Speaker: Kyle Daniels, Assistant Professor of Genetics, Stanford University
Title: Decoding the language of signaling domains to control cell function
Abstract: Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasingly complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. Combining experimental library screens and AI to build create predictive models, design rules, and improved designs could accelerate the development of cell therapies. 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 ~2,300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse 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 which bind tumor necrosis factor receptor-associated factors (TRAFs) and phospholipase C gamma 1 (PLCg1) 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.