Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T-cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune resistance in pancreatic cancer was induced in response to antitumor T-cell immune responses and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide evidence that T-cell–dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune resistance. Three KPC mouse models of pancreatic cancer were used: the mT3-2D (Kras+/LSL-G12D; Trp53+/LSL-R172H; Pdx1-Cre) subcutaneous and orthotopic models, as well as the KP1 (p48-CRE/LSL-Kras/Trp53flox/flox) subcutaneous model. KPC cancer cells were grown in immunocompetent and immunodeficient C57BL/6 mice and analyzed to determine the impact of adaptive immunity on malignant epithelial cells, as well as on whole tumors. We found that induced T-cell antitumor immunity, via signal transducer and activator of transcription 1 (STAT1), stimulated malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and altered intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA-approved drug ruxolitinib overcame these tumor-protective responses and improved anti–PD-1 therapeutic efficacy. These findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.
Worldwide, nearly half a million people are diagnosed with pancreatic cancer every year (1), and the mortality of these patients within five years is more than 90% (2). Therefore, there is an urgent need to develop more effective therapeutic strategies.
Pancreatic tumors typically have minimal infiltration of T cells, and thus pancreatic cancer is considered an immunologically cold neoplasm. When T cells infiltrate pancreatic tumors, they are generally skewed toward exhibiting protumor properties rather than antitumor cytotoxic functions (3). Even when effective cytotoxic cells successfully infiltrate pancreatic tumors, they likely get suppressed or physically trapped within the stroma (4). The pancreatic cancer myelosuppressive microenvironment has been shown to be derived by tumor-intrinsic properties (5, 6). Although these observations could suggest that T-cell immunity is not relevant to pancreatic cancer and that the pancreas has organ site-specific resistance to immunotherapy, studies have shown otherwise. Indeed, tumors of rare long-term pancreatic cancer survivors elicit more potent cytotoxic T-cell responses compared with the rest of pancreatic cancer patients (7). Immune-checkpoint blockade, which promotes T-cell activity, has demonstrated efficacy in ∼2% of pancreatic cancer patients that exhibit high microsatellite instability (MSI-high; ref. 8). These findings suggest that when T-cell antitumor immune responses are sufficiently induced, T cells become effective weapons.
Many therapeutic interventions have been developed to target known immune-escape routes in pancreatic cancer. However, the vast majority of pancreatic cancer immunotherapy clinical trials have failed (9). Hence, further resolution of the complex biology of pancreatic cancer immunity is needed. Here, we hypothesized that immune resistance in pancreatic cancer is induced in response to T-cell antitumor immunity, and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. To test this hypothesis, mT3-2D pancreatic tumors were grown subcutaneously in immunocompetent wild-type (WT) and severe combined immunodeficiency (SCID) C57BL/6 mice. Tumors were then analyzed to determine the impact of adaptive immunity on malignant epithelial cells and whole tumors. The major findings from this mouse model were then validated using the mT3-2D orthotopic model, as well as the KP1 subcutaneous model. First, we determined that despite the induction of T-cell immunity in immunocompetent mice bearing mT3-2D tumors, T cells failed to completely regulate tumor growth. Subsequently, we showed that stimulated antitumor T-cell responses, potentially via JAK/STAT signaling, induced the expression of myeloid-associated genes in malignant epithelial cells and stimulated the infiltration of PD-L1–expressing myeloid cells. Targeting the JAK/STAT signaling pathway using the FDA-approved drug ruxolitinib inhibited the expression of myeloid cell–associated genes and improved anti–PD-1 therapeutic efficacy in mice. Together, these data suggest that some known features of the pancreatic cancer immunosuppressive microenvironment were driven in response to T-cell antitumor immunity. Disabling such responses offers new approaches to pancreatic cancer–directed immunotherapy.
Materials and Methods
mT3-2D, mT4-2D, and mT5-2D murine pancreatic cancer cell lines were gifts from David Tuveson, Cold Spring Harbor Laboratory, Laurel Hollow, NY (obtained in 2016; ref. 10). The KP1 murine pancreatic cancer cell line was a gift from David DeNardo, Washington University Medical School, St. Louis, MO (obtained in 2016; ref. 11). PANC02 murine pancreatic cancer cells were a gift from Jill Smith, Georgetown University Lombardi Comprehensive Cancer Center, Washington, DC (obtained in 2015). The mT3-2D GFP cell line was a gift from Chunling Yi, Georgetown University Lombardi Comprehensive Cancer Center, Washington, DC (obtained in 2018). Briefly, pHAGE PGK-GFP-IRES-LUC-W (addgene, #46793) was transfected into 293T cells to generate the virus. The virus was infected into mT3-2D cells. For infection, 1 × 105/well mT3-2D cells were cultured in 6-well plates and the MOI was 0.1 to 1. GFP+ cells were sorted as described below. All cell lines are syngeneic in C57BL/6 mice. HEK293T cells, C3H/10T 1/2 clone 8, and RAW 264.7 cell lines were obtained from the Georgetown Lombardi Comprehensive Cancer Center Tissue Culture Shared Resource (TCSR), Georgetown University, Washington, DC. All cell lines, except C3H/10T 1/2 clone 8, were grown in standard conditions and maintained in Dulbecco's Modified Eagle Medium (DMEM; Fisher Scientific, # SH30022LS) supplemented with heat-inactivated fetal bovine serum (HI-FBS) to a final concentration of 10% and 2 mmol/L L-glutamine. C3H/10T 1/2 clone 8 was cultured in Eagle's Basal medium (Thermo Fisher, #21010-046) supplemented with HI-FBS to a final concentration of 10% and 2 mmol/L L-glutamine. Mouse short tandem repeat profiling was done in 2017, which validated the mouse origin of the cell lines, and no contamination with any human or non-human primate cells. All cell lines were tested and determined to be free of Mycoplasma and other rodent pathogens (IMPACT II profile was done several times before using the cells for animal experiments). All cell lines were used for in vivo and in vitro experiments within the third and tenth passages.
All mice used in the study were 6 to 8 weeks of age. C57BL/6J WT and SCID mice were purchased from The Jackson Laboratory. All studies involving animals were reviewed and approved by the Georgetown University Institutional Animal Care and Use Committee (GU IACUC). 1 × 105 mT3-2D or mT3-2D-GFP cells were injected subcutaneously into the right flank of WT, WT with T-cell depletion (T-cell depletion; details in the next section), or B6.CB17-Prkdcscid/SzJ (SCID) mice. KP1 cells (2 × 105) were injected subcutaneously into the right flank of C57BL/6J WT or T-cell–depleted mice. PANC02 cells (2 × 106) were injected subcutaneously into the right flank of WT and SCID mice.
For in vivo ruxolitinib (LC Laboratories) experiments, WT mice were first randomized into groups based on their tumor size. Mice were then treated with ruxolitinib daily by oral gavage at a dose of 50 mg/kg. Anti–PD-1 (clone: RMP1-14, Bio X Cell) was used at a dose of 200 μg/i.p. injection twice per week. Both ruxolitinib and anti–PD-1 treatment started when tumors reached about 50 to 100 mm3 and continued for 4 weeks. All tumors were measured twice weekly using calipers. Volume was calculated using (length × width2)/2. When tumors reached 1 to 2 cm3, or when mice showed signs of pain or distress, mice were euthanized using CO2 inhalation. For orthotopic experiments, 5 × 104 mT3-2D cells expressing GFP and luciferase (mT3-2D-GFP/Luc) were orthotopically implanted into the tail of the pancreas. Tumor growth was monitored weekly using the IVIS imaging system (Xenogen Corp). Luciferin (30 mg/kg; Nanolight Technology) was administered to mice i.p. to image tumors.
Mice were euthanized using CO2 inhalation when orthotopic tumors reached total flux of 1 × 1010 or when mice showed signs of pain or distress. After euthanization, tumors were excised, and spleens were collected for downstream analysis.
In vivo T-cell depletion
For the subcutaneous mouse models, T-cell depletion started one week before tumor cell inoculation. 1 × 105 of mT3-2D cells or 2 × 105 of KP1 cells were then injected subcutaneously in the right flank of C57BL/6J mice. For the orthotopic mT3-2D mouse model, T-cell depletion started four days after 5 × 104 mT3-2D-GFP/Luc cells were implanted in the pancreas. For depletion of CD4+ and CD8+ T cells, each mouse was given 200 μg of anti-mouse CD4 (GK1.5, rat IgG2b; Bio X Cell) + 200 μg anti-CD8 (2.43, rat IgG2b; Bio X Cell) i.p., and the treatment continued twice weekly until the mice were sacrificed. After euthanizing the mice, splenocytes were collected to evaluate T-cell depletion by flow cytometry.
Preparation of solid tumor tissue for single-cell suspension
Tumors were chopped into small pieces and transferred into gentleMACS C tubes (MACS Miltenyi Biotec, #130-093-237), containing 10 mL of DMEM and collagenase D (1 mg/mL; Sigma-Aldrich, COLLD-RO Roche, #11088866001). The tubes were placed on a gentleMACS Dissociator (MACS Miltenyi Biotec, #130-095-937), and the program 37_m_TDK2 was used. After dissociation, cells were filtered using 70-μm cell strainer and recovered by centrifugation.
Preparation of splenocytes for single-cell suspension
Spleen tissue was gently ground between frosted glass microscope slides (Corning, #2948). The tissue was passed 10× through a 1,000 μL pipette tip. Red cells were removed by incubating the splenocytes for 3 minutes with 3 mL eBioscience 1X RBC Lysis Buffer (Invitrogen, Thermo Fisher, #00-4333-57). Cells were pelleted by centrifugation, and then recovered in 10 mL RPMI media with 10% HI-FBS, and filtered using a 70-μm cell strainer.
NanoString nCounter mouse PanCancer immune profiling screen
Five WT mice and five SCID mice were injected with 1 × 105 of mT3-2D cells subcutaneously. When tumors reached 1 cm3, mice were euthanized, and tumor tissues were collected. Then, RNA was extracted from the whole tumor tissue of the five WT tumor samples, the five SCID tumor samples, and the mT3-2D cell line using the PureLink RNA Mini Kit (Ambion). RNA concentration and quality were assessed using the Agilent BioAnalyzer (RINs between 5.9 and 9.7). Total RNA (50 ng) from each sample was obtained, and gene expression was measured on the NanoString nCounter Analysis System (NanoString Technologies). The NanoString PanCancer Immune CodeSet (#XT-CSO-MIP1-12) was used to perform the nCounter Gene-Expression Assay, and no custom probes were added. Samples were processed on the NanoString Technologies nCounter Sprint Profiler according to NanoString protocols in the Georgetown University Genomics and Epigenomics Shared Resource (GESR). Data from the NanoString nCounter system were normalized to the internal positive controls and housekeeping genes (Rpl19, Abcf1, Hdac3, Oaz1, Sap130, Ppia, Eef1g, Polr2a, Tbp, Polr1b, Edc3, Sdha, Gusb, Nubp1, Eif2b4, Alas1, Sf3a3, and Hprt) using the recommended settings in the nSolver 4.0 software normalization module (NanoString Technologies). Normalized data were also used with NanoString Advanced Analysis Module 2.0 (NanoString Technologies). For the summary heat map analysis, all normalized genes of the cell line, WT tumor and SCID tumor samples were included in the analysis, and the cell line sample was used as the reference. For the differential gene-expression analysis, the expression of all normalized genes in WT tumor samples was compared with the expression of all normalized genes in SCID tumor samples, and the SCID samples were used as the reference. The data obtained from the PanCancer Immune CodeSet were deposited on the public database GEO under the accession number GSE165165.
Whole-exome sequencing and neoantigen prediction
Five WT mice and five SCID mice were injected with 1 × 105 of mT3-2D cells subcutaneously. When tumors reached 1 cm3, mice were euthanized, and tumor tissues were collected. Then, DNA was extracted from the whole tumor tissue of the five WT mT3-2D subcutaneous tumors, the five SCID mT3-2D subcutaneous tumors, and the mT3-2D cell line using a DNeasy Blood and Tissue Kit (Qiagen, #69504). Indexed, paired-end whole-exome libraries were constructed from 1.0 μg gDNA using the Agilent SureSelect XT HS Mouse All Exon Library Kit. Libraries were sequenced on an Illumina HiSeq 4000 using PE 150 bp (300 cycle) chemistry to a mean depth of 200× per sample. Raw and processed whole-exome sequencing (WES) data were deposited in the public database Gene Expression Omnibus (GEO) under the accession number GSE165167. For Sanger sequencing, Genewize (www.genewiz.com) was used to design primers that target the neoepitope regions. Each targeted region was amplified with PCR and sequenced with Sanger sequencing. These targeted regions are shown in Supplementary Table S1.
Reads were aligned to the mm10 reference genome using the Burrows–Wheeler Alignment tool version 0.7.17 (12). Genome Analysis Toolkit (GATK) best practices were used for sorting, duplicate marking, and variant calling with the HaplotypeCaller (13). Variants were annotated using Ensemble-VEP version 90.9 (14), and all possible 9-mer peptides that included a missense variant were identified using pVACtools version 1.0.8 (15). MHCI binding affinities (H-2-Db and H-2-Kb) were then predicted for each WT and corresponding mutant peptide with NetMHC 4.0 (16). The following filtering steps were performed. Mutant peptides with predicted binding affinity >500 nmol/L were removed. Peptides from olfactory genes were excluded because they are prone to somatic mutations, are not highly expressed, and yield false positives as cancer-associated genes (17). Mutations that corresponded to known polymorphic genes were also excluded. Peptides with low predicted agretopic indices (18), i.e., ratio of mutant to WT binding affinity <4, were removed.
Sorting malignant epithelial cells
For the RNA-sequencing (RNA-seq) experiments, six mT3-2D-GFP WT and five SCID tumors were processed into single-cell suspensions as described above. The cells were stained with Helix NP Blue (BioLegend, #425305) and live GFP+ cells were isolated using a BD FACSAria IIu fluorescence-activated cell sorting (FACS) cytometer. Sorted cells were then snap frozen in liquid nitrogen. For sorting mT3-2D-GFP/Luc malignant cells from the tumors, Sytox Blue (Thermo Fisher Scientific, #34857) staining was used, and live GFP+ cells were isolated via FACS. To sort KP1 cells, tumors were processed into single-cell suspensions as stated. Cells were then stained with Sytox Blue (Thermo Fisher Scientific, #34857), CD45 (clone 30-F11; BioLegend #103115), and CD44 (clone IM7; BioLegend #103047), and Live CD44+CD45− cells were selected.
RNA was extracted from WT GFP+ sorted cells, SCID GFP+ sorted cells, and the mT3-2D-GFP cell line using the RNeasy Micro Kit (Qiagen). RNA concentration and quality were assessed using an Agilent BioAnalyzer (RINs >7). Equal amounts of RNA from each corresponding group were combined as one sample (1 WT sample, 1 SCID sample, and 1 cell line). Indexed, paired-end sequencing libraries were prepared from 1.0 μg total RNA using the Illumina TruSeq Stranded mRNA Library Preparation Kit (polyA cDNA synthesis) in the GESR in the Lombardi Comprehensive Cancer Center at Georgetown University Medical Center. Libraries were sequenced on an Illumina NextSeq550 using 75 bp (150-cycle) paired-end chemistry to a mean depth of 40M reads per sample. Raw reads were aligned to the mm9 reference genome with BWA (12, 19) and quantified into read counts with the annotation model (Partek E/M). All subsequent RNA-seq analyses were performed in R version 3.5.1. Read counts were variance-stabilized with the regularized log (rlog) function in the R/Bioconductor package DESeq2 version 1.20.0 (20). Log fold changes were computed on the rlog-transformed expression data, and genes with absolute log fold changes greater than or equal to one were retained as significant. One-sided gene set statistics were performed for log fold changes on Hallmark version 6.2 pathways from the MSigDB (21) and KEGG pathways (22) from the R/Bioconductor package KEGG.db version 3.2.3, and sets with FDR adjusted P values below 0.05 were considered statistically significant. Raw and processed RNA-seq data were deposited in the public database GEO under the accession number GSE165166.
Sample preparation for global proteomics profiling
Solid mT3-2D tumor tissue was ground with a mortar and pestle in the presence of liquid nitrogen to create a powdered tissue. The powdered tumor tissues or cell pellets in the case of the cell line sample were resuspended in lysis buffer containing 50 mmol/L Tris HCl pH 7.5, 100 mmol/L NaCl, 1% Triton X-100 (Fisher Scientific, #BP151-100), 10 mmol/L EDTA (VWR, #VW3654-1), 1 mmol/L phenylmethylsulfonyl fluoride (PMSF; Crystalgene, #225-011), 1 mL/10 mL 1× Protease Inhibitor Cocktail (Roche, #04693132001), and 100 μL/10 mL 1× Phosphatase Inhibitor Cocktail (Sigma, #P5726). The single-cell suspensions were vortexed vigorously for 5 minutes, sonicated using a probe-tip ultrasonic processor (Vibra Cell; with the AMPL setting of 30%), and then maintained in agitation for 30 minutes at 4°C. The suspension was centrifuged at 12,000 × g for 15 minutes. The supernatant was collected, and samples were processed for NanoUPLC-MS/MS and analyzed as described previously (23).
Solid mT3-2D tumor tissue was ground with a mortar and pestle in the presence of liquid nitrogen to create a powdered tissue. Lysis buffer was added to the powdered tissue or cell pellets and maintained in agitation for 30 minutes at 4°C. The lysis buffer consists of RIPA buffer (Thermo Scientific, #89900), 1 mL/10 mL 10x Protease Inhibitor (Sigma, #S8820), and 100 μL/10 mL 1× Phosphatase Inhibitor Cocktail (Sigma, #P5726-1ML). Western blotting was done as described previously (24). The antibodies used, with dilutions, are listed in Supplementary Table S2.
RNA isolation and quantitative reverse-transcription PCR
RNA extraction and quantitative reverse-transcription PCR (RT-qPCR) were performed using mT3-2D, mT4-2D, mT5-2D, RAW264.7, and KP1 cell lines, as well as using the mT3-2D, mT3-2D-GFP/Luc, and KP1 tumor tissue. For the solid tumor samples, the tissue was ground with a mortar and pestle in the presence of liquid nitrogen to create a powdered tissue. Then, the powdered tumor tissue or cell pellets in the case of the cell lines were resuspended in lysis buffer. RNA extraction and RT-qPCR were performed as described previously (24). Gene expression was normalized to Hrpt or Gapdh and analyzed using the ΔCT or 2−ΔΔCT method in triplicate. The primer sequences are listed in Supplementary Table S3. For lipopolysaccharide (LPS) treatment, 1 × 105 RAW264.7 cells/mL (2 × 105 cells/well of a 6-well plate) were treated with 1 μg/mL LPS (Sigma-Aldrich, # L3129) for 24 hours. For in vitro ruxolitinib treatment, cells were treated with ruxolitinib (1 μg/mL; MCE, #HY-50856) for the indicated time periods.
For solid mT3-2D tumors, tissues were fixed in 10% formalin overnight at room temperature and then stored in 70% ethanol until paraffin embedding. For HistoGel sample preparation, 1 to 5 million cells were fixed in 10% formalin overnight at 4°C. Cells were washed with PBS and then left in HistoGel for 2 to 5 minutes, followed by the addition of 100% ethanol. Samples were sent to the Georgetown University Histopathology and Tissue Shared Resource for embedding, sectioning, and staining. The hematoxylin and eosin (H&E) staining was scored blinded by the histopathologist in the Georgetown University Histopathology and Tissue Shared Resource. For Masson's trichrome, Ki67, CD4, CD8, S100a8, and S100a9 IHC, ImageJ (v1.48) and FIJI (v2.0.0-rc-69/1.52n) were used for the analysis. ImageJ and FIJI allowed for automated unbiased quantification of the staining intensity and cell count for all slides from both groups. The antibodies are listed in Supplementary Table S4.
Arginase activity assay
Arginase activity was measured colorimetrically using Abcam's Arginase Activity Assay Kit (#ab180877). 5 × 106 cells of mT3-2D, mT4-2D, mT5-2D, KP1, and CH3/10T1/1 clone 8 were lysed with 100 μL of the kit's lysis buffer (2 × 106 cells per well) in duplicate in a flat-bottom, low-retention plate. Samples were processed and analyzed per the manufacturer's instructions. Briefly, samples were incubated for 20 minutes at 37°C with H2O2 substrate solution, while background wells were incubated with additional buffer. Standards were prepared per kit instructions, and the enzymatic reaction mixture was prepared and added to all wells. Absorbance values were immediately obtained over a 60-minute period using a plate reader (the 2104 EnVision Multilabel Plate Reader) at OD = 570 nm.
Crystal violet staining
Crystal violet staining was done as described previously (24). Briefly, 1 × 103 mT3-2D cells per well were cultured in 96-well plates. At indicated time points, cells were rinsed and subjected to 0.52% Crystal Violet (Fisher Scientific, C581-100) in 25% methanol. For quantitative analysis, cells were dissolved in 100 mmol/L sodium citrate with 50% ethanol. Then, the absorbance was measured using a plate reader (the 2104 EnVision Multilabel Plate Reader) at OD = 570 nm.
Multiparametric flow cytometry analysis of intratumoral immune cell infiltrates
Single-cell suspensions from mT3-2D tumors harvested from WT and SCID mice were generated as outlined above. 2 × 106 cells were prepared for staining as described previously (24). Briefly, cells were labeled with Live/Dead (Invitrogen, Thermo Fisher, Ref: l-23105). Subsequently, cells were washed and incubated for 10 minutes with CD16/CD32 (clone 2.4G2; 553141) to block Fc receptors before adding a cocktail of surface monoclonal antibodies (mAb): anti-mouse-CD45 (clone 30-F11, #564590), NK-1.1 (clone PK136, #562864), B220 (clone RA3-6B2, #563103), CD3e (clone 145-2C11, #564661), CD4 (clone RM4-5, #563151), CD8a (clone 53-6.7, #564920), PD-1 (clone J43, #744549), PD-L1 (clone MIH5, #564715), CD25 (clone PC61, #565134), I-A/I-E (clone M5/114.15.2, #563415), CD11b (clone M1/70, #564443), CD11c (clone N418, #565452), all from BD Biosciences, and F4/80 (BM8, #123114), Gr-1 (clone RB6-8C5, #108428), Ly6G (clone 1A8; #127645), and Ly6C (clone HK1.4, #128030) from BioLegend. After 30 minutes of staining, cells were washed, and samples were acquired with a FACS Symphony cytometer (BD Biosciences).
For the analysis, cells were manually gated on size and granularity. Dead cells and doublets were excluded, and CD45+ cells were selected. Three thousand CD45+ live cells from each WT (n = 5) and SCID mouse (n = 5) were concatenated. A total of 30,000 CD45+ cells were analyzed using unsupervised T-distributed stochastic neighbor embedding (t-SNE), FlowSOM (Flow Self-Organizing Map, FlowJo), ClusterExplorer, and PhenoGraph algorithms using FlowJo software (version 10.5.3; FlowJo, LLC).
For FlowSOM analysis, Ly6C, Ly6G, F4/80, PD-L1, and MHCII were evaluated. The same FlowSOM map was applied to WT and SCID groups. In this analysis, visualized cell clusters were distributed in a minimal spanning tree. Each cluster was colored and contained nodes, where the size of each node corresponded with the number of cells represented by the node. These nodes connected to others with the greatest similarities, but the analysis also considered the multidimensional topology of the data. Inside each node, a pie chart indicated the mean fluorescence intensities of the selected markers (25). t-SNE clustering was done based on CD45, NK1.1, CD3, Gr-1, CD11b, CD11c, MHCII, Ly6C, Ly6G, B220, CD4, CD8, PD1, PD-L1, CD25, and F4/80. To determine the enrichment of generated clusters, CD45+ cells from WT tumors and SCID tumors were overlaid on the generated t-SNE map. These clusters were further analyzed based on their phenotypic similarities, using PhenoGraph. ClusterExplorer analysis was done based on the median fluorescent intensity of selected surface markers expressed by each of the studied clusters. Here, the expression of CD45, CD3, CD4, CD8, CD25, PD1, and Ly6C in T cells was evaluated.
Flow cytometry analysis and sorting
For CCR1 myeloid cell analysis of the mT3-2D tumor model, and for CD11b+Ly6G−Ly6Clow/− and CD11b+Ly6G+ Ly6Cint splenocyte myeloid cell sorting from WT tumor-free/naïve mice, cells were labeled with either a Zombie NIR Fixable Viability Kit (BioLegend, #423105) before Fc blocking and cocktail antibody staining, or with Helix NP Green (BioLegend, #425303) after cocktail antibody staining. Cells were washed and incubated for 10 minutes with CD16/CD32 (clone 2.4G2; 553141) to block Fc receptors. Cells were then washed, and a cocktail of antibodies was added: CD11b (BioLegend, clone M1/70 #101207), Ly-6C (BioLegend, clone HK1.4, #128015), Ly-6G (BioLegend, clone 1A8, #127627) with and without CCR1 (BioLegend, clone S15040E, #152505). After staining, cells were acquired with a BD LSRFortessa Cell Analyzer (BD Biosciences) and analyzed with FlowJo, or CD11b+Ly6G−Ly6Clow/− and CD11b+Ly6G+ cells were isolated using a BD FACSAria IIu cytometer.
For PD-L1–expressing tumor-associated macrophages (TAM; CD11b+PD-L1+F4/80+) flow-cytometric analysis, cells were labeled with the LIVE/DEAD Fixable Violet Dead Cell Stain Kit (Invitrogen, #L34964) in PBS before Fc blocking and cocktail antibody staining. Cells were then washed with 1% BSA in 1× PBS and incubated for 20 minutes with TruStain fcX (anti-mouse CD16/CD32, clone 93; BioLegend, #101320) to block Fc receptors. Cells were washed, and a cocktail of antibodies was added: CD45 (clone 30-F11; BioLegend #103115), CD11b (BioLegend, clone M1/70 #101255), F4/80 (clone BM8; BioLegend #123110), PD-L1 (clone MIH5; BD Biosciences, #564715). After staining, cells were fixed in 1% paraformaldehyde in 1× PBS and then acquired with a BD LSRFortessa Cell Analyzer (BD Biosciences) and analyzed with FlowJo.
For flow-cytometric analysis of CD45.2 in the mT3-2D GFP cell line, RAW264.7 cell line, and WT mT3-2D-GFP tumors, cells were stained with Helix NP Blue (BioLegend, #425305) and anti-CD45.2 (Miltenyi Biotec, clone 104-2, #130-102-471). After staining, cells were acquired with a BD FACSAria IIu cytometer and analyzed with FlowJo.
Statistical analyses were done for in vivo tumor growth, in vitro cell proliferation, flow cytometry analysis, RT-qPCR analysis, global proteomics profiling, Western blot analysis, NanoString nCounter analysis, IHC analysis, and RNA-seq analysis. Differences in variables were assessed using GraphPad Prism version 8.2.1 by performing Mann–Whitney test or Student t test for two groups, one-way ANOVA or Kruskal–Wallis test for three groups, or two-way ANOVA followed by a multiple comparison test for multiple groups. Kaplan–Meier survival analyses were done using the Human Protein Atlas online tool (https://www.proteinatlas.org/). Differential gene-expression analysis and Hallmark pathway analysis for the RNA-seq data were conducted using R (version 3.5.1). Statistical analyses of the RNA-seq data are described in detail above in the “RNA-seq” section.
T-cell immunity incompletely regulates pancreatic cancer tumor growth
To determine if T-cell immunity can control pancreatic tumor growth, mT3-2D cells were inoculated into syngeneic WT and SCID C57BL/6 mice. mT3-2D tumors in SCID mice grew more rapidly than did mT3-2D tumors in WT mice (Fig. 1A; Supplementary Fig. S1A). A similar tumor growth pattern was observed in the PANC02 model (Supplementary Fig. S1B and S1C). Using H&E, Masson's trichrome, and Ki67 IHC staining, no apparent difference in the histology, fibrosis, or the cellular proliferation rate was observed between mT3-2D WT and SCID tumors, respectively (Supplementary Fig. S2).
To validate that the reduced mT3-2D tumor growth rate in WT mice was T-cell dependent, we depleted CD4+ and CD8+ T cells. We observed that mT3-2D tumors in T-cell–depleted mice grew similarly to those in SCID mice (Fig. 1B; Supplementary Fig. S3A and S3B). To demonstrate that this observation was not limited to the mT3-2D model nor to subcutaneous models, we utilized the KP1 KPC (p48-CRE/LSL-KRas/p53flox/flox) subcutaneous mouse model, as well as the mT3-2D orthotopic model. In both mouse models, tumor growth rate was slower in the immunocompetent mice compared with T-cell–depleted mice (Fig. 1C and D; Supplementary Fig. S3C–S3F).
Because missense mutations may generate T-cell neoepitopes that can drive antitumor T-cell responses, we performed WES. To determine the influence of in vivo tumor growth and antitumor immune responses on the generation of tumor neoepitopes, we analyzed the mT3-2D cell line, five WT tumors, and five SCID tumors. In total, we identified 1,248 peptides with predicted binding affinities >500 nmol/L. After filtering, 125 of these mutations were predicted to be putative cancer neoantigens (Fig. 1E; Supplementary Table S5). Among the 14 potential neoepitopes that were shared among the three groups, 13 belong to unique genes and seven were validated by Sanger sequencing (Supplementary Table S1). We also observed that many mutations were generated during in vivo tumor growth compared with the mT3-2D cell line. However, the majority of these mutations were not shared between the tumor samples (Supplementary Table S5). These observations suggest that antitumor T-cell responses in WT mice were likely tumor neoantigen-specific.
To evaluate intratumoral T-cell infiltration, we used flow cytometry and IHC. A modest infiltration of T cells was observed (Fig. 1F; Supplementary Fig. S4A). To comprehensively assess the immune milieu of mT3-2D WT and SCID tumors, we used NanoString nCounter immune profiling and multiparametric flow cytometry. The NanoString heat map analysis showed a clear separation of the WT and SCID samples and indicated greater upregulation of immune-related genes in mT3-2D WT tumors than in SCID tumors (Supplementary Fig. S4B). NanoString nCounter Cell Type Score analysis showed that “T cell” and “cytotoxic cell” scores were significantly higher in the WT tumors (Supplementary Fig. S4C and S4D). In line with that observation, the analysis further indicated that IFNγ (Ifng) expression was induced at significantly higher levels in the WT group than in the SCID group (Supplementary Fig. S5A). This was confirmed using RT-qPCR (Supplementary Fig. S5B). In addition to Ifng, NanoString nCounter analysis indicated that several genes associated with T-cell activation and exhaustion were induced in WT tumors compared with SCID tumors and the mT3-2D cell line (Supplementary Fig. S5C). However, unlike Ifng, no significant differential expression of the type I interferon genes Ifna1, Ifna4, and Ifnb1 between the two groups was detected, except Ifna2 (Supplementary Fig. S6).
Consistent with the NanoString nCounter analysis, t-SNE analysis of multiparametric flow cytometry data indicated that cells from WT and SCID tumors were well separated, suggesting that WT and SCID intratumoral immune profiles were different (Fig. 1G). PhenoGraph analysis showed that intratumoral T cells were heterogeneous and clustered into two CD8+ T-cell subsets (clusters A and B) and two CD4+ T-cell subsets (clusters C and D; Fig. 1H). ClusterExplorer analysis was used to generate a heat map to determine the distinct phenotypes of the CD8+ and CD4+ T-cell subsets (Fig. 1I), which were likely to possess different effector functions.
These findings suggest that mT3-2D WT and SCID tumors exhibited distinct immune profiles. Although T-cell immunity was clearly induced in immunocompetent mice, T-cell–based antitumor responses incompletely regulated KPC tumor growth.
Antitumor T-cell responses induce myeloid mimicry by malignant cells
We hypothesized that T-cell immunity failed to completely control tumor growth because malignant pancreatic epithelial cells deployed immunosuppressive mechanisms. To investigate how these cells specifically responded to antitumor T-cell immune responses, GFP-labeled mT3-2D cells were grown in C57BL/6J WT and SCID mice. RNA-seq was used to examine the gene-expression profiles of the mT3-2D-GFP cell line and sorted mT3-2D-GFP cancer cells isolated from the WT and SCID tumors. Fifty-two genes were differentially expressed between the WT and SCID malignant epithelial pancreatic cells (Fig. 2A and B; listed in Supplementary Table S6). Among the genes that were upregulated in sorted WT cancer cells compared with SCID cancer cells were genes for CCL9 (Ccl9), arginase (Arg1), and CD68 (Cd68). The selective upregulation of Ccl9, Arg1, and Cd68 in the WT malignant cells was validated by RT-qPCR (Fig. 2C). Because these genes are typically expressed by myeloid cells, using RT-qPCR and flow cytometry, we confirmed that the sorted tumor cells did not express CD45 (Supplementary Fig. S7A–S7C). In line with that observation, protein tyrosine phosphatase, receptor type, C (Ptprc) mRNA expression was below the detection limit by RNA-seq in sorted malignant cells, as well as in the mT3-2D cell line. We then validated that induced expression of these myeloid-associated genes in response to T-cell immunity was not limited to the mT3-2D subcutaneous model. A similar pattern was seen in both the mT3-2D orthotopic and KP1 subcutaneous models (Supplementary Fig. S7D–S7G). Due to the limited number of sorted cells, it was technically challenging to evaluate CCL9, ARG1, and CD68 protein expression. However, we validated that the mT3-2D cell line, as well as three additional murine KPC cell lines (mT4-2D, mT5-2D, and KP1), possessed basal expression of CCL9, ARG1, and CD68 at the mRNA and protein levels (Supplementary Fig. S8). These observations suggest no apparent stromal contamination and no immune cell–cancer cell fusion. Because the expression of Cd68 was very low in sorted malignant cells, only Ccl9 and Arg1 were investigated in subsequent experiments. Together, these results suggest that malignant epithelial pancreatic cells expressed some genes associated with myeloid cells in response to antitumor T-cell immunity.
Antitumor T-cell immunity induces intratumoral PD-L1–expressing myeloid cells
To determine the overall impact of antitumor T-cell immune responses on mT3-2D tumors, including cancer cells and the surrounding stroma, we used unbiased global proteomics and multiparametric flow cytometry analyses. Global proteomics profiling of WT and SCID tumors indicated that S100a8 and S100a9 proteins, which are typically associated with myeloid cells, were higher in the WT tumors than in the SCID tumors (Fig. 3A). We validated this observation using IHC (Supplementary Fig. S9A and S9B).
Flow cytometry analysis of the immune infiltrates determined that CD11b+ myeloid cells accounted for approximately 80% of total leukocytes in WT and SCID tumors (Supplementary Fig. S9C and S9D). The frequency of CD11b+ cells expressing PD-L1 was significantly higher in the WT tumors (Fig. 3B). To better characterize intratumoral myeloid cell populations, we performed an unsupervised multidimensional single-cell analysis on CD45+ cells using the FlowSOM clustering approach (Supplementary Fig. S9E). Based on the expression of Ly6C, Ly6G, F4/80, PD-L1, and MHCII, eight clusters were identified. Among these eight clusters, most cells segregated into two clusters: cluster 1 and cluster 3 (Fig. 3C).
The cells segregated in cluster 3 were F4/80+PD-L1+ MHCIIlow, suggesting that they were PD-L1–expressing TAMs. This cluster was predominantly noted in the WT group. A standard flow cytometry gating strategy was used to confirm this FlowSOM observation (Supplementary Fig. S9F). Consistent with the FlowSOM analysis, the percentage of PD-L1–expressing TAMS (CD11b+F4/80+PD-L1+Ly6G−Ly6Clow/−MHCII−) was significantly higher in WT tumors (Fig. 3D). To validate this observation in the context of antitumor T-cell immunity, intratumoral PD-L1–expressing TAMs were induced, and we compared the frequency of this population in immunocompetent tumors and T-cell–deficient tumors using the KP1 subcutaneous model, as well as the mT3-2D orthotopic model. In both mouse models, the frequency of PD-L1–expressing TAMs (CD11b+F4/80+PD-L1+) was significantly higher in WT tumors compared with T-cell–depleted tumors (Fig. 3E and F; Supplementary Fig. S10A–S10C).
The cells segregated in cluster 1 were characterized by Ly6Ghigh and intermediate Ly6C expression (Ly6Cint). This cluster was predominantly in the SCID group. Cluster 1 contained heterogeneous populations, and nodes containing PD-L1+ cells were similar in WT and SCID tumors (Fig. 3C). This FlowSOM observation was validated using standard flow cytometry gating strategies (Supplementary Fig. S9F). The percentage of CD11b+Ly6G+Ly6Cint cells was significantly higher in mT3-2D subcutaneous SCID tumors (Supplementary Fig. S10D), but no significant difference in the frequency of CD11b+Ly6G+Ly6Cint cells expressing PD-L1 was seen between groups (Supplementary Fig. S10E). In line with literature (26), the frequency of conventional dendritic cells (cDC; CD45+CD11c+MHCII+) was also significantly higher in mT3-2D subcutaneous SCID tumors compared with WT tumors (Supplementary Fig. S11).
The expression of CCL9 in cancer cells is known to be associated with immune evasion and resistance via the recruitment of C–C motif chemokine receptor 1 (CCR1)-expressing immunosuppressive myeloid cells into the tumors (27–31). Hence, we evaluated the expression of CCR1 in intratumoral CD11b+ cells using flow cytometry. We determined that the average of CD11b+ cells expressing CCR1 in WT tumors was 44.5% (Supplementary Fig. S12A and S12B). To evaluate the expression of S100a8 and S100a9 in myeloid cell populations, we sorted CD11b+Ly6G−Ly6Clow/− and CD11b+Ly6G+Ly6Cint splenocytes, and performed RT-qPCR. We determined that both myeloid cell populations had high expression of S100a8 and S100a9 (Supplementary Fig. S12C). Collectively, these observations suggest that the antitumor T-cell immune responses altered intratumoral myeloid cell profiles. These alterations included induction of intratumoral PD-L1–expressing CD11b+ myeloid cells, potentially via CCL9/CCR1 signaling.
Induction of STAT1 expression in tumors and malignant epithelial pancreatic cells
To identify the molecular determinants of immune resistance in mT3-2D tumors, differentially regulated genes and pathways between immunocompetent and immunodeficient tumors were examined. NanoString nCounter analysis identified nine genes that were upregulated in the WT tumors compared with SCID tumors (Fig. 4A). The common upstream regulator of all of these genes, except chitinase-like protein 3 (Chil3), an M2 macrophage marker, is Stat1 (Fig. 4B). The analysis of Stat1 expression by NanoString nCounter analysis (Fig. 4C) was validated by Western blot (Fig. 4D), and a similar pattern was shown by mass spectrometry (Fig. 4E).
In addition to tumor analysis, RNA-seq Hallmark pathway analysis of the mT3-2D-GFP cell line and sorted mT3-2D-GFP cancer cells isolated from WT and SCID tumors was used to identify pathways that were modified specifically in the malignant cells. IFNγ response and IFNα responses were the most significantly enriched pathways in sorted mT3-2D-GFP cancer cells isolated from tumors grown in WT mice compared with tumors grown in SCID mice (FDR adjusted P values of 1.72 × 10−11 and 1.29 × 10−06, respectively; Supplementary Table S7). Malignant epithelial cell Stat1 was selectively induced in the WT malignant cells (Supplementary Table S6), which was validated by RT-qPCR (Fig. 4F). Using the Human Protein Atlas online tool (https://www.proteinatlas.org/), it was found that high STAT1 expression was significantly associated with shorter overall survival for all pancreatic cancer patients (Fig. 4G), suggesting a potential role for STAT1 in resistance to human pancreatic cancer immune control.
Differences in the induction of other Stat genes (Stat2, Stat3, Stat4, Stat5b, and Stat6) were less prominent between WT and SCID tumors compared with Stat1 by NanoString nCounter analysis (Supplementary Fig. S13A–S13E). Similarly, using mass spectrometry, none of the other STAT proteins were selectively induced in the WT tumors compared with the SCID tumors (Supplementary Fig. S13F). Also, Human Protein Atlas survival analysis of The Cancer Genome Atlas data indicated that although STAT2, STAT3, STAT5A, and STAT6 were not significant prognostic factors, high STAT4 and STAT5B expression was associated with longer overall survival in pancreatic cancer (Supplementary Fig. S14). Together, these results suggest that STAT1 was selectively induced both in WT malignant cells and in WT tumor microenvironments compared with SCID samples, and that STAT1 may play a role in immune resistance.
Ruxolitinib downregulates CCL9 and ARG1 expression and promotes anti–PD-1 efficacy
It has been reported that STAT1 signaling regulates TAM-mediated T-cell inhibition by regulating arginase activity and nitric oxide production (32). CCL9 expression is induced by the interferon consensus sequence-binding protein (ICSBP; ref. 33). To investigate the effect of JAK/STAT signaling on the expression of Ccl9 and Arg1, we treated mT3-2D cells in vitro with ruxolitinib for up to 12 hours. Using RT-qPCR, we determined that the expression of Ccl9 and Arg1 was downregulated upon ruxolitinib treatment (Supplementary Fig. S15A and S15B). The effect was not unique to mT3-2D cells, as the effect was reproduced in multiple murine KPC cell lines (Supplementary Fig. S15C and S15D).
To determine if the ruxolitinib-mediated inhibition of CCL9 and ARG1 was maintained in vivo and if ruxolitinib had antitumor effects, we treated mT3-2D immunocompetent mice with either PBS, ruxolitinib, anti–PD-1, or the combination of anti–PD-1 and ruxolitinib. Although ruxolitinib monotherapy did not result in significant tumor growth inhibition, ruxolitinib improved the antitumor efficacy of anti–PD-1 treatment. In fact, although anti–PD-1-treated mice had an initial reduction in tumor size, the majority of the tumors eventually progressed. However, all tumors in the combination group were completely rejected (Fig. 5A; Supplementary Fig. S16). Using Western blotting, CCL9 protein was reduced in tumors from the ruxolitinib-treated mice compared with the control group (Fig. 5B), which was consistent with the expression of S100a9 (S100a8 was not detectable by Western blot in both groups; Fig. 5C). A similar but less consistent pattern of inhibition was also seen in ARG1 protein expression (Fig. 5D). Ruxolitinib did not affect the cellular proliferation rate of mT3-2D cells in vitro even at high concentrations (Supplementary Fig. S17A), suggesting that the ruxolitinib effect was host immunity-dependent. Knowing that JAK/STAT signaling is essential for T-cell differentiation (34), the impact of ruxolitinib in vivo on T-cell frequency was evaluated using flow cytometry. No significant difference in the frequency of intratumoral CD4+ and CD8+ T cells was detected (Supplementary Fig. S17B and S17C). The effect of ruxolitinib on the major CD11b+ myeloid cell populations expressing PD-L1 was also evaluated and showed a trend toward lower frequencies in the ruxolitinib monotherapy group compared with the control group (Supplementary Fig. S17D and S17E).
The goal of this study was to determine the extent to which antitumor T-cell immunity shapes the immunosuppressive tumor microenvironment (TME) in murine models of pancreatic ductal adenocarcinoma (PDAC). Here, we demonstrated that, despite the clear induction of T-cell responses in syngeneic mT3-2D and KP1 PDAC-bearing immunocompetent WT mice, T-cell immunity incompletely regulated tumor growth. Stimulated antitumor T-cell responses induced a myeloid mimicry phenomenon by malignant epithelial pancreatic cells and stimulated intratumoral infiltration of PD-L1–expressing myeloid cells. These tumor cell–based responses to antitumor immunity promoted immune escape in these model systems, which could be reversed by inhibiting JAK/STAT signaling (Fig. 6).
T cells in human pancreatic tumors are generally infrequent and incapable of eliciting antitumor immunity (3, 9). Thus, T-cell immunity has been considered irrelevant to pancreatic cancer, and it has been argued that the pancreas might have organ site–specific resistance to immunotherapy. This hypothesis was supported by the observation that the vast majority of pancreatic cancer immunotherapy clinical trials have failed (9). However, studies show that tumors of long-term pancreatic cancer survivors exhibit potent cytotoxic T-cell responses (7). Immune-checkpoint inhibitors, which promote T-cell responses, have demonstrated efficacy in pancreatic cancer patients with MSI-high tumors (8). Induced T-cell immunity has also been shown to be associated with the induction of a fibro-inflammatory microenvironment that drives pancreatic cancer disease progression and metastasis in mouse models (26). Hence, T-cell immunity is likely to play a critical role in pancreatic cancer biology, but further resolution of the molecular determinants of immune resistance and evasion in pancreatic malignancy is needed. The goal of this study was to address the extent to which immune resistance in pancreatic cancer is a tumor-intrinsic property and if it is induced in response to antitumor T-cell responses. If the latter is true, identifying immunosuppressive mechanisms that cancer cells use in order to survive in the face of T-cell recognition and attack may facilitate the development of novel immunotherapies and improve responses to currently available treatment strategies.
To address this goal, we investigated the effects of T-cell antitumor response on pancreatic cancer using the mT3-2D subcutaneous murine model, the mT3-2D orthotopic model, and the KP1 subcutaneous model. Two types of analyses were performed: (i) malignant epithelial cell analysis and (ii) whole tumor analysis. The former determined how malignant cells specifically responded to antitumor immune responses, whereas the latter determined the effect of antitumor immunity on the TME, including cancer cells and non-cancer stromal elements. Combining these two approaches provided a comprehensive understanding of the complex interactions between cancer cells and the immune system.
The mT3-2D cell line may reflect the immunogenicity of human PDAC. In this mouse model, we identified 125 putative cancer neoantigens within 11 samples, with only 14 potential neoepitopes predicted to be shared between the three groups, half of which were validated by Sanger sequencing. The slow tumor growth rate in immunocompetent mice suggested that at least some of these mutations stimulated T-cell responses. However, it is likely that T-cell responses were also directed against tumor-associated antigens (35). Although pancreatic cancers generally have lower mutational load compared with other cancers, such as melanoma, several comprehensive genomic analyses have shown that human pancreatic tumors exhibit a considerable number of mutations. For example, Waddell and colleagues report that the average number of mutations/Mb in 100 human PDAC samples is 2.64, ranging from 0.65 to 28.2 mutations/Mb. More importantly, pancreatic cancers are also likely to express immunogenic mutations (36). Balachandran and colleagues reported that the median number of putative neoantigens detected per pancreatic cancer patient is 38 in the MSKCC cohort and 32 in the ICGC cohort (7). Bailey and colleagues demonstrate that the number of neoantigen-related mutations in human PDAC ranges from 4 to 4,000 neoantigens per sample (37, 38). As a result, the detection of 125 putative cancer neoantigens within 11 samples suggested that the mT3-2D mouse model was reminiscent of human disease, supporting the notion that pancreatic tumors may use mechanisms to evade antitumor T-cell immunity even when PDAC tumors exhibit sufficient immunogenicity. We were able to validate our major findings using the KP1 KPC model, suggesting that our observations were not limited to tumors derived from the mT3-2D cell line.
The myeloid mimicry phenomenon in cancer cells has been reported previously in several cancer types, including breast cancer, colorectal cancer, and pancreatic cancer, and it is associated with advanced disease and worse outcomes (39–41). Some studies determined that the expression of myeloid markers by cancer cells was a result of cancer cell–immune cell fusion, and thus created hybrids that express characteristics of both cellular origins (42–44). In our study, we determined that sorted malignant epithelial cells and murine KPC cell lines expressed chemokine CCL9, ARG1, and CD68, but did not express CD45, the transmembrane protein expressed by all hematopoietic cells, suggesting that the myeloid mimicry phenomenon we reported here was not a consequence of cell fusion. Our data suggest that antitumor immune responses play an important role in regulating this phenomenon.
Whole tumor analysis using global proteomics profiling and multiparametric flow cytometry revealed selective increases of the myeloid cell–associated proteins S100a8 and S100a9 and increased infiltration of PD-L1+ myeloid cells in WT compared with SCID and T-cell–depleted tumors, respectively, potentially via CCL9/CCR1 signaling. CCL9 induction by cancer cells is shown to be associated with the recruitment of CCR1-expressing immunosuppressive myeloid cells, and that targeting CCL9/CCR1 interaction limited tumor growth in mice. However, the phenotype of these CCR1-expressing myeloid cell populations varies among cancer models (27–31). Together, our findings suggest that antitumor T-cell immunity, potentially through myeloid mimicry, was associated with the infiltration of PD-L1–expressing myeloid cells that contributed to establishing an immunosuppressive pancreatic TME.
Whole tumor analysis consistently indicated upregulation of STAT1 protein in WT tumors compared with SCID tumors, but other STAT proteins were less different between the two groups. The upregulation of STAT1 expression in the TME of immunocompetent tumors compared with tumors from T-cell-deficient SCID mice is expected, and is likely caused by the induction of IFNγ signaling in response to T-cell immunity. However, we found that STAT1 expression was also upregulated selectively in epithelial cells isolated from WT tumors, suggesting a role for STAT1 as an orchestrator of this myeloid mimicry phenomenon. Interestingly, a study has identified an interferon-stimulated gene (ISG) resistance signature (ISG.RS), predominantly expressed in human cancer cells and associated with resistance to immune-checkpoint blockade. STAT1, but not any other STAT genes, is a component of this ISG.RS signature (45). We found that JAK/STAT signaling regulated the expression of CCL9 and ARG1, which were reduced in mT3-2D cells following ruxolitinib exposure. Although in vivo ruxolitinib treatment targets malignant cells as well as the tumor stroma, the reduction of CCL9 in tumors from ruxolitinib-treated mice compared with control tumors suggests that the ruxolitinib effect involved targeting the malignant cells. Muthalagu and colleagues describe a role for mutant KRAS and MYC cooperation in suppressing STAT1 signaling in a murine model of pancreatic cancer (46). In contrast to our study, they did not examine the impact of antitumor immunity on these molecular interactions. STAT1 overexpression was an adverse prognostic factor in human pancreatic cancer. Together, these data suggest that potentially immunosuppressive CCL9 and ARG1 expression in cancer cells was elicited in response to the antitumor immune responses through JAK/STAT signaling.
We found that ruxolitinib treatment improved anti–PD-1 therapeutic effects in immunocompetent mice bearing mT3-2D subcutaneous tumors. A similar observation was reported in a study by Lu and colleagues using the PANC02 orthotopic murine pancreatic cancer model (47). Lu and colleagues also demonstrate that ruxolitinib induces cytotoxic T-cell frequency and activity in the TME. In a related study, Nagathihalli and colleagues report that AZD1480 (a JAK inhibitor) can improve survival in several PDAC mouse models (48). Mace and colleagues show a benefit of blocking IL6, which leads to the activation of JAK/STAT signaling, in combination with PD-1 blockade in mouse models of pancreatic cancer (49). Hence, our findings may be generally applicable and translatable into the clinical trial setting.
Myeloid cell populations are highly heterogeneous (50, 51). Although many myeloid cell populations can stimulate immune evasion and resistance via a wide range of immunosuppressive mechanisms, some myeloid cells have antitumor properties (52). We found that PD-L1–expressing myeloid cells, mainly TAMs, were selectively induced in WT tumors compared with SCID and T-cell–depleted tumors in the mT3-2D subcutaneous and orthotopic models, as well as the KP1 subcutaneous model. Consistent with this observation, PD-L1–expressing myeloid cells have been reported to promote immune escape (53, 54). Thus, our observations suggest that the antitumor T-cell immune responses altered intratumoral myeloid cell profiles that likely contributed to immune resistance in PDAC.
Despite the induction of tumor-directed T-cell immunity in multiple immunocompetent models, T-cell immunity incompletely controlled tumor growth. We have shown that the antitumor immune responses, via JAK/STAT signaling, stimulated a myeloid mimicry phenomenon, and this was potentially associated with the intratumoral infiltration of PD-L1–expressing myeloid cells that likely played an important role in immune resistance. These observations could be relevant to human pancreatic cancer and potentially other malignancies. Future studies will further elucidate the specific role of STAT1 signaling using genetically engineered mouse models that permit study of the influence of immune selection pressure on the evolution of tumor-derived immunosuppression.
E.J. Fertig reports grants from NIH/NCI, Lustgarten Foundation, Allegheny Foundation, and NIH/National Institute of Dental and Craniofacial Research (NIDCR) during the conduct of the study; a grant from AACR/SU2C outside the submitted work; and is on the scientific advisory board for Viosera Therapeutics. L.M. Weiner reports grants from NCI during the conduct of the study, as well as personal fees from Celldex Therapeutics, Jounce Therapeutics, Tessa Therapeutics, Samyang BioPharma USA, Menarini, and Molecular Templates, personal fees and other from Cytomx Therapeutics (scientific advisory board, stock options) and Immunome (scientific advisory board, stock options), other from Targeted Diagnostics & Therapeutics (shareholder), Klus Pharmaceuticals (scientific advisory board), and Xiconic (scientific advisory board), and grants from Bioexcel Therapeutics outside the submitted work. No disclosures were reported by the other authors.
R. Ajina: Conceptualization, formal analysis, validation, investigation, visualization, methodology, writing–original draft. Z.X. Malchiodi: Validation, investigation. A.A. Fitzgerald: Investigation. A. Zuo: Investigation. S. Wang: Investigation. M. Moussa: Investigation, visualization. C.J. Cooper: Data curation and formal analysis. Y. Shen: Data curation and formal analysis. Q.R. Johnson: Data curation, formal analysis. J.M. Parks: Data curation, formal analysis, validation. J.C. Smith: Data curation, formal analysis, validation. M. Catalfamo: Validation, visualization. E.J. Fertig: Data curation, formal analysis, validation. S.A. Jablonski: Supervision, validation, project administration. L.M. Weiner: Conceptualization, resources, supervision, funding acquisition, validation, writing–original draft.
This research was supported by NCI R01 CA50633 (L.M. Weiner) and NIH/NCI grant P30-CA051008 (L.M. Weiner). Lombardi Comprehensive Cancer Center Shared Resources supporting this study include the following: the Genomics and Epigenomics Shared Resource, the Flow Cytometry and Cell Sorting Shared Resource, the Tissue Culture Shared Resource, the Proteomics and Metabolomics Shared Resource, the Biostatistics and Bioinformatics Shared Resource, and the Histopathology and Tissue Shared Resource. R. Ajina was supported by King Saud bin Abdulaziz University for Health science (KSAU-HS) and the Saudi Arabian Cultural Mission (SACM). This research was sponsored by the Laboratory Directed Research and Development Program at Oak Ridge National Laboratory (ORNL), which is managed by UT-Battelle, LLC, for the U.S. Department of Energy (DOE) under contract number DE-AC05-00OR22725. C.J. Cooper was supported by NIH/National Institute of General Medical Sciences (NIGMS) Initiative for Maximizing Student Development (IMSD) grant R25GM086761 (J.M. Parks). This research was supported by Lustgarten Foundation, the Emerson Foundation (640183), and the Allegheny Foundation (E.J. Fertig). This research was supported by Leidos Biomedical Research, Inc. and has been funded in whole or in part with federal funds from the NCI, NIH, under contract HHSN261200800001E (M. Catalfamo and M. Moussa) and R01AI145549-02 (M. Moussa).
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