Computational Modeling of Chimeric Antigen Receptor (CAR) T-Cell Therapy of a Binary Model of Antigen Receptors in Breast Cancer

Among the various types of breast cancer, Triple-Negative Breast Cancer (TNBC) has proven to be one of the most potent with poor prognosis and limited treatment options. In this paper, we build upon a previous 3D agent-based model of TNBC to incorporate Chimeric Antigen Receptor (CAR) T-cell therapy. CAR T-cell therapy is a promising method of treating cancers. Rather than limiting the cancer’s resources or targeting rapidly dividing cells (e.g. anti-angiogenic and chemo therapies) this method modifies the patients’ T-cells to directly target cancer cells. We varied the percentage of antigen-presenting tumor cells to see the overall effectiveness of CAR T-cell therapy on tumor growth. We used a binary antigen presentation, where each tumor cell either does or does not have the antigen necessary for their detection and elimination by a CAR T-cell. We found that the therapy statistically reduced the tumor growth but does not completely eliminate the tumor. We observed a negative trend between the percentage of antigen-presenting tumor cells and the growth of tumor. Our simulations also showed some cases where antigen non-presenting cells formed a shield over antigen-presenting cells. This shield blocked the access of CAR T-cells to antigen-presenting cells. Finally, we observed a decrease in cancerous stem cells after the introduction of CAR T-cells. Our findings suggest that CAR T-cell therapy is effective in decreasing the size of solid tumors in case of binary heterogeneity but may not be effective at completely eliminating the tumor.