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 0.92. 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.
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 0.92. 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.
Authors: Robert J Fontana; Herbert L Bonkovsky; Deepa Naishadham; Jules L Dienstag; Richard K Sterling; Anna S F Lok; Grace L Su Journal: Clin Gastroenterol Hepatol Date: 2008-11-07 Impact factor: 11.382
Authors: Tarik Asselah; Emilie Estrabaud; Ivan Bieche; Martine Lapalus; Simon De Muynck; Michel Vidaud; David Saadoun; Vassili Soumelis; Patrick Marcellin Journal: Liver Int Date: 2010-10 Impact factor: 5.828
Authors: C Mancone; F Ciccosanti; C Montaldo; A B Perdomo; M Piacentini; T Alonzi; G M Fimia; M Tripodi Journal: Clin Microbiol Infect Date: 2012-10-03 Impact factor: 8.067