UNICORN (Unified Cohorts Research Network): Disaggregating asthma
Lead Research Organisation:
Imperial College London
Department Name: National Heart and Lung Institute
Abstract
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.
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.
Technical Summary
The development of new methodologies for improving causal inference in epidemiological studies creates an opportunity for a step change in understanding mechanisms underlying asthma development. We propose that the best way to scale up research in asthma is to integrate unselected birth cohorts with patient cohorts and randomised controlled trials (RCTs) for joint analyses, as these different settings provide complementary windows on distinct aspects of understanding disease aetiology. UNICORN will form an alliance between the STELAR consortium of 5 birth cohorts aimed at studying asthma and allergies (in total >15000 participants), and patient cohorts with large numbers of carefully phenotyped patients with asthma (Breathing Together consortium, U-BIOPRED, RBH Severe Asthma cohort). In Workstream 1, we will build on earlier investments (eLab, tranSMART/eTRIX) to develop efficient scalable informatics solutions enabling integration, management, harmonisation and secure co-analysis of birth cohorts, patient cohorts, and RCTs. The development of an integrated data management and analysis platform at the heart of the UNICORN research engine will be a unique resource for the UK health science and will provide a template for implementation in other complex non-communicable disease areas where data integration provides the only realistic prospect of solving the complex and heterogeneous biology of these conditions. Workstream 2 will extend the detailed information collected from ante-natal period to adulthood in STELAR cohorts, with a routinely acquired data in primary care and hospital records, facilitating more sophisticated analyses. In Workstream 3, in an iterative discovery process, we will capitalise on a unique combination of expertise, well characterised birth and patient cohorts, and our novel research engine to promote the discovery of asthma endotypes, and identify and understand mechanisms underpinning such endotypes, thereby advancing stratified medicine.
Planned Impact
Who might benefit from this research?
We will develop the capacity to handle increasing quantities of complex data across different types of studies and ensure that these rich and unique data sets are used to their maximum potential. The project will multiply the effects of previous investments, thereby having an overall scientific impact much greater than its level of requested funding. We will provide an 'engine' for large scale transdisciplinary collaborations to conduct cutting edge science, using existing data resources, to produce health benefit for the UK population, and broader.
Our approach will support greater reproducibility and transparency of research, and enable researchers to explore the sets of variables and analytical methods selected by their colleagues in other disciplines. There are clear and immediate economic benefits in allowing researchers to build on the methods and expertise of others.
Our results will identify risk factors and mechanisms that influence the onset and progression of asthma-related diseases from infancy to early adulthood, and associated adverse lung function trajectories. The discovery of mechanisms underpinning different asthma endotypes may form the basis for identification of novel therapeutic targets, and biomarkers which are predictive of health or disease, or the response to treatment. This will be of great value to patients, society, health-care professionals and industry.
The impact of the programme will include conceptual, methodological and analytical contributions towards data integration and their efficient exploitation. It can lead to advances in artificial intelligence and machine learning, with widespread applications for technology companies (see letter of support from Prof C Bishop, Director of Microsoft Research Cambridge). UNICORN's science-focused approach, nurturing innovation in computational epidemiology while advancing asthma research, represents pull-through of data science from leading, cognate biomedical research, and complements infrastructural approaches such as HDRUK.
How might they benefit from this research?
The ability to access shared analysis resources will be of great value for training and development of researchers, and the ability to access example analyses and expert advice will reduce their learning curve. Enabling the networking of datasets, expertise and methods for data preparation and analysis can help drive greater value from existing investments.
The outputs from our research will support UK Industrial Strategy by enabling Pharmaceutical and Biotechnology companies to identify novel therapeutic targets, which are endotype-specific. Diagnostic companies may develop biomarkers and/or algorithms which can be used as tools to assess future risk, and the response to currently available or novel treatments. Such biomarker and associated algorithms could form the basis of diagnostic or prognostic tests, which may be used to make informed life-style choices to prevent or attenuate disease development, and impact long-term health. The discovered biomarkers may also be used to stratify patients in clinical trials, and facilitate the selection of the most appropriate therapies in a stratified manner.
Technology companies will be well-placed to abstract the underpinning methodology, and help MRC translate it to other domains of research, de-risking the investment for MRC, and enhancing methodological advances for industry.
Our findings may represent potentially valuable intellectual property, which we will seek to commercialise in collaboration with companies invested in diagnostics and/or therapeutics. Participating universities have mechanisms and structures in place for exploring industrial applications. Partnerships such as the one described in this application help to make the UK an attractive location to retain research activities, and help expose academics to the process of translating science into products.
We will develop the capacity to handle increasing quantities of complex data across different types of studies and ensure that these rich and unique data sets are used to their maximum potential. The project will multiply the effects of previous investments, thereby having an overall scientific impact much greater than its level of requested funding. We will provide an 'engine' for large scale transdisciplinary collaborations to conduct cutting edge science, using existing data resources, to produce health benefit for the UK population, and broader.
Our approach will support greater reproducibility and transparency of research, and enable researchers to explore the sets of variables and analytical methods selected by their colleagues in other disciplines. There are clear and immediate economic benefits in allowing researchers to build on the methods and expertise of others.
Our results will identify risk factors and mechanisms that influence the onset and progression of asthma-related diseases from infancy to early adulthood, and associated adverse lung function trajectories. The discovery of mechanisms underpinning different asthma endotypes may form the basis for identification of novel therapeutic targets, and biomarkers which are predictive of health or disease, or the response to treatment. This will be of great value to patients, society, health-care professionals and industry.
The impact of the programme will include conceptual, methodological and analytical contributions towards data integration and their efficient exploitation. It can lead to advances in artificial intelligence and machine learning, with widespread applications for technology companies (see letter of support from Prof C Bishop, Director of Microsoft Research Cambridge). UNICORN's science-focused approach, nurturing innovation in computational epidemiology while advancing asthma research, represents pull-through of data science from leading, cognate biomedical research, and complements infrastructural approaches such as HDRUK.
How might they benefit from this research?
The ability to access shared analysis resources will be of great value for training and development of researchers, and the ability to access example analyses and expert advice will reduce their learning curve. Enabling the networking of datasets, expertise and methods for data preparation and analysis can help drive greater value from existing investments.
The outputs from our research will support UK Industrial Strategy by enabling Pharmaceutical and Biotechnology companies to identify novel therapeutic targets, which are endotype-specific. Diagnostic companies may develop biomarkers and/or algorithms which can be used as tools to assess future risk, and the response to currently available or novel treatments. Such biomarker and associated algorithms could form the basis of diagnostic or prognostic tests, which may be used to make informed life-style choices to prevent or attenuate disease development, and impact long-term health. The discovered biomarkers may also be used to stratify patients in clinical trials, and facilitate the selection of the most appropriate therapies in a stratified manner.
Technology companies will be well-placed to abstract the underpinning methodology, and help MRC translate it to other domains of research, de-risking the investment for MRC, and enhancing methodological advances for industry.
Our findings may represent potentially valuable intellectual property, which we will seek to commercialise in collaboration with companies invested in diagnostics and/or therapeutics. Participating universities have mechanisms and structures in place for exploring industrial applications. Partnerships such as the one described in this application help to make the UK an attractive location to retain research activities, and help expose academics to the process of translating science into products.
