Biomarker Discovery for Hepatitis C Progression using Machine Learning Techniques

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DESCRIPTION (provided by applicant): The incidence of Hepatitis C Virus (HCV) infection in the general population is growing significantly which is of great concern. The progression of the viral infection as well as treatment modality critically depends on the patient's stage of fibrosis. Thus, we need to be able to clearly distinguish between the five stages of liver fibrosis associated with HCV infection if we are to prescribe the proper treatment. Little is known about how HCV infection progresses to liver cancer in patients with advanced fibrosis, so one important goal of this project is to discover biomarkers for the detection of early stage liver cancer. The different stages of fibrosis associated with HCV infection will be compared to discover which proteins and metabolites are differentially expressed; the goal of which will be the development of a biomarker panel for fibrosis stage determination. Also, patient's proteomic and metabonomic responses to therapy will be examined to determine a priori which individuals will respond well to therapy. Patients with liver cancer will also be compared to HCV infected patients to develop a biomarker panel for the detection of early stage liver cancer. For the development of such biomarkers, Surface Enhanced Laser Desorption/ lonization and metabonomics experiments will be conducted on serum and urine samples from infected patients. I will use classical methods of data analysis such as principal components analysis, hierarchical clustering, neural networks, and logistic regression to create a first order biomarker panel. In phase two, I will utilize more sophisticated machine learning techniques such as kernel methods and support vector machines (SVM). My research will especially focus on making advances in SVMs to create high quality biomarker panels for HCV infection and disease progression. The specific aims are to 1) analyze the HCV data using classical techniques to identify proteins and metabolites which are differentially expressed in the various stages of fibrosis; 2) analyze the data using SVMs to more accurately classify the stage of fibrosis; 3) improve the SVM methodology to obtain a more reliable diagnostic of liver fibrosis stage; 4) analyze patient response to treatment using improved SVM techniques to determine which patients are more likely to respond to therapy; and 5) develop markers that distinguish cancer versus non-cancer patients with HCV from applications of SVMs.