Hyperglycemia Enhances Cancer Immune Evasion by Inducing Alternative Macrophage Polarization through Increased O-GlcNAcylation

Diabetes mellitus (DM) is one of the most common metabolic disorders with a high morbidity and mortality leading to uncontrolled hyperglycemia (1). Prevalence of DM is increasing at an alarming rate and on a global scale (2). Epidemiologic evidence shows that patients with DM display a significantly higher risk of developing multiple types of cancers (3–6) and also demonstrated that cancer recurrence, metastasis, or fatal outcome occurs with much higher incidence in patients with cancer with hyperglycemia (7–9). Although an association of a poor prognosis with hyperglycemia in patients with cancer has been shown, mechanistic studies often focus on metabolic changes in tumor cells (10). Studies on the influence of hyperglycemia on the tumor immune microenvironment are often lacking, and further investigations are needed.

Glucose metabolism, and in particular the hexosamine biosynthetic pathway (HBP), emerged as an important cascade linking cancer progression and hyperglycemia (10–13). The HBP provides UDP-N-acetyl-D-glucosamine (UDP-GlcNAc) and its derivatives for glycosylation using glucose, glutamine, Acetyl-CoA, and UTP as substrates (14). UDP-GlcNAc is highly responsive to cell nutrient fluctuations as its synthesis depends on products of the several metabolic pathways and is therefore considered to be a metabolic sensor (14). Our recent work showed that increasing extracellular glucose concentrations provoke aberrant glycosylation, increasing cell proliferation and invasion in vivo due to increased flux through the HBP (14). Hyperglycemia increased levels of intracellular O-GlcNAcylation in tumor cells (12), a posttranslational modification of intracellular proteins catalyzed by O-GlcNAc transferase (15) and counteracted by O-GlcNAcase (OGA; ref. 16). Increase of O-GlcNAc levels induced by high glucose (HG) can affect the expression of genes and deregulation of biochemical pathways to respond to environmental changes (10).

Metabolic changes can strongly influence immune cell function (17–21). Although glucose is needed for proper effector function as T-cell activation and M1 macrophage polarization (21, 22), it is largely unknown how a systemic increase of glucose in circulation of diabetic patients with cancer instructs the local tumor immune microenvironment. In order to improve our understanding of the influence of hyperglycemia, we used a mouse model of hyperglycemia to study the changes in the tumor immune microenvironment. We characterized alterations of cell infiltration, activation, and polarization and provide a molecular mechanism for the effect of hyperglycemia on tumor growth.

All experiments involving mice were approved by the local ethical committees in Basel, Switzerland (approval number 2747), and in Rio de Janeiro, Brazil (approval number IBCCF214-09/16), and were performed in accordance with the regulations. All human tumor samples were anonymized and obtained from the Marcílio Dias Hospital following informed written consent (approval 39063014.8.0000.5256, Brazil).

Mouse colon adenocarcinoma MC38 cells were donated by Dr. M. Smyth (QIMR Biomedical Research Institute, Brisbane, Queensland, Australia) and mouse melanoma B16D5 cells by Dr. L. Weiner (Georgetown University, Washington, DC). Tumor cell lines were cultured in DMEM (25 mmol/L Glucose, Sigma-Aldrich), supplemented with 2 mmol/L l-glutamine, 1 mmol/L sodium pyruvate, 1% MEM nonessential amino acids, 500 U/mL streptomycin/penicillin, and 10% heat-inactivated FBS (Sigma-Aldrich) at 37°C and 5% CO₂. All cells were regularly tested for Mycoplasma by PCR.

Male C57BL/6 and NOD scid gamma (NSG) mice were obtained from Janvier Labs (23), bred in-house at the Department of Biomedicine, University Hospital Basel, Switzerland, and were used at 10 to 12 weeks old. Hyperglycemia was induced with a single i.p. injection of 150 mg/kg streptozotocin (STZ, Sigma-Aldrich) diluted with 0.1 mmol/L sodium citrate buffer as vehicle (pH 4.3). Euglycemic control mice (EuG) were treated with vehicle only. Seven days after STZ injection, 5 × 10⁵ syngeneic tumor cells in 100 μL PBS were injected s.c. into the flank of mice. Tumor growth and health scores were measured 3 times per week until a maximum size of 1,500 mm³ (advanced tumor stage) or 300 to 400 mm³ (early tumor stage). Perpendicular tumor diameters were measured using a caliper, and tumor volume was calculated according to the following formula: tumor volume (mm³) = (d²*D)/2, where d and D are the shortest and longest diameters (in mm) of the tumor, respectively. Mice that developed ulcerated tumors were sacrificed and excluded from further analysis.

Blood sampling was performed from the tail vein 7 days after STZ injection and on the day of tumor resection. Glucose levels were measured using a validated glucose meter (FreeStyle lite, Abbott). STZ-treated mice were considered to be hyperglycemic if the blood glucose levels exceeded 250 mg/dL at the end of the experiment.

Tumors were collected, weighed, cut in small pieces, and mechanically dissociated using a micropipette. Each tumor was resuspended in 1 mL PBS, filtered, glucose concentrations measured by the glucose colorimetric detection Kit (Invitrogen), and normalized to tumor mass.

Depletion of intratumoral macrophages was achieved by i.p. anti-CSF1R antibody treatment (CD115, clone AFS98, BioXCell), with a total of 7 injections, 1 mg before tumor inoculation, followed by 6 injections of 400 μg each every 3 to 4 days over 3 weeks.

HBP inhibition in vivo was achieved by treatment of tumor-bearing mice with 3 doses (10 mg/kg) of DON (6-Diazo-5-oxo-L-norleucine, Sigma-Aldrich) every 3 days, beginning when tumors reached 300 to 400 mm³. In addition, mice were treated with a single i.p. injection of DON (10 mg/kg) or the OGA-inhibitor Thiamet G (TMG, 20 mg/kg, Sigma-Aldrich), and animals sacrificed 2 days after the treatment.

Tumors were surgically collected, cut in small pieces, and digested in a solution of collagenase IV (Worthington), DNase I (Sigma-Aldrich), Hyaluronidase (Sigma-Aldrich), and accutase (PAA Laboratories) for 1 hour at 37°C. After incubation, cells were filtered, washed, and separated by density centrifugation with Histopaque-1119 (Sigma-Aldrich), and cell suspensions frozen at -80°C for future analyses. All samples were stained using LIVE/DEAD Fixable Blue Dye (Invitrogen) and various panels of antibodies to identify different population of immune cells. All samples were acquired with the Fortessa LSR II Flow Cytometer (BD Biosciences) and analyzed in the FlowJo software (BD Biosciences).

Bone marrow–derived macrophages (BMDM) were differentiated in petri dishes with M-CSF (20 ng/mL, Peprotech) or 20% L929 supernatant for 6 days in HG (25 mmol/L) or low glucose (LG, 5 mmol/L) DMEM. Cells were plated at 5 × 10⁵/well in 12-well plates and treated for 24 hours with 10 μmol/L of the inhibitors DON, TMG, and OSMI-1 (Sigma-Aldrich). BMDMs were polarized to M1 with IFNγ 50 ng/mL (Peprotech) and lipopolysaccharide (LPS) 20 ng/mL (Sigma-Aldrich) or to M2 with IL4 20 ng/mL (Peprotech) for 48 hours. BMDMs were washed in FACS buffer, followed by incubation with anti-CD16/CD32 blocking antibodies (eBioscience) for Fc-receptor blocking. Polarization was confirmed by staining against CD206, CD86, and MHC-II. Cells were also collected for metabolite extraction and immunoblotting.

BMDMs were differentiated in petri dishes with M-CSF (20 ng/mL, Peprotech) for 6 days in HG (25 mmol/L) or LG (5 mmol/L) DMEM. MC38 cells were cocultured with macrophages in the ultralow attachment 96-well U-bottom plates (Corning). The coculture was carried out using serum-free LG or HG DMEM at different time points (2, 4, or 24 hours), and in each well was added 1 × 10⁵ MC38 cells and 5 × 10⁴ macrophages. After the incubation time, polarization was accessed by staining against CD206, CD86, and MHC-II.

Polar metabolites were extracted from 5 × 10⁶ macrophages with chloroform, methanol, and water (1:1:1). Each sample was subjected to chromatographic separation on a Hypercarb PGC column (Thermo Fisher) running in HPLC Prominence (Shimadzu). Nucleotide sugars were eluted using a discontinuous linear gradient of mobile phases A (0.2% formic acid and 0.75% ammonium hydroxide in water) and B (95% acetonitrile with 0.1% formic acid and 0.07% ammonium hydroxide). The detection was achieved using a diode array detector (SPM-M20A Prominence, Shimadzu) set to 250 to 400 nm, coupled to a Q-TOF mass spectrometer (maXis Impact, Bruker Daltonics). The retention times of nucleotide sugars were previously established using standards. The chromatograms and mass spectra were analyzed with DataAnalysis 4.2 (Bruker Daltonics). UDP-GlcNAc concentration was quantified by measuring the area of its correspondent UV peak at 262 nm normalized to the total area of the chromatogram from 5 to 30 minutes, using a calibration curve. For the other nucleotide sugars, the peaks of their respective extracted ion chromatograms were evaluated similarly.

BMDMs were washed with PBS and homogenized in lysis buffer [150 mmol/L NaCl, 30 mmol/L Tris-HCl, pH 7.6, 1 mmol/L EDTA, 1 mmol/L EGTA, 0.1% SDS, 1 mmol/L phenylmethylsulfonyl fluoride, and 1 μmol/L O-(2-acetamido-2-deoxy-d-glucopyranosylidene)amino-N-phenylcarbamate (PUGNAc)]. Cell lysates were sonicated and centrifuged. Supernatant was collected, protein concentration was determined, and modified Laemmli buffer was added. Samples were separated on SDS-polyacrylamide gels and were subsequently electroblotted to nitrocellulose membrane (Bio-Rad). The membranes were blocked in Tris-buffered saline with 0.1% (v/v) Tween 20 with either 3% (w/v) BSA or 3% (w/v) nonfat dry milk. The blocked membranes were then incubated overnight at 4°C with primary antibodies against O-GlcNAc (CTD 110.6 or RL-2, Santa Cruz Biotechnology), arginase-1 (clone H52, Santa Cruz Biotechnology), NOS2 (clone N20, Santa Cruz Biotechnology), and β-actin (clone 13E5, Cell Signaling Technology). The blots were then washed, incubated with the appropriate secondary antibody, developed using ECL (GE Healthcare), and exposed to Image Quant LAS 4000 (GE Healthcare). ImageJ software was used for densitometric analysis of immunoblots, and measurements were normalized against β-actin.

Paraffin sections of patients with colorectal cancer with DM and control patients were obtained from the Marine Hospital Marcílio Dias, Rio de Janeiro, Brazil. All samples were from surgical resections and were evaluated by a board-certified pathologist. Sections were deparaffinized automatically, and antigen retrieval was performed in citrate buffer. Tissues were permeabilized with 0.1% Triton X-100 and blocked with 1% BSA. Primary antibodies were incubated overnight in 1% BSA solution. The antibodies used were anti-CD68 (clone Y1-82A, BD Biosciences), anti-CD206 (polyclonal, Abcam, Cat.: 64693), and anti-CD163 (polyclonal, Abcam, Cat.: 87099). After incubation, slides were washed and incubated with the respective secondary antibody and then washed. Slides were further stained with DAPI (Thermo Fisher) and mounted with ProLong diamond antifade mountant. Pictures were taken in a Nikon Eclipse Ti-S microscope and analyzed using ImageJ.

Graphs and statistical tests were done with Prism 8 (GraphPad). Statistical significance between two groups was tested using paired Student t tests. Comparison between multiple time points was analyzed by one-way ANOVAs with multiple comparisons and Tukey posttest. Differences in survival curves were analyzed by the Gehan–Breslow–Wilcoxon test.

In order to study the influence of systemic hyperglycemia on tumor progression, we used a mouse model of acute hyperglycemia by applying the cytotoxic drug STZ, destroying pancreatic beta cells (24). We tested the growth of MC38 and highly metastatic mouse melanoma B16D5 cells subcutaneously transplanted into C57BL/6 mice treated with STZ (Fig. 1A). Survival was significantly shorter, and tumor growth increased in hyperglycemic mice bearing subcutaneous B16D5 tumors (Fig. 1B and C). Similarly, hyperglycemic mice injected with MC38 tumor cells showed a significantly shorter survival than euglycemic mice (Fig. 1D and E). In addition, hyperglycemia was observed in the peripheral blood as well as within the extracellular space of the tumor microenvironment, demonstrated by increased glucose concentrations in the interstitial tumor fluid (Fig. 1F). Tumor growth was not due to STZ treatment because STZ reduced MC38 viability in vitro in a concentration-dependent manner (Supplementary Fig. S1A). These experiments demonstrate that STZ treatment, which induces hyperglycemia, increases tumor growth and thereby reduces survival in two different syngeneic mouse models.

Recent studies have shown that hyperglycemia can lead to an increase of sialylation in tumor microenvironment (12). More importantly, hypersialylation of tumors can mediate immune evasion by engaging the sialoglycan-Siglec immune checkpoint (25, 26). In our model system, hyperglycemia induced an increase in sialylation demonstrated by increased Siglec-E-Fc binding when compared with cells grown in normoglycemia (Supplementary Fig. S2A). We therefore tested the role of sialylation in the hyperglycemia model by using GlcNAc-epimerase (GNE)–deficient MC38 tumor cells, which display a strong reduction in cell surface sialylation (25). As previously published (25), GNE-deficient MC38 cells grew slower compared with wild-type cells. Yet, hyperglycemia still increased tumor growth, conserving the key difference in tumor growth between hyperglycemic and euglycemic mice (Supplementary Fig. S2B). This finding was confirmed in mice deficient for Siglec-E, which showed similar tumor growth as wild-type animals, but were smaller in the hyperglycemic compared with euglycemic mice (Supplementary Fig. S2D). We concluded that engagement of the sialoglycan-Siglec pathway is not the main mechanism of enhanced tumor growth in hyperglycemic mice.

To delineate the cellular immune mechanisms involved in hyperglycemia-induced tumor progression, we characterized the immune cell infiltrates in size-matched tumors by flow cytometry (see gating strategy, Supplementary Fig. S3). We found a significant reduction of CD8⁺ tumor-infiltrating lymphocytes (TIL; Fig. 2A), as well as of conventional CD11c⁺MHC-II⁺ dendritic cells (Supplementary Fig. S3A) in tumors from hyperglycemic mice. We found a reduction of CD4⁺CD25⁺FOXP3⁺ regulatory T cells (Fig. 2B), whereas no significant differences in the frequencies of CD4⁺ TILs (Fig. 2C) and B cells (Fig. 2D) were observed in the infiltrates of hyperglycemic tumors. In addition, we found that the total percentages of CD3⁺ cells were the same among both groups (Supplementary Fig. S4B). The presence of Ly6G⁺ cells was decreased in tumors from hyperglycemic mice (Supplementary Fig. S4C), whereas no difference was seen in the Ly6C⁺ cells (Supplementary Fig. S4D). The numbers of CD11b⁺F4/80⁺Gr1⁻ tumor-associated macrophages (TAM) were similar in both conditions (Supplementary Fig. S4E). However, a significant increase in CD206 expression was found in hyperglycemic MC38 (Fig. 2E; Supplementary Fig. S4F) and B16D5 tumors (Supplementary Fig. S4I). This result was accompanied by a decrease of the M1 marker MHC-II in MC38 (Fig. 2F; Supplementary Fig. S4G), but not in B16D5 tumors (Supplementary Fig. S4J), whereas MHC-II⁺CD206⁺ cells were not altered in both cases (Supplementary Fig. S4H and S4K).

Taken together, the analysis of inflammatory infiltrates suggests that hyperglycemia affects the immune response against the tumor.

The interaction between the cancer cells, its metabolites, and other soluble factors are known to influence TAM function (21). Previously, we have shown that HG concentration induces aberrant glycosylation in colon cancer cell. By modulating the activity of the HBP rate-limiting enzyme, glutamine-fructose-6-phosphate amidotransferase (GFAT), we abrogate hyperglycemia effects on tumor cells (12). To address the influence of tumor cell surface glycosylation in macrophage polarization, shGFAT-MC38 and the control MC38 cells, transduced with a scramble GFAT, were injected in hyperglycemic mice treated with STZ or in the euglycemic group, and cell infiltrate was analyzed. As shown in Supplementary Fig. S5A and S5B, GFAT downregulation does not affect the expression of TAM polarization markers MHCII and CD206. These results are supported by experiments using GNE-deficient MC38 tumors (Supplementary Fig. S2C) and by in vitro coculture experiments (Fig. 2G and H; Supplementary Fig S5C–S5J). Indeed, the increase of CD206 (Fig. 2I; Supplementary Fig. S5C and S5D) and decrease of MHCII (Supplementary Fig. S5E–S5G) were observed coculturing MC38 and BMDMs in HG medium at different time points (2, 4, and 24 hours). Furthermore, we observed the decrease of the M1 marker CD86 in the coculture using HG medium comparing with LG (Supplementary Fig. S5H–S5J). Coculture experiment and data showing that hyperglycemia also accumulate M2 macrophages in the peritoneum after pristine injection in vivo, an inflammatory murine model of systemic Lupus erythematosus, support that macrophage polarization is affected by HG concentrations rather than by tumor cells or its metabolites. Analysis of pristane-induced macrophages in the peritoneal lavage showed a decrease of MHC-II (Supplementary Fig. S5K) and increase of CD206 expression (Supplementary Fig. S5L) in hyperglycemia. In addition, the proportions of Ly6C⁺ (Supplementary Fig. S5M) and Ly6G⁺ cells (Supplementary Fig. S5N) were not affected by hyperglycemia.

Next, we aimed to determine the functional contribution of the immune system and specific immune cell types to the observed effect of hyperglycemia-promoted tumor growth. Therefore, we induced hyperglycemia in NSG mice that are devoid of a functional immune system. Tumor growth of s.c. injected MC38 cells (Fig. 3A) was faster than in immunocompetent mice (Fig. 3B), whereas no differences were observed between hyperglycemic and euglycemic mice (Fig. 3A). The analysis of the TAMs showed no significantly difference in the expression of CD206 (Supplementary Fig. S5O) and MHCII (Supplementary Fig. S5P) comparing hyperglycemic and euglycemic mice. This finding indicates that growth difference observed in our mouse model was mainly mediated by changes in the immune response. Further characterization by gene expression analysis showed a decreased presence of inflammation-associated genes, using immunocompetent C57bl/6 mice. Genes involved in immune cell recruitment and activation such as Il1b, Cxcl1, Ccl5, S100a9, and S100a8 were significantly downregulated in hyperglycemic tumors compared with euglycemic tumors (Supplementary Fig. S6A). In contrast, genes involved in tumor-promoting inflammation as the M2 macrophage markers Cd163 and Cd276, an immune checkpoint member, were increased supporting an important function of macrophages in this model (Supplementary Fig. S6A). We therefore tested the contribution of macrophages to the hyperglycemia-induced tumor growth by treating mice with anti-CSF1R antibodies. Hyperglycemic mice treated with anti-CSF1R showed similar tumor growth as treated euglycemic mice (Fig. 3B). The treatment of mice with anti-CSF1R showed a significant reduction of intratumoral macrophages (Fig. 3C–E). These results suggest a key role for intratumoral macrophages in mediating the effect of hyperglycemia on tumor growth and suggest an interplay between innate and adaptive responses in the observed effect.

Hyperglycemia can increase the rate of flux through the HBP, resulting in increased levels of UDP-GlcNAc in cells in HG environments (12). Previous work has shown that UDP-GlcNAc is a characteristic of M2 polarization (27) and acts as a substrate for O-GlcNAcylation (14). In addition, increased O-GlcNAcylation has been demonstrated to influence macrophage function (28–30). We therefore investigated whether hyperglycemia could change macrophage polarization through increased O-GlcNAcylation. First, we tested whether polarization of in vitro cultured BMDMs would be affected by the glucose concentration of the cell culture medium (Supplementary Fig. S6B). HG medium augmented UDP-GlcNAc levels (Fig. 4A) and O-GlcNAcylation in BMDMs, with the highest levels being reached under M2-polarizing conditions (Fig. 4B; Supplementary Fig S6C). Other activated monosaccharides were differentially affected by HG, with CMP-Neu5Ac levels decreasing under HG conditions (Supplementary Fig. S6D–S6F). HG changed the macrophage phenotype measured as a decrease of the M1 markers CD86 (Fig. 4C, F, and I), MHC-II (Fig. 4D, G, and J), and NOS2 (Supplementary Fig. S6G), whereas M2 markers such as CD206 (Fig. 4E, H, and K) and Arginase1 (Supplementary Fig. S6G) were increased.

To determine whether O-GlcNAcylation would induce an M2 phenotype in BMDMs, we inhibited the enzyme O-GlcNAc-transferase (15) using the inhibitor OSMI-1 (Fig. 4C–E). Treatment decreased overall cell O-GlcNAcylation (Supplementary Fig. S6H) and reversed the effects induced by HG, leading to an increase in CD86 and MHC-II expression (Fig. 4C and D) and a decrease in CD206 (Fig. 4E). Applying the glutamine antagonist DON, which inhibits the rate-limiting enzyme of HBP GFAT (Fig. 4F–H) and leads to a decrease in O-GlcNAcylation (Supplementary Fig. S6I), showed similar effects.

Conversely, increasing O-GlcNAcylation by inhibiting OGA with TMG (Supplementary Fig. S6J) augmented the expression of CD206 (Fig. 4K) and decreased CD86 in both HG and LG cultured macrophages (Fig. 4I). These experiments suggest that HG concentrations enhance M2 polarization in murine BMDMs by increasing the flux of glucose through the HBP, resulting in an increase of O-GlcNAcylation.

To further investigate the influence of O-GlcNAcylation on hyperglycemia-promoted tumor growth and macrophage fate in vivo, we treated hyperglycemic and euglycemic mice with the inhibitor of the HBP, DON, thereby inhibiting O-GlcNAcylation. Treatment impaired tumor growth in both hyperglycemic and euglycemic mice (Fig. 5A) and resulted in a similar tumor growth in both groups, abrogating the effect of hyperglycemia (Fig. 5A). We observed a clear reduction of polarization to a protumorigenic phenotype of macrophages (Fig. 5B; Supplementary Fig. S7A) and an increase in M1 macrophage markers (Supplementary Fig. S7B and S7C) in the tumor microenvironment of hyperglycemic mice. Confirming this finding, opposite effects were seen when mice were treated with the inhibitor of the enzyme that cleaves O-GlcNAc (OGA), TMG, increasing O-GlcNAcylation in mice. We found an increase in protumorigenic M2-like macrophages (Fig. 5B), whereas no differences were observed in the CD206⁺ and MHC-II⁺ double-positive population (Supplementary Fig. S7D). Together, these results show that the inhibition of O-GlcNAcylation reverts the effect of hyperglycemia on tumor growth and intratumoral macrophage polarization in vivo, reprograming macrophages toward an antitumor phenotype.

Hyperglycemia correlates with a worse prognosis in colorectal cancer (31), and our data from preclinical models provided evidence that hyperglycemia could induce intratumoral macrophage polarization to a protumorigenic M2-like phenotype. Analysis of The Cancer Genome Atlas gene expression data showed that the expression of M2 macrophage markers correlated with a reduced overall survival for all types of cancers (Supplementary Fig. S7E) and that the increased expression of OGT was similarly associated with poor survival (Supplementary Fig. S7F). To investigate if hyperglycemia was associated with a protumorigenic M2-like polarization of macrophages in patients with colorectal cancer, we analyzed samples from patients with colorectal cancer with type 2 DM for changes in macrophage infiltration and polarization (Fig. 5C). No significant differences in the frequency of CD68⁺ intratumoral macrophages were observed between nondiabetic and diabetic patients (Fig. 5D). However, IHC analysis of CD68⁺ macrophages for the M2 markers CD206 (Fig. 5E; Supplementary Fig. S8A) and CD163 (Fig. 5F; Supplementary Fig. S8B) showed a higher frequency of M2-like macrophages in diabetic patients. This analysis suggests that patients with colorectal cancer with hyperglycemia have a higher frequency of M2 macrophages, although larger studies are needed to further corroborate a direct association between DM2 and M2 macrophage polarization.

Here, we show that systemic and intratumoral hyperglycemia significantly alters the tumor immune microenvironment and increases the number of alternatively M2-like polarized tumor-promoting macrophages. We demonstrate that these macrophages enhance tumor growth by mediating immune evasion and secondary inhibition of the adaptive immune response. Intratumoral macrophages are known to support cancer progression by creating an inhibitory microenvironment and to strongly inhibit antitumor immunity (32–34). M2-like polarized macrophages secrete mediators such as IL10 and TGFβ leading to the dampening of adaptive antitumor immunity (32–34). M2-like polarized TAMs are also defective of phagocytosing tumor cells (35–37). Due to the heavy involvement in immune evasion and cancer progression, TAMs have become an interesting target for cancer immunotherapy. For example, blockade of CD47 on tumor cells or its receptor SIRPα on macrophages was successfully tested in preclinical models, and antibodies against CD47 are currently in clinical development (35–37). Targeting the recruitment and polarization of macrophages has also been tested by using blocking agents against CSF1 or CSF1R (38, 39). In our model, treatment with anti-CSF1R led to a significant reduction in TAMs and M2-like polarization. This reduction of protumorigenic macrophages reversed the tumor-promoting effect of hyperglycemia, which clearly implicates intratumor macrophages in the observed effect.

Polarization of TAMs is not binary but rather a continuum (32–34), with LPS and IFNγ inducing an antitumoral M1 phenotype, whereas cytokines including IL4 and IL13 lead to an alternative, anti-inflammatory M2-like phenotype. Previous evidence showed that M2 polarization in murine macrophages is accompanied by an increase in UDP-GlcNAc (27). We demonstrate that hyperglycemia increases M2 macrophage polarization through an increase in HBP flux and enhanced UDP-GlcNAc production. Inhibition of the HBP with DON showed a reduction in M2 polarization and also abolished the effect of hyperglycemia in vivo, although the results of systemic DON treatment need to be interpreted cautiously due to its pleiotropic effects on many different cell types including direct effects on tumor cells. In further support of an important role of O-GlcNAcylation in hyperglycemia-driven M2 polarization, inhibition of the O-GlcNAc transferase by OSMI-1 inhibited the M2 polarization in hyperglycemic condition. O-GlcNAcylation has been associated in regulation of inflammatory responses and other immune reactions (21, 40).

Our data support a role for O-GlcNAcylation in regulating macrophage polarization and differentiation, with increased O-GlcNAcylation directing macrophages toward M2-like phenotype. We show for the first time that intratumoral hyperglycemia leads to mediate cancer immune evasion by O-GlcNAc–mediated macrophage polarization. In agreement, recent work has shown that LPS-activated macrophages (M1 phenotype) display reduced HBP activity and protein O-GlcNAcylation (30). Previous studies have reported how O-GlcNAc cycling can influence macrophage function. Depletion of OGT in the human macrophage cell line THP-1 induces M1 polarization, whereas M2 genes were downregulated (41). In addition, pharmacologic inhibition of OGA in an experimental model of ischemic stroke showed an increased expression of M2 markers and decreased expression of M1 markers in microglia cells (42). OGT-mediated O-GlcNAcylation of STAT3 in macrophages inhibits STAT3 phosphorylation and IL10 production (43). Several proteins which regulate macrophage function, i.e., Akt/mTOR, HIFα, as well as the NF-κB and NFAT families (34, 44–46), were shown to be O-GlcNAcylated in other models. Together, these studies point to OGT as an important mediator of M2 polarization in macrophages.

A limitation of our study is the use of STZ to induce hyperglycemia, since the model induces hypoinsulinemia, where the acute type of hyperglycemia resembles more the situation in patients with type 1 DM. Future experiments will make use of other DM models, including diet-induced mouse models that mimic type 2 DM. We have also not identified exact target proteins of O-GlcNAcylation in our experiments, but have focused on overall levels of O-GlcNAcylation. Further studies are needed to determine the distinct influence of enhanced O-GlcNAcylation on different signaling pathways.

Our results support a metabolic reprogramming of the tumor immune microenvironment by hyperglycemia resulting in an immune-inhibitory, tumor-promoting condition mediated by increased M2-like macrophage polarization. Accordingly, diabetic patients with colorectal cancer had an increased number of M2-like polarized macrophages compared with patients with colorectal cancer without diabetes. Although our study advocates for a strict control of glycemia in diabetic patients with cancer, further functional analyses and clinical examinations are needed to determine the target range of blood glucose in patients with cancer. One could also speculate that diabetic patients with cancer could benefit from an additional macrophage-targeting agent such as CSF-1R blockade. Furthermore, the description of O-GlcNAcylation mediating hyperglycemia-induced tumor progression provides new targets for cancer immunotherapy, including enzymes involved in the HBP and O-GlcNAcylation. We demonstrated that OGT inhibition reprograms TAMs to an antitumor profile. By directly targeting macrophages, the specific delivery of OGT inhibitors could be achieved, greatly reducing the toxicity of the compound and influencing the tumor microenvironment only, reducing potential side effects.

No potential conflicts of interest were disclosed.

N. Rodrigues Mantuano: Conceptualization, formal analysis, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing. M.A. Stanczak: Formal analysis, investigation, methodology. I.A. Oliveira: Investigation. N. Kirchhammer: Investigation. A.A. Filardy: Formal analysis, investigation. G. Monaco: Data curation, software, investigation. R.C. Santos: Resources, investigation. A.C. Fonseca: Resources. M. Fontes: Resources. C.S. Bastos Jr: Resources. W.B. Dias: Conceptualization, data curation. A. Zippelius: Conceptualization, resources, supervision, funding acquisition. A.R. Todeschini: Conceptualization, resources, supervision, funding acquisition, writing–original draft, writing–review and editing. H. Läubli: Conceptualization, resources, supervision, funding acquisition, investigation, writing–original draft, writing–review and editing.

This work was supported by funding from the Goldschmidt-Jacobson Foundation (to H. Läubli), Promedica Foundation (to M.A. Stanczak and A. Zippelius), Krebsliga Beider Basel (KLBB, to H. Läubli), Schoenemakers Foundation (to H. Läubli), Swiss National Science Foundation (SNSF grant #310030-184720, to H. Läubli), Swiss Government Excellence Scholarships for Foreign Scholars and Artists (FCS, to N. Rodrigues Mantuano), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, to N. Rodrigues Mantuano and R.C. Santos), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, to A.R. Todeschini, I.A. Oliveira, N. Rodrigues Mantuano, and W.B. Dias), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ, to A.R. Todeschini), and Fundação do Câncer (to W.B. Dias). The authors thank the Centro de Espectrometria de Massas de Biomoléculas (CEMBIO) and plataforma de Imuno-análise (PIA; UFRJ, Rio de Janeiro, Brazil). They also thank all the patients that allowed the authors to use their material and made this work possible.

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