Our goal is to prepare for the worst-case scenario of cancer biology: that the best treatment for each patient is unique and that most tumors will adapt even to that best treatment. Our goal is to increase how much of the vast space of therapeutic interventions we search for this scenario. We combine computational biology, mathematical modeling and pharmacology to up our odds in the race against tumor evolution.
Our research program runs on two parallel tracks, the first complementing and supporting the second. The first track aims to improve consilience among clone perspectives: developing methods to quantify the clonal composition of a tumor from different perspectives, such as their genome, transcriptome, or appearance, and integrating those methods with each other. In short, the first track characterizes clones. The second track uses these clone characteristics to make treatment predictions.