Functions of Immune Checkpoint Molecules Beyond Immune Evasion

Zhang, Yaping; Zheng, Junke

doi:10.1007/978-981-15-3266-5_9

Cited by 186 publications

(135 citation statements)

References 157 publications

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“…Immune checkpoints were defined as ligand receptor pairs that inhibit or stimulate immune responses [ 9 ]. Tumor cells evaded the attack of immune cells by activating immune checkpoints [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Zhou

Yang

Tang³

et al. 2021

Journal of Oncology

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Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

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Section: Introductionmentioning

confidence: 99%

Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Zhou

Yang

Tang³

et al. 2021

Journal of Oncology

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“…In the present study, we did not analyze the role of individual ICs but rather grouped the ligands and receptors as activating or inhibitory ICs based on their main reported function, mainly in T cells ( 5 ). However, the function of ICs activation vs. inhibition depends on the cell type and the context of the signaling ( 5 , 29 , 30 ). Interestingly, the gene expression of activating IC ligands and receptors correlated with improved survival of AML patients, whereas, the gene expression of inhibitory IC ligands or receptors was a negative prognostic factor.…”

Section: Discussionmentioning

confidence: 99%

Epigenetic Silencing of Immune-Checkpoint Receptors in Bone Marrow- Infiltrating T Cells in Acute Myeloid Leukemia

et al. 2021

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BackgroundImmune-checkpoint (IC) inhibitors have revolutionized the treatment of multiple solid tumors and defined lymphomas, but they are largely ineffective in acute myeloid leukemia (AML). The reason why especially PD1/PD-L1 blocking agents are not efficacious is not well-understood but it may be due to the contribution of different IC ligand/receptor interactions that determine the function of T cells in AML.MethodsTo analyze the interactions of IC ligands and receptors in AML, we performed a comprehensive transcriptomic analysis of FACS-purified leukemia stem/progenitor cells and paired bone marrow (BM)-infiltrating CD4+ and CD8+ T cells from 30 patients with AML. The gene expression profiles of activating and inhibiting IC ligands and receptors were correlated with the clinical data. Epigenetic mechanisms were studied by inhibiting the histone deacetylase with valproic acid or by gene silencing of PAC1.ResultsWe observed that IC ligands and receptors were mainly upregulated in leukemia stem cells. The gene expression of activating IC ligands and receptors correlated with improved prognosis and vice versa. In contrast, the majority of IC receptor genes were downregulated in BM-infiltrating CD8+ T cells and partially in CD4+ T cells, due to pathological chromatin remodeling via histone deacetylation. Therefore, treatment with histone deacetylase inhibitor (HDACi) or silencing of PAC1, as a T cell-specific epigenetic modulator, significantly increased the expression of IC receptors and defined effector molecules in CD8+ T cells.ConclusionsOur results suggest that CD8+ T cells in AML are dysfunctional mainly due to pathological epigenetic silencing of activating IC receptors rather than due to signaling by immune inhibitory IC receptors, which may explain the limited efficacy of antibodies that block immune-inhibitory ICs in AML.

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“…Checkpoint molecules are regulatory molecules that play an inhibitory role in the immune system and are critical to maintain tolerance, prevent an autoimmune response and minimize tissue damage by controlling the timing and intensity of the immune response (100,101). The expression of immunological checkpoint molecules on immune cells suppresses the immune cell function as the host fails to produce an effective antitumor immune response.…”

Section: Adenovirus Modification Combined With Immune Checkpoints In mentioning

confidence: 99%

Oncolytic adenovirus: A tool for reversing the tumor microenvironment and promoting cancer treatment (Review)

2021

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Immunogene therapy can enhance the antitumor immune effect by introducing genes encoding co-stimulation molecules, cytokines, chemokines and tumor-associated antigens into treatment cells or human cells through genetic engineering techniques. Oncolytic viruses can specifically target tumor cells and replicate indefinitely until they kill tumor cells. If combined with immunogene therapy, oncolytic viruses can play a more powerful antitumor role. The high pressure, hypoxia and acidity in the tumor microenvironment (TME) provide suitable conditions for tumor cells to survive. To maximize the potency of oncolytic viruses, various methods are being developed to promote the reversal of the TME, thereby maximizing transmission of replication and immunogenicity. The aim of the present review was to discuss the basic mechanisms underlying the effects of oncolytic adenoviruses on the TME, and suggest how to combine the modification of the adenovirus with the TME to further combat malignant tumors. Contents 1. Introduction 2. Oncolytic adenovirus 3. TME of solid tumors 4. Adenovirus modification combined with immune cells in the TME 5. Adenovirus modification combined with stromal cells in the TME 6. Adenovirus modification combined with immune checkpoints in the TME 7. Outlooks

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Functions of Immune Checkpoint Molecules Beyond Immune Evasion

Cited by 186 publications

References 157 publications

Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Epigenetic Silencing of Immune-Checkpoint Receptors in Bone Marrow- Infiltrating T Cells in Acute Myeloid Leukemia

Oncolytic adenovirus: A tool for reversing the tumor microenvironment and promoting cancer treatment (Review)

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