To show this, I will present a relatively simple modeling framework that captures the complexity of cancer-immune interactions and provides biological insights into the immune response to cancer. The model incorporates the combined effects of various immune cell types, exploiting general principles of self-limited logistic growth and the physical process of inflammation. The results underscore the ultimately polar nature of final tumor fate (escape or elimination), while at the same time showing how transient periods of dormancy may precede either of these two outcomes.
A Markov chain Monte Carlo method is used to determine parameter sets that predict tumor growth equally well, but that at the same time also predict fundamentally different underlying dynamics. The striking variability observed even in this simple model demonstrates the significance of intrinsic and possibly immeasurable patient-specific factors determining the complex biological response involved in tumor growth in an immune competent host.
Ultimately, this work demonstrates that near- and long-term responses of a tumor to immune interaction may be opposed. That is to say, a response dynamic that appears to be more promoting of tumor growth than another in the near term, may be superior at curtailing tumor growth in the long-term, even to the point of establishing dormancy while the other allows for tumor escape. These dynamics result from a contest of tumor-promoting and tumor-inhibiting immune response timescales.