Chalmers Conferences, 9th European Conference on Mathematical and Theoretical Biology

The effect of T-cell homeostasis on solid and liquid tumors
Mark Robertson-Tessi, Derek Park, Adam Mailloux, Kimberly Luddy, Pearlie Burnette, Alexander Anderson

Last modified: 2014-06-09


T-cell populations are subject to homeostatic control from cytokines and microenvironmental signaling.  Disruption of homeostasis can cause changes to the dynamics of the system that have implications for the progression of cancer.  Here we present two mathematical models that examine the progression of tumors in the context of T-cell homeostasis.  Model 1: During a chronic disease such as cancer, T cells often become tolerant to the antigens presented by the disease.  This tolerant state effectively limits the response of the immune system to the tumor.  Experimental evidence has shown that depletion of T-cells can lead to a loss of T-cell tolerance.  During the homeostatic phase of T-cell compartment repopulation, there is a temporary window of opportunity during which T cells lose their tolerant state, allowing them to respond to tumor antigens.  In addition, clonal expansion of the tumor-specific T-cell clone may be enhanced during the regrowth phase due to increased stimulation.  We use an ordinary differential equation (ODE) model to explore the effect of T-cell depletion and homeostatic repopulation on the loss of tolerance in the T-cell compartment and subsequent effectiveness of immune-mediated tumor cytotoxicity.   The model predicts different outcomes for the tumor and T-cell compartment, dependent on the strength and schedule of the depletion therapy.  The optimal regimen can lead to tumor control in some cases, but T-cell exhaustion is also common dynamic predicted by the model.  By understanding the effects of T-cell depletion, immune depleting therapies can be optimized to enhance immune potential.  Model 2: Large Granular Lymphocytic Leukemia (LGLL) is a T-cell lymphoproliferative disorder that exhibits clonal expansion of a subset of T cells.  Since there are no clinical biomarkers to predict the aggressiveness of the disease, treatment decisions are often made on a watch and wait approach.  Using a set of ODEs, we develop a model of LGLL that uses clinical patient data from diagnosis to predict the timeframe for progression of the disease.  Our experimental results have suggested that the disease is caused by a change in sensitivity to both positive and negative regulators of T-cell homeostasis.  The model incorporates these cell-specific mechanisms to investigate their effect when placed in a homeostatic setting.  The level of dysregulation as measured from patient-specific data determines the rate of outgrowth of the diseased T-cell clone, and therefore serve as a useful predictive tool for managing treatment decisions in the clinic.