A Multi-Omics Approach to Identify a Biomarker Panel for the Early Detection of Hepatocellular Carcinoma in Hepatitis C+ Individuals

University Of Texas Med Br Galveston, Galveston TX

Investigators

Abstract

PROJECT SUMMARY/ABSTRACT Our work to develop novel algorithms for predictive modeling will for the first time combine results from the use of both metabolomics and proteomics to identify multiple biomarkers that can be used to identify those Hepatitis C (HCV) patients who have developed early-stage Hepatocellular Carcinoma (HCC). Our long-term goal is to create a non-invasive diagnostic test that is both highly specific and sensitive for the detection of early-stage liver cancer. The overall objective of this project is the creation of a biomarker panel, developed from a predictive model, that can be used for the detection of early-stage HCC. Our central hypothesis is that machine learning algorithms can be used on urine and serum metabolomic data and/or serum proteomic data to create a predictive model for identifying those individuals with HCV that have developed early-stage HCC. The rationale underlying this project is that a combination of metabolites and proteins will yield a highly specific and sensitive predictive model to identify HCV subjects that have developed early stage HCC. Our central hypothesis will be objectively tested by pursuing two Specific Aims. Aim 1 is to test the hypothesis that combining proteomics data with metabolomics data will create a better predictive model for HCC. Based on preliminary data, our hypothesis is that a biomarker panel consisting of several metabolites and proteins will be more specific and sensitive than either set of data alone. Aim 2 is to test the hypothesis that combining different types of metabolomics data specific to Stage IV HCV will improve the predictive accuracy of a model for the early detection of HCC. Based on preliminary data, we hypothesize that by combining results from multiple platforms, we will create a more specific and sensitive predictive model for the early detection of HCC in Stage IV HCV individuals than either platform alone is capable of achieving. This project is significant because it will be the first step towards the early detection of HCC, and will lead to the development of a sensitive and specific diagnostic test for early-stage HCC, and thus to improved patient outcomes and long- term survival. This research is innovative because we are using Stage IV HCV individuals with liver cirrhosis as a control for HCC individuals (as all HCC patients have some amount of cirrhosis), and in using both metabolomics and proteomics on both urine and serum samples from infected patients to identify potential biomarkers.