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: 10452535
• 负责人: Nicholas Mancuso
• 金额: $ 60.09万
• 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/10452535
• 关键词: Achievement; African; African ancestry; 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; 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.

项目概要 在美国，前列腺癌 (PCa) 是男性癌症死亡的第二大原因，其中非洲男性 发病率和死亡率最高的血统。事实上，患有前列腺癌的非洲血统男性 与非非洲血统相比，平均而言，他们的前列腺肿瘤更具侵袭性和致命性 同行。虽然造成这种健康差异的原因尚不清楚，但有证据表明遗传因素很可能 一个促成因素。事实上，PCa 的大规模全基因组关联研究 (GWAS) 已经确定了 300 基因组风险变异；然而，绝大多数位于非编码区域，这使得识别邻近的 靶基因具有挑战性并阻碍转化工作。大量研究表明 PCa 风险 在基因组的功能区域中高度富集，这表明风险是通过扰动介导的 对相关易感基因的调控作用。多项证据表明，整合组学 大规模遗传数据增强了识别新基因组风险区域并揭示的统计能力 风险的目标分子机制。这些分析首先依赖于确定遗传学和 各种组学数据（即分子数量性状位点或 molQTL），然后使用这些关联来估算 或对大规模 PCa GWAS 数据进行组学预测。然而，迄今为止，分析仅限于三个主要领域 原因。首先，之前对 PCa 风险的综合分析依赖于跨组织测量的不同组学数据 除前列腺外，转化为前列腺特异性结果可能不准确。以前的组学数据集 前列腺中的测量与基因型一起仅限于小样本量，导致准确性较低 与较大样本量的数据集进行比较时的预测。其次，测量了先前的组学数据集 主要发生在欧洲血统的男性中。最近的多项研究发现基于基因的组学预测可以转化为 跨人群的效果较差，这限制了现有组学数据对非欧洲男性的效用。三、上一篇 研究表明，将基因表达以外的组学数据与 PCa 风险整合起来非常重要，因此 证明多组学研究有助于采用更公正的方法来提供生物学见解 进入疾病机制。迄今为止，大多数基于估算的方法已应用于大型 大规模 GWAS，但是最近的工作通过估算癌症风险在癌症生物学方面取得了重要发现 从 GWAS 到分子队列。在这里，了解前列腺组织的基因调控机制 在整个分子级联中，我们建议检测甲基化、转录组学、蛋白质组学和代谢组学 前列腺组织中的数据，为非洲和欧洲血统的男性进行大规模的 molQTL 定位。到 阐明导致 PCa 风险的潜在机制并确定新的遗传风险因素，我们将 将已识别的 molQTL 与最大的可用 PCa GWAS 整合。总的来说，我们的建议旨在描述 前列腺组织的基因调控格局、其对 PCa 风险的影响以及该疾病的健康差异。