Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy

Cao, Yuqing; Wang, Xiaoyu; Jin, Tianqiang; Tian, Yu; Dai, Chaoliu; Widarma, Crystal; Song, Rui; Xu, Feng

doi:10.1038/s41392-020-00348-8

Cited by 98 publications

(71 citation statements)

References 306 publications

(309 reference statements)

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“…In the tumor microenvironment, different immune cells confer different tumor responses. T-helper-1 cells, natural killer cells, M1 phenotype macrophages, and DC1 phenotype dendritic cells are involved in the suppression of tumorigenesis and development, while T-helper-2 cells, M2 macrophages, DC2 dendritic cells, and regulatory T cells (Treg) suppress immune responses [ 43 – 45 ]. Analysis of the unique properties of immune cells in the tumor microenvironment may inform the design of cancer immunotherapy targets.…”

Section: Discussionmentioning

confidence: 99%

Gene expression and immune infiltration in melanoma patients with different mutation burden

Wang

et al. 2021

BMC Cancer

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Background Immunotherapy is a vital component in cancer treatment. However, due to the complex genetic bases of cancer, a clear prediction index for efficacy has not been established. Tumor mutation burden (TMB) is one of the essential factors that affect immunotherapeutic efficacies, but it has not been determined whether the mutation is associated with the survival of Skin Cutaneous Melanoma (SKCM) patients. This study aimed at evaluating the correlation between TMB and immune infiltration. Methods Somatic mutation profiles (n = 467), transcriptome data (n = 471), and their clinical information (n = 447) of all SKCM samples were downloaded from The Cancer Genome Atlas (TCGA) database. For each sample, TMB was calculated as the number of variants per megabase. Based on K-M survival analysis, they were allocated into the high-TMB and low-TMB groups (the optimal cutoff was determined by the ‘surv_cutpoint’ algorithm of survival R package). Then, Gene ontology (GO) and Gene Set Enrichment Analyses (GSEA) were performed, with immune-associated biological pathways found to be significantly enriched in the low-TMB group. Therefore, immune genes that were differentially expressed between the two groups were evaluated in Cox regression to determine their prognostic values, and a four-gene TMB immune prognostic model (TMB-IP) was constructed. Results Elevated TMB levels were associated with better survival outcomes in SKCM patients. Based on the cutoff value in OS analysis, they were divided into high-TMB and low-TMB groups. GSEA revealed that the low-TMB group was associated with immunity while intersection analysis revealed that there were 38 differentially expressed immune-related genes between the two groups. Four TMB-associated immune genes were used to construct a TMB-IP model. The AUC of the ROC curve of this model reached a maximum of 0.75 (95%CI, 0.66–0.85) for OS outcomes. Validation in each clinical subgroup confirmed the efficacy of the model to distinguish between high and low TMB-IP score patients. Conclusions In SKCM patients, low TMB was associated with worse survival outcomes and enriched immune-associated pathways. The four TMB-associated immune genes model can effectively distinguish between high and low-risk patients.

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Section: Discussionmentioning

confidence: 99%

Gene expression and immune infiltration in melanoma patients with different mutation burden

Wang

et al. 2021

BMC Cancer

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“…Very recent data have shown that ICIs can act not only on T cells but also on NK cells, which express several ICs that inhibit their cytotoxic function, such as PD-1, TIM3, TIGIT, LAG-3 and CD96 ( 184 ). Several clinical trials are ongoing investigating the effects of ICIs on NK cells in different solid cancers, as reviewed in ( 185 ).…”

Section: Strategies To Revert Immune Suppression and Improve Cancer Imentioning

confidence: 99%

The Crosstalk Between Tumor Cells and the Immune Microenvironment in Breast Cancer: Implications for Immunotherapy

et al. 2021

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Breast cancer progression is a complex process controlled by genetic and epigenetic factors that coordinate the crosstalk between tumor cells and the components of tumor microenvironment (TME). Among those, the immune cells play a dual role during cancer onset and progression, as they can protect from tumor progression by killing immunogenic neoplastic cells, but in the meanwhile can also shape tumor immunogenicity, contributing to tumor escape. The complex interplay between cancer and the immune TME influences the outcome of immunotherapy and of many other anti-cancer therapies. Herein, we present an updated view of the pro- and anti-tumor activities of the main immune cell populations present in breast TME, such as T and NK cells, myeloid cells, innate lymphoid cells, mast cells and eosinophils, and of the underlying cytokine-, cell–cell contact- and microvesicle-based mechanisms. Moreover, current and novel therapeutic options that can revert the immunosuppressive activity of breast TME will be discussed. To this end, clinical trials assessing the efficacy of CAR-T and CAR-NK cells, cancer vaccination, immunogenic cell death-inducing chemotherapy, DNA methyl transferase and histone deacetylase inhibitors, cytokines or their inhibitors and other immunotherapies in breast cancer patients will be reviewed. The knowledge of the complex interplay that elapses between tumor and immune cells, and of the experimental therapies targeting it, would help to develop new combination treatments able to overcome tumor immune evasion mechanisms and optimize clinical benefit of current immunotherapies.

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“…The suppression of MDSCs by anti-CSF-1R neutralizing antibodies or small molecule inhibitors has been shown to reduce tumor growth and metastasis ( Noy and Pollard, 2014 ; Szebeni et al, 2016 ; Ridker et al, 2017 ). Immune checkpoint molecules in NKs were also reported to be potential targets for immunotherapy ( Cao et al, 2020 ). To date approximately 174 clinical trials involving CTLA-4 and 750 involving PD-1 and its receptor PD-L1 have been reported (as reviewed in Boohaker et al, 2018 ; Darvin et al, 2018 ; Chen T. et al, 2019 ).…”

Section: Targeting the Tumor Microenvironment For Therapymentioning

confidence: 99%

Adaptive Mechanisms of Tumor Therapy Resistance Driven by Tumor Microenvironment

Gao

et al. 2021

Front. Cell Dev. Biol.

103

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Cancer is a disease which frequently has a poor prognosis. Although multiple therapeutic strategies have been developed for various cancers, including chemotherapy, radiotherapy, and immunotherapy, resistance to these treatments frequently impedes the clinical outcomes. Besides the active resistance driven by genetic and epigenetic alterations in tumor cells, the tumor microenvironment (TME) has also been reported to be a crucial regulator in tumorigenesis, progression, and resistance. Here, we propose that the adaptive mechanisms of tumor resistance are closely connected with the TME rather than depending on non-cell-autonomous changes in response to clinical treatment. Although the comprehensive understanding of adaptive mechanisms driven by the TME need further investigation to fully elucidate the mechanisms of tumor therapeutic resistance, many clinical treatments targeting the TME have been successful. In this review, we report on recent advances concerning the molecular events and important factors involved in the TME, particularly focusing on the contributions of the TME to adaptive resistance, and provide insights into potential therapeutic methods or translational medicine targeting the TME to overcome resistance to therapy in clinical treatment.

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Immune checkpoint molecules in natural killer cells as potential targets for cancer immunotherapy

Cited by 98 publications

References 306 publications

Gene expression and immune infiltration in melanoma patients with different mutation burden

Gene expression and immune infiltration in melanoma patients with different mutation burden

The Crosstalk Between Tumor Cells and the Immune Microenvironment in Breast Cancer: Implications for Immunotherapy

Adaptive Mechanisms of Tumor Therapy Resistance Driven by Tumor Microenvironment

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