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.
Materials and Methods
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.
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).
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).
|Gene .||Forward primer sequence (5′ to 3′) .||Reverse primer sequence (5′ to 3′) .||Gene accession number .|
|Gene .||Forward primer sequence (5′ to 3′) .||Reverse primer sequence (5′ to 3′) .||Gene accession number .|
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.
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).
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.
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).
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.
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).
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.
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.
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).
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).
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.
Disclosure of Potential Conflicts of Interest
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.
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