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Computational model for tumor response to adoptive cell transfer therapy

One of the barriers to the development of effective adoptive cell transfer therapies (ACT), specifically for genetically engineered T-cell receptors (TCRs), and chimeric antigen receptor (CAR) T-cells, is target antigen heterogeneity. It is thought that intratumor heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model to rationalize the outcomes of two types of ACT therapies over heterogeneous tumors: antigen specific ACT therapy and multi-antigen recognition ACT therapy. We found that one dose of antigen specific ACT therapy should be expected to reduce the tumor size as well as its growth rate, however it may not be enough to completely eliminate it. A second dose also reduced the tumor size as well as the tumor growth rate, but, due to the intratumor heterogeneity, it turned out to be less effective than the previous dose. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low oncoprotein expression. This shield turns out to protect cells with high oncoprotein expression. On the other hand, our studies suggest that the earlier the multi-antigen recognition ACT therapy is applied, the more efficient it turns. In fact, it could completely eliminate the tumor. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and explore the characteristics of successful and unsuccessful treatments.