Characterizing noncoding GWAS variants in acute lymphoblastic leukemia treatment outcome

表征急性淋巴细胞白血病治疗结果中的非编码 GWAS 变异

基本信息

批准号：
10227006
负责人：
Daniel Savic
金额：
$ 48.61万
依托单位：
ST. JUDE CHILDREN'S RESEARCH HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10227006
关键词：
Acute Lymphocytic Leukemia Address Alleles Biological Assay Biology Bypass CRISPR/Cas technology Catalogs Cell Line Cellular biology Childhood Acute Lymphocytic Leukemia Chromatin Chromosome Mapping Clinic Clinical Clustered Regularly Interspaced Short Palindromic Repeats DNA Sequence Data Data Set Development Disease Outcome Drug resistance Enhancers Follow-Up Studies Gene Expression Regulation Genes Genetic Genetic Determinism Genetic Variation Genome Genomics Goals Human Inherited Knowledge Leukemic Cell Link Linkage Disequilibrium Location Malignant Childhood Neoplasm Maps Modeling Nature Newly Diagnosed Patients Pharmaceutical Preparations Phenotype Publishing Recurrent disease Regulator Genes Regulatory Element Relapse Reporter Research Proposals Residual Neoplasm Resistance Resources Role Saint Jude Children&apos s Research Hospital Sampling Site Survival Rate System Treatment Failure Treatment Protocols Treatment outcome Untranslated RNA Variant acute lymphoblastic leukemia cell antileukemic agent causal variant chemotherapeutic agent cohort follow-up functional genomics gene function genetic variant genome editing genome wide association study genomic data improved leukemia leukemia relapse mouse model novel novel therapeutics outcome prediction patient derived xenograft model personalized medicine precision medicine promoter relapse patients resistance mechanism response transcriptome treatment response treatment strategy

项目摘要

PROJECT SUMMARY Acute lymphoblastic leukemia (ALL) is the most prevalent childhood cancer, and although overall survival rates of ALL have substantially improved, resistance to antileukemic agents remains a major clinical problem. Antileukemic drug resistance is predictive of poor disease outcome and is commonly observed in ALL patients that have relapsed, who have a low overall survival rate of only 40%. The mechanisms that cause ALL relapse and drug resistance remain poorly understood. To address how inherited genomic variability contributes to these mechanisms, genome wide association studies (GWASs) have identified DNA sequence variation associated with ALL treatment outcome. However, since these variants are noncoding in nature, their connection to gene function, ALL biology and antileukemic drug resistance has been difficult to establish. Moreover, given that hundreds of variants are typically in strong linkage disequilibrium with the associated variant, pinpointing causal variants at GWAS loci has been challenging. To address these challenges, we have generated functional genomic maps of the ALL genome, including the precise locations of noncoding regulatory elements in >40 ALL samples. Through rigorous open chromatin-fine mapping using our ALL genome maps, and by integrating our results with drug resistance phenotypes from primary ALL cells obtained from patients at St. Jude (SJ), we identified 3229 variants at 125 GWAS loci associated with ALL treatment outcome that are predicted to have an impact on gene regulation, leukemic cell biology and antileukemic drug resistance. Using these fine-mapped variants, we propose an integrative strategy for identifying candidate causal variants associated with ALL treatment outcome, and a rational experimental system for functionally linking these variants and their target genes to antileukemic drug resistance. In Aim 1, we will perform massively parallel reporter assays (MPRAs) on >3200 fine-mapped variants to assess their gene regulatory activities and to identify allele-specific differences in activity. In Aim 2, we will employ a polygenomic strategy by integrating our MPRA results with diverse genomic datasets to prioritize and rank fine-mapped variants by their likelihood of being causal. For this effort, we will capitalize on the unique and rich resources available at SJ, including extensive genomic characterizations and drug resistance phenotypes from large ALL patient cohorts, as well as ongoing genomic characterizations for all new and/or relapsed patients. We will functionally validate the role of the top 20 ranked candidate causal variants on antileukemic drug resistance using CRISPR technology and chemotherapeutic drug viability assays in human ALL cell lines. We will also identify GWAS target genes and functionally assess their role in antileukemic drug resistance in human ALL cell lines and in patient-derived xenograft mouse models of pediatric ALL. Collectively, our proposal will uncover novel genomic mechanisms of antileukemic drug resistance. Ultimately, these data can inform approaches to circumvent resistance in the clinic, and be used to improve initial treatment, as well as guide therapy for relapsed disease through precision medicine and more personalized treatment regimens.

项目概要急性淋巴细胞白血病 (ALL) 是最常见的儿童癌症，尽管总体生存率 ALL 的发病率已得到显着改善，但抗白血病药物的耐药性仍然是一个主要的临床问题。抗白血病药物耐药性预示着疾病结果不佳，并且常见于 ALL 患者复发的患者总体生存率较低，仅为 40%。导致 ALL 复发的机制和耐药性仍然知之甚少。为了解决遗传基因组变异如何促成这些机制，全基因组关联研究（GWAS）已经确定了与 DNA 序列变异相关的与所有治疗结果。然而，由于这些变体本质上是非编码的，它们与基因的联系功能、ALL 生物学和抗白血病药物耐药性一直难以确定。此外，鉴于数百个变体通常与相关变体处于强连锁不平衡状态，从而精确定位因果关系 GWAS 基因座的变异一直具有挑战性。为了应对这些挑战，我们生成了功能 ALL 基因组的基因组图谱，包括 >40 ALL 中非编码调控元件的精确位置样品。通过使用我们的所有基因组图谱进行严格的开放染色质精细映射，并通过整合我们的从 St. Jude (SJ) 患者获得的原代 ALL 细胞的耐药表型结果，我们在 125 个 GWAS 位点上鉴定出 3229 个与 ALL 治疗结果相关的变异，预计这些变异具有对基因调控、白血病细胞生物学和抗白血病耐药性的影响。使用这些精细映射变异，我们提出了一种综合策略来识别与 ALL 相关的候选因果变异治疗结果，以及用于将这些变体及其靶点在功能上联系起来的合理实验系统抗白血病药物耐药性的基因。在目标 1 中，我们将进行大规模并行报告分析 (MPRA) >3200 个精细映射的变体，用于评估其基因调控活性并识别等位基因特异性差异在活动中。在目标 2 中，我们将采用多基因组策略，将 MPRA 结果与不同的基因组相结合数据集，根据其因果关系的可能性对精细映射的变体进行优先级排序和排名。为了这个努力，我们将利用 SJ 独特而丰富的资源，包括广泛的基因组特征和来自大型 ALL 患者队列的耐药表型，以及所有患者正在进行的基因组特征新的和/或复发的患者。我们将从功能上验证排名前 20 的候选因果变体的作用使用 CRISPR 技术和人体化疗药物活力测定研究抗白血病药物耐药性所有细胞系。我们还将确定 GWAS 靶基因并对其在抗白血病药物中的作用进行功能评估人类 ALL 细胞系和儿童 ALL 患者来源的异种移植小鼠模型中的耐药性。总的来说，我们的提案将揭示抗白血病耐药性的新基因组机制。最终这些数据可以为临床上规避耐药性的方法提供信息，并用于改善初始治疗，以及通过精准医疗和更加个性化的治疗方案指导复发性疾病的治疗。