Improving cancer therapy through unconventional strategies

Abstract

In this thesis, I aimed to improve cancer therapy through rational and innovative/unconventional approaches. The adage “if you do what you did, you get what you got” is used to remind us that meaningful change often requires fundamentally different approaches. This also applies to cancer therapy, where for several decades the majority of novel cancer drugs have been developed as single-agent therapies and delivered to patients at maximum-tolerated-dose (MTD). The main work of this thesis describes the development of a fundamentally different approach to treat cancer – called the Multiple Low Dose (MLD). Unlike conventional MTD regimens, where one drug is given at high dose, in the (unconventional) MLD regimen several drugs that act in the same signalling pathway are combined at low doses. This results in a lower chance of resistance as well as reduced toxicity. The promising pre-clinical data in mice suggest that MLD therapy could deliver clinical benefit in several types of cancer. However, testing all combinations experimentally, especially with the increasing number of cancer compounds is becoming an impossible task. To tackle this problem, we developed a mathematical model of signal transduction which we fed with data from perturbation experiments and showed that it’s possible to predict the best multi-drug combinations in silico. In this thesis I also developed a new fluorescent-based reporter to identify the acquisition of microsatellite instability by cancer cells in real-time, and showed that by optimizing Cas9 expression levels gene editing time can be reduced, which can improve performance of CRISPR based screening.