OBJECTIVES/SPECIFIC AIMS: Given the poor prognosis of HCC and its increasing incidence worldwide, identifying biomarkers of HCC has been an active area of research. While biomarkers are being identified at a rapid pace, many are still in early phases of clinical study and very few have proven clinical utility. The objective of this study is to identify novel biomarkers of HCC and evaluate their clinical utility as predictors of disease development and prognosis with specific emphasis on disease recurrence after liver transplantation. Biomarkers will be identified through GWAS, as well as through analysis of qualitative and quantitative liver traits by magnetic resonance imaging (MRI). These novel biomarkers will then by implemented into risk prediction models aimed to assess an individual’s risk for development of HCC and stratify their level of risk according to predicted disease prognosis. METHODS/STUDY POPULATION: This will be a case-control study, analyzing data from previously created biorepositories from four cohorts of recipients across multiple centers which have undergone liver transplant. First, a GWAS will be performed to identify genetic variant(s). Second, pre-transplant MRI’s will be evaluated using CAVASS software to assess liver quantitative and qualitative traits, including visceral adiposity. Lastly, these findings will be implemented into risk stratification models to assess each individual’s level of risk for development of HCC and for recurrence of HCC after transplant. RESULTS/ANTICIPATED RESULTS: We hypothesize that genetic variant(s) are associated with positive HCV status and the development of HCC. Additionally, we hypothesize that increased visceral adiposity measured by MRI will have an association with recurrence of HCC after transplant. Lastly, we hypothesize that possession of these aforementioned features will be associated with an increased risk of HCC development and recurrence after transplant. DISCUSSION/SIGNIFICANCE OF IMPACT: As more is learned about the nature and reliability of these biomarkers, their potential clinical applications will be revealed. Ideally these proposed risk score models will ultimately be used by clinicians to provide personalized disease management while optimizing the allocation of health care resources. For instance, this may lead to changes in the MRI screening frequency of patients considered to be at high risk for HCC. The ability to diagnose patients early and provide personalized therapies may ultimately result in fewer disease related mortalities in the future.