Preventing evolutionary rescue in cancer
link to preprint
Extinction therapy aims to eradicate tumours by optimally scheduling multiple treatment strikes to exploit the vulnerability of small cell populations to stochastic extinction. This concept was recently shown to be theoretically sound but has not been subjected to thorough mathematical analysis. In this project with Dr. Robert Noble, we obtained quantitative estimates of tumour extinction probabilities using a deterministic analytical model and a stochastic simulation model of two-strike extinction therapy, based on evolutionary rescue theory. We found that the optimal time for the second strike is when the tumour is close to its minimum size before relapse. Given that this exact time point may be difficult to determine in practice, we showed that striking slightly after the relapse has begun is typically better than switching too early. We further explained how demographic and environmental parameters influence the treatment outcome. Surprisingly, a low dose in the first strike paired with a high dose in the second was shown to be optimal. As one of the first investigations of extinction therapy, our work established a foundation for further theoretical and experimental studies of extinction therapy. This work was done as a part of my master's thesis, which was then extended and released as a preprint as well.