The current standard therapy in chronic hepatitis C (CHC) consists of pegylated interferon-α and ribavirin (pegIFNα/RBV) , which helps to eradicate the virus in nearly 80% of patients infected with hepatitis C virus (HCV) genotype 2 or 3, whereas only about 40–50% of HCV genotype 1 and roughly 60% of genotype 4 patients undergoing therapy clear the virus. Therapy with pegIFNα/RBV is also used as part of combination treatment that includes the new direct acting antivirals telaprevir or boceprevir . HIV/HCV-coinfected patients show pronounced difficulty at achieving sustained virologic response (SVR) . Moreover, current HCV treatment is prolonged, costly, and causes dose-limiting side effects . Thus, the identification of successful predictors of response to therapy is particularly desirable in HIV/HCV-coinfected patients in order to ensure an adequate selection of patients to receive therapy.
HCV treatment response is defined as a reduction or absence of plasma HCV viral load (HCV-RNA), leading to several types of virologic responses, which are designated according to their timing relative to treatment. Thus, viral kinetics is a key factor for determining the optimal duration of therapy that allows for limited exposure to treatment resulting in reduced toxicity and cost savings . Apart from baseline plasma HCV-RNA level, HCV genotype, and liver fibrosis are other baseline predictors of response to current HCV therapy . Additionally, several single-nucleotide polymorphisms (SNPs) around the interleukin 28B (IL28B) gene have been associated with HCV treatment success [5–7]. This gene is clustered with the IFN-λ1 and IFN-λ2 genes on chromosome 19, and encodes IFN-λ3, an IL that belongs to the type III Interferon . IFN-λs induce antiviral activity and have been shown to induce interferon-stimulated genes (ISGs) . Beyond the identification of genomic variants, little is yet known about the mechanisms of the association of IL28B genotype with HCV clearance. The genetic association between IL28B variants and baseline levels of ISGs is a controversial issue, as contradictory results have been observed so far .
To date, many articles have assessed the influence of IL28B polymorphisms on virologic response in HIV/HCV-coinfected patients . The rs8099917 SNP has shown an influence on treatment response among HIV/HCV-coinfected patients [12,13]. However, fewer results have been published regarding the impact on HCV treatment response for other relevant IL28B SNPs such as rs12980275, rs7248668, and rs11881222, which are located either in the IL28B gene or at its untranslated regions [11,14]. Those SNPs located in untranslated regions could be involved in the regulation of gene expression and function.
Cytokines are immunomodulation molecules that include interferons (released by host cells in response to the presence of pathogens/tumors) and interleukins (immune response modulators synthesized by helper CD4+ T lymphocytes, monocytes, macrophages, and endothelial cells). The cytokine IL28B is functionally considered an IFN, but it has also been reported to be structurally related to the IL-10 family and the IFN-λ family . Resistance or susceptibility to viral infections such as HCV, is critically linked to cytokine release, which can tend toward different types of responses. Thus, depending on the pattern of signals that CD4+ T cells receive during their initial interaction with pathogens, distinct cell subsets have been reported: Th1, Th2, Th17 and induced regulatory T (iTreg) cells . The cytokine profile plays an important role in treatment response to pegIFNα and ribavirin, and probably modulates the immune response against HCV. Thus, it has been found that a Th2 immune response has been associated with HCV infection persistence  and HCV treatment failure [17,18]. During anti-HCV therapy, IFN-α stimulates Th1 cells but suppresses the Th2 response in HCV-monoinfected patients, suggesting that Th1 rather than Th2 cytokines may be important for eliminating HCV infection [19–21]. Furthermore, Th17 cells produce cytokines that play an important role in autoimmunity and pathogen clearing during host defense reactions  making it vital for the clearance of viral infections such as influenza virus  and hepatitis B virus . Th17 cells have also been implicated in host defense against HIV  and HCV . However, scarce data have been published on plasma levels of Th17 cytokines in HIV/HCV-coinfected patients on anti-HCV therapy .
Hence, our aim was to estimate the impact of IL28B polymorphisms (rs12980275, rs8099917, rs7248668, and rs11881222) and to investigate whether their haplotypes could improve HCV treatment success in 326 HIV/HCV-coinfected patients. In addition, we also explored the behavior of plasma cytokine levels in this population.
Patients and methods
We carried out a retrospective follow-up study on 324 HIV/HCV-coinfected patients, who started treatment with pegIFNα/RBV with regular follow-up at two reference HIV hospitals located in Madrid, Spain. All patients were HIV/HCV positive, had completed a course of pegIFNα/RBV therapy, and were genotyped for IL28B SNPs. Only IFN-naive patients were included. Patients with hepatitis B virus (HBV) were excluded. All patients were of European ancestry.
This study was conducted in accordance with the Declaration of Helsinki. Patients gave their written consent for the study and the Institutional Ethics Committee approved it.
The following information was obtained from medical records: age, sex, risk category, weight, height, nadir CD4+ T-cell count, antiretroviral therapy, and HCV genotype. In addition, during HCV therapy, blood samples were taken from each patient to analyze complete blood counts, CD4+ T cells, plasma HIV viral load (HIV-RNA) and HCV-RNA. Liver fibrosis was assessed by different methods depending on the Hospital: HGUGM protocol has been described previously . Briefly, the fibrosis score was estimated following the criteria established by the METAVIR Cooperative Study Group : F0, no fibrosis; F1, portal fibrosis; F2, periportal fibrosis or rare portal-portal septa; F3, fibrous septa with architectural distortion but with no obvious cirrhosis (bridging fibrosis); and F4, definite cirrhosis. HCIII used transient elastometry (FibroScan, Echosens) as has also been described previously . Briefly, liver stiffness values 7.0 or less, between 7.1 and 9.4, and between 9.5 and 12.4, and 12.5 or more were considered to correspond with Metavir scores F0-F1, F2, F3, and F4, respectively .
Plasma markers analyzed
Baseline plasma levels of 13 cytokines were assessed (IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, IL-13, IL-17A, IL-22, and TNF-α) in a subpopulation of 57 patients, by using Human Th1/Th2/Th9/Th17/Th22 13plex FlowCytomix Multiplex (Bender MedSystems, Vienna, Austria) and FACSCalibur flow cytometer (BD FACSCalibur; BD Biosciences, San Jose, California, USA) according to the manufacturer's instructions.
Four IL28B single nucleotide polymorphisms (rs12980275, rs8099917, rs7248668, and rs11881222) were genotyped at the Spanish National Genotyping Centre (CeGen; http://http://www.cegen.org/). Genomic DNA was extracted from peripheral blood with Qiagen columns (QIAamp DNA Blood Midi/Maxi; Qiagen, Hilden, Germany). Genotyping was performed by GoldenGate assay with VeraCode Technology (Illumina Inc., San Diego, California, USA) following manufacturer protocol.
Hepatitis C therapy
HCV treatment regimens included pegIFNα 2a or 2b at standard doses (180 μg/week or 1.5 μg/kg per week, respectively) and weight-adjusted RBV dosing (1000 mg per day for patients weighing <75 kg and 1200 mg per day for patients weighing ≥75 kg). Following international guidelines , patients with HCV genotypes 1 or 4 (GT1/4) received either 48 or 72 weeks of treatment, and patients with HCV genotype 3 (GT3) were treated for 24 or 48 weeks, depending on the virologic response at week 4. None of the patients in this series were infected with HCV genotype 2. Early stopping rules were applied to patients with suboptimal virologic responses at weeks 12 and 24 .
HCV-RNA viral load was measured by quantitative polymerase chain reaction (qPCR; Cobas Amplicor HCV Monitor Test, Branchburg, New Jersey, USA; and COBAS AmpliPrep/COBAS TaqMan HCV test); results were reported in International Units per milliliter (IU/ml), with a lower limit of detection of 10 IU/ml.
Virologic response to HCV treatment was measured by assessing plasma HCV viral load at different time points : rapid virologic response (RVR): no detectable viral load (<10 IU/ml) after 4 weeks of treatment; early virologic response (EVR): viral load dropped by 99% (2 log10) after 12 weeks of treatment; end-of-treatment virologic response (ETVR): no detectable viral load at completion of treatment; SVR: no detectable viral load 6 months after treatment cessation.
Statistical analysis was carried out by on-treatment analysis of observed data. Patients that prematurely interrupted their HCV treatment due to adverse events, abandonment, or loss of follow-up were discarded from the analysis.
We performed multivariate logistic regression analysis to investigate the association between favorable IL28B genotypes and HCV treatment responses. A stepwise algorithm was used to identify the most significant covariates associated with treatment outcome in our data for the regression model. Hosmer–Lemeshow test was performed in order to evaluate the Goodness-of-fit of the regression logistic model. Analysis was performed by using the Statistical Package for the Social Sciences (SPSS) 15.0 (SPSS Inc., Chicago, Illinois, USA).
The Hardy–Weinberg equilibrium (HWE) of all SNPs was assessed by a χ2 test, considering equilibrium when P value was more than 0.001 . In addition, haplotype structure was analyzed. Pair-wise linkage disequilibrium analysis was computed using the standardized D′ and r2 values by Haploview software . Only those haplotypes with a frequency more than 0.01 were studied. Each haplotype was analyzed versus all others by a multivariate logistic regression analysis adjusted by the most significant covariates cited above. Therefore, odds ratio (OR) and P values of each comparison were calculated in respect to the overall data. These analyses were performed with PLINK software .
We also carried out a classification and regression tree (CART) to identify higher-order interactions between SVR and IL28B variants using SPSS 15.0. CART is a prognostic system with a hierarchical structure based on recursive portioning that build a decision tree to identify subgroups at higher odds of SVR. CART was performed with SPSS 15.0.
In regard to cytokine levels, data were analyzed according to IL28B genotypes by the Mann–Whitney U test, and P values were adjusted by Bonferroni correction. All P values were two-tailed and statistical significance was defined as P value less than 0.05. This analysis was also carried out by SPSS 15.0.
Our study included 324 HIV/HCV-coinfected patients on HCV treatment, whose baseline characteristics (before starting anti-HCV therapy) are shown in Table 1. A total of 184 out of 324 patients, (56.8%) were GT1, 103 (31.6%) were GT3, and 37 (11.4%) were GT4. We did not find significant differences between patients according to HCV genotype (Table 1).
The overall rates of the four efficacy endpoints (RVR, EVR, ETVR, and SVR) were 33.2, 72.3, 64.2 and 54.6%, respectively [see Supplemental Digital Content (SDC) 1, http://links.lww.com/QAD/A275]. When we stratified these data by HCV genotype, different rates of HCV treatment response were found for the four efficacy endpoints, with the highest response rates for GT3 patients (58.9, 92.5, 88.1 and 82.5% for RVR, EVR, ETVR and SVR, respectively), whereas lower response rates were observed for GT1 and GT4 patients (see SDC1, http://links.lww.com/QAD/A275).
Linkage disequilibrium among interleukin 28B single-nucleotide polymorphisms
We analyzed four IL28B SNPs (rs12980275, rs11881222, rs8099917, and rs7248668). All of them showed a call rate of 99.53%, fulfilled the minimum allele frequency (MAF) more than 0.05, and were in HWE (P > 0.001). Linkage disequilibrium was identified among the four IL28B SNPs, especially between rs12980275/rs11881222 (D′ = 0.97; r2 = 0.94) and rs8099917/rs7248668 (D′ = 1.00; r2 = 0.99) (see SDC2, http://links.lww.com/QAD/A275).
Interleukin 28B variants and virologic response to hepatitis C virus treatment
Due to the high linkage disequilibrium among the cited IL28B polymorphisms, we obtained the same or highly similar results for rs12980275/rs11881222 and rs8099917/rs7248668 couples. In order to avoid redundancy, we only show results for rs12980275 and rs8099917.
As is shown in Fig. 1, interestingly the rate of virologic response at different time points (RVR, EVR, ETVR, and SVR) was significantly higher in patients harboring favorable IL28B genotypes [rs12980275 (AA) and rs8099917 (TT)]. These differences were mainly observed for genotype 1 (Fig. 1). All numerical data are provided in SDC3, http://links.lww.com/QAD/A275.
In order to analyze the effect of IL28B variants on virologic response, we performed a multivariate logistic regression adjusted by the most significant variables, which were selected by stepwise algorithm [HCV genotype, HCV-RNA ≥500 000 IU/ml and significant fibrosis (F ≥ 2)]. The Hosmer–Lemeshow goodness-of-fit test showed no evidence of lack of fit (SDC4, http://links.lww.com/QAD/A275). The dominant model was the best fit to our data. The association of IL28B variants with virologic response during HCV treatment (RVR, EVR, and ETVR) and after completion of HCV therapy (SVR) was statistically significant for all patients and for GT1/4 patients (Table 2). It is noteworthy that patients harboring favorable IL28B genotypes [either rs12980275 (AA) or rs8099917 (TT)] showed, in some cases, virologic response rates around four times higher than those patients with unfavorable genotypes (P < 0.001) (Table 2). When patients were stratified by HCV genotype, the association was observed for GT1/4 patients, and for GT1 and GT4 patients, separately. Overall, both IL28B SNPs showed similar results. Regarding HCV genotype, the results for GT4 patients are less conclusive, because the low number of patients (n = 37) prevents us from drawing firm conclusions (Table 2). For HCV GT3 patients, we did not find any significant association (data not shown).
Next, we examined whether the combination of all four IL28B SNPs could improve HCV treatment outcome. Thus, any association of IL28B haplotypes defined by all four SNPs was assessed. The haplotype AATG, which harbors all favorable alleles, was the most prevalent (67%), followed by GGGA (19%) with all nonprotective alleles, and GGTG (12%) possessing two protective alleles (Table 3). Therefore, patients harboring AATG showed increased OR of achieving EVR, ETVR and SVR, whereas GGGA showed decreased OR for all variables. The haplotype GGTG, which contains two protective alleles, was only significant for EVR and ETVR (OR = 0.50, P = 0.023 and OR = 0.56, P = 0.045, respectively). When patients were analyzed by HCV genotype, we found that only significant differences were found for GT1 and GT4 patients, wherein ORs were higher for favorable haplotype (AATG) and more reduced for unfavorable haplotype (GGGA) compared with the overall analysis (Table 3). For GT3 patients, we did not find any significant association (data not shown).
Finally, we explored, by a decision tree algorithm, whether two IL28B SNPs may predict SVR better than one single SNP in those patients with HCV GT1/4, wherein IL28B SNPs are associated with virologic response rates. For this purpose we used genotype data from rs12980275 and rs8099917 polymorphisms. The overall SVR rate of 41.9% significantly increased to 59.1% in those patients carrying the rs12980275 favorable genotype (AA), but this rate did not improve when considering patients simultaneously carrying protective genotypes for both SNPs (Fig. 2). Moreover, patients carrying the rs12980275 unfavorable genotypes (AG/GG) showed an increase in SVR rate (from 30.6 to 45.2%) when they also possessed the rs8099917 favorable genotype (AA). On the contrary, SVR decreased from 30.6 to 23.9% when one of the rs8099917 unfavorable genotypes (GT/GG) was present in these patients (Fig. 2).
Influence of interleukin 28B genotype on cytokines level and sustained virologic response
We analyzed baseline plasma levels of Th1/Th2/Th9/Th17/Th22 cytokines (13plex) and their relevance to IL28B genotypes in the 57 out of 324 patients for whom baseline plasma samples were available. Unfortunately, we were unable to analyze the effect of rs8099917 due to the unequal distribution of patients in each group (one of the groups displayed only one patient). Hence, patients were classified according to their favorable or unfavorable rs12980275 genotype. Only data for cytokines significantly associated with other variables are shown (IFN-γ, IL-5, IL-9, TNF-α, and IL-6).
First, patients were stratified by IL28B rs12980275 genotype (see SDC5, http://links.lww.com/QAD/A275). Patients harboring the favorable genotype (AA) showed reduced cytokine levels in general, with the difference being significant for IFN-γ, IL-5, and IL-9. After adding a second factor of stratification (HCV genotype) (see SDC6, http://links.lww.com/QAD/A275), we found that patients with the best prognosis in terms of treatment response (favorable IL28B genotype (AA) and HCV genotype 3) had significantly reduced IFN-γ and IL-9 plasma levels compared with those less likely to respond to treatment (IL28B genotype (GG+AG) and HCV genotype 1/4). Finally, patients were stratified according to rs12980275 genotype and SVR (Fig. 3). Again, the most interesting results were found when comparing the extreme groups. Nonresponder patients with unfavorable IL28B genotype (no-SVR, AG/GG) had significantly higher values of IFN-γ, IL-6, IL-9, and TNF-α than responder patients with favorable IL28B genotype (SVR, AA) (Fig. 3).
We performed a multiple linear regression to assess the influence of these three factors together (SVR, IL28B genotype, and HCV genotype) on cytokine levels (logarithm10 transformed, dependent variable). The slope (b) of the regression equation (y = a + b × X) allows us to quantify the effect of each variable on cytokine levels. We found that IFN-γ levels were lower in rs12980275 (AA) than in rs12980275 (AG/GG) patients, (b = −0.257 ± 0.081; P = 0.003), but this cytokine was not influenced by HCV genotype or SVR. However, IL-6 (b = −0.380 ± 0.146; P = 0.012), IL-9 (b = −0.293 ± 0.102; P = 0.006), and TNF-α (b = −0.218 ± 0.099; P = 0.032) levels were all lower in SVR than in no-responder patients, but these cytokines, in contrast, were not influenced by IL28B or HCV genotype.
In the present study, we have selected four seldom-studied IL28B SNPs for their assessment in HIV/HCV-coinfected patients. Our results showed a significant association of all studied SNPs alone (rs12980275, rs11881222, rs8099917, and rs7248668) or in combination (IL28B haplotypes) with HCV-treatment response in HCV/HIV-coinfected patients.
It is noteworthy that this is the first study reporting treatment response data on rs11881222 and rs7248668 in HIV/HCV-coinfected patients. To our knowledge only one study on this topic reported data from white HCV-monoinfected patients , as it has mainly been studied in Asian populations [7,36,37]. Clausen et al. also reported data for rs11881222 in HIV/HCV-coinfected patients, but their results are not comparable to our study, as they analyzed the association with a different variable (spontaneous clearance), and some of the patients were also coinfected with HBV. In addition, we have shown for the first time the association of seldom-studied IL28B SNPs with all virologic responses during and after HCV therapy (RVR, EVR, ETVR, and SVR) in HIV/HCV-coinfected patients. Although results for all four of the SNPs were highly similar, it seems that rs12980275 shows higher association with on-treatment responses (EVR and ETVR). In addition to being commonly used, these data on virologic responses are important for the monitoring of patients and the identification of nonresponders and relapses. For example, EVR is a pivotal decision criterion for treatment guidelines, as it has been shown that the absence of EVR is the most robust characteristic for identifying nonresponders .
In addition to host factors, HCV genotype is also a well known predictor of response to pegIFNα/RBV therapy , as patients with GT1/4 show poorer responses (40–50%) than GT2/3 (80%) . About two-thirds of our cohort had GT1/4 viruses, and we confirmed the strong association of IL28B polymorphisms with the rate of on-treatment virologic responses for both groups. Results for GT1 and GT4 were similar, although GT4 patients showed less significant results than GT1. This lack of consistency in GT4 patients is probably due to the limited sample size. On the contrary, the genotyping of IL28B SNPs was useless for predicting therapy outcome in HIV/HCV patients infected with GT3. This attenuated association for HCV GT3 patients could be related to the high rate of SVR present with these IFN-sensitive genotypes, wherein a larger sample size would be required to tease out statistical differences .
IL28B haplotypes were investigated in order to evaluate whether the association with treatment outcome was better explained using this approach. AATG (four favorable alleles) was the most prevalent and highly significant haplotype for predicting all virologic responses (RVR, EVR, ETVR, and SVR). However, in contrast, GGGA (four nonfavorable alleles) showed decreased odds for all virologic response, and was, therefore, an excellent predictor of HCV treatment failure. Nevertheless, as has been previously reported for spontaneous HCV clearance , haplotype analysis did not improve the prediction of any of the virologic responses compared with the SNPs alone.
However, the use of a decision tree tool appeared to improve treatment success prediction. Thus, it was found that HCV GT1/4 patients with an unfavorable rs12980275 genotype significantly improved the prediction of SVR when they were combined with the rs8099917 genotype information. Therefore, the genotyping of only rs12980275 may be sufficient for predicting treatment response in patients with a favorable genotype (AA), whereas for patients harboring unfavorable rs12980275 (AG or GG), genotyping of rs8099917 is advisable. This promising result will allow us to identify patients with a very low likelihood of responding to pegIFNα/RBV treatment prior to prescribing it, which is extremely important in order to avoid unnecessary side effects of an ineffective treatment . In this context, patients with a double-poor-responder genotype should be treated with new direct-acting antivirals (DAAs), such as boceprevir and telaprevir, instead of pegIFNα/RBV alone or wait until additional new DAAs become available. As HIV/HCV-coinfected patients have generally poorer responses compared with HCV-monoinfected individuals [42,43], HCV therapy is particularly not recommended for coinfected patients with a low likelihood of achieving a virologic response, and screening for IL28B SNPs is a valid pretreatment approach for maximizing treatment success and minimizing HCV therapy-related toxicity.
Both viral and host factors are independent predictors of HCV treatment outcome , nonetheless both have an effect on the immune response. To further investigate this, we studied whether IL28B variants (rs12980275) affect baseline levels of different cytokines related to different types of immune responses. In order to achieve this goal, patients were also stratified by HCV genotype and SVR. In our study, patients with the poorest prognosis (AG or GG at rs12980275 and HCV GT1/4) were those who showed the highest values of Th1 (IFN-γ) and Th2 (IL-9) cytokines. Likewise, patients with unfavorable rs12980275 genotype (AG or GG) that did not achieve SVR, showed the highest values of Th1 (IFN-γ), Th2 (IL-6 and IL-9), and proinflammatory (TNF-α) cytokines before starting anti-HCV treatment.
The rs12980275 SNP seems to be located at the promoter region of the IL28B gene, therefore, it could modify its expression and the expression of related interferon-stimulated genes (ISGs) . ISGs play a critical role in anti-HCV therapy response . It is tempting to speculate that patients with the favorable genetic variation of this SNP may have a more effective immune response to HCV infection, which, therefore, results in a better response to anti-HCV treatment.
IFN-γ is secreted by cytotoxic T lymphocytes and plays a pivotal role in mechanisms of HCV control . HCV infection induces the endogenous IFN system in the liver of some (but not all) patients with CHC; however, patients with a preactivated IFN system are refractory to both endogenous and exogenous IFN-α . In our study, we found that patients with nonfavorable IL28B genotypes and HCV GT1/4 who do not achieve SVR showed the highest values of IFN-γ, which may indicate a greater degree of the preactivated IFN system associated to HCV treatment failure. In this sense, our results agree with previous studies that have detected an increase in plasma levels of the IFN-γ inducible protein 10 (IP-10; CXCL10) in CHC nonresponder patients with HCV genotype 1 [47–49]. These high CXCL10 levels may also indicate a preactivation of the IFN system in the liver .
TNF-α is a proinflammatory cytokine released by different types of cells, and it has been implicated in nonresponse to interferon therapy in CHC . In our study, high levels of TNF-α were found in patients with nonfavorable IL28B genotype and without SVR. TNF-α may influence the response to anti-HCV therapy via overexpression of suppressors of cytokine signaling three (SOCS3) in the livers of CHC patients [51,52].
The Th2 immune response has been associated with HCV treatment failure in HIV/HCV-coinfected patients  and HCV-monoinfected patients . Our data showed that patients achieving SVR displayed reduced levels of Th2 cytokines (IL-9 and IL-6), but interestingly, a different expression pattern was found for each cytokine. Plasma levels of IL-9 seemed to be dependent on IL28B genotype and HCV genotype, whereas IL-6 did not. As for TNF-α, it is possible that IL-6 and IL-9 may influence the response to anti-HCV therapy via overexpression of SOCS3, a potent inhibitor of the type I IFN pathway, by producing IFN-α resistance through suppression of interferon signaling [53–55].
This study has several limitations that must be taken into account for the correct interpretation of the data. Our study design is retrospective and has a low number of patients. All selected patients met a set of criteria for starting HCV treatment (e.g. no alcohol abuse, high CD4 cell counts, controlled HIV replication, and good treatment adherence), and this may have introduced a selection bias. As P value is dependent on the sample size, it may be possible that we did not find any significant adjusted P values in some comparisons (GT4, for instance) due to such a size-limited population. Thus, only big effects would be detected in small populations. Finally, as with any study measuring plasma levels of cytokines, any additional unknown infection(s) could potentially influence our results, although we have no evidence. With this in mind, we confirmed the absence of HBV infection or other active opportunistic infections in all patients.
In conclusion, the four IL28B polymorphisms tested alone or by haplotypes showed a significant association with HCV clearance during and after HCV therapy. Although haplotypes did not improve the prediction of SVR, the decision tree with rs12980275 and rs8099917 was useful for improving the prediction of HCV treatment success in the most difficult-to-treat patients, for whom adding more factors to predictive algorithms could improve therapeutic decision-making. Furthermore, the cytokine profile was much more favorable in patients with rs12980275 (AA) who achieved SVR.
The authors thank the Spanish National Genotyping Center (CeGen) for providing the SNP genotyping services (http://http://www.cegen.org).
Authors contributions: A.F.R. and S.R. performed all statistical analysis, interpretation of the data and wrote the manuscript.
J.B., V.S., and S.R. participated in the study concept and design. J.B., V.S., J.C., J.C.L., and P.M. participated in patient selection, collection of samples and acquisition of data. M.A.J.S., M.G.F., M.G.A., J.M.B., C.R., and N.R. participated in sample preparation, DNA isolation and genotyping preprocedure, and contributed with critical revision of the manuscript. S.R. supervised the study.
Funding/support: This work has been supported by grants given by ‘Instituto de Salud Carlos III’ [grant numbers PI08/0738, PI11/00245; ISCIII-RETIC RD06/006, PI08/0928 to JB; and PI11/00870], ‘Fundación para la Investigación y la Prevención del Sida en España’ (FIPSE) [grant numbers 36443/03 and 361020/10] and ‘Fundación para la Investigación y la Educación en SIDA’ (IES Foundation).
A.F.R., M.G.F., M.G.A., and M.A.J.S. are supported by ‘Instituto de Salud Carlos III’ (grant numbers UIPY-1377/08, CM09/00031, CM08/00101, and CM10/00105, respectively). J.B. is supported by ‘Programa de Intensificación de la Actividad Investigadora en el SNS’ (grant number I3SNS).
All authors revised the manuscript from a draft by A.F.R.
Conflicts of interest
Transparency declarations: The authors do not have any commercial or other association that might pose a conflict of interest.
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