Chimeric 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 on-target, off-tumor toxicities. This dissertation presents computational approaches harnessing single-cell RNA sequencing data to identify both single and combinatorial CAR cell targets with optimal safety and efficacy profiles.
Our work first established a comprehensive framework for evaluating CAR target safety and selectivity using patient tumor single-cell transcriptomics data. Through 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, I developed LogiCAR Designer, a genetic algorithm that identifies logical combinations of surface antigens using Boolean operators. Applied to 17 breast cancer cohorts comprising nearly 2 million cells from 342 patients, LogiCAR Designer identified triplet antigen combinations that outperform clinically approved single-antigen targets. We further demonstrated individualized circuit design, achieving >99% tumor-targeting efficacy for most patients in a new 82-patient multi-ethnic cohort.
Finally, I applied this framework to lung cancer in non-smokers (LCINS), a critical unmet clinical need with rising incidence. Using single-cell transcriptomics data, we identified promising CAR target combinations with superior efficacy and safety profiles specific to this population.
This work provides computational tools for rational CAR target selection and precision cellular immunotherapy design, establishing a generalizable framework applicable across diverse cancer types and patient populations.
Sanna Madan is a PhD Candidate in Computer Science at the University of Maryland, College Park, primarily conducting her research at the National Cancer Institute's Cancer Data Science Laboratory through the NCI-UMD Partnership for Integrative Cancer Research. Her research focuses on developing computational methods to advance CAR-T cell therapy, particularly through the analysis of single-cell RNA sequencing data. She has developed novel approaches for identifying both single and combinatorial CAR targets, including LogiCAR Designer, a genetic algorithm framework that identifies optimal logical combinations of surface antigens for enhanced cancer cell targeting. Her work addresses fundamental challenges in precision cancer therapeutic design, aiming to improve both the efficacy and safety of cellular immunotherapy treatments. She works under the supervision of Dr. Eytan Ruppin and Professor Aravind Srinivasan.