bioRxivImmunologyPreprint
PAIR-Scan: Single-step identification of highly potent TCR-neoantigen pairs through library-on-library screening
Marius MessemakeriD, Connor A. RichterichiD, Jule Wenner, Yiting Ma, Živa MoraveciD, Rhianne VoogdiD, Maike Mussmann, Sam de PaauwiD, Bjørn P. Y. KweeiD, Jeroen GeerligsiD, Ben MorrisiD, Yael WinkleriD, Babet O. Springer, Floris van den BrekeliD, Jos H. M. UrbanusiD, Aurélie GuislainiD, Nuno AlfaiateiD, Frank N.J. van Diepen, Martijn v. BaaleniD, Benoît P. NicoletiD, John B.A.G. HaaneniD, Catherine J. WuiD, Jani HuuhtaneniD, Wouter ScheperiD, Ton N. SchumacheriD
Abstract
Tumors contain a mixture of T cells with bystander reactivities and reactivities towards different, frequently patient-specific, cancer (neo)antigens. The one-step identification of highly active TCR-antigen pairs in human tumors would be valuable, both as a monitoring tool and to boost T cell reactivities of interest. Here, we develop PAIR-Scan, an HLA-agnostic library-on-library screening technology that identifies functionally active TCR-neoantigen pairs among tens of thousands of candidate pairs in a single step. We demonstrate the value of PAIR-Scan on a range of tumor samples and for the direct identification of TCR-recognized minimal peptides. In addition, we demonstrate that PAIR-Scan correctly ranks TCRs reactive to the same antigen by their relative tumor-killing efficiency. Together, these data demonstrate the value of PAIR-Scan for both the dissection of T cell responses in clinical samples and to generate large-scale datasets for the development of predictive models of TCR reactivity.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
- Citations
- 0
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