To Measure is to Predict

Summary

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Successful translational research on plasma cell neoplasms relies on a combination of expertise in plasma cell biology, pathophysiology of multiple myeloma (MM), disease evolution and progression, mechanism of (immune)therapies, and an understanding of potential clinical implications. The research that is presented in this thesis forms a bridge between preclinical research and clinical applications. Together, the studies presented here explore the intelligent use of therapy in patients with MM and AL amyloidosis. We highlight the significance of finding predictive biomarkers of treatment response and the use of patient-derived culture approaches in working towards personalized medicine for patients with plasma cell neoplasms. Our findings have potential clinical implications for MM treatment strategies.

MM is characterized by heterogeneity; intra- and interpatient genetic differences correspond with a high degree of clinical heterogeneity and ultimately result in resistance to therapy and relapse of disease.1–3 As the number of available treatment options is growing, selecting the optimal class, combination and sequence of treatment will be a clinical challenge in years to come. Therefore, we are in need of informed precision treatment strategies, targeting the Achilles heel of MM disease biology and thereby improving duration and depth of response2. Broadly, there seem to be two approaches to achieve personalized medicine in plasma cell neoplasms. Firstly, further unravelling disease biology should reveal novel predictive biomarkers of therapy response. Secondly, ex vivo measurement in patient-derived MM cells pragmatically informs on drug sensitivity. Both of these approaches could be used to guide choice of treatment in individual patients before initiating therapy and both of these approaches have been explored in this thesis.