Affinity Gradient of Chimeric Antigen Receptor T-Cells Against Low-Antigen Density Target

Adoptive Cell Therapy (ACT) is a novel approach to cancer treatment, which implements the use of the patient T-cells redirected to eradicate tumour cells. The T-cells are engineered to express a Chimeric Antigen Receptor (CAR) that bestows specificity for an arbitrary antigen. CAR-T cell clinical trials have shown tumour eradication of leukaemia, but modest results against solid tumours. There is an ongoing debate regarding the correlation of CAR affinity and antigen-density recognition threshold, which we sought to investigate. Although the TCRs sensitivity against low antigen targets is superior at low-affinity, the effect of affinity in CAR efficacy and sensitivity is controversial. In order to examine the correlation between CAR efficacy and affinity, we designed a CAR against an intracellular protein in melanoma. Tyrosinase-Related Protein 1 (Tyrp1) resides in the surface of the organelles called melanosomes. However, Tyrp1 is also trafficked to the surface at a low density. We mutated an anti-Tyrp1 single-chain Variable Fragment (scFv) to acquire eight mutants of various affinities. The affinity range was 0.74nM-54.3nM. Including the wild-type scFv, these nine affinity gradient CARs were challenged with a cell line expressing low, medium and high densities of cell-surface Tyrp1. Three mutant scFv CARs were superior to the wild-type scFV. However, the affinities of those superior CARs was variant. There was no pattern observed to suggest a direct correlation between the affinity and the CAR efficacy. In contrast to previous publications, this study shows that affinity does not play a key role in determining the CAR efficacy. We hypothesise that the difference in CAR efficacy depends on a combination of factors, such as scFv stability and affinity, CAR density, and antigen density. However, the complex interaction of these parameters, as well as further confounding factors, renders the deduction of a pattern challenging. In order overcome this complexity, a multivariate analysis of all the parameters together is necessary