Cystic Fibrosis: a real-world challenge to predict individual outcomes

Cystic Fibrosis (CF) is a rare monogenic multisystem disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. More than 2000 different CFTR variants have been identified that differentially affect CFTR protein function. As a result, CF disease manifestations and response to recently developed CFTR-modulating treatments is highly variable among individuals. Without an effective treatment, people with CF can die at a young age. To develop and select the best personalized therapy for all people with CF, we need more sensitive tests and outcome measures to predict long-term disease progression and response to CFTR-modulating drugs in real-life practice. This thesis showed that the forskolin-induced swelling (FIS) assay of intestinal organoids is associated with long-term CF disease progression in multiple organs, including long-term lung function decline and the odds of developing CF-related comorbidities such as pancreatic insufficiency, diabetes and liver disease. The sweat chloride test, which is the reference standard biomarker of CF diagnosis, was not associated with long-term CF disease progression. These findings demonstrate the potential value of the FIS assay as a prognostic biomarker of long-term CF disease progression, which is particularly useful to inform individuals with rare CFTR variants with unclear clinical consequences about their long-term prognosis. On the other hand, this thesis could not identify predictors that were associated with long-term response to CFTR-modulating drugs in people with CF homozygous for the most prevalent F508del mutation. This indicates that prediction of long-term treatment response remains challenging with the currently available tests and outcome measures in this context, emphasizing the need for alternative outcomes and analysis strategies. In the last part of this thesis, the Q-Life app was developed as a novel personalized electronic patient-reported outcome measure (ePROM) to measure quality of life of people with CF on an individual level. Two observational studies described in this thesis showed that the Q-Life app is a reliable, valid and sensitive ePROM to measure quality of life in a personalized way. The Q-Life app could be a potential new sensitive and relevant outcome measure that can help to optimize personalized treatment of people with CF. As suggested in this thesis, future research in extensive datasets studying various long-term outcomes in real-life settings should reveal at what precision individual disease states and therapeutic responses can be resolved and predicted through a combination of CFTR and non-CFTR dependent individual assessments