Baseline Prediction of Combination Therapy Outcome in Hepatitis C Virus 1b Infected Patients by Discriminant Analysis Using Viral and Host Factors
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Verónica Saludes1,2, Maria Alma Bracho2,3, Oliver Valero4, Mercè Ardèvol5, Ramón Planas6,7, Fernando González-Candelas2,3, Vicente Ausina1,8, Elisa Martró1,2*
1 Microbiology Service, Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain, 2 CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain, 3 Unidad Mixta Genómica y Salud, Centro Superior de Investigación en Salud Pública - Universitat de València (CSISP-UV)/Instituto Cavanilles, Valencia, Spain, 4 Statistics Service, Universitat Autònoma de Barcelona, Cerdanyola, Spain, 5 Hospital Pharmacy, Hospital Universitari Germans Trias i Pujol, Badalona, Spain, 6 Liver Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain, 7 CIBER Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain, 8 CIBER Enfermedades Respiratorias (CIBERES), Bunyola, Spain
1 Microbiology Service, Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain, 2 CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain, 3 Unidad Mixta Genómica y Salud, Centro Superior de Investigación en Salud Pública - Universitat de València (CSISP-UV)/Instituto Cavanilles, Valencia, Spain, 4 Statistics Service, Universitat Autònoma de Barcelona, Cerdanyola, Spain, 5 Hospital Pharmacy, Hospital Universitari Germans Trias i Pujol, Badalona, Spain, 6 Liver Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain, 7 CIBER Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain, 8 CIBER Enfermedades Respiratorias (CIBERES), Bunyola, Spain
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Received: July 1, 2010; Accepted: November 8, 2010;
Published: November 30, 2010
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Background
Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline.
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Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline.
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Methodology
Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1–E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A “leave-one-out” cross-validation method was used to assess the reliability of the discriminant models.
Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1–E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A “leave-one-out” cross-validation method was used to assess the reliability of the discriminant models.
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Principal Findings
Lower alanine transaminase levels (ALT, p = 0.009), a higher number of quasispecies variants in the E1–E2 region (number of haplotypes, nHap_E1–E2) (p = 0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1–E2, and gamma-glutamyl transferase and ALT levels.
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Lower alanine transaminase levels (ALT, p = 0.009), a higher number of quasispecies variants in the E1–E2 region (number of haplotypes, nHap_E1–E2) (p = 0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1–E2, and gamma-glutamyl transferase and ALT levels.
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Conclusions and Significance
Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.
Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.
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Discussion Only
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As combination treatment failure occurs in about half of all patients with chronic hepatitis C infected by genotype 1 [6], [7], prediction of treatment outcome at baseline would be highly beneficial. Although several factors have been identified as predictors of treatment outcome, none of them can provide a reliable individualized prediction when used independently. Based on our results in Spanish patients infected with HCV-1b, we propose the use of discriminant statistical models based on host and viral characteristics to provide an aggregate prediction of the treatment outcome at baseline.
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Among the host-related factors studied baseline ALT levels, which are an indicator of liver damage, were significantly higher in responder patients than in non-responders (p = 0.009), as previously reported [11], [12].
Among the host-related factors studied baseline ALT levels, which are an indicator of liver damage, were significantly higher in responder patients than in non-responders (p = 0.009), as previously reported [11], [12].
Conversely, the GGT quotient tended to be higher in the non-responder group in agreement with other studies [12], [23]; higher GGT levels have been related to advanced fibrosis, steatosis and insuline resistance, which are more common among non-responders [24]. The body weight tended to be higher in non-responder patients; in fact, it has been suggested that obese subjects have an increased expression of the IFN-α signalling inhibitor factor SOCS-3 [25]. Some of the host factors that have previously been associated with treatment failure, such as male gender, advanced age, advanced liver fibrosis stage and cirrhosis [6], [7], [13] did not reach statistical significance in our study probably due to a limited sample size, especially regarding the liver biopsy, which was not performed in 37.2% of patients.
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In relation to virus-related factors, HCV baseline viral load has been suggested as a predictor of SVR, but several cut-offs have been proposed [24]. In our study, average viral loads were higher in non-responders but differences were not significant.
In relation to virus-related factors, HCV baseline viral load has been suggested as a predictor of SVR, but several cut-offs have been proposed [24]. In our study, average viral loads were higher in non-responders but differences were not significant.
Additionally, several studies have reported an association between the level of variability in the HCV genome at baseline and treatment outcome. Envelope glycoprotein coding regions are highly variable; the HVR-1, which is the most variable region in the whole genome, is targeted by host neutralizing antibodies and plays a role in immune escape [26]. While the variability in this region has also been associated with treatment outcome [27]–[32], discrepancies on this matter have been noted probably due to the different treatment regimens, the different genetic variability estimates employed, and limitations in statistical analyses [33]–[35].
While our results show that treatment outcome was not related to the presence of a common evolutionary origin, in general terms, the E1–E2 genetic variability estimators suggested that a high heterogeneity in the baseline viral population could be involved in combination therapy failure, either through the pre-existence or the generation of drug-resistant viral variants. A higher number of quasispecies variants in the E1–E2 region (nHap_E1–E2) was significantly associated with treatment failure (p = 0.003). Additionally, when the analysis focussed on the HVR-1 subregion, nHap and Ks were marginally significant with higher values in the non-responder group.
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Although significant differences between groups at the amino acid level were not found, synonymous substitutions may have an effect on the secondary structure of the genomic RNA, which is an important selection target [36].
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Pre-treatment Core amino acid substitutions at positions 70 (R by Q) and/or 91 (L by M) have been described as useful independent predictors of treatment failure in Japanese HCV-1b infected patients [37]. Similarly, our results show an association between the absence of both R70 and L91 amino acids and treatment failure (p = 0.039). Although it has been suggested that the Core protein may inhibit the transcription of antiviral genes induced by IFN-α [38], further studies are needed to clarify the role of the observed amino acid substitutions in treatment failure.
Pre-treatment Core amino acid substitutions at positions 70 (R by Q) and/or 91 (L by M) have been described as useful independent predictors of treatment failure in Japanese HCV-1b infected patients [37]. Similarly, our results show an association between the absence of both R70 and L91 amino acids and treatment failure (p = 0.039). Although it has been suggested that the Core protein may inhibit the transcription of antiviral genes induced by IFN-α [38], further studies are needed to clarify the role of the observed amino acid substitutions in treatment failure.
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Since factors that significantly differed between groups in the bivariate analyses were not completely reliable in predicting treatment outcome when used independently, we developed predictive models that included a combination of variables. The logistic regression analysis identified the nHap_E1–E2 (OR = 1.47) and the Core amino acid pattern (OR = 25.47) as independent risk factors for treatment failure
Since factors that significantly differed between groups in the bivariate analyses were not completely reliable in predicting treatment outcome when used independently, we developed predictive models that included a combination of variables. The logistic regression analysis identified the nHap_E1–E2 (OR = 1.47) and the Core amino acid pattern (OR = 25.47) as independent risk factors for treatment failure
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However, predictive models obtained by discriminant analysis including additional variables showed better AUC values and more accurate predictions in our study population (90.5–95.2% sensitivity and 95.5–100% specificity). The most significant variables in both discriminant models were the Core amino acid pattern, nHap_E1–E2, and GGT and ALT quotients. Although prediction accuracy may deteriorate in an independent sample, the internal cross-validation pointed to a better reproducibility for model 2 in a comparable population (identifying 85.7% and 81.8% of the responder and non-responder patients, respectively), despite the fact that model 1 best predicted treatment outcome in our population
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Besides, using model 2 the detection of those patients likely to respond to therapy could be maximized by adjusting the cut-off, leading to a higher NPV at the cost of a lower specificity (93.3% and 63.6%, respectively, after cross-validation). Thus, the results suggest that non-response could be predicted at baseline with high accuracy (NPV after cross-validation of 81.8% to 93.3% depending on the cut-off) in patient groups comparable to ours in terms of ethnicity, clinical background, and HCV subtype.
To our knowledge, this is the first study that describes a model for predicting individual combination therapy outcomes on the basis of baseline host and viral characteristics using a discriminant multivariate analysis
To our knowledge, this is the first study that describes a model for predicting individual combination therapy outcomes on the basis of baseline host and viral characteristics using a discriminant multivariate analysis
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This comprehensive statistical method integrates the information of all variables included in the model thus improving the prediction with respect to more commonly used statistical approaches. Additionally, discriminant models may be adjusted to include the most significant predictors of treatment outcome in each population. However, our study has several limitations: i) other viral genome regions not included in the study might also be involved in resistance to therapy, such as the ISDR. Nevertheless, a meta-analysis suggested that the association between the number of mutations in this region and SVR achievement was more pronounced in Japanese than in European patients [39].
As most European HCV-1b strains present less than 3 mutations, large sample sizes would be required to find significant associations; ii) recent studies have suggested that single nucleotide polymorphisms in several human genes involved in the IFN mediated response are associated to treatment outcome in HCV-1 infected patients, especially the IL28B gene polymorphisms [14]–[18]
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Since our study was retrospective, whole-blood samples were not available to assess host genetic polymorphisms; iii) the sample size was limited to 43 patients. However, a similar number of patients were included in each group, accounting for the fact that about 50% of patients infected by HCV-1b achieve an SVR. Although an independent but similar population was not available, we performed an internal cross-validation. This method is commonly used to reduce classification bias and estimate future model performance [40]
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Our results show that both host and viral factors are involved in treatment failure, although the exact mechanisms should be further characterized. The host-related variables included in the prediction models are routinely used for patient management and relatively easy to obtain, while viral variability estimates are obtained through laborious methods. Even so, and if confirmed in further studies, the information obtained may help physicians to restrict treatment to those patients that are likely to benefit from it, thus reducing overall treatment costs. Those patients that are unlikely to respond could avoid current therapy and related side effects, and wait for more effective treatment regimens
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In conclusion, discriminant analysis using both baseline HCV genetic variability and host characteristics has been shown as a useful statistical tool allowing us to accurately predict combination treatment outcome in a high proportion of Spanish HCV-1b infected patients. Further studies including host genetic polymorphisms and larger numbers of patients are under way, and similarly generated models will probably have an increased predictive power.
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