Grant Number:	1R01CA312098-01 Interpret this number
Primary Investigator:	Darst, Burcu
Organization:	Fred Hutchinson Cancer Center
Project Title:	Metabolomic Mechanisms Contributing to Prostate Cancer Risk Across Multiple Populations
Fiscal Year:	2026

1 2 Prostate cancer (PCa) is the most common cancer and second leading cause of cancer death in US men, with 3 Black men having the highest incidence and mortality rates. Despite being a leading cause of cancer and cancer 4 mortality, little is known about modifiable risk factors that could inform PCa prevention. Metabolic dysregulation 5 contributes to PCa pathogenesis, as it accommodates for increased energy demands during cancer growth. 6 Accordingly, we and others have found metabolites associated with PCa risk, progression, and aggressiveness, 7 including phospholipids and amino acids involved in glutamate, taurine, and tryptophan metabolism, with some 8 findings supported by dietary studies. As such, metabolomics could inform novel PCa risk stratification and 9 preventive strategies. However, many other reported metabolites have not been replicated due to differences in 10 metabolomic platforms and limited sample sizes. Further, previous metabolomic studies of PCa risk were limited 11 to European descent populations, limiting discovery and the generalizability of findings. Given the multi-factorial 12 nature of the metabolome, being strongly influenced by both genetic factors and exposures, it is uniquely 13 positioned to illuminate the functional consequences of genetic risk factors and to identify potentially modifiable 14 environmental risk factors, which could have implications for preventive and treatment targets, as well as PCa 15 risk modeling. We propose to investigate the contribution of circulating pre-diagnostic metabolomics to overall 16 and aggressive PCa risk across populations. We will generate circulating metabolomic data on 3,006 PCa cases 17 and 3,006 controls from Black, Hispanic, Japanese American, and White individuals from the Multiethnic Cohort, 18 which includes detailed health, dietary, and lifestyle information, genetic data, blood specimens, and up to 30 19 years of follow-up data. We will leverage existing PCa metabolomic studies, bringing our sample size to >12K 20 (>5K cases) with untargeted metabolomic data (quantified on the same platform) across six deeply characterized 21 longitudinal cohorts to ensure well-powered and robust findings, with validation in >225K (>18K cases; quantified 22 on multiple metabolomic platforms). In Aim 1, we will establish metabolomic profiles of overall and aggressive 23 PCa risk within and across populations. In Aim 2, we will integrate germline genetic and metabolomic data to 24 disentangle genetic and environmental mechanisms underlying PCa-metabolite associations. Specifically, we 25 will identify metabolites reflecting causal mechanisms of PCa risk and metabolites reflecting environmentally- 26 driven mechanisms. In Aim 3, we will investigate the combined effect of metabolomic, genetic, lifestyle, and 27 clinical factors on risk of overall and aggressive PCa, building comprehensive clinical models of PCa disease. 28 Results are expected to provide novel modifiable and genetic mechanisms to target for prevention and improve 29 our ability to identify high-risk patients who would benefit from earlier or more intensive PCa screening across 30 populations, which could ultimately reduce PCa mortality and outcome differences between populations. PROJECT SUMMARY