Baseline profile of intrinsic cytokines predicting prognosis of chronic hepatitis B patients responding to HBV therapeutic vaccinations

Abstract

ABSTRACTObjectiveTo explore relevant biomarkers in chronic HBV (CHB) infected individuals, and whether their presence can be related to the prognosis of CHB (i.e., used as a prediction tool) and used as inclusion and exclusion criteria in clinical trials.MethodsThirty-four (34) cytokines and chemokines were analyzed in the baseline plasma of 130 chronic HBV infected patients and were matched with the clinical outcomes of these patients regarding to their responses to anti-HBV treatment by a mathematic model based on the Boolean method. A retrospective analysis was implemented to establish the prediction model, and a perspective analysis was performed to verify the prediction efficacy.ResultsThrough analyzing 34 cytokines and chemokines in the baseline plasma of 130 chronic HBV infected patients by Boolean methods, we generated a predicting model successfully capable of screening out therapy non-responded patients. In this prediction model, six cytokines, including IL-8, IL-10, IL-17, IL-1RA, IFN-α, IL-18, defined as expressed or not-expressed, contributed to 21 possibilities, every of which predicts a clinical outcome. The model was verified in a separate chronic HBV infected population database, which included 76 patients, with 100% responders and 50% who are not responded to the immunotherapy identified.ConclusionsThe prediction model can be used to screen CHB patients as the inclusion incorporated into HBV clinical design and practice. By screening out inappropriate participants in clinical trials, therapy response rate may rise and lead to a more homogeneous responding population. For Boolean method which requires continuous iteration, more accurate prediction models will be established with more homogeneous data. This is very helpful for revealing the reason why certain CHB individuals can be functionally cured and others were not. The method may also have great potential and possible applications for other immunotherapies in the future.Significance of this studyWhat is already known about the subject?Chronic hepatitis B virus (CHB) infection can be controlled while rarely cured, or functionally cured. The exact reason why certain CHB individuals can be functionally cured and others were not, regarding to different treatment strategies, remains unclear.Lack of relevant immunological biomarkers are often to blame clinical failures in immunotherapeutic treatments, particularly for the hepatitis B virus (HBV) therapeutic vaccination, since such trials use virological parameters as inclusion and exclusion criteria of patients, but seldom more relevant immunological biomarkers.What are the new findings?Using patterns of cytokines, instead of single cytokines, to present CHB individuals’ immune status can help discovering the prognosis of their responses or not response to HBV therapeutic vaccination.By utilizing the model, we predicted 10 patients out of 10 who were sensitive to the anti-HBV immunotherapy and 33 out of 66 who were not, in a distinct CHB population, and verified the predicting efficacy.How might it impact on clinical practice in the foreseeable future?Immune status, presented by different patterns of cytokines/chemokines, might be used as an in/exclusion criteria in clinical trials to select a more appropriate treatment for CHB individuals.By screening out inappropriate participants in clinical trials, therapy response rate may rise and lead to a more homogeneous responding population. For Boolean method which requires continuous iteration, more accurate prediction models will be established with such more homogeneous data. This is very helpful for revealing the reason why certain CHB individuals can be responsive to the treatments and toward the functionally cured and others could not.