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: 10657510
• 负责人: Nicholas Mancuso
• 金额: $ 12.29万
• 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 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657510
• 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; Disparity; Ethnic Origin; Ethnic Population; European; European ancestry; Gene Expression; Genes; Genetic; Genetic Risk; Genome; Genomics; Genotype; Goals; Heterogeneity; Incidence; Individual; Investigation; Japanese; Latino; Lead; Malignant neoplasm of prostate; Maps; 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; Proteins; 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; 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; 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.

项目总结 在美国，前列腺癌(PCA)是男性癌症死亡的第二大原因，非洲裔男性 具有最高发病率和死亡率的祖先。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，他们患有更具侵袭性和致命性的前列腺癌 对口单位。虽然这种健康差距的原因尚不清楚，但有证据表明，遗传因素很可能是 这是一个促成因素。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，这使得识别近端 目标基因具有挑战性，阻碍了翻译工作。大量的研究表明，PCA存在风险 在基因组的功能区域中高度丰富，这表明风险是通过扰动传递的 对相关易感基因的调控作用。多条证据表明，整合组学 随着大规模基因数据的增加，识别新的基因组风险区域和发现 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或分子QTL)，然后使用这些关联来归因于 或将组学预测纳入大规模的PCA Gwas数据。然而，到目前为止，分析仅限于三个主要方面 理由。首先，先前对PCA风险的综合分析依赖于跨组织测量的不同组学数据 除了前列腺癌，在那里转换为前列腺特异性结果可能是不准确的。以前的组学数据集 在前列腺癌中测量的基因和基因只限于小样本量，导致准确性降低。 与更大样本量的数据集相比时的预测。其次，之前的组学数据集已经被测量 主要是欧洲血统的男人。最近的多项研究发现，基于基因的组学预测可以翻译成 在人群中表现不佳，这限制了现有组学数据对非欧洲男性的效用。第三，以前的 研究表明，将基因表达以外的组学数据与前列腺癌风险相结合的重要性，因此 证明多组学研究促进了提供生物学见解的更公正的方法 转化为疾病机制。到目前为止，大多数基于归责的方法已经被应用于大型- 然而，最近的研究通过计算癌症风险，在癌症生物学方面取得了至关重要的发现 从GWAs到分子队列。在这里，为了了解前列腺组织中的遗传调控机制 在分子级联中，我们建议分析甲基化、转录组、蛋白质组和代谢组 用于对非洲和欧洲血统的男性进行大规模分子QTL定位的前列腺组织数据。至 阐明前列腺癌风险的潜在机制，并确定新的遗传风险因素，我们将 将已识别的分子QTL与最大可用主成分分析进行整合。总体而言，我们的建议旨在 前列腺组织的基因调控格局，它对前列腺癌风险的影响，以及这种疾病的健康差异。

项目成果

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics.

一种使用 GWAS 摘要统计来表征数千种人类疾病和复杂性状的多效性的可扩展方法。

• DOI: 10.1016/j.ajhg.2023.09.015
• 发表时间: 2023
• 期刊: American journal of human genetics
• 影响因子: 9.8
• 作者: Zhang,Zixuan;Jung,Junghyun;Kim,Artem;Suboc,Noah;Gazal,Steven;Mancuso,Nicholas
• 通讯作者: Mancuso,Nicholas

A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics.

一种可扩展的变分方法，使用 GWAS 摘要统计来表征数千种人类疾病和复杂性状的多效性成分。

• DOI: 10.1101/2023.03.27.23287801
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Zhang,Zixuan;Jung,Junghyun;Kim,Artem;Suboc,Noah;Gazal,Steven;Mancuso,Nicholas
• 通讯作者: Mancuso,Nicholas