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

背景：正常的免疫反应对生存至关重要，但高炎症反应是有害的，可导致细胞因子风暴、多器官衰竭和死亡。高炎症或细胞因子风暴综合征的例子包括由COVID-19引起的高炎症性肺炎，与CAR-T细胞治疗相关的细胞因子释放综合征，以及多系统综合征嗜血淋巴组织细胞增多症（HLH）。继发性HLH是一种典型的高炎症综合征，由恶性肿瘤、风湿病和包括SARS-CoV-2在内的感染引起[1,2]。它的死亡率约为50%，在淋巴瘤患者中增加到80%。HLH的治疗包括细胞因子风暴的治疗以及致病驱动因素的识别和治疗。人们对这种综合征了解甚少，而且这种罕见的症状阻碍了研究。我们不明白为什么有些人对这些刺激产生了夸张和有害的免疫反应，而另一些人却没有。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。迄今为止，由于缺乏专业知识的集中，这方面一直缺乏。UCLH的HLH服务就是为了解决这个问题而设立的，在2022年的前6个月，我们已经看到了20名患者，而在该服务建立之前，每年大约有2-4名患者。患者样本被招募到生物库和匹配的临床数据库（通过UCLH生物库每月招募5-10名患者）。我们计划利用这一独特的资源来开发一种机制模型，以提高我们对高炎症反应的理解。该项目将使用免疫学和多组学（multi-omics）分析方法来表征炎症表型和异质性，以提高对这种异常免疫反应的理解。目的：1：整理文献，识别相关致病途径，设计多组学分析方法。2：查询多组学[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[3-10]，在轮转项目期间开发的模型和参加“数据马拉松”期间获得的知识，重点是使用临床/注册数据进行患者分层。3：根据多组学、疾病和患者结局特征，建立和识别HLH疾病的内源性类型和异质性。结果：首次研究确定HLH异质性，确定潜在的HLH疾病内型，提高对HLH疾病机制的理解。

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.