Cancer Immunotherapy and the Nectin Family

Johnston, Robert J.; Lee, Peter S.; Strop, Pavel; Smyth, Mark J.

doi:10.1146/annurev-cancerbio-060920-084910

Cited by 16 publications

(12 citation statements)

References 137 publications

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“…For another example, we also identified the involvement of NECTIN pathway during the interaction between the activated macrophages (source) and TRM CD8 T cells (target). This is consistent with the previous report that NECTIN (CD155) expressed on tumor-infiltrating macrophages could be immunosuppressive when interacting with surface receptors on CD8+ T cells in the lung cancer TME 21,22 . Protein Gene…”

Section: Deepmaps Accurately Identify Cell Types In Pbmc and Lung Tumor Immune Citeseq Datasupporting

confidence: 93%

“…Previous studies have shown that ALCAM on antigenpresenting DCs interact with CD6 on the T cell surface and contribute to T cell activation and proliferation [18][19][20] . As another example, we identified the involvement of the NECTIN-TIGIT signaling pathway during the interaction between the TAM (source) and TRM CD8 + T cells (target), which is supported with a previous report that NECTIN (CD155) expressed on TAM could be immunosuppressive when interacting with surface receptors, TIGIT, on CD8 + T cells in the lung cancer TME 21,22 . Protein Gene…”

Section: Deepmaps Accurately Identify Cell Types and Infer Cell-cell Communication In Pbmc And Lung Tumor Immune Cite-seq Datasupporting

confidence: 89%

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DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

Wang

et al. 2021

Preprint

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We present DeepMAPS, a deep learning platform for cell-type-specific biological gene network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to infer cell-cell, cell-gene, and gene-gene relations simultaneously. The graph attention neural network considers a cell and a gene with both local and global information, making DeepMAPS more robust to data noises. We benchmarked DeepMAPS on 18 datasets for cell clustering and network inference, and the results showed that our method outperforms various existing tools. We further applied DeepMAPS on a case study of lung tumor leukocyte CITE-seq data and observed superior performance in cell clustering, and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. To improve the feasibility and ensure the reproducibility of analyzing scMulti-omics data, we deployed a webserver with multi-functions and various visualizations. Overall, we valued DeepMAPS as a novel platform of the state-of-the-art deep learning model in the single-cell study and can promote the use of scMulti-omics data in the community.

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Section: Deepmaps Accurately Identify Cell Types In Pbmc and Lung Tumor Immune Citeseq Datasupporting

confidence: 93%

Section: Deepmaps Accurately Identify Cell Types and Infer Cell-cell Communication In Pbmc And Lung Tumor Immune Cite-seq Datasupporting

confidence: 89%

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

Wang

et al. 2021

Preprint

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“…The T Cell Immunoreceptor With Ig and ITIM Domains (TIGIT) family of receptors and ligands (hereafter, receptors/ligands) functions as an immune checkpoint to limit immune responses in model systems of viral infections and cancer (12)(13)(14). TIGIT is expressed on T cells and natural killer (NK) cells (15).…”

Section: Resultsmentioning

confidence: 99%

TIGIT as a therapeutic target of HPV-positive head and neck squamous cell carcinomas

Dang

Hegde

et al. 2021

Preprint

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The tumor immune microenvironment (TIME) of treatment-naïve, human papillomavirus-positive head and neck squamous cell carcinoma (HPV-positive HNSCC) was interrogated at single-cell level to identify influential immune checkpoints as therapeutic targets. Single-cell transcriptome profiling revealed enrichment of numerous cell-cell interactions mediated by TIGIT-PVR/NECTIN2 in the TIME of HPV-positive HNSCC versus normal tonsil. TIGIT was the most differentially upregulated immune checkpoint on clonally expanded CD8+ T cells and was abundant on antigen-experienced, tissue-resident memory CD8+ T cell and T-regulatory subsets. TIGIT ligands PVR/NECTIN1/2 were abundant on mature regulatory dendritic cells, immunosuppressive plasmacytoid DCs, and macrophages. TIGIT and PD-1 co-blockade in the mEER murine model of HPV-positive HNSCC significantly reduced tumor growth, improved survival, restored effector function of HPV16 E7-specific CD8+ T cells, natural killer cells, and DCs, and conferred tumor re-challenge protection. This immunogenetic analysis at single-cell resolution focusing on HPV-positive HNSCC identified TIGIT as a rational therapeutic target.

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“…Nectin family members are expressed by different cell types within the TME [56]. Although primarily regulators of cell adhesion, migration, and proliferation, several nectins have been shown to modulate immune responses in the TME via interactions with the T cell activating receptor CD226 (DNAM-1), or with the inhibitory receptors T cell immunoglobulin and ITIM domain (TIGIT), CD96 and CD112R (PVRIG) [57][58][59][60][61][62]. While the role for CD96 in tumor immunity is controversially discussed, TIGIT is a well-established immune checkpoint [63].…”

Section: Immunosuppressive Molecules Expressed On the Cell Surface Of...mentioning

confidence: 99%

Targeting inflamed and non-inflamed melanomas: biological background and clinical challenges

Indini¹,

Massi²,

Pirro³

et al. 2022

Seminars in Cancer Biology

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Cancer Immunotherapy and the Nectin Family

Cited by 16 publications

References 137 publications

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

TIGIT as a therapeutic target of HPV-positive head and neck squamous cell carcinomas

Targeting inflamed and non-inflamed melanomas: biological background and clinical challenges

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