School of Mathematics

Alexander R.A. Anderson

Exploiting evolution to design better cancer therapies

Our current approach to precision medicine is dictated by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose. These therapies generally achieve impressive short-term responses, that unfortunately give way to treatment resistance and tumor relapse. Here we present an integrated theoretical/experimental/clinical approach to develop multiscale models that capture the temporal and spatial heterogeneity in cancers and govern tumor response and resistance to therapy. Specifically, we examine the impact of microenvironmental modulation on cancer evolution both in silico, using a hybrid multiscale mathematical model, and in vivo, using three different spontaneous murine cancers. These models illustrate how we can steer the tumor into a less invasive state through the smart sequential application of drug treatment and drug holidays. Furthermore, in the clinical setting, we illustrate the importance of using treatment response as a key driver of treatment decisions, rather than fixed strategies that ignore it. Our models explicitly account for the underlying tumor heterogeneity and try to exploit it through the principle of competitive release. Our results strongly indicate that the future of precision medicine shouldn’t be in the development of new drugs but rather in the smarter evolutionary application of preexisting ones.