Speaker
Description
Developing a mathematical model requires balancing biological realism against model tractability. We investigate this question through a case study of mechanistic models of ovarian cancer tumour growth and treatment response, assessing which biological hypotheses can be addressed at different levels of model complexity. We use data from Paffenholz et al. (2022), who developed mouse models of high-grade serous ovarian cancer to better understand therapeutic responses to platinum-based chemotherapy (i.e., cisplatin), ICB, and their combinations in homologous recombination (HR)-deficient and HR-proficient immunocompetent mice.
Identifying and interpreting mechanisms of response in these scenarios can be challenging because of the overlapping and nonlinear interactions among tumour, immune, and treatment dynamics. To this end, we developed a hierarchy of ordinary differential equation (ODE) models with increasing biological complexity, progressing from simple exponential tumour growth to mechanistic models incorporating tumour and immune cell populations. Comparing these models reveals that some treatment-response patterns can be captured using relatively simple formulations, whereas others require explicit representation of immune-mediated mechanisms, highlighting how the appropriate level of biological realism depends on the hypothesis being investigated.
Bibliography
@article{paffenholz2022senescence,
title={Senescence induction dictates response to chemo-and immunotherapy in preclinical models of ovarian cancer},
author={Paffenholz, Stella V and Salvagno, Camilla and Ho, Yu-Jui and Limjoco, Matthew and Baslan, Timour and Tian, Sha and Kulick, Amanda and de Stanchina, Elisa and Wilkinson, John E and Barriga, Francisco M and others},
journal={Proceedings of the National Academy of Sciences},
volume={119},
number={5},
pages={e2117754119},
year={2022},
publisher={National Academy of Sciences}
}
