Precision medicine and patient stratification are expanding as a result of
innovations in high-throughput technologies applied to clinical medicine.
Stratification can explain differences in disease trajectories and outcomes in
heterogeneous cohorts. Thus, approaches employed for patient treatment can
be tailored by taking into account individual variabilities and specificities.
This thesis focuses on clustering approaches and how they can be applied to
both single time points and time-series high-dimensional data for the
identification of disease subtypes defined by distinct mechanisms, also called
endotypes, in complex and/or heterogeneous diseases. Multiple carefully
selected clustering strategies were compared to highlight which would produce
the most relevant stratification in terms of mathematical robustness and
biological meaning, both of which quantified using standardised methods.
More specifically, this strategy was applied to time-series multi-omics data
from a cohort of patients with acute pancreatitis, an inflammatory disease of
the pancreas. Using this high-dimensional multi-omics data as well as routine
lab and clinical measurements, the cohort was stratified into four subgroups.
Findings from the analysis of acute pancreatitis data showed that two of the
four subgroups could be detected in another syndrome, acute respiratory
distress syndrome, suggesting that inflammatory signatures are comparable
between diseases.
With the aim of applying these principles to other diseases and using
preliminary results from other studies suggesting that relevant subgroups
might be highlighted, data from inflammatory bowel disease and Parkinson's
disease cohorts was analysed. Results from our analyses confirmed that
disease knowledge could be gained using this approach. Work from this thesis provides novel approaches for the application and
evaluation of stratification methods. Furthermore, results may constitute a
basis for the development of tailored treatment approaches for acute
pancreatitis, acute respiratory distress syndrome, inflammatory bowel disease
and Parkinson’s disease. Also, the observation of commonalities between
distinct inflammatory diseases will broaden the perspectives when analysing
disease data and more specifically, in biomarker discovery and drug
development processes
