CAR Architecture Optimization Using Strategical Motif Combinations and Predictive Modeling.

Abstract

CAR T cell therapy has emerged as a promising approach for treating various forms of cancer. Despite its success, the effectiveness of CAR T cell therapy can be influenced by the specific configurations of signaling motifs within the CAR architecture. This study focuses on optimizing CAR architecture using strategic motif combinations and predictive modeling to enhance therapeutic outcomes. The research utilized a dataset of CAR T cell configurations, each characterized by different motif combinations, to explore the impact on cytotoxicity and stemness. Various machine learning models, including Random Forest, Support Vector Machine, Neural Networks, Linear Regression, Decision Tree, and Gradient Boosting, were employed to predict cytotoxicity and stemness based on these configurations. The transformer-based model was also implemented to predict cytotoxicity from protein sequences, showcasing the potential for integrating generative AI models into the healthcare domain. The results demonstrated that traditional machine learning models, such as Decision Tree and Gradient Boosting, effectively captured key features of cytotoxicity and stemness, particularly at moderate levels, and did so with significantly less computational power and time compared to more complex models like CNNs and LSTMs. This highlights the first major objective of the research: to show that machine learning models can achieve comparable performance to CNNs and LSTMs in feature extraction, while being more efficient in terms of computational resources. This study contributes to the growing field of CAR T cell therapy by providing a detailed analysis of how different motif combinations influence therapeutic outcomes. The research offers key insights that can lead to the refinement of CAR designs, making them more effective and safer. These findings support the development of improved CAR T cell therapies and pave the way for personalized treatment strategies that use predictive modeling to tailor interventions to individual patients.