The T-cell surface molecule TIGIT is an immune checkpoint molecule that inhibits T-cell responses, but its roles in cancer are little understood. In this study, we evaluated the role TIGIT checkpoint plays in the development and progression of gastric cancer. We show that the percentage of CD8 T cells that are TIGIT+ was increased in gastric cancer patients compared with healthy individuals. These cells showed functional exhaustion with impaired activation, proliferation, cytokine production, and metabolism, all of which were rescued by glucose. In addition, gastric cancer tissue and cell lines expressed CD155, which bound TIGIT receptors and inactivated CD8 T cells. In a T cell–gastric cancer cell coculture system, gastric cancer cells deprived CD8 T cells of glucose and impaired CD8 T-cell effector functions; these effects were neutralized by the additional glucose or by TIGIT blockade. In gastric cancer tumor cells, CD155 silencing increased T-cell metabolism and IFNγ production, whereas CD155 overexpression inhibited T-cell metabolism and IFNγ production; this inhibition was neutralized by TIGIT blockade. Targeting CD155/TIGIT enhanced CD8 T-cell reaction and improved survival in tumor-bearing mice. Combined targeting of TIGIT and PD-1 further enhanced CD8 T-cell activation and improved survival in tumor-bearing mice. Our results suggest that gastric cancer cells inhibit CD8 T-cell metabolism through CD155/TIGIT signaling, which inhibits CD8 T-cell effector functions, resulting in hyporesponsive antitumor immunity. These findings support the candidacy of CD155/TIGIT as a potential therapeutic target in gastric cancer. Cancer Res; 77(22); 6375–88. ©2017 AACR.

Gastric cancer is one of the most common malignancies worldwide (1). Five-year survival rate of gastric cancer is lower than 30%, and the current therapeutic methods show little improvement for gastric cancer survival (2). Tumor-specific cytotoxic T cells are present in gastric cancer tissue, but are unable to contain tumors because of poor immune responses in the tumor microenvironment (3). Reversing this effect would be a potential therapeutic approach to enhance the effectiveness of current treatments. The mechanisms by which gastric cancer inhibits antitumor immune responses are poorly understood that targets cannot be identified.

The balance of positive and negative signals is crucial to maintain host immune tolerance and activation (4, 5). Immune checkpoints are molecules in the immune system that either turn on a positive (costimulatory) signal or a negative (coinhibitory) signal. Antibody treatments that target immune checkpoints have significantly improved clinical outcomes of solid and hematologic malignancies (6, 7). Malignant tumors, including gastric cancer, escape antitumor immune responses by upregulating coinhibitory signals, such as PD-1/PD-L1, in the tumor microenvironment (8, 9). Phase I clinical trial has provided promising antitumor activity by targeting PD-1/PD-L1 signal (10), warranting further investigation to the immune checkpoints to have better outcomes for gastric cancer.

The T-cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) has emerged as an important immune checkpoint in recent years. TIGIT, which belongs to the CD28 family (11), and CD226 share the common ligand CD155; binding of CD155 to TIGIT suppresses T-cell activation (12), whereas binding to CD226 enhances T-cell activation (13). TIGIT expression is increased in tumor-infiltrating lymphocytes (TIL) and tumor antigen-specific CD8 T cells in melanoma patients (14). Blocking TIGIT enhances CD8 T-cell effector functions in tumor-bearing mice (15). In addition, CD155 expression is increased in melanoma cells, and the T-cell response is inhibited via TIGITCD155 interactions (16). However, the mechanisms of CD155/TIGIT–induced immune suppression and subversion in gastric cancer remain poorly understood.

Costimulatory and coinhibitory signals interact to activate T cells by regulating metabolic activity (17, 18). T-cell metabolism is highly dynamic and controls T-cell activation, proliferation, and differentiation (19, 20). Upon initial antigenic stimulation, T cells increase in size and switch their metabolism to glycolysis, which permits proliferation and effector functions (21, 22). During T-cell clonal expansion, T cells preferentially metabolize glucose to fulfill their increased energy requirements (23). Failure to fulfill the increased bioenergetic demands of cell growth results in deleted or unresponsive T cells (20).

T-cell activation, which is critical for the antitumor immune response (24), depends on the AKT/mTOR signaling pathway. AKT promotes glucose metabolism by increasing glucose transporter 1 (Glut1) expression, which facilitates glucose uptake in T cells (17), so mTOR signaling integrates immune signals and metabolic cues in T cells (25). Previous studies have demonstrated that AKT/mTOR signaling and T-cell metabolism are decreased in the tumor microenvironment (26), and that limited nutrients in the tumor microenvironment impair the T-cell antitumor immune response (27).

Here, we report that TIGIT+ T cells were significantly increased in gastric cancer patients. TIGIT+ CD8 T cells underwent metabolic reprogramming and exhibited functional exhaustion. CD155 expressed by gastric cancer cells interacted with TIGIT, resulting in the inhibition of glucose uptake and impaired T-cell effector functions. CD155/TIGIT pathway blockade enhanced T-cell effector functions and suppressed tumor progression. This study provides a potential treatment target for gastric cancer.

Patients

Peripheral blood and primary tumor tissues were collected from 138 clinically and pathologically verified gastric cancer patients from the First Affiliated Hospital, Sun Yat-sen University, Guangzhou China; Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen China; Sun Yat-sen University Cancer Center; and Guangdong Provincial Hospital, Guangzhou, China. Infection and autoimmune diseases were excluded. A previous study has shown that TIGIT is upregulated in CMV+ CD8 T cells. To exclude the contaminated effects in CD8 T cells induced by CMV infection, CMV-infected cases were excluded from the current study. This study was approved by the Institutional Review Board of First Affiliated Hospital, Sun Yat-sen University. Consent was informed and consent forms were obtained from every patient. Patient studies were conducted in accordance with ethical guideline: Declaration of Helsinki. Clinical and pathologic characteristics of the included patients are shown in Supplementary Table S1.

Cell isolation

Blood samples were collected from gastric cancer patients or age- and sex-matched healthy controls (HC). Peripheral blood mononuclear cells (PBMC) were isolated with Ficoll–Hypaque by density gradient centrifugation within 2 hours of sample collection. Fresh tumor tissues were obtained from gastric cancer patients during surgical tumor resection. Samples were minced and digested with type I collagenase (Sigma) in RPMI1640. Digested cells were filtered through a nylon mesh (70 μm) and washed with PBS. CD8+TIGIT+, CD8+TIGIT, CD4+TIGIT+, or CD4+TIGIT cells were sorted using a BD FACS Influx. Total and naïve CD8 T cells were purified from PBMCs by negative selection using the EasySep human total or naïve CD8+ T Cell Enrichment Kits (STEMCELL Technologies Inc.). Cell purity was checked (>95%, Supplementary Fig. S1A–S1C).

Cell culture

Human gastric cancer cell lines SGC7901, HGC27, and BGC823 were obtained from the type Culture Collection of Chinese Academy of Sciences (Shanghai, China; ref. 28). Cell lines were authenticated by cell viability analysis, short tandem repeat profiling, and isoenzyme analysis. Cell lines were screened for mycoplasma contamination. Cells were grown in RPMI1640 medium supplemented with 10% FBS, 50 U/mL penicillin, and 50 mg/mL streptomycin in a humidified atmosphere at 37°C with 5% CO2.

Plasmids, retroviral infection, and transfection

CD155 constructs were generated by subcloning PCR-amplified full-length human CD155 cDNA into pcDNA3.1. To deplete CD155, siRNA sequences were cloned into GV248 to generate GV248-RNAi(s) targeting CD155. siRNA duplexes were synthesized and purified by RiboBio Inc. The CD155 siRNA sequences were as follows: sense, 5′-GGUAUCCAUCUCUGGCUAUTT-3′; antisense, 5′-AUAGCCAGAGAUGGAUACCTT-3′. siRNA transfection was carried out using Lipofectamine 2000 reagent (Invitrogen Co.) according to the manufacturer's instructions. Stable cell lines expressing CD155 or CD155 RNAi(s) were selected via treatment with 0.5 μg/mL puromycin for 10 days beginning 48 hours after infection. Following selection, gastric cancer cell lysates prepared from the pooled cell populations in sampling buffer were fractionated by SDS–PAGE to detect protein levels via Western blotting (29).

Flow cytometry

PBMCs isolated from gastric cancer patients or HCs were stained with the following antibodies: FITC-conjugated anti-CD4, PE-conjugated anti-CD8, APC-conjugated anti-TIGIT, and FITC-conjugated CD226. Single-cell suspensions from gastric tumor tissues were stained with FITC-conjugated anti-CD4, PE-conjugated anti-CD8, and APC-conjugated anti-TIGIT antibodies. Sorted CD8+TIGIT+ or CD8+TIGIT cells were stained with PE-Cy7–conjugated CD69, FITC-conjugated anti-PD-1, and V450-conjugated anti–TIM-3 antibodies. Cultured T cells were stained with APC-conjugated p-AKT, PE-CY7–conjugated p-S6K, and Pe-Cy5.5–conjugated p-4EBP1 antibodies. Gastric cancer tumor cell lines were stained with an APC-conjugated CD155 antibody. A commercial kit from Roche was used to analyze cell apoptosis. Samples were analyzed using a BD FACS ARIA (BD Biosciences).

T-cell function assays

For T-cell activation assays, cells were seeded in 96-well plates and stimulated with anti-CD3/CD28 Dynabeads (αCD3/CD28) for 12 hours to measure CD69 expression by flow cytometry. For the proliferation assay, cells were labeled with carboxyfluorescein diacetate succinimidyl ester (CFSE) and stimulated with αCD3/CD28 at 37°C with 5% CO2 for 4 days. Cells were collected, and the dilution of intracellular CFSE caused by proliferation was calculated using a flow cytometer (30). For intracellular cytokine stimulation assays, cells were stimulated with 500 ng/mL PMA and 1 μg/mL ionomycin (Sigma-Aldrich) for 5 hours at 37°C with 5% CO2. During the last 2 hours, 1 μg/mL brefeldin A was included. Cells were collected and stained with V450-conjugated anti-IFNγ and PE-Cy7–conjugated anti-TNFα antibodies.

Coculture

CD8+TIGIT+ or CD8 T cells were sorted and cocultured with gastric cancer SGC7901 cells in 48-well plates at a ratio of 5:1. Cells were stimulated with αCD3/CD28 in the presence of 5 μg/mL anti-TIGIT blocking antibody (BPS Biosciences) or isotype control. Alternatively, cells were treated with 10 mmol/L glucose. T cells were collected to determine the activation, proliferation, and cytokine production using the described T-cell function assays. The phosphorylation of AKT, mTOR, S6K, and 4E-BP1 in CD8 T cells was measured by Western blotting or flow cytometry.

Glucose consumption assay

2-Deoxyglucose (2-DG) is a glucose analogue taken up by glucose transporters and metabolized to 2-DG-6-phosphate (2-DG6P). 2-DG6P cannot be further metabolized and accumulates in cells. 2-DG6P is oxidized to generate NADPH, which can be measured by an enzymatic recycling amplification reaction. T cells (2 × 105/well) were stimulated with αCD3/CD28 for 8 hours. Cells were washed with PBS three times and then glucose starved by plating with 100 μL of Krebs-Ringer-Phosphate-HEPES buffer containing 2% BSA for 40 minutes. Then, cells were stimulated with or without insulin (1 μmol/L) for 20 minutes. A total of 10 μL 10 mmol/L 2-DG was added to the cells for 20 minutes. Glucose levels in the cells were analyzed using a Glucose Assay Kit (Sigma-Aldrich) according to the manufacturer's instructions.

Lactate production assay

CD8+TIGIT+ or CD8+TIGIT cells (2 × 105/well) were stimulated with αCD3/CD28 for 8 hours and then cultured with fresh complete medium containing glucose. Lactate concentrations were analyzed in triplicate using a Lactate Assay Kit (Abcam) according to the manufacturer's instructions.

Humanized NOG mouse tumor model

NOD.Cg-PrkdcscidIl2rgtm1Sug/JicCrl (NOG) mice (Weitong Lihua Experimental Animal Co., Ltd.) were immunodeficient so that they could receive human immune cells. In the humanized mice, we can study immune reaction of human cells against tumor and the underlying mechanisms. PMBCs were isolated from HCs, and 2 × 107 human PBMCs were injected into the mice peritoneally to reconstitute human immune system. Circulating human T cells were evaluated by flow cytometry. To investigate the antitumor effects by target human T cells, mice were subcutaneously inoculated with 2 × 106 SGC7901 or SGC7901-CD155 RNAi. Mice were treated with an anti–PD-L1 antibody or isotype control. The mice were monitored three times per week for evidence of morbidity and mortality associated with tumor growth and metastasis. In vivo bioluminescence imaging was performed by using the IVIS Imaging System. The Living Image acquisition and analysis software (Caliper Life Sciences) were used together as described before (31).

Statistical analyses

Data are expressed as means ± SEM. Statistical analysis was performed using SPSS version 13.0. Differences were assessed using either the Student t test or one-way ANOVA with or without repeated measurements, followed by Bonferroni multiple comparison posttest, as appropriate. Two-tailed P values <0.05 were considered statistically significant.

TIGIT+ T cells are associated with immune subversion in patients with gastric cancer

TIGIT+ T cells expand during malignancy (32). Here, to determine whether TIGIT+ T cells are expanded in gastric cancer, we compared TIGIT expression in T cells from gastric cancer patients or HCs by flow cytometry. The percentage of CD4+TIGIT+ and CD8+TIGIT+ T cells was increased significantly in gastric cancer patients compared with age- and sex-matched HCs (Fig. 1A–C). The percentage of TIGIT+ T cells decreased after surgical removal of the tumor tissue and increased again after tumor recurrence (Fig. 1D–F). In addition, TIGIT was strongly expressed in TILs (Fig. 1G–I). TIGIT+ T cells from PBMCs displayed a memory phenotype. TIGIT was expressed in CD45RACD45RO+ memory T cells, but not in CD45RA+CD45RO naïve T cells (Supplementary Fig. S2A and S2B). CD226 is the costimulatory molecule competing with TIGIT for CD155 and the initiation of CD226 results in T-cell activation (33). Compared with HCs, fewer CD226+ CD8 T cells had been identified in gastric cancer patients (Supplementary Fig. S3A and S3B). CD4 and CD8 T-cell compartments were not significantly different in HCs and gastric cancer patients (Supplementary Fig. S4A and S4B), indicating that increased TIGIT expression on T cells is responsible for the higher number of TIGIT+ T cells in gastric cancer patients.

Figure 1.

Increased TIGIT+ T cells disrupt the immune response in gastric cancer patients. A, TIGIT expression in CD4 and CD8 T cells from PBMCs of gastric cancer patients and HCs was analyzed by flow cytometry. B and C, Percentage of CD4+TIGIT+ or CD8+TIGIT+ cells in PBMCs [age (y): controls, 66.23 ± 6.627 n = 16; patients, 68.87 ± 6.146 n = 24]. D, The percentages of TIGIT+ T cells were measured in PBMCs from postsurgery or recurrent gastric cancer patients by flow cytometry. Percentages of CD4+TIGIT+ (E) and CD8+TIGIT+ (F) cells in PBMCs (n = 8). G, Single-cell suspensions were prepared from gastric tumor tissues. Percentages of TIGIT+ CD4 or CD8 T cells from tumor tissues were measured by flow cytometry. H and I, Individual percentages of TIGIT+ and TIGIT T cells from eight independent samples. J–P, CD8+TIGIT+ and CD8+TIGIT cells in PBMCs from gastric cancer patients were sorted by flow cytometry. Cells were stimulated with anti-CD3/CD28 Dynabeads (αCD3/CD28). J, CD69 expression was determined by flow cytometry after 12 hours of stimulation. Representative flow histograms are shown. K, CD69+ rates are summarized from 12 samples. L, Cells were stained with CFSE, and proliferation rates were measured by flow cytometry. M, Proliferation of CD8+TIGIT+ versus CD8+TIGIT cells in 12 independent samples. N, IFNγ and TNFα production by CD8 T cells was measured by flow cytometry. Representative flow charts are shown. O, Percentages of IFNγ- and TNFα-producing CD8+TIGIT+ or CD8+TIGIT cells. P, CD8+TIGIT+ or CD8+TIGIT cell migration was measured in a Transwell assay, and percentages of cells that migrated to the lower chamber were calculated by flow cytometry. Data were collected from 12 samples. **, P < 0.01; ***, P < 0.001.

Figure 1.

Increased TIGIT+ T cells disrupt the immune response in gastric cancer patients. A, TIGIT expression in CD4 and CD8 T cells from PBMCs of gastric cancer patients and HCs was analyzed by flow cytometry. B and C, Percentage of CD4+TIGIT+ or CD8+TIGIT+ cells in PBMCs [age (y): controls, 66.23 ± 6.627 n = 16; patients, 68.87 ± 6.146 n = 24]. D, The percentages of TIGIT+ T cells were measured in PBMCs from postsurgery or recurrent gastric cancer patients by flow cytometry. Percentages of CD4+TIGIT+ (E) and CD8+TIGIT+ (F) cells in PBMCs (n = 8). G, Single-cell suspensions were prepared from gastric tumor tissues. Percentages of TIGIT+ CD4 or CD8 T cells from tumor tissues were measured by flow cytometry. H and I, Individual percentages of TIGIT+ and TIGIT T cells from eight independent samples. J–P, CD8+TIGIT+ and CD8+TIGIT cells in PBMCs from gastric cancer patients were sorted by flow cytometry. Cells were stimulated with anti-CD3/CD28 Dynabeads (αCD3/CD28). J, CD69 expression was determined by flow cytometry after 12 hours of stimulation. Representative flow histograms are shown. K, CD69+ rates are summarized from 12 samples. L, Cells were stained with CFSE, and proliferation rates were measured by flow cytometry. M, Proliferation of CD8+TIGIT+ versus CD8+TIGIT cells in 12 independent samples. N, IFNγ and TNFα production by CD8 T cells was measured by flow cytometry. Representative flow charts are shown. O, Percentages of IFNγ- and TNFα-producing CD8+TIGIT+ or CD8+TIGIT cells. P, CD8+TIGIT+ or CD8+TIGIT cell migration was measured in a Transwell assay, and percentages of cells that migrated to the lower chamber were calculated by flow cytometry. Data were collected from 12 samples. **, P < 0.01; ***, P < 0.001.

Close modal

TIGIT is associated with T-cell exhaustion. Next, we investigated the effect of increased TIGIT expression on T-cell effector functions in patients with gastric cancer. CD8+TIGIT+ or CD8+TIGIT cells were sorted from gastric cancer PBMCs by flow cytometry and stimulated with anti-CD3/CD28 Dynabeads (αCD3/CD28). More TIGIT T cells were CD69+ than TIGIT+ T cells (Fig. 1J and K). In addition, proliferation rate was significantly lower in TIGIT+ CD8 T than TIGIT CD8 T cells (Fig. 1L and M). Cytokines of IFNγ and TNFα production (Fig. 1N and O) and cell migration (Fig. 1P) were impaired in TIGIT+ CD8 T cells, while apoptosis was higher in TIGIT+ CD8 T cells (Supplementary Fig. S5A and B). Furthermore, TIGIT+ CD4 T cells from gastric cancer patients exhibited characteristic functional exhaustion that was similar with CD8 T cells (Supplementary Fig. S6A–S6F).

In summary, the expansion of TIGIT+ T cells was in accordance with immune subversion in gastric cancer, implying immune escape in gastric cancer through the upregulation of TIGIT.

Metabolic reprogramming of TIGIT+ CD8 T cells in gastric cancer patients

Glucose uptake and glycolysis increase rapidly when T cells are activated (20). To explore metabolic changes in TIGIT+ CD8 T cells, we evaluated the expression of genes involved in metabolic reprogramming by RT-PCR first. Expression of metabolism-associated genes was significantly reduced in TIGIT+ CD8 T cells compared with TIGIT CD8 T cells (Fig. 2A). Glut1 has been very important for glucose uptake in T cells, and hexokinase 1 and hexokinase 2 (HK1/HK2) are the key kinases that initiate the process of glycolysis. The downregulation of Glut1 and HK1/HK2 in TIGIT+ T cells was confirmed by flow cytometry and Western blotting, respectively (Fig. 2B–D). The AKT/mTOR pathway regulates glycolysis and is important for cell growth and proliferation. Western blot analysis revealed that phosphorylation of AKT and mTOR was significantly downregulated in TIGIT+ T cells (Fig. 2E). Furthermore, flow cytometric analysis showed decreased expression of p-S6K and p-4EBP1, which are downstream targets of mTOR, in TIGIT+ T cells (Fig. 2F–H).

Figure 2.

Metabolic reprogramming in TIGIT+ CD8 T cells from gastric cancer patients. CD8+TIGIT+ and CD8+TIGIT cells were sorted from PBMCs of gastric cancer patients. A, Cells were stimulated with αCD3/CD28 for 4 hours. Heatmap shows expression of metabolism-associated gene expression quantified by RT-PCR. B, CD8+TIGIT+ or CD8+TIGIT cells were stimulated with αCD3/CD28 for 24 hours. Glucose transporter 1 (Glut1) expression in CD8+TIGIT+ and CD8+TIGIT cells was determined by flow cytometry. Representative histograms are shown. C, Glut1 mean fluorescence intensity (MFI) is summarized from six independent samples. D, CD8+TIGIT+ and CD8+TIGIT cells were stimulated with αCD3/CD28 for 24 hours. Hexokinase 1 (HK1) and hexokinase 2 (HK2) expression was measured by Western blotting. Representative blots are shown. E, CD8+TIGIT+ and CD8+TIGIT cells were stimulated with αCD3/CD28 for 5 minutes. AKT and mTOR phosphorylation was quantified by Western blotting. F, Cells were stimulated with αCD3/CD28 for 3 minutes; p-S6K and p-4EBP1 were measured by flow cytometry. Representative histograms are shown. Phosphorylation of S6K (G) and 4EBP1 (H) is summarized. CD8+TIGIT+ or CD8+TIGIT cells were stimulated with αCD3/CD28. I, Glucose uptake by T cells was measured indirectly using a 2-deoxyglucose (2-DG)–based assay. 2-DG is a glucose analogue that is taken up by glucose transporters and metabolized to 2-DG-6-phosphate (2-DG6P). 2-DG6P cannot be further metabolized and accumulates in cells. J, Lactate production was measured by a colorimetric assay 4 hours after stimulation. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 2.

Metabolic reprogramming in TIGIT+ CD8 T cells from gastric cancer patients. CD8+TIGIT+ and CD8+TIGIT cells were sorted from PBMCs of gastric cancer patients. A, Cells were stimulated with αCD3/CD28 for 4 hours. Heatmap shows expression of metabolism-associated gene expression quantified by RT-PCR. B, CD8+TIGIT+ or CD8+TIGIT cells were stimulated with αCD3/CD28 for 24 hours. Glucose transporter 1 (Glut1) expression in CD8+TIGIT+ and CD8+TIGIT cells was determined by flow cytometry. Representative histograms are shown. C, Glut1 mean fluorescence intensity (MFI) is summarized from six independent samples. D, CD8+TIGIT+ and CD8+TIGIT cells were stimulated with αCD3/CD28 for 24 hours. Hexokinase 1 (HK1) and hexokinase 2 (HK2) expression was measured by Western blotting. Representative blots are shown. E, CD8+TIGIT+ and CD8+TIGIT cells were stimulated with αCD3/CD28 for 5 minutes. AKT and mTOR phosphorylation was quantified by Western blotting. F, Cells were stimulated with αCD3/CD28 for 3 minutes; p-S6K and p-4EBP1 were measured by flow cytometry. Representative histograms are shown. Phosphorylation of S6K (G) and 4EBP1 (H) is summarized. CD8+TIGIT+ or CD8+TIGIT cells were stimulated with αCD3/CD28. I, Glucose uptake by T cells was measured indirectly using a 2-deoxyglucose (2-DG)–based assay. 2-DG is a glucose analogue that is taken up by glucose transporters and metabolized to 2-DG-6-phosphate (2-DG6P). 2-DG6P cannot be further metabolized and accumulates in cells. J, Lactate production was measured by a colorimetric assay 4 hours after stimulation. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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To determine whether these changes were associated with changes in T-cell metabolism, we measured glucose uptake and lactate production in TIGIT+ and TIGIT T cells. Glucose uptake was impaired in TIGIT+ CD8 T cells compared with TIGIT CD8 T cells (Fig. 2I). In addition, lactate production was significantly lower in TIGIT+ CD8 T cells (Fig. 2J).

Taken together, these findings indicated that TIGIT reduced glucose uptake and inhibited T-cell metabolism in gastric cancer patients.

Glucose reverses impaired TIGIT+ T-cell metabolism and rescues T-cell functional exhaustion

As glucose is the major cellular fuel that promotes T-cell proliferation and survival, we investigated whether the addition of exogenous glucose could reverse TIGIT-associated T-cell exhaustion. To do this, TIGIT CD8 T or TIGIT+ CD8 T cells were sorted from gastric cancer PBMCs. Glucose treatment reversed metabolic activities of TIGIT+ CD8 T cells. Glucose uptake and lactate production were increased by glucose. Glucose also increased glucose uptake and lactate production in TIGIT CD8 T cells (Supplementary Fig. S7A and S7B). Interestingly, fructose, another hexoses, could not reverse metabolic activities of TIGIT+ CD8 T cells (Fig. 3A and B). Next, we were to explore whether glucose can reverse the exhaustion of TIGIT+ CD8 T cells. We found that glut1 expression was increased by glucose treatment, as determined by flow cytometry (Fig. 3C). This increased metabolism in TIGIT+ T cells was accompanied by enhanced T-cell effector functions, as the percentage of CD69+ T cells and T-cell proliferation were increased after glucose treatment (Fig. 3D–G). In addition, TIGIT+ T-cell migration and cytokine production were restored by glucose (Fig. 3H–J). In addition, glucose increased IFNγ production in a dose-dependent manner (Fig. 3K–L).

Figure 3.

Glucose reverses the effects of CD8+TIGIT+ on T-cell metabolism and rescues T-cell exhaustion. CD8+TIGIT or CD8+TIGIT+ T cells were sorted from PBMCs of gastric cancer patients. Cells were stimulated with αCD3/CD28 in the presence or absence of 10 mmol/L glucose (Glu) or 10 mmol/L fructose. A and B, Cells were stimulated with αCD3/CD28 in the presence or absence of 10 mmol/L glucose for 24 hours. Cells were then collected to measure glucose consumption (A) and lactate production (B) as described above. C, Glut1 expression in T cells was determined by flow cytometry after stimulation for 24 hours. Representative histograms of 6 experiments were presented. D, CD69 expression was determined by flow cytometry after stimulation for 12 hours. E, The percentages of CD69+ CD8 T cells. F, CD8 T-cell proliferation was quantified by flow cytometric analysis of CFSE dilution after 4 days of stimulation. Representative histograms of 6 experiments are shown. G, CD8 T-cell proliferation rates are summarized from 6 experiments. H, CD8 T-cell migration was measured using a Transwell system. Transmigrated cells were enumerated by flow cytometry. I, IFNγ production was measured by flow cytometry after 24 hours of stimulation. J, Percentages of IFNγ-producing CD8 T cells. K, CD8 T cells were stimulated with αCD3/CD28 for 48 hours and treated with different concentrations of glucose. IFNγ production was measured by flow cytometry. Representative flow charts are shown. L, Percentages of IFNγ-producing CD8 T cells. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 3.

Glucose reverses the effects of CD8+TIGIT+ on T-cell metabolism and rescues T-cell exhaustion. CD8+TIGIT or CD8+TIGIT+ T cells were sorted from PBMCs of gastric cancer patients. Cells were stimulated with αCD3/CD28 in the presence or absence of 10 mmol/L glucose (Glu) or 10 mmol/L fructose. A and B, Cells were stimulated with αCD3/CD28 in the presence or absence of 10 mmol/L glucose for 24 hours. Cells were then collected to measure glucose consumption (A) and lactate production (B) as described above. C, Glut1 expression in T cells was determined by flow cytometry after stimulation for 24 hours. Representative histograms of 6 experiments were presented. D, CD69 expression was determined by flow cytometry after stimulation for 12 hours. E, The percentages of CD69+ CD8 T cells. F, CD8 T-cell proliferation was quantified by flow cytometric analysis of CFSE dilution after 4 days of stimulation. Representative histograms of 6 experiments are shown. G, CD8 T-cell proliferation rates are summarized from 6 experiments. H, CD8 T-cell migration was measured using a Transwell system. Transmigrated cells were enumerated by flow cytometry. I, IFNγ production was measured by flow cytometry after 24 hours of stimulation. J, Percentages of IFNγ-producing CD8 T cells. K, CD8 T cells were stimulated with αCD3/CD28 for 48 hours and treated with different concentrations of glucose. IFNγ production was measured by flow cytometry. Representative flow charts are shown. L, Percentages of IFNγ-producing CD8 T cells. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

Taken together, these results indicate that TIGIT may have negative regulatory effects on T-cell metabolism, which can be reversed by glucose.

Gastric cancer cells deprive CD8 T cells of glucose

To investigate why T-cell metabolism is inhibited from the gastric cancer, we used the coculture of CD8 T cells from HC PBMCs and gastric cancer SGC7901 cells. As shown in the heatmap in Fig. 4A, metabolism-associated gene expression was suppressed in CD8 T cells cocultured with SGC7901 compared with CD8 T cells without coculture with gastric cancer cells. Glut1 expression was reduced and CD8+ T-cell expression of the downstream molecules, HK2, was also downregulated when compared with CD8 T cells without coculture with gastric cancer cells (Fig. 4B–D). Glucose uptake by CD8 T cells was significantly inhibited when CD8 T cells were cocultured with SGC7901 (Fig. 4E). In addition, lactate production in CD8 T cells was much lower in SGC7901-T-cell cocultures (Fig. 4F). Furthermore, we found that the phosphorylation of mTOR and its downstream molecules, S6K and 4E-BP1, was inhibited in T cells when cocultured with SGC7901 (Fig. 4G–K). In addition, T-cell cytokine production (IL2, TNFα, and IFNγ) was decreased significantly in SGC7901-T-cell cocultures (Fig. 4L–N). We confirmed these findings in another gastric cancer cell line, HGC27 (Supplementary Fig. S8A–S8D). These data demonstrate that gastric cancer cells could inhibit T-cell metabolism in tumor microenvironment to turnover T-cell effector functions.

Figure 4.

Gastric cancer cells deprive CD8 T cells of glucose. A–L, Naïve CD8 T cells isolated from HCs were stimulated with αCD3/CD28 and cocultured with gastric cancer cells (SGC7901) at a 5:1 ratio. A, Glycolytic gene expression levels in CD8 T cells were measured by RT-PCR after 12 hours of stimulation. Relative gene expression is shown as a heatmap. B, Glut1 expression in CD8 T cells was determined by flow cytometry after 48-hour stimulation. A representative histogram is shown. C, Summary of Glut1 mean fluorescence intensity (MFI). D, HK1 and HK2 expression in CD8 T cells was determined by Western blotting. Glucose consumption (E) and lactate production (F) in CD8 T cells were measured as described above after 24 hours of coculture. G, Phosphorylation of mTOR in CD8 T cells was measured by flow cytometry. H, p-mTOR (S2448) MFI is summarized. I, Phosphorylation of SK6 and 4EBP1 was measured by flow cytometry. J and K, Summary of MFIs. L, CD8 T cells were stimulated with αCD3/CD28 and cocultured with SGC7901 for 48 hours. IFNγ production by CD8 T cells was determined by flow cytometry. M, Percentages of IFNγ-producing CD8 T cells. N, CD8 T-cell cytokine production in the supernatant was measured by ELISA. Data were analyzed relative to the control group. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 4.

Gastric cancer cells deprive CD8 T cells of glucose. A–L, Naïve CD8 T cells isolated from HCs were stimulated with αCD3/CD28 and cocultured with gastric cancer cells (SGC7901) at a 5:1 ratio. A, Glycolytic gene expression levels in CD8 T cells were measured by RT-PCR after 12 hours of stimulation. Relative gene expression is shown as a heatmap. B, Glut1 expression in CD8 T cells was determined by flow cytometry after 48-hour stimulation. A representative histogram is shown. C, Summary of Glut1 mean fluorescence intensity (MFI). D, HK1 and HK2 expression in CD8 T cells was determined by Western blotting. Glucose consumption (E) and lactate production (F) in CD8 T cells were measured as described above after 24 hours of coculture. G, Phosphorylation of mTOR in CD8 T cells was measured by flow cytometry. H, p-mTOR (S2448) MFI is summarized. I, Phosphorylation of SK6 and 4EBP1 was measured by flow cytometry. J and K, Summary of MFIs. L, CD8 T cells were stimulated with αCD3/CD28 and cocultured with SGC7901 for 48 hours. IFNγ production by CD8 T cells was determined by flow cytometry. M, Percentages of IFNγ-producing CD8 T cells. N, CD8 T-cell cytokine production in the supernatant was measured by ELISA. Data were analyzed relative to the control group. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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Next, we were to investigate whether glucose can affect the effector function of CD8 T cells that are cocultured with gastric cancer cells. CD8 T cells from HC PBMCs were cocultured with SGC7901 with or without the additional glucose. We found that the inhibition T-cell function was neutralized by glucose when cocultured with SGC7901. The percentage of IFNγ-producing T cells increased after supplementation of the coculture system with glucose (Supplementary Fig. S9A and S9B). However, TIGIT expression was not affected by glucose supplementation (Supplementary Fig. S9C and S9D).

These findings suggest that gastric cancer cells impair T-cell function by depriving them of glucose and that this impairment can be reversed by the addition of exogenous glucose.

TIGIT blockade reverses the inhibition of T-cell metabolism and cytokine production by gastric cancer cells

As data show above that gastric cancer cells can inhibit T-cell metabolism, we were to investigate how gastric cancer cells affect T-cell metabolism and effector functions. CD8 T cells were isolated from HC PBMCs and cocultured with gastric cancer cell line of SC7901. TIGIT expression was unregulated in T cells when cocultured with SGC7901 (Fig. 5A). Circulating tumor cells (CTC) were enumerated using a NanoVelcro system, as described previously (34). The number of CTCs correlated closely with the percentage of CD8+TIGIT+ T cells (Fig. 5B), indicating CTCs might contribute to the increased TIGIT+ CD8 T cells in the circulation of gastric cancer patients.

Figure 5.

TIGIT blockade neutralizes gastric cancer cell–induced inhibition of T-cell metabolism and cytokine production. TIGIT+ CD8 T cells were sorted from HCs by flow cytometry. Cells were stimulated with αCD3/CD28 and cocultured with SGC7901 at a ratio of 5:1. TIGIT was inhibited by an anti-TIGIT blocking antibody (αTIGIT). An isotype control was used as a control. A, TIGIT expression in CD8 T cells was measured by flow cytometry. Representative histograms are shown. B, Circulating tumor cells in gastric cancer patients were determined by NanoVelcro. Correlation of CTCs and CD8+TIGIT+ cells are shown (n = 25). C, Cells were stimulated with αCD3/CD28 for 2 days, and Glut1 expression was measured by flow cytometry. D, Summary of Glut1 mean fluorescence intensity (MFI). E, AKT and mTOR phosphorylation in CD8 T cells was determined by Western blotting. F and G, Semiquantification of p-AKT and p-mTOR. H, S6K and 4EBP1 phosphorylation in CD8 T cells was measured by flow cytometry. The phosphorylation rates of S6K and 4EBP1 are summarized in I and J, respectively. Glucose (glu) consumption (K) and lactate production (L) in CD8 T cells. M, Total CD8 T cells were isolated from healthy PBMCs. Cells were cocultured with gastric cancer cells at a ratio of 5:1 in the presence of αTIGIT or isotype control. IFNγ production was measured by flow cytometry after 2 days of stimulation with αCD3/CD28. N, Percentages of IFNγ-producing CD8 T cells. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 5.

TIGIT blockade neutralizes gastric cancer cell–induced inhibition of T-cell metabolism and cytokine production. TIGIT+ CD8 T cells were sorted from HCs by flow cytometry. Cells were stimulated with αCD3/CD28 and cocultured with SGC7901 at a ratio of 5:1. TIGIT was inhibited by an anti-TIGIT blocking antibody (αTIGIT). An isotype control was used as a control. A, TIGIT expression in CD8 T cells was measured by flow cytometry. Representative histograms are shown. B, Circulating tumor cells in gastric cancer patients were determined by NanoVelcro. Correlation of CTCs and CD8+TIGIT+ cells are shown (n = 25). C, Cells were stimulated with αCD3/CD28 for 2 days, and Glut1 expression was measured by flow cytometry. D, Summary of Glut1 mean fluorescence intensity (MFI). E, AKT and mTOR phosphorylation in CD8 T cells was determined by Western blotting. F and G, Semiquantification of p-AKT and p-mTOR. H, S6K and 4EBP1 phosphorylation in CD8 T cells was measured by flow cytometry. The phosphorylation rates of S6K and 4EBP1 are summarized in I and J, respectively. Glucose (glu) consumption (K) and lactate production (L) in CD8 T cells. M, Total CD8 T cells were isolated from healthy PBMCs. Cells were cocultured with gastric cancer cells at a ratio of 5:1 in the presence of αTIGIT or isotype control. IFNγ production was measured by flow cytometry after 2 days of stimulation with αCD3/CD28. N, Percentages of IFNγ-producing CD8 T cells. n = 6; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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As we observed that gastric cancer cells could induce TIGIT expression in CD8 T cells, and TIGIT was associated with T-cell metabolism and T-cell exhaustion, we next investigated whether TIGIT blockade could reverse SGC7901-induced changes in T-cell metabolism and effector functions. CD8 T cells were sorted from HC PBMCs and cocultured with gastric cancer cell line of SGC7901. TIGIT-blocking antibody was used to block TIGIT signal. Blocking TIGIT increased metabolism-associated gene expression in CD8 T cells (Supplementary Fig. S10A). Increased Glut1 expression in CD8 T cells was confirmed at the protein level by flow cytometry (Fig. 5C and D). AKT and mTOR phosphorylation was increased in CD8 T cells after TIGIT blockade (Fig. 5E–G) compared with isotype control. Furthermore, blocking TIGIT increased p-S6K and p-4EBP1 expression by CD8 T cells when cocultured with SGC7901 (Fig. 5H–J). These metabolic changes were associated with increased glucose uptake and lactate production in CD8 T cells (Fig. 5K and L). Consistent with this, gastric cancer cell-mediated inhibition of IFNγ production by CD8 T cells was reversed by blocking TIGIT (Fig. 5M and N).

These observations demonstrate that gastric cancer cells induce TIGIT expression on CD8 T cells, through which gastric cancer cells inhibit T-cell metabolism and impair T-cell effector function.

Gastric cancer cells inhibit T-cell metabolism through CD155/TIGIT signaling

Melanoma cells suppress T-cell responses through CD155–TIGIT interactions (16). To investigate whether CD155 is involved in the inhibition on T cells mediated by gastric cancer cells, we tested CD155 expression on gastric cancer tissue and gastric cancer cell lines. We found that CD155 expression was detected and significantly increased in gastric cancer tissue compared with normal gastric tissue (Fig. 6A and B), and the gastric cancer cell lines SGC7901, HGC27, and BGC823 all expressed CD155 (Fig. 6C) as detected by flow cytometry. These data indicate that gastric cancer could interact with T cells through CD155-TIGIT and affect T-cell functions.

Figure 6.

Gastric cancer cells inhibit T-cell metabolism through CD155/TIGIT. A, CD155 expression in normal gastric tissue or gastric cancer tissue was measured by Western blotting. Representative blots are shown. B, Relative expression of CD155. C, CD155 expression in gastric cancer cell lines SGC7901, HGC27, and BGC823 determined by flow cytometry. Representative histograms are shown. D, CD155 knockdown efficiency was confirmed by flow cytometry. E–I, CD8 T cells were stimulated with αCD3/CD28 and cocultured with SGC7901-vector or SGC7901-CD155-RNAi for 48 hours. E, Phosphorylation of AKT, S6K, and 4E-BP1 in CD8 T cells was determined by flow cytometry. Representative histograms are shown. Glucose uptake (F) or lactate production (G) in CD8 T cells. H, IFNγ production in CD8 T cells measured by flow cytometry. I, Percentages of IFNγ-producing CD8 T cells. J, CD155 overexpression was confirmed by flow cytometry. K, CD8 T cells were cocultured with SGC7901-CD155 or SGC7901-vector cells. TIGIT was blocked using αTIGIT. IFNγ production in CD8 T cells determined by flow cytometry. Representative flow charts are shown. L, Percentages of IFNγ-producing CD8 T cells. n = 6; N, normal gastric tissue; P, gastric cancer tumor tissue. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 6.

Gastric cancer cells inhibit T-cell metabolism through CD155/TIGIT. A, CD155 expression in normal gastric tissue or gastric cancer tissue was measured by Western blotting. Representative blots are shown. B, Relative expression of CD155. C, CD155 expression in gastric cancer cell lines SGC7901, HGC27, and BGC823 determined by flow cytometry. Representative histograms are shown. D, CD155 knockdown efficiency was confirmed by flow cytometry. E–I, CD8 T cells were stimulated with αCD3/CD28 and cocultured with SGC7901-vector or SGC7901-CD155-RNAi for 48 hours. E, Phosphorylation of AKT, S6K, and 4E-BP1 in CD8 T cells was determined by flow cytometry. Representative histograms are shown. Glucose uptake (F) or lactate production (G) in CD8 T cells. H, IFNγ production in CD8 T cells measured by flow cytometry. I, Percentages of IFNγ-producing CD8 T cells. J, CD155 overexpression was confirmed by flow cytometry. K, CD8 T cells were cocultured with SGC7901-CD155 or SGC7901-vector cells. TIGIT was blocked using αTIGIT. IFNγ production in CD8 T cells determined by flow cytometry. Representative flow charts are shown. L, Percentages of IFNγ-producing CD8 T cells. n = 6; N, normal gastric tissue; P, gastric cancer tumor tissue. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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To determine whether CD155 expressed by gastric cancer cells regulates T-cell metabolism and effector functions, we generated a SGC7901 cell line with stable downregulation of CD155 expression, SGC7901-CD155 RNAi. The downregulation of CD155 was confirmed by flow cytometry (Fig. 6D). CD8 T cells sorted from HC PBMCs were cocultured with SGC7901-CD155 RNAi or SGC7901-vector. AKT, S6K, and 4EBP1 phosphorylation in CD8 T cells was decreased by coculture with SGC7901-vector cells. Downregulation of CD155 in SGC7901 cells (SGC7901-CD155 RNAi) increased AKT, S6K, and 4EBP1 phosphorylation in CD8 T cells (Fig. 6E). Glucose uptake and lactate production in CD8 T cells were both decreased by SGC7901-vector cells, but this effect was reversed when CD155 was downregulated in SGC7901 cells (Fig. 6F and G). In addition, SGC7901-vector cells inhibited IFNγ production in CD8 T cells, and this inhibition was reversed by CD155 downregulation in SGC7901 cells (Fig. 6H and I).

We also investigated the effect of CD155 overexpression in SGC7901 cells (SGC7901-CD155) on IFNγ production in T cells. CD8 T cells were cocultured with SGC7901-vector or SGC7901-CD155 cells. Overexpression of CD155 in SGC7901 was confirmed by flow cytometry (Fig. 6J). SGC7901-vector inhibited IFNγ production compared with T cells stimulated with αCD3/CD28 alone. SGC7901-CD155 cells further decreased IFNγ production in CD8 T cells compared with SGC7901-vector cells, which could be neutralized by blocking TIGIT (Fig. 6K and L).

Taken together, these findings indicated that gastric cancer cells inhibit T-cell metabolism through CD155/TIGIT signaling pathways.

TIGIT and PD-1 are coexpressed in CD8 T cells, and combined inhibition of TIGIT and PD-1 signals has synergistic effects

A study has shown that exhausted CD8 T cells coexpressed TIGIT and PD-1, and combined blockade of these two signals demonstrates stronger effects in T-cell activation compared with blocking either. To study whether TIGIT+ CD8 T cells from gastric cancer coexpress PD-1, we analyzed PD-1 expression in CD8+TIGIT+ and CD8+TIGIT cells by flow cytometry. PD-1 expression was significantly higher in TIGIT+ CD8 T cells than in TIGIT CD8 T cells (Fig. 7A and B). In addition, the expression of another checkpoint molecule, TIM-3, was also higher in TIGIT+ CD8 T cells (Supplementary Fig. S11A–S11D). We next investigated whether TIGIT and PD-1 have synergistic effects on T-cell activation when cocultured with gastric cancer cells. Blocking TIGIT or PD-1 increased IFNγ production in CD8 T cells that were cocultured with SGC7901 cells. Blocking both TIGIT and PD-1 further enhanced IFNγ production in CD8 T cells (Fig. 7C and D). These findings indicate that TIGIT and PD-1 act synergistically to induce CD8 T-cell exhaustion.

Figure 7.

Combined inhibition of TIGIT and PD-1 signaling has synergistic effects in vitro and in vivo. A, PBMCs from gastric cancer patients were stained with anti-human CD8, anti-human TIGIT, and anti-human PD-1 antibodies. Representative flow charts were gated on CD8+TIGIT+ or CD8+TIGIT cells. B, Percentages or mean fluorescence intensity (MFI) of PD-1 in CD8+TIGIT+ or CD8+TIGIT cells. C and D, Total CD8 T cells were isolated from healthy PBMCs. Cells were stimulated with αCD3/CD28 and cocultured with or without SGC7901 for 2 days. Anti-TIGIT (αTIGIT), anti-PD-1 (αPD-1), isotype control, or a combination of αTIGIT and αPD-1 was included. IFNγ production in CD8 T cells was determined by flow cytometry. Percentages of IFNγ-producing CD8 T cells were summarized from 8 samples. E–H, NOG mice were inoculated subcutaneously with Vector-SGC7901 or SGC7901-CD155-RNAi gastric carcinoma cells. At the same time, mice were reconstituted with human PBMCs. When tumor sizes reached approximately 200 mm3, mice were treated with isotype control or anti–PD-1 antibody (αPD-L1) for 3 weeks. E, Representative images of showing CD8 T-cell infiltration in tumor microenvironment as detected by IHC. F, Mouse survival over time (n = 12). G, Mean (top) or individuals of tumor volume (bottom 4 panels) over time (n = 8). H,In vivo bioluminescence images of the tumor-bearing mice. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 7.

Combined inhibition of TIGIT and PD-1 signaling has synergistic effects in vitro and in vivo. A, PBMCs from gastric cancer patients were stained with anti-human CD8, anti-human TIGIT, and anti-human PD-1 antibodies. Representative flow charts were gated on CD8+TIGIT+ or CD8+TIGIT cells. B, Percentages or mean fluorescence intensity (MFI) of PD-1 in CD8+TIGIT+ or CD8+TIGIT cells. C and D, Total CD8 T cells were isolated from healthy PBMCs. Cells were stimulated with αCD3/CD28 and cocultured with or without SGC7901 for 2 days. Anti-TIGIT (αTIGIT), anti-PD-1 (αPD-1), isotype control, or a combination of αTIGIT and αPD-1 was included. IFNγ production in CD8 T cells was determined by flow cytometry. Percentages of IFNγ-producing CD8 T cells were summarized from 8 samples. E–H, NOG mice were inoculated subcutaneously with Vector-SGC7901 or SGC7901-CD155-RNAi gastric carcinoma cells. At the same time, mice were reconstituted with human PBMCs. When tumor sizes reached approximately 200 mm3, mice were treated with isotype control or anti–PD-1 antibody (αPD-L1) for 3 weeks. E, Representative images of showing CD8 T-cell infiltration in tumor microenvironment as detected by IHC. F, Mouse survival over time (n = 12). G, Mean (top) or individuals of tumor volume (bottom 4 panels) over time (n = 8). H,In vivo bioluminescence images of the tumor-bearing mice. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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Targeting CD155/TIGIT suppresses tumor progression in vivo

To assess the antitumor effects of targeting CD155/TIGIT signaling, we used a humanized mouse tumor model using NOG mice. NOG mice are immunodeficient mice that can be used for reconstitution of human immune cells in the mice. Mice were first injected with HC PBMCs. NOG mice were then subcutaneously inoculated with SGC7901-vector cells or SGC7901-CD155-RNAi one week later when human immune system is established. Mice were treated with a PD-L1–blocking antibody or isotype control. CD8+ T-cell infiltration in the tumor was increased in mice that received SGC7901-CD155-RNAi or mice that received anti–PD-1 treatment, when compared with mice that received SGC7901-vector as measured by IHC (Fig. 7E). Anti–PD-1 treatment combined with SGC7901-CD155-RNAi further enhanced T-cell infiltration to the tumor (Fig. 7E). In addition, TCR, IL2, and IFNγ production in the tumor quantified by RT-PCR further confirmed the increased T cell and enhanced immune response in the mice that received SGC7901-CD155-RNAi or anti–PD-1 treatment (Supplementary Fig. S12A–S12C). Mice that received SGC7901-CD155-RNAi showed inhibited tumor progression and improved survival when compared with mice that received SGC7901-vector. Also, tumor progression was inhibited by anti–PD-1 treatment and survival was improved by anti–PD-1 treatment. Tumor progression was further inhibited in mice that received SGC7901-CD155-RNAi and anti–PD-1 treatment. Survival was further improved in mice that received SGC7901-CD155-RNAi and anti–PD-1 treatment (Fig. 7F–H).

Taken together, these results suggest that downregulation of CD155 in gastric cancer cells combined with PD-L1 blockade mediated synergistic effects in terms of the inhibition of tumor progression and increased survival compared with the effects downregulation of CD155 in gastric cancer cells or PD-L1 blockade alone.

Immunosurveillance is important for maintaining cellular homeostasis and preventing carcinogenesis (35). Immune escape is a defect of the immune system that facilitates carcinogenesis. It is promoted by upregulation of immune checkpoints, such as PD-1, and induces T-cell exhaustion (36). CD8 T cells are the major effector cells in antitumor immunity. These cells are exhausted and rendered dysfunctional by immune checkpoints in tumor-bearing hosts (37). In this study, we found that the percentage of CD8+ T cells that are TIGIT+ is dramatically increased in gastric cancer patients and that these cells exhibit functional exhaustion and reduced metabolic activity. We showed that gastric cancer cells inhibited glucose uptake and reduced metabolic activity in CD8 T cells and that these effects were reversed by the addition of glucose or blocking CD155/TIGIT. Targeting CD155 pathway suppressed tumor progression and improved survival in tumor-bearing mice.

We demonstrated that activation, proliferation, migration, and cytokine production are impaired in TIGIT+ T cells from gastric cancer patients. These findings are in agreement with previous studies that have shown that TIGIT deficiency causes T-cell hyperproliferation and increased susceptibility to autoimmunity (12, 33). Our results suggest that TIGIT+ T cells in gastric cancer patients contribute to immune dysfunction, leading to impaired antitumor immunity and accelerated tumor progression. TIGIT may represent a potential therapeutic target to enhance antitumor immunity and control gastric cancer progression.

The immune system plays a key role in controlling tumor initiation and progression (38). Activated T cells require adequate energy supplies and changes in cellular metabolism for antitumor immune responses (26, 39). In the current study, we revealed abnormal metabolic reprogramming and lower metabolic activity in TIGIT+ CD8 T cells than TIGIT CD8 T cells. Glucose uptake and lactate production were low in TIGIT+ CD8 T cells than TIGIT CD8 T cells from gastric cancer patients. Strikingly, glucose could reverse the hypometabolic profile of TIGIT+ T cells. Glucose reversed the metabolic pathway of AKT/mTOR in TIGIT+ CD8 T cells. Cytokine production was increased in TIGIT+ CD8 T cells along with reversed metabolism. These observations suggest that TIGIT regulates T-cell metabolism and induces T-cell dysfunction in gastric cancer patients. Harnessing T-cell metabolism may be the potential method in reversing metabolic activity and effector function of TIGIT+ CD8 T cells.

T cells from leukemia patients are metabolically impaired (40), and tumor cells outcompete T cells for glucose consumption and induce T-cell exhaustion, (26), implying tumor cells may inhibit the metabolism in T cells and damage T-cell antitumor effects subsequently. In the current study, we found that CD8 T cells were metabolically impaired when cocultured with gastric cancer cells. Gastric cancer cells deprived CD8 T cells of glucose and downregulated the AKT/mTOR metabolic pathway in CD8 T cells. These findings suggest that gastric cancer cells inhibit AKT/mTOR signaling pathway in CD8 T cells, which resulted in reduced glucose uptake and lactate production. In agreement with our findings, extremely low levels of glucose and high lactate have been reported in gastric tumor tissues (41). Furthermore, we found that gastric cancer cells induced TIGIT expression in CD8 T cells. TIGIT blockade activated metabolic pathway in CD8 T cells. The phosphorylation of AKT/mTOR pathway was increased by TIGIT blockade, resulting in increased metabolism and cytokine production in CD8 T cells. These findings demonstrate that TIGIT signaling in CD8 T cells leads to decreased phosphorylation of AKT/mTOR pathway, resulting in the inhibition of metabolism. PD-1 inhibits mTOR pathway activation and suppresses glycolysis in T cells (42). The immune checkpoints may share some common mechanism in the regulation of T-cell function. However, this signaling is different from the previous report that TIGIT signals through ZAP70 and ERK1/2 in NK cells (43). Together, gastric cancer cells inhibit AKT/mTOR pathway in CD8 T cells through upregulating TIGIT expression on CD8 T cells.

Next, we demonstrated that CD155, ligand of TIGIT, is overexpressed in gastric cancer tumor tissue and cell lines, which was in accordance with the previous report that soluble CD155 is increased in gastric cancer serum (44). The expression of CD155 on gastric cancer cells downregulated AKT/mTOR pathway and inhibited glucose uptake in CD8 T cells when they were cocultured together. Downregulation of CD155 in gastric cancer cells increased T-cell metabolism and cytokine production in the coculture system. Moreover, mice inoculated with CD155-knockdown gastric cancer cells showed enhanced immune responses and improved survival. Conversely, CD155 overexpression in gastric cancer cells decreased T-cell metabolism and inhibited cytokine production. The effects of CD155 overexpression were reversed by inhibiting TIGIT. Together, gastric cancer cells upregulate CD155 expression and inhibit CD8 T-cell metabolism through CD155–TIGIT interaction.

Our finding that coexpression of TIGIT and PD-1 promoted cell exhaustion in CD8 T cells from gastric cancer patients indicates that combined blockade of these immune checkpoints is a potential therapeutic option for gastric cancer. In the T cell–tumor cell coculture, combined blockade of TIGIT and PD-1 showed stronger in cytokine production. In addition, combined blockade of TIGIT and PD-1 further enhanced immune response in the tumor-bearing mice and had better tumor control and survival compared with targeting either one. These findings indicate the potential of combined immunotherapies to treat cancer, and this is now receiving more attention from researchers (45, 46). Targeting PD-1/PD-L1 signal has improved the clinical outcome in cancer patients dramatically, but treatment responses vary, from 24% in renal cell cancer and 87% in non-Hodgkin lymphoma (6, 47, 48). A combination of PD-1 and CTLA-4 blockade has been shown to exert synergistic antitumor effects on B16 melanoma tumors (49), and the combined blockade of TIGIT and PD-1 demonstrates further enhancement of immune activation as reported before (14). Together, TIGIT or TIGIT combined with PD-1 may be the potential therapy for gastric cancer.

In conclusion, our findings provide insights into the mechanism by which CD8 T-cell metabolism and function is impaired by TIGIT. Mediators that enhance CD8 T-cell metabolism and promote maximum antitumor immunity may be novel therapeutic targets for the treatment of gastric cancer.

No potential conflicts of interest were disclosed.

Conception and design: H. Zhang, Z. Ke

Development of methodology: W. He, F. Han

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): W. He, R. Lin, W. Zhang, H. Wang

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): W. He, F. Han, X. Chen, Q. Liao, Z. Ke

Writing, review, and/or revision of the manuscript: W. He, H. Zhang, F. Han, Z. Ke

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): W. Jiang

Study supervision: Y. Cui

Other (patient sample collection): W. Wang, H.-B. Qiu, Z. Zhuang

Other (collection and processing of human samples): Q. Cai

This work was supported by grants from the National Natural Science Foundation of China (30900650, 81372501, and 81572260), Guangdong Natural Science Foundation (2011B031800025, S2012010008378, and 2015A030313036), and Guangdong/Guangzhou Science and Technology Planning Program (2014J4100132, 2015A020214010, 2013B02180021, 2016A020215055, 201704020094, 16ykjc08 and 2015ykzd07, 2012B031800115, and 2013B021800281).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Chen
W
,
Zheng
R
,
Baade
PD
,
Zhang
S
,
Zeng
H
,
Bray
F
, et al
Cancer statistics in China, 2015
.
CA Cancer J Clin
2016
;
66
:
115
32
.
2.
Crew
KD
,
Neugut
AI
. 
Epidemiology of gastric cancer
.
World J Gastroenterol
2006
;
12
:
354
62
.
3.
Hoshino
T
,
Seki
N
,
Kikuchi
M
,
Kuramoto
T
,
Iwamoto
O
,
Kodama
I
, et al
HLA class-I-restricted and tumor-specific CTL in tumor-infiltrating lymphocytes of patients with gastric cancer
.
Int J Cancer
1997
;
70
:
631
8
.
4.
Carreno
BM
,
Collins
M
. 
The B7 family of ligands and its receptors: new pathways for costimulation and inhibition of immune responses
.
Annu Rev Immunol
2002
;
20
:
29
53
.
5.
Keir
ME
,
Butte
MJ
,
Freeman
GJ
,
Sharpe
AH
. 
PD-1 and its ligands in tolerance and immunity
.
Annu Rev Immunol
2008
;
26
:
677
704
.
6.
Topalian
SL
,
Hodi
FS
,
Brahmer
JR
,
Gettinger
SN
,
Smith
DC
,
McDermott
DF
, et al
Safety, activity, and immune correlates of anti-PD-1 antibody in cancer
.
N Engl J Med
2012
;
366
:
2443
54
.
7.
Berger
R
,
Rotem-Yehudar
R
,
Slama
G
,
Landes
S
,
Kneller
A
,
Leiba
M
, et al
Phase I safety and pharmacokinetic study of CT-011, a humanized antibody interacting with PD-1, in patients with advanced hematologic malignancies
.
Clin Cancer Res
2008
;
14
:
3044
51
8.
Mittal
D
,
Gubin
MM
,
Schreiber
RD
,
Smyth
MJ
. 
New insights into cancer immunoediting and its three component phases–elimination, equilibrium and escape
.
Curr Opin Immunol
2014
;
27
:
16
25
.
9.
Thompson
ED
,
Zahurak
M
,
Murphy
A
,
Cornish
T
,
Cuka
N
,
Abdelfatah
E
, et al
Patterns of PD-L1 expression and CD8 T cell infiltration in gastric adenocarcinomas and associated immune stroma
.
Gut
2017
;
66
:
794
801
.
10.
Muro
K
,
Chung
HC
,
Shankaran
V
,
Geva
R
,
Catenacci
D
,
Gupta
S
, et al
Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): a multicentre, open-label, phase 1b trial
.
Lancet Oncol
2016
;
17
:
717
26
.
11.
Levin
SD
,
Taft
DW
,
Brandt
CS
,
Bucher
C
,
Howard
ED
,
Chadwick
EM
, et al
Vstm3 is a member of the CD28 family and an important modulator of T-cell function
.
Eur J Immunol
2011
;
41
:
902
15
.
12.
Joller
N
,
Hafler
JP
,
Brynedal
B
,
Kassam
N
,
Spoerl
S
,
Levin
SD
, et al
Cutting edge: TIGIT has T cell-intrinsic inhibitory functions
.
J Immunol
2011
;
186
:
1338
42
.
13.
Bottino
C
,
Castriconi
R
,
Pende
D
,
Rivera
P
,
Nanni
M
,
Carnemolla
B
, et al
Identification of PVR (CD155) and Nectin-2 (CD112) as cell surface ligands for the human DNAM-1 (CD226) activating molecule
.
J Exp Med
2003
;
198
:
557
67
.
14.
Chauvin
JM
,
Pagliano
O
,
Fourcade
J
,
Sun
Z
,
Wang
H
,
Sander
C
, et al
TIGIT and PD-1 impair tumor antigen-specific CD8(+) T cells in melanoma patients
.
J Clin Invest
2015
;
125
:
2046
58
.
15.
Johnston
RJ
,
Comps-Agrar
L
,
Hackney
J
,
Yu
X
,
Huseni
M
,
Yang
Y
, et al
The immunoreceptor TIGIT regulates antitumor and antiviral CD8(+) T cell effector function
.
Cancer Cell
2014
;
26
:
923
37
.
16.
Inozume
T
,
Yaguchi
T
,
Furuta
J
,
Harada
K
,
Kawakami
Y
,
Shimada
S
. 
Melanoma cells control anti-melanoma CTL responses via interaction between TIGIT and CD155 in the effector phase
.
J Invest Dermatol
2016
;
136
:
255
63
.
17.
Frauwirth
KA
,
Riley
JL
,
Harris
MH
,
Parry
RV
,
Rathmell
JC
,
Plas
DR
, et al
The CD28 signaling pathway regulates glucose metabolism
.
Immunity
2002
;
16
:
769
77
.
18.
Parry
RV
,
Chemnitz
JM
,
Frauwirth
KA
,
Lanfranco
AR
,
Braunstein
I
,
Kobayashi
SV
, et al
CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms
.
Mol Cell Biol
2005
;
25
:
9543
53
.
19.
Gerriets
VA
,
Rathmell
JC
. 
Metabolic pathways in T cell fate and function
.
Trends Immunol
2012
;
33
:
168
73
.
20.
Fox
CJ
,
Hammerman
PS
,
Thompson
CB
. 
Fuel feeds function: energy metabolism and the T-cell response
.
Nat Rev Immunol
2005
;
5
:
844
52
.
21.
Krauss
S
,
Brand
MD
,
Buttgereit
F
. 
Signaling takes a breath–new quantitative perspectives on bioenergetics and signal transduction
.
Immunity
2001
;
15
:
497
502
.
22.
Rathmell
JC
,
Vander Heiden
MG
,
Harris
MH
,
Frauwirth
KA
,
Thompson
CB
. 
In the absence of extrinsic signals, nutrient utilization by lymphocytes is insufficient to maintain either cell size or viability
.
Mol Cell
2000
;
6
:
683
92
.
23.
Maciver
NJ
,
Jacobs
SR
,
Wieman
HL
,
Wofford
JA
,
Coloff
JL
,
Rathmell
JC
. 
Glucose metabolism in lymphocytes is a regulated process with significant effects on immune cell function and survival
.
J Leukoc Biol
2008
;
84
:
949
57
.
24.
Mockler
MB
,
Conroy
MJ
,
Lysaght
J
. 
Targeting T cell immunometabolism for cancer immunotherapy; understanding the impact of the tumor microenvironment
.
Front Oncol
2014
;
4
:
107
.
25.
Chi
H
. 
Regulation and function of mTOR signalling in T cell fate decisions
.
Nat Rev Immunol
2012
;
12
:
325
38
.
26.
Chang
CH
,
Qiu
J
,
O'Sullivan
D
,
Buck
MD
,
Noguchi
T
,
Curtis
JD
, et al
Metabolic competition in the tumor microenvironment is a driver of cancer progression
.
Cell
2015
;
162
:
1229
41
.
27.
Mellor
AL
,
Munn
DH
. 
Creating immune privilege: active local suppression that benefits friends, but protects foes
.
Nat Rev Immunol
2008
;
8
:
74
80
.
28.
Li
G
,
Wang
Z
,
Ye
J
,
Zhang
X
,
Wu
H
,
Peng
J
, et al
Uncontrolled inflammation induced by AEG-1 promotes gastric cancer and poor prognosis
.
Cancer Res
2014
;
74
:
5541
52
.
29.
Liu
L
,
Wu
J
,
Ying
Z
,
Chen
B
,
Han
A
,
Liang
Y
, et al
Astrocyte elevated gene-1 upregulates matrix metalloproteinase-9 and induces human glioma invasion
.
Cancer Res
2010
;
70
:
3750
9
.
30.
Hawkins
ED
,
Hommel
M
,
Turner
ML
,
Battye
FL
,
Markham
JF
,
Hodgkin
PD
. 
Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data
.
Nat Protoc
2007
;
2
:
2057
67
.
31.
Zhang
C
,
Yan
Z
,
Arango
ME
,
Painter
CL
,
Anderes
K
. 
Advancing bioluminescence imaging technology for the evaluation of anticancer agents in the MDA-MB-435-HAL-Luc mammary fat pad and subrenal capsule tumor models
.
Clin Cancer Res
2009
;
15
:
238
46
.
32.
Kong
Y
,
Zhu
L
,
Schell
TD
,
Zhang
J
,
Claxton
DF
,
Ehmann
WC
, et al
T-cell immunoglobulin and ITIM Domain (TIGIT) associates with CD8+ T-cell exhaustion and poor clinical outcome in AML patients
.
Clin Cancer Res
2016
;
22
:
3057
66
.
33.
Lozano
E
,
Dominguez-Villar
M
,
Kuchroo
V
,
Hafler
DA
. 
The TIGIT/CD226 axis regulates human T cell function
.
J Immunol
2012
;
188
:
3869
75
.
34.
He
W
,
Xu
D
,
Wang
Z
,
Xiang
X
,
Tang
B
,
Li
S
, et al
Detecting ALK-rearrangement of CTC enriched by nanovelcro chip in advanced NSCLC patients
.
Oncotarget
2016
.
35.
Swann
JB
,
Smyth
MJ
. 
Immune surveillance of tumors
.
J Clin Invest
2007
;
117
:
1137
46
.
36.
Chen
L
. 
Co-inhibitory molecules of the B7-CD28 family in the control of T-cell immunity
.
Nat Rev Immunol
2004
;
4
:
336
47
.
37.
Sakuishi
K
,
Apetoh
L
,
Sullivan
JM
,
Blazar
BR
,
Kuchroo
VK
,
Anderson
AC
. 
Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity
.
J Exp Med
2010
;
207
:
2187
94
.
38.
Schreiber
RD
,
Old
LJ
,
Smyth
MJ
. 
Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion
.
Science
2011
;
331
:
1565
70
.
39.
Pearce
EL
,
Poffenberger
MC
,
Chang
CH
,
Jones
RG
. 
Fueling immunity: insights into metabolism and lymphocyte function
.
Science
2013
;
342
:
1242454
.
40.
Siska
PJ
,
van der Windt
GJ
,
Kishton
RJ
,
Cohen
S
,
Eisner
W
,
MacIver
NJ
, et al
Suppression of glut1 and glucose metabolism by decreased Akt/mTORC1 signaling drives T cell impairment in B cell leukemia
.
J Immunol
2016
;
197
:
2532
40
.
41.
Hirayama
A
,
Kami
K
,
Sugimoto
M
,
Sugawara
M
,
Toki
N
,
Onozuka
H
, et al
Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry
.
Cancer Res
2009
;
69
:
4918
25
.
42.
Bengsch
B
,
Johnson
AL
,
Kurachi
M
,
Odorizzi
PM
,
Pauken
KE
,
Attanasio
J
, et al
Bioenergetic insufficiencies due to metabolic alterations regulated by the inhibitory receptor PD-1 are an early driver of CD8(+) T cell exhaustion
.
Immunity
2016
;
45
:
358
73
.
43.
Sarhan
D
,
Cichocki
F
,
Zhang
B
,
Yingst
A
,
Spellman
SR
,
Cooley
S
, et al
Adaptive NK Cells with Low TIGIT expression are inherently resistant to myeloid-derived suppressor cells
.
Cancer Res
2016
;
76
:
5696
706
.
44.
Iguchi-Manaka
A
,
Okumura
G
,
Kojima
H
,
Cho
Y
,
Hirochika
R
,
Bando
H
, et al
Increased Soluble CD155 in the serum of cancer patients
.
PLoS One
2016
;
11
:
e0152982
.
45.
Callahan
MK
,
Postow
MA
,
Wolchok
JD
. 
CTLA-4 and PD-1 pathway blockade: combinations in the clinic
.
Front Oncol
2014
;
4
:
385
.
46.
Sharma
P
,
Allison
JP
. 
The future of immune checkpoint therapy
.
Science
2015
;
348
:
56
61
.
47.
Ansell
SM
,
Lesokhin
AM
,
Borrello
I
,
Halwani
A
,
Scott
EC
,
Gutierrez
M
, et al
PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma
.
N Engl J Med
2015
;
372
:
311
9
.
48.
Hamid
O
,
Robert
C
,
Daud
A
,
Hodi
FS
,
Hwu
WJ
,
Kefford
R
, et al
Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma
.
N Engl J Med
2013
;
369
:
134
44
.
49.
Curran
MA
,
Montalvo
W
,
Yagita
H
,
Allison
JP
. 
PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors
.
Proc Natl Acad Sci U S A
2010
;
107
:
4275
80
.