Abstract LB245: Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design

Abstract The advancement of chimeric antigen receptor (CAR) T-cell therapy has been groundbreaking in the treatment of hematological malignancies. However, its application in solid tumors is constrained by antigen variability and unintended off-tumor, on-target toxicities. Addressing these issues, we introduce a genetic algorithm to analyze single-cell transcriptomics data from patient tumors. This method pinpoints sets of surface antigens exclusive to cancer cells, utilizing the logical operators "AND, " "OR, " and "NOT. " Our technique scales to gene combinations of any size and exhibits robust performance across diverse parameter settings. When applied to five breast cancer datasets, this algorithm successfully uncovered triplet antigen combinations superior to current clinical single antigen targets in distinguishing between cancerous and healthy cells. This strategy offers a promising pathway toward the design of more selective and safe CAR therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Saugato Rahman Dhruba, Alejandro A. Schäffer, Eytan Ruppin. Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr LB245.