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，非洲人 祖先的发病率和死亡率最高。确实，发展PCA的非洲血统的人 与非非洲血统相比，平均具有更具侵略性和致命的前列腺肿瘤 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。实际上，PCA的大规模基因组关联研究（GWAS）已鉴定出300 基因组风险变体；但是，绝大多数在非编码区域，这使得识别近端 目标基因具有挑战性，并阻碍转化工作。大量作品证明了PCA风险 在基因组的功能区域高度富集，这表明风险是通过扰动介导的 对相关易感基因的调节作用。多种证据表明，将OMICS整合 随着大规模遗传数据，增加了统计能力，以鉴定新的基因组风险区域并发现 靶标分子机制。这些分析依赖于首先识别遗传学与 各种OMICS数据（即分子定量性状基因座或molqtls），然后使用这些关联来估算 或将OMICS预测到大型PCA GWAS数据中。但是，迄今为止，三个主要的分析受到限制 原因。首先，先前与PCA风险的综合分析取决于跨组织测量的多种OMIC数据 除了前列腺以外，转化为前列腺特异性结果可能不准确。以前的OMICS数据集 在前列腺中与基因型一起测量的样本量仅限于小样本量，从而较少准确 与较大样本量数据集相比，预测。其次，已经测量了先前的OMICS数据集 主要是欧洲血统的人。最近的多项作品发现，基于遗传的OMIC预测转化了 在整个人群中都很差，这将现有的OMIC数据的效用限制在非欧洲男性身上。第三，以前 研究表明，将基因表达以外的OMIC数据与PCA风险相结合的重要性，因此 证明多摩学调查促进了一种提供生物学见解的更无偏见的方法 进入疾病机制。迄今为止，大多数基于归合的方法已应用于大型 比例GWAS，但是最近的作品通过推出癌症风险来使癌症生物学的发现至关重要 从GWAS到分子队列。在这里，了解前列腺组织中的遗传调节机制 在整个分子级联反应中，我们建议分析甲基化，转录组，蛋白质组学和代谢组学 前列腺组织中的数据以对非洲和欧洲人的男性进行大规模的MOLQTL映射。到 阐明负责PCA风险的基本机制并确定新的遗传风险因素，我们将 将确定的molqtl与最大可用的PCA GWAS整合在一起。总体而言，我们的建议旨在表征 前列腺组织的遗传调节景观，其对PCA风险的影响以及该疾病的健康差异。