New strategies to boost tumor immunotherapy: deciphering the heterogeneity of tumor immune microenvironment with single-cell sequencing

doi:10.21203/rs.3.rs-1748190/v1

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Research Article

New strategies to boost tumor immunotherapy: deciphering the heterogeneity of tumor immune microenvironment with single-cell sequencing

https://doi.org/10.21203/rs.3.rs-1748190/v1

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posted 14 Jun, 2022

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Immunotherapy has brought great benefits to tumor patients. However, due to the relatively low response rate, its further application is limited. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing transcriptome at the single-cell resolution, and the application of scRNA-seq has revolutionized the knowledge of tumor immune microenvironment heterogeneity. It can accurately measure individual immune cells in the tumor and find fine differences in cell subsets, and offer new candidate which have potential to be therapeutics targets. With the development of scRNA-seq technologies, more immunotherapy targets are being discovered, which may benefit the effectiveness. In this review, we discuss novel therapeutic targets identified by scRNA-seq and their therapeutic value.

Single-cell RNA sequencing (scRNA-seq)

Heterogeneity

Tumor immune microenvironment

Tumor immunotherapy

The heterogeneity of tumor immune microenvironment is closely related to the therapeutic response to tumor immunotherapy in individual patients¹. High-throughput single-cell RNA sequencing (scRNA-seq) is an effective method to detect cell heterogeneity and discover new subsets of cells at the single-cell level^{2, 3}. It also reveals the transcriptome characteristics of cancer cells and the microenvironment components and their interactions⁴. Furthermore, with the improvement of detection accuracy, scRNA-seq is used to classify tumor cells, especially to identify the distinct forms of immune cells^{5, 6}. scRNA-seq studies have also been used to characterize gene changes after immune checkpoint inhibitor (ICI) treatment⁷. Thus, the use of scRNA-seq in tumor is the foundation for further understanding of immune microenvironment heterogeneity, finding therapeutic mechanisms, and discovering more effective therapeutic strategies⁸. In this review, we focus on the recent investigations of scRNA-seq in tumor-infiltrating T cells including CD8⁺T cells, CD4⁺T cells, Treg cells, tumor-associated macrophages, myeloid-derived suppressor cells, DCs and NK cells(Fig. 1). In addition, we discuss the heterogeneity of their gene expression, compositional differences among cancer types and their potential as therapeutic targets in tumor immunotherapy.

CD8⁺ T cells

T cells constitute the major compartment of tumor-infiltrated lymphocytes in tumor microenvironment⁹. Cytotoxic T cells, T helper (TH) cells, and regulatory T cells (Tregs), constitute the T cell-mediated immune responses¹⁰. Different T cell subsets affect the effect of immunotherapy in tumor patients¹¹. For example, Sara et al. detected circulating CD8⁺T cells of 28 patients with metastatic melanoma who started anti-PD-1 therapy by combining scRNA-seq and multiparameter flow cytometry. They found the proportion of proliferating activating CD8⁺T cells and mucosal-associated invariant T (MAIT) cells are significantly increased in patients who responded to immunotherapy¹². They suggested that MAIT may be a key biomarker in tumor immunotherapy. Another study showed that B7-H3 (CD276) inhibited the activation of T cells, which are expressed in a variety of tumor cell lines including tumor infiltrating dendritic cells and macrophages¹³. Inhibition of CD276 increased the level of CD8⁺T cells expressing IFN-γ and Granzyme B (GZMB). GZMB which largely produced by CD8⁺ T and NK cells, induces tumor cell apoptosis. Cheng Wang et al. found cancer stem cells (CSCs) evade the host immune response by upregulating CD276 expression, and blocking CD276 greatly increased the infiltration degree of CD8⁺T cells in mouse and human head and neck squamous cell carcinoma (HNSCC)¹⁴. Anti-CD276 antibodies eliminate CSCs in a CD8⁺ T cell-dependent manner and inhibit tumor growth and metastases. In addition, scRNA-seq showed that blocking of CD276 could reshape the heterogeneity of squamous cell carcinoma. Thus, targeting CD276 may provide a new treatment strategy for tumor immunotherapy. Uveal melanoma (UM) is a highly metastatic cancer that does not respond to existing checkpoint immunotherapy. Using scRNA-seq, Michael analyzed tumor cells and nonneoplastic cells from 8 primary and 3 metastatic samples, revealing a variety of previously unrecognized cell types and transcriptional states¹⁵. They revealed CD8⁺T cells predominantly expressing the checkpoint marker lymphocytic-activation gene 3 (LAG3), rather than PD1 or cytotoxic T-lymphocyte antigen 4 (CTLA4), providing a new approach for immune checkpoint suppression in patients with UM. Another study showed that LAG3 is expressed not only on CD8⁺ T cells but also on NK cells and regulatory T cells, and has unique properties that could significantly expand the efficacy of checkpoint inhibitor therapy¹⁶. A large number of clinical trial about LAG3 inhibitors are being verified in multiple cancer types¹⁷. Christian et.al found that increased LAG3 expression on leukemia cells is associated with shorter treatment duration and poorer prognosis in chronic lymphocytic leukemia (CLL) patients¹⁸. Currently, a clinical trial about relatlimab which is a novel anti-LAG-3 blocking monoclonal antibody verified that it has at least partially restored NK and T cell-mediated antitumor responses in a variety of solid and hematological malignancies including CLL.

Tregs

Tregs are one of the major immunosuppressive cell types in tumor microenvironment. Tumor-infiltrating Tregs are reprogrammed to exhibit high glucose-depleting properties and adapt to the glucose-restricted tumor microenvironment. The glucose-responsive transcription factor MondoA is highly expressed in Tregs. A study analysis of single-cell RNA sequencing data of patients with colorectal cancer showed that intratumoral MondoA–thioredoxin-interacting protein (TXNIP) axis activity was lower and glucose uptake increased¹⁹. At the same time, they found that Treg-specific MondoA knockout mice were more likely to develop colorectal cancer. Suppression of the MondoA-TXNIP axis could induce hyperglycolytic Th17-like Tregs, which facilitated Th17 inflammation, promoted interleukin 17A (IL-17A)-induced of CD8⁺T-cell exhaustion, and drove colorectal carcinogenesis. Blockade of IL-17A reduced tumor progression and reduced the sensitivity of MondoA-defificient mice to colorectal carcinogenesis. Zhang et al. found that esophageal squamous-cell carcinoma (ESCC) was rich of Treg cells, exhausted T (TEX) cells and myeloid cells, indicating an immunosuppressive status in the tumor and the immunosuppressive status became worse with tumor progression⁸. Furthermore, Myung-Chul Kim and his cooperators using scRNA sequencing of immune cells from renal clear cell carcinoma (ccRCC) patients identified two distinct transcriptional fates for tumor-infiltrating Treg cells, Fate-1 and Fate-2²⁰. Compared with Treg cells with cell fate #2 (CF2), Treg cells with cell fate #1 (CF1) had increased expression of genes associated with immune regulation and suppression. The expression of ribosome related genes in CF2 Treg cells was increased. The CF1 Treg cells specifically expressed CD177 in several solid cancer types, but not on other tumor-infiltrating or peripheral Treg cells. While, CXCR4 and EGR1 genes were only expressed in CF2 Treg cells. They found that the Fate-1 signature is associated with a poorer prognosis in ccRCC and several other solid cancers. Blocking CD177 can reduce the suppressive activity of Treg cells, while Treg-specific deletion of Cd177 leads to decreased tumor growth and reduced TI Treg frequency in mice. Thus, CD177⁺ TI Treg population may serve as a target for TI Treg-specific immunotherapy.

Classical CD4⁺ Treg cells are characterized by expression of the transcription factor forkhead box protein P3 (FOXP3)²¹. However, suppression is not limited to FOXP3⁺ CD4⁺ Treg cells. Several studies described FOXP3⁻CD4⁺ T cells, also named Tr1 cells, with potent immunosuppressive properties based on the display of cytotoxic functions and production of interleukin-10 (IL-10)²². Eomesodermin (EOMES) acted as a lineage-defining transcription factor in human IFN-γ/IL-10 coproducing Tr1-like cells. Raoul et al. identified two subsets of CD4⁺ T cells that are highly enriched in primary and metastatic colorectal cancer and non-small-cell lung cancer²³. The two types of CD4+T cells differ in size and suppressive function, one is the classical FOXP3+Treg, the other is IL-10-producing EOMES+ Tr1-like cells. They found that EOMES+ Tr1-like cells not only correlated with disease progression but were also associated with response to programmed cell death protein 1–targeted immunotherapy. Thus, EOMES+ Tr1-like cells is also a potential target for tumor immunotherapy.

T cell receptors (TCRs)

T cell receptors (TCRs) are expressed on the surface of all T cells, which make T cells immune specificity by recognizing antigens and binding to the major histocompatibility complex (MHC)²⁴. Conventional T cells express a vast range of TCRs. The diversity of TCR is generated during random, somatic rearrangement of variable (V), joining (J), and diversity (D) gene segments in TCR chains. Moreover, the TCR repertoire can accurately describe T cell dynamics^{25, 26} and endow T cells with diversity and specificity²⁷. Thus, how to figure out the characteristic of the TCR has always been the focus of researchers. Currently, the development of scRNA-seq technologies bring huge breakthrough for TCR analysis²⁸(Fig1). For example, Zheng et al. conducted scRNA-seq analysis of 39 matched hepatocellular carcinoma (HCC) (T), nontumor (N), and leading-edge (L) specimens and found that the tumor-associated CD4/CD8 double-positive T (DPT) cells enriched in L regions²⁹. DPT cells enriched in L regions are associated with favorable prognosis due to high expression of PD-1/HLA-DR/ICOS/CD45RO and high level of IFN-r, TNF-α, and PD-1 upon stimulation. Using scRNA-seq and TCRs sequencing (TCR-seq), they identified 11 cell subpopulations of DPT cells and found that DPT clusters have more shared TCR clones with TCD4 and TCD8 than PBMC, especially with CD8⁺T cells. This suggests that PD-1⁺DPT cells most likely transformed from intratumoral CD8⁺T cells. Furthermore, they also demonstrated that DPT cells not only exist in HCCs but also in other tumors. In addition to TCR-seq and scRNA-seq can also link T cell specificity by interrogates transcriptional activity of individual cells³⁰. Another study identified a comprehensive characterization of T cells in tumors and adjacent tissues by scRNA-seq, TCR-seq and in vitro neoantigen stimulation to establish a personalized TCR-T cell therapy³¹. They found that tumor antigen-specific T (Tas) are characterized by tumor enrichment, tumor-specific clonal amplification and neoantigen specificity. TCR-T cells constructed with Tas-TCRs will specifically target individual tumor-specific antigens (TSAs). CXCL13 is a unique marker for both CD4 and CD8 Tas cells. Tas cell level measured by CXCL13 expression can accurately predict the response to immune checkpoint blockade. Furthermore, CD200 and ENTPD1 were identified as surface markers of CD4 and CD8 Tas cells. TCR-T cells constructed with Tas-TCRs are a promising personalized immunotherapy target.

Tumor associated macrophages

Tumor-associated macrophages (TAMs) are important components of the tumor immune microenvironment³². Desmoplastic malignancies such as cholangiocarcinoma (CCA) have an abundant tumor immune microenvironment. Only a few patients response to ICB immunotherapy in these cancers³³. A study found that tumor-associated macrophages (TAMs) as the primary source of programmed death–ligand 1 (PD-L1) in human and murine CCA³⁴. However, TAM blockade failed to reduce CCA tumor burden due to compensatory accumulation of immunosuppressive granulocytic myeloid-derived suppressor cells (G-MDSCs). scRNA-seq analysis uncovers a distinct ApoE G-MDSC subset in murine tumor G-MDSCs and a human scRNA-Seq data set demonstrated the presence of a similar G-MDSC subset in human. ApoE, a transcriptional target of liver-X receptors (LXR), is a conserved gene³⁵. ApoE G-MDSCs displayed an immunosuppressive gene signature with enhanced expression of Arginase 1 (Arg1), S100 calcium binding protein A4 (S100a4), CD74 antigen (Cd74), and Peroxiredoxin 1 (Prdx1), those genes implicated in MDSC-related immunosuppression, tumorigenesis, and metastasis. Therefore, combination immunotherapies that targeting both TAMs and G-MDSCs may provide promising therapeutic approach for desmoplastic cancers³⁴. Xiong et al. re-analyzed a publicly available scRNA-seq database of melanoma samples and found that patients who did not respond to immune checkpoint therpy (ICT) were enriched with a subset of TAMs overexpressing TREM2, as well as a subset of gammadelta T cells³⁶. TREM2hi TAMs displayed a unique signature with overexpression of SPP1, RNASE1, MT1G, SEPP1, FOLR2, NUPR1, KLHDC8B, CCL18, MMP12, and APOC2 along with key complement system genes (C3, C1QA, C1QB, and C1QC). TREM2hi TAMs also overexpressed M2 polarization genes (MMP14, CD276, FN1, MRC1, CCL13, CCL18, LYVE1, PDCD1LG2 (PD-L2), MMP9, TGFB2, and ARG2). Therefore, TREM2hi macrophages may be functionally proximal to M2 polarization macrophages and could block the anti-tumor activities of ICT and contributed to ICT resistance.

Myeloid-derived suppressor cells

Myeloid-derived suppressor cells (MDSCs) are considered as the primary cellular mediators of cancer immunotherapy resistance and may serve as potential biomarkers to predict cancer treatment response. According to morphology, density and surface markers, MDSCs can be divided into two main subtypes: polymorphonuclear MDSCs (PMN-MDSCs) and monocytic MDSCs (M-MDSCs)³⁷. Another type lacking macrophage and granulocyte markers is called early-stage MDSCs (e-MDSCs). In human renal cell carcinoma, increased MDSCs infiltration within the tumor immune microenvironment is associated with increased expression of a number of cytokines including interleukin-1β (IL-1β), interleukin-8 (IL-8), and CXCL5 and overall poor prognosis³⁸. In these factors IL-1β has numerous pro-tumorigenic properties including recruitment of immunosuppressive cells like PMN-MDSCs, inflammasome activation and promotion of tumor angiogenesis³⁹. Anti-IL-1β reduced the infiltration of PMN-MDSCs³⁸. IL-1β is a potential target which can improve successful anti-tumor immunity. Hamad et.al used the mouse model based on the mouse mammary tumor virus (MMTV) promoter–driven expression of the polyomavirus middle T oncoprotein (MMTV-PyMT) found that CD84 as a surface marker for improved detection and enrichment of MDSCs in breast cancers by the method of single-cell transcriptomics⁴⁰. In mice, MDSCs are defined by CD11b⁺Gr1⁺expression and can be further divided into CD11b⁺Ly6C^lowLy6G⁺ granulocyte MDSCs (G-MDSCs) and CD11b⁺Ly6C⁺Ly6G⁻ Monocyte MDSCs (M-MDSCs). They found that CD11b⁺Gr1⁺cells form tumor-bearing mice significantly suppressed CD4⁺ and CD8⁺ T cell proliferation. MDSCs can mediate immunosuppression through multiple mechanisms including immunosuppressive activity and proliferation on T cells. Hence, targeting myeloid cells may represent a potential strategy to overcome tumor tolerance.

Filippo and his cooperators found the heterogeneity of polymorphonuclear neutrophils (PMNs) in cancer⁴¹. They divided PMNs into three populations in tumor-bearing mice: classical PMNs, polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), and activated PMN-MDSCs with potent immune suppressive activity. They found that two distinct populations of PMN-MDSCs gradually replaced classical PMNs during tumor progression. The expression of CD14, a marker of monocytes, was increased in PMN-MDSCs and activated PMN-MDSCs. They found that CD14⁻PMNs were most similar to classical PMNs, and CD14^highgroup were most similar to activated PMN-MDSCs. CD14⁻PMNs demonstrated no suppressive activity. CD14^int PMNs were weakly suppressive, whereas CD14^high PMNs showed stronger suppression. However, unlike mouse PMNs, CD14 is not expressed in human PMNs. They also found that in cancer patients, there are two populations of PMNs with characteristics of classical PMNs and PMN-MDSCs, of which the gene signature was similar to mouse activated PMN-MDSCs and the existence of which was closely associated with negative clinical outcome. Thus, PMN-MDSCs are a distinct population of PMNs with unique features and potential for selective targeting opportunities. Mastio et..al found that the down-regulated expression of retinoblastoma 1 (Rb1) gene is involved in the differentiation of PMN-MDSCs cells and can induce part of M-MDSCs to differentiate into PMN-MDSCs in cancer⁴². At the same time, they found the expression of Rb1 was closely related to CD117 and the Rb1^hi monocytic cells expressed CD117. They identified two populations: CD117⁻CD11b⁺Ly6C^hiLy6G⁻cells are true M-MDSCs with T cell suppressive activity and can differentiate into macrophages and dendritic cells, whereas CD117⁺CD11b⁺Ly6C^hiLy6G⁻ cells have no suppression effect but can differentiate into granulocytes. The author defined CD11b⁺Ly6C^hiLy6G^-CD117⁺ monocytes as monocyte-like precursors of granulocytes (MLPGs). MLPGs substantially promoted the accumulation of PMN-MDSCs in cancer.

Dendritic cells

Dendritic cells (DCs) as the important antigen-presenting cells (APCs) are the crucial component that initiates and maintains antitumor immunity⁴³. There are three major DC subsets, classical or conventional DCs (cDCs), tolerogenic DC (tDCs) and plasmacytoid DCs (pDCs)⁸. Based on different phenotypes, functions, and transcriptional factor dependencies, cDCs are divided into cDC1s and cDC2s⁴⁴. Zhang et.al performed scRNA-seq analysis of immune and stromal populations in colorectal cancer patients, and identified that cDCs subpopulations are key to myeloid targeted immunotherapy in the tumor microenvironment⁴⁵. They found that CD40 was expressed on multiple DCs and macrophage subpopulations, particularly cDC1s cluster, by scRNA-seq analysis of human CRC and mouse MC38 tumor samples. Anti-CD40 Antibody enhanced the frequency of Ccl22⁺cDC1s, and increased cDC1s cell production of IL-12, a cytokine that enhances Th1 development and IFNg production of CD8⁺T cells. This suggests that the ability to anti-CD40 to activate these cells may be relevant for human cancer treatments.

Previous studies have suggested that cDC1s control the response to checkpoint blockade in preclinical models and are associated with better overall survival in patients with cancer⁴⁶. However, cDC1s can also be found in tumors that resist checkpoint blockade. Maier et.al using single-cell RNA sequencing in human and mouse non-small-cell lung cancers identified a cluster of DCs that coexpressed of immunoregulatory genes (Cd274, Pdcd1lg2 and Cd200) and maturation genes (Cd40, Ccr7 and Il12b) and they named this cluster cell ‘mature DCs’ enriched in immunoregulatory molecules (mregDCs) ⁴⁷. They found a key checkpoint molecule in mregDCs is the programmed death ligand 1 protein which is partially driven by AXL and IL-4 signalling and that IL-4-blocking antibodies rescue DC1s functionality in tumour lesions and enhance cytolytic antitumour immunity. Furthermore, Zhang and her cooperators found that tDCs had the highest expression of the immune checkpoint genes (IDO1, PD-L1 and PD-L2) that have been shown to induce the T cell anergy and produce Treg cells⁸. At the same time, they also found that esophageal squamous-cell carcinoma (ESCC) was rich in tDCs and the expression of PD-L1/L2 in tDCs was significantly higher in tumors compared with adjacent normal tissues and tDCs can suppress the activation of CD8⁺ T cells. Thus, tDCs may be a potential immunotherapy target in ESCC even in other tumors.

Natural killer cells

Natural killer (NK) cells, the first subtype of innate lymphoid cells (ILCs) to be identified, are cytotoxic lymphocytes of the innate immune system capable of killing virus-infected cells and/or cancer cells⁴⁸. The function of NK cells is mainly reflected in cell killing and production of pro-inflammatory cytokines. CTLA-4 is an important regulator of T cells, and there is growing evidence suggesting that CTLA-4 regulates other human immune cell types, including B cells ⁴⁹, monocytes⁵⁰, and DCs⁵¹^. Recently, a study using scRNA-seq datasets found that human NK cells express CTLA-4 which expression is affected by cytokine mediated and target cell mediated NK cell activation⁷. They also found anti-CTLA-4 (ipilimumab) directly interact with human NK cells via a CD16-independent mechanism. In addition, NK cell activation characteristics were associated with longer overall survival and could predict anti-CTLA-4 (ipilimumab) response in cancer. Anti-CTLA-4 antibodies also enhance T cell effector activity^{52, 53} and deplete Tregs which express CTLA-4 ^{54, 55}^. This analysis identifies NK cell activation in anti-CTLA-4-treated human tumors and provided novel insights into the role of NK cells in anti-CTLA-4 efficacy and represent a general strategy.

In addition to the findings in the article, more key discoveries on cells and cancer treatment in the past year are listed in Table 1.

Table 1: Using single-cell sequencing obtained key findings about cell heterogeneity and treatment among tumors

Tumor	Technology	Key findings	References
Esophageal squamous cell carcinoma	ScRNA-seq Flow cytometry	Identified seven CD8 T clusters and found GZMK and EOMES were positively correlated in both CD8^-C3^-GZMK and total CD8 T cells	⁵⁶
Colorectal cancer	ScRNA-seq Flow cytometry	A previously unrecognized population of innate lymphocytes (Lin-CD7+CD127-CD56+CD45RO+) was found to be enriched in CRC tissues and to show cytotoxic activity	⁵⁷
Hepaocellular Carcinoma	Real-time PCR ScRNA-seq	Human cluster of differentiation positive (hCD14+) cells could produce IL-33 through damage-associated molecular pattern/Toll-like receptor 4/activator protein 1. Specific knockdown of the CD14 gene in human monocytes could impair IL-33 production induced by cell lysates	⁵⁸
Clear cell renal carcinoma (ccRCC)	Single-cell protein activity analysis	TREM2/APOE/C1Q-positive macrophage infiltration is a potential prognostic biomarker for recurrence of ccRCC and a candidate therapeutic target	⁵⁹
Cervical cancer	ScRNA-seq	CD96 and PD-1 cooperatively and negatively regulate the function of CD8⁺ TILs	⁶⁰
Ovarian cancer	ScRNA-seq Bulk RNA sequencing	Four M2 TAM-associated genes (SLAMF7, GNAS, TBX2-AS1, and LYPD6) correlated with the prognostic survival of ovarian cancer patients and knockdown of SLAMF7 or GNAS mRNA repressed malignancy and cisplatin resistance of ovarian cancer cells	⁶¹
Chronic lymphocytic leukemia	ScRNA-seq Flow cytometry	BCL-2 expression in the T cells of Chronic lymphocytic leukemia patients is associated with immunosuppression by promotion of Treg abundance and CTL exhaustion	⁶²
Pancreatic cancer	ScRNA-seq	Pancreatic cancer cells may inhibit CD8⁺ T cell infiltration through CA9	⁶³
Breast cancers	ScRNA-seq	MDSCs specific gene signature was established to identify CD84 as a surface marker to improve detection and enrichment of MDSCs in breast cancer	⁴⁰
Melanoma	ScRNA-seq CyTOF	GCN2 would be a prominent modulator of myeloid phenotype	⁶⁴

The application of scRNA-seq has extensively promoted the exploration of heterogeneity of tumor immune microenvironment and greatly improved the development of immunotherapy. scRNA-seq has revealed a variety of immune cells and identified new immunotherapy targets. However, scRNA-seq also has its limitations, including low transcript capture efficiency, low sequencing coverage, and data requires quality control, normalization and imputation. In addition, due to technical restrictions and biological factors, scRNA-seq data is more complicated than bulk RNA-seq data. In the future, with the development of scRNA-seq techniques, our understanding of the complexity and heterogeneity of tumor immune microenvironment will be greatly improved. In addition, we can obtain more accurate information on spatial organization and spatial gene expression patterns of tumor-associated immune cells by combining with other technologies.

CD276: B7-H3; CSCs: Cancer stem cells; CTLA4: Cytotoxic T-lymphocyte antigen 4; CLL: Chronic lymphocytic leukemia; ccRCC: Renal clear cell carcinoma; CF2: Cell fate #2; CF1: Cell fate #1; CCA: Cholangiocarcinoma; DPT: CD4/CD8 double-positive T; DCs: Dendritic cells; EOMES: Eomesodermin; ESCC: esophageal squamous-cell carcinoma; FOXP3: Forkhead box protein P3; GZMB: Granzyme B; G-MDSCs: Granulocytic myeloid-derived suppressor cells; IL-17A: Interleukin 17A; IL-10: Interleukin-10; ICT: Immune checkpoint therapy; IL-1β: interleukin-1β; LAG3: Lymphocytic-activation gene 3; MAIT: Mucosal-associated invariant T; MDSCs: Myeloid-derived suppressor cells; MLPGs: Monocyte-like precursors of granulocytes ; Rb1: Retinoblastoma 1; scRNA-seq: Single-cell RNA sequencing; Tregs: Regulatory T cells; UM: Uveal melanoma; TXNIP: Thioredoxin-interacting protein; TCRs: T cell receptors; TCR-seq: TCRs sequencing; Tas: Antigen-specific T; TSAs: Tumor-specific antigens; TAMs: Tumor-associated macrophages;

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Competing interests

The authors declare that they have no competing interests.

Funding

National Natural Science Foundation of China：32170910

Natural Science Foundation of Jiangsu Province：BK20211124

Zhenjiang Key Research and Development Program：SH2021037

Authors' contributions

SQF, QJ, HWT, and JLW collected the related papers and drafted the manuscript. WYS participated in the design of the review. RKLand XW initiated the study and revised and finalized the manuscript. All authors read and approved the final manuscript.

Acknowledgments

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No competing interests reported.

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New strategies to boost tumor immunotherapy: deciphering the heterogeneity of tumor immune microenvironment with single-cell sequencing

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