Leveraging multi-omics to maximize the scientific value of pediatric sepsis biorepository and advance patient endotyping

利用多组学最大限度地提高儿科脓毒症生物储存库的科学价值，并推进患者的内分型。 项目概要： 败血症是主要的儿科健康问题，在美国每年死亡的儿童人数超过癌症。主要 由宿主对感染的功能障碍性反应驱动，一部分持续或进行性 多器官功能障碍不成比例地导致脓毒症发病率和死亡率。然而，没有 除了早期抗生素和器官支持之外，目前可用的疾病改善疗法。生物 患者之间的异质性已经严重阻碍了科学进步和患者护理的进步。 尽管精准医疗方法已被用于开始筛选患者水平的差异，但我们 从根本上缺乏对疾病机制的全面了解。因此，迫切需要 最大限度地利用现有的儿科脓毒症生物储存库，以解开因果途径，促进快速 识别更可能从靶向治疗中获益的生物学相关患者亚类。 为了弥合这一差距，我们寻求利用最先进的多组学方法来探索其科学价值 我们收集的大量败血症危重患儿的生物样本虽然基因表达谱分析 已经被用于鉴定生物学相关的内型，越来越明显的是，询问单层 分子数据可能还不够最近的研究表明，表观基因组的变化，包括 CpG岛的差异DNA甲基化密切调节人类脓毒症中的基因表达。通过这个 阶段性创新奖，我们试图确定是否甲基化和转录组学的综合分析， 大规模的数据集可以提供对机制的全面理解，并为患者内定型提供信息。 我们进一步寻求确定与患者生物标本相关的临床数据是否可用于预测 内型成员资格，在未来的临床试验中预测富集的操作意义。 R21阶段的里程碑式开发活动将侧重于严格的DNA质量控制 和RNA样品，以确定高通量方法的适用性。然后我们将 为研究计划生成试点DNA甲基化分析和RNA测序数据，并证明 综合分析的可行性。在R33阶段，我们将扩大努力，以产生强大的甲基化， 转录组数据集。我们将利用研究团队的生物信息学能力， 验证新的多组学内型并确定其临床意义。最后，我们将开发一个 分类器模型，以使用衍生群组内的临床数据预测内型成员资格，并测试其 在一个大型的基于电子健康记录的数据集中的普遍性，该数据集包含> 15，000名患有败血症的重症儿童， 多器官功能障碍通过成功执行这一建议，我们寻求产生丰富的 数据集，以推动人类脓毒症的未来机制研究，并制定一个可行的框架， 和公平鉴定可能受益于靶向脓毒症治疗的儿科脓毒症亚类。

Leveraging multi-omics to maximize the scientific value of pediatric sepsis biorepository and advance patient endotyping. PROJECT SUMMARY: Sepsis is major pediatric health problem and kills more children than cancer in the U.S each year. Primarily driven by a dysfunctional host response to an infection, a subset of patients with persistent or progressive multiple organ dysfunctions disproportionately contribute to sepsis morbidity and mortality. Yet, there are no disease modifying therapies currently available beyond early antibiotics and organ support. Biological heterogeneity among patients has significantly impeded scientific progress and advances in patient care. Although precision medicine approaches have been used to begin to sift through patient-level differences, we fundamentally lack a comprehensive understanding of disease mechanisms. Thus, there is a crucial need to maximize the use of existing pediatric sepsis biorepositories to unravel causal pathways, facilitate rapid identification of biologically relevant patient subclasses more likely to benefit from targeted therapies. To bridge this gap, we seek to utilize state-of-the-art multi-omics approaches to explore the scientific value of our vast collection of biospecimens from critically ill children with sepsis. While gene-expression profiling has been used to identify biologically relevant endotypes, it is increasingly evident that interrogation of single layer of molecular data is likely insufficient. Recent studies suggest that the epigenomic changes, including differential DNA methylation at CpG islands, closely regulate gene-expression in human sepsis. Through this phased innovation award, we seek to determine whether integrated analyses of methylomic and transcriptomic datasets at scale can provide a comprehensive understanding of mechanisms and inform patient endotyping. We further seek to determine whether clinical data linked with patient biospecimens can be used to predict endotype membership, with operational implications for predictive enrichment in future clinical trials. Milestone-driven developmental activities in the R21 phase will focus on stringent quality control of DNA and RNA samples within our biorepository to determine suitability for high throughput methods. We will then generate pilot DNA methylation profiling and RNA sequencing data for study planning and to demonstrate feasibility of integrated analyses. In the R33 phase, we will scale efforts to generate robust methylomic and transcriptomic datasets. We will leverage the bioinformatic capabilities of the investigator team to derive and validate novel multi-omic endotypes and determine their clinical significance. Finally, we will develop a classifier model to predict endotype membership using clinical data within the derivation cohort and test its generalizability in a large electronic health record-based dataset of >15,000 critically ill children with sepsis and multiple organ dysfunctions. Through the successful execution of this proposal, we seek to generate a rich dataset to drive future mechanistic research in human sepsis and develop an actionable framework for rapid and equitable identification of pediatric sepsis subclasses who may benefit from targeted sepsis therapeutics.