Leveraging machine learning and combinatorial signaling motif libraries for engineering CAR T cells

Abstract

During the past decade the use of machine learning (ML) approaches has been growing rapidly. ML models are deeply rooted in statistical methods and their applications represents a data-driven strategy to solve complex and often mathematically intractable scientific problems. The success of ML approaches is due to the ability of learning patterns and complex interactions inherently hidden in the data. Here I will discuss how we applied machine learning in combination with combinatorial signaling motif libraries to guide efficiently engineering of receptors with desired phenotype. Chimeric antigen receptor (CAR) costimulatory domains are constructed from native immune receptors and govern the phenotypic output of therapeutic T cells. In our work, we built a combinatorial library of 13 signaling motifs that can be allocated in 3 different positions in a CAR T cell. Therefore, the combinatorial library of CAR T cells contains ~2300 elements and each CAR promotes different T cell phenotypes. Our ML model trained on few experimental data enabled us to unveil the different effects of signal motifs, motif combinations, and motif positions on CAR T cell phenotype. Our work demonstrates that ML approaches can be used jointly with more traditional design methods to engineer cells with desired functions.