Abstract LB_B06: Single cell transcriptomics guided identification of antigen combinations for the design of logic-gated CAR therapies

Abstract Chimeric antigen receptor (CAR) T-cell therapy has demonstrated significant efficacy in treating hematological cancers but faces challenges in treating solid tumors due to antigen heterogeneity and off-tumor, on-target expression in normal tissues. To combat these challenges, we developed a novel genetic algorithm approach to analyze patient tumor single-cell transcriptomics data. This algorithm identifies combinations of cell surface antigens specific to cancer cells by incorporating "AND," "OR," and "NOT" logic. Our method is generalizable to combinations of any size and demonstrates rapid convergence across different parameter choices. When applied to breast cancer datasets, our algorithm identified optimal combinations of triplet antigens that outperformed clinically tested single target antigens in discriminating tumor cells from non-tumor cells across patient samples. In sum, our approach may guide the design of logic-gated CAR therapies aimed at multiple target antigens, thereby increasing the safety and selectivity of these therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Alejandro A. Schäffer, Eytan Ruppin. Single cell transcriptomics guided identification of antigen combinations for the design of logic-gated CAR therapies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr LB_B06.