Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies

The past 5 years has seen an exponential increase in the amount of clinical, genetic and biological data that has become available. However, this data explosion has not been accompanied by a parallel capacity to analyse such data and understand how this data can be translated into solutions for curing diseases. We therefore need to explore statistical models which allow us to identify meaningful patterns in this 'big data' so that we can understand the nature of human diseases. Recent news headlines have identified the need for a "global medical revolution" to rethink the way we understand disease so that we move towards more personalised medicine strategies which incorporate knowledge from a patient's genetic and biological profile. This is an exciting era of medical research where we are moving towards using statistical modelling techniques to translate scientific understanding of human genetics and immunology into a better understanding of disease aetiology and as a consequence improve the health of individuals within the global community. To achieve this requires a truly multi-disciplinary approach to combining clinical knowledge and mathematical-computational expertise so that we can understand the underlying causes of different diseases which will allow us to improve our strategies for identifying the correct treatment.

The abundance of genetic and biological information has led to the recognition that there needs to be a reclassification of diseases as we understand them today. One such disease is asthma. Asthma has featured in the recent news as is the most common chronic disease of childhood which causes many hospital admissions each year and many preventable deaths. The number of children suffering from asthma has increased dramatically over the past 30 years. It is unclear why some people get asthma and others do not. Many factors in the environment may contribute to the development of asthma (for example diet, immunizations, antibiotics, pets and tobacco smoke) but we don't know how to modify the environment to reduce the risks. One reason for the difficulty in understanding causes of asthma is that what we once thought to be a single condition may be a collection of different diseases which cause similar symptoms. A promising approach to investigating these different "asthmas" and allergic diseases is the use of statistical machine learning which allows us to create models which recognise patterns of how disease changes over time. Since most asthma is initiated from childhood, the data I would use for this project looks at various clinical and biological measurements from children starting at birth. These children are in different parts of the UK and data is collected from them approximately every 2-3 years to see how their patterns change over time and whether differences in patterns among children who would generally be classified as "asthmatics" allow us to identify different asthma subtypes or "endotypes". Building models which are appropriately complex for endotype discovery allows us to incorporate more complex datasets including molecular and biological data which tell us how these children respond to viruses and bacteria over time. The complexity of the data and these models needs to reflect the complexity of the diseases. If we construct optimal models which are able to capture and predict change over time, these techniques could also be applied on a global scale that can be used to better identify children that would benefit from different treatment regimens. It is hoped that more refined endotype discovery will lead to a better understanding of the causes and nature of disease and therefore lead to more targeted treatment and management strategies. This approach of using statistical science to inform our understanding of disease by incorporating a large scale of data which is collected over time is applicable not just to asthma and allergy, but is generalizable to other diseases.

Aim: To determine a unified probabilistic latent variable modelling strategy for integrating immunological, molecular, biological and clinical phenotypes for endotype discovery in longitudinal studies.

Objectives:
1. To build a unified graphical model that represents a broad range of important variables associated with asthma and allergic disease.
2. To use innovative computational statistical latent variable modelling methods to discover novel subtypes of childhood asthma and allergy across multiple cohorts.
3. To extend these graphical models to generate novel insights into a systems modelling framework that is able to upscale in order to integrate clinical data, immunological data, genetic data and epigenetic data.
5. To extend this unified graphical modelling framework to explore principled methods for using probabilistic latent variable models to deal with missing variables in the context where not all variables are available at every time-point.
6. To formally assess the strengths and weakness of Bayesian and Frequentist methods within a longitudinal birth cohort setting.

Methods: The proposal will use data from the STELAR consortium for clinical endotype discovery and data from MAAS for extending graphical models to a systems biology framework. Current Bayesian machine learning and classical-statistical probabilistic modelling approaches will be extended to identify latent disease subtypes. Dimensionality reduction techniques will be developed and explored to understand the latent space which best describes high-dimensional clinical and immunological data - considering viral and bacterial stimuli-cytokine responses separately.

Scientific and Medical Opportunities: It is hoped that more refined endotype discovery will lead to understanding their underlying biological mechanisms and therefore lead to more targeted treatment and management strategies. This is applicable not just to asthma and allergy, but is generalizable to other diseases.

This research will have a direct impact on the scientific community by evaluating novel asthma endotypes or pathways. By understanding these, it is hoped to gain an understanding of the underlying biological, molecular, immunological and genetic mechanisms which need to be targeted in order to better control asthma and allergic disease.
Patients, health professionals, taxpayers, policy makers and UK businesses will benefit from the applications of this research which is well aligned to the MRC's Strategic plan to deliver research that changes lives. By understanding the underlying mechanisms of different asthma and allergy endotypes, this would lead to identifying potential groups of patients who may respond to different asthma and allergy management strategies.

The Pharmaceutical industry would be encouraged towards more targeted medication strategies and better stratification included in randomised controlled trials. A unified statistical graphical modelling framework may be applied to research into other disease areas as well in attempt to move towards stratified treatment.
This will hopefully lead to new approaches to medicine which, as well as benefiting the groups identified, has a global impact.

Understanding the existence of distinct subtypes of disease in this project is aligned with the Department of Health's NHS Outcomes Strategy for COPD and Asthma (2012) to help to "provide proactive chronic disease management appropriate for the severity level assessed - mild, moderate or severe"

This project is also directly aligned to the UK Trade and Investment which outlines the UK's commitment to stratified medicine. It states that "this smarter model of medicine, in which tools are used to stratify cohorts of patients by subclass of disease or the likelihood of responding to a particular therapy, intervention, or disease management strategy. A more stratified approach to medicine has the potential to increase patient benefit and at the same time unlock business and economic benefits. Effective development and delivery of stratified medicine will require collaboration across sectors. There is scope for innovators in drug discovery, research tools, diagnostics, devices, informatics, clinical decision making and health systems to pull together in this refocused approach to medicine."

This project is an extension of the government's commitment to scientific research through its capital and infrastructure investments from 2015 highlighted in the HM Treasury's document "Investing in Britain's Future". This document outlines the government's commitment to "ensuring that UK science and research stays at the cutting edge of developments and ensure that the UK is a major competitor in the global race. Investment in science and research capital and infrastructure projects will be made on the basis of scientific excellence so there are no further details to announce at the current time." Two of the "Great technologies" highlighted in this document were "Big Data" and "Regenerative Medicine" of which this proposal forms a part.

At a higher level, the multidisciplinary nature of this work is aligned to the WHO report 'Working together for Health' which states that '.....working alone with no regular exchanges of experience for mutual improvement can no longer be considered professionally satisfactory'. Working in a team enables the professions to solve 'complex health problems that cannot be adequately dealt with by one profession alone'. In order to understand disease aetiology, investment is needed in young researchers who will have the skills to train others in seeking solutions to healthcare problems.

As stated in the communications plan, dissemination of the research within this proposal through public engagement, participation in conferences and publication will have a significant impact on all levels of society: the local community, academic interdisciplinary research and the private sector.