Project 2: Multi-Omics of high-risk MM

项目2：高风险MM的多组学

基本信息

批准号：
10706328
负责人：
Esteban Braggio
金额：
$ 33.98万
依托单位：
MAYO CLINIC ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10706328
关键词：
Accounting Address Atlases Automobile Driving Biological Blood Bone Marrow Caring Cells Clinic Clinical Clinical Data Clinical Trials Collaborations Complex Complex Analysis Core Facility DNA DNA sequencing Data Data Analyses Data Analytics Data Set Disease Disease Outcome Environment Epigenetic Process Event Evolution Generations Genes Genetic Genome Genomics Goals Immune Immuno-Chemotherapy Immunophenotyping Individual Link Maps Marrow Mass Spectrum Analysis Mononuclear Multiomic Data Multiple Myeloma Mutation Network-based Outcome Pathogenesis Pathway interactions Patients Pattern Phase II Clinical Trials Phenotype Pilot Projects Population Positioning Attribute Process Proteome RNA Relapse Resistance Resolution Resources Rewards Role Sampling Sampling Studies Series Specimen Technology Time Variant Work biobank bioinformatics tool clinical database clinical phenotype clinical risk data integration data resource deep learning design drug sensitivity epigenome experience gene environment interaction gene interaction high risk high risk population immunoregulation improved insight lipidome mRNA sequencing metabolome multidisciplinary multiple omics neoplastic cell patient population personalized approach response single cell analysis therapeutically effective transcriptome transcriptomics translational clinical trial treatment response tumor tumor-immune system interactions ultra high resolution whole genome

项目摘要

PROJECT SUMMARY/ABSTRACT While our own extensive studies have confirmed the significant role of the disrupted genome in Multiple Myeloma (MM), they have also re-emphasized the gaps in understanding, and the importance of immune regulation and gene-environment interaction. It is indeed likely that the evolution of MM, both before or during therapy, is the result of a complex interplay of biological perturbations driven by genetic changes and environmental influences. Our past work has also demonstrated that studying small numbers of patients at great depth can be as rewarding for scientific understanding as studies of superficial genomic events in thousands of patients. Thus we will strive to generate the first, longitudinal, translational clinical trial and comprehensive data resource of environmental genetic interactions for the highest-risk MM population. It is these patients who continue to rapidly fail highly effective therapeutics for reasons which are still completely opaque. New and bold approaches using state-of-the-art technology are required to reverse this decades-old lack of progress. Our hypothesis is that analysis of data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing MM responsiveness to therapy and subsequent outcomes. First, we will leverage a carefully studied and homogeneously treated high-risk group of “double hit” patients in a Phase 2 clinical trial with large control clinical databases and bio-repositories to derive, for each patient, a detailed map of environmental gene interactions linked to clinical outcome over time. Second, we will perform a series of complex analyses to identify MM-associated changes in and across the genome, transcriptome, epigenome, immune environment, proteome, lipidome and metabolome. Third, we will study these samples at the highest resolution technically feasible today, and seek to define gene-environment interaction changes over time that associate with response to therapy. Finally, high resolution data capturing these interaction changes and clinical response data will be linked to improve our understanding of the mechanisms underlying MM variability among patients in regards to disease outcomes. This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for MM.

项目摘要/摘要虽然我们自己的广泛研究证实了中断基因组在多个中的重要作用骨髓瘤（MM），他们还重新强调了理解差距以及免疫的重要性调节和基因环境相互作用。 MM的演变确实很可能是在之前或期间的治疗是由遗传变化和环境影响。我们过去的工作还表明，研究少数患者与对肤浅基因组事件的研究，深度对科学理解的理解是有意义的成千上万的患者。我们将努力生成第一个，纵向的，翻译的临床试验，最高风险的MM人群的环境遗传相互作用的综合数据资源。这是这些因素仍然完全是完全有效治疗的患者不透明。需要使用最先进的技术进行新的和大胆的方法来扭转数十年历史的历史缺乏进步。我们的假设是，以高分辨率捕获基因环境相互作用的数据分析将揭示对生物学途径的见解会影响MM对治疗和随后结果的反应。第一的，我们将利用经过精心研究和同质治疗的高风险组的“双重命中”患者 2阶段的临床试验具有大型对照临床数据库和生物重复剂，以衍生每个患者随着时间的推移，环境基因相互作用与临床结果相关的详细图。第二，我们将执行一系列复杂分析，以鉴定基因组和跨基因组中与MM相关的变化，转录组，表观基因组，免疫环境，蛋白质组，脂肪组和代谢组。第三，我们将研究这些样本当今技术在技术上最可行的最高分辨率，并试图定义基因环境的相互作用变化随着时间的流逝，与对治疗的反应有关。最后，捕获这些交互的高分辨率数据变化和临床响应数据将链接到我们对我们对基本机制的理解在疾病结局方面，患者的MM变异性。这种全面的资源将使对MM进行临床监测和治疗的更多个性化方法。