权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

An integrative multi-omics approach to characterize prostate cancer risk in diverse populations

一种综合多组学方法来表征不同人群中前列腺癌的风险

基本信息

批准号：
10186390
负责人：
Nicholas Mancuso
金额：
$ 69.16万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-16 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10186390
关键词：
Achievement African Biological Biological Assay Biology Cancer Biology Cancer Etiology Categories Cessation of life Chromosome Mapping DNA Methylation Data Data Set Disease Ethnic Origin European Gene Expression Genes Genetic Genetic Risk Genome Genomics Genotype Goals Heterogeneity Incidence Individual Investigation Japanese Population Latino Lead Light Malignant neoplasm of prostate Measurement Measures Mediating Messenger RNA Methylation Modeling Molecular Molecular Target Outcome Population Population Heterogeneity Procedures Process Prostate Prostate Cancer Outcomes Study Prostatic Diseases Prostatic Neoplasms Protein Isoforms Proteomics Quantitative Trait Loci RNA Splicing Resources Risk Risk Factors Sample Size Screening for Prostate Cancer Statistical Methods Subgroup Susceptibility Gene Testing Tissues Training Transcript Translating Translations Untranslated RNA Variant Work base cancer health disparity cancer risk clinical phenotype cohort disease disparity functional genomics genetic information genetic risk factor genetic variant genome wide association study genome-wide genomic data health disparity improved insight mRNA Expression men metabolomics molecular phenotype mortality multi-ethnic multiple omics novel novel strategies polygenic risk score predictive modeling prostate cancer risk protein metabolite risk variant screening guidelines trait transcriptomics

项目摘要

PROJECT SUMMARY 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.

项目总结