Using machine learning and multi-omic analysis to understand the hyperinflammatory response that leads to haemophagocytic lymphohistiocytosis (HLH).

使用机器学习和多组学分析来了解导致噬血细胞性淋巴组织细胞增多症 (HLH) 的过度炎症反应。

• 批准号: 2550144
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2550144
• 关键词: Using; machine; learning; multi; omic

Background: Normal immune responses are critical to survival, but hyperinflammatory responses are harmful, and can lead to cytokine storm, multi-organ failure and death. Examples of hyperinflammation, or cytokine storm syndromes, include the hyperinflammatory pneumonia caused by COVID-19, the cytokine release syndrome related to CAR-T cell therapy, and the multisystem syndrome haemophagocytic lymphohistiocytosis (HLH). Secondary HLH is a prototypic hyperinflammatory syndrome caused by malignancies, rheumatological conditions, and infections including SARS-CoV-2 [1, 2]. It has a mortality of ~50%, increasing to 80% in people with lymphoma. Management of HLH involves treatment of the cytokine storm alongside identification and treatment of the pathogenic driver.This syndrome is poorly understood, and a perceived rarity has hampered research. We do not understand why some people mount an exaggerated and harmful immune response to these stimuli and others do not.Objective: To improve patient outcomes, we need to develop a strong evidence base. This has been lacking to date because of a lack of concentration of expertise. The UCLH HLH Service was set up to address this and we have seen 20 patients in the first 6 months of 2022, compared to approximately 2-4 patients per year prior to the establishment of this service. Patient samples are recruited to a biobank and a matched clinical database (recruitment of 5-10 patients per month ongoing via UCLH biobank). We plan to harness this unique resource to develop a mechanistic model to improve our understanding of hyperinflammatory responses. This project will use immunological and multiple 'omics (multi-omics) analysis approaches to characterise hyperinflammation phenotypes and heterogeneity for improved understanding of this aberrant immune response.Aims:1: Curate literature to identify relevant pathogenic pathways and design multi-omic analysis approach.2: Interrogate multi-omic [immune phenotype (spectral cytometry), targeted transcriptomics/proteomics and metabolomics] and rich clinical data to establish signatures associated with HLH disease using already established analysis pipelines[3-10],models developed during the rotation project and knowledge gained from attending a 'Datathon' focused on using clinical/registry data for patient stratification.3: Establish and identify HLH disease endotypes and heterogeneity according to multi-omic, disease and patient outcome signatures.Outcome: First study to define HLH heterogeneity, identify potential HLH disease endotypes and improve understanding of HLH disease mechanisms.

背景资料：正常的免疫反应对生存至关重要，但过度炎症反应是有害的，可导致细胞因子风暴，多器官衰竭和死亡。炎症过度或细胞因子风暴综合征的实例包括由COVID-19引起的炎症过度性肺炎、与CAR-T细胞疗法相关的细胞因子释放综合征和多系统综合征噬血细胞性淋巴组织细胞增多症（HLH）。继发性HLH是由恶性肿瘤、风湿性疾病和包括SARS-CoV-2在内的感染引起的典型炎症性综合征[1，2]。它的死亡率约为50%，在淋巴瘤患者中增加到80%。HLH的管理包括对细胞因子风暴的治疗以及对致病驱动因素的识别和治疗。我们不明白为什么有些人会对这些刺激产生夸张和有害的免疫反应，而另一些人则不会。目的：为了改善患者的预后，我们需要建立一个强有力的证据基础。由于缺乏专门知识的集中，迄今为止一直没有做到这一点。UCLH HLH服务旨在解决这一问题，我们在2022年的前6个月已经看到了20名患者，而在建立这项服务之前，每年约有2-4名患者。将患者样本招募到生物库和匹配的临床数据库中（通过UCLH生物库每月招募5-10名患者）。我们计划利用这种独特的资源来开发一种机制模型，以提高我们对炎症反应的理解。该项目将使用免疫学和多组学（多组学）分析方法来分析过度炎症表型和异质性，以提高对这种异常免疫应答的理解。目的：1：整理文献以鉴定相关致病途径并设计多组学分析方法。询问多组[免疫]表型（光谱细胞术），靶向转录组学/蛋白质组学和代谢组学]和丰富的临床数据，使用已经建立的分析管道建立与HLH疾病相关的特征[3-10]，在轮换项目期间开发的模型和参加“数据库”获得的知识，重点是使用临床/登记数据进行患者分层。3：根据多组学、疾病和患者结局特征，建立和识别HLH疾病内在型和异质性。首次研究定义HLH异质性，确定潜在的HLH疾病内在型，并提高对HLH疾病机制的理解。