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

• 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
• 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

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.

项目摘要 在美国，前列腺癌（PCa）是男性癌症死亡的第二大原因，非洲男性 发病率和死亡率最高的祖先。事实上，患有前列腺癌的非洲血统的男性 平均而言，与非非洲血统相比， 同行虽然这种健康差异的原因尚不清楚，但有证据表明，遗传学可能是 一个促成因素。事实上，大规模的PCa全基因组关联研究（GWAS）已经确定了300个 基因组风险变异;然而，绝大多数是在非编码区，这使得确定近端 挑战靶基因并阻碍翻译努力。大量的研究表明， 在基因组的功能区域高度富集，这表明风险是通过干扰介导的。 对相关易感基因的调控作用。多种证据表明，整合组学 大规模遗传数据增加了识别新的基因组风险区域和发现的统计能力 针对风险的分子机制。这些分析依赖于首先确定遗传学和 各种组学数据（即，分子数量性状基因座（molQTL），然后利用这些关联来估算 或预测组学到大规模PCa GWAS数据中。然而，迄今为止，分析仅限于三个主要的 原因首先，以前对PCa风险的综合分析依赖于跨组织测量的不同组学数据 而不是前列腺，其中转化为前列腺特异性结果可能不准确。以前的组学数据集 在前列腺中测量的基因型仅限于小样本量， 与更大样本量的数据集相比，其次，先前的组学数据集已经被测量 主要是欧洲血统的男性。最近的多项研究发现，基于遗传学的组学预测 这限制了现有组学数据对非欧洲男性的效用。第三，此前 研究表明，将基因表达以外的组学数据与PCa风险相结合的重要性， 表明多组学研究有助于更公正地提供生物学见解 疾病机制。到目前为止，大多数基于估算的方法都适用于大型企业， 规模的GWAS，然而，最近的工作已经通过估算癌症风险在癌症生物学中取得了重要发现 从GWAS到分子队列。为了了解前列腺组织中的遗传调控机制， 在整个分子级联反应中，我们建议测定甲基化、转录组学、蛋白质组学和代谢组学 前列腺组织中的数据进行大规模molQTL作图非洲和欧洲血统的男子。到 阐明PCa风险的潜在机制，并确定新的遗传风险因素，我们将 将鉴定的molQTL与最大的可用PCa GWAS整合。总的来说，我们的建议旨在描述 前列腺组织的遗传调控景观，其对前列腺癌风险的影响，以及这种疾病的健康差异。