  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.
