Molecular Prognostic Indicators in Liver Cirrhosis and Cancer

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DESCRIPTION (provided by applicant): The accelerating incidence and dismal mortality of hepatocellular carcinoma (HCC) (5-yr survival <12%) highlight the urgent need to improve prediction and prevention of this deadly neoplasm. HCC develops from advanced fibrosis due to hepatitis B virus (HBV), hepatitis C virus (HCV), alcohol, or non-alcoholic fatty liver disease (NAFLD). A 186-gene prognostic signature in stromal cirrhotic liver, previously identified and validated in HCV-related cirrhosis, holds promise to identify patients with highest HCC risk and provide clues to HCC prevention targets. It remains unknown whether this signature is also prognostic for other HCC etiologies. Moreover, the signature has not yet been adapted as a clinically applicable test. Our long-term goal is to develop clinically applicable prognostic biomarkers for the global cirrhosis population caused by variety of etiologies to enable more effective, personalized HCC surveillance and prevention. The objective here is to establish prognostic relevance of the 186-gene signature in the non-HCV etiologies, identify etiology-specific molecular prognostic factors, implement and refine the signature as a clinically applicable assay, and develop genomic database for prognostic biomarker assessment. Our central hypotheses are that the 186-gene signature is universally prognostic irrespective of etiology, and that there are also complementary, etiology-specific prognostic indicators. We will test these hypotheses and address the unmet need by the following interrelated Specific Aims: 1. Establish a gene signature of HCC risk and poor prognosis in patients with non-HCV etiologies of advanced liver disease. We will perform whole-genome expression profiling of stromal cirrhotic liver tissues (905 cirrhosis and 1025 HCC cases affected by HBV, HCV, alcohol, or NAFLD), and validate predictive capability for HCC development as well as cirrhosis progression and death. Etiology-specific prognostic gene signatures will also be identified using the whole-genome datasets. 2. Develop a clinically applicable prognostic test using gene signature that defines HCC risk. We will implement the signature in the digital transcript counting technology specifically designed for clinical lab (nCounter assay, NanoString), and optimize experimental and analytical methods. Transcriptome sequencing-based prediction will also be evaluated as a potential future platform of a clinical test. 3. Develop a web resource for in silico prognostic biomarker assessment for cirrhosis and HCC. Our new and existing datasets, representing a global cirrhosis patient population, will be assembled as a public database that enables quick and easy clinical utility assessment of a user's prognostic molecular signatures.