A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Author:

Singh Akashdip12ORCID,Bedate Alberto Miranda13,von Richthofen Helen J.12,van der Vlist Michiel12ORCID,Kuhn Raphael4,Yermanos Alexander14,Kuball Jurgen13ORCID,Keşmir Can5,Pascoal Ramos M. Ines12,Meyaard Linde12ORCID

Affiliation:

1. Center for Translational Immunology, University Medical Centre Utrecht, Utrecht University

2. Oncode Institute

3. Department of Haematology, University Medical Centre Utrecht, Utrecht University

4. Department of Biosystems Science and Engineering

5. Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University

Abstract

Blocking inhibitory receptors like PD-1 and CTLA-4 has revolutionized cancer treatment in recent years. However, despite major successes in melanoma and lung cancer, the majority of cancer types are not responsive to these immunotherapies. As such, there is an ongoing need for the identification of novel inhibitory receptors as drug targets. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies have estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, further testing and development of this large number of candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor.To assist in the selection of novel inhibitory receptor as therapeutic targets, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify 51 known and 390 putative inhibitory receptors. Using publicly available transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories.Known and putative inhibitory receptors were expressed across a wide variety of immune cells, and we found cell type-specific patterns in expression of these receptors. We used our pipeline to study inhibitory receptor expression patterns in single cell transcriptomics data of tumour infiltrating T cells. We determined that putative immune inhibitory receptors were expressed differentially in CD4 + and CD8 + T cell subsets, including exhausted CD8 + T cells and CD4 + memory T cells, which could allow for subset-specific targeting.In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

Publisher

eLife Sciences Publications, Ltd

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