Intratumoral T-cell dysfunction is a hallmark of pancreatic tumors, and efforts to improve dendritic cell (DC)–mediated T-cell activation may be critical in treating these immune therapy unresponsive tumors. Recent evidence indicates that mechanisms that induce dysfunction of type 1 conventional DCs (cDC1) in pancreatic adenocarcinomas (PDAC) are drivers of the lack of responsiveness to checkpoint immunotherapy. However, the impact of PDAC on systemic type 2 cDC2 development and function has not been well studied. Herein, we report the analysis of 3 cohorts, totaling 106 samples, of human blood and bone marrow (BM) from patients with PDAC for changes in cDCs. We found that circulating cDC2s and their progenitors were significantly decreased in the blood of patients with PDAC, and repressed numbers of cDC2s were associated with poor prognosis. Serum cytokine analyses identified IL6 as significantly elevated in patients with PDAC and negatively correlated with cDC numbers. In vitro, IL6 impaired the differentiation of cDC1s and cDC2s from BM progenitors. Single-cell RNA sequencing analysis of human cDC progenitors in the BM and blood of patients with PDAC showed an upregulation of the IL6/STAT3 pathway and a corresponding impairment of antigen processing and presentation. These results suggested that cDC2s were systemically suppressed by inflammatory cytokines, which was linked to impaired antitumor immunity.

The advent of checkpoint immunotherapy has revolutionized care for patients with cancer (1). Targeting the immune checkpoint molecules programmed death 1 (PD-1), programmed death ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen-4 (CTLA-4) has dramatically improved patient outcomes in once-challenging diseases such as advanced melanoma and non–small cell lung cancer (NSCLC; refs. 2–4). In several cancers, including triple-negative breast cancer and NSCLC, the addition of chemotherapy to PD1/PDL1 checkpoint inhibition has substantially expanded the number of patients who benefit from these therapies (5, 6). However, even in the above “responsive cancers,” not all patients benefit, and for some cancers, such as glioblastoma, microsatellite stable colorectal cancer, and pancreatic adenocarcinoma (PDAC), neither checkpoint inhibitors alone nor in combination with chemotherapy has proven to be beneficial. This is especially true of PDAC, where, aside from a small subset of patients with mismatch repair–deficient tumors, the benefits of immune checkpoint blockade remain largely unrealized (7–9), and overall survival (OS) at 5 years remains at 10% (10). One factor likely contributing to this phenomenon is the characteristically dense stroma associated with the disease, whose role as a support network for immunosuppressive cell types and associated T-cell scarcity and dysfunction have been well characterized (11–14). However, at least 30% of PDAC tumors have significant T-cell infiltrates that correlate with positive clinical outcomes (15), suggesting that other aspects of the tumor–immunity cycle may be impacted. Recent evidence suggests that the presence of dysfunctional intratumoral T-cell responses and poor responsiveness to immunotherapy may be the result of impaired T-cell priming (16–18). Thus, efforts should be directed toward better understanding the cells responsible for orchestrating effector T-cell priming.

Although multiple professional antigen-presenting cells (APC) reside in the tumor microenvironment (TME), conventional dendritic cells (cDC) are particularly proficient at T-cell priming and are necessary for sufficient T cell–mediated antitumor immunity (19). The cDCs are produced in the bone marrow (BM) and recruited from the periphery to facilitate antigen-specific T-cell priming, both at the sites of the tumor and the tumor draining lymph nodes. They can be divided into two main lineages with separate immunological functions, Batf3-dependent CD103+/XCR1+ (CD141+ in human) type 1 cDCs (cDC1) and IRF4-dependent CD172α+ (CD1c+) type 2 cDCs (cDC2s). Conventionally, although not an exclusive rule, cDC1s specialize in cross-presenting antigens on MHCI and in activating CD8+ T cells, whereas cDC2s show enhanced MHCII antigen presentation and preferentially regulate CD4+ T-cell responses (20–22). Tumor-mediated alterations in the cDC compartment, particularly the CD141+ cDC1s, have been increasingly recognized as a mechanism of immune evasion in solid tumors (19, 23–30). However, the extent to which tumor-mediated suppression influences the cDC2 compartment is poorly understood.

In contrast with cDC1s, cDC2s exhibit substantial heterogeneity, consisting of both pro- and anti-inflammatory populations with the capacity to promote tumor clearance or tolerance. In this study, we used single-cell RNA sequencing (scRNA-seq) of circulating cDC2 populations from human PDAC samples to comprehensively characterize tumor-derived alterations in the diversity and function of cDC2s, and determined how this, in turn, influenced antitumor immunity. We identified elevated levels of inflammatory cytokines, including IL6, as a key mediator of impairments in cDC2 development and antigen-presenting capacity, which in turn correlated with poor patient outcomes.

Tissue banking

BM and peripheral blood were obtained from patients diagnosed with resectable, locally advanced, or unresectable pancreatic ductal adenocarcinoma at the Barnes-Jewish Hospital at Washington University (St. Louis, MO) from 2011 to 2017, and who were followed for recurrence and survival in a prospectively collected database. Samples were collected from patients who provided written informed consent in concordance with Washington University Institutional Review Board (IRB) approval (IRB protocol numbers 201108117 and 201309087). At the time of collection, the patients had received no prior cancer-related treatment. PBMC analysis was performed on samples from 46 patients with resectable pancreatic cancer and 17 patients with locally advanced disease. Healthy donor BM (n = 10) and blood (n = 15) were collected from cancer-free volunteers. Blood was collected into vacuum tubes containing sodium heparin (BD Biosciences). The cells were isolated by Ficoll density centrifugation and frozen in 90% FBS (R&D Systems, Cat. No S11150) with 10% dimethyl sulfoxide. BM from patients with PDAC was isolated as previously described (n = 7). Primary PDAC tissues were collected during surgical resection and verified by standard pathology. The study was performed in accordance with the Declaration of Helsinki statement on ethical biomedical research and with the International Conference on Harmonization Guidelines for Good Clinical Practice.

Statistical analysis

Baseline patient demographics and clinical characteristics from patient cohorts were summarized with count and percentage for categorical variables and mean or median and interquartile range (Q1–Q3) for continuous variables. The Kaplan–Meier method was conducted to estimate empirical OS probability by each covariate [Age, Sex, Resectability, Stage, Margin, Hypertension, Diabetes, Chronic Kidney Disease (CKD), Chronic Obstructive Pulmonary Disease (COPD), Coronary Artery Disease (CAD), Congestive Heart Failure (CHF), and Smoking status]. The log-rank test was used to compare survival differences across groups as determined by a covariate. A multivariate Cox proportional hazards model was also applied to compute hazard ratios (HR) and 95% confidence intervals (95% CI), adjusting for known covariates. The backward selection algorithm was used to retain variables in the final multivariate Cox model, where we started with the full model with all the relevant variables (Age, Sex, Resectability, Stage, Margin, Hypertension, Diabetes, CKD, COPD, CAD, CHF, and Smoking status) and sequentially remove one least significant variable until all the variables in the model had a P value ≤ 0.1. Two-sided P < 0.05 is considered statistically significant. All analyses were performed within SAS (version 9.4; SAS).

Murine models

All mice were maintained in the Laboratory for Animal Care barrier facility at Washington University School of Medicine. All studies were approved by the Washington University School of Medicine Institutional Animal Studies Committee. PDAC genetically engineered mouse models were monitored biweekly for the development of tumors and were enrolled at the onset of tumor identification. They were followed for 15 days, and then serum, blood, BM, and tumors were collected for analyses. KPPC (p48-CRE+/LSL-KrasG12D/p53flox/flox C57BL/6) component mice were either obtained from The Jackson Laboratory (Kras and p53) or from Dr. Sunil Hingorani (p48; University of Washington, Seattle, WA). C57BL/6 mice were purchased from The Jackson Laboratory (Cat. No. 000664). Breeding pairs of OTII and Zbtb46-GFP mice were purchased from The Jackson Laboratory and bred in house (Cat. No. 004194 and 027618, respectively).

Tissue harvest

For tumor tissue analysis, mice were euthanized by intracardiac perfusion with 15 mL of PBS-heparin under isoflurane anesthesia. Blood was obtained by cardiac puncture and deposited in heparin-PBS (Alfa Aesar, Lonza) solution. Blood was then incubated in red blood cell (RBC) lysis buffer (BioLegend) for 10 minutes on ice and quenched with PBS. BM was obtained from bilateral femurs and suspended in RBC lysis buffer for 1 minute on ice, then quenched with PBS. Normal and tumor tissues were manually minced and digested in 15 mL Hank's Balanced Salt Solution (Thermo Fisher Scientific) supplemented with 2 mg/mL collagenase A (Roche) and DNAse (Sigma-Aldrich) for 25 minutes at 37°C with agitation. After digestion, the cell suspensions were quenched with 5 mL of FBS and filtered through 40-μm nylon mesh. The filtered suspensions were then pelleted by centrifugation (680 × g for 5 minutes at 4°C) and resuspended in flow cytometry buffer (PBS containing 1% BSA) as a single-cell suspension.

Flow cytometry

Following tissue digestion, single-cell suspensions were blocked with rat anti-mouse CD16/CD32 (eBioscience, Cat. No. 14–0161–82, Supplementary Table S1) for 10 minutes on ice and then pelleted by centrifugation. The cells were subsequently labeled with 100 μL of fluorophore-conjugated anti-mouse extracellular antibodies (Supplementary Table S1) at recommended dilutions for 25 minutes on ice in flow cytometry buffer. The samples were then washed with staining buffer and fixed with fixation buffer (BD Biosciences, Cat. No. 554655). When intracellular staining was performed, a fixation/permeabilization kit (eBioscience, Cat. No. 00–5523–00) was used after extracellular staining according to the manufacturer's instructions. Human BM and blood were thawed from cryopreservation into PBS (Lonza). The samples were then processed in the same manner as described above. Data were acquired using a Fortessa X20 or Aria II (BD Biosciences). FlowJo, v.10 (Tree Star software) was used for compensation and analysis.

IL6 neutralization

KPPC mice were monitored for tumor development, and treatment was begun at the time of first diagnosis. Mice received 1 mg of anti-mouse IL6 (MP5–20F3; BioXCell) or isotype control (HPRN; BioXCell; Supplementary Table S1) three times per week for 2 weeks. Mice were euthanized at 15 days and the blood, BM, and tumors were profiled by flow cytometry to evaluate immune populations. Serum was collected by cardiac puncture, allowed to clot for 1 hour, and centrifuged at 1,000 × g for 15 minutes.

In vitro DC differentiation assay

BM from C57BL/6 mice was collected by spinning cutoff ends of bilateral femurs. The cells were resuspended in 1× RBC lysis buffer according to the manufacturer's protocol and quenched with PBS. The cells were then resuspended in RPMI media with 1:1,000 beta-mercaptoethanol, penicillin, streptomycin, and 100 ng/mL Flt3L, with or without the addition of 100 ng/mL IL6 (Peprotech). Details of all reagents listed in Supplementary Table S1. Additional fresh medium was added on day 5. Nonadherent and semi-adherent cells were harvested at 9 days for further use.

CFSE dilution assay

After cDCs were generated according to the above protocol the pool of cells was sorted for SIRPα+ cDC2s (antibodies listed in Supplementary Table S1). The cDC2s were pulsed with class II ova peptide (ISQA VHAAHAEINEAGR, Invivogen; Supplementary Table S1) for 3 hours and washed twice. OTII mice were sacrificed using CO2 and the spleens were harvested under sterile conditions. A single-cell suspension was generated by crushing the spleen through a 100-μm filter. The cells were pelleted and resuspended in 10 mL of 1× RBC lysis buffer (BioLegend) according to the manufacturer's protocol. The cells were washed, counted, and enriched for CD4+ T cells by magnetic bead purification (Miltenyi, Bergisch Gladbach; Supplementary Table S1). CD4+ T cells were stained with Carboxyfluorescein succinimidyl ester (CFSE; Life Technologies Cat. No. C34554) for 10 minutes at 37°C, washed, and added on top of ova-pulsed cDC2s in RPMI media (Supplementary Table S1). The ratio of DCs to T cells was 100 to 50 K in a round bottom 96-well plate. The experiments were conducted in triplicate and repeated (3 times). The CD4+ T cells were analyzed at 72 hours for CFSE by flow cytometry using a Fortessa X20 (BD Biosciences) and analyzed using FlowJo v10 software (TreeStar).

ELISA and the cytokine array

Murine serum was collected as previously described. The serum was thawed on ice and assessed for IL6 levels using a murine IL6 ELISA Duoset kit (R&D Systems; No. DY406–05). Human plasma was collected from the Washington University Tissue Bank under IRB 201108117, thawed on ice, and sent to Olink Proteomics. The Olink Proteomics Immuno-Oncology Panel (article number 95311) was used for analysis.

TCGA analysis

TCGA data were analyzed using the online tool GEPIAZ2 (http://gepia2.cancer-pku.cn/#survival) to look for IL6 gene expression across 178 patients (PAAD dataset). These data use RNA-seq of patient samples correlated with OS. Patients were stratified by IL6 expression, and then median OS was compared.

scRNA-seq

Human peripheral blood mononuclear cells and BM

Peripheral blood mononuclear cells (PBMC) were isolated from patients and healthy donors using Ficoll-Paque centrifugation (Cytiva), and then frozen. Before submission, samples were rapidly thawed at 37°C, Fc blocked, live/dead (7AAD) stained, extracellular stains applied (CD3 BD HIT3a, CD14 BD M5E2, CD16 BD 3G8, CD19 BD HIB19, CD20 BioLegend 2H7, HLA-DR Thermo Fisher Scientific L243, CD45 BD 2D1, and sorted for live APCs (7AAD,CD45+,CD3CD16CD19CD20HLA-DR+) by using an Aria II Cell Sorter (BD Biosciences). All antibodies are listed in Supplementary Table S1. Samples from three healthy donors and three patients with PDAC were submitted for scRNA-seq.

BM was collected from patients, mononuclear cells were isolated via Ficoll-Paque centrifugation, and then frozen. Before submission, the samples were rapidly thawed at 37°C and lineage-depleted using anti-biotin MACS beads (Miltenyi) and biotinylated antibodies against CD3/15/16/20/56/235a (Supplementary Table S1). Samples from four healthy donors and three patients with PDAC were submitted for scRNA-seq.

Cells from each sample were submitted to McDonnell Genome Institute (MGI) at Washington University School of Medicine for library preparation using Chromium Single Cell 3′v3 kit (10X Genomics) according to the manufacturer's protocol. The generated libraries were sequenced by MGI using a NovaSeq 6000 sequencing system (Illumina) to an average of 50 K mean reads per cell. Cellranger mkfastq pipeline (10X Genomics) was used to demultiplex the Illumina base call files to FASTQ files. All BM and PBMC files were demultiplexed with greater than 96% valid barcodes and greater than 93% q30 reads. Fastq files from each sample were then processed with Cellranger count (Qiagen) and aligned to the human GRCh38 reference genome (v3.1.0, 10X Genomics).

Human Peng scRNA-seq data were obtained from a publicly available dataset (12). FASTQ files were realigned to the human GRCh38 reference and generated a feature barcode matrix, including 21 PDAC samples and 6 normal samples. Mouse PDAC scRNA-seq data were also obtained from a publicly available dataset GSE125588 (31).

PBMC and BM scRNA-seq data analyses

The filtered feature barcode matrix from PBMCs and BM were loaded into Seurat as Seurat objects (Seurat, v.3). For each Seurat object, genes that were expressed in less than three cells and cells that expressed less than 200 were excluded. Cells having fewer than 200 or greater than 5,000 genes, and cells having fewer than 100 or greater than 250,000 reads were excluded. Cells with greater than 15% mitochondrial RNA content were also excluded, resulting in 30,830 cells for normal PBMC, 20,963 cells for PDAC PBMCs, 23,724 cells for normal BM, and 27,883 cells for PDAC BM. SCTransform with default parameters was used on each individual sample to normalize and scale the expression matrix against the sequence depth and percentage of mitochondrial genes. Variable features were independently calculated for each sample and ranked on the basis of the number of samples independently identified (SelectIntegrationFeatures). The top 2,000 shared variable features were used for multi-set canonical correlation analysis to reduce dimensions and identify projection vectors that defined shared biological states among samples and maximized overall correlation across datasets. Mutual nearest neighbors (pairs of cells, with one from each dataset) were calculated and identified as “anchors” (FindIntegrationAnchors). Multiple datasets were then integrated on the basis of these calculated “anchors” and a guided order tree with default parameters (IntegrateData). Principle component analysis (PCA) was performed on the 2,000 variable genes calculated earlier (function RunPCA). A Uniform Manifold Approximation and Projection (UMAP) dimensional reduction was performed on the scaled matrix using the first 30 PCA components to obtain a two-dimensional representation of the cell states. The 30 defined dimensions were then used to refine the edge weights between any two cells based on Jaccard similarities (FindNeighbors) and to cluster cells using the FindClusters function, which implemented shared nearest neighbor modularity optimization with a resolution of 0.3, leading to 18 PBMC and 20 BM clusters. To characterize clusters, the FindAllMarkers function with log-fold-change threshold = 0.5 and minimum percentage of each cluster expressing genes of 25%, and MAST test was used to identify signatures alone with each cluster. For PBMC analyses, clusters representing cDC2s and monocytes were each selected and the top 2,000 variable features were recalculated to recluster to a resolution of 0.1. Similar analysis was performed on BM for clusters representing cDC2s and their precursors. These ranked gene sets were used by Gene Set Enrichment Analysis (GSEA) to test for Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes pathways, and Reactome and Molecular Signatures Database (MSigDB) gene sets with FDR < 0.05.

Four additional healthy donor PBMCs and four additional BM sequencing data (4 PBMC and 4 BM) were obtained from public datasets. Two patients with PBMC were obtained from the Broad Institute sequenced at 2 sites (BST and NYC, Broad Institute Study SCP424, https://singlecell.broadinstitute.org/single_cell/study/SCP424/single-cell-comparison-pbmc-data) and two PBMC samples from the dataset GSE132044. Four healthy BM datasets were obtained from GSE116256. These were integrated together with previously obtained healthy samples and reanalyzed as above.

Trajectory and pseudotime analyses were performed using the “slingshot” package in R according to default parameters and recommendations of the authors (https://github.com/kstreet13/slingshot; ref. 32). Briefly, following SCTransform, data integration, and clustering with Seurat, slingshot function was used to calculate global lineage structure and identify best principal curves using a cluster-based minimum spanning tree model with user provided starting and ending clusters.

Data accessibility

The data generated in this study are available within the article and its Supplementary Data Files or from the corresponding author upon reasonable request. scRNA-seq data generated and analyzed in this study have been deposited in Gene Expression Omnibus at GSE213096.

Patients with pancreatic cancer have a diminished number of circulating cDCs

To evaluate the impact of PDAC on circulating DCs, we evaluated PBMCs from three independent patient cohorts. Consistent with previous findings, we observed a decrease in the numbers of both mature CD141+ cDC1s and their pre-DC1 precursors, when compared with healthy controls (Fig. 1AD; refs. 23, 33). In addition, we also observed a reduction in the numbers of both pre-cDC2s and mature CD1c+ cDC2 (Fig. 1A). These changes in systemic pre-DCs, cDC1, and cDC2 were consistent across all three cohorts of patients with PDAC (Fig. 1BD). To address the variations in raw values between runs, which precluded us from pooling PBMC cohorts, we also compared the fold change between normal and patients with PDAC within groups and pooled these data, revealing a consistent reduction in circulating cDC2s of approximately 50% (Supplementary Fig. S1A–S1D). When assessing the impact of changes in cDC2s, we observed that decreased numbers of pre-cDC2s or mature cDC2s were indicative of poor clinical outcomes (Fig. 1E and F). Baseline clinical and demographic information of the two surgical cohorts analyzed were reviewed and demonstrated no significant differences between high and low cDC2 groups (Supplementary Table S2A and S2B). In addition, covariates were analyzed for correlation with OS by both univariate and multivariate analysis where diabetes was the only covariate associated with OS (Supplementary Table S3). Together, these results suggested that even before metastasis of PDAC, there were deleterious systemic changes in cDCs; however, the mechanism(s) mediating this impairment were unclear.

Figure 1.

Human patients with PDAC have reduced systemic numbers of cDCs. A, Representative flow cytometry gating strategy for human PBMC CD45RA+ pre-DCs, CD141+ cDC1s, and CD1c+ cDC2s, including representative final plots from a patient with PDAC and healthy control. B–D, Frequency of CD141+ cDC1s, pre-cDC1s as well as CD1c+ cDC2s and their precursors in patients with PDAC, when compared with healthy controls. Data from three separate PDAC patient cohorts, two with resectable and one with locally advanced diseases. Healthy controls: n = 15, surgical cohort #1: n = 30, locally advanced cohort: n = 17, surgical cohort #2: n = 16. E, Correlation of CD1c+ cDC2s and pre-cDC2s with overall survival (days), and survival data from surgical cohort #1. E, Kaplan–Meier survival curves for patients with PDAC with above average levels of cDC2s (%CD45) compared with patients with below average cDC2s. Survival data presented from surgical cohort #1, n = 30. Data are displayed as a box and whisker plot with maximum and minimum values. For comparisons between two groups, the unpaired Student two-tailed t test was used. For correlation analyses, the coefficient of determination, R2, was calculated from Pearson's correlation coefficient, r. For survival analyses, the log-rank (Mantel–Cox) test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 1.

Human patients with PDAC have reduced systemic numbers of cDCs. A, Representative flow cytometry gating strategy for human PBMC CD45RA+ pre-DCs, CD141+ cDC1s, and CD1c+ cDC2s, including representative final plots from a patient with PDAC and healthy control. B–D, Frequency of CD141+ cDC1s, pre-cDC1s as well as CD1c+ cDC2s and their precursors in patients with PDAC, when compared with healthy controls. Data from three separate PDAC patient cohorts, two with resectable and one with locally advanced diseases. Healthy controls: n = 15, surgical cohort #1: n = 30, locally advanced cohort: n = 17, surgical cohort #2: n = 16. E, Correlation of CD1c+ cDC2s and pre-cDC2s with overall survival (days), and survival data from surgical cohort #1. E, Kaplan–Meier survival curves for patients with PDAC with above average levels of cDC2s (%CD45) compared with patients with below average cDC2s. Survival data presented from surgical cohort #1, n = 30. Data are displayed as a box and whisker plot with maximum and minimum values. For comparisons between two groups, the unpaired Student two-tailed t test was used. For correlation analyses, the coefficient of determination, R2, was calculated from Pearson's correlation coefficient, r. For survival analyses, the log-rank (Mantel–Cox) test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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Systemic IL6 elevations in PDAC drive impair cDC development and function

To better identify the cytokines involved, we performed high-throughput cytokine analysis on plasma samples from patients with PDAC and healthy donors. A total of 83 of 92 cytokines analyzed were detectable and of these, 59 were differentially expressed between PDAC and normal donors (Supplementary Fig. S2A). Eleven of these factors were increased by greater than 3-fold in patients with PDAC (Fig. 2A). The elevated cytokines included classic inflammatory mediators (IL6, IL8, IL10, CCL20, CXCL9, and CCL3). Among these cytokines, IL6 was the most elevated in patients with PDAC (Fig. 2A and B) and circulating levels of IL6, CCL3, CXCL9, and CXCL10 all showed an inverse correlation with cDC2 numbers in the blood (Fig. 2C; Supplementary Fig. S2B). To determine which of these cytokines might impact cDC2 development, we evaluated their abilities to block cDC development from BM precursors in response to FLT3 L (34). We found that only IL6 led to diminished cDC1 and cDC2 numbers in vitro (Supplementary Fig. S2C and S2D). In vivo, IL6 has been implicated in driving increased apoptosis of cDC1s, leading to impairments in cDC1-mediated CD8+ T-cell priming (35). We first tested the hypothesis that increased cell death was the mechanism for IL6-mediated cDC suppression. To accomplish this aim, we differentiated cDCs from BM using Flt3 L for 5 days and then added exogenous IL6. We observed that although coculture with exogenous IL6 significantly increased cell death in cDC1s, no such increase occurred in cDC2s in vitro (Fig. 2D), suggesting that IL6-mediated cDC2 impairment may have instead been due to altered differentiation from progenitors in the BM. To test this possibility, we assessed the impact of exogenous IL6 during Flt3L-driven cDC differentiation using BM from Zbtb-GFP reporter mice and found that IL6 significantly reduced the number of cDC2s that Flt3 L produced in culture (Fig. 2E; Supplementary Fig. S2D). Together, these results suggested that IL6 impaired the development of cDC2s by inhibiting their differentiation from BM progenitors.

Figure 2.

IL6 is elevated in the blood of patients with PDAC and reduces cDC2 differentiation. A and B, Fold-change of plasma cytokine levels in patients with PDAC (n = 30) compared with healthy controls (n = 15), as measured by high-throughput cytokine analysis (A), where IL6 represents the most significantly elevated cytokine compared with healthy controls (B). C, Correlation between plasma IL6 concentrations and circulating CD1c+ cDC2s (%CD45) in patients with PDAC (red, n = 30) and healthy (blue, n = 15) patients. D,In vitro system for the BMDC differentiation assay. BM from Zbtb46GFP+ mice were cultured in the presence of Flt3 L for 5 days before addition of IL6 (100 ng/mL). DCs were collected after 3 days and analyzed using FACS to determine the percentage of dead (7-AAD+) cDC1 and cDC2, samples were run in triplicate by condition. Data were consistent across two independent experiments. E, BM from Zbtb46GFP+ mice were cultured with Flt3 L ± IL6 for 9 days and analyzed via FACS for cDC2 production, three wells per condition. Data were consistent across three independent experiments. F and G, The scRNA-seq analysis from human PDAC (F) and three murine (G) PDAC models; late-stage KrasLSL−G12D/+Trp53fl/flPdx1Cre/+(LKPFC), late-stage KrasLSL−G12D/+Trp53LSL−R172H/+Ptf1aCre/+ (LKPC), and late-stage KrasLSL−G12D/+Ink4afl/flPtf1aCre/+ (LKIC) were analyzed for IL6 expression (31). H, Kaplan–Meier survival analyses comparing patients with PDAC with high and low IL6 expressions in the primary tumor site using TCGA data. I, KPPC mice were analyzed at 15 days for immune cell changes in the BM compared with nontumor-bearing C57BL/6 mice. J, KPPC mice were treated with 1 mg anti-IL6 or Ig-control three times/wk; n = 3 vehicle, n = 4 aIL6. Data shown as fold-changes compared with the vehicle, representative of three independent experiments. All data are displayed as violin plots or bar graphs with error bars representing the mean ± SEM. For comparisons between two groups, the unpaired Student two-tailed t test was used. For correlation analyses, the coefficient of determination, R2, was calculated from Pearson's correlation coefficient, r. For survival analyses, the log-rank (Mantel–Cox) test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001, ****, P < 0.0001. BMDC, bone marrow–derived dendritic cell. MDP, monocyte-dendritic progenitor; CDP, common DC progenitor; MDSC, myeloid-derived suppressor cell.

Figure 2.

IL6 is elevated in the blood of patients with PDAC and reduces cDC2 differentiation. A and B, Fold-change of plasma cytokine levels in patients with PDAC (n = 30) compared with healthy controls (n = 15), as measured by high-throughput cytokine analysis (A), where IL6 represents the most significantly elevated cytokine compared with healthy controls (B). C, Correlation between plasma IL6 concentrations and circulating CD1c+ cDC2s (%CD45) in patients with PDAC (red, n = 30) and healthy (blue, n = 15) patients. D,In vitro system for the BMDC differentiation assay. BM from Zbtb46GFP+ mice were cultured in the presence of Flt3 L for 5 days before addition of IL6 (100 ng/mL). DCs were collected after 3 days and analyzed using FACS to determine the percentage of dead (7-AAD+) cDC1 and cDC2, samples were run in triplicate by condition. Data were consistent across two independent experiments. E, BM from Zbtb46GFP+ mice were cultured with Flt3 L ± IL6 for 9 days and analyzed via FACS for cDC2 production, three wells per condition. Data were consistent across three independent experiments. F and G, The scRNA-seq analysis from human PDAC (F) and three murine (G) PDAC models; late-stage KrasLSL−G12D/+Trp53fl/flPdx1Cre/+(LKPFC), late-stage KrasLSL−G12D/+Trp53LSL−R172H/+Ptf1aCre/+ (LKPC), and late-stage KrasLSL−G12D/+Ink4afl/flPtf1aCre/+ (LKIC) were analyzed for IL6 expression (31). H, Kaplan–Meier survival analyses comparing patients with PDAC with high and low IL6 expressions in the primary tumor site using TCGA data. I, KPPC mice were analyzed at 15 days for immune cell changes in the BM compared with nontumor-bearing C57BL/6 mice. J, KPPC mice were treated with 1 mg anti-IL6 or Ig-control three times/wk; n = 3 vehicle, n = 4 aIL6. Data shown as fold-changes compared with the vehicle, representative of three independent experiments. All data are displayed as violin plots or bar graphs with error bars representing the mean ± SEM. For comparisons between two groups, the unpaired Student two-tailed t test was used. For correlation analyses, the coefficient of determination, R2, was calculated from Pearson's correlation coefficient, r. For survival analyses, the log-rank (Mantel–Cox) test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001, ****, P < 0.0001. BMDC, bone marrow–derived dendritic cell. MDP, monocyte-dendritic progenitor; CDP, common DC progenitor; MDSC, myeloid-derived suppressor cell.

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To evaluate the possible sources of IL6, we analyzed scRNA-seq data. In both human and mouse PDAC tissues, we found the highest levels of IL6 expression in stromal cells, including cancer-associated fibroblasts (CAF), macrophages, and endothelial cells (Fig. 2F and G; Supplementary Fig. S2E) and much lower levels of IL6 mRNA in malignant cells, consistent with previous findings (36, 37). These results suggested that the fibro-inflammatory TME of PDAC may be a source of systemic IL6 that, in turn, dysregulated cDC differentiation. Correspondingly, high levels of IL6 expression in PDAC tumor tissues were associated with poorer outcomes (Fig. 2H), suggesting that the impact of increased IL6 occurred both locally and systemically.

To further characterize this possibility, we analyzed the p48-CRE; KrasLSL-G12D;Trp53fl/fl (KPC) genetically engineered mouse model of PDAC. KPC mice have abundant inflammatory stroma and high circulating IL6 levels (35, 38, 39), in a similar manner as human PDAC. We first assessed the impact of tumor formation in the BM and observed evidence of myelopoiesis, including increased numbers of BM inflammatory monocytes (CD11b+LyC+Ly6G) and immature granulocytes (CD11b+LyC+Ly6Ghi, Fig. 2I). In contrast, we observed decreased numbers of common DC progenitors (CDP) and cDC2s in the BM, when compared with healthy controls (Fig. 2I). To determine the role of IL6 in this context, mice were systemically treated with IL6-neutralizing IgGs. IL6 neutralization increased the number of pre-DCs in the BM (Supplementary Fig. S3A and S3B), restored the numbers of intratumoral cDC2s by 2-fold, and trended toward an increase in intratumoral cDC1s (Fig. 2J). Together, these results suggested systemic elevation of IL6-reduced BM development of cDCs, which in turn, limited cells in circulation and in tumor tissues.

Circulating cDC2s in PDAC upregulate IL6/STAT3 signaling and corresponding inflammatory pathways

We next sought to better understand what the potential impact of circulating inflammatory cytokines present in patients with PDAC might have on the cDC phenotype. This was particularly relevant for cDC2s, given their heterogeneous roles in regulating both pro-inflammatory and anti-inflammatory phenotypes with the potential for dichotomous impacts on pro- versus antitumor immunities (40–43). To assess the cDC phenotype, we analyzed circulating APCs. APCs defined as CD45+Lin (CD3,CD16,CD19,CD20) and HLA-DR+ were isolated from the blood of untreated patients with PDAC with only local disease, as well as from healthy controls, followed by scRNA-seq analysis (Fig. 3A; Supplementary Fig. S4A). Unbiased hierarchical clustering led to the identification of 18 distinct clusters representing seven different cell types, visualized using UMAP dimensional reduction (Fig. 3B). Cell identities were established using canonical gene expression (Fig. 3C). Monocytes were the predominant cell type observed, identified as classical (S100A8, S100A9, and CD14), nonclassical (FCGR3A, CDKN1C, and KLF2), and intermediate (ISG15, IFIT2, and LY6E) subsets. The cDC2s were also readily identified by high expression levels of CD1C, FCER1A, and CLEC10A, with smaller fractions of cDC1s (XCR1 and CLEC9A), pDC (IL3RA and IRF7), and circulating CD34+ progenitors (CD34 and SOX4).

Figure 3.

Circulating cDC2s in patients with PDAC have reduced antigen-presenting capacity and increased inflammatory signatures with associated IL6 exposure signatures. A, Flow cytometry plot of gating strategy used for live FACS for CD45+, lineage (CD3/16/19/20)-negative, and HLA-DR+ cells for scRNA analysis. B, UMAP plot of integrated PDAC (n = 3) and healthy (n = 3) patient samples; the colors represent clusters identified by Seurat clustering and/or by canonical gene expression. C, Heat map of the top 10 differentially expressed genes based on log(FC) for each cluster identified. D, Volcano plot of differentially expressed genes for the cDC2 cluster, where genes on the right are those with increased expressions in PDAC samples. E and F, GSEA (E) and over representation analysis (F) performed on the cDC2 cluster using the MsigDB database. G and H, Cells identified as cDC2 (B) were subset on and reclustered, UMAP was colored to represent new Seurat clustering (G) with the corresponding heat maps (H) identifying differentially expressed genes between the two clusters. I, GSEA of the APcDC2 cluster using the Gene Ontology biological processes database. J, Over-representation analysis of the APcDC2 cluster using the Kyoto Encyclopedia of Genes and Genomes. K, GSEA of the icDC2 cluster using the MsigDB hallmark (H) database. L, HLA-DR MFI expressions of circulating CADM1+ cDC1s, and CD1c+ cDC2s from patients with PDAC compared with healthy controls. Data shown from the locally advanced cohort. M, MHCII, CD86, and PDL1 expressions by fluorescence-activated cell sorting of cDC2 differentiated in the presence of Flt3 and IL6 for 9 days. N, Histogram representing CFSE dilution in OTII CD4+ T cells after 72 hours of coculture with ISQ-pulsed DCs differentiated in Flt3L, with or without IL6; corresponding Division Index of OTII CD4+ T cells. Error bars represent the mean ± SEM; for comparisons between two groups, the unpaired Student two-tailed t test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 3.

Circulating cDC2s in patients with PDAC have reduced antigen-presenting capacity and increased inflammatory signatures with associated IL6 exposure signatures. A, Flow cytometry plot of gating strategy used for live FACS for CD45+, lineage (CD3/16/19/20)-negative, and HLA-DR+ cells for scRNA analysis. B, UMAP plot of integrated PDAC (n = 3) and healthy (n = 3) patient samples; the colors represent clusters identified by Seurat clustering and/or by canonical gene expression. C, Heat map of the top 10 differentially expressed genes based on log(FC) for each cluster identified. D, Volcano plot of differentially expressed genes for the cDC2 cluster, where genes on the right are those with increased expressions in PDAC samples. E and F, GSEA (E) and over representation analysis (F) performed on the cDC2 cluster using the MsigDB database. G and H, Cells identified as cDC2 (B) were subset on and reclustered, UMAP was colored to represent new Seurat clustering (G) with the corresponding heat maps (H) identifying differentially expressed genes between the two clusters. I, GSEA of the APcDC2 cluster using the Gene Ontology biological processes database. J, Over-representation analysis of the APcDC2 cluster using the Kyoto Encyclopedia of Genes and Genomes. K, GSEA of the icDC2 cluster using the MsigDB hallmark (H) database. L, HLA-DR MFI expressions of circulating CADM1+ cDC1s, and CD1c+ cDC2s from patients with PDAC compared with healthy controls. Data shown from the locally advanced cohort. M, MHCII, CD86, and PDL1 expressions by fluorescence-activated cell sorting of cDC2 differentiated in the presence of Flt3 and IL6 for 9 days. N, Histogram representing CFSE dilution in OTII CD4+ T cells after 72 hours of coculture with ISQ-pulsed DCs differentiated in Flt3L, with or without IL6; corresponding Division Index of OTII CD4+ T cells. Error bars represent the mean ± SEM; for comparisons between two groups, the unpaired Student two-tailed t test was used. ns, not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

To assess phenotypic changes in circulating cDC2s, differential gene analysis was performed, which identified greater than 700 differentially expressed genes between blood cDC2s in healthy donors and patients with PDAC (Fig. 3D). Among the genes most enriched in PDAC were multiple inflammatory mediators, including S100A8, S100A9, and LYZ, which have been shown to inhibit the differentiation of DCs in mice (44), as well as AREG, which has been shown to promote protumorigenic properties in DCs (45). MT2A was also elevated, whose expression has been shown to increase following lipopolysaccharide stimulation among circulating monocyte derived-DCs (mo-DCs; ref. 46). Genes most enriched in cDC2s from healthy donors included several zinc-finger proteins (ZFPs; ZFP36L1, ZFP36L2, and KLF6), as well as CIITA, which regulates both MHCII expression and the immunostimulatory capacity of DCs (47). Together, these results suggested transcriptional shifts in cDC2s from patients with PDAC toward inflammatory immunosuppressive phenotypes, accompanied by a reduction in antigen-processing capability.

Analysis using GSEA also suggested dramatic shifts in cDC phenotypes. Consistent with high circulating IL6 levels in human patients with PDAC, in cDC2s, we observed an upregulation in hallmark IL6/JAK/STAT3 pathways in PDAC using multiple analyses (Fig. 3E and F). In addition, we observed metabolic shifts in PDAC cDC2s characterized by enrichment of glycolysis and fatty acid metabolism, as well as enrichment in pathways responsible for hypoxia, reactive oxygen species (ROS) production, and inflammatory and IFN alpha responses (Fig. 3E). Conversely, normal samples showed enrichment in IL12/STAT4 signaling pathways, which are associated with enhanced priming of TH1 CD4+ T cells, as well as FOXO pathway enrichment, which has been shown to enhance DC activation of lymphocytes in vivo (Fig. 3F; ref. 48). AP-1 and HIF1 pathways were also enhanced in PDAC cDC2s. These analyses were repeated with the addition of healthy donor PBMC data obtained from public datasets. As with our internal data, we observed upregulation of inflammatory pathways in cDC2s of patients with PDAC compared with noncancer patients (Supplementary Fig. S4B–S4D). Taken together, these results suggested that inflammatory pathways were hyperactivated in circulating cDC2s, and that this process was related to decreased T cell–priming capacity.

Suspecting that these alterations may have influenced subset heterogeneity of the cDC2 pool, we isolated and reclustered cDC2s and were able to identify two separate clusters with distinct phenotypes (Fig. 3G). One was characterized by markers traditionally associated with monocyte lineage and an inflammatory state, the so-called “icDC2” (CD14, VCAN, S100A8, and S100A9), whereas the other exhibited canonical cDC2 markers related to antigen-presenting capabilities, the so-called “APcDC2” (FCER1A, CD1C, and HLA-DRA; Fig. 3H; Supplementary Fig. S4E and S4F). The observed divergent nature of cDC2s is consistent with previous reports of cDC2 compartment heterogeneity (28–32). Aside from the overall reduction in total circulating cDC2 numbers in PDAC, similar to Fig. 1, we did not observe a shift within the cDC2s toward either cDC2 subcluster. Notably, GSEA performed on the two subtypes showed that phenotypic changes in PDAC varied between the clusters. APcDC2s in PDAC were enriched in pathways associated with negative regulation of T-cell activation and immune effector processes (Fig. 3I), whereas APcDC2s in healthy donors were enriched in antigen processing and presentation (Fig. 3J). Conversely, icDC2s in PDAC were enriched in TGFβ and ROS pathways, as well as inflammatory and IFN alpha responses (Fig. 3K). In addition, the upregulation in glycolysis observed in the total cDC2 compartment was only observed in the icDC2s of PDAC, and not APcDC2s. Moreover, using module scores generated from gene sets of CD5+ and CD5 cDC2s reported by Yin and colleagues (41), we observed enrichment of the CD5 gene set in the icDC2s and decreased expression of the CD5+ gene set by the APcDC2s of PDAC (Supplementary Fig. S4E and S4F). These data suggested that although PDAC induced a global shift in cDC2s toward an inflammatory phenotype, the influence within the cDC2 compartment was more subtle, involving reduction of the antigen-presenting capacity of APcDC2s, while simultaneously enhancing the inflammatory phenotype of the icDC2s.

In support of these scRNA-seq results, we found a reduction in HLA-DR expression in cDC2s of patients with PDAC (Fig. 3L). This phenomenon was also observed in cDC1s (Fig. 3L). In support of the premise that circulating IL6 may have been partially responsible, we found that cDC2s that were differentiated in vitro from murine BM progenitors in the presence of exogenous IL6 had decreased MHCII and CD86 expressions and higher PDL1 expression (Fig. 3M). Moreover, FACS-isolated cDC2s produced from BM in the presence of IL6 showed a reduced capacity to present antigens to CD4+ T cells to induce proliferation (Fig. 3N). Together, these results suggested that the cDC2 phenotype was dramatically altered in the circulation of patients with PDAC; these circulating cells had the molecular signature of IL6 exposure, and elevated circulating IL6 levels led to reduced APC function in cDC2s.

Circulating inflammatory cytokines impact the monocyte phenotype

As they share a common precursor with DCs and represent the other predominant APCs in circulation, we next evaluated circulating monocytes for corresponding phenotypic alterations. We isolated and reclustered cells initially identified as monocytes based on FCAR and C5AR1 expressions (42), and observed five distinct subsets within the monocyte compartment (Fig. 4A; Supplementary Fig. S4G). These consisted of a classical monocyte cluster with increased CD14 expression and a nonclassical monocyte cluster sharing many similarities with the “Mono2” subset (FCGR3A and HLA-DRA) described by Villani and colleagues (43), along with three additional likely intermediate monocyte subsets (Fig. 4B and C; ref. 32). The final three clusters likely reflected heterogeneities within the intermediate monocyte compartment. One shared similar gene expression to “Mono3” from Villani and colleagues (CXCL8, CCL3, and NFKBIA), the fourth cluster expressed many IFN response and regulatory genes (IFI44L, IFIT3, and MX1), and the final cluster had high expression of THBS1 and EREG.

Figure 4.

Circulating monocytes have altered phenotypes in patients with PDAC. A, UMAP of the cell subset initially identified as monocytes (see Fig. 3B), colored to represent new Seurat clustering. B and C, Corresponding heat map and violin plot illustrating differentially expressed genes for each cluster. D, GSEA of classical monocyte clusters using the Gene Ontology biological processes database. E, Over-representation analysis of classical monocyte clusters using the MsigDB PID database. F, GSEA of nonclassical monocyte clusters using the Gene Ontology biological processes database. G, Over-representation analysis of nonclassical monocyte clusters using the MsigDB PID database.

Figure 4.

Circulating monocytes have altered phenotypes in patients with PDAC. A, UMAP of the cell subset initially identified as monocytes (see Fig. 3B), colored to represent new Seurat clustering. B and C, Corresponding heat map and violin plot illustrating differentially expressed genes for each cluster. D, GSEA of classical monocyte clusters using the Gene Ontology biological processes database. E, Over-representation analysis of classical monocyte clusters using the MsigDB PID database. F, GSEA of nonclassical monocyte clusters using the Gene Ontology biological processes database. G, Over-representation analysis of nonclassical monocyte clusters using the MsigDB PID database.

Close modal

As with the cDC2 compartment, DGA and GSEA revealed significant differences between normal and PDAC conditions, with each subset differing greatly between normal and PDAC samples. DGA analysis revealed between 700 and 1,500 differentially expressed genes for each cluster, and when GSEA was performed, many alterations were seen across subsets. Consistent with high IL6 exposure, we observed enrichment in IL6-related pathways, along with GM-CSF pathways. Within classical monocytes, we saw enrichments in pathways related to negative regulation of T-cell activation and proliferation, as well as positive regulation of angiogenesis and the Wnt-signaling pathway (Fig. 4D). Pathways enriched in normal samples highlighted processes critical for translation, including RNA processing and mRNA metabolism. In addition, we observed enrichment in PDGFRB, CXCR4, and the angiogenesis-related VEGFR1/2 pathways in PDAC classical monocytes (Fig. 4E).

Like classical monocytes, we observed that nonclassical monocytes in PDAC were enriched in pathways responsible for negative regulation of immune effector processes and regulation of T-cell proliferation (Fig. 4F), whereas in normal samples, we found enrichment of RNA processing and metabolic functions, including peptide metabolic processes and organic nitrogen compound biosynthesis. Like cDC2s, we found evidence of IL6 exposure with enrichment of IL6 signaling pathways (Fig. 4G). Together, these results suggested that the monocyte compartment underwent substantial remodeling in PDAC, including enrichment in pathways detrimental to the generation of robust T cell–mediated immunity, as well as a potential protumorigenic influence by supporting angiogenic pathways.

BM cDC2s in PDAC

To evaluate phenotypic changes in patients with PDAC in cDC development in the BM, cDCs and their progenitors were analyzed by scRNA-seq. Cells enriched for cDCs and their progenitors were isolated by magnetic bead-lineage depletion of BM from 4 healthy donors and 3 untreated resectable patients with PDAC and underwent scRNA-seq. Unbiased hierarchical clustering led to the identification of 16 distinct clusters representing 11 different cell types, visualized using UMAP dimensional reduction and heat map of differentially expressed genes (Fig. 5A; Supplementary Fig. S5A). cDCs and potential cDC progenitors were isolated and reclustered (Fig. 5B). As seen in PBMC samples, GSEA analysis of cDC2s in the BM showed substantial numbers of differentially expressed genes and upregulation of the IL6 pathway in PDAC patient's BM samples compared with healthy donor's BM (Supplementary Fig. S5B and S5C). As above, additional data from healthy BM samples were obtained from public datasets and reanalyzed (Supplementary Fig. S6A). This confirmed findings previously seen, namely upregulation of inflammatory pathways and IL6/JAK/STAT pathways (Supplementary Fig. S6B). However, using either dataset, traditional UMAP clustering was unable to distinguish cDC progenitors. To overcome this, we performed pseudo time analysis, which identified multiple lineages of progenitors leading to both cDC1s and cDC2s (Fig. 5C; Supplementary Fig. S6C). In this analysis, we could identify two potential cDC progenitor populations related to both cDC1s and cDC2s, as well as a proliferating population of cDC2s that was related to mature cDC2s (here called “pro-cDC2s” but could be considered pre-cDC2s). Analysis of both pro-cDC2s and mature cDC2s found substantial numbers of DEGs and changes in potential pathways by GSEA, including upregulation of the IL6 pathway in PDAC patient cDC2 progenitors (Fig. 5D and E; Supplementary Fig. S5D). Taken together these data suggest that, similar to the circulating blood pool of cDC2s, PDAC distorts the heterogeneity of cDC2s and their progenitors in the BM of patients and this potentially biases the type of immune response that can be generated.

Figure 5.

Dendritic cells from the BM are altered in pancreatic cancer. A, UMAP of lineage (CD3/CD15/CD16/CD19/CD56/CD235) negative BM samples FACS isolated from patients with PDAC (n = 3) and healthy donors (n = 4) submitted for scRNA-seq–integration and clustered using Seurat. B, UMAP analysis of selected subsets of clusters from (A) and associated heat map of differentially expressed genes for each cluster identified. C, Pseudotime analysis of cell in (B). Line delineates relationships. D and E, Volcano plot of differentially expressed genes in BM-cDC2s and pro-cDC2s from (C) and over-representation analysis of cDC2s using the MsigDB database (PID) for these comparisons.

Figure 5.

Dendritic cells from the BM are altered in pancreatic cancer. A, UMAP of lineage (CD3/CD15/CD16/CD19/CD56/CD235) negative BM samples FACS isolated from patients with PDAC (n = 3) and healthy donors (n = 4) submitted for scRNA-seq–integration and clustered using Seurat. B, UMAP analysis of selected subsets of clusters from (A) and associated heat map of differentially expressed genes for each cluster identified. C, Pseudotime analysis of cell in (B). Line delineates relationships. D and E, Volcano plot of differentially expressed genes in BM-cDC2s and pro-cDC2s from (C) and over-representation analysis of cDC2s using the MsigDB database (PID) for these comparisons.

Close modal

The poor response of pancreatic cancer to immunotherapy has multiple contributing factors. Its characteristically dense fibrotic stroma simultaneously restricts intratumoral T-cell trafficking and functions, leading to loss of tumor control (49, 50), while effectively reducing the delivery of therapeutics to the TME (51–53). In addition, CAFs and tumor-associated macrophages predominate in the stroma, representing major producers of a variety of inflammatory mediators, and contribute to an immunosuppressive TME. Compounding these effects, direct tumor-induced impairments of antitumor mechanisms have also been described previously, both locally on T cells (54) and systemically on DCs (23, 35), and have been extensively reviewed in (30). In the present study, we demonstrated the extent to which the cDC2 compartment was impaired in pancreatic cancer, which exacerbated an already compromised antitumor immune response.

Our findings further support an understanding of cDC2s as a heterogeneous cell type, and provided evidence that this heterogeneity is already established at the time of their origin in the BM. Previous scRNA-seq on human blood (43) and tumor-draining lymph nodes (55) have shown heterogeneity within cDC2s, but this has not been characterized in human BM. In addition, we showed how cDC2 subsets were differentially affected in the context of PDAC, where APcDC2s exhibited reduced antigen-presenting capacity, whereas icDC2s were increasingly inflammatory. These results suggest that PDAC leveraged heterogeneity in the compartment from the earliest stages in development in the BM in favor of immunosuppression. As critical initiators of CD4+ T-cell activation (55), a smaller circulating pool of phenotypically altered cDC2s likely contributes to an impaired antitumor adaptive immune response in PDAC.

The inflammatory cytokine profile of PDAC has far-reaching immunological implications, which drives alterations in many cell types (56). Prior studies have shown that IL6, IL8, IL10, IL12, and IL18, as well as TGFβ and TNFα are elevated in PDAC (56–58). In addition, elevated circulating levels of IL6 and IL10 have been correlated with poor outcomes in PDAC (58). Regarding DCs, IL6 has been shown to suppress the differentiation of CD14+ mo-DCs and, in combination with G-CSF, has been shown to be capable of reducing the allostimulatory capacity of immature mo-DCs (59). Separately, Bellone and colleagues (60) showed that tumor-derived TGFβ, IL10, and IL6 work in concert to inhibit immature monocyte-derived DCs (iMo-DC) in vitro. However, because there was substantial evidence supporting biological disparities between mo-DCs and cDCs, the extent to which these cytokines influenced cDC differentiation was unclear (61). Evidence from previous studies indicates that IL6 inhibits the differentiation of mature cDCs in the steady-state as non–tumor-bearing mice IL6 knockout mice exhibit fewer activated, mature DCs in superficial lymph nodes (62). IL6 has recently been implicated in driving increased apoptosis of cDC1s, leading to impairments in cDC1-mediated CD8+ T-cell activation (35). Our findings identified an additional role of IL6-mediated impairment of the antitumor immune response via reduced differentiation of cDC2s in the BM, leading to fewer circulating cDC2s with a dramatically altered phenotype.

The IL6 pathway is currently being targeted in ongoing clinical trials in pancreatic cancer using mAbs against either IL6 (NCT04191421 and NCT00841191) or its receptor (NCT02767557 and NCT04258150). Our findings provide additional support for these studies and suggest that combination with DC-targeted therapies may augment responses to IL6-targeted therapies. Thus, these results provide the rationale for further combined immunotherapy regimens to be tested in future clinical trials.

C.A. James reports grants from NIH during the conduct of the study. J.M. Baer reports grants from NIH during the conduct of the study. L.-I. Kang reports grants from NIH during the conduct of the study. W.G. Hawkins reports a clinical trial support for an ongoing trial with Celldex Corporation. This is unrelated to this work with the exception that both focus on DCs. D.G. DeNardo reports grants from NIH/NCI during the conduct of the study; as well as grants from BMS, Pfizer, and Verastem outside the submitted work. No disclosures were reported by the other authors.

C.A. James: Conceptualization, data curation, formal analysis, supervision, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing. J.M. Baer: Formal analysis, writing–review and editing. C. Zuo: Formal analysis, writing–review and editing. U.Y. Panni: Investigation, methodology, writing–review and editing. B.L. Knolhoff: Formal analysis, investigation. G.D. Hogg: Formal analysis, investigation, writing–review and editing. N.L. Kingston: Formal analysis, investigation. L.-I. Kang: Formal analysis, investigation. V.E. Lander: Data curation, software, writing–review and editing. J. Luo: Data curation, formal analysis, writing–review and editing. Y. Tao: Data curation, software, writing–review and editing. M.A. Watson: Resources, supervision. R. Aft: Resources, supervision. R.C. Fields: Resources, supervision. W.G. Hawkins: Conceptualization, resources, supervision, writing–original draft, project administration, writing–review and editing. D.G. DeNardo: Conceptualization, data curation, formal analysis, supervision, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing.

D.G. DeNardo and team were supported by NCI R01CA273190, R01CA177670, R01CA203890, R01CA248917, P30CA09184215, P50CA196510, and the BJC Cancer Frontier Fund. R. Aft was supported by R01 CA262555.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).

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