From Big Data to Better Breathing: Decoding COPD Heterogeneity Through Multi-omics and Treatable Trait Clustering

Superviors:

• Dr Jing Chen  jing.chen@leicester.ac.uk
• Dr Catherine John  catherine.john@leicester.ac.uk
• Dr Tom Ward  tom.ward@leicester.ac.uk

Project Description:

This project aims to uncover the biological factors that shape lung disease by combining state-of -the-art multi-omics data with advanced computational, statistical and machine learning techniques.
Chronic obstructive pulmonary disease (COPD) is a long-term lung condition which causes millions of deaths worldwide each year. The disease process varies significantly from person to person, with some people experiencing severe damage to the lung’s air sacs (emphysema) and others predominantly experiencing inflammation of the airways. Traditionally, researchers have tried to classify COPD into subtypes based on clinical assessments including spirometry (lung function tests) and imaging [1-4]. 

However, with the exception of eosinophilic disease (i.e. with high levels of inflammatory cells called eosinophils), these approaches showed inconsistent findings that were difficult to reproduce across populations. Treatment strategies tailored to specific subtypes remain constrained by limited mechanistic understanding and a lack of standardised measurements and definitions. 
A newer way of thinking about lung disease is the “treatable traits” approach, which recognises that chronic airway diseases arise from interactions across complex biological, behavioural, and environmental networks[5, 6]. Instead of placing people into broad disease types, this approach focuses on holistic, multidimensional assessment of pulmonary, extrapulmonary and lifestyle-related traits that are clinically measurable and treatable. Integrating this framework with “omics” and the environment represents a critical opportunity for insight into the biology underlying individual clinical presentations, with potential to transform personalised prevention, diagnosis and treatment.[5] 

This PhD project will bring together large-scale data from biobanks, including multi-omics, biomarkers and environmental data, to identify and predict COPD clusters defined by treatable traits. You will use advanced computational, statistical and machine-learning approaches to uncover novel biological insights and support the development of personalized risk prediction tools.  This is an exciting opportunity to work at the forefront of respiratory precision medicine and generate results with direct clinical relevance, with potential to improve COPD care for patients. 

Training and Environment
You will join a world-leading Genetic Epidemiology research group. The group currently hosts 17 PhD students and has an exceptional track record in developing talented postgraduate researchers who go on to successful postdoctoral careers in academia and in industry. 

You will benefit from:

• expert supervision in statistical genetics, genetic epidemiology and bioinformatics,
• high-performance computing facilities for large-scale data analysis,
• links with the Leicester NIHR Biomedical Research Centre,
• opportunities to collaborate with leading clinicians and functional genomics experts to support translational insight,
• chances to get involved in public engagement and science communication.

Your PhD training will foster well-rounded expertise and skills spanning data science, population health and translational research, positioning you well for further opportunities in this rapidly-growing field.>

Expected Outcome
By the end of the project you will:

• generate new insights into COPD heterogeneity,
• identify biomarkers and trait-based clusters relevant for disease stratification,
• improve understanding of disease mechanisms underlying individual patient presentations,
• Contribute to development of tools for personalised risk prediction. 

Your findings will help lay the groundwork for future personalised medicine approaches, enabling patients and clinicians to make better decisions about COPD care. You will present your emerging findings to academics and clinicians at relevant local, national and international conferences, and will be encouraged to contribute to peer-reviewed publications.

References

1. Castaldi, P.J., et al., Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease. Am J Epidemiol, 2023. 192(10): p. 1647-1658.
2. Young, K.A., et al., Subtypes of COPD Have Unique Distributions and Differential Risk of Mortality. Chronic Obstr Pulm Dis, 2019. 6(5): p. 400-413.
3. Ross, J.C., et al., Longitudinal Modeling of Lung Function Trajectories in Smokers with and without Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med, 2018. 198(8): p. 1033-1042.
4. Lowe, K.E., et al., COPDGene(®) 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease. Chronic Obstr Pulm Dis, 2019. 6(5): p. 384-399.
5. Papi, A., et al., From treatable traits to GETomics in airway disease: moving towards clinical practice. European Respiratory Review, 2024. 33(171): p. 230143.
6. McDonald, V.M., et al., Treatable traits: a new paradigm for 21st century management of chronic airway diseases: Treatable Traits Down Under International Workshop report. European Respiratory Journal, 2019. 53(5): p. 1802058.

Please refer to the entry requirements, funding and application advice below before submitting your application