PhD Proposal: Harnessing single-cell transcriptomics data for the identification of single and combinatorial CAR-T cell targets

himeric antigen receptor (CAR) T cell therapy has demonstrated remarkable success in treating hematological malignancies but faces significant challenges in solid tumors due to antigen heterogeneity and potential on-target, off-tumor toxicities. This dissertation presents computational approaches leveraging single-cell RNA sequencing data to identify both single and combinatorial CAR-T cell targets with optimal safety and efficacy profiles.Our work first established a framework for evaluating CAR target safety and selectivity using patient tumor single-cell transcriptomics data. Through a pan-cancer analysis, we demonstrated the near-optimality of existing CAR targets in most cancers while identifying novel promising targets for head and neck squamous cell carcinoma. Building on this foundation, we developed LogiCAR, a novel genetic algorithm that identifies logical combinations of surface antigens using AND, OR, and NOT operators. When applied to breast cancer cohorts, LogiCAR successfully identified triplet antigen combinations that outperform clinically approved single-antigen targets in efficacy while maintaining high safety profiles.Building on these results, we will adapt the LogiCAR framework to address the unique challenges of pediatric acute myeloid leukemia (AML), where the similarity between malignant and healthy blood cells demands particularly precise target selection. Additionally, we will explore larger combinations of 4-5 antigens to potentially achieve even greater specificity in target cell recognition. This work aims to advance the field of CAR-T cell therapy by providing computational tools for rational target selection and combination strategies in both solid and liquid tumors.