||5R01CA258808-02 Interpret this number
||University Of Southern California
||An Integrative Multi-Omics Approach to Characterize Prostate Cancer Risk in Diverse Populations
In the US, prostate cancer (PCa) is the second leading cause of cancer death in men, with men of African
ancestry having the highest incidence and mortality rates. Indeed, men of African ancestry who develop PCa
have more aggressive and lethal prostate tumors on average, compared to their non-African ancestry
counterparts. While the reasons for this health disparity are unknown, evidence suggests that genetics is likely
a contributing factor. Indeed, large-scale genome-wide association studies (GWAS) of PCa have identified 300
genomic risk variants; however, the vast majority are in non-coding regions, which makes identifying the proximal
target gene challenging and hinders translational efforts. A large body of works have demonstrated that PCa risk
is highly enriched in functional regions of the genome, which indicates that risk is mediated through perturbed
regulatory action on relevant susceptibility genes. Multiple lines of evidence have shown that integrating omics
with large-scale genetic data increases statistical power to identify novel genomic risk regions and uncovers
target molecular mechanisms of risk. These analyses rely on first identifying associations between genetics and
various omics data (i.e., molecular quantitative trait loci, or molQTLs) and then using these associations to impute
or predict omics into large-scale PCa GWAS data. However, to date, analyses have been limited for three primary
reasons. First, previous integrative analyses with PCa risk relied on diverse omics data measured across tissues
other than prostate, where translation to prostate-specific results may be inaccurate. Previous omics datasets
measured in prostate together with genotype have been limited to small sample sizes, resulting in less accurate
prediction when compared with larger sample size datasets. Second, prior omics datasets have been measured
primarily in men of European ancestry. Multiple recent works find that genetic-based omics prediction translates
poorly across populations, which limits the utility of existing omics data to non-European men. Third, previous
studies have shown the importance of integrating omics data beyond gene expression with PCa risk, thus
demonstrating that multi-omics investigations facilitate a more unbiased approach to provide biological insights
into disease mechanisms. To date, the majority of imputation-based approaches have been applied to large-
scale GWAS, however recent works have made crucial discoveries in cancer biology by imputing cancer risk
from GWAS into molecular cohorts. Here, to understand the genetic regulatory mechanisms in prostate tissues
across the molecular cascade, we propose to assay methylation, transcriptomic, proteomic, and metabolomic
data in prostate tissue to perform large-scale molQTL mapping for African- and European-ancestry men. To
elucidate the underlying mechanisms responsible for PCa risk and identify novel genetic risk factors, we will
integrate identified molQTLs with the largest-available PCa GWAS. Overall, our proposal aims to characterize
the genetic regulatory landscape of prostate tissue, its effect on PCa risk, and health disparities of this disease.
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