Validation of polymorphic Gompertzian model of cancer throughin vitroandin vivodata

Abstract

Mathematical modeling plays an important role in our understanding of ther- apy resistance mechanisms in cancer. The polymorphic Gompertzian model, analyzed theoretically by Viossat and Noble, describes a heterogeneous can- cer population consisting of therapy sensitive and resistant cells. This theo- retically promising model has not previously been validated with real-world data. In this study, we provide this validation. We demonstrate that the polymorphic Gompertzian model successfully captures trends in both in vitro and in vivo data on non-small cell lung cancer (NSCLC) dynamics under treatment. For the in vivo data of tumor dynamics in patients undergoing treatment, we compare the polymorphic Gompertzian model to the classical oncologic models, which were previously identified as the models fitting this data the best. We showed that the polymorphic Gompertzian model can successfully capture the U-shape trend in tumor size during cancer relapse, which can not be fitted with the classical oncologic models. In general, the polymorphic Gompertzian model corresponds well to both in vitro and in vivo real-world data, suggesting it as a candidate for improving the efficacy of cancer therapy, for example through evolutionary/adaptive therapies.