56 Questions). For instance, serological antibody-based signatures could be integrated into machine-learning algorithms while relying on detailed patient phenotypes to infer serological antibody signatures that are associated with a particular disease outcome. However, the main future challenge for such efforts will lie in the current complexity and impracticality of this approach in clinical practice. We should therefore strive to stimulate the parallel development of easy-to-use, robust, cost-effective clinical applications that incorporate these data-driven predictive signatures. For example, the development of arrays capable of assessing sets of key antibodies could become promising decision-support tools to aid treating physicians in disease management. Antibody repertoire profiling represents a promising approach to improve our understanding of IBD immunopathogenesis and expose novel targets for disease diagnostics and management, potentially paving the way for preventive opportunities. To advance the field, we should prioritize large and carefully designed studies that take relevant patient characteristics and other layers of biological data into account (i.e. adopting a multi-omics strategy). This would enable the identification of personalized “immunological fingerprints” for patients while also including relevant determinants, culminating in an immunology-driven precision medicine approach for IBD. Acknowledgments All authors would like to express their gratitude towards scientific and medical illustrator Nikola Kolundzic (King’s College London, United Kingdom) for his valuable help in the graphical design of the figures. In addition, the authors would like to thank Kate McIntyre (Scientific Editor, Department of Genetics, University Medical Center Groningen) for language editing. T.V. gratefully acknowledges support from the Austrian Science Fund (FWF, Erwin Schrödinger fellowship J 4256). Chapter 2
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