UNICORN (Unified Cohorts Research Network): Disaggregating asthma

哮喘和过敏是儿童和青少年最常见的慢性疾病。这些疾病通常在学龄之前就开始了，造成了健康不良的沉重负担，包括过早死亡。人们越来越认识到，哮喘是一个涵盖几种不同疾病的总括性术语，这为提供个性化治疗（即针对个体患者量身定制的治疗）造成了障碍。我们提出了一个创新的科研项目（UNICORN，统一队列研究网络），该项目采用团队科学的方法来了解哮喘和过敏的异质性。我们有许多研究疾病的方法：(1)从出生开始研究儿童（出生队列）；(2)重症患者的研究；(3)随机对照试验（rct，随机分配患者接受几种治疗中的一种）。我们建议，如果我们把所有这些放在一起研究，我们就可以开始理解哮喘和过敏等常见疾病中的变异。这将帮助我们预测谁对不同的治疗反应最好。由于缺乏一个共同管理和分析来自不同研究的数据的系统，这种协作方法目前受到阻碍。我们将在5项旨在研究哮喘和过敏性疾病的英国出生队列研究的STELAR联盟（总共超过15,000名参与者，从出生前一直随访到成年期）和招募大量严重哮喘患者的临床研究（超过1000名）之间建立联盟。我们的出生队列测量了疾病发病前的环境暴露，并包含了从儿童早期到成年期哮喘和过敏发展的详细信息。在UNICORN中，这些信息将由在严重哮喘患者研究中收集的信息补充。这些研究测量了额外的临床标记（例如，更详细的肺功能），并收集了出生队列中无法获得的生物样本（例如痰液、鼻分泌物和气道活检）。需要这些样本来了解不同类型哮喘的潜在机制。随机对照试验提供了关于治疗反应的进一步重要和准确的信息。因此，出生队列、患者队列和随机对照试验是互补的，通过适当地连接数据将它们结合起来，将为不同哮喘亚型的机制、预测未来风险的标志物和个体对治疗的反应提供宝贵的见解。UNICORN建立在对哮喘研究的科学和基础设施的大量先前投资基础上。我们将齐心协力，在数据管理平台和帮助数据协调和联合分析的工具方面进行早期投资。在工作流程1中，我们将开发有效的软件解决方案来整合、管理、协调和分析不同类型的研究。将队列研究中的详细研究观察与常规临床记录中不太彻底但更频繁的信息相结合，具有巨大的潜力。在工作流程2中，我们将利用来自初级保健和医院记录的数据，丰富从STELAR出生队列出生前到成年早期收集的详细信息。我们的工作计划将为合作研究创造条件。共享的数字环境将为我们的科学家团队提供有效分析现有和新收集的数据的工具，并帮助解释发现，并快速实施，以造福患者。在工作流程3中，我们将以哮喘为例，开发和应用方法来联合分析来自不同环境的数据。我们的发现将支持针对特定亚型的哮喘和过敏预防和治疗的新试验，并可能有助于确定个性化药物所需的亚型特异性治疗的新靶点。

Asthma and allergies are the most common chronic diseases in childhood and adolescence. They usually start before school age and are responsible for a heavy burden of ill health, including premature death. It is increasingly recognised that asthma is an umbrella term covering several different diseases, which creates a barrier to delivering personalised treatments (i.e., treatments tailored to the individual patient). We propose an innovative scientific research program (UNICORN, Unified Cohorts Research Network), which embraces a team-science approach to understand heterogeneity of asthma and allergies. We have many ways of researching illnesses: (1) studies of children from their birth (birth cohorts); (2) studies of patients with severe disease; (3) randomised controlled trials (RCTs, where patients are allocated by chance to receive one of several treatments). We propose that we can begin to understand the variation seen in common diseases such as asthma and allergies if we look at all these together. This will help us to predict who will respond best to different treatments. Such a collaborative approach is currently prevented by the lack of a system to jointly manage and analyse the data from different studies. We will form an alliance between the STELAR consortium of 5 UK birth cohort studies aimed at studying asthma and allergic diseases (in total more than 15,000 participants who have been followed from before birth to adulthood) and clinical studies which recruited large numbers of patients with severe asthma (more than 1000). Our birth cohorts measured environmental exposures before the onset of the disease and contain detailed information on the development of asthma and allergies from early childhood to adulthood. In UNICORN, these will be supplemented by the information collected in studies of patients with severe asthma. These studies measure additional clinical markers (for example, more detailed lung function), and collect biological samples which are not available in birth cohorts (such as sputum, nasal secretions, and airway biopsies). These samples are needed to understand the mechanisms underlying different types of asthma. RCTs provide further important and accurate information about responses to treatment. Thus, birth cohorts, patient cohorts, and RCTs are complementary, and combining them by linking the data appropriately will provide invaluable insights into the mechanisms of different asthma subtypes, markers to predict future risk, and individual responses to treatment. UNICORN builds on substantial prior investments in the science and infrastructure underpinning asthma research. We will pull together and build upon several earlier investments in data management platforms and in tools that have been created to help data harmonisation and joint analysis. In Workstream 1, we will develop efficient software solutions to integrate, manage, harmonise and analyse different types of studies together. Combining detailed research observations in cohort studies, with less thorough, but more frequent, information from routine clinical records, holds huge potential. In Workstream 2, we will enrich detailed information collected from before birth to early adulthood in STELAR birth cohorts with data from primary care and hospital records. Our programme of work will create conditions that enable collaborative research. The shared digital environment will provide our team of scientists with tools to efficiently analyse existing and newly collected data and help interpretation of findings, and rapid implementation for patient benefit. In Workstream 3, we will use asthma as exemplar to develop and apply methods to jointly analyse data from different settings. Our findings will underpin new trials of asthma and allergy prevention and treatment, personalised for specific subtypes, and may help identify novel targets for the discovery of subtype-specific treatments required for personalised medicine.