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Extrinsic KRAS Signaling Shapes the Pancreatic Microenvironment Through Fibroblast Reprogramming

Open AccessPublished:March 01, 2022DOI:https://doi.org/10.1016/j.jcmgh.2022.02.016

      Background & Aims

      Oncogenic Kirsten Rat Sarcoma virus (KRAS) is the hallmark mutation of human pancreatic cancer and a driver of tumorigenesis in genetically engineered mouse models of the disease. Although the tumor cell–intrinsic effects of oncogenic Kras expression have been widely studied, its role in regulating the extensive pancreatic tumor microenvironment is less understood.

      Methods

      Using a genetically engineered mouse model of inducible and reversible oncogenic Kras expression and a combination of approaches that include mass cytometry and single-cell RNA sequencing we studied the effect of oncogenic KRAS in the tumor microenvironment.

      Results

      We have discovered that non–cell autonomous (ie, extrinsic) oncogenic KRAS signaling reprograms pancreatic fibroblasts, activating an inflammatory gene expression program. As a result, fibroblasts become a hub of extracellular signaling, and the main source of cytokines mediating the polarization of protumorigenic macrophages while also preventing tissue repair.

      Conclusions

      Our study provides fundamental knowledge on the mechanisms underlying the formation of the fibroinflammatory stroma in pancreatic cancer and highlights stromal pathways with the potential to be exploited therapeutically.

      Graphical abstract

      Keywords

      Abbreviations used in this paper:

      ADM (acinar-to-ductal metaplasia), ARG1 (arginase 1), BSA (bovine serum albumin), CAF (cancer-associated fibroblast), CK19 (_), CM (conditioned media), CXCL1 (__), DAPI (4′,6-diamidino-2-phenylindole), DMEM (Dulbecco’s modified Eagle medium), DOX (doxycycline), ESI (Electrospray ionization), iCAF (inflammatory CAFs), IF (immunofluorescence), IL (interleukin), JAK (Janus kinase), JAKi (JAK inhibitor), Kras (Kirsten Rat Sarcoma virus), LC (Liquid Chromatography), MS (mass spectrometer), PanIN (pancreatic intraepithelial neoplasia), Pdx1 (Pancreatic and Duodenal Homeobox 1), p-ERK (phosphorylated Extracellular signal-regulated kinase), PME (precursor lesion microenvironment), p-STAT3 (phosphorylated signal transducer and activator of transcription 3), Ptf1a (Pancreas Associated Transcription Factor 1a), QqQ (triple quadrupole), SAA (Serum amyloid a), scRNAseq (single-cell RNA sequencing), SMA (smooth muscle actin), STAT3 (signal transducer and activator of transcription 3), TAM (tumor-associated macrophage), TBST (Tris buffered saline solution with Tween 20), TME (tumor microenvironment), UMAP (uniform and manifold approximation and projection)
      Here, we show that epithelial oncogenic Kirsten Rat Sarcoma virus (KRAS) signals in a non–cell autonomous (ie, extrinsic) manner to drive accumulation and maintenance of an immune-suppressive microenvironment during the onset of pancreatic cancer. Furthermore, we show that KRAS-reprogrammed fibroblasts constitute a key mediator of the transformation process.
      See editorial on page 1845.
      Pancreatic ductal adenocarcinoma is the third leading cause of cancer-related death in the United States and is among the most lethal malignancies, with an expected 5-year survival of approximately 10%.
      American Cancer Society
      Cancer facts & figures.
      More than 90% of pancreatic ductal adenocarcinoma instances harbor an oncogenic mutation in the KRAS gene, most commonly KRASG12D.
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      Results

      Extrinsic Signaling by Oncogenic Kras Reprograms the Pancreas Microenvironment

      To study the recruitment of immune cells by oncogenic Kras-expressing tumor cells, we used Ptf1aCre/+; R26rtTa-ires-EGFP/rtTa-ires-EGFP;TetO-KrasG12D mice (hereby iKras) that express oncogenic KrasG12D in pancreatic epithelial cells in an inducible and reversible manner upon doxycycline (DOX) administration.
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      Initially, we activated KrasG12D in adult mice (age, 6–12 wk) by feeding iKras mice or littermate controls (lacking either Cre or KrasG12D expression) DOX chow for 3 days, 1 week, or 2 weeks (Figure 1A). Pancreata from iKras mice appeared largely histopathologically normal 3 days after KrasG12D activation, with the parenchyma largely composed of acinar cells and Cytokeratin 19 (CK19) expression confined to the ducts (Figure 1B–D). One week after KrasG12D induction, although the pancreas largely was histologically normal, we observed focal ADM accompanied by an increase in the proliferation marker Ki67 that became more widespread by 2 weeks (Figure 1B, C, and E). Three days after KrasG12D induction, we observed patchy epithelial expression of phosphorylated Extracellular signal-regulated kinase (ERK) (p-ERK), indicating activation of the MAPK pathway (Figure 1F) in otherwise histologically normal areas. At later time points, areas of apparent ADM had increased p-ERK staining (Figure 1F), while the remainder of the pancreas had patchy expression of p-ERK. The limited expression of p-ERK even as all pancreas epithelium expresses KrasG12D in this model was consistent with the known requirement of stimulation by various upstream factors to fully activate KrasG12D signaling, including myeloid-derived Epidermal growth factor receptor (EGFR) ligands.
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      Figure thumbnail gr1
      Figure 1Oncogenic Kras drives immune cell recruitment during the onset of tumorigenesis. (A) Experimental design. Control (lacking either the Kras or Ptf1aCre allele) and iKras mice were given DOX chow to activate oncogenic Kras (KrasG12D) and pancreata were harvested 3 days (3d), 1 week (1w), or 2 weeks (2w) after induction of Kras. N = 3–5 mice per group. (B) Quantification of ADM in pancreatic tissue from control or iKras mice that received DOX chow for 3 days, 1 week, or 2 weeks. (C) Representative images of H&E staining of control and iKras pancreata at the indicated time points. N = 3–5 mice per group. Scale bar: 100 μm. (D) Representative images of CD3 (green), CK19 (red), and SMA (magenta) co-immunofluorescent staining in control and iKras pancreata at the indicated time points, arrowheads point to CD3+ T cells. N = 3 mice per group. Scale bar: 50 μm. (E) Immunostaining for Ki67 (green), F4/80 (red), and SMA (magenta). Scale bar: 50 μm. (F) Representative images of F4/80 (green), p-ERK (red), and SMA (magenta) co-immunofluorescent staining in control and iKras pancreata at the indicated time points. N = 3 mice per group. Scale bar: 50 μm.
      Next, we investigated the dynamics of immune infiltration and the presence of activated fibroblasts (as marked by expression of smooth muscle actin [SMA]). Co-immunofluorescence and immunohistochemistry analysis showed the presence of macrophages surrounding untransformed p-ERK–expressing acinar cells as early as 3 days after KrasG12D activation (Figures 1F and 2A), whereas T cells were rare at this stage (Figures 1D and 2B). Macrophages increased by 1 and 2 weeks after KrasG12D activation, and T cells, including CD8+ T cells, were detected easily at these time points, at least in the ADM areas (Figures 1D and F, and 2A and B). Infiltrating macrophages were largely negative for the immunosuppressive marker arginase 1 (ARG1) (Figure 2C). ADM clusters at this stage also were surrounded by SMA+ fibroblasts (Figure 1D–F). We then examined the areas of the pancreas that were morphologically normal at the 1-week time point (majority of the pancreas) (Figure 2D). Interestingly, high-magnification images showed morphologically normal clusters of acini with increased p-ERK surrounded by both macrophages and a thin layer of SMA+ fibroblasts, indicating that changes in the microenvironment precede acinar transdifferentiation. Careful analysis of the tissue from the 3-day time point also showed some p-ERK–expressing acini with surrounding macrophages but without SMA+ fibroblasts (Figure 1F), but these instances were rare and we cannot exclude that fibroblasts were present outside the plane of the section. Of note, the fibroblasts had no expression of p-ERK at this stage (Figure 2E). Taken together, our findings are consistent with a model whereby epithelial activation of p-ERK, downstream of oncogenic Kras, is accompanied by fibroblast activation and macrophage infiltration, which precede acinar transdifferentiation.
      Figure thumbnail gr2
      Figure 2Macrophage expansion precedes acinar transdifferentiation. (A) Immunohistochemistry for F4/80. N = 3 mice per group. (B) Representative images of F4/80 (green), CD8 (red), and CK19 (magenta) co-immunofluorescent staining in control and iKras∗ pancreata. Scale bar: 50 μm. (C) Co-immunostaining for F4/80 (green), ARG1 (red), and CK19 (gray) in iKras∗ pancreata at 3 days ON. Note that macrophages surround CK19- acinar cells. Right: Image shows a CK19+ duct in the same section. Scale bar: 50 μm. (D) Representative images of F4/80 (green), p-ERK (red), and SMA (magenta) co-immunofluorescence staining of an iKras pancreas after activating KrasG12D for 1 week. Regions with high expression of p-ERK are enlarged and single-channel images are included. N = 3 mice. Scale bar: 50 μm. (E) Representative images of PDGFRβ (green), p-ERK (red), and SMA (magenta) co-immunofluorescence staining of an iKras pancreas after activating KrasG12D for 1 week. Single-channel images are included. N = 3 mice.

      Epithelial Oncogenic KrasG12D Is Required to Maintain Acinar Transdifferentiation and Fibroblast Accumulation, but Not Immune Infiltration

      Acute pancreatitis synergizes with oncogenic Kras to drive neoplastic transformation.
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      ,
      • Carriere C.
      • Young A.L.
      • Gunn J.R.
      • Longnecker D.S.
      • Korc M.
      Acute pancreatitis markedly accelerates pancreatic cancer progression in mice expressing oncogenic Kras.
      ,
      • Guerra C.
      • Collado M.
      • Navas C.
      • Schuhmacher A.J.
      • Hernandez-Porras I.
      • Canamero M.
      • Rodriguez-Justo M.
      • Serrano M.
      • Barbacid M.
      Pancreatitis-induced inflammation contributes to pancreatic cancer by inhibiting oncogene-induced senescence.
      Previously, we have shown that iKras mice on DOX (KrasG12D ON) develop widespread early PanIN lesions at 3 weeks after pancreatitis induced by caerulein, and withdrawal of DOX (KrasG12D OFF) at this stage leads to PanIN regression and tissue repair.
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      Thus, epithelial oncogenic KrasG12D is required to maintain acinar transdifferentiation. Here, using the same experimental strategy, we sought to study the potential role of KrasG12D in regulating the PME. Accordingly, we placed iKras and control mice on DOX and then induced acute pancreatitis and harvested the pancreata 3 weeks after pancreatitis (Figure 3A). Pancreata in iKras mice presented with widespread ADM and low-grade PanINs (with intracellular mucin accumulation as shown by periodic acid–Schiff staining) and accumulation of fibrotic stroma (Figure 3B and C). Expression of p-ERK was confined to the epithelium when KrasG12D was ON, as previously shown (Figure 3D).
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      In contrast, control mice treated in parallel had normal pancreas architecture. iKras pancreata also were marked by extensive collagen deposition, fibroblast expansion (as detected by podoplanin), and activation (by SMA expression) (Figure 3C–H). To study the effect of KrasG12D inactivation on the PME, we replaced DOX chow with DOX-free chow to inactivate KrasG12D expression in iKras mice (for 3 days or 1 week) 3 weeks after administration of caerulein (Figure 3A). The 3-day and 1-week time points were chosen to coincide with the early and middle phases of the pancreatic remodeling process.
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      As previously described, KrasG12D inactivation led to a drastic reduction in epithelial p-ERK and redifferentiation of acinar cells progressively over time (Figure 3B–D).
      • Liou G.Y.
      • Doppler H.
      • Necela B.
      • Krishna M.
      • Crawford H.C.
      • Raimondo M.
      • Storz P.
      Macrophage-secreted cytokines drive pancreatic acinar-to-ductal metaplasia through NF-kappaB and MMPs.
      ,
      • Zhang Y.
      • Yan W.
      • Mathew E.
      • Kane K.T.
      • Brannon 3rd, A.
      • Adoumie M.
      • Vinta A.
      • Crawford H.C.
      • Pasca di Magliano M.
      Epithelial-myeloid cell crosstalk regulates acinar cell plasticity and pancreatic remodeling in mice.
      We then investigated changes in the stroma after inactivation of KrasG12D. We observed a reduction in both total fibroblasts (podoplanin+ cells) and activated, SMA+ fibroblasts (Figure 3D–F, and quantification in G and H), as well as a reduction in collagen deposition (Figure 3C). These results are consistent with a requirement for continued oncogenic KrasG12D activity to maintain fibroblast expansion and pancreatic fibrosis.
      Figure thumbnail gr3
      Figure 3Sustained expression of KrasG12D is required to maintain PanIN and fibrosis. (A) Experimental design. Wild-type control and iKras mice were given DOX chow to activate KrasG12D, followed by induction of acute pancreatitis. Mice either remained on DOX chow for 3 weeks and pancreata were harvested (3 weeks [3w] ON), or DOX chow was removed and the pancreata were harvested after 3 days (3d) or 1 week (1w) (labeled 3d OFF or 1w OFF, respectively). N = 8 mice per group. (B) Representative images of H&E staining of control and iKras∗ pancreata at the indicated time points. N = 8 mice per group. Scale bar: 100 μm. (C) Representative images of Gomori trichrome and periodic acid–Schiff Stain (PAS; inset) of control or iKras pancreata at the indicated time points. N = 2–3 mice per group. Scale bar: 100 μm. (D) Immunofluorescent staining for SMA (white) and p-ERK (red). Scale bar: 50 μm. N = 2–3 mice per group. (E) Immunostaining for podoplanin. Scale bar: 100 μm. (F) Immunofluorescent staining for SMA (single channel) used for quantification. Scale bar: 50 μm. N = 2–3 mice per group. Quantification of (G) podoplanin and (H) SMA staining, shown as means ± SEM. Statistical differences were determined by multiple analysis of variance, all compared with the 3w ON group. BV, blood vessels; HPF, high-power field; PDPN, Podoplanin.
      Myeloid cells, including abundant macrophages, infiltrate early during pancreatic carcinogenesis and are required for sustained ADM and progression to PanIN.
      • Liou G.Y.
      • Doppler H.
      • Necela B.
      • Krishna M.
      • Crawford H.C.
      • Raimondo M.
      • Storz P.
      Macrophage-secreted cytokines drive pancreatic acinar-to-ductal metaplasia through NF-kappaB and MMPs.
      ,
      • Zhang Y.
      • Yan W.
      • Mathew E.
      • Kane K.T.
      • Brannon 3rd, A.
      • Adoumie M.
      • Vinta A.
      • Crawford H.C.
      • Pasca di Magliano M.
      Epithelial-myeloid cell crosstalk regulates acinar cell plasticity and pancreatic remodeling in mice.
      ,
      • Storz P.
      Acinar cell plasticity and development of pancreatic ductal adenocarcinoma.
      Therefore, we endeavored to study the effect of KrasG12D inactivation on macrophages and other myeloid cell infiltration in the pancreas. We performed a combination of flow cytometry (gating strategy in Figure 4A) and immunostaining, including multiplex immunofluorescent staining (Opal multiplex immunohistochemistry; Akoya Biosciences, Menlo Park, CA). As expected, we observed an increase in CD45+ immune cells in the iKras mice at 3 weeks ON compared with control mice (Figure 4B and C). This increase included total myeloid cells (CD45+CD11b+) as well as macrophages (CD45+CD11b+F4/80+) and immature myeloid cells (CD45+CD11b+F4/80-Ly6C+Ly6G+, often referred to as Myeloid-derived suppressor cells, MDSCs), with the majority being macrophages (Figure 4B and D–F), consistent with the increase in leukocyte infiltration observed in KC mice.
      • Clark C.E.
      • Hingorani S.R.
      • Mick R.
      • Combs C.
      • Tuveson D.A.
      • Vonderheide R.H.
      Dynamics of the immune reaction to pancreatic cancer from inception to invasion.
      Surprisingly, KrasG12D inactivation resulted in little to no reduction in total immune infiltration, total myeloid cells, or macrophages (Figure 4B–F). These results are consistent with the role of myeloid cells during the pancreatic tissue repair process.
      • Zhang Y.
      • Yan W.
      • Mathew E.
      • Kane K.T.
      • Brannon 3rd, A.
      • Adoumie M.
      • Vinta A.
      • Crawford H.C.
      • Pasca di Magliano M.
      Epithelial-myeloid cell crosstalk regulates acinar cell plasticity and pancreatic remodeling in mice.
      Figure thumbnail gr4
      Figure 4Immune infiltration persists during tissue repair after epithelial Kras inactivation. (A) Flow cytometry gating strategy for myeloid cells in FlowJo. (B) Quantification of flow cytometry results. Immune cell populations are expressed as the percentage of total cells in control or iKras pancreata at the indicated time points, shown as means ± SD. N = 9–11 mice per group. Statistical analysis by multiple comparison analysis of variance and multiple comparison Kruskal–Wallis. Triangles represent females and circles represent males. Representative immunohistochemistry images for (C) CD45 and (D) F4/80 in the pancreatic tissue of control or iKras∗ mice at the indicated time points. Scale bar: 100 μm. N = 3 mice per group. (E) Top: Representative images of F4/80, Arg1, CD3, CD8, and CK19 immunofluorescent multiplex staining at the indicated time points. N = 5–7 mice per group. Bottom: Identification of cell types using inForm Cell Analysis software. T cell, macrophage, and epithelial cells were selected based on single staining of CD3 (Opal 520), F480 (Opal 540), and CK19 (Opal 690), respectively. Acinar cells were labeled based on the lack of listed fluorophores and by tissue morphology. (F) OPAL staining quantification of the percentage of F4/80-positive cells from total cells in the pancreatic tissue of control or iKras mice at the indicated time points. Data are shown as means ± SEM. N = 5–7 mice per group. Statistical analysis was performed by the 2-tailed unpaired t test. FSC-A, forward scatter area.

      Extrinsic Signaling From KrasG12D-Transformed Cells Drives Myeloid Polarization

      Because myeloid cell numbers did not change upon KrasG12D inactivation, we sought to determine whether their polarization status was affected. For this purpose, we used mass cytometry (CyTOF, Fluidigm, South San Francisco, CA) with a panel of approximately 20 antibodies (Table 1). We limited the scope of the CyTOF experiment to 3 weeks Kras∗ ON (N = 2) and 3 days OFF (N = 3) time points (Figure 5A) to highlight the short-term effect of KrasG12D inactivation, before extensive tissue remodeling. We visualized the multidimensional CyTOF data using t-distributed stochastics neighbor embedding plots and identified 17 distinct immune cell clusters (Figure 5B–D). Among T cells, we observed a trend toward an increase upon KrasG12D inactivation, but no statistical difference (Figure 5E). The myeloid immune landscape was complex, with multiple populations, including heterogeneous macrophage subtypes (Figure 5B, C, and E). Although some populations did not change or trended toward an increase when we compared KrasG12D OFF with KrasG12D ON samples, other populations decreased drastically (Figure 5E). Notably, immunosuppressive ARG1+ Ly6C+ macrophages, granulocytic myeloid-derived suppressor cells (Gr-MDSCs), defined as CD45+CD11b+F4/80-Ly6CloLy6Ghi, and CCR1+CD206+ macrophages, decreased significantly upon KrasG12D inactivation (Figure 5E and F). Down-regulation of ARG1+ macrophages was validated by co-immunofluorescent and multiplex immunofluorescent staining (Figure 6A and B). Furthermore, we discovered that phosphorylated signal transducer and activator of transcription 3 (p-STAT3), indicating activation of JAK/STAT3 signaling and known to promote the immune-suppressive function of macrophages,
      • Yu H.
      • Kortylewski M.
      • Pardoll D.
      Crosstalk between cancer and immune cells: role of STAT3 in the tumour microenvironment.
      was similarly down-regulated in macrophages and other stromal cells, as well as in epithelial cells, by inactivation of epithelial oncogenic KrasG12D (Figure 6C).
      Table 1CyTOF Antibodies
      AntibodiesCloneIsotopeDilution
      CD45Fluidigm30-F11Y891:200
      Ly6GFluidigm1ABPr1411:400
      CD11bFluidigmM1/70Nd1431:300
      CD4FluidigmRM4/5Nd1451:200
      F480FluidigmBM8Nd1461:100
      CD140aFluidigmAPA5Nd1481:100
      CD19Fluidigm6D5Nd1491:200
      Ly6CFluidigmHK1.4Nd1501:400
      CD3eFluidigm145-2C11Sm1521:100
      CD105CustomMJ7/18Dy1641:100
      TCR gdFluidigmGL3Tb1591:100
      CD191 CCR1CustomS15040EGd1601:100
      iNOSFluidigmCXNFTDy1611:100
      Arginase1CustomPolyclonalEr1661:400
      CD8aFluidigm53-6.7Er1681:200
      CD206FluidigmC068C2Tm1691:200
      CD161 NK1.1FluidigmPK136Er1701:100
      CD11cFluidigmN418Bi2091:100
      Figure thumbnail gr5
      Figure 5Oncogenic Kras regulates myeloid cell polarization status in the PME. (A) Experimental design. N = 8–9 mice per group. (B) CyTOF analysis of CD45+ cells from iKras pancreata visualized by t-distributed stochastics neighbor embedding (tSNE) plot for 3 weeks (3w) ON and 3 days (3d) OFF time points. Nineteen distinct cell clusters were identified by FlowSOM (Bioconductor, Buffalo, NY) using 18 markers and 1061 randomly selected cells per group. N = 2–3 mice per group. (C) Heatmap of the median marker intensity generated with FlowSOM of the CyTOF samples (iKras 3w ON and 3d OFF combined). Colors on the left represent the clusters shown in the t-SNE plot. N = 2–3 per group. All population were gated from CD45+ cells. (D) tSNE plots showing the expression of different immune lineage markers in the clusters from the CyTOF data. (E) Quantification of identified clusters proportion by CyTOF analysis at the indicated time points. Statistical analysis by Wrapper function. (F) Heatmap showing differential abundance of clusters from CyTOF analysis of iKras pancreata for 3w ON and 3d OFF time points. N = 2–3 mice per group.
      Figure thumbnail gr6
      Figure 6Oncogenic Kras inactivation results in transcriptional reprogramming of infiltrating macrophages. (A) Immunostaining for F4/80 (green), ARG1 (red), and E-cadherin (Ecad) (white) in control and iKras pancreata at the indicated time points. N = 3 mice per group. Scale bar: 50 μm. (B) OPAL staining quantification of ARG1+ macrophages in pancreatic tissue of control or iKras mice at the indicated time points. Data are shown as means ± SEM. Statistical analysis by the 2-tailed unpaired t test. (C) Immunostaining for F4/80 (green), p-STAT3 (magenta), and Ecad (white). N = 3 mice per group. Scale bar: 50 μm. (D) UMAP visualization of scRNAseq data showing unsupervised clustering of cells from iKras pancreatic samples (3 weeks [3w] ON, N = 2; 3 days [3d] OFF, N = 3). Each color represents a distinct cellular cluster. (E) Dot plot of the key genes used to identify different cell populations shown in the UMAP, identified by unsupervised clustering of the scRNAseq samples (3w ON and 3d OFF samples combined). Size of the dots reflects the proportion of cells expressing a determined gene and color represents the average expression level. (F) Heatmap showing the averaged scRNAseq expression data (relative to the highest expressor) for genes in macrophages selected from a curated list of macrophage polarization and functional markers.
      Because CyTOF and immunostaining are limited by available antibodies, we performed single-cell RNA sequencing (scRNAseq) to elucidate how epithelial KrasG12D expression alters the microenvironment, and, in particular, macrophage polarization. We harvested pancreata from mice with KrasG12D ON for 3 weeks plus 3 days (N = 2, pooled for submission) or ON for 3 weeks and then OFF for 3 days (N = 3, pooled for submission). The experiment was designed such that pancreata were harvested and processed at the same time to avoid batch effects. The multidimensional scRNAseq data included a total of 5073 cells overall, with 1984 cells from the 3 weeks ON sample and 3089 cells from the 3 days OFF sample. The data were analyzed using the Seurat (Satija Lab, New York Genome Center, New York, NY) package in R (version 3.2.2) and visualized by uniform and manifold approximation and projection (UMAP) (Figure 6D). By comparing the transcriptome of each cluster with known markers of epithelial, immune, and stromal cell types (Figure 6E), we identified acinar cells, CK19+ epithelial cells (PanIN or ducts), fibroblasts, endothelial cells, and 16 distinct immune cell populations. Similar cell populations were present in both the ON and OFF condition, allowing us to compare gene expression changes upon inactivation of KrasG12D within each. We first assessed gene expression changes in macrophages (Figure 6F). Notably, tumor-associated macrophage (TAM) markers such as Arg1 and Ccr1 were down-regulated upon KrasG12D inactivation. We also observed down-regulation of apolipoprotein E (Apoe), a secreted protein that our group recently described as highly expressed in TAMs and as promoting immune suppression in invasive pancreatic cancer.
      • Kemp S.B.
      • Carpenter E.S.
      • Steele N.G.
      • Donahue K.L.
      • Nwosu Z.C.
      • Pacheco A.
      • Velez-Delgado A.
      • Menjivar R.E.
      • Lima F.
      • The S.
      • Espinoza C.E.
      • Brown K.
      • Long D.
      • Lyssiotis C.A.
      • Rao A.
      • Zhang Y.
      • Pasca di Magliano M.
      • Crawford H.C.
      Apolipoprotein E promotes immune suppression in pancreatic cancer through NF-kB-mediated production of CXCL1.
      In addition, we observed down-regulation of the complement genes C1qa, C1qb, C1qc, and the transcription factor Trem2, which together define TAMs in several malignancies including, as described by our group, primary and metastatic pancreatic cancer.
      • Katzenelenbogen Y.
      • Sheban F.
      • Yalin A.
      • Yofe I.
      • Svetlichnyy D.
      • Jaitin D.A.
      • Bornstein C.
      • Moshe A.
      • Keren-Shaul H.
      • Cohen M.
      • Wang S.Y.
      • Li B.
      • David E.
      • Salame T.M.
      • Weiner A.
      • Amit I.
      Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer.
      ,
      • Kemp S.B.
      • Steele N.G.
      • Carpenter E.S.
      • Donahue K.L.
      • Bushnell G.G.
      • Morris A.H.
      • The S.
      • Orbach S.M.
      • Sirihorachai V.R.
      • Nwosu Z.C.
      • Espinoza C.
      • Lima F.
      • Brown K.
      • Girgis A.A.
      • Gunchick V.
      • Zhang Y.
      • Lyssiotis C.A.
      • Frankel T.L.
      • Bednar F.
      • Rao A.
      • Sahai V.
      • Shea L.D.
      • Crawford H.C.
      • Pasca di Magliano M.
      Pancreatic cancer is marked by complement-high blood monocytes and tumor-associated macrophages.
      These findings are consistent with KrasG12D being required to maintain TAM-like macrophage polarization. Conversely, inactivation of KrasG12D induced a repair-associated gene expression program, including Tgfb, Il10, Il4, Pdgfb, and Mmps (Figure 6F). Although oncogenic Kras is not required to maintain immune infiltration, it extrinsically (non–cell autonomously) regulates macrophage gene expression in PanIN.

      Epithelial KrasG12D Drives Inflammatory Reprogramming of Pancreatic Fibroblasts

      We next sought to understand how KrasG12D expression in epithelial cells directed cellular crosstalk to account for the changes in macrophage polarization. To achieve this, we filtered the scRNAseq data for expression of known ligand and receptor pairs using a published list further curated in our laboratory.
      • Ramilowski J.A.
      • Goldberg T.
      • Harshbarger J.
      • Kloppmann E.
      • Lizio M.
      • Satagopam V.P.
      • Itoh M.
      • Kawaji H.
      • Carninci P.
      • Rost B.
      • Forrest A.R.
      A draft network of ligand-receptor-mediated multicellular signalling in human.
      ,
      • Steele N.G.
      • Carpenter E.S.
      • Kemp S.B.
      • Sirihorachai V.R.
      • The S.
      • Delrosario L.
      • Lazarus J.
      • E-aD Amir
      • Gunchick V.
      • Espinoza C.
      • Bell S.
      • Harris L.
      • Lima F.
      • Irizarry-Negron V.
      • Paglia D.
      • Macchia J.
      • Chu A.K.Y.
      • Schofield H.
      • Wamsteker E.-J.
      • Kwon R.
      • Schulman A.
      • Prabhu A.
      • Law R.
      • Sondhi A.
      • Yu J.
      • Patel A.
      • Donahue K.
      • Nathan H.
      • Cho C.
      • Anderson M.A.
      • Sahai V.
      • Lyssiotis C.A.
      • Zou W.
      • Allen B.L.
      • Rao A.
      • Crawford H.C.
      • Bednar F.
      • Frankel T.L.
      • Pasca di Magliano M.
      Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer.
      We thus identified potential predicted interactions within the tissue (Figure 7A). We then plotted interactions that were induced by oncogenic Kras (hence down-regulated upon KrasG12D inactivation). Strikingly, most Kras-driven interactions connected fibroblast-expressed ligands with receptors on epithelial cells and immune cells (Figure 7B and C). We then queried the fibroblast gene expression data to identify cytokines specifically expressed in an epithelial KrasG12D-dependent manner (Figure 7D). Finally, we selected from that list the genes encoding for cytokines expressed mainly, or exclusively, by fibroblasts, including Il33, Il6, C-X-C motif chemokine ligand 1 (Cxcl1), and the inflammatory mediators Serum amyloid a1 (Saa1) and Saa3 (the ortholog of human SAA1) (Figures 7E and 8). Intriguingly, these aforementioned cytokines all have known roles in pancreatic cancer.
      • Lesina M.
      • Kurkowski M.U.
      • Ludes K.
      • Rose-John S.
      • Treiber M.
      • Kloppel G.
      • Yoshimura A.
      • Reindl W.
      • Sipos B.
      • Akira S.
      • Schmid R.M.
      • Algul H.
      Stat3/Socs3 activation by IL-6 transsignaling promotes progression of pancreatic intraepithelial neoplasia and development of pancreatic cancer.
      • Mace T.A.
      • Shakya R.
      • Pitarresi J.R.
      • Swanson B.
      • McQuinn C.W.
      • Loftus S.
      • Nordquist E.
      • Cruz-Monserrate Z.
      • Yu L.
      • Young G.
      • Zhong X.
      • Zimmers T.A.
      • Ostrowski M.C.
      • Ludwig T.
      • Bloomston M.
      • Bekaii-Saab T.
      • Lesinski G.B.
      IL-6 and PD-L1 antibody blockade combination therapy reduces tumour progression in murine models of pancreatic cancer.
      • Zhang Y.
      • Yan W.
      • Collins M.A.
      • Bednar F.
      • Rakshit S.
      • Zetter B.R.
      • Stanger B.Z.
      • Chung I.
      • Rhim A.D.
      • di Magliano M.P.
      Interleukin-6 is required for pancreatic cancer progression by promoting MAPK signaling activation and oxidative stress resistance.
      • Djurec M.
      • Grana O.
      • Lee A.
      • Troule K.
      • Espinet E.
      • Cabras L.
      • Navas C.
      • Blasco M.T.
      • Martin-Diaz L.
      • Burdiel M.
      • Li J.
      • Liu Z.
      • Vallespinos M.
      • Sanchez-Bueno F.
      • Sprick M.R.
      • Trumpp A.
      • Sainz Jr., B.
      • Al-Shahrour F.
      • Rabadan R.
      • Guerra C.
      • Barbacid M.
      Saa3 is a key mediator of the protumorigenic properties of cancer-associated fibroblasts in pancreatic tumors.
      • Li J.
      • Byrne K.T.
      • Yan F.
      • Yamazoe T.
      • Chen Z.
      • Baslan T.
      • Richman L.P.
      • Lin J.H.
      • Sun Y.H.
      • Rech A.J.
      • Balli D.
      • Hay C.A.
      • Sela Y.
      • Merrell A.J.
      • Liudahl S.M.
      • Gordon N.
      • Norgard R.J.
      • Yuan S.
      • Yu S.
      • Chao T.
      • Ye S.
      • Eisinger-Mathason T.S.K.
      • Faryabi R.B.
      • Tobias J.W.
      • Lowe S.W.
      • Coussens L.M.
      • Wherry E.J.
      • Vonderheide R.H.
      • Stanger B.Z.
      Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy.
      Conversely, the respective receptors for these secreted factors were expressed by immune cells, including Cxcr2 (CXCL1 receptor) on immature myeloid cells, Il6r (interleukin [IL]6 receptor), P2rx7 and Scarb1 (SAA3 and SAA1 receptors) on several myeloid populations, and Il1rl1 (IL33 receptor also known as Suppression Of Tumorigenicity 2, ST2) on macrophages, regulatory T cells, mast cells, and innate lymphoid cells type 2. Thus, fibroblasts are extrinsically reprogrammed by KrasG12D epithelial cells to activate a gene expression profile that includes cytokines with cognate receptors expressed by myeloid cells, and with a known role in driving myeloid cell/macrophage-mediated immune suppression.
      Figure thumbnail gr7
      Figure 7Fibroblasts express inflammatory cytokines in response to epithelial oncogenic Kras. (A) Interactome analysis showing all predicted ligand-receptor interactions between different cell populations identified in iKras pancreatic scRNAseq analysis. Each line represents a ligand-receptor pair, color-coded by the cell type expressing the ligand. (B) Differential interactions from panel A positively regulated by oncogenic Kras (higher in 3 weeks [3w] ON compared with 3 days [3d] OFF) (adjusted P value < .05). (C) Circos plot showing the average expression of fibroblast ligands connected to their predicted receptors on various cell populations as measured in the pancreatic scRNAseq analysis. Ligands shown are from panel B (adjusted P value < .05). (D) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in fibroblasts from a curated list of immunomodulatory factors. (E) Violin plots showing expression of Il33, Cxcl1, Il6, Saa3, and Saa1 and their respective receptors Il1rl1, Cxcr2, Il6ra, P2rx7, and Scarb1 across all identified cell populations in both Kras∗ ON and OFF samples combined. DC, Dendritic cell; NK T, Natural killer T cell.
      Figure thumbnail gr8
      Figure 8Fibroblasts secrete immunomodulatory factors with receptors in myeloid cells. Dot plot showing average expression of Il33, Il6, Cxcl1, Saa1, and Saa3 and their respective receptors Il1rl1, Il6ra, Cxcr2, P2rx7, and Scarb1 across cell populations. Size of the dots reflects the proportion of cells expressing a determined gene and color represents the average expression level. NK T, Natural killer T cell.
      To investigate the mechanism of fibroblast reprogramming, we used an in vitro approach. We harvested conditioned media (CM) from iKras cells derived from a Ptf1aCre/+; R26rtTa-ires-EGFP/rtTa-ires-EGFP;TetO-KrasG12D;Trp53R172H/+ (iKras;P53) mouse tumor
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      ; the inclusion of mutant Trp53 being required for growth in cell culture. In brief, iKras cells
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      Metastatic pancreatic cancer is dependent on oncogenic Kras in mice.
      were grown in the presence of DOX, thus expressing KrasG12D. Then, media was changed to either DOX-containing or DOX-free media, thus maintaining KrasG12D expression or inactivating it, respectively. After 48–72 hours, we harvested CM from each condition and boiled half of it to denature heat-labile components. CM then was administered to cultured primary mouse fibroblasts (CD1WT and B6318) derived from healthy adult pancreata as previously described
      • Kemp S.B.
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      Apolipoprotein E promotes immune suppression in pancreatic cancer through NF-kB-mediated production of CXCL1.
      ,
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      The transcription factor GLI1 modulates the inflammatory response during pancreatic tissue remodeling.
      (Figure 9A). Compared with control Dulbecco’s modified Eagle medium (DMEM) media, CM from iKras cells induced expression of Cxcl1, Il6, Il33, and Saa3 in fibroblasts in a KrasG12D-dependent manner (Figure 9B), recapitulating the in vivo results. To distinguish between a KrasG12D-dependent protein factor or metabolite signaling to fibroblasts, we compared intact CM with boiled CM, and found that the latter induced Il6 but none of the genes encoding the other cytokines of interest (Figure 9B). Thus, a combination of cancer cell–derived, KrasG12D-dependent heat-labile factors such as proteins and metabolites mediated fibroblast reprogramming. Oncogenic Kras regulates the intracellular metabolome of pancreatic cancer cells.
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      Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism.
      We inquired whether the extracellular metabolome is similarly dependent on expression of oncogenic Kras. Indeed, mass spectrometry–based metabolomic profiling showed numerous extracellular metabolites whose abundance changed depending on KrasG12D expression (Figure 9C and D). Among these, several released metabolites have been reported to act as direct extracellular signaling molecules (eg, pyruvate, lactate, α-ketoglutarate) while, alternatively, the decreased abundance of other metabolites may impact signaling downstream of Mammalian target of rapamycin (mTOR) (eg, arginine, isoleucine, cystine, glutamine) (Figure 9D).
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      mTOR signaling in growth, metabolism, and disease.
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      The immunomodulatory potential of the metabolite itaconate.
      We then investigated the extracellular metabolite composition after incubation of the iKrasG12D-conditioned medium from the various conditions described earlier. We observed that the lactate concentration was not affected while pyruvate was depleted upon culturing with fibroblasts (Figure 9E). Thus, it appears that fibroblasts consume pyruvate from the medium, an intriguing finding given that fibroblasts serve as a source of pyruvate in breast cancer.
      • Becker L.M.
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      • Tampe D.
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      • Lovisa S.
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      Epigenetic reprogramming of cancer-associated fibroblasts deregulates glucose metabolism and facilitates progression of breast cancer.
      Figure thumbnail gr9
      Figure 9Fibroblasts are reprogrammed through epithelial KrasG12D-induced secreted molecules. (A) Experimental design. CM was collected from iKras∗ cancer cells cultured with +DOX to activate KrasG12D expression (Kras ON) or without -DOX (Kras OFF). The media samples then were boiled to denature protein factors, or left intact and used to culture pancreatic fibroblasts. DMEM was used as control. (B) Quantitative reverse-transcription polymerase chain reaction for Cxcl1, Il33, Il6, and Saa3 expression in fibroblasts (CD1WT) that were cultured with CM either from Kras ON, Kras OFF, or DMEM, either boiled or not boiled. Gene expression was normalized to Peptidylprolyl Isomerase A (Ppia). Data are shown as means ± SD. N = 3 per group. The statistical differences were determined by multiple-comparison analysis of variance. (C) Experimental design. CM was collected from iKras cancer cells cultured with +DOX to activate KrasG12D expression (Kras∗ ON) or without -DOX (KrasG12D OFF), and used for metabolomic analysis. (D) Heatmap showing levels of extracellular metabolites in iKras cancer cell CM with KrasG12D ON or OFF. Both conditions are compared with regular DMEM media. (E) Fibroblasts were exposed to +DOX CM and -DOX CM for 48 hours. The resulting CM was analyzed for metabolites, in parallel with iKras cell CM and DMEM. Relative metabolite abundance of lactate and pyruvate from all conditions are shown. (F) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes encoding for ligands that are secreted by fibroblasts and epithelial cells. FB, Fibroblast; Rel., relative.
      We then investigated the list of secreted factors expressed by KrasG12D-expressing epithelial cells in the single-cell sequencing data. We noted, among others, expression of Hbegf, Tnf, Pdgfa, Pdgfb, Hgf, and Il6 (Figure 9F). Expression of Hbegf, Tnf, and Pdgfa was higher in epithelial cells, whereas Hgf, Il6, and Pdgfb were higher in fibroblasts. Given the multitude of factors, we reasoned that we were unlikely to identify a single factor driving fibroblast reprogramming. We focused instead on signaling pathways that might be activated in fibroblasts. In vivo, we observed expression of p-STAT3, but not p-ERK, in Platelet-derived growth factor receptors (PDGFR)+ or SMA+ fibroblasts when KrasG12D is ON in iKras mice (Figure 10A and B). Similar to the p-STAT3 expression within macrophages (Figure 6C), p-STAT3 expression in fibroblasts was down-regulated upon KrasG12D inactivation, identifying it as a potential mediator of fibroblast reprogramming (Figure 10A). In contrast, p-ERK was up-regulated in fibroblasts upon KrasG12D inactivation (Figure 10B), consistent with its role during tissue repair.
      • Collins M.A.
      • Yan W.
      • Sebolt-Leopold J.S.
      • Pasca di Magliano M.
      MAPK signaling is required for dedifferentiation of acinar cells and development of pancreatic intraepithelial neoplasia in mice.
      ,
      • Zhang Y.
      • Yan W.
      • Mathew E.
      • Kane K.T.
      • Brannon 3rd, A.
      • Adoumie M.
      • Vinta A.
      • Crawford H.C.
      • Pasca di Magliano M.
      Epithelial-myeloid cell crosstalk regulates acinar cell plasticity and pancreatic remodeling in mice.
      We then repeated the in vitro experiment described earlier, this time inhibiting JAK/STAT signaling using ruxolitinib (JAK inhibitor, JAKi) (Figure 10C and D). CM-induced fibroblast expression of Cxcl1, Il33, Saa3, and Il6 was inhibited by JAKi (Figure 10E and F). Thus, in vitro activation of fibroblast JAK/STAT3 signaling is required for epithelial cell–induced reprogramming.
      Figure thumbnail gr10
      Figure 10Fibroblast reprogramming requires JAK/STAT signaling. (A) Immunofluorescent staining of p-STAT3 (red), Platelet-derived growth factor receptor beta (PDGFRβ) (green), and SMA (gray). N = 2–3 mice per group. Arrowheads point to p-STAT3–positive fibroblasts and arrow points to positive epithelial cells. (B) Immunofluorescent staining for PDGFRβ (green), SMA (white), and p-ERK (red). Scale bar: 50 μm. N = 2–3 mice per group. (C) Experimental design. Fibroblasts were exposed to iKras∖tumor cell CM either from KrasG12D ON, KrasG12D OFF, or DMEM. Fibroblasts cultured with KrasG12D ON CM media were treated with a JAK/STAT2/3 inhibitor (ruxolitinib) or vehicle. (D) Western blot showing protein levels of total STAT3 or phospho-STAT3 from CD1WT fibroblasts treated as indicated. α-tubulin was used as a loading control. (E) Quantitative reverse-transcription polymerase chain reaction results for Cxcl1, Il33, Il6, and Saa3 expression in fibroblasts (CD1WT) treated with CM as described in panel C. Data are shown as means ± SD. N = 3 per group. The statistical differences were determined by multiple-comparison analysis of variance. (F) Quantitative reverse-transcription polymerase chain reaction for Cxcl1, Il33, Il6, and Saa3 expression in fibroblasts (B6318) treated with iKras cell CM either from KrasG12D ON, KrasG12D OFF, or DMEM. Fibroblasts cultured with KrasG12D ON CM medium were treated with a JAK/STAT2/3 inhibitor (ruxolitinib) or vehicle. Data are shown as means ± SD. The statistical differences were determined by multiple-comparison analysis of variance. mRNA, messenger RNA.
      We then studied the effect of JAK/STAT inhibition in vivo. We administered DOX to iKras mice and induced acute pancreatitis; 3 weeks later, the mice were randomized to ruxolitinib (JAKi, 180 mg/kg) or vehicle daily for 3 days (Figure 11A). As expected, p-STAT3 was reduced upon JAKi treatment (Figure 11B and C). Intriguingly, histology analysis showed that the pancreata of mice treated with JAKi had fewer PanIN lesions and more acini areas when compared with vehicle-treated mice (Figure 11B, D, and E). We also observed a reduction in SMA+ fibroblasts (Figure 11F). Although the number of F4/80+ macrophages did not change in JAKi-treated mice, the number of ARG1+ macrophages tended toward decreasing (Figure 11G and H). Thus, inhibition of JAK/STAT in early PanINs partially reversed tumorigenesis and drove stromal remodeling.
      Figure thumbnail gr11
      Figure 11Inhibition of the JAK/STAT pathway mimics KrasG12D inactivation-induced tissue repair. (A) Experimental design. iKras mice were given DOX and after 3 days acute pancreatitis was induced. After pancreatitis, KrasG12D was left ON for 3 weeks and at 3 weeks mice either received ruxolitinib (180 mg/kg) daily for 3 days or were treated with vehicle. N = 2–3 mice per group. (B) H&E, periodic acid–Schiff (PAS), and immunofluorescent staining for p-STAT3 (red), F4/80 (green), and SMA (white), or ARG1 (red), F4/80 (green), and CK19 (white). Scale bar: 50 μm. N = 2–3 mice per group. (C) Quantification for the p-STAT3 staining. (D) Quantification for the PAS staining. (E) Quantification of CK19-positive area. (F) Quantification of SMA-positive area. (G) Quantification of ARG1+-positive area within the F4/80+ macrophages. (H) Quantification of F4/80+-positive area. All data were analyzed using a t test. Blue, male; pink, females. Rux, ruxolitinib; Veh, vehicle.

      Discussion

      In summary, our results showed that oncogenic KrasG12D in epithelial cells drives the recruitment of macrophages and activates fibroblasts before ADM formation. Furthermore, continuous KrasG12D expression is required for fibroblast reprogramming, including expression of an inflammatory gene expression panel with cytokines known to be key mediators of macrophage polarization in pancreatic cancer. Thus, KrasG12D signaling drives formation and maintenance of an immune-suppressive stroma from early stages of carcinogenesis in an extrinsic manner.
      Mutations in the KRAS gene are present in most human pancreatic cancers
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      • Zetter B.R.
      • Stanger B.Z.
      • Chung I.
      • Rhim A.D.
      • di Magliano M.P.
      Interleukin-6 is required for pancreatic cancer progression by promoting MAPK signaling activation and oxidative stress resistance.
      ,
      • Fukuda A.
      • Wang S.C.
      • Morris J.P.
      • Folias A.E.
      • Liou A.
      • Kim G.E.
      • Akira S.
      • Boucher K.M.
      • Firpo M.A.
      • Mulvihill S.J.
      • Hebrok M.
      Stat3 and MMP7 contribute to pancreatic ductal adenocarcinoma initiation and progression.
      ; Saa3 (ortholog to human SAA1), an apolipoprotein required for neoplastic progression
      • Djurec M.
      • Grana O.
      • Lee A.
      • Troule K.
      • Espinet E.
      • Cabras L.
      • Navas C.
      • Blasco M.T.
      • Martin-Diaz L.
      • Burdiel M.
      • Li J.
      • Liu Z.
      • Vallespinos M.
      • Sanchez-Bueno F.
      • Sprick M.R.
      • Trumpp A.
      • Sainz Jr., B.
      • Al-Shahrour F.
      • Rabadan R.
      • Guerra C.
      • Barbacid M.
      Saa3 is a key mediator of the protumorigenic properties of cancer-associated fibroblasts in pancreatic tumors.
      and CXCL1, has been studied in epithelial cells, and shown to be a key moderator of T-cell exclusion in malignant disease.
      • Li J.
      • Byrne K.T.
      • Yan F.
      • Yamazoe T.
      • Chen Z.
      • Baslan T.
      • Richman L.P.
      • Lin J.H.
      • Sun Y.H.
      • Rech A.J.
      • Balli D.
      • Hay C.A.
      • Sela Y.
      • Merrell A.J.
      • Liudahl S.M.
      • Gordon N.
      • Norgard R.J.
      • Yuan S.
      • Yu S.
      • Chao T.
      • Ye S.
      • Eisinger-Mathason T.S.K.
      • Faryabi R.B.
      • Tobias J.W.
      • Lowe S.W.
      • Coussens L.M.
      • Wherry E.J.
      • Vonderheide R.H.
      • Stanger B.Z.
      Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy.
      In our study, fibroblasts emerged as mediators of epithelial/immune cell crosstalk during the onset of pancreatic carcinogenesis. We found that fibroblasts are reprogrammed when exposed to CM from oncogenic Kras-expressing epithelial cells, supporting direct communication between these cell types. Interestingly, although the majority of cytokines we assessed in fibroblasts required a heat-labile component of the conditioned media, Il6 did not, implying that a combination of KrasG12D-dependent signals are required for establishing a complete reprogramming of pancreatic fibroblasts, potentially including both peptide and metabolic secreted factors (Figure 6).
      The role and origin of fibroblasts in pancreatic cancer remains poorly understood. Fibroblasts secrete a number of cytokines that influence the immune milieu (eg, CXCL12, which has been shown to reduce T-cell infiltration in pancreatic cancer), and as such they promote cancer growth.
      • Feig C.
      • Jones J.O.
      • Kraman M.
      • Wells R.J.
      • Deonarine A.
      • Chan D.S.
      • Connell C.M.
      • Roberts E.W.
      • Zhao Q.
      • Caballero O.L.
      • Teichmann S.A.
      • Janowitz T.
      • Jodrell D.I.
      • Tuveson D.A.
      • Fearon D.T.
      Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer.
      However, in other models, fibroblast depletion promotes carcinogenesis.
      • Ozdemir B.C.
      • Pentcheva-Hoang T.
      • Carstens J.L.
      • Zheng X.
      • Wu C.C.
      • Simpson T.R.
      • Laklai H.
      • Sugimoto H.
      • Kahlert C.
      • Novitskiy S.V.
      • De Jesus-Acosta A.
      • Sharma P.
      • Heidari P.
      • Mahmood U.
      • Chin L.
      • Moses H.L.
      • Weaver V.M.
      • Maitra A.
      • Allison J.P.
      • LeBleu V.S.
      • Kalluri R.
      Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival.
      These contradictory findings might be explained by the heterogeneity of fibroblast populations. In recent years, the nature and origin of fibroblasts in advanced disease have been addressed by several studies, and previous assumptions regarding these cell populations have been challenged. Cancer-associated fibroblasts (CAFs) have long been assumed to derive from a pancreatic stellate cell population in the pancreas (similar to hepatic stellate cells), but recent lineage tracing work by our group has shown that a substantial proportion of CAFs derive from perivascular fibroblasts present in the normal pancreas.
      • Garcia P.E.
      • Adoumie M.
      • Kim E.C.
      • Zhang Y.
      • Scales M.K.
      • El-Tawil Y.S.
      • Shaikh A.Z.
      • Wen H.J.
      • Bednar F.
      • Allen B.L.
      • Wellik D.M.
      • Crawford H.C.
      • Pasca di Magliano M.
      Differential contribution of pancreatic fibroblast subsets to the pancreatic cancer stroma.
      Accordingly, a lineage tracing study following pancreatic stellate cells in pancreatic cancer showed that they only contribute to a small subset of CAFs.
      • Helms E.J.
      • Berry M.W.
      • Chaw R.C.
      • DuFort C.C.
      • Sun D.
      • Onate M.K.
      • Oon C.
      • Bhattacharyya S.
      • Sanford-Crane H.
      • Horton W.
      • Finan J.M.
      • Sattler A.
      • Makar R.
      • Dawson D.W.
      • Xia Z.
      • Hingorani S.R.
      • Sherman M.H.
      Mesenchymal lineage heterogeneity underlies non-redundant functions of pancreatic cancer-associated fibroblasts.
      In vitro characterization and, more recently, scRNAseq studies have identified CAF subsets with specific transcriptional signatures and functional roles.
      • Biffi G.
      • Oni T.E.
      • Spielman B.
      • Hao Y.
      • Elyada E.
      • Park Y.
      • Preall J.
      • Tuveson D.A.
      IL1-Induced JAK/STAT signaling is antagonized by TGFbeta to shape CAF heterogeneity in pancreatic ductal adenocarcinoma.
      • Elyada E.
      • Bolisetty M.
      • Laise P.
      • Flynn W.F.
      • Courtois E.T.
      • Burkhart R.A.
      • Teinor J.A.
      • Belleau P.
      • Biffi G.
      • Lucito M.S.
      • Sivajothi S.
      • Armstrong T.D.
      • Engle D.D.
      • Yu K.H.
      • Hao Y.
      • Wolfgang C.L.
      • Park Y.
      • Preall J.
      • Jaffee E.M.
      • Califano A.
      • Robson P.
      • Tuveson D.A.
      Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts.
      • Ohlund D.
      • Handly-Santana A.
      • Biffi G.
      • Elyada E.
      • Almeida A.S.
      • Ponz-Sarvise M.
      • Corbo V.
      • Oni T.E.
      • Hearn S.A.
      • Lee E.J.
      • Chio II,
      • Hwang C.I.
      • Tiriac H.
      • Baker L.A.
      • Engle D.D.
      • Feig C.
      • Kultti A.
      • Egeblad M.
      • Fearon D.T.
      • Crawford J.M.
      • Clevers H.
      • Park Y.
      • Tuveson D.A.
      Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer.
      In advanced disease, fibroblasts have been classified as myofibroblastic CAFs (myCAFs), expressing high levels of SMA and genes encoding for extracellular matrix components, and inflammatory CAFs (iCAFs), inflammatory fibroblasts secreting cytokines such as IL6.
      • Ohlund D.
      • Handly-Santana A.
      • Biffi G.
      • Elyada E.
      • Almeida A.S.
      • Ponz-Sarvise M.
      • Corbo V.
      • Oni T.E.
      • Hearn S.A.
      • Lee E.J.
      • Chio II,
      • Hwang C.I.
      • Tiriac H.
      • Baker L.A.
      • Engle D.D.
      • Feig C.
      • Kultti A.
      • Egeblad M.
      • Fearon D.T.
      • Crawford J.M.
      • Clevers H.
      • Park Y.
      • Tuveson D.A.
      Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer.
      ,
      • Biffi G.
      • Tuveson D.A.
      Diversity and biology of cancer-associated fibroblasts.
      Formation of iCAFs is dependent on IL1 and JAK/STAT3 signaling.
      • Biffi G.
      • Oni T.E.
      • Spielman B.
      • Hao Y.
      • Elyada E.
      • Park Y.
      • Preall J.
      • Tuveson D.A.
      IL1-Induced JAK/STAT signaling is antagonized by TGFbeta to shape CAF heterogeneity in pancreatic ductal adenocarcinoma.
      Although fibroblast heterogeneity has been described in precursor lesions,
      • Hosein A.N.
      • Huang H.
      • Wang Z.
      • Parmar K.
      • Du W.
      • Huang J.
      • Maitra A.
      • Olson E.
      • Verma U.
      • Brekken R.A.
      Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution.
      whether the biological function of different fibroblast subsets is similar in early and advanced disease is not known. Here, we show that fibroblasts activated during the earliest stages of carcinogenesis have increased SMA, but also are a source of inflammatory cytokines such as IL6; furthermore, fibroblast activation, including SMA expression, requires JAK/STAT3 signaling. This suggests that the reported iCAF/myCAF dichotomy in established tumors is not as distinct in premalignant lesions. Whether other populations, such as the antigen-presenting CAFs,
      • Elyada E.
      • Bolisetty M.
      • Laise P.
      • Flynn W.F.
      • Courtois E.T.
      • Burkhart R.A.
      • Teinor J.A.
      • Belleau P.
      • Biffi G.
      • Lucito M.S.
      • Sivajothi S.
      • Armstrong T.D.
      • Engle D.D.
      • Yu K.H.
      • Hao Y.
      • Wolfgang C.L.
      • Park Y.
      • Preall J.
      • Jaffee E.M.
      • Califano A.
      • Robson P.
      • Tuveson D.A.
      Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts.
      are recapitulated in early lesions remains to be determined. Analysis of the myeloid cells showed other differences in the PME compared with the TME. In the earliest stages of Kras activation, macrophages have low/no expression of ARG1, while expression is increased in established PanINs and in pancreatic cancer. This observation recapitulates earlier functional studies on the role of macrophages in pancreatic carcinogenesis. In the PME, macrophages promote PanIN formation/progression independently from their ability to inhibit CD8+ T-cell responses, likely through direct signaling to epithelial cells.
      • Zhang Y.
      • Yan W.
      • Mathew E.
      • Kane K.T.
      • Brannon 3rd, A.
      • Adoumie M.
      • Vinta A.
      • Crawford H.C.
      • Pasca di Magliano M.
      Epithelial-myeloid cell crosstalk regulates acinar cell plasticity and pancreatic remodeling in mice.
      In contrast, in advanced disease, the main role of macrophages is to inhibit anti–tumor immunity.
      • Mitchem J.B.
      • Brennan D.J.
      • Knolhoff B.L.
      • Belt B.A.
      • Zhu Y.
      • Sanford D.E.
      • Belaygorod L.
      • Carpenter D.
      • Collins L.
      • Piwnica-Worms D.
      • Hewitt S.
      • Udupi G.M.
      • Gallagher W.M.
      • Wegner C.
      • West B.L.
      • Wang-Gillam A.
      • Goedegebuure P.
      • Linehan D.C.
      • DeNardo D.G.
      Targeting tumor-infiltrating macrophages decreases tumor-initiating cells, relieves immunosuppression, and improves chemotherapeutic responses.
      ,
      • Zhang Y.
      • Velez-Delgado A.
      • Mathew E.
      • Li D.
      • Mendez F.M.
      • Flannagan K.
      • Rhim A.D.
      • Simeone D.M.
      • Beatty G.L.
      • Pasca di Magliano M.
      Myeloid cells are required for PD-1/PD-L1 checkpoint activation and the establishment of an immunosuppressive environment in pancreatic cancer.
      ,
      • Steele C.W.
      • Karim S.A.
      • Leach J.D.
      • Bailey P.
      • Upstill-Goddard R.
      • Rishi L.
      • Foth M.
      • Bryson S.
      • McDaid K.
      • Wilson Z.
      • Eberlein C.
      • Candido J.B.
      • Clarke M.
      • Nixon C.
      • Connelly J.
      • Jamieson N.
      • Carter C.R.
      • Balkwill F.
      • Chang D.K.
      • Evans T.R.
      • Strathdee D.
      • Biankin A.V.
      • Nibbs R.J.
      • Barry S.T.
      • Sansom O.J.
      • Morton J.P.
      CXCR2 inhibition profoundly suppresses metastases and augments immunotherapy in pancreatic ductal adenocarcinoma.
      Our findings thus highlight profound differences between the PME and the TME, and raise the question as to whether fibroblasts are equally required to regulate production of inflammatory cytokines in the TME, or whether they become dispensable at later stages. It also is possible that different tumor types, such as basal and classic, have a different reliance on fibroblasts, an area of future exploration.
      In this study, we close the loop on communication between epithelial cells, fibroblasts, and immune cells. Reprogramming of myeloid cells to alleviate the profound immune suppression of pancreatic cancer is a key concept in potential therapeutic approaches.
      • Zhu Y.
      • Knolhoff B.L.
      • Meyer M.A.
      • Nywening T.M.
      • West B.L.
      • Luo J.
      • Wang-Gillam A.
      • Goedegebuure S.P.
      • Linehan D.C.
      • DeNardo D.G.
      CSF1/CSF1R blockade reprograms tumor-infiltrating macrophages and improves response to T-cell checkpoint immunotherapy in pancreatic cancer models.
      ,
      • Beatty G.L.
      • Chiorean E.G.
      • Fishman M.P.
      • Saboury B.
      • Teitelbaum U.R.
      • Sun W.
      • Huhn R.D.
      • Song W.
      • Li D.
      • Sharp L.L.
      • Torigian D.A.
      • O'Dwyer P.J.
      • Vonderheide R.H.
      CD40 agonists alter tumor stroma and show efficacy against pancreatic carcinoma in mice and humans.
      Unfortunately, the initial results from clinical trial testing of a CD40 agonist administered to reprogram myeloid cells combined with immune checkpoint inhibition and chemotherapy did not benefit the majority of patients,
      • O'Hara M.H.
      • O'Reilly E.M.
      • Wolff R.A.
      • Wainberg Z.A.
      • Ko A.H.
      • Rahma O.E.
      • Fisher G.A.
      • Lyman J.P.
      • Cabanski C.R.
      • Karakunnel J.J.
      • Gherardini P.F.
      • Kitch L.J.
      • Bucktrout S.
      • Christopher E.
      • Mick R.
      • Chen R.
      • Trifan O.C.
      • Salvador L.
      • O'Donnell-Tormey J.
      • Vonderheide R.H.
      Gemcitabine (Gem) and nab-paclitaxel (NP) ± nivolumab (nivo) ± CD40 agonistic monoclonal antibody APX005M (sotigalimab), in patients (Pts) with untreated metastatic pancreatic adenocarcinoma (mPDAC): phase (Ph) 2 final results.
      pointing to the need for yet additional avenues by which to target the microenvironment. Our current work suggests that fibroblast reprogramming should be considered in high-risk patients to prevent malignant progression and also could be explored to prevent tumor relapse in surgical patients. Finally, our study provides another piece of the puzzle in our understanding of the events that lead to the onset and progression of pancreatic cancer, and as such contribute to our fundamental knowledge of this deadly disease.

      Methods

      Mice

      Mice were housed in the specific pathogen-free animal facility at the Rogel Cancer Center at the University of Michigan and overseen by the Unit for Laboratory Animal Medicine. Ptf1a(p48)-Cre;Rosa26rtTa/rtTa mice were crossed to TRE-KrasG12D;R26rtTa/rtTa to generate the p48-Cre;TRE-KrasG12D;R26rtTa/rtTa (iKras∗), as described.
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      Expression of KrasG12D was induced in adult mice (age, 8–14 wk) by replacing regular chow with doxycycline chow (1 g/kg; Bio-Serv, Flemington, NJ). Acute pancreatitis was induced by administering 8 hourly doses of caerulein (75 μg/kg; Sigma-Aldrich, St. Louis, MO) intraperitoneally for 2 consecutive days. For JAK/STAT pathway inhibition, the JAK1/2 inhibitor ruxolitinib (INCB18424; MedChemExpress, Monmouth Junction, NJ) was administered orally once a day for a total of 4 doses at 180 mg/kg in 100 μL of 10% dimethyl sulfoxide in corn oil. Control mice received vehicle in parallel. All animal studies were conducted in compliance with the guidelines of the Institutional Animal Care and Use Committee at the University of Michigan.

      Histology and Immunohistochemistry

      Pancreatic tissue samples from experimental and control mice were fixed in 10% neutral-buffered formalin (FisherBrand, Fisher Scientific, Pittsburgh, PA) overnight and then embedded in paraffin and sectioned into slides. H&E, Gomori trichome, periodic acid–Schiff, and immunofluorescence (IF) staining were performed as previously described.
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      For immunohistochemistry, fresh cut paraffin sections were rehydrated using 2 series of xylene, 2 series of 100% ethanol, and then 2 series of 95% ethanol. Water was used to wash all residues from previous washes. Antigen retrieval was performed using Antigen Retrieval CITRA Plus (BioGenex, Fremont, CA) and microwaved for a total of 8 minutes. Upon cool down, sections were blocked using 1% bovine serum albumin (BSA) in phosphate-buffered saline for 30 minutes and then primary antibodies (Table 2) were used at their corresponding dilutions. Primary antibody incubation was performed at 4°C overnight. Biotinylated secondary antibodies were used in 1:300 dilution and applied to sections for 45 minutes at room temperature. After secondary antibody incubation, sections were incubated for 30 minutes with the ABC reagent from the Vectastain Elite ABC Kit (peroxidase), followed by 3,3′-diaminobenzidine tetra hydrochloride (Vector, Burlingame, CA). For IF, Alexa Fluor secondary antibodies (Invitrogen) were used, then slides were mounted with Prolong Diamond Antifade Mountant with 4′,6-diamidino-2-phenylindole (DAPI) (Invitrogen, Waltham, MA). The TSA Plus Fluorescein system (PerkinElmer, Waltham, MA) was used in IF for mouse primary antibodies. An Olympus BX53F microscope, Olympus DP80 digital camera (Olympus, Tokyo, Japan), and CellSens Standard software (Olympus, Tokyo, Japan) were used for imaging. Quantification of positive cell number or area was performed using ImageJ (National Institutes of Health, Bethesda, MD) using 3–5 images/slide (magnification, 200× or 400×) taken from 2–4 samples per group. We also used the Leica STELLARIS 8 FALCON Confocal Microscopy System and the LAS X software to acquire and visualize images (Leica Microsystems, Wetzlar, Germany).
      Table 2Immunohistochemistry, IF, and Western Blot Antibodies
      AntibodySupplierCatalog numberImmunohistochemistry dilutionIF dilutionWB dilution
      CD45BD Pharmingen, San Diego, CA5530761:200
      F4/80Cell Signaling, Danvers, MA70076S1:2501:250
      CD4Cell Signaling25229S1:00
      CD8Cell Signaling98941S1:4001:100
      Foxp3Cell Signaling12653S1:100
      Arginase 1Cell Signaling93668S1:250
      αSMASigma Aldrich, Burlington, MAA25471:1000
      CK19 (Troma III)Developmental Studies Hybridoma Bank, Iowa City, IA-1:50
      Perk1/2Cell Signaling4370L1:100
      Ki67Abcam, Cambridge, UKab155801:100
      CC3Cell Signaling9661L
      E-cadherinCell Signaling14472S1:100
      CD3Abcamab56901:100
      p-Stat3 Y705Cell Signaling9145S1:500
      Total STAT3Cell Signaling9139S1:1000
      p-ERK (T202/Y204)Cell Signaling470L1:1000
      Total ERKCell Signaling4695S1:1000
      VinculinCell Signaling139011:2000
      a-tubulinCell Signaling3873S1:2000
      PDGFRβAbcamab325701:100
      WB, Western blot.

      Multiplex Immunohistochemistry Staining and Analysis

      Multiplex immunohistochemistry staining was performed on paraffin-embedded pancreatic tissue sections as follows. Slides were baked in a hybridization oven for 1 hour at 60ºC, cooled for 10 minutes at room temperature, then dipped sequentially (×3) into xylene for 10 minutes each for removal of paraffin. Slides then were rehydrated in alcohol with dilutions of 100%, 95%, and then 70% for 10 minutes each, followed by a wash in deionized water for 2 minutes. Slides then were placed in neutral buffered formalin for 30 minutes. The slides then were washed for 2 minutes in deionized water, and then microwaved at 100% power in Rodent Decloaker (Biocare Medical, Pacheco, CA) for 30 seconds, the power level was reduced to 20% and microwaving continued for an additional 10 minutes, followed by a resting step of 15 minutes at room temperature. Microwaving continued at 10% power for an additional 10 minutes. Before microwaving with Rodent Decloaker, plastic wrap was secured on top of the microwave-proof slide box with rubber bands, with a partial opening for steam escape to prevent loss of solution. After the last microwaving step, slides were left to cool until slides and solution achieved room temperature. The multiplex staining was performed for each primary color combination. Slides were placed in a deionized water wash for 2 minutes followed by a Tris buffered saline solution with Tween 20 (TBST) wash for 2 minutes. Slides were placed in a slide incubation chamber and Bloxall (Vector, Burlingame, CA) was applied for 10 minutes followed by an additional blocking step of 1% BSA (in TBST) for 20 minutes, primary antibody was applied after tapping the slide to remove the primary antibody and was left to incubate for 1 hour, slides were washed in TBST (×3) for 2 minutes each, secondary antibody was applied, followed by TBST wash (×3) for 2 minutes each, Opal color was applied for 10 minutes, and a TBST wash (×3) for 2 minutes each was performed. The slides then were microwaved with either AR6 or AR9 for 45 seconds at 100% followed by 15 minutes at 20%. The previous steps then were repeated for each of the following antibodies and Opal colors in exact listed order: (1) F4/80 at 1:600 (ab6640; Abcam, Cambridge, UK); (2) CD3 at 1:400 (A0452; Dako, Agilent, Santa Clara, CA) TSA 520; (3) CD8 at 1:400 (98941; Cell Signaling, Danvers, MA); (4) ARG1 at 1:100 (93668; Cell Signaling, Danvers, MA); and (5) CK19 at 1:400 (Troma III, Developmental Studies Hybridoma Bank, Iowa City, IA). After the last application of multiplex was completed, slides were washed as described previously and placed in AR6, and then microwaved. After cooling, the slides were washed in deionized water followed by TBST for 2 minutes each. Opal spectral DAPI solution was applied (3 drops diluted in 1 mL TBST for 10 minutes followed by a wash in TBST for 30 seconds). Coverslips were mounted with Prolong Diamond, and slides were left to lie flat overnight away from light. If the entire multiplex could not be completed without interruption, the slides were left in AR6 or AR9 after a microwaving step, and covered from light until the next day. All primary antibodies were diluted in 1% BSA and all TSA Opal colors were diluted in TSA diluent at 1:50.
      The Mantra Quantitative Pathology Work Station was used to image sections of each of the slides. One to 3 images per slide was captured at a magnification of ×20. Cube filters (DAPI, CY3, CY5, CY7, Texas Red, Qdot, Thermo Fisher Scientific, Waltham, MA) were used in taking each image capture. All images were analyzed using inForm Cell Analysis software (Akoya Biosciences, Marlborough, MA). Sixty-three images encompassing the 26 different control and experimental slides were batch analyzed after using a mouse formulated library consisting of each single TSA fluorophore listed previously. Unmixing was performed and no spectral overlap was found between fluorophores. Using inForm version 2.3.0 training software, cell compartments were segmented into the nucleus, cytoplasm, and membrane. DAPI was used to identify the nucleus of the cells and to determine their shape and size. The cytoplasm was segmented using ARG1 (Opal 650), F4/80 (Opal 540), and CD3 (Opal 520). The inner distance to the nucleus was set at 0 pixels and the outer distance of the nucleus was set at 6 pixels. The membrane was segmented using CK19 (Opal 690) with the maximum cell size set at 25 pixels from the nucleus to the membrane, and each pixel is 0.496 μm. X and Y coordinates were assigned to each identified cell in each image. Cell phenotypes (T cell, macrophage, epithelial cell, acinar cell, other cell) were selected manually at random throughout the 63 images. T cell, macrophage, and epithelial cells were selected based on single staining of CD3 (Opal 520), F4/80 (Opal 540), and CK19 (Opal 690), respectively. Other cells were selected manually based on the lack of staining of the earlier-listed fluorophores, and acinar cells were labeled based on the lack of listed fluorophores and tissue morphology. A fluorescent intensity of ARG1 (Opal 650) in the nucleus at a maximum threshold of 200 and a positivity threshold level of 13 was used to identify ARG1+ cells. R programs were used to make complex phenotypes by combining the primary cell phenotypes (T cell, macrophage, epithelial cell, acinar cell, and other cell) and the positivity thresholds for CD8+ and ARG1+ (Table 3).
      Table 3Multiplex Immunohistochemistry Antibodies and Cell Phenotyping
      Complex phenotypeOpalPrimary phenotypeAntibody scoringCell segmentMean signal intensity
      MacrophageF4/80-Opal 540MacrophageNoneN/AN/A
      Arg1+ macrophageArg1-Opal 650MacrophageArg1Nucleus13 and above
      Arg1- macrophageMacrophageArg1Nucleus<13
      Epithelial cellCK19-Opal 690EpithelialNoneN/AN/A
      Acinar cellAcinar cellNoneN/AN/A
      N/A, not applicable.

      Flow Cytometry

      Pancreata were harvested and dissociated to single cells by mincing the tissue finely using scissors followed by collagenase IV (1 mg/mL; Sigma) digestion for 30 minutes at 37°C while shaking. A 40-um mesh strainer was used to separate single cells. Red blood cell lysis buffer (eBioscience, San Diego, CA) was used to lyse all the red blood cells. Live cells were stained for surface markers using the antibodies listed in Table 4. The cells were either fixed after the primary antibody staining and used for analysis or the cells were fixed and permeabilized before intracellular staining using the antibodies listed in Table 4. Flow-cytometric analysis was performed on the Cyan ADP analyzer (Beckman Coulter, Brea, CA) and the ZE5 analyzer (Bio-Rad, Hercules, CA). Data were analyzed using FlowJo v10 software (Flowjo, LCC; Ashland, OR).
      Table 4Flow Cytometry Antibodies
      AntibodiesSupplierCatalog numberCloneDilution
      CD45Invitrogen, Waltham, MAMCD453030-F111:100
      CD45BD Horizon, Franklin Lakes, NJ56389130-F111:100
      CD11bBD Pharmingen557657M1/701:100
      F4/80Invitrogen15-4801-82BM81:100
      Ly6GBD Pharmingen5514601A81:100
      Ly6CBD Pharmingen560592AL-211:100
      Arg1R&D Systems, Minneapolis, MNIC5868FPolyclonal1:50
      iNOSInvitrogen12-5920-82CXNFT1:100
      CD206BD Pharmingen565250MR5D31:100

      CyTOF

      Pancreas was harvested and disrupted to single cells as described earlier. Three mesh strainers were used: 500 um, 100 um, and 40 um to separate single cells. Cells were washed twice in phosphate-buffered saline and incubated with Cell-ID cisplatin (1.67 umol/L) for 5 minutes at room temperature as a viability marker. Surface and intracellular staining was performed as detailed in the manufacturer’s instructions (Fluidigm) with the antibodies listed in Table 1. Cells were shipped in intercalator buffer on ice overnight to the Flow Cytometry Core at the University of Rochester Medical Center at Rochester, NY, where sample preparation was finalized, and CyTOF2 Mass Cytometer (Helios) analysis was performed. Data analysis was performed using the Premium CytoBank Software (cytobank.org) and R studio using the CyTOF workflow from Nowicka et al.
      • Nowicka M.
      • Krieg C.
      • Crowell H.L.
      • Weber L.M.
      • Hartmann F.J.
      • Guglietta S.
      • Becher B.
      • Levesque M.P.
      • Robinson M.D.
      CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets.

      scRNAseq

      Pancreatic tissues were mechanically minced and enzymatically digested with collagenase IV (1 mg/mL in Roswell Park Memorial Institute (RPMI) 1640). Cell suspensions subsequently were filtered through 500-μm, 100-μm, and 40-μm mesh to obtain single cells. Dead cells were removed using the MACS Dead Cell Removal Kit (Miltenyi Biotec, Bergish Gladbas, DE)). The resulting single-cell suspensions were pooled by experimental group (Kras ON/Kras OFF). Single-cell complementary DNA libraries were prepared and sequenced at the University of Michigan Advanced Genomics Core using the 10× Genomics Platform (raw and processed data are available at GSM4175981 and GSE179846). Samples were run using 50-cycle, paired-end reads on the HiSeq 4000 (Illumina, San Diego, CA) to a depth of 100,000 reads. The raw data were processed and aligned by the University of Michigan DNA Sequencing Core. Cell Ranger count version 3.0.0 (10× Genomics, Pleasanton, CA) was used with default settings, with an initial expected cell count of 10,000. R version 3.6.2, RStudio version 1.2.5033, and R package Seurat version 3.2.2 were used for scRNAseq data analysis (RStudio Team RStudio: Integrated Development for R, RStudio, 2015; http://www.rstudio.com; R Core Development Team R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing, 2017; https://www.R-project.org
      • Stuart T.
      • Butler A.
      • Hoffman P.
      • Hafemeister C.
      • Papalexi E.
      • Mauck 3rd, W.M.
      • Hao Y.
      • Stoeckius M.
      • Smibert P.
      • Satija R.
      Comprehensive integration of single-cell data.
      ,
      • Butler A.
      • Hoffman P.
      • Smibert P.
      • Papalexi E.
      • Satija R.
      Integrating single-cell transcriptomic data across different conditions, technologies, and species.
      ). Data initially were filtered to include only cells with at least 100 genes and genes that appeared in more than 3 cells. Data were normalized using the NormalizeData function with a scale factor of 10,000 and the LogNormalize normalization method. Data then were filtered manually to include only cells with 1000–60,000 transcripts and <15% mitochondrial genes. Variable genes were identified using the FindVariableFeatures function. Data were scaled and centered using linear regression of transcript counts. PCA was run with the RunPCA function using the previously defined variable genes. Cell clusters were identified via the FindNeighbors and FindClusters functions, using dimensions corresponding to approximately 90% variance as defined by PCA. UMAP clustering algorithms were performed with RunUMAP. Clusters were defined by user-defined criteria. The complete R script including figure-specific visualization methods is publicly available on GitHub (https://github.com/PascaDiMagliano-Lab).
      For interactome analysis, ligand-receptor pairs were defined based on a curated literature-supported list in Ramilowski et al,
      • Ramilowski J.A.
      • Goldberg T.
      • Harshbarger J.
      • Kloppmann E.
      • Lizio M.
      • Satagopam V.P.
      • Itoh M.
      • Kawaji H.
      • Carninci P.
      • Rost B.
      • Forrest A.R.
      A draft network of ligand-receptor-mediated multicellular signalling in human.
      further curated as described in Steele et al.
      • Steele N.G.
      • Carpenter E.S.
      • Kemp S.B.
      • Sirihorachai V.R.
      • The S.
      • Delrosario L.
      • Lazarus J.
      • E-aD Amir
      • Gunchick V.
      • Espinoza C.
      • Bell S.
      • Harris L.
      • Lima F.
      • Irizarry-Negron V.
      • Paglia D.
      • Macchia J.
      • Chu A.K.Y.
      • Schofield H.
      • Wamsteker E.-J.
      • Kwon R.
      • Schulman A.
      • Prabhu A.
      • Law R.
      • Sondhi A.
      • Yu J.
      • Patel A.
      • Donahue K.
      • Nathan H.
      • Cho C.
      • Anderson M.A.
      • Sahai V.
      • Lyssiotis C.A.
      • Zou W.
      • Allen B.L.
      • Rao A.
      • Crawford H.C.
      • Bednar F.
      • Frankel T.L.
      • Pasca di Magliano M.
      Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer.
      The average expression values of ligands and receptors in each cell population for both experimental groups (Kras ON/Kras OFF) were calculated individually, and ligands and receptors expressed below a user-defined threshold (median average expression) were removed from analysis. Ligand-receptor pairs also were excluded if the ligands and receptors were not expressed in both experimental groups. Differences in the ligands and receptors between groups were determined using the Wilcoxon ranked test, and P values were adjusted for multiple comparisons using the Bonferroni correction method. Ligands and receptors were considered significantly different if P < .05. The resulting data table was visualized using Cytoscape (version 3.7.2) software (https://cytoscape.org/).
      • Shannon P.
      • Markiel A.
      • Ozier O.
      • Baliga N.S.
      • Wang J.T.
      • Ramage D.
      • Amin N.
      • Schwikowski B.
      • Ideker T.
      Cytoscape: a software environment for integrated models of biomolecular interaction networks.
      The complete R script is publicly available on GitHub (https://github.com/PascaDiMagliano-Lab). The Circos plot was built with Circos software (version 0.69-9) http://circos.ca/ and the heatmap values within the circus plot show the average expression of each fibroblast ligand and their corresponding receptor across the iKras pancreatic tissue.

      Cell Culture

      All cell lines were cultured in DMEM with 10% fetal bovine serum and 1% penicillin streptomycin. The tumor cell line (9805) was derived from an iKras G12DP53R172H (ptf1a-Cre; TetO-KrasG12D;Rosa26rtTa/+; p53R172H/+) pancreatic cancer.
      • Collins M.A.
      • Brisset J.C.
      • Zhang Y.
      • Bednar F.
      • Pierre J.
      • Heist K.A.
      • Galban C.J.
      • Galban S.
      • di Magliano M.P.
      Metastatic pancreatic cancer is dependent on oncogenic Kras in mice.
      The fibroblast cell lines, CD1WT and B6318, were derived from wild-type pancreata of a mixed or C57BL/6J background, respectively. The 9805 cell line was maintained in DOX-containing (1 ug/mL) medium. To generate conditioned media, fresh medium containing DOX (KrasG12D ON) or without DOX (KrasG12D OFF) was replaced and harvested after 2–3 days based on cell confluency. The media then were centrifuged (300 × g, 10 minutes at 4ºC) to remove contaminating cancer cells. The CM was used to culture fibroblasts with a 1:1 ratio of CM to normal DMEM for 2–3 days. An aliquot of the CM before and after culturing fibroblasts was collected for metabolomics analysis (described later). To test the effect of heat labile factors, media were boiled for 10 minutes to denature secondary and tertiary peptide structures. For inhibitor experiments, ruxolitinib (INCB018424; Selleckchem, Houston, TX) at 0.1 μmol/L, 0.5 μmol/L, and 5 μmol/L, or trametinib (GSK1120212; Selleckchem) at 0.1 μmol/L, 0.5 μmol/L, and 2 μmol/L or vehicle control were added to the medium.

      Quantitative Reverse-Transcription Polymerase Chain Reaction

      RNA was extracted using the QIAGEN kit (Hilden, Germany). RNA samples went through reverse-transcription polymerase chain reaction using the complementary DNA kit (Applied Biosystems, Waltham, MA). Complementary DNA samples for quantitative real-time polymerase chain reaction were prepared using a mix of 1× Fast-SYBR Green PCR Master Mix (Applied Biosystems) and the primers listed in Table 5. The reaction conditions were used as previously described (Collins et al. 2012
      • Collins M.A.
      • Bednar F.
      • Zhang Y.
      • Brisset J.C.
      • Galban S.
      • Galban C.J.
      • Rakshit S.
      • Flannagan K.S.
      • Adsay N.V.
      • Pasca di Magliano M.
      Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice.
      ). Cyclophilin A (Ppia) was used as housekeeping control.
      Table 5Primers for Quantitative Reverse-Transcription Polymerase Chain Reaction
      GenesForward primerReverse primer
      Cxcl15’-CTGGGATTCACCTCAAGAACATC-3’5’-CAGGGTCAAGGCAAGCCTC-3’
      Il65’-TTCCATCCAGTTGCCTTCTTGG-3’5’-TTCTCATTTCCACGATTTCCCAG-3’
      Il335’-TGAGACTCCGTTCTGGCCTC-3’5’-CTCTTCATGCTTGGTACCCGA T-3’
      Saa35’-TGCCATCATTCTTTGCATCTTGA-3’5’-CCGTGAACTTCTGAACACCCT-3’

      Western Blot

      Cells were lysed using RIPA buffer (Sigma-Aldrich) with protease and phosphatase inhibitors (Sigma-Aldrich). Protein levels were quantified and the same amount of protein was loaded to the wells in a 4%–15% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (Bio-Rad). Protein was transferred to a Polyvinylidene fluoride or polyvinylidene difluoride (PVDF) membrane (Bio-Rad) that was blocked with milk and then incubated with primary antibodies listed in Table 2 overnight. Horseradish-peroxidase–conjugated secondary anti-rabbit and anti-mouse (1:5000) were used and detected by using the enhance chemiluminescent substrate (PerkinElmer). The bands were visualized using the ChemiDoc Imaging System (Bio-Rad).

      Metabolomics Analysis

      The conditioned medium from tumor cells and fibroblasts (described earlier) was aspirated and 1 mL of 80% methanol—cooled in dry ice—was added per well. The plates were incubated on dry ice for 10 minutes, then scraped and transferred to sample tubes. The samples were vortexed and centrifuged for 10 minutes at 13,000 × g at 4°C. Then, 1 mL of the supernatant was aspirated from each tube, transferred to a tightly capped sample tube, and stored at -80°C until analysis.
      Samples were run on an Agilent 1290 Infinity II LC-6470 triple quadrupole (QqQ) tandem mass spectrometer (MS/MS) system with the following components: Agilent Technologies Triple Quad 6470 LC-MS/MS system with a 1290 Infinity II LC Flexible Pump (Quaternary Pump), multisampler, and multicolumn thermostat with 6 port valves. Agilent Masshunter Workstation Software Liquid Chromatography Mas Spectometry (LC/MS) Data Acquisition for 6400 Series Triple Quadrupole MS with version B.08.02 was used for compound optimization, calibration, and data acquisition.
      The following solvents were used for LC analysis. Solvent A (97% water, 3% methanol, 15 mmol/L acetic acid, and 10 mmol/L tributylamine, pH 5), solvent B (15 mmol/L acetic acid and 10 mmol/L tributylamine in methanol), and washing solvent C (acetonitrile). LC system seal washing solvent (90% water and 10% isopropanol) and needle wash solvent (75% methanol and 25% water) were used. Solvents were purchased from the following vendors: GC grade tributylamine 99% (ACROS ORGANICS, Geel, Belgium), LC/MS grade acetic acid Optima (Fisher Chemical, Waltham, MA), InfinityLab (Dallas, TX) deactivator additive, Electrospray ionization (ESI)–L low concentration tuning mix (Agilent Technologies), LC-MS grade water, acetonitrile, methanol (Millipore, Burlington, MA), and isopropanol (Fisher Chemical).
      For LC analysis, 2 μL sample was injected into an Agilent ZORBAX RRHD Extend-C18 column (2.1 × 150 mm, 1.8 μm) with ZORBAX Extend Fast Guards. The LC gradient profile was as follows: flow rate, 0.25 mL/min, 0–2.5 minutes, 100% A; 2.5–7.5 minutes, 80% A and 20% B; 7.5–13 minutes 55% A and 45% B; 13–24 minutes, 1% A and 99% B; 24–27 minutes, 1% A and 99% C; and 27–27.5 minutes, 1% A and 99% C. Then, at 0.8 mL/min, 27.5–31.5 minutes, 1% A and 99% C; at 0.6 mL/min, 31.5–32.25 minutes, 1% A and 99% C; at 0.4 mL/min, 32.25–39.9 minutes, 100% A; and at 0.25 mL/min, 40 minutes, 100% A. Column temperature was maintained at 35ºC while the samples were at 4ºC.
      For MS analysis, a 6470 Triple Quad MS calibrated with the Agilent ESI-L low concentration tuning mix was used. Source parameters: gas temperature, 150ºC; gas flow, 10 L/min; nebulizer, 45 psi; sheath gas temperature, 325ºC; sheath gas flow, 12 L/min; capillary, -2000 V; and delta electron multiplier voltage (EMV), -200 V. Dynamic MRM scan type was used with 0.07-minute peak width and 24-minute acquisition time. Delta retention time of ±1 minute, fragmentor of 40 eV, and cell accelerator of 5 eV were incorporated in the method.
      The MassHunter (Agilent) Metabolomics Dynamic multiple reaction monitoring (MRM) Database and Method was used for target identification. Key parameters of AJS ESI were as follows: gas temperature, 150ºC; gas flow, 13 L/min; nebulizer, 45 psi; sheath gas temperature, 325ºC; sheath gas flow, 12 L/min; capillary, 2000 V; and nozzle, 500 V. Detector delta EMV, (-) 200. The QqQ data were preprocessed with the Agilent MassHunter Workstation QqQ Quantitative Analysis Software (B0700). The abundance level of each metabolite in every sample was divided by the median of all abundance levels across all samples for proper comparisons, statistical analyses, and visualization. The statistical significance test was performed by a 2-tailed t test with a significance threshold level of 0.05.
      Heatmaps were generated with Morpheus (https://software.broadinstitute.org/morpheus).

      Statistics

      We used GraphPad Prism (San Diego, CA) version 8 software for most of our analysis. The normality was checked in all data sets and either a t test or the Mann–Whitney test was performed for statistical analysis, with statistical significance at P < .05. Quantitative reverse-transcription polymerase chain reaction data were analyzed using multiple comparison analysis of variance and considered statistically significant when P < .05.

      Acknowledgment

      The authors thank Daniel Long for histologic services.

      CRediT Authorship Contributions

      Ashley Velez-Delgado (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Funding acquisition: Lead; Investigation: Lead; Methodology: Lead; Validation: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead)
      Katelyn L. Donahue (Data curation: Lead; Formal analysis: Lead; Software: Lead; Visualization: Lead; Writing – review & editing: Supporting)
      Kristee L. Brown (Data curation: Supporting; Formal analysis: Supporting; Investigation: Supporting; Validation: Supporting)
      Wenting Du (Formal analysis: Equal; Investigation: Equal; Validation: Equal; Writing – review & editing: Equal)
      Valerie Irizarry-Negron (Formal analysis: Equal; Investigation: Equal; Validation: Equal)
      Rosa E. Menjivar (Formal analysis: Supporting; Investigation: Supporting; Writing – review & editing: Equal)
      Emily L. Lasse Opsahl (Investigation: Supporting; Validation: Supporting)
      Nina Steele (Formal analysis: Supporting; Writing – review & editing: Equal)
      Stephanie The (Software: Lead)
      Jenny Lazarus (Data curation: Lead; Methodology: Lead; Software: Lead; Visualization: Lead; Writing – review & editing: Supporting)
      Veerin R. Sirihorachai (Software: Lead; Visualization: Supporting)
      Wei Yan (Investigation: Supporting; Validation: Supporting)
      Samantha B. Kemp (Investigation: Supporting)
      Samuel A. Kerk (Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Visualization: Equal)
      Murali Bollampally (Investigation: Supporting)
      Sion Yang (Investigation: Supporting)
      Michael K. Scales (Formal analysis: Supporting)
      Faith R. Avritt (Formal analysis: Supporting)
      Fatima Lima (Formal analysis: Supporting; Investigation: Supporting)
      Costas A. Lyssiotis (Resources: Supporting; Supervision: Supporting)
      Arvind Rao (Conceptualization: Supporting; Resources: Supporting; Software: Supporting; Supervision: Supporting)
      Howard C. Crawford (Resources: Supporting; Writing – review & editing: Supporting)
      Filip Bednar (Conceptualization: Supporting; Software: Supporting; Visualization: Supporting; Writing – review & editing: Supporting)
      Timothy L. Frankel (Conceptualization: Supporting; Resources: Supporting; Supervision: Supporting; Writing – review & editing: Supporting)
      Benjamin L. Allen (Resources: Equal; Supervision: Supporting)
      Yaqing Zhang (Conceptualization: Lead; Supervision: Lead; Visualization: Supporting; Writing – review & editing: Supporting)
      Marina Pasca di Magliano (Conceptualization: Lead; Funding acquisition: Lead; Methodology: Lead; Project administration: Lead; Resources: Lead; Supervision: Lead; Writing – review & editing: Lead)

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