 51.3% (n = 387) and CC: 35.7% (n = 269); for rs8099917: GG: 3.7% (n = 28), TG: 37.7% (n = 284) and TT: 58.6% (n = 442). All demographic data according to different HCV genotypes are summarised in Table 1.

Table 1. Demographic data
 OverallGT1GT2GT3GT4
  1. a

    Continuous data shown as mean and 95% confidence interval. GT, genotype.

  2. b

    Liver biopsy was performed in 456 subjects.

  3. c

    Data available in 740 subjects.

N (%)754432 (57.3)23 (3.1)185 (24.5)114 (15.1)
Male, n (%)484 (64.2)259 (60.0)12 (52.2)113 (61.1)100 (87.7)
Age, yearsa42.8 (42.0–43.6)45.1 (44.0–46.1)44.7 (39.9–49.4)37.9 (36.4–39.4)41.7 (40.1–43.3)
BMI, kg/m²a25.1 (24.8–25.4)25.2 (24.8–25.6)25.1 (23.4–26.8)23.9 (23.3–24.5)26.6 (25.7–27.4)
Fibrosisb, n (%)
F023 (5.0)18 (5.0) 0 (0.0)5 (5.6)
F172 (15.8)56 (15.6) 1 (11.1)15 (16.8)
F2207 (45.4)170 (47.5) 0 (0.0)37 (41.6)
F368 (14.9)56 (15.6) 1 (11.1)11 (12.4)
F486 (18.9)58 (16.2) 7 (77.8)21 (23.6)
Baseline viral load, log10 IU/mLa5.87 (5.82–5.93)5.98 (5.91–6.05)5.91 (5.53–6.29)5.87 (5.74–5.99)5.44 (5.31–5.56)
RVRc, n (%)259 (35.0)88 (20.4)17 (81.0)107 (61.5)47 (41.2)
Treatment outcome, n (%)
Null-response82 (10.9)61 (14.1)1 (4.3)1 (0.5)19 (16.7)
Partial response70 (9.3)49 (11.3)0 (0.0)6 (3.2)15 (13.2)
Breakthrough10 (1.3)5 (1.2)0 (0.0)3 (1.6)2 (1.8)
Relapse117 (15.5)67 (15.5)3 (13.0)34 (18.4)13 (11.4)
SVR475 (63.0)250 (57.9)19 (82.6)141 (76.2)65 (57.0)
IL28B (rs12979860)
CC269 (35.7)145 (33.6)8 (34.8)79 (42.7)37 (32.5)
TC387 (51.3)227 (52.5)14 (60.9)83 (44.9)63 (55.3)
TT98 (13.0)60 (13.9)1 (4.3)23 (12.4)14 (12.3)
IL28B (rs8099917)
TT442 (58.6)247 (57.2)13 (56.5)105 (56.8)77 (67.5)
TG284 (37.7)166 (38.4)10 (43.5)72 (38.9)36 (31.6)
GG28 (3.7)19 (4.4)0 (0.0)8 (4.3)1 (0.9)
IFNL4
TT/TT270 (35.8)146 (33.8)8 (34.8)79 (42.7)37 (32.5)
TT/ΔG387 (51.3)226 (52.3)14 (60.9)84 (45.4)63 (55.3)
ΔG/ΔG97 (12.9)60 (13.9)1 (4.3)22 (11.9)14 (12.3)

Overall, 63.0% (n = 475) of the patients had a SVR, 10.9% (n = 82) were null-responders, 9.3% (n = 70) were partial responders, only 1.3% (n = 10) had a breakthrough during treatment and 15.5% (n = 117) relapsed within the 6-month follow-up period.

IFNL4 and treatment response

In HCV GT1 patients (n = 432), SVR was strongly associated with the IFNL4 genotype: 78.1% (n = 114/146) of patients with ss469415590 TT/TT had SVR, whereas only 47.3% (n = 107/226) of the heterozygotes with TT/ΔG and 48.3% (n = 29/60) of ΔG/ΔG homozygotes had SVR (P < 0.001). After adjustment with age, sex, BMI, fibrosis stage and baseline viral load, multivariate logistic regression identified age (continuous, OR: 0.971, CI 95%: 0.949–0.993, P = 0.01), baseline viral load (OR: 0.562, CI 95%: 0.394–0.803, P = 0.002) and IFNL4 (OR: 2.539, CI 95%: 1.745–3.693, P < 0.001) as independent predictors for SVR (Table 2).

Table 2. Univariate and multivariate logistic regression of factors associated with SVR according to different HCV genotypes
 UnivariateMultivariate
ORCI 95%P-valueORCI 95%P-value
  1. Significant differences are shown in bold letters.

Genotype 1 (n = 432)
Age, years (cont.) 0.956 0.938–0.974 <0.001 0.971 0.949–0.993 0.01
Sex (male)0.7910.535–1.1710.242   
BMI, kg/m² (cont.)0.9690.918–1.0220.242   
Advanced fibrosis (F3/4)0.4490.285–0.7050.0010.6080.363–1.0170.058
Virus load, log10 IU/mL (cont.) 0.573 0.432–0.761 <0.001 0.562 0.394–0.803 0.002
IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG) 2.219 1.629–3.021 <0.001 2.539 1.745–3.693 <0.001
Genotype 2/3 (n = 208)
Age, years (cont.) 0.954 0.923–0.985 0.005 0.964 0.929–0.999 0.046
Sex (male)0.5430.271–1.0900.086   
BMI, kg/m² (cont.) 0.862 0.791–0.940 0.001 0.864 0.789–0.946 0.002
Virus load, log10 IU/mL (cont.) 0.549 0.345–0.875 0.012 0.565 0.342–0.934 0.026
IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG)1.5140.933–2.4580.093   
Genotype 4 (n = 114)
Age, years (cont.)0.9320.888–0.9780.0040.9350.858–1.0180.120
Sex (male)0.3200.084–1.2180.095   
BMI, kg/m² (cont.)0.9590.877–1.0490.363   
Advanced fibrosis (F3/4) 0.223 0.088–0.561 0.001 0.144 0.037–0.560 0.005
Virus load, log10 IU/mL (cont.) 0.180 0.067–0.479 0.001 0.030 0.004-0.229 0.001
IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG) 5.363 2.462–11.681 <0.001 12.573 3.427-46.133 <0.001

In HCV GT4 patients (n = 114), IFNL4 genotype had a significant impact on treatment response. SVR rates were higher in patients carrying the beneficial TT/TT genotype [86.5% (n=32/37)] than in patients with TT/ΔG [47.6% (n = 30/63)] or ΔG/ΔG [21.4% (n = 3/14), P < 0.001]. Multivariate logistic regression identified fibrosis stage (OR: 0.144, CI 95%: 0.037–0.560, P = 0.005), baseline viral load (OR: 0.030, CI 95%: 0.004–0.229, P = 0.001) and IFNL4 (OR: 12.573, CI 95%: 3.427–46.133, P < 0.001) as independent predictors for SVR.

Due to the low number of HCV GT2 patients (n = 23), they were analysed together with GT3 patients (n = 185). In these subjects, no significant association between IFNL4 and SVR could be observed. SVR rates were 80.5% (n = 70/87) in TT/TT, 77.6% (n = 76/98) in TT/ΔG and 60.9% (n = 14/23) in ΔG/ΔG respectively (P = 0.137). In multivariate analysis, SVR was associated with age (OR: 0.964, CI 95%: 0.929–0.999, P = 0.046), BMI (OR: 0.864, CI 95%: 0.789–0.946, P = 0.002) and baseline viral load (OR: 0.565, CI 95%: 0.342–0.934, P = 0.026; Table 2). As only nine patients had a pre-treatment liver biopsy, fibrosis stage was not included into the logistic regression analysis.

IFNL4 and rapid virological response (RVR)

HCV-RNA data at week 4 were available in 740 patients; 259 (35.0%) of them had a RVR. IFNL4 genotype was significantly and independently associated with RVR in all viral genotypes, including GT2/3. Table 3 shows the univariate and multivariate logistic regression analysis of factors associated with RVR according to different viral genotypes.

Table 3. Univariate and multivariate logistic regression of factors associated with RVR according to different HCV genotypes
 UnivariateMultivariate
ORCI 95%P-valueORCI 95%P-value
  1. Significant differences are shown in bold letters.

Genotype 1 (n = 431)
Age, years (cont.) 0.963 0.943–0.983 <0.001 0.962 0.935–0.989 0.007
Sex (male)1.0190.632–1.6450.937   
BMI, kg/m² (cont.)0.9560.895–1.0210.181   
Advanced fibrosis (F3/4)0.4430.236–0.8340.0120.6310.292–1.3670.243
Virus load, log10 IU/mL (cont.) 0.268 0.183–0.393 <0.001 0.253 0.159–0.408 <0.001
 IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG) 3.227 2.119–4.913 <0.001 5.548 3.109-9.898 <0.001
Genotype 2/3 (n = 195)
Age, years (cont.)0.9710.943–0.9990.0430.9710.941–1.0020.070
Sex (male)0.6210.337–1.1440.126   
BMI, kg/m² (cont.)0.9700.897–1.0490.443   
Virus load, log10 IU/mL (cont.) 0.442 0.287–0.680 <0.001 0.394 0.248–0.626 <0.001
 IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG) 1.949 1.227–3.095 0.005 2.366 1.416–3.955 0.001
Genotype 4 (n = 114)
Age, years (cont.)0.9580.915–1.0040.072   
Sex (male)0.4800.155–1.4880.203   
BMI, kg/m² (cont.)0.9680.883–1.0600.480   
Advanced fibrosis (F3/4)0.5430.222–1.3280.181   
Virus load, log10 IU/mL (cont.) 0.125 0.045–0.349 <0.001 0.029 0.006–0.140 <0.001
 IFNL4 (TT/TT*TT/ΔG* ΔG/ΔG) 7.237 3.204–16.345 <0.001 16.658 5.241–52.943 <0.001

Correlation between IFNL4 and IL28B

In the overall study population, ss469415590 (IFNL4) showed a strong correlation with rs12979860 (IL28B): ρ = 0.988, P < 0.001. Only 5 of 754 patients (0.66%) had a discrepancy between rs12979860 and IFNL4 genotype. On the other hand, the correlation between rs8099917 (IL28B) and ss469415590 (IFNL4) was only moderate: ρ = 0.598, P < 0.001. In 442 subjects carrying the beneficial TT genotype in rs8099917, only 59.0% (n = 261) had the ss469415590 TT/TT, 35.7% (n = 158) were heterozygotes and 5.2% (n = 23) had ΔG/ΔG. 71.5% (n = 316/442) of the patients carrying the beneficial TT allele in rs8099917 achieved SVR. When observations were restricted to those with TT/TT in IFNL4, SVR rates raised to 80.8% (n = 211/261) (see fig.1). Both sensitivity (66.5%) and specificity (54.8%) for prediction of SVR for rs8099917 alone were low. After inclusion of IFNL4, specificity raised to 82.1%, but sensitivity remained low (44.4%). Correlations between several SNPs in IL28B and IFNL4 are shown in Table 4.

Table 4. Correlation between SNPs in IFNL4 and rs12979860 (a: ρ = 0.988, P < 0.001) and rs8099917 (b: ρ = 0.598, P < 0.001). c: shows the correlation between rs8099917 and rs12979860 (ρ = 0.597, P < 0.001)
IL28B IFNL4
TT/TTSVRTT/ΔGSVRΔG/ΔGSVR
(a)
rs12979860
CC268215 (80.2)11 (100)0 
TC11 (100)385211 (54.8)11 (100)
TT10 (0.0)11 (100)9645 (46.9)
  IFNL4
IL28B TT/TTSVRTT/ΔGSVRΔG/ΔGSVR
(b)
rs8099917
TT 261211 (80.8)15899 (62.7)236 (26.1)
TG 95 (55.6)229114 (49.8)4625 (54.3)
GG0 0 2815 (53.6)
IL28B rs12979860
CCSVRTCSVRTTSVR
(c)
rs8099917
TT260210 (80.8)159100 (62.9)236 (26.1)
TG96 (66.7)228113 (49.6)4725 (53.2)
GG0 0 2815 (53.6)
image

Figure 1. SVR rates according to IFNL4 genotypes in 442 rs8099917 TT homozygotes.

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Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Our study confirmed the observations of Prokunina-Olsson et al.[15] and of Bibert et al.[24] on the prediction of response to PEG/RBV therapy by IFNL4 polymorphism in a large cohort of Caucasian patients infected with GT1 and GT4. IFNL4 was not associated with SVR in patients infected with GT2 and GT3. The predictive value was almost identical to that of SNP rs12979860 (IL28B) genotype. Thus, testing for IFNL4 had no additional effect on prediction of SVR in our predominantly Caucasian study population. In contrast, the correlation of SNP rs8099917 (which is commonly used in Far Eastern countries) with IFNL4 was surprisingly only moderate.

In our study, there were only five of 754 patients with discrepancies between ss469415590 and rs12979860: two patients were homozygotes for IFNL4 TT, but IL28B heterozygotes; one achieved SVR and the other was a partial responder. Two patients with TT/ΔG had noncorresponding genotypes in IL28B (one CC, one TT) and both achieved SVR. Finally, one patient with ΔG/ΔG was an IL28B heterozygote with SVR. In contrast, there was only a limited, but still significant, correlation between rs8099917 and IFNL4 (ρ = 0.598, P < 0.001). The SVR rate in rs8099917 TT and ss469415590 TT/TT homozygotes was >80%, but decreased in rs8099917 TT homozygotes if they were ss469415590 ΔG/TT heterozygotes or ΔG/ΔG homozygotes (Figure 1, Table 3). The same differences were documented between the distributions of the two IL28B SNPs. These findings might suggest that pre-treatment testing for rs12979860 is a better predictor of response than rs8099917. The difference between the two commonly used IL28B polymorphisms in relation to ss469415590 (IFNL4) may be due to their different distances from ss469415590. IL28B rs12979860 resides only 3 kilobases (kb) upstream of IFNL3 within intron 1 of IFNL4, whereas rs8099917 lies 9 kb upstream of IFNL3 and hence outside the IFNL4 gene.[15, 25] Thus, rs8099917 may frequently segregate from IFNL4, making it a less reliable marker for interferon response.

It is questionable if genetic markers will be still needed if the upcoming new treatments will become available, but in many countries these treatments will not be available for the foreseeable future. In these countries, interferon-based therapies will be used for many years. For these patients, the IL28B rs8099917 polymorphism (commonly used in Asia) is a less accurate predictor of response than IFNL4 or the IL28B rs12979860. Conflicting data were found with respect to spontaneous HCV clearance in patients with African ancestry.[9, 15, 26, 27] Furthermore, in the recent paper on the interferon-free combination of faldaprevir and deleobuvir,[28] patients with IL28B rs12979860 CC had better response rates than those carrying a T allele. Hence, genetics may still play a role for IFN-free therapy.

Finally, it has to be pointed out, that even in subjects carrying the beneficial IFNL4 TT/TT (and IL28B CC) genotype, almost 20% of GT1- and GT4-infected patients do not achieve SVR. Thus, well-known pre-treatment predictors for nonresponse like high baseline viral load, older age and advanced fibrosis stage should further be considered prior to initiation of anti-viral therapy.

In conclusion, IFNL4 is strongly associated with SVR in both GT1 and GT4, but not in GT2 and GT3. Thus, due to its strong correlation with rs12979860 in IL28B, it provides no additional information for treatment prediction, at least in Caucasian patients. The role of IFNL4 in patients with African or Asian ancestry needs to be further studied. At least in African patients, it may improve the performance of genetic markers in the clinical setting.

Authorship

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Guarantor of the article: P Ferenci.

Author contributions: AF Stättermayer: data collection, analysis and interpretation of data, writing of the manuscript. R Strassl, A Maieron, K Rutter, M Strasser, R Stauber, S Beinhardt, C Datz, TM Scherzer, P Steindl-Munda, M Gschwandtler and H Hofer: acquisition of data, critical revision of the manuscript for important intellectual content. M Trauner: critical revision of the manuscript for important intellectual content. P Ferenci: study concept and design, principal investigator, analysis and interpretation of data, outlining and revising the manuscript. All authors approved the final version of the manuscript.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References

Declaration of personal interests: The authors would like to thank Claudia Willheim and Elisabeth Eder for performing the genetic assays.

Declaration of funding interests: Peter Ferenci is a member of the global advisory board and of the speaker's bureau of Roche, Basel, Switzerland, and of Rottapharm-Madaus, Monza, Italy. He is also advisor to Boehringer Ingelheim, Vertex/Tibotec, Gilead, Novartis, GSK and MSD. He has also received an unrestricted research grant from Roche Austria. Harald Hofer, Petra Steindl-Munda, Rudolf Stauber and Michael Trauner serve as speakers for Roche Austria and MSD Austria. Michael Gschwantler is advisor for Vertex/Tibotec, MSD, BMS, Gilead and GlaxoSmithKline Pharma. He has also served as speaker for Roche Austria, Vertex/Tibotec, MSD, BMS, Gilead and GlaxoSmithKline Pharma.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Authorship
  8. Acknowledgements
  9. References
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