Moriah Pellowe | Applied Mathematics, University of Waterloo
Mathematical approaches to the study of cellular heterogeneity, treatment design, and immune response in cancer
One of the complicating factors in treating cancer patients are the different levels of heterogeneity involved. In this thesis, we use a combination of mathematical methods (in silico experiments) and experimental data (in vitro and ex vivo experiments) to study cellular heterogeneity, treatment design, and immune response in cancer. This thesis demonstrates the importance and value of interdisciplinary communication and collaboration.
In studying cellular heterogeneity, we demonstrate a framework that uses in vitro and in silico experiments to characterize cancer cell lines and identify the cellular dynamics during early cancer development. This framework can be used to identify the key cellular behaviours for specific cancer cell lines. We demonstrate this process with the breast cancer cell line, MCF-7, and identify progenitor cells as the significant cancer subpopulation. In a following project, we modify and build on the agent-based model to characterize the effect of pressure on phenotypic plasticity during mammosphere formation with and without the presence of a chemotherapy drug. We demonstrated that pressure induces phenotypic plasticity, which can sensitize cells to chemotherapy but also result in a highly resistant colony of cells.
In studying treatment design, we identify the Hsp90 protein network as a means by which drug resistance can be overcome in a drug-tolerant cell. We constructed a minimal in silico model of this network based on systems biology to design a treatment schedule for docetaxel and radicicol. In silico experiments were used to show that radicicol could reverse the development of drug resistance to docetaxel with the proper treatment sequence, which could also be accomplished with a nanoparticle formulation. We identified the intake rate and the decay rate of radicicol as drug formulation properties that would have the greatest impact on increasing the efficacy of the docetaxel-radicicol treatment sequence.
In studying immune response in cancer, we investigate the variability in immune system response to anti-PD-1 immunotherapy. In this work, we construct a systems biology model and use sensitivity analysis to identify potential biomarkers for a positive response to anti-PD-1 immunotherapy. We identified two important interaction networks with regards to response to anti-PD-1 immunotherapy: the interaction between cancer cells and CD8+ cytotoxic Tc cells, and the balance between CD4+ Th1 and Th2 helper cells.
In each of the projects, we investigate heterogeneity at a different level: cellular heterogeneity, variability in protein expression, and variability in immune system response. We also consider the effect of the microenvironment on cellular dynamics. By developing an in silico model to describe the biological phenomena, we can identify the underlying mechanisms at work and provide potential biomarkers and potential improvements that could be tested further.