bioRxivImmunologyPreprint
Massively parallel base editing screens to map variant effects on anti-tumor hallmarks of primary human T cells
Z. H. Walsh, P. Shah, N. Kothapalli, G. Nikolenyi, S. Shah, G. Leuzzi, M. Mu, P. Ho, S. Abuzaid, Z. D. Brodtman, N. Vasan, M. N. AlQuaraishi, J. D. Milner, A. Ciccia, J. C. Melms, B. Izar
Abstract
Base editing enables generation of single nucleotide variants, but large-scale screening in primary human T cells is limited due to low editing efficiency, among other challenges1. Here, we developed a high-throughput approach for high-efficiency and massively parallel adenine and cytosine base-editor screening in primary human T cells. We performed multiple large-scale screens editing 102 genes with central functions in T cells and full-length tiling mutagenesis of selected genes, and read out variant effects on hallmarks of T cell anti-tumor immunity, including activation, proliferation, and cytokine production. We discovered a broad landscape of gain- and loss-of-function mutations, including in PIK3CD and its regulatory subunit encoded by PIK3R1, LCK, AKT1, CTLA-4 and JAK1. We identified variants that affected several (e.g., PIK3CD C416R) or only selected (e.g. LCK Y505C) hallmarks of T cell activity, and functionally validated several hits by probing downstream signaling nodes and testing their impact on T cell polyfunctionality and proliferation. Using primary human T cells in which we engineered a T cell receptor (TCR) specific to a commonly presented tumor testis antigen as a model for cellular immunotherapy, we demonstrate that base edits identified in our screens can tune specific or broad T cell functions and ultimately improve tumor elimination while exerting minimal off-target activity. In summary, we present the first large-scale base editing screen in primary human T cells and provide a framework for scalable and targeted base editing at high efficiency. Coupled with multi-modal phenotypic mapping, we accurately nominate variants that produce a desirable T cell state and leverage these synthetic proteins to improve models of cellular cancer immunotherapies.
- organism: human
- assay: CRISPR screen
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
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