Grant Number:	5U01CA262678-03 Interpret this number
Primary Investigator:	Shu, Xiao-Ou
Organization:	Vanderbilt University Medical Center
Project Title:	Searching the Blood Metabolome to Identify Risk Biomarkers for Biliary Tract Cancer
Fiscal Year:	2024

Summary Biliary tract cancer (BTC), which includes cancers of the gallbladder and bile ducts, is the second most common primary hepatobiliary cancer worldwide. BTC is highly fatal, with ~90% of its patients dying within five years after diagnosis. A better understanding of the etiology of BTC is critical for designing cost-effective prevention strategies to reduce the morbidity and mortality of this fatal cancer. The biliary tract plays a central role in the metabolism and absorption of fat-soluble endogenous and exogenous compounds and in the maintenance of normal liver functions. Many known risk factors for BTC are related to metabolic disturbance, suggesting a significant role of metabolic perturbance in BTC pathogenesis. Thus, a systematic investigation of circulating metabolites could provide valuable information regarding biomarkers for BTC risk and biological mechanisms of BTC pathogenesis. Herein, we propose a well-powered, multi-ancestry study, using resources from 17 large prospective cohort studies around the world and five large genetic consortia/studies of BTC, to identify and validate metabolomic biomarkers for BTC risk. In Aim 1, we propose to analyze blood samples collected prior to any cancer diagnosis from 750 incident cases and 750 matched controls to systematically search the blood metabolome to identify promising metabolite biomarkers for replication. In Aim 2, we will use genetic variants associated with circulating metabolites and data from large genome-wide association studies (GWAS) of BTC to search for additional promising metabolite biomarkers for replication. In Aim 3, we will quantify 50 promising metabolites selected from Aims 1 and 2 and evaluate their associations with risk of BTC overall, and by its subsites, in an independent sample of nearly 2000 cases and their matched controls. Finally, in Aim 4, we will build risk prediction models for BTC using metabolite biomarkers, genetic risk variants and traditional risk factors. This is the first large study ever conducted to systematically search the blood metabolome to identify risk biomarkers for BTC, and the first study to integrate metabolomic and genomic data in BTC biomarker research. Including multi-ancestry populations will allow us to cross-validate research findings and identify potential racial differences in biomarker associations. This study will provide substantial novel data to improve the understanding of BTC etiology and identify biomarkers for BTC risk assessment.





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