Idiopathic pulmonary fibrosis kills over 6,000 people in the UK each year, with only a quarter of patients surviving five years after diagnosis. The disease causes progressive scarring of the lungs, and while anti-fibrotic drugs can slow this process, many patients continue to worsen—and others with similar scarring from different causes are not eligible for these drugs at all. This research aims to identify blood-based biomarkers that can predict which patients will benefit most from anti-fibrotic therapy. The researcher will analyse data from several large cohort studies, including one tracking early lung abnormalities seen on CT scans that may affect over half a million people in the UK. By linking genetic risk markers with serum biomarkers of disease progression, the team will build a computational model to forecast which patients are likely to deteriorate rapidly. They will also test how known and novel biomarkers respond to treatment in samples from a clinical trial, and develop a health-economic model for biomarker-guided treatment decisions. If successful, this work could transform pulmonary fibrosis from a one-size-fits-all diagnosis into a condition managed by molecular subtype, allowing clinicians to target expensive therapies to those most likely to benefit while sparing others unnecessary side effects and cost.
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Background Idiopathic Pulmonary Fibrosis (IPF) is a devastating, progressive fibrotic lung disease with a median survival of 3 years and a 25% 5-year survival rate. The incidence continues to rise throughout the world, with >6000 deaths/year in the UK alone. Development of 'anti-fibrotic' drugs has lead to improved outcomes for patients with IPF, although patients continue to progress despite therapy. However, disease progression is highly variable and recognition of two related conditions has added further complexity disease management. Patients with progressive fibrotic lung disease of known aetiology, or not sharing classical radiological or pathological characteristics of IPF, are not eligible for anti-fibrotic therapy even though disease trajectory and molecular pathogenesis may be shared. Furthermore, recent data suggests Interstitial Lung Abnormalities (ILAs) on CT scanning may represent early progressive fibrotic lung disease, which could affect over half a million people in the UK, who have no clear management pathway. Research Question The overarching hypothesis underpinning this application is that 'genetic and serum biomarkers can identify a subgroup of patients with pulmonary fibrosis who will benefit most from anti-fibrotic therapy'. Methods This proposal will use information gained from the PROFILE study to guide analysis from two cohort studies, one observing progressive pulmonary fibrosis (INJUSTIS study) and one observing early ILAs (SUMMIT study), and an on-going interventional clinical trial (INMARK trial), to identify molecular endotypes of patients with rapidly progressive pulmonary fibrosis. The information gained will be used to develop a feasibility study of anti-fibrotic therapy in patients with early progressive fibrotic lung disease. Specific Aims Aim 1) Identify endotypic biomarkers reflecting progressive pulmonary fibrosis. I will further analyse data from the PROFILE cohort as well as analysing data from the Its Not Just IPF Study (INJUSTIS) cohort to determine whether endotypes of progressive pulmonary fibrosis are conserved regardless of disease aetiology or clinical phenotype. Results will be validated in cohorts obtained from US collaborators. Aim 2) Identify biomarkers that predict progressive fibrosis in people with ILAs. I will undertake a discovery analysis in ILAs identified in patients participating in a CT Screening study for lung cancer (The SUMMIT study). Results from the UK screening study will be validated in US cohorts of ILA in collaboration with US investigators. Aim 3) Develop a prediction model to determine which patients with pulmonary fibrosis are likely to progress. I will assess the interaction between markers of genetic risk of disease susceptibility, progression and mortality with serum biomarkers of disease progression and outcome to develop a computational model for prediction of disease progression. Aim 4) Validate biomarkers of response to therapy. I will assess the response of known, and novel, biomarkers in samples from patients treated with anti-fibrotic drugs (INMARK trial), and develop a health economic model for the use of biomarker driven therapeutic decision-making. Aim 5) Determine the feasibility of using biomarker-guided strategy for managing patients with ILAs. Impact These studies will define endotypes of progressive fibrotic lung disease that will clarify management and improve outcomes for patients with these complex conditions.
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