Transforming growth factor β (TGFβ) is an effector of immune suppression and contributes to a permissive tumor microenvironment that compromises effective immunotherapy. We identified a correlation between TGFB1 and genes expressed by myeloid cells, but not granulocytes, in The Cancer Genome Atlas lung adenocarcinoma data, in which high TGFB1 expression was associated with poor survival. To determine whether TGFβ affected cell fate decisions and lineage commitment, we studied primary cultures of CD14+ monocytes isolated from peripheral blood of healthy donors. We discovered that TGFβ was a survival factor for CD14+ monocytes, which rapidly executed an apoptotic program in its absence. Continued exposure to TGFβ in combination with granulocyte-macrophage colony stimulating factor (GM-CSF) and interleukin 6 (IL6) amplified HLA-DRlowCD14+CD11b+CD33+ myeloid-derived suppressor cells (MDSCs) at the expense of macrophage and dendritic cell (DC) differentiation. MDSCs generated in the presence of TGFβ were more effective in suppressing T-cell proliferation and promoted the T regulatory cell phenotype. In contrast, inhibition of TGFβ signaling using a small-molecule inhibitor of receptor kinase activity in CD14+ monocytes treated with GM-CSF and IL6 decreased MDSC differentiation and increased differentiation to proinflammatory macrophages and antigen-presenting DCs. The effect of autocrine and paracrine TGFβ on myeloid cell survival and lineage commitment suggests that pharmacologic inhibition of TGFβ-dependent signaling in cancer would favor antitumor immunity.

The pleiotropic cytokine transforming growth factor β (TGFβ) is regulated under physiologic conditions, for which it has roles in development, wound healing, and tissue homeostasis (1). TGFB1 encodes a polypeptide that forms a secreted complex consisting of latency-associated peptide noncovalently associated with TGFβ. Latent TGFβ is sequestered in the extracellular matrix and must be activated to release TGFβ to bind the ubiquitous type I and II receptors that initiate signaling, which in turn causes nuclear localization of phosphorylated SMAD 2/3 (pSMAD). In cancer, malignant cells can activate abundant TGFβ, which also regulates the tumor microenvironment (TME; refs. 1, 2) and mediates the response to cytotoxic therapy (3). TGFβ is implicated in failure to respond to immunotherapy (4–6), and it is thought to stimulate monocyte chemotaxis (7), promote immunosuppressive cell types, including regulatory T cells (Treg) and macrophages, and suppresses T-cell proliferation (1). TGFβ activation is also associated with activated immune cells, raising the potential for positive reinforcement of immunosuppression.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population that functionally limits antitumor immunity (8, 9). MDSCs are defined by their suppression of T-cell proliferation (10). In humans, MDSCs can be subdivided into monocytic MDSCs, which are characterized by the expression of markers that include CD14, CD33 (Siglec-3), and CD11b and by the lack of the lineage differentiation marker HLA-DR, whereas granulocytic MDSCs display CD15 and dim CD33 staining (11). MDSCs suppress cytotoxic T-cell activation and proliferation by several mechanisms, including depleting arginine through the expression of arginase-1, increasing nitric oxide, and by activating TGFβ (12). MDSCs can also promote differentiation of naïve T cells to an immunosuppressive Treg phenotype. MDSCs are reported to suppress immune rejection of cancer, compromise immunotherapy, promote tumor growth, and impede response to cytotoxic therapy (8, 11). Patients with abundant MDSCs have lower overall survival and decreased progression-free survival in several cancer types (13).

Under physiologic conditions, CD14+ monocytes exit the bone marrow to circulate in the peripheral blood and can be recruited into tissues to differentiate into macrophages and dendritic cells (DCs), particularly at sites of injury, tissue remodeling, and infection. In certain states and diseases, granulocyte-macrophage colony stimulating factor (GM-CSF) and interleukin 6 (IL6) favor MDSC generation (14). Here, we focused on expanding the understanding of the role that TGFβ plays in the accumulation and differentiation of monocytes once they enter the TGFβ-rich TME. We used primary cultures of CD14+ monocytes isolated from human peripheral blood to investigate the contribution of TGFβ to monocyte lineage commitment and MDSC function.

The Cancer Genome Atlas (TCGA) data analysis using cBioPortal

cBioPortal for cancer genomics (15, 16) was used to study gene expression on samples from TCGA corresponding to glioblastoma, non–small cell lung adenocarcinoma (NSCLC), squamous cell carcinoma, melanoma, head and neck, pancreatic adenocarcinoma, liver, kidney, ovarian, bladder, and prostate carcinomas. Kaplan–Meier survival analysis was performed using cBioPortal.

Healthy donor buffy coat

Buffy coats from 30 donors were obtained from New York City Blood Center (Queens, NY) and from the Pacific Blood Center (San Francisco, CA) donations under standard operating conditions, following all applicable FDA regulations. Individual consent was not required as all donors consented to the possibility of research use at the time of donation. No prescreening based on age, sex, or ethnicity was applied. Buffy coats were kept on ice and processed shortly after reception (less than 8 hours) for peripheral blood mononuclear cells (PBMC) isolation.

CD14+ cell isolation, separation, and culture

CD14+ cells were freshly isolated from healthy donor buffy PBMCs using CD14+ magnetic beads (Miltenyi Biotech; cat. #130-050-201; Supplementary Fig. S1A). The purity of CD14 selection was greater than 95% (Supplementary Fig. S1B). The CD14 fraction was viably frozen in 95% FBS and 5% DMSO using a temperature gradient and stored for 5 days at −80°C. CD14+ monocytes were then cultured in 10% fetal bovine serum-RPMI (complete) media, and the indicated cytokines at the following concentrations: GM-CSF (10 ng/mL; Milteny Biotech; cat. #130-095-372), IL6 (10 ng/mL; Miltenyi Biotech; cat. #130-093-929), and TGFβ (500 pg/mL; R&D Systems; cat. #240-B-002). In some experiments, the TGFβ type I receptor kinase inhibitor (LY2109761, 2 μmol/L) obtained from Eli Lilly under a Material Transfer Agreement, or a pan TGFβ-neutralizing antibody 1D11 (1 μg/mL; R&D Systems; cat. #MAB-1835), was added to complete media.

Immunofluorescence

A lung tumor tissue microarray (TMA) was obtained from US Biomax (BC041114). Ninety NSCLC cases were used for the analysis. Tumors were fixed in formalin and embedded in paraffin (FFPE) in the histology core facility (NYU and UCSF). Three-μm-thin sections were blocked with 0.5% casein and incubated with primary antibodies overnight at 4°C. Slides were incubated at room temperature for 1 hour with fluorescent-conjugated secondary antibodies (Life Technologies), and 4,6-diamidino-2-phenylindole (DAPI) for nuclear DNA content and mounted with Vectashield (Vector Labs; cat. #H-1000) mounting media and stored at 4°C in the dark.

For cell immunofluorescence, CD14+ cells were fixed with 4% paraformaldehyde (PFA) for 20 minutes, blocked with 0.5% casein, and incubated with primary antibodies overnight at 4°C. Slides were incubated at room temperature for 1 hour with fluorescent-conjugated secondary antibodies (Life Technologies), and DAPI, and mounted with Vectashield mounting media and stored at 4°C in the dark. High-power field images (20×) were acquired within 3 days on a Zeiss Axiovert 135TV fluorescence microscope equipped with Metamorph software (Molecular Devices, Inc.), and fluorescence was quantified by Fiji/ImageJ using in-house macros to measure mean intensity with a user-defined region of interest. Antibodies and dilutions are listed in Supplementary Table S1.

RNA sequencing (RNA-seq)

Specimens for RNA-seq were obtained as described in Supplementary Fig. S1A. RNA was extracted, and RNA-seq was performed at the NYU genomics core facility. RNA-seq data were analyzed using the Aroma Framework (17) and Bioconductor (18) implemented in R (19). Data were normalized using the weighted trimmed mean of M-values method from edgeR package (20) and mapped to the reference assembly hg38. Treated and untreated samples were compared separately for each time point, excluding genes with fewer than 10 counts for any sample for that time point. Linear modeling with weights based on mean-variance relationship (21) and with empirical Bayes moderation (22) from the Bioconductor limma package (23) was used to determine differentially expressed genes. Q-values, computed from q-value package (24), P < 0.05 were considered significant. Gene set enrichment analysis (GSEA; Broad Institute) was done using the romer method (25) from the limma package in R/Bioconductor and the reference gene sets from the molecular signatures database (MSigDB; ref. 26). The P values were adjusted for multiple testing by controlling the false discovery rate using the Benjamini and Hochberg method for each database and time point. Adjusted P ≤ 0.01 were considered significant. Fastq RNA-seq files were deposited at the Gene Expression Omnibus (GEO) database (GSE96885).

Gene expression

CD14+ cells were treated as described in the figure legends and lysed with RNA lysis buffer (Qiagen), and RNA was extracted following the manufacturer's instructions (RNAeasy, QIAGEN). RNA concentration was measured using Nanodrop. One hundred to 300 ng of RNA was used to generate cDNA using the Life Technologies SuperScript kit. Gene expression of the following transcripts was assessed by quantitative real-time PCR (qRT-PCR) using SYBR green (Thermo Fisher/Life Technologies; cat. #4309155). Expression of genes was measured using SYBR green quantitative real-time PCR (qRT-PCR) normalized by the expression of 2 endogenous housekeeping genes, GAPDH and RPL13, using the (2−ΔΔCt) method. Results are shown as mean ± SEM as arbitrary units (AU) and normalized to the untreated or GM-CSF + IL6–treated control sample. Primers (Table 1) were designed using Roche Universal Probe Library Design Center (https://lifescience.roche.com/en_us/brands/universal-probe-library.html#assay-design-centre).

Table 1.

Target genes and primers

GeneForward primer sequence (5′ to 3′)Reverse primer sequence (5′ to 3′)Gene accession number
TGFB1 GCAGAAGTTGGCATGGTAGC CCCTGGACACCAACTATTGC NM_000660.6 
SMAD7 AGACAACGTGCTCTTTGTTTTG AGAGACACCGCTTGGGACT NM_005904.3 
SERPINEA1 CCAGCTGACAACAGGAGGAG CCCATGAGCTCCTTGTACAGAT NM_000602.4 
RUNX1 CTCCCTGAACCACTCCACTG TGGGGATGGTTGGATCTG NM_001754.4 
SOX4 AGCCGGAGGAGGAGATGT TTCTCGGGTCATTTCCTAGC NM_003107.2 
TNFSF14 AGCGAAGGTCTCACGAGGT CGGTCAAGCTGGAGTTGG NM_003807.4 
CASP1 CCAGGACATTAAAATAAGGAAACTGT CCAAAAACCTTTACAGAAGGATCTC NM_033292.3 
CD36 CCTCCTTGGCCTGATAGAAA GTTTGTGCTTGAGCCAGGTT NM_001001548.2 
CARD16 GCCAAATTTGCATCACATACA GTCCTGCACTGCCTGAAGA NM_001017534.1 
CARD17 CAAGATTCTCAAATAGTACTTCCTTCC GCTGGGCATCTGTGCTTTAT NM_001007232.1 
THBS1 CAATGCCACAGTTCCTGATG TGGAGACCAGCCATCGTC NM_003246.3 
VNN1 TCCTGAGGTGTTGCTGAGTG AGCGTCCGTCAGTTGACAC NM_004666.2 
GADD45G CAGCCAAAGTCTTGAACGTG CCTGGATCAGCGTAAAATGG NM_006705.3 
CD274 TATGGTGGTGCCGACTACAA TGCTTGTCCAGATGACTTCG NM_014143.3 
NOS2 ATTCTGCTGCTTGCTGAGGT TTCAAGACCAAATTCCACCAG NM_000625.4 
ARG1 GTTTCTCAAGCAGACCAGCC GCTCAAGTGCAGCAAAGAGA NM_001244438.1 
CYBB GACAGAGGGGCTGTTCAATG GCCCATCAACCGCTATCTT NM_000397.3 
CEBPB CGCTTACCTCGGCTACCA ACGAGGAGGACGTGGAGAG NM_001285879.1 
STAT3 CCTCTGCCGGAGAAACAG CTGTCACTGTAGAGCTGATGGAG NM_139276.2 
S100A8 GCCAAGCCTAACCGCTATAA ATGATGCCCACGGACTTG NM_001319196.1 
S100A9 CTCCCACGAGAAGATGCAC GAGGCCTGGCTTATGGTG NM_002965.3 
HLA-A TGCAAAGGCACCTGAATGT ACAGGTCAGTGTGGGGACA NM_002116.7 
HLA-DR-A AGCACTGGGAGTTTGATGCT GGCACACACCACGTTCTCT M60334.1 
HLA-DR-B ACTGGAACAGCCAGAAGGAC TGTGTCTGCAGTAGGTGTCCA A06805.1 
B2M TTCTGGCCTGGAGGCTATC TCAGGAAATTTGACTTTCCATTC NM_004048.2 
GAPDH CAGCCTCCAGATCATCAGCA TGTGGTCATGAGTCCTTCCA NM_002046.5 
RPL13 CAGCGGCTGAAGGAGTACC GGTGGCCAGTTTCAGTTCTT NM_000977.3 
GeneForward primer sequence (5′ to 3′)Reverse primer sequence (5′ to 3′)Gene accession number
TGFB1 GCAGAAGTTGGCATGGTAGC CCCTGGACACCAACTATTGC NM_000660.6 
SMAD7 AGACAACGTGCTCTTTGTTTTG AGAGACACCGCTTGGGACT NM_005904.3 
SERPINEA1 CCAGCTGACAACAGGAGGAG CCCATGAGCTCCTTGTACAGAT NM_000602.4 
RUNX1 CTCCCTGAACCACTCCACTG TGGGGATGGTTGGATCTG NM_001754.4 
SOX4 AGCCGGAGGAGGAGATGT TTCTCGGGTCATTTCCTAGC NM_003107.2 
TNFSF14 AGCGAAGGTCTCACGAGGT CGGTCAAGCTGGAGTTGG NM_003807.4 
CASP1 CCAGGACATTAAAATAAGGAAACTGT CCAAAAACCTTTACAGAAGGATCTC NM_033292.3 
CD36 CCTCCTTGGCCTGATAGAAA GTTTGTGCTTGAGCCAGGTT NM_001001548.2 
CARD16 GCCAAATTTGCATCACATACA GTCCTGCACTGCCTGAAGA NM_001017534.1 
CARD17 CAAGATTCTCAAATAGTACTTCCTTCC GCTGGGCATCTGTGCTTTAT NM_001007232.1 
THBS1 CAATGCCACAGTTCCTGATG TGGAGACCAGCCATCGTC NM_003246.3 
VNN1 TCCTGAGGTGTTGCTGAGTG AGCGTCCGTCAGTTGACAC NM_004666.2 
GADD45G CAGCCAAAGTCTTGAACGTG CCTGGATCAGCGTAAAATGG NM_006705.3 
CD274 TATGGTGGTGCCGACTACAA TGCTTGTCCAGATGACTTCG NM_014143.3 
NOS2 ATTCTGCTGCTTGCTGAGGT TTCAAGACCAAATTCCACCAG NM_000625.4 
ARG1 GTTTCTCAAGCAGACCAGCC GCTCAAGTGCAGCAAAGAGA NM_001244438.1 
CYBB GACAGAGGGGCTGTTCAATG GCCCATCAACCGCTATCTT NM_000397.3 
CEBPB CGCTTACCTCGGCTACCA ACGAGGAGGACGTGGAGAG NM_001285879.1 
STAT3 CCTCTGCCGGAGAAACAG CTGTCACTGTAGAGCTGATGGAG NM_139276.2 
S100A8 GCCAAGCCTAACCGCTATAA ATGATGCCCACGGACTTG NM_001319196.1 
S100A9 CTCCCACGAGAAGATGCAC GAGGCCTGGCTTATGGTG NM_002965.3 
HLA-A TGCAAAGGCACCTGAATGT ACAGGTCAGTGTGGGGACA NM_002116.7 
HLA-DR-A AGCACTGGGAGTTTGATGCT GGCACACACCACGTTCTCT M60334.1 
HLA-DR-B ACTGGAACAGCCAGAAGGAC TGTGTCTGCAGTAGGTGTCCA A06805.1 
B2M TTCTGGCCTGGAGGCTATC TCAGGAAATTTGACTTTCCATTC NM_004048.2 
GAPDH CAGCCTCCAGATCATCAGCA TGTGGTCATGAGTCCTTCCA NM_002046.5 
RPL13 CAGCGGCTGAAGGAGTACC GGTGGCCAGTTTCAGTTCTT NM_000977.3 

Cytokine analysis

Freshly isolated CD14+ cells (2 × 106) were treated with cytokines GM-CSF and IL6 at the specified concentrations in combination with either TGFβ or LY2109761 at the specified concentrations in complete media for 48 hours. Cytokines were measured in undiluted cell-free supernatants using a multiplexed panel of 42 cytokines and chemokines (MILLIPLEX MAP Human Cytokine/Chemokine Panel I Kit; HCYTOMAG-60K). All samples were acquired on a Luminex 200 instrument (Millipore) using the manufacturer's instructions and software. Results were normalized by the number of cells counted at the time of collection (viability was determined by trypan blue exclusion on automatic Bio-Rad cell counter). Cytokines were compared with control (GM-CSF + IL6–treated) cells. TGFβ1 and latency-associated peptide 1 were measured in supernatants, using a multiplex assay based on Mesoscale Discovery (MSD) electrochemiluminescence-based ELISA (Mesoscale Diagnostics, LLC) as previously published (27). Protein concentration baselines for complete media with or without cytokines (including TGFβ) were subtracted, and values were normalized to the number of live cells (determined by trypan blue exclusion using Bio-Rad cell counter) for each condition.

FACS staining

CD14+ cells treated under the conditions described in the figure legends were diluted in FACS buffer (PBS + 10% FBS) and stained using a panel of fluorescent labeled antibodies (Supplementary Table S2) for 45 minutes at 4°C. Cells were then washed with FACS buffer and incubated with LIVE/DEAD Fixable Yellow Dead Cell Stain (cat. #L-34959 Invitrogen/Life Technologies, now Thermo Fisher) according to the manufacturer's protocol. Stained cells were washed and resuspended in FACS buffer and analyzed immediately in a BD LSRII Cytometer. Appropriate compensation was performed using compensation beads (UltraComp Beads; cat. #01-2222-42, Invitrogen/Thermo Fisher), and isotype controls (Supplementary Table S2) were run in parallel in each experiment. Data were analyzed using FlowJo v9 and v10 (FlowJo, LLC).

Mass cytometry (CyTOF)

Freshly isolated CD14+ cells (2 × 106) were treated with cytokines GM-CSF and IL6 (at the concentrations specified above) in combination with either TGFβ or LY2109761 (at the concentrations indicated above) in complete media for 5 days. Cells were incubated for 1 minute with cisplatin (25 μmol/L) to assay nonviable cells resulting in a platinum signal quantifiable by mass cytometry (28). Cells were then fixed with paraformaldehyde and stained with commercially available metal-labeled antibodies (Fluidigm; Supplementary Table S3). Cell populations were analyzed using unsupervised clustering performed using the clara algorithm in R and visualized as a force-directed graph in the open-sourced software Gephi (https://gephi.org/) as previously described (29, 30).

Phagocytosis assay

Phagocytic capacity of differentiated myeloid cells was assessed by using a commercially available phagocytosis assay kit IgG-FITC (Cayman; Item no. 500290), following the manufacturer's instructions. Briefly, CD14+ cells were differentiated for 5 days with GM-CSF + IL6 and with or without the addition of TGFβ or TGFβ inhibitor (at the concentrations indicated above). Cells were counted, and 106 cells were incubated with FITC-IgG latex beads (Item no. 500290; Cayman) at 37°C. Cells were then stained with CD45-APC-Cy7 and CD11b-Pacific Blue and analyzed by flow cytometry. Alternatively, cells were fixed in 4% PFA on positively charged slides, and stained with anti-CD11b (see Supplementary Table S1) and analyzed using immunofluorescence microscopy. To quantify phagocytosis, the median fluorescence intensity and percentage of positive FITC+ cells were quantified on CD11b+ cells using FlowJo (FlowJo, LLC).

Cytotoxic T-cell assay

Antigen-presentation capacity of CD11b+ cells was tested by coculturing with autologous naïve CD3+ T cells and a human lung cancer cell line, NCI-H1299 (ATCC CRL-5803). Verified Mycoplasma-negative NCI-H1299 cells were purchased from ATCC (lot number 58483200) and passaged according to ATCC recommendation of fewer than 6 passages. CD14+ myeloid cells were differentiated for 5 days under the listed conditions, collected, counted, and cocultured at a 1:1 ratio with autologous naïve CD3+ T cells and adherent H1299. After 48 hours, supernatants containing nonadherent immune cells were discarded, and tumor cells were trypsinized for annexin V flow cytometry.

Statistical analysis

Differences between values measured as a function of treatment of specimens from independent donors (N indicated in figure legends) were analyzed on Prism 7.0 software (GraphPad), using paired t tests for normal distributions, and Wilcoxon signed-rank test for nonparametric variables, unless otherwise indicated. A P value of <0.05 was considered statistically significant.

TGFB1 and myeloid markers are correlated in cancer, including lung adenocarcinoma

To evaluate the relationship between TGFβ and myeloid cells across cancer types, we interrogated TCGA samples using the cBioPortal (15, 16). The expression of TGFB1 was significantly correlated to ITGAM (gene encoding for the canonical myeloid marker CD11b) in non–small cell lung adenocarcinoma (NSCLC) samples (Fig. 1A). A similar relationship was observed in glioblastoma, melanoma, colorectal carcinoma, and ovarian cancers (Supplementary Table S4; Supplementary Fig. S2A). TGFB1 expression was also correlated in NSCLC for myeloid markers CD14 and CD33, which were expressed on monocytic MDSCs (10) but not with granulocyte or neutrophil marker CEACAM (CD66b), cross-presenting DC marker ITGAE (CD103), or NK marker NCAM (CD56; Fig. 1B).

Figure 1.

TGFβ expression correlates with myeloid cell markers in TCGA human cancers. A, cBioPortal for cancer genomics (15, 16) was used to assess the correlation between TGFB1 gene expression and myeloid cell marker CD11b (ITGAM) in TCGA NSCLC (N = 586), and Pearson and Spearman correlations were calculated. B, cBioPortal was used to test correlations between expression of TGFB1 and myeloid cell markers CD14, CD33, CSF1R, CD11c (ITGAX), and CD103, NK cell marker CD56 (NCAM1), and neutrophil/granulocyte marker CD66b (CEACAM8) using TCGA NSCLC adenocarcinoma specimens (N = 586; ns, not statistically significant correlation). C, Examples of NSCLC tissue microarray stained for active TGFβ (green) and CD11b (red). DAPI (blue) was used to stain the nuclei. D, Correlation between CD11b and TGFβ activity immunostaining in NSCLC tissue array (N = 66 samples, P = 0.02; Pearson, Spearman = 0.3). E, Kaplan–Meier survival curves of TCGA NSCLC patients stratified according to the expression of TGFB1 (z-score threshold = 2; N = 514 patients TGFB1hi, N = 514 patients TGFB1lo). HR = 3.4. Median survival = 49.8 months (TGFB1lo) versus 32.4 months (TGFB1hi).

Figure 1.

TGFβ expression correlates with myeloid cell markers in TCGA human cancers. A, cBioPortal for cancer genomics (15, 16) was used to assess the correlation between TGFB1 gene expression and myeloid cell marker CD11b (ITGAM) in TCGA NSCLC (N = 586), and Pearson and Spearman correlations were calculated. B, cBioPortal was used to test correlations between expression of TGFB1 and myeloid cell markers CD14, CD33, CSF1R, CD11c (ITGAX), and CD103, NK cell marker CD56 (NCAM1), and neutrophil/granulocyte marker CD66b (CEACAM8) using TCGA NSCLC adenocarcinoma specimens (N = 586; ns, not statistically significant correlation). C, Examples of NSCLC tissue microarray stained for active TGFβ (green) and CD11b (red). DAPI (blue) was used to stain the nuclei. D, Correlation between CD11b and TGFβ activity immunostaining in NSCLC tissue array (N = 66 samples, P = 0.02; Pearson, Spearman = 0.3). E, Kaplan–Meier survival curves of TCGA NSCLC patients stratified according to the expression of TGFB1 (z-score threshold = 2; N = 514 patients TGFB1hi, N = 514 patients TGFB1lo). HR = 3.4. Median survival = 49.8 months (TGFB1lo) versus 32.4 months (TGFB1hi).

Close modal

Due to TGFβ's production as a latent complex, extracellular modification is necessary to release, i.e., activate, TGFβ to bind to ubiquitous receptors. TGFβ receptor activation results in the phosphorylation of SMAD (i.e., pSMAD) that is evident in the nucleus. To further assess the relationship between expression of TGFB1 and myeloid markers, we used antibodies that recognized active TGFβ (after its release from latency-associated peptide), pSMAD, and CD11b+ monocytes in NSCLC tissue microarrays. Controls using secondary antibodies alone were negative (Supplementary Fig. S2B). TGFβ immunostaining correlated with nuclear pSMAD intensity, as expected (Supplementary Fig. S2C). TGFβ and pSMAD immunostaining of NSCLC was heterogeneous (Supplementary Fig. S2D). TGFβ activity colocalized with CD11b+ cells in NSCLC (Fig. 1C), and TGFβ staining intensity significantly correlated with frequency of CD11b+ cells (Fig. 1D), which supports a relationship between TGFβ activity and myeloid cells, as was indicated by the TCGA analysis. We found that high TGFB1 was associated with poor prognosis in lung adenocarcinoma TCGA patients (Fig. 1E; Supplementary Table S5; ref. 31).

TGFβ is a survival factor for myeloid cells

To investigate the effect of TGFβ on myeloid lineage differentiation, we established primary cultures of CD14+ monocytes isolated from the peripheral blood of healthy donors (Supplementary Fig. S1A). The purity of CD14+ cell cultures was greater than 95% (Supplementary Fig. S1B). We noted that the number of viable CD14+ cells in the absence of TGFβ for 24 hours was less than half that seen in TGFβ-treated cultures (Fig. 2A). The proportion of CD14+ cells significantly decreased in the absence of TGFβ compared with TGFβ-treated cultures (Fig. 2B). In contrast, exposure to TGFβ maintained CD14 status and increased cell viability, which resulted in 3 times more CD14+ viable cells after 5 days in culture (Fig. 2C). Thus, TGFβ conferred a specific survival benefit and maintains expression of CD14 on monocytes isolated from PBMCs.

Figure 2.

TGFβ treatment increases CD14+ monocytes. A, Number of CD14+ live cells in control (C, solid circles) or TGFβ-treated samples (TGFβ, open circles; N = 8 donors in 4 independent experiments). Representative FACS profile is shown on the right. B, Percentage of CD14+ cells in cultures treated with TGFβ for 5 days (open circles) (N = 11). Representative FACS profiles are shown on the right. C, Percentage of CD14+ live cells measured at indicated time points treated with TGFβ (open circles) or without (solid circles; N = 3 donors). P = 0.005 (two-way ANOVA).

Figure 2.

TGFβ treatment increases CD14+ monocytes. A, Number of CD14+ live cells in control (C, solid circles) or TGFβ-treated samples (TGFβ, open circles; N = 8 donors in 4 independent experiments). Representative FACS profile is shown on the right. B, Percentage of CD14+ cells in cultures treated with TGFβ for 5 days (open circles) (N = 11). Representative FACS profiles are shown on the right. C, Percentage of CD14+ live cells measured at indicated time points treated with TGFβ (open circles) or without (solid circles; N = 3 donors). P = 0.005 (two-way ANOVA).

Close modal

To further explore the consequences of TGFβ in CD14+ monocytes, we used RNA-seq to analyze gene expression at 8, 12, and 36 hours. As expected, GSEA demonstrated enrichment of targets of SMAD2/3 (Supplementary Fig. S3A). TGFβ changed 3,400 genes (q < 0.001) within 8 hours (Fig. 3A). Over 5,000 genes were differentially expressed after 12 hours. The genes regulated in TGFβ-treated monocytes overlapped less than 50% with published gene-expression signatures from other myeloid cells and were distinct from those of different myeloid cell populations from Newman and colleagues (ref. 32; Supplementary Fig. S3B and S3C). TGFβ induced upregulation of known target genes that included SMAD7, SERPINE1, SNAI1, MMP2, ADAM12, LTBP2, PLAU, CADH26, PDGFA, and PDGFB (Supplementary Table S5). GSEA indicated significant enrichment of published TGFβ signatures and SMAD2/3 regulated genes (33, 34), as well as signatures associated with macrophages, cytokines, and hematopoiesis (refs. 35, 36; Fig. 3B). TGFβ-regulated transcriptional responses revealed differential expression of myeloid differentiation and survival-related genes (Fig. 3C). Selected genes, validated by qRT-PCR (Fig. 3D), confirmed that TGFβ significantly increased the expression of the transcription factor RUNX1, which is known to regulate myeloid cell survival and differentiation (37); SOX4, known to prevent p53-mediated apoptosis (38); and TNFSF14, which is required for monocyte survival (39). Conversely, TGFβ repressed the expression of CASP1, 4, and 5, and the caspase-interacting CARD16 and CARD17, responsible for the proteolysis and release of the proinflammatory cytokines interleukin 1 beta (IL1β) and IL18, mediators of innate immunity (40).

Figure 3.

TGFβ regulates survival and differentiation pathways in CD14+ cells. A, Volcano plot showing the top differentially regulated genes in TGFβ-treated or untreated CD14+ cells after 8 hours of culture. Red dots correspond to the differentially expressed genes with q < 0.001 (3,412/18,047 total; 3,127/14,956 unique). B, Summary of the main pathways significantly correlated with TGFβ treatment ranked by NES obtained by GSEA. C, List of selected target genes significantly upregulated or downregulated after TGFβ treatment. Genes are grouped according to their biological function. D, Validation of some of TGFβ-regulated genes using qRT-PCR (N = 3). Gene expression was normalized using GAPDH and RPL13 and expressed as relative to untreated cells in arbitrary units (AU). Mean and SEM are shown. E, Percentage of CD14+ cells incorporating annexin V after 24 hours (N = 9). F, Representative images of cleaved caspase-3 immunofluorescence on CD14+ monocytes without (left) or with TGFβ for 24 hours. Fluorescence intensity per cell was quantified using ImageJ (N = 4, with 4–5 fields imaged and quantified). G, Representative images of CD14+ cell pS6 ribosomal protein immunofluorescence are shown. Fluorescence intensity was quantified using ImageJ (N = 3).

Figure 3.

TGFβ regulates survival and differentiation pathways in CD14+ cells. A, Volcano plot showing the top differentially regulated genes in TGFβ-treated or untreated CD14+ cells after 8 hours of culture. Red dots correspond to the differentially expressed genes with q < 0.001 (3,412/18,047 total; 3,127/14,956 unique). B, Summary of the main pathways significantly correlated with TGFβ treatment ranked by NES obtained by GSEA. C, List of selected target genes significantly upregulated or downregulated after TGFβ treatment. Genes are grouped according to their biological function. D, Validation of some of TGFβ-regulated genes using qRT-PCR (N = 3). Gene expression was normalized using GAPDH and RPL13 and expressed as relative to untreated cells in arbitrary units (AU). Mean and SEM are shown. E, Percentage of CD14+ cells incorporating annexin V after 24 hours (N = 9). F, Representative images of cleaved caspase-3 immunofluorescence on CD14+ monocytes without (left) or with TGFβ for 24 hours. Fluorescence intensity per cell was quantified using ImageJ (N = 4, with 4–5 fields imaged and quantified). G, Representative images of CD14+ cell pS6 ribosomal protein immunofluorescence are shown. Fluorescence intensity was quantified using ImageJ (N = 3).

Close modal

This RNA-seq analysis suggested that TGFβ suppressed apoptotic programs, which could increase survival of CD14+ monocytes. Accordingly, TGFβ reduced the number of cells positive for annexin V, a surrogate marker of cell death (Fig. 3E) and significantly reduced cleavage of caspase-3, a main effector caspase of the apoptotic cascade (Fig. 3F). GSEA also suggested increased activity of PI3-kinase/AKT signaling, which is a survival pathway for monocytes (41, 42). Immunoreactivity of phosphorylated ribosomal protein S6, which is a surrogate for AKT pathway activity, was increased when cells were treated with TGFβ (Fig. 3G). These data indicate that CD14+ monocytes execute an apoptotic program in the absence of TGFβ. Thus, high TGFβ activity in the TME could promote survival of recently recruited CD14+ monocytes.

TGFβ promotes MDSC accumulation at the expense of lineage differentiation

Pathway analysis also suggested that TGFβ influenced differentiation in CD14+ monocytes (Supplementary Fig. S3B), which can differentiate into MDSCs, macrophages, or antigen-presenting DCs, depending on the cytokine composition of the microenvironment. For example, GM-CSF can promote differentiation toward macrophages or DCs (14). The cytokine spectrum of culture supernatants of CD14+ monocytes cultured with GM-CSF and IL6 with or without the addition of TGFβ were analyzed for 42 anti- or proinflammatory cytokines and chemokines (Supplementary Fig. S4). The addition of TGFβ to GM-CSF and IL6 significantly reduced the amount of proinflammatory and macrophage-secreted cytokines and chemokines, such as chemokine (C-X-C motif) ligand 1 (CXCL1) or growth-regulated oncogene (GRO), monocyte chemoattractant protein 1 and 3 (MCP-1, 3; also known as chemokine (C-C motif) ligand 2 (CCL2)), macrophage inflammatory protein 1 alpha (MIP-1α), also known as chemokine (C-C motif) ligand 3 (CCL3), and transforming growth factor alpha (TGFα). Simultaneously, TGFβ-treated cultures significantly increased the secretion of the anti-inflammatory cytokine interleukin-1 receptor antagonist (IL1Rα) and the Th2-promoting chemokine macrophage-derived cytokine (MDC), also known as chemokine (C-C motif) ligand 22 (CCL22; Fig. 4A). TGFβ also increased the production of GM-CSF and IL6. Together, these results suggest that CD14+ monocytes shifted from a proinflammatory phenotype toward a more suppressive cytokine profile when exposed to TGFβ.

Figure 4.

TGFβ increases cells with MDSC markers, MDSC-related genes, and immunosuppressive function. A, CD14+ selected cells were treated for 48 hours with the combination of GM-CSF and IL6 with or without TGFβ (N = 3). A multiplexed panel of cytokines and chemokines was used to assay the conditioned media after 48 hours. Heat map representing the relative protein concentration of selected cytokines and chemokines. Normalized protein concentrations are shown in Supplementary Fig. S2B. B, Percentage of live HLA-DRlowCD11b+CD33+ cells after being cultured for 5 days with GM-CSF + IL6 and adding TGFβ (open circles). MDSCs were quantified within the live CD45+ leukocyte and CD14+ population (N = 36). Representative FACS plots of CD11b+CD33+ within the CD14+HLA-DRlow population are shown on the right. C, Percentage of HLA-DR+HLA-1+ mature antigen-presenting cells (mDC) after being cultured for 5 days with GM-CSF + IL6 and with the addition of TGFβ (open circles; N = 23). Representative FACS plots showing double-positive HLA-DR and HLA-1 mature DCs within the CD45+ population are shown on the right. D, Percentage of CD68+ macrophages after being cultured for 5 days with GM-CSF + IL6 and adding TGFβ (open circles; N = 20). E, Immunosuppressive assay was performed, as depicted in Supplementary Fig. S5C. T-cell proliferation was assessed by CFSE staining after CD3+ T cells were cocultured with an equal number of CD11b+ MDSCs that had been generated under the conditions indicated. The percentage of CFSE+ nonproliferating CD8+ T cells was used as readout for the immunosuppressive potential of MDSCs as a function of TGFβ (open circles; N = 5). The gray shadowed area shows CFSE intensity of T cells cocultured with GM-CSF/IL6-induced MDSCs. Dotted line shows the CFSE intensity of T cells after coculture with MDSCs generated in the presence of cytokines plus TGFβ. F, Percentage of IFNγ+ active CD8+ cytotoxic T cells was measured after coculture with myeloid cells that had been generated under the listed conditions (N = 6). G, Percentage of CD4+FoxP3+ Tregs was measured after coculture with myeloid cells that had been generated under the listed conditions (N = 6).

Figure 4.

TGFβ increases cells with MDSC markers, MDSC-related genes, and immunosuppressive function. A, CD14+ selected cells were treated for 48 hours with the combination of GM-CSF and IL6 with or without TGFβ (N = 3). A multiplexed panel of cytokines and chemokines was used to assay the conditioned media after 48 hours. Heat map representing the relative protein concentration of selected cytokines and chemokines. Normalized protein concentrations are shown in Supplementary Fig. S2B. B, Percentage of live HLA-DRlowCD11b+CD33+ cells after being cultured for 5 days with GM-CSF + IL6 and adding TGFβ (open circles). MDSCs were quantified within the live CD45+ leukocyte and CD14+ population (N = 36). Representative FACS plots of CD11b+CD33+ within the CD14+HLA-DRlow population are shown on the right. C, Percentage of HLA-DR+HLA-1+ mature antigen-presenting cells (mDC) after being cultured for 5 days with GM-CSF + IL6 and with the addition of TGFβ (open circles; N = 23). Representative FACS plots showing double-positive HLA-DR and HLA-1 mature DCs within the CD45+ population are shown on the right. D, Percentage of CD68+ macrophages after being cultured for 5 days with GM-CSF + IL6 and adding TGFβ (open circles; N = 20). E, Immunosuppressive assay was performed, as depicted in Supplementary Fig. S5C. T-cell proliferation was assessed by CFSE staining after CD3+ T cells were cocultured with an equal number of CD11b+ MDSCs that had been generated under the conditions indicated. The percentage of CFSE+ nonproliferating CD8+ T cells was used as readout for the immunosuppressive potential of MDSCs as a function of TGFβ (open circles; N = 5). The gray shadowed area shows CFSE intensity of T cells cocultured with GM-CSF/IL6-induced MDSCs. Dotted line shows the CFSE intensity of T cells after coculture with MDSCs generated in the presence of cytokines plus TGFβ. F, Percentage of IFNγ+ active CD8+ cytotoxic T cells was measured after coculture with myeloid cells that had been generated under the listed conditions (N = 6). G, Percentage of CD4+FoxP3+ Tregs was measured after coculture with myeloid cells that had been generated under the listed conditions (N = 6).

Close modal

A panel of fluorescently labeled antibodies was used to assess the expression of markers typically associated with MDSCs, DCs, and macrophages (gating strategy shown in Supplementary Fig. S5A and S5B). The addition of TGFβ to GM-CSF and IL6 significantly increased the population of HLA-DRlowCD11b+CD33+ cells (Fig. 4B), indicative of MDSCs (10). Concomitantly, the population of mature DCs expressing the antigen-presenting proteins HLA-DR and HLA-1 was significantly reduced after treatment with TGFβ (Fig. 4C). TGFβ addition also reduced the population expressing macrophage marker CD68 (Fig. 4D) and significantly reduced phagocytic capacity, concordant with a decrease in macrophage differentiation (Supplementary Fig. S6A). We further evaluated the gene expression of a panel of immunosuppressive genes (12, 14). Expression of CD274 (PD-L1), NOS2, ARG1, and CYBB (NOX2) was significantly increased when more MDSCs were generated in the presence of TGFβ (Supplementary Fig. S6B). TGFβ also increased the expression of the CEBP/B and STAT3 transcription factors, as well as S100A8 and A9 genes, all of which are implicated in the regulation and function of MDSCs (43–45).

MDSCs are defined by their immunosuppressive capacity. To functionally validate MDSCs, CD11b+ cells were sorted from each condition, counted, and cocultured 1:1 with autologous activated CD3+ T cells (Supplementary Fig. S6C–S6E). CD11b+ cells generated in the presence of TGFβ were more effective at inhibiting CD8+ T-cell proliferation than cells generated by GM-CSF and IL6 alone (Fig. 4E). Concordant with decreased proliferation, TGFβ-treated CD11b+ cells also reduced the percentage of IFNγ+ cytotoxic T cells (Fig. 4F) and were more proficient in inducing CD4+CD25+FoxP3+ Treg conversion (Fig. 4G). To further characterize the cells generated in the absence or presence of TGFβ, we used CyTOF and a panel of 35 myeloid cell phenotypic and functional markers (29). The addition of TGFβ to GM-CSF and IL6 did not result in a novel population but rather increased the existing population displaying MDSC markers (Supplementary Fig. S7A and S7B). Collectively, these results indicated that TGFβ promoted MDSC differentiation.

TGFβ promotes MDSC via an autocrine feedback loop

MDSCs are known to activate TGFβ as part of their immunosuppressive repertoire (11). TGFβ frequently regulates its own expression in a positive feedback loop (46). Consistent with this, TGFB1 expression increased as early as 4 hours after exposure (Fig. 5A). Hence, we asked whether autocrine TGFβ contributed to MDSC generation. To measure TGFβ activity, we analyzed the media conditioned by CD14+ monocytes using a multiplex ELISA that measures active and latent TGFβ1 (27). The amount of active TGFβ1 tripled in short-term (48-hour) cultures (Fig. 5B), whereas the amount of latent TGFβ1 did not change (Fig. 5C), which indicated rapid induction of TGFβ1 activation. In long-term cultures (5 days), CD14+ cells treated with GM-CSF, IL6, and TGFβ produced 5 times as much active TGFβ and almost twice as much latent TGFβ than cultures treated with GM-CSF and IL6 alone (Fig. 5D and E). TGFB1 expression was also increased (Fig. 5F). We then stained cells using antibodies that recognized active TGFβ and pSMAD2, indicative of downstream signaling (Fig. 5G). Both indices significantly increased in monocytes exposed to TGFβ (Fig. 5H–J). These data indicate that TGFβ elicited a feedback loop in monocytes to further amplify its activity.

Figure 5.

TGFβ-treated myeloid cells activate more TGFβ. A,TGFB1 mRNA expression of CD14+ cells after 4 hours of TGFβ treatment measured by qRT-PCR relative to untreated cells (N = 3). B, Active TGFβ1 protein concentration measured in supernatants of CD14+ cells treated with TGFβ for 48 hours (N = 3). C, Latent TGFβ1 was measured by MSD in supernatants of CD14+ cells treated with or without TGFβ for 48 hours (N = 3). D, Active TGFβ1 was measured by MSD assay in supernatants of CD14+ cells treated with cytokines (GM-CSF + IL6) and either TGFβ or a TβRI inhibitor, LY2109761 (N = 3). E, Latent TGFβ1 was measured by MSD in the supernatants of CD14+ cells treated as in D (N = 3). F,TGFB1 mRNA expression on CD14+ cells treated as in D and E. G, CD14+ cells were stained with antibodies that selectively detect active TGFβ1 (green) and pSMAD2 (red). DAPI was used to counterstain the nuclei. H, Mean fluorescence intensity of TGFβ was measured (N = 3). I, Nuclear fluorescence intensity of phosphorylated-SMAD2 was measured (N = 3). J, The frequency of pSMAD-positive nuclei was determined (N = 3).

Figure 5.

TGFβ-treated myeloid cells activate more TGFβ. A,TGFB1 mRNA expression of CD14+ cells after 4 hours of TGFβ treatment measured by qRT-PCR relative to untreated cells (N = 3). B, Active TGFβ1 protein concentration measured in supernatants of CD14+ cells treated with TGFβ for 48 hours (N = 3). C, Latent TGFβ1 was measured by MSD in supernatants of CD14+ cells treated with or without TGFβ for 48 hours (N = 3). D, Active TGFβ1 was measured by MSD assay in supernatants of CD14+ cells treated with cytokines (GM-CSF + IL6) and either TGFβ or a TβRI inhibitor, LY2109761 (N = 3). E, Latent TGFβ1 was measured by MSD in the supernatants of CD14+ cells treated as in D (N = 3). F,TGFB1 mRNA expression on CD14+ cells treated as in D and E. G, CD14+ cells were stained with antibodies that selectively detect active TGFβ1 (green) and pSMAD2 (red). DAPI was used to counterstain the nuclei. H, Mean fluorescence intensity of TGFβ was measured (N = 3). I, Nuclear fluorescence intensity of phosphorylated-SMAD2 was measured (N = 3). J, The frequency of pSMAD-positive nuclei was determined (N = 3).

Close modal

We noted that GM-CSF and IL6 increased TGFβ activation compared with untreated control. To test whether autocrine TGFβ contributed to the effects of GM-CSF and IL6, we blocked TGFβ signaling using LY2109761, a selective TGFβ type I receptor (TβRI) kinase inhibitor, in combination with GM-CSF and IL6 conditioning of CD14+ cells. Treatment with LY2109761 prevented the increase of active TGFβ in the conditioned media (Fig. 5D and E) and resulted in reduced downstream signaling (Fig. 5F–I). We noticed that cells cultured with TGFβ inhibitor for 5 days underwent changed morphology, becoming more adherent and resembling mature macrophages or DCs (Fig. 6A).

Figure 6.

Inhibition of TGFβ signaling alters differentiation of myeloid cells, reducing MDSCs and increasing secretion of proinflammatory molecules. A, Representative bright field images of CD14+ cultures with GM-CSF + IL6 and with the addition of TβRI inhibitor showing the different morphology of the cells after treatment. B, Quantification of proinflammatory cytokines and chemokines measured in the supernatants of CD14+ cells cultured with GM-CSF + IL6 with the addition of a TβRI inhibitor, LY2108761 (N = 3). Normalized protein concentration is shown in Supplementary Fig. S7. C, Percentage of HLA-DRlowCD11b+CD33+ cells within the CD14+ fraction after 5 days of treatment with GM-CSF and IL6 in combination with TβRI inhibitor (N = 35) or with 1D11 (N = 17). Representative FACS plots showing CD11b+/CD33+ cells within the CD14+/HLA-DRlow population are shown on the right. D, Percentage of CD68+ macrophages and MFI of CD68 measured by FACS after 5 days of treatment with GM-CSF and IL6 in combination with LY2108761 (N = 8). Representative FACS plots of CD68+ macrophages are shown on the right. Histogram representing CD68 fluorescence intensity (dark gray = GM-CSF + IL6; light gray = GM-CSF + IL6 + LY2109761). E, Percentage of FITC+ cells within the CD11b+ population (N = 3). Representative histogram for the FITC fluorescence on CD11b+ cells treated with GM-CSF and IL6 (dark gray) and with the addition of LY2109761 (light gray). Representative immunofluorescence images are shown on the right. CD11b+ cells are shown in red, and FITC+ beads are shown in green. F, Percentage CD80+ M1 and CD163+ M2 macrophages within the CD68+ macrophage population generated after 5 days of treatment as in A (N = 6). Representative overlay graphs are shown in the right (light gray, GM-CSF + IL6; dark gray, GM-CSF + IL6 + LY2109761).

Figure 6.

Inhibition of TGFβ signaling alters differentiation of myeloid cells, reducing MDSCs and increasing secretion of proinflammatory molecules. A, Representative bright field images of CD14+ cultures with GM-CSF + IL6 and with the addition of TβRI inhibitor showing the different morphology of the cells after treatment. B, Quantification of proinflammatory cytokines and chemokines measured in the supernatants of CD14+ cells cultured with GM-CSF + IL6 with the addition of a TβRI inhibitor, LY2108761 (N = 3). Normalized protein concentration is shown in Supplementary Fig. S7. C, Percentage of HLA-DRlowCD11b+CD33+ cells within the CD14+ fraction after 5 days of treatment with GM-CSF and IL6 in combination with TβRI inhibitor (N = 35) or with 1D11 (N = 17). Representative FACS plots showing CD11b+/CD33+ cells within the CD14+/HLA-DRlow population are shown on the right. D, Percentage of CD68+ macrophages and MFI of CD68 measured by FACS after 5 days of treatment with GM-CSF and IL6 in combination with LY2108761 (N = 8). Representative FACS plots of CD68+ macrophages are shown on the right. Histogram representing CD68 fluorescence intensity (dark gray = GM-CSF + IL6; light gray = GM-CSF + IL6 + LY2109761). E, Percentage of FITC+ cells within the CD11b+ population (N = 3). Representative histogram for the FITC fluorescence on CD11b+ cells treated with GM-CSF and IL6 (dark gray) and with the addition of LY2109761 (light gray). Representative immunofluorescence images are shown on the right. CD11b+ cells are shown in red, and FITC+ beads are shown in green. F, Percentage CD80+ M1 and CD163+ M2 macrophages within the CD68+ macrophage population generated after 5 days of treatment as in A (N = 6). Representative overlay graphs are shown in the right (light gray, GM-CSF + IL6; dark gray, GM-CSF + IL6 + LY2109761).

Close modal

Consistent with this, TGFβ signaling blockade increased proinflammatory MDCs present in the supernatants (Fig. 6B; Supplementary Fig. S8), as well as increased type I IFN (IFNα) and IFNγ, which are both implicated in the activation of a Th1 immune response (47). TGFβ inhibition also increased IL2 and other related cytokines, such as IL7 and IL15, which are all implicated in sustaining activation and proliferation of T cells and NK cells (48). T-cell, NK cell, and neutrophil chemoattractant chemokines IL8 (CXCL8), IP-10 (CXCL10), MIP1α, and MIP1β (49) were also significantly increased in CD14+ cell cultures after LY2109761 treatment. At the same time, the immunosuppressive cytokines IL1Rα and CCL22 were significantly reduced when TGFβ was inhibited.

Either inhibition of TGFβ signaling by LY2109761 or ligand blockade using the TGFβ-neutralizing antibody 1D11 produced significantly fewer MDSCs in both total number and percentage (Fig. 6C), whereas the proportion of CD68+ macrophages significantly increased (Fig. 6D). Consistent with an increase in functional macrophages, the phagocytic capacity of cells in these cultures was significantly enhanced (Fig. 6E). CD68+ macrophages expressing M1 marker CD80 were significantly increased, whereas the percentage of CD163+ M2 macrophages significantly decreased upon TGFβ inhibition (Fig. 6F), as previously reported (50, 51).

Blocking TGFβ increases DC maturation and antigen-presentation capacity

To further examine monocyte lineage differentiation upon TGFβ blockade, we used CyTOF analysis of populations using unsupervised clustering and visualization as a force-directed graph (30). A separate group of clusters was evident in the presence of the TGFβ inhibitor (Fig. 7A), which was characterized by lower CD11b and high expression of CD14, CD11c, HLA-DR and CD38, which are indicative of antigen-presenting DCs (ref. 52; Fig. 7B; Supplementary Fig. S9A). We confirmed that DC maturation and activation markers HLA-1, HLA-DR, CD86, and CD11c were significantly increased after treatment with LY2109761 (Fig. 7C). Mature HLA+ DCs were increased when TGFβ ligand was blocked using neutralizing antibody 1D11 (Fig. 7D). TGFβ inhibition increased the gene expression of the molecules involved with antigen presentation—HLA-A, HLA-DRA, HLA-DRB, and B2M—indicative of greater antigen-presenting capacity (Supplementary Fig. S9B). The expression of HLA-DR was validated by flow cytometry. TGFβ inhibition significantly increased cell-surface HLA-DR and HLA-1 compared with cytokine alone–treated cells (Supplementary Fig. S9C and S9D).

Figure 7.

Inhibition of TGFβ promotes mature antigen-presenting dendritic cells. A, CyTOF force–directed scaffold map of GM-CSF + IL6–treated cells (gray) and with the addition of LY2109781 (green; N = 4). B, Relative abundance of surface markers CD11c and HLA-DR on the previous populations. C, DC activation was measured by quantification of median fluorescence intensity of DC activation markers: HLA1, HLA-DR, CD86, CD11c, and CD40 on samples treated as in A–B (N = 9). D, Similar results were obtained when cells were treated with 1D11 to inhibit TGFβ (N = 6). Representative FACS plots showing the percentage of mature antigen-presenting DC (HLA1+/HLA-DR+). E, Quantification of tumor cell killing in cocultures of human tumor cells (H1299), myeloid cells, and autologous naïve T cells as described in Supplementary Fig. S7. Percentage of annexin V+ on CD45-negative tumor cells. T cells activated with CD3/CD28 beads were used as a positive control (N = 5). F, Representative bright field images of myeloid cells, T cells and tumor cells cocultures. G, Annexin V+ MFI on tumor cells after coculture with myeloid cells and autologous naïve CD3+ T cells. H, Representative histogram of annexin V fluorescence intensity. Light gray, tumor cells were cocultured with CD3+ T cells and CD11b+ myeloid cells that were generated under TGFβ inhibition (LY2109761); dark gray, tumor cells after coculture with T cells and CD11b+ cells that were generated with cytokines GM-CSF and IL6 alone.

Figure 7.

Inhibition of TGFβ promotes mature antigen-presenting dendritic cells. A, CyTOF force–directed scaffold map of GM-CSF + IL6–treated cells (gray) and with the addition of LY2109781 (green; N = 4). B, Relative abundance of surface markers CD11c and HLA-DR on the previous populations. C, DC activation was measured by quantification of median fluorescence intensity of DC activation markers: HLA1, HLA-DR, CD86, CD11c, and CD40 on samples treated as in A–B (N = 9). D, Similar results were obtained when cells were treated with 1D11 to inhibit TGFβ (N = 6). Representative FACS plots showing the percentage of mature antigen-presenting DC (HLA1+/HLA-DR+). E, Quantification of tumor cell killing in cocultures of human tumor cells (H1299), myeloid cells, and autologous naïve T cells as described in Supplementary Fig. S7. Percentage of annexin V+ on CD45-negative tumor cells. T cells activated with CD3/CD28 beads were used as a positive control (N = 5). F, Representative bright field images of myeloid cells, T cells and tumor cells cocultures. G, Annexin V+ MFI on tumor cells after coculture with myeloid cells and autologous naïve CD3+ T cells. H, Representative histogram of annexin V fluorescence intensity. Light gray, tumor cells were cocultured with CD3+ T cells and CD11b+ myeloid cells that were generated under TGFβ inhibition (LY2109761); dark gray, tumor cells after coculture with T cells and CD11b+ cells that were generated with cytokines GM-CSF and IL6 alone.

Close modal

To functionally test the antigen-presentation capacity of the resulting cells, we cocultured CD11b+ cells with autologous naïve CD3+ T cells and a human lung cancer cell line (NCI-H1299). We assessed activity of cytotoxic T cells by measuring tumor cell apoptosis (Supplementary Fig. S10A). CD11b+ cells cultured in the presence of the cytokines GM-CSF and IL6 were less efficient at priming naïve T cells compared with those treated with LY2109761. The enhanced priming of T cells resulted in increased tumor cell killing, measured by either the percentage or the intensity of annexin V+ tumor cells (Fig. 7E–H). The cross-talk between myeloid cells and T cells mediated the increase because naïve T cells or myeloid cells alone were inefficient at eliciting tumor cell apoptosis (Supplementary Fig. S10B and S10C). These results collectively indicate that monocyte autocrine TGFβ supported generation of immunosuppressive MDSCs, whereas inhibition of TGFβ signaling or ligand blockade promoted lineage commitment to macrophages and antigen-presenting DCs, which promote antitumor immunity.

Using CD14+ monocytes from normal blood, we asked how TGFβ, a prominent cytokine in cancer, affects lineage commitment. Gene-expression profiles revealed that TGFβ is a survival factor, without which monocytes undergo rapid apoptotic death. Both autocrine and paracrine TGFβ regulated myeloid lineage commitment by promoting monocyte survival and endorsing differentiation to MDSCs at the expense of antigen-presenting DCs and macrophages. Although MDSCs identified by cell-surface markers are neither homogeneous nor acting alone, this population is an important part of a network of innate immune cells in cancer that impede adaptive immunity by shifting T-cell lineages and skewing the TME toward immunosuppression.

Our studies revealed that autocrine TGFβ enforces human MDSC differentiation as well as their immunosuppressive potential, consistent with prior studies implicating TGFβ in MDSC generation in murine inflammatory models (53). Our experiments using isolated CD14+ cells expand on previous reports using PBMCs to study conditions favoring MDSC generation (14, 54). We determined that exogenous TGFβ increased autocrine TGFβ activation that is crucial for MDSC function. Consistent with this, TGFβ-induced MDSCs had greater capacity to suppress cytotoxic CD8+ T cells. TGFβ production by MDSCs suppresses T-cell responses (8, 9). This shift, accompanied by a significant increase in inflammatory cytokines and expression of antigen-presenting molecules, could increase the efficiency of an antitumor immune response (55). This interplay was evident in that TGFβ-generated MDSCs both inhibited T-cell proliferation and increased the frequency of Tregs, which are an additional source of TGFβ (56).

The high activity of TGFβ in the TME likely promotes monocyte recruitment (7) and regulates macrophage polarization, skewing differentiation toward an M2 tumor–promoting phenotype (51, 57). TGFβ is also implicated in the reprograming of PMN-MDSCs or tumor-associated neutrophils with immunosuppressive capacity (58). Interestingly, TGFβ regulates micro-RNA miR-494, which promotes MDSC accumulation in murine cancer models (59). As both chemotherapy and radiotherapy rapidly and persistently stimulate TGFβ activity in cancers (3, 4, 60, 61), treatment could further increase monocyte survival and promote an immunosuppressive TME in a manner that compromises the response to immunotherapy. Studies of patients who do not respond to checkpoint blockade suggest that TGFβ represents a major obstacle (4–6).

Targeting the increase in MDSCs observed in cancer patients has been proposed as a strategy to prevent tumor progression (62). One model for MDSC accumulation in cancer patients suggests two signals are needed: the first expands myeloid progenitors and arrests terminal differentiation, followed by a second signal that endorses immunosuppressive activity and converts the immature myeloid cells into functional MDSCs (8, 9). Here, we showed that TGFβ executed both steps: the amplification of myeloid precursors via increased survival and differentiation into immunosuppressive MDSCs. Autocrine TGFβ is essential to maintenance of this differentiation block, thus raising the potential of TGFβ inhibitors to release the immunosuppressive TME via multiple mechanisms.

The translational potential of these findings is supported by demonstration that inhibiting TGFβ could reorient monocyte differentiation toward proinflammatory and antigen-presenting macrophages and DCs. Viable TGFβ inhibition therapies are in clinical trials for the treatment of various diseases, including cancer (63, 64). The abundance of MDSCs in cancer correlates with resistance to checkpoint inhibitors (65–68), as well as with poor responses to standard-of-care treatment (69). Our studies showing that highly active TGFβ could skew monocyte differentiation support the proposition that clinical strategies using TGFβ inhibitors in cancer could reprogram the myeloid component to potentiate responses to immunotherapy.

A. Gonzalez-Junca is a consultant/advisory board member for Senti Biosciences. K. Driscoll has ownership interest in Eli Lilly. R. Parry has ownership interest in Varian Medical Systems. M.H. Spitzer reports receiving commercial research funding from Roche/Genentech. M.H. Barcellos-Hoff reports receiving commercial research funding from Lilly and Varian Inc., has received speakers bureau honoraria from Varian Inc., and is a consultant/advisory board member for Varian Inc. and Tilos Therapeutics, Inc. No potential conflicts of interest were disclosed by the other authors.

Conception and design: A. Gonzalez-Junca, K.E. Driscoll, I. Pellicciotta, R. Parry, M.H. Barcellos-Hoff

Development of methodology: A. Gonzalez-Junca, I. Pellicciotta, S. Du, C.H. Lo, M.H. Spitzer

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Gonzalez-Junca, S. Du, C.H. Lo, I. Tenvooren, D.M. Marquez, M.H. Spitzer

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Gonzalez-Junca, K.E. Driscoll, R. Roy, M.H. Spitzer, M.H. Barcellos-Hoff

Writing, review, and/or revision of the manuscript: A. Gonzalez-Junca, K.E. Driscoll, I. Pellicciotta, R. Roy, R. Parry, M.H. Spitzer, M.H. Barcellos-Hoff

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): K.E. Driscoll, C.H. Lo, M.H. Spitzer

Study supervision: A. Gonzalez-Junca, M.H. Spitzer, M.H. Barcellos-Hoff

Personnel effort and supplies were supported by Eli Lilly and Company through the Lilly Research Award Program (M.H. Barcellos-Hoff and A. Gonzalez-Junca), Varian Medical Systems, Inc. (M.H. Barcellos-Hoff, S. Du, C.H. Lo, and I. Pellicciotta), NIH DP5OD023056 (M.H. Spitzer, I. Tenvooren, and D.M. Marquez), and National Cancer Institute Cancer Center Support Grant 5P30CA082103 (R. Roy). The CyTOF mass cytometer at UCSF was supported by NIH grant S10OD018040.

The authors would like to thank Dr. David Schaer for helpful suggestions and Mr. William Chou and Ms. Xiaohong Xu for technical support. The authors also thank Dr. Ann Lazar for advice on biostatistics and data analysis. The authors thank members of the NYUMC Genome Technology Center, which is partially supported by the Cancer Center Support Grant, P30CA016087, at the Laura and Isaac Perlmutter Cancer Center.

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.
Pickup
M
,
Novitskiy
S
,
Moses
HL
. 
The roles of TGFbeta in the tumour microenvironment
.
Nat Rev Cancer
2013
;
13
:
788
99
.
2.
Massagué
J
. 
TGF[beta] in cancer
.
Cell
2008
;
134
:
215
30
.
3.
Du
S
,
Bouquet
F
,
Lo
C-H
,
Pellicciotta
I
,
Bolourchi
S
,
Parry
R
, et al
Attenuation of the DNA damage response by TGFβ inhibitors enhances radiation sensitivity of NSCLC cells in vitro and in vivo
.
Int J Radiat Oncol Biol Phys
2014
;
91
:
91
9
.
4.
Vanpouille-Box
C
,
Diamond
J
,
Pilones
KA
,
Zavadil
J
,
Babb
JS
,
Formenti
SC
, et al
Transforming growth factor (TGF) β is a master regulator of radiotherapy-induced anti-tumor immunity
.
Cancer Res
2015
;
75
:
2232
42
.
5.
Mariathasan
S
,
Turley
SJ
,
Nickles
D
,
Castiglioni
A
,
Yuen
K
,
Wang
Y
, et al
TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
.
Nature
2018
;
554
:
544
8
.
6.
Tauriello
DVF
,
Palomo-Ponce
S
,
Stork
D
,
Berenguer-Llergo
A
,
Badia-Ramentol
J
,
Iglesias
M
, et al
TGFbeta drives immune evasion in genetically reconstituted colon cancer metastasis
.
Nature
2018
;
554
:
538
43
.
7.
Wahl
SM
,
Hunt
DA
,
Wakefield
LM
,
McCartney-Francis
N
,
Wahl
M
,
Roberts
AB
, et al
Transforming growth-factor beta (TGF-beta) induces monocyte chemotaxis an growth factor production
.
Proc Natl Acad Sci U S A
1987
;
84
:
5788
91
.
8.
Gabrilovich
DI
. 
Myeloid-derived suppressor cells
.
Cancer Immunol Res
2017
;
5
:
3
8
.
9.
Condamine
T
,
Gabrilovich
DI
. 
Molecular mechanisms regulating myeloid-derived suppressor cell differentiation and function
.
Trends Immunol
2011
;
32
:
19
25
.
10.
Bronte
V
,
Brandau
S
,
Chen
SH
,
Colombo
MP
,
Frey
AB
,
Greten
TF
, et al
Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards
.
Nat Commun
2016
;
7
:
12150
.
11.
Marvel
D
,
Gabrilovich
DI
. 
Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected
.
J Clin Invest
2015
;
125
:
3356
64
.
12.
Gabrilovich
DI
,
Nagaraj
S
. 
Myeloid-derived suppressor cells as regulators of the immune system
.
Nat Rev Immunol
2009
;
9
:
162
74
.
13.
Zhang
S
,
Ma
X
,
Zhu
C
,
Liu
L
,
Wang
G
,
Yuan
X
. 
The role of myeloid-derived suppressor cells in patients with solid tumors: a meta-analysis
.
PLoS One
2016
;
11
:
e0164514
.
14.
Lechner
MG
,
Liebertz
DJ
,
Epstein
AL
. 
Characterization of cytokine-induced myeloid-derived suppressor cells from normal human peripheral blood mononuclear cells
.
J Immunol
2010
;
185
:
2273
84
.
15.
Cerami
E
,
Gao
J
,
Dogrusoz
U
,
Gross
BE
,
Sumer
SO
,
Aksoy
BA
, et al
The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data
.
Cancer Discov
2012
;
2
:
401
4
.
16.
Gao
J
,
Aksoy
BA
,
Dogrusoz
U
,
Dresdner
G
,
Gross
B
,
Sumer
SO
, et al
Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
.
Sci Signal
2013
;
6
:
pl1
.
17.
Bengtsson
H
,
Simpson
K
,
Bullard
J
,
Hansen
K
.
aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory
.
Berkeley, CA
:
University of California
; 
2008
.
18.
Huber
W
,
Carey
VJ
,
Gentleman
R
,
Anders
S
,
Carlson
M
,
Carvalho
BS
, et al
Orchestrating high-throughput genomic analysis with Bioconductor
.
Nat Methods
2015
;
12
:
115
21
.
19.
R Core Team
.
R: A language and environment for statistical computing
.
Vienna, Austria:
R Foundation for Statistical Computing
; 
2015
.
20.
Robinson
MD
,
McCarthy
DJ
,
Smyth
GK
. 
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
.
Bioinformatics
2010
;
26
:
139
40
.
21.
Law
CW
,
Chen
Y
,
Shi
W
,
Smyth
GK
. 
voom: Precision weights unlock linear model analysis tools for RNA-seq read counts
.
Genome Biol
2014
;
15
:
R29
.
22.
Smyth
GK
. 
Linear models and empirical bayes methods for assessing differential expression in microarray experiments
.
Stat Appl Genet Mol Biol
2004
;
3
:
Article3
.
23.
Ritchie
ME
,
Phipson
B
,
Wu
D
,
Hu
Y
,
Law
CW
,
Shi
W
, et al
limma powers differential expression analyses for RNA-sequencing and microarray studies
.
Nucleic Acids Res
2015
;
43
:
e47
.
24.
Storey
D
,
Bass
A
,
Dabney
A
,
Robinson
D
. 
qvalue: Q-value estimation for false discovery rate control
.
R package version 2.4.22015
.
25.
Wu
D
,
Lim
E
,
Vaillant
F
,
Asselin-Labat
ML
,
Visvader
JE
,
Smyth
GK
. 
ROAST: rotation gene set tests for complex microarray experiments
.
Bioinformatics
2010
;
26
:
2176
82
.
26.
Subramanian
A
,
Tamayo
P
,
Mootha
VK
,
Mukherjee
S
,
Ebert
BL
,
Gillette
MA
, et al
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
.
Proc Natl Acad Sci USA
2005
;
102
:
15545
50
.
27.
Pellicciotta
I
,
Marciscano
AE
,
Hardee
ME
,
Francis
D
,
Formenti
S
,
Barcellos-Hoff
MH
. 
Development of a novel multiplexed assay for quantification of transforming growth factor-β (TGFβ)
.
Growth Factors
2014
;
33
:
79
91
.
28.
Fienberg
HG
,
Simonds
EF
,
Fantl
WJ
,
Nolan
GP
,
Bodenmiller
B
. 
A platinum-based covalent viability reagent for single-cell mass cytometry
.
Cytometry A
2012
;
81
:
467
75
.
29.
Spitzer
MH
,
Nolan
GP
. 
Mass cytometry: single cells, many features
.
Cell
2016
;
165
:
780
91
.
30.
Spitzer
MH
,
Gherardini
PF
,
Fragiadakis
GK
,
Bhattacharya
N
,
Yuan
RT
,
Hotson
AN
, et al
IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system
.
Science
2015
;
349
:
1259425
.
31.
Gentles
AJ
,
Newman
AM
,
Liu
CL
,
Bratman
SV
,
Feng
W
,
Kim
D
, et al
The prognostic landscape of genes and infiltrating immune cells across human cancers
.
Nat Med
2015
;
21
:
938
45
.
32.
Newman
AM
,
Liu
CL
,
Green
MR
,
Gentles
AJ
,
Feng
W
,
Xu
Y
, et al
Robust enumeration of cell subsets from tissue expression profiles
.
Nat Methods
2015
;
12
:
453
7
.
33.
Koinuma
D
,
Tsutsumi
S
,
Kamimura
N
,
Taniguchi
H
,
Miyazawa
K
,
Sunamura
M
, et al
Chromatin immunoprecipitation on microarray analysis of Smad2/3 binding sites reveals roles of ETS1 and TFAP2A in transforming growth factor beta signaling
.
Mol Cell Biol
2009
;
29
:
172
86
.
34.
Plasari
G
,
Calabrese
A
,
Dusserre
Y
,
Gronostajski
RM
,
McNair
A
,
Michalik
L
, et al
Nuclear factor I-C links platelet-derived growth factor and transforming growth factor beta1 signaling to skin wound healing progression
.
Mol Cell Biol
2009
;
29
:
6006
17
.
35.
Fulcher
JA
,
Hashimi
ST
,
Levroney
EL
,
Pang
M
,
Gurney
KB
,
Baum
LG
, et al
Galectin-1-matured human monocyte-derived dendritic cells have enhanced migration through extracellular matrix
.
J Immunol
2006
;
177
:
216
26
.
36.
Foster
SL
,
Hargreaves
DC
,
Medzhitov
R
. 
Gene-specific control of inflammation by TLR-induced chromatin modifications
.
Nature
2007
;
447
:
972
8
.
37.
Himes
SR
,
Cronau
S
,
Mulford
C
,
Hume
DA
. 
The Runx1 transcription factor controls CSF-1-dependent and -independent growth and survival of macrophages
.
Oncogene
2005
;
24
:
5278
86
.
38.
Tiwari
N
,
Tiwari
VK
,
Waldmeier
L
,
Balwierz
PJ
,
Arnold
P
,
Pachkov
M
, et al
Sox4 is a master regulator of epithelial–mesenchymal transition by controlling Ezh2 expression and epigenetic reprogramming
.
Cancer Cell
2013
;
23
:
768
83
.
39.
Liebermann
DA
,
Hoffman
B
. 
Gadd45 in the response of hematopoietic cells to genotoxic stress
.
Blood Cells Mol Dis
2007
;
39
:
329
35
.
40.
Guo
H
,
Callaway
JB
,
Ting
JP
. 
Inflammasomes: mechanism of action, role in disease, and therapeutics
.
Nat Med
2015
;
21
:
677
87
.
41.
Goyal
A
,
Wang
Y
,
Graham
MM
,
Doseff
AI
,
Bhatt
NY
,
Marsh
CB
. 
Monocyte survival factors induce Akt activation and suppress caspase-3
.
Am J Respir Cell Mol Biol
2002
;
26
:
224
30
.
42.
Song
G
,
Ouyang
G
,
Bao
S
. 
The activation of Akt/PKB signaling pathway and cell survival
.
J Cell Mol Med
2005
;
9
:
59
71
.
43.
Wang
T
,
Niu
G
,
Kortylewski
M
,
Burdelya
L
,
Shain
K
,
Zhang
S
, et al
Regulation of the innate and adaptive immune responses by Stat-3 signaling in tumor cells
.
Nat Med
2004
;
10
:
48
54
.
44.
Sinha
P
,
Okoro
C
,
Foell
D
,
Freeze
HH
,
Ostrand-Rosenberg
S
,
Srikrishna
G
. 
Proinflammatory S100 proteins regulate the accumulation of myeloid-derived suppressor cells
.
J Immunol
2008
;
181
:
4666
75
.
45.
Cheng
P
,
Corzo
CA
,
Luetteke
N
,
Yu
B
,
Nagaraj
S
,
Bui
MM
, et al
Inhibition of dendritic cell differentiation and accumulation of myeloid-derived suppressor cells in cancer is regulated by S100A9 protein
.
J Exp Med
2008
;
205
:
2235
49
.
46.
Rodon
L
,
Gonzalez-Junca
A
,
Inda Mdel
M
,
Sala-Hojman
A
,
Martinez-Saez
E
,
Seoane
J
. 
Active CREB1 promotes a malignant TGFbeta2 autocrine loop in glioblastoma
.
Cancer Discov
2014
;
4
:
1230
41
.
47.
Gonzalez-Navajas
JM
,
Lee
J
,
David
M
,
Raz
E
. 
Immunomodulatory functions of type I interferons
.
Nat Rev Immunol
2012
;
12
:
125
35
.
48.
Sim
GC
,
Radvanyi
L
. 
The IL-2 cytokine family in cancer immunotherapy
.
Cytokine Growth Factor Rev
2014
;
25
:
377
90
.
49.
Liu
M
,
Guo
S
,
Stiles
JK
. 
The emerging role of CXCL10 in cancer (review)
.
Oncol Lett
2011
;
2
:
583
9
.
50.
Gong
D
,
Shi
W
,
Yi
SJ
,
Chen
H
,
Groffen
J
,
Heisterkamp
N
. 
TGFbeta signaling plays a critical role in promoting alternative macrophage activation
.
BMC Immunol
2012
;
13
:
31
.
51.
Mantovani
A
,
Sozzani
S
,
Locati
M
,
Allavena
P
,
Sica
A
. 
Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes
.
Trends Immunol
2002
;
23
:
549
55
.
52.
Fedele
G
,
Frasca
L
,
Palazzo
R
,
Ferrero
E
,
Malavasi
F
,
Ausiello
CM
. 
CD38 is expressed on human mature monocyte-derived dendritic cells and is functionally involved in CD83 expression and IL-12 induction
.
Eur J Immunol
2004
;
34
:
1342
50
.
53.
Lee
CR
,
Kwak
Y
,
Yang
T
,
Han
JH
,
Park
SH
,
Ye
MB
, et al
Myeloid-derived suppressor cells are controlled by regulatory T cells via TGF-beta during murine colitis
.
Cell Rep
2016
;
17
:
3219
32
.
54.
Lechner
MG
,
Megiel
C
,
Russell
SM
,
Bingham
B
,
Arger
N
,
Woo
T
, et al
Functional characterization of human Cd33+ and Cd11b+ myeloid-derived suppressor cell subsets induced from peripheral blood mononuclear cells co-cultured with a diverse set of human tumor cell lines
.
J Transl Med
2011
;
9
:
90
.
55.
Kao
JY
,
Gong
Y
,
Chen
CM
,
Zheng
QD
,
Chen
JJ
. 
Tumor-derived TGF-beta reduces the efficacy of dendritic cell/tumor fusion vaccine
.
J Immunol
2003
;
170
:
3806
11
.
56.
Liu
VC
,
Wong
LY
,
Jang
T
,
Shah
AH
,
Park
I
,
Yang
X
, et al
Tumor evasion of the immune system by converting CD4+CD25 T cells into CD4+CD25+ T regulatory cells: role of tumor-derived TGF-b
.
J Immunol
2007
;
178
:
2883
92
.
57.
Yang
WC
,
Ma
G
,
Chen
SH
,
Pan
PY
. 
Polarization and reprogramming of myeloid-derived suppressor cells
.
J Mol Cell Biol
2013
;
5
:
207
9
.
58.
Fridlender
ZG
,
Sun
J
,
Kim
S
,
Kapoor
V
,
Cheng
G
,
Ling
L
, et al
Polarization of tumor-associated neutrophil phenotype by TGF-b: N1 versus N2 TAN
.
Cancer Cell
2009
;
16
:
183
94
.
59.
Liu
Y
,
Lai
LH
,
Chen
QY
,
Song
YJ
,
Xu
S
,
Ma
F
, et al
MicroRNA-494 is required for the accumulation and functions of tumor-expanded myeloid-derived suppressor cells via targeting of PTEN
.
J Immunol
2012
;
188
:
5500
10
.
60.
Hardee
ME
,
Marciscano
AE
,
Medina-Ramirez
CM
,
Zagzag
D
,
Narayana
A
,
Lonning
SM
, et al
Resistance of glioblastoma-initiating cells to radiation mediated by the tumor microenvironment can be abolished by inhibiting transforming growth factor-β
.
Cancer Res
2012
;
72
:
4119
29
.
61.
Bouquet
SF
,
Pal
A
,
Pilones
KA
,
Demaria
S
,
Hann
B
,
Akhurst
RJ
, et al
Transforming growth factor b1 inhibition increases the radiosensitivity of breast cancer cells in vitro and promotes tumor control by radiation in vivo
.
Clin Cancer Res
2011
;
17
:
6754
65
.
62.
Ugel
S
,
Delpozzo
F
,
Desantis
G
,
Papalini
F
,
Simonato
F
,
Sonda
N
, et al
Therapeutic targeting of myeloid-derived suppressor cells
.
Curr Opin Pharmacol
2009
;
9
:
470
81
.
63.
Akhurst
RJ
,
Hata
A
. 
Targeting the TGFb signalling pathway in disease
.
Nat Rev Drug Discov
2012
;
11
:
790
811
.
64.
Rodon
J
,
Carducci
MA
,
Sepulveda-Sanchez
JM
,
Azaro
A
,
Calvo
E
,
Seoane
J
, et al
First-in-human dose study of the novel transforming growth factor-beta receptor I kinase inhibitor LY2157299 monohydrate in patients with advanced cancer and glioma
.
Clin Cancer Res
2015
;
21
:
553
60
.
65.
Dammeijer
F
,
Lau
SP
,
van Eijck
CHJ
,
van der Burg
SH
,
Aerts
J
. 
Rationally combining immunotherapies to improve efficacy of immune checkpoint blockade in solid tumors
.
Cytokine Growth Factor Rev
2017
;
36
:
5
15
.
66.
De Henau
O
,
Rausch
M
,
Winkler
D
,
Campesato
LF
,
Liu
C
,
Cymerman
DH
, et al
Overcoming resistance to checkpoint blockade therapy by targeting PI3Kgamma in myeloid cells
.
Nature
2016
;
539
:
443
7
.
67.
Lu
X
,
Horner
JW
,
Paul
E
,
Shang
X
,
Troncoso
P
,
Deng
P
, et al
Effective combinatorial immunotherapy for castration-resistant prostate cancer
.
Nature
2017
;
543
:
728
32
.
68.
Sade-Feldman
M
,
Kanterman
J
,
Klieger
Y
,
Ish-Shalom
E
,
Olga
M
,
Saragovi
A
, et al
Clinical significance of circulating CD33+CD11b+HLA-DR myeloid cells in patients with stage IV melanoma treated with ipilimumab
.
Clin Cancer Res
2016
;
22
:
5661
72
.
69.
Tada
K
,
Kitano
S
,
Shoji
H
,
Nishimura
T
,
Shimada
Y
,
Nagashima
K
, et al
Pretreatment immune status correlates with progression-free survival in chemotherapy-treated metastatic colorectal cancer patients
.
Cancer Immunol Res
2016
;
4
:
592
9
.