MICROARRAYS AND OTHER NEW TECHNOLOGIES

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1 GASTROENTEROLOGY 2006;130: MICROARRAYS AND OTHER NEW TECHNOLOGIES Serum Proteome to Predict Virologic Response in Patients With Hepatitis C Treated by Pegylated Interferon Plus Ribavirin VALÉRIE PARADIS,* TARIK ASSELAH, DELPHINE DARGERE,* MARIE PIERRE RIPAULT, MICHÈLE MARTINOT, NATHALIE BOYER, DOMINIQUE VALLA, PATRICK MARCELLIN, and PIERRE BEDOSSA* *Service d Anatomie Pathologie, Hôpital Beaujon, and CNRS UMR 8149; and Service d Hépatologie and CRB3, Hôpital Beaujon, Clichy, France Background & Aims: Surface-enhanced laser desorption ionization time-of-flight mass spectrometry is a proteomic technique that enables the global profiling of proteins. We used this approach to monitor the kinetics of serum proteome in patients with chronic hepatitis C virus infection receiving a standard bitherapy regimen to predict treatment response. Methods: Ninety-six patients with chronic hepatitis C were retrospectively selected. All patients received complete treatment with pegylated interferon in combination with ribavirin. Patients had serum sampling before starting treatment and at the end of treatment. Results were validated in an independent cohort of 51 patients. Results: Comparison of protein profiles in pretreatment and after-treatment serum allowed us to characterize 50 protein peaks, the level of which significantly varied. In the group of patients with sustained virologic response, 37 peaks displayed significant variation during treatment, whereas only one peak differed in nonresponders. A logistic regression analysis allowed us to define an algorithm composed of 2 protein peaks (fibrosis stage and genotype) that correctly predicted, in pretreatment serum, response to treatment in 89% of all patients with an area under the receiver operating characteristic curve of In the independent testing group, the same difference in proteome kinetics was observed between sustained responders and nonresponders. The algorithm correctly predicted treatment response in 81% of patients in the testing group. Conclusions: This study suggests that the kinetics of proteome are significantly different in serum of patients according to treatment response. Serum protein profiling allows prediction of response to antiviral treatment in a significant proportion of patients. Hepatitis C virus (HCV) infection is among the leading causes of chronic liver disease, with approximately 170 million people affected worldwide. 1 Morbidity and mortality are mainly related to the complications of long-term chronic hepatitis and, due to the slow and progressive natural evolution of the disease, liverrelated mortality is expected to increase several fold in the next decade. 2,3 The main treatment goal in patients with chronic HCV infection is the prevention of progressive hepatic fibrosis by virus eradication. Recently, advances have been made in the treatment of chronic hepatitis C by combination therapy using pegylated interferon plus ribavirin, thereby achieving higher sustained virologic response rates than with a regimen of standard interferon alfa plus ribavirin. 4 7 Despite such recent progress, the treatment of patients with chronic HCV infection remains a challenge both in terms of clinical effectiveness and cost-effectiveness. Interferon plus ribavirin therapy produces a number of well-described deleterious side effects, such as fatigue, influenza-like symptoms, hematologic abnormalities, and neuropsychiatric symptoms. 8 Combination therapy with pegylated interferons (peginterferon alfa-2a and alfa-2b) yields an adverse event profile similar to that of standard interferon, although the frequency of certain adverse events may vary. 9,10 Premature withdrawal from therapy due to adverse events was necessary in 10% 14% of participants in registration trials of these agents. 5 Considering the number of side effects and treatment costs, 11 prediction of virologic nonresponse early in therapy or even before starting therapy is potentially useful. Indeed, several cofactors significantly influence patient Abbreviations used in this paper: AUROC, area under the receiver operating characteristic curve; m/z, mass-to-charge ratio; SEFU, serum sampled at the end of the follow-up; SELDI-TOF/MS, surface-enhanced laser desorption ionization time-of-flight mass spectrometry; SEOT, serum sampled at the end of treatment; SO, serum sampled before starting treatment by the American Gastroenterological Association Institute /06/$32.00 doi: /j.gastro

2 2190 PARADIS ET AL GASTROENTEROLOGY Vol. 130, No. 7 response, such as genotype and advanced liver fibrosis, which may help to predict drug efficacy. In patients with a non-1 genotype (particularly genotypes 2 and 3), the optimal response rate is 75% 81%. In contrast, in patients with genotype 1 infection, the optimal regimen achieves a response rate of 41% 52%. 4 7,12,13 In this unfavorable group, prediction of treatment response is difficult to assess. The recent development of proteomic array technology, including protein profiling coupling ProteinChip array with surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF/MS ProteinChip technology; Cyphergen, Freemont, CA), provides a potentially powerful tool for global visualization of the proteome in a biological milieu It enables the obtaining of spectra composed of hundreds of protein peaks, each characterized by its mass-to-charge ratio (m/z). Comparison of protein profiles according to patient phenotypes led to the identification of isolated or clustered peaks characteristic of pathologic conditions. This approach has led to the discovery of new biomarkers, as recently shown in human immunodeficiency virus infection and in patients with prostate, pancreatic, ovarian, and liver malignancies The aim of our study was to investigate the global serum protein profile in patients with chronic HCV infection receiving antiviral treatment using Chip array proteomic technology. Our results show that serum profile variations differed substantially according to the response to antiviral treatment and that this approach might enable us to predict the efficacy (ie, the virologic response) of combination therapy with pegylated interferon plus ribavirin in patients with chronic hepatitis C. Patients and Methods Patients Inclusion criteria. Patients with chronic hepatitis C followed up at the Hôpital Beaujon were included in this study if they met the following criteria: (1) an established diagnosis of chronic hepatitis C with the presence of anti-hcv antibodies, detectable serum HCV RNA by reverse-transcription polymerase chain reaction, and findings consistent with chronic hepatitis C on liver biopsy and (2) the absence of other causes of chronic liver disease (undetectable hepatitis B surface antigen, no excessive alcohol consumption [defined as intake 30 g/day], hemochromatosis, autoimmune hepatitis, Wilson s disease, 1 -antitrypsin deficiency, primary sclerosing cholangitis, or primary biliary cirrhosis). Standard treatment regimen. All were treatmentnaive patients and received the same complete treatment schedule consisting of pegylated interferon alfa-2b (ViraferonPeg; Schering Corp, Kenilworth, NJ) at a dose of 1.5 g kg 1 wk 1 in combination with ribavirin at a dose adjusted according to body weight ( 65 kg, 800 mg/day; kg, 1000 mg/day; 85 kg, 1200 mg/day). Duration of treatment was 24 weeks for patients with HCV genotype 2 or 3 infection and 48 weeks for patients with HCV genotype 1 or 4 infection. Only patients with good observance (ie, those who received more than 80% dose of each drug and more than 80% of the duration of treatment) were selected. Follow-up. Patients were assessed as outpatients. Detection of HCV RNA in serum was performed at week 12, at the end of treatment, and 6 months after the end of treatment by reverse-transcription polymerase chain reaction (HCV Amplicor 2.0; Roche Diagnostics, Mannheim, Germany). Assessment of efficacy. Sustained virologic response was defined as undetectable HCV RNA determined 6 months after completion of treatment. Nonresponse was defined as detectable serum HCV RNA at the end of treatment in patients who received the complete treatment (more than 80% dose of each drug [pegylated interferon alfa-2b and ribavirin] and more than 80% of the duration). Serum sampling. For each patient, serum was sampled before starting treatment (SO), at the end of treatment (SEOT), and at the end of the follow-up (24 weeks after the end of treatment; SEFU). Serum samples were aliquoted and stored at 80 C until use for SELDI analysis. Biopsy. A liver biopsy specimen 1 cm was obtained before starting therapy. Biopsy specimens were read according to the METAVIR scoring system. 21 Patient cohorts. Ninety-six patients met all of the criteria and constituted the training group. Among them, 68 were sustained virologic responders and 28 were nonresponders. Baseline characteristics of the patients are shown in Table 1. Another cohort of 51 patients that met the same criteria of selection was also retrieved and used to validate the results at distance from the initial experiment. This cohort constituted the testing group. Ciphergen ProteinChip SELDI-TOF/MS Analysis Each serum aliquot was thawed and diluted (1:10) in denaturing buffer (7M urea, 2M thiourea, 4% CHAPS 4%, 1% dithiothreitol). Serum samples were processed using 3 different experimental conditions according to the manufacturer s protocols (Ciphergen Biosystems, Freemont, CA): cationic exchange (CM10) ProteinChip array/binding buffer containing 50 mmol/l sodium acetate, ph 5, and Triton 0.1%; anionic exchange (Q10) protein chip array/binding buffer containing sodium acetate 50 mmol/l, ph 6; and immobilized metal ion affinity capture (IMAC30) ProteinChip array loaded with zinc/ binding buffer containing NaCl 0.5 mol/l, 1 phosphatebuffered saline, and Triton X %. The array spots were preactivated with 100 mmol/l ZnCl 2 for 15 minutes at room temperature, followed by one wash with H 2 O. Five microliters of each diluted serum was spotted onto array chips and incubated with 95 L of binding buffer for 30 minutes. After 2 washes with binding buffer (5 minutes each)

3 June 2006 SERUM PROTEOME TO PREDICT VIROLOGIC RESPONSE 2191 Table 1. Baseline Characteristics of the Training Cohort Patients with chronic hepatitis C (n 96) Nonresponders (n 28) Sustained responders (n 68) Male sex 58 (60) 20 (71) 38 (57) Age (y), mean SD (range) (28 70) 47 9 (33 68) 45 9 (28 70) Source of infection Blood transfusion 15 (16) 6 (21) 9 (13) Intravenous drug use 31 (32) 11 (39) 20 (30) Unknown 50 (52) 11 (39) 39 (57) Alanine aminotransferase (IU/L), (25 397) (40 397) (25 390) median SD (range) HCV genotypes 1 43 (45) 19 (68) 24 (35) 2 16 (17) 0 (0) 16 (24) 3 24 (25) 3 (11) 21 (31) 4 12 (13) 6 (21) 6 (9) 5 1 (1) 0 (0) 1 (1) Grade A1 45 (47) 9 (32) 35 (52) A2 45 (47) 17 (61) 28 (42) A3 6 (6) 2 (7) 4 (6) Stage F1 40 (42) 6 (21) 34 (50) F2 34 (35) 10 (36) 24 (35) F3 14 (15) 6 (21) 8 (12) F4 8 (8) 6 (21) 2 (3) NOTE. Values are expressed as n (%) unless otherwise indicated. and one wash with binding buffer without Triton X-100 and HEPES 1 mmol/l, the air-dried arrays were saturated with sinapinic acid in 0.5% trifluoroacetic acid and 50% acetonitrile before being read on the instrument (Ciphergen Protein- Chip Reader; Ciphergen Biosystems). All samples were tested during the same experiment. The arrays were analyzed with the Ciphergen ProteinChip Reader (model PBS II). The mass spectra of proteins were generated using an average of 195 laser shots. For data acquisition of low-molecular-weight proteins, the detection size range was between 3 and 30 kilodaltons. Detector sensitivity was set at 7, and laser intensity was set at 190. For the high-molecular-weight proteins, the detection size range was between 30 and 150 kilodaltons. The detector sensitivity was set at 7, and the laser intensity was set at 210. The m/z of each of the proteins captured on the array surface was determined according to externally calibrated standards (All-in-one Protein II; Ciphergen Biosystems). According to the manufacturer, the mass accuracy of the spectrometer was 0.1%. Intra-ProteinChip array reproducibility was checked by spotting 8 different aliquots of one sample on the same array, and inter-proteinchip array reproducibility was checked by including one given sample on each different array. The intra- and inter- ProteinChip array coefficients of variation were assessed for all protein peaks above background according to the setting of detection. The mean intra- and inter-proteinchip array coefficients of variation were 10% and 25%, respectively. 20 Data Mining and Statistical Analysis Spectra were analyzed with ProteinChip software version (Ciphergen Biosystems). For each comparison, the raw intensity data were normalized by using the total ion current of all profiles. The peak intensities were normalized to the total ion current of m/z between 1500 and 30,000 daltons for the low-molecular-weight range and between 1500 and 150,000 for the high-molecular-weight range. To characterize protein peaks of potential interest differentially expressed between 2 phenotype classes of patients, intensity of each protein peak was compared according to groups using nonparametric tests (Mann Whitney). Peaks were considered as significantly different for P.001. To generate the best index for discrimination, the logistic regression function that combined the most discriminatory peaks was calculated using NCSS 2001 software (Kaysville, UT). Peaks of potential interest included in the model were those selected in previous univariate analysis. To assess the diagnostic value of a peak, individually or in combination, determination of the area under the receiver operating characteristic curve (AUROC) was performed. For class prediction, we used the class prediction tool from BRB-ArrayTools, version (available at The class prediction tool constructs predictors for classifying experiments into phenotype classes based on expression levels. Five methods of prediction are used: compound covariate predictor, diagonal linear discriminant analysis, k-nearest neighbor (using k 1 and 3), nearest centroid, and support vector machines. The class prediction tool determines the cross-validated misclassification rate and performs a permutation test to determine whether the cross-validated misclassification rate is lower than would be expected by chance.

4 2192 PARADIS ET AL GASTROENTEROLOGY Vol. 130, No All patients Genotype non 1 Genotype 1 F0-F2 F3-F4 A1 A Genotype Fibrosis Activity Figure 1. Number of peaks that significantly changed in intensity between the beginning and the end of treatment according to genotype, stage of fibrosis, and grade of activity in the training group. Results Serum Protein Profiling With SELDI-TOF/MS Using SELDI-TOF/MS and 3 different Protein- Chip arrays (CM10, Q10, and IMAC-Zn), each serum yielded a mean of 695 peaks with m/z varying from 3000 to 100,000 daltons. For each of the 96 patients, SO, SEOT, and SEFU were studied and serum protein profiles were compared. Longitudinal Evolution of Serum Protein Profiles of Patients With Chronic Hepatitis C Before, at the End of Antiviral Treatment, and at the End of Follow-Up in the Training Group Comparison of SO and SEOT serum profiles showed that among 695 peaks, 50 (7.1%) varied significantly in intensity (P.001). Among them, 23 peaks decreased and 27 increased under treatment. Comparison of SEOT and SEFU serum protein profiles also showed significant variation for 16 peaks (2%; 9 increased and 7 decreased). Peaks that changed in intensity between SO and SEOT were different from those that changed between SEOT and SEFU except for one peak (cm36012,8). Thus, SELDI-TOF/MS protein profiling distinguished sets of protein peaks, the intensity of which significantly changed during and after cessation of antiviral treatment. Serum Protein Profile Variations According to Genotype and Histology in the Training Group We compared longitudinal serum protein profiles under antiviral therapy according to hepatitis C genotype. When comparing patients with genotype 1 (n 43) and those with other genotypes (n 53), a significant difference was observed. While only 6 peaks significantly differed between SO and SEOT in the group of patients with genotype 1, 33 peaks differed in the group of patients with other genotypes (Figure 1). When taking into consideration the group of patients with non clinically significant fibrosis (F1 and F2; n 74) and comparing serum proteome modifications before and at the end of treatment (SO vs SEOT), 41 peaks significantly differed. In contrast, the serum proteome profile differed by only 1 peak in the 22 patients with significant liver fibrosis (F3 or F4). In patients with mild necroinflammatory activity at the time of initial biopsy (A1; n 45), the serum proteome profile differed significantly in 29 peaks (SO vs SEOT). The level of 16 peaks changed according to treatment in patients with moderate or severe activity (A2 or A3; n 51) (Figure 1). These results suggest that the usual predictive factors of treatment response might influence the serum proteome variation. Serum Protein Profile Evolution According to Treatment Response in the Training Group Because deleterious factors in response to treatment seem to influence proteome variations, we compared longitudinal evolution of serum protein profiles according to the presence or absence of a sustained virologic response. In the group of patients who obtained sustained virologic response to treatment, 37 peaks showed significant variation in intensity when comparing SO and SEOT (16 increased and 21 decreased). For the group of nonre-

5 June 2006 SERUM PROTEOME TO PREDICT VIROLOGIC RESPONSE cm32835_ S0 1J0 2W24 SEOT 3F24 SEFU 0 1J0 2W24 3F24 S0 SEOT TEMPS SEFU Figure 2. Variation in intensity of the cm32835 peak before beginning of treatment, at the end of treatment, and in the follow-up serum sample in the group of (A) sustained responders and (B) nonresponders in the training group. sponders, only one peak differed significantly between SO and SEOT (cm ; decreased). Figure 2 shows the longitudinal variation of individual intensity of the most significant peak (cm32835) in the group of responders (Figure 2A) and in the group of nonresponders (Figure 2B). When comparing serum protein profiles in SEOT and SEFU, a similar conclusion was reached. Whereas none of the peaks showed significant changes in the group of nonresponders, the level of 12 peaks significantly changed (8 increased and 4 decreased) in the group of sustained responders. All protein peaks that changed between SEOT and SEFU were different from those that significantly changed when comparing SO with SEOT except for two (qm and qm8465). Results are summarized in Figure 3. Therefore, while no significant variation in serum protein profiling was observed in the group of nonresponders, sustained responders displayed significant variation during treatment and posttreatment follow-up. Prediction of Response to Antiviral Treatment According to Initial Serum Proteome Profiles in the Training Group We analyzed whether the initial serum protein profile could help to predict antiviral treatment response. Therefore, the protein profiles of SO sera were compared according to subsequent response to therapy. We identified a set of 6 peaks, the levels of expression of which significantly differed between responders and nonresponders in pretreatment sera (P.001). Four of them were significantly higher in the group of nonresponders, whereas 2 were increased in the group of sustained responders. Results are shown in Figure 4. Diagnostic value for the prediction of response to therapy of each peak taken individually was assessed with AUROC. AU- ROC ranged from 0.69 to 0.74 according to the different protein peaks. To increase the performance of the prediction of response to treatment, the most efficient peak combination predictive of treatment response was determined using regression analysis. The 6 peaks that significantly differed in univariate analysis were included in the model. The most accurate index allowed correct classification of Figure 3. Number of peaks that significantly changed in intensity between the beginning (SO) and end of treatment (SEOT) and between the end of treatment (SEOT) and follow-up (SEFU) in all patients, the group of sustained responders, and the group of nonresponders in the training group.

6 2194 PARADIS ET AL GASTROENTEROLOGY Vol. 130, No. 7 Figure 4. Box plot of peaks that distinguish the group of nonresponders from the group of sustained responders in the initial serum sample in the training cohort. 80% of patients with an AUROC of For a threshold of 0.8, the index had a sensitivity of 81%, a specificity of 80%, a positive predictive value of 91%, and a negative predictive value of 62%. Interestingly, in the subgroup of genotype 1 patients, the algorithm had an AUROC of For a threshold of 0.8, sensitivity was 77% and specificity was 78% with a positive predictive value of 81% and a negative predictive value of 74%. Prediction of Treatment Response According to Genotype, Serum Protein Profile, and Histology in the Training Group Finally, an algorithm that included not only significantly different protein peaks but also fibrosis stage (F0 2 vs F3 4) and genotype (1 vs non-1) was constructed by logistic regression. The best algorithm including 4 variables (cm , fibrosis stage, genotype, and qm ) led to accurate prediction of the response to treatment in 89% of all patients with an AUROC of By comparison, the best algorithm including genotype and fibrosis stage only had an AU- ROC of For a threshold of 18, sensitivity was 93%, specificity was 93%, positive predictive value was 84%, and predictive negative value was 98%. Validation in an Independent Testing Group To validate this experimental approach, the same study was performed in an independent experiment with another cohort of patients that met the same inclusion criteria. This validation cohort included 51 treatmentnaive patients who followed the same complete treatment schedule. Duration of treatment was 24 weeks for patients with HCV genotype 2 or 3 infection and 48 weeks for patients with HCV genotype 1 or 4 infection. Patients had serum measured at time of starting treatment and at the end of treatment. The group included 29 men and 22 women (mean age, 45.1 years; range, years). Thirty-eight were sustained responders and 13 nonresponders. Twenty-three patients were genotype 1, and 28 were a non-1 genotype. Thirty-five patients had mild fibrosis (F1 2), and 16 patients had significant fibrosis (F3 4). Data are shown in detail in Table 2. Comparison of serum proteome profiles before and at the end of treatment in the 51 patients of the validation cohort showed that 77 peaks significantly varied in intensity. As observed in the training group, there was a significant difference in proteome kinetics according to histology and genotype. In the group of patients with non clinically significant fibrosis (F1 2), 69 peaks significantly differed when serum proteome before and at the end of treatment was compared. In contrast, profile differed by only 13 peaks in the 16 patients with significant fibrosis (F3 4). Whereas 19 peaks significantly differed in the group of patients with genotype 1, 36 peaks differed in the group of patients with other genotypes. As observed in the initial cohort, kinetics of proteome were significantly different according to treatment response. Whereas only 2 peaks significantly differed before and after treatment in the nonresponder group, 23 peaks significantly varied in the sustained responders. Comparison of initial serum proteome according to treatment response identified 14 protein peaks, the level of which was significantly different according to treatment response. Among this set of protein, 2 of the 6 initially characterized peaks reached statistical significance (qm and cm ; P.001). The other 4 had marginal significance level when responder and nonresponder groups were compared. Using the algorithm previously defined in the training group that included fibrosis stage, virus genotype, qm , and cm , treatment response could be predicted in 81% of patients with an AUROC of Discussion SELDI-TOF/MS ProteinChip is a potentially powerful technique for monitoring the overall protein

7 June 2006 SERUM PROTEOME TO PREDICT VIROLOGIC RESPONSE 2195 Table 2. Baseline Characteristics of the Testing Cohort Patients with chronic hepatitis C (n 51) Nonresponders (n 13) Sustained responders (n 38) Male sex 29 (57) 7 (54) 22 (58) Age (y), mean SD (range) (21 70) 51 9 (36 70) (21 69) Source of infection Blood transfusion 11 (22) 4 (31) 7 (18) Intravenous drug use 17 (33) 5 (38) 12 (32) Unknown 23 (45) 4 (31) 19 (50) Alanine aminotransferase (IU/L), (25 258) (27 258) (25 217) median SD (range) HCV genotypes 1 23 (45) 9 (69) 14 (37) 2 8 (16) 0 (0) 8 (21) 3 11 (21) 1 (8) 10 (26) 4 9 (18) 3 (23) 6 (16) 5 0 (0) 0 (0) 0 (0) Grade A1 22 (43) 6 (46) 15 (39) A2 26 (51) 5 (38) 20 (53) A3 3 (6) 2 (15) 3 (8) Stage F1 18 (35) 5 (38) 13 (34) F2 17 (33) 3 (23) 15 (40) F3 11 (22) 4 (31) 5 (13) F4 5 (10) 1 (8) 5 (13) NOTE. Results are expressed as n (%) unless otherwise indicated. profile of all biological samples. Comparison of proteome profiles in groups of patients with different phenotypes enables the identification of specific biomarkers Using this approach and comparing proteome profiles before and at the end of treatment, we clearly showed that antiviral treatment is associated with longitudinal serum proteome changes in patients with chronic hepatitis C. Although these changes were expected, no study had previously investigated, via a global approach, the magnitude, timing, and influence of antiviral drugs on whole serum proteome kinetics. More surprisingly, comparison of serum proteomes at the end of treatment and 24 weeks after the end of treatment also showed that the serum protein continued to evolve. Because patients did not receive any further treatment, this strongly suggests that these peaks reflect delayed host protein production and regulation. Because, in the second phase, no peak except one was identical to peaks previously identified in the first phase, it is presumed that a biphasic host protein response might be produced in response to antiviral treatment, and it also clearly shows that despite treatment arrest, the healing process remains active. The primary goal of treatment of hepatitis C is eradication of the virus. Although sustained virologic response rates have considerably increased recently due to current optimal treatment consisting of pegylated interferon in association with ribavirin, a significant number of patients fail to respond. Interestingly, our study shows significant differences in proteome kinetics between sustained responders and nonresponders. Although several protein peaks in sustained responders changed in intensity in the first or second phase, no changes (except for one peak) were observed in the group of nonresponders. This strongly suggests that longitudinal serum protein profiling can mirror the response to treatment and that a favorable outcome is associated with a high degree of proteome variation. Whether or not these changes occur very early after the beginning of therapy is not known, and it will be of major interest in patient monitoring to investigate the kinetics of serum protein profiles at earlier time points with respect to further response to treatment. Wide-scale clinical trials have shown that several virus- and host-related factors are associated with significantly lower virologic response rates. Among these factors, genotype is the most important. In patients with HCV genotype 1 infection, virologic response rates are 50%, whereas in patients with HCV genotype 2 or 3 infection, virologic response rates are approximately 80%. 4 7,12,13 Host-related factors associated with lower virologic response include older age, cirrhosis, obesity, and immunodeficiency. 22 Interestingly, when comparing longitudinal proteome variations according to genotype or histologic fibrosis, a difference was also observed. While patients with genotype 1 or clinically significant fibrosis did not display longitudinal proteome modifica-

8 2196 PARADIS ET AL GASTROENTEROLOGY Vol. 130, No. 7 tions, those in the non-1 genotype group or with mild fibrosis displayed significant variations in serum proteome. These results also support a relationship between significant proteome modifications and a favorable response to treatment. Because numerous patients fail to respond to optimal antiviral treatment or have significant side effects that necessitate treatment arrest, it is of major interest for patient welfare, and from an economic standpoint, to be able to predict failure to response as early as possible, ideally at baseline, before initiating treatment. Among predictive factors of response, the predictive value of HCV kinetics during treatment seems to be of greater importance than any virus- or host-related baseline factors. A reduction in HCV RNA serum levels of 2 log 10 IU/mL after the first 12 weeks of treatment compared with baseline is clearly associated with virologic nonresponse. 23 Although of potential importance, these data were generated in clinical trials using centralized laboratories and must be confirmed in other settings. Furthermore, the positive predictive value is low, because among patients who achieved an early virologic response (defined as a 2-log decrease from baseline HCV RNA levels), 65% subsequently had a sustained virologic response. 5 Finally, information on HCV kinetics is available only after 12 weeks of treatment. Because protein profiles differ in patients before and after treatment, and because we had shown that proteome kinetics differ between groups, we sought to determine whether initial serum proteome could distinguish those patients who would respond. Indeed, a set of 6 protein peaks in pretreatment serum had highly significantly different values when both groups were compared; 4 were overexpressed in the nonresponder group, and 2 were increased in the group of sustained responders. An index combining these 6 peaks enabled us to correctly predict classification of 80% of patients in the 2 groups. Finally, when including not only proteomic data but also genotype and stage of fibrosis, we were able to define an index including 2 protein peaks that led us to correctly predict drug response in the vast majority of patients and especially in the group of patients with genotype 1 virus. To validate these data, we tested an independent cohort of patients with the same inclusion criteria. Similar trends were observed with major proteome changes in the sustained responder group whether proteome of nonresponders did not change significantly. Furthermore, the prediction was equally accurate in the training set as in the independent testing sample set of patients, achieving an accurate prediction of 81% of patient responses. This index, which must be validated in populations from other centers, might be very useful in the management of patients with chronic HCV infection. It is noteworthy that SELDI-TOF/MS enables characterization of proteins only by their m/z. Identification of protein peaks of potential interest can be achieved using sophisticated tools such as the SELDI-TOF interface and tandem mass spectrometry. 21 This technique might be useful for characterizing molecular species associated with these peaks to develop more simple serum tests for predicting treatment response. Lastly, and if proteome changes occur early after starting treatment in sustained responders, comparison of serum proteome before and during therapy on an individual basis should help to predict sustained treatment response even in the absence of protein peak identification. Further studies are needed to confirm the potential of this direct approach. In conclusion, our study shows that antiviral treatment induces longitudinal changes in serum proteome and that these variations are closely associated with the virologic response to treatment. Moreover, baseline serum proteome analysis in naive patients enables the prediction of the response to therapy in most of our patients both in a training and in an independent testing cohort. Molecular identification of these peaks would be of major interest for developing serum tests useful in patient care. References 1. Global surveillance and control of hepatitis C. 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