Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics

Abstract

Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificit toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantl achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production o TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and can didate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithm may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using dee learning tools, we demonstrate accurate prediction of TCR-transgenic CD8+ T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provide the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.