Abstract
OBJECTIVES/GOALS: Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients prognosis. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. METHODS/STUDY POPULATION: Current predictive models for GBC outcomes often utilize clinical data only. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Rochester, Minnesota, Daegu, Korea, Vilnius, Lithuania, and Calgary, Canada. We will perform next-generation RNA sequencing on these tissue samples. The patients clinical, pathologic and survival data will be abstracted from the medical record. Random forests, support vector machines, and gradient boosting machines will be applied to train the data. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. RESULTS/ANTICIPATED RESULTS: Our preliminary analysis of next generation RNA sequencing from 18 GBC tissue samples identified recurrent mutations in genes enriched in pathways in cytoskeletal signaling, cell organization, cell movement, extracellular matrix interaction, growth, and proliferation. The top three most significantly altered pathways, actin cytoskeleton signaling, hepatic fibrosis/hepatic stellate cell activation, and epithelial adherens junction signaling, emphasized a molecular metastatic and invasive fingerprint in our patient cohort. This molecular fingerprint is consistent with the previous knowledge of the highly metastatic nature of gallbladder tumors and is also manifested physiologically in the patient cohort. DISCUSSION/SIGNIFICANCE: Integrative analysis of molecular and clinical characterization of GBC has not been fully established, and minimal improvement has been made to the survival of these patients. If overall survival can be better predicted, we can gain a greater understanding of key biomarkers driving the tumor phenotype.
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