PhD defence J. (Joske) Ubels

Many cancer drugs only benefit a subset of the patients that receive them. Because these drugs are often associated with serious side effects, it is very important to be able to predict who will benefit and who will not. This is a difficult problem, as we cannot know how a patient would have responded to another drug, and thus cannot know for sure whether the drug given was the best choice. This thesis presents several algorithms that can build models that can predict whether a patient will benefit more from a drug of interest than an alternative treatment. In multiple myeloma, we use tumor gene expression measurements taken at diagnosis, to predict which patients will benefit from treatment with proteasome inhibitors. Proteasome inhibitors are an important part of multiple myeloma treatment. We identify approximately 30% of patients who benefit, while other patients do not see a significant benefit from this treatment. We also present another algorithm that can use germline DNA to predict benefit and apply this to colorectal cancer data to predict cetuximab benefit. Taken together, these algorithms form a new approach to facilitate personalized medicine and pick the best drug for every patient.

Due to corona, the PhD defences do not take place publicly in the usual way in the Senate Hall of the Professor Andries Querido Room. The candidates will defend their dissertation either in a small group or online.