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

  1. Akashdip Singh
  2. Alberto Miranda Bedate
  3. Helen J. von Richthofen
  4. Michiel van der Vlist
  5. Raphael Kuhn
  6. Alexander Yermanos
  7. Jurgen Kuball
  8. Can Keşmir
  9. M. Ines Pascoal Ramos
  10. Linde Meyaard

  • Curated by eLife

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    eLife assessment

    This work is potentially useful because it has generated a mineable yield of new candidate immune inhibitory receptors, which can serve both as drug targets and as subjects for further biological investigation. It is noted however that the work is rather incomplete, in that it does little to validate the putative new receptors, and instead makes a study of their putative distribution across cell types. Experimental follow-up to demonstrate the claimed properties for the proteins identified, or mining existing experimental data sources on gene expression across tissues to at least show that the pipeline correctly identified genes likely to be specific to immune cells, would make this work more complete.

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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.

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  1. Version published to 10.7554/elife.92870.1 on eLife
  2. Version published to 10.7554/elife.92870 on eLife
  3. eLife

    eLife assessment

    This work is potentially useful because it has generated a mineable yield of new candidate immune inhibitory receptors, which can serve both as drug targets and as subjects for further biological investigation. It is noted however that the work is rather incomplete, in that it does little to validate the putative new receptors, and instead makes a study of their putative distribution across cell types. Experimental follow-up to demonstrate the claimed properties for the proteins identified, or mining existing experimental data sources on gene expression across tissues to at least show that the pipeline correctly identified genes likely to be specific to immune cells, would make this work more complete.

  4. eLife

    Reviewer #1 (Public Review):

    This manuscript proposes a new bioinformatics approach identifying several hundreds of previously unknown inhibitory immunoreceptors. When expressed in immune cells (such as neutrophils, monocytes, CD8+, CD4+, and T-cells), such receptors inhibit the functional activity of these cells. Blocking inhibitory receptors represents a promising therapeutic strategy for cancer treatment.

    As such, this is a high-quality and important bioinformatics study. One general concern is the absence of direct experimental validation of the results. In addition to the fact that the authors bioinformatically identified 51 known receptors, providing such experimental evaluation (of at least one, or better few identified receptors) would, in my opinion, significantly strengthen the presented evidence.

    I will now briefly summarize the results and give my comments.

    First, using sequence comparison analysis, the authors identify a large set of putative receptors based on the presence of immunoreceptor tyrosine-based inhibitory motifs (ITIMs), or immunoreceptor tyrosine-based switch motifs (ITSMs). They further filter the identified set of receptors for the presence of the ITIMs or ITSMs in an intracellular domain of the protein. Second, using AlphaFold structure modeling, the authors select only receptors containing ITIMs/ITSMs in structurally disordered regions. Third, the evaluation of gene expression profiles of known and putative receptors in several immune cell types was performed. Fourth, the authors classified putative receptors into functional categories, such as negative feedback receptors, threshold receptors, threshold disinhibition, and threshold-negative feedback. The latter classification was based on the available data from Nat Rev Immunol 2020. Fifth, using publicly available single-cell RNA sequencing data of tumor-infiltrating CD4+ and CD8+ cells from nearly twenty types of cancer, the authors demonstrate that a significant fraction of putative receptors are indeed expressed in these datasets.

    In summary, in my opinion, this is an interesting, important, high-quality bioinformatics work. The manuscript is clearly written and all technical details are carefully explained.

    One comment/suggestion regarding the methodology of evaluating gene expression profiles of putative receptors: perhaps it might be important to look at clusters of genes that are co-expressed with putative inhibitory receptors.

  5. eLife

    Reviewer #2 (Public Review):

    Summary:

    The authors developed a bioinformatic pipeline to aid the screening and identification of inhibitory receptors suitable as drug targets. The challenge lies in the large search space and lack of tools for assessing the likelihood of their inhibitory function. To make progress, the authors used a consensus protein membrane topology and sequence motif prediction tool (TOPCOS) combined with both a statistical measure assessing their likelihood function and a machine learning protein structural prediction model (AlphaFold) to greatly cut down the search space. After obtaining a manageable set of 398 high-confidence known and putative inhibitory receptors through this pipeline, the authors then mapped these receptors to different functional categories across different cell types based on their expression both in the resting and activated state. Additionally, by using publicly available pan-cancer scRNA-seq for tumor-infiltrating T-cell data, they showed that these receptors are expressed across various cellular subsets.

    Strengths:

    The authors presented sound arguments motivating the need to efficiently screen inhibitory receptors and to identify those that are functional. Key components of the algorithm were presented along with solid justification for why they addressed challenges faced by existing approaches. To name a few:

    • TOPCON algorithm was elected to optimize the prediction of membrane topology.
    • A statistical measure was used to remove potential false positives.
    • AlphaFold is used to filter out putative receptors that are low confidence (and likely intrinsically disordered).

    To examine receptors screened through this pipeline through a functional lens, the authors proposed to look at their expression of various immune cell subsets to assign functional categories. This is a reasonable and appropriate first step for interpreting and understanding how potential drug targets are differentially expressed in some disease contexts.

    Weaknesses:

    The paper has strength in the pipeline they presented, but the weakness, in my opinion, lies in the lack of concrete demonstration on how this pipeline can be used to at least "rediscover" known targets in a disease-specific manner. For example, the result that both known and putative immune inhibitory receptors are expressed across a wide variety of tumor-infiltrating T-cell subsets is reassuring, but this would have been more informative and illustrative if the authors could demonstrate using a disease with known targets, as opposed to a pan-cancer context. Additionally, a discussion that contrasts the known and putative receptors in the context above would help readers better identify use cases suitable for their research using this pipeline. Particularly,
    • For known receptors, does the pipeline and the expression analysis above rediscover the known target in the disease of interest?
    • For putative receptors, what do the functional category mapping and the differential expression across various tumor-infiltrating T-cell subsets imply on a potential therapeutic target?

  6. Version published to 10.1101/2023.10.24.563834v1 on bioRxiv