Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning

Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, non-native combinations of motifs that bind tumor necrosis factor receptor–associated factors (TRAFs) and phospholipase C gamma 1 (PLCγ1) enhanced cytotoxicity and stemness associated with effective tumor killing. Thus, libraries built from minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes. Description Exploring receptor design principles Chimeric antigen receptor T cell technology, in which cells of the immune system are modified with customized receptors, has proved effective in cancer therapy. To explore the range of cell responses that can be encoded in such receptors and to make their design more quantitative and predictive, Daniels et al. tested about 200 of 2400 possible combinations of 13 signaling motifs found in such receptors and used machine learning to predict other effective combinations. Using these design rules, the authors constructed receptors in human T cells with improved signaling characteristics that contributed to better tumor control in a mouse model. —LBR Combinations of signaling motifs and machine learning can guide engineering of receptors with the desired phenotypes.