Cellular Heterogeneity During Mouse Pancreatic Ductal Adenocarcinoma

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bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

1 Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at 2 single-cell resolution 3 4 Abdel Nasser Hosein1,3,7, Huocong Huang1, Zhaoning Wang2, Kamalpreet Parmar5, Wenting 5 Du1, Jonathan Huang7, Anirban Maitra6,7, Eric Olson2, Udit Verma3, Rolf A. Brekken1,4 6 7 1Hamon Center for Therapeutic Oncology Research, Division of Surgical Oncology, Department 8 of Surgery, 2Department of Molecular Biology, 3Department of Internal Medicine – Division of 9 Hematology & Oncology, 4Department of Pharmacology, and 5Department of Pathology, 10 University of Texas Southwestern Medical Center, Dallas, Texas, USA. 6Department of 11 Pathology and 7Department of Translational Molecular Pathology, University of Texas MD 12 Anderson Cancer Center, Houston, TX, USA. 13 14 Short title: scRNA-seq of PDA GEMMs 15 16 Corresponding author 17 Rolf A. Brekken, PhD 18 Hamon Center for Therapeutic Oncology Research 19 University of Texas Southwestern Medical Center 20 6000 Harry Hines Blvd. 21 Dallas, TX 75390-8593 22 Tel: 214.648.5151; Fax: 214.648.4940 23 [email protected] 24 25 Disclosures 26 The authors have no conflicts of interest to report. 27 28 Writing assistance 29 Editorial assistance was provided by Dave Primm of the UT Southwestern Department of 30 Surgery. 31 32 Funding 33 This work was supported by NIH grants R01 CA192381 and U54 CA210181 Project 2 to RAB, 34 the Effie Marie Cain Fellowship to RAB, the H. Ray and Paula Calvert Pancreatic Cancer 35 Research Fund to UV, NIH grants U01 CA200468, U01 CA196403, R01 CA218004, R01 36 CA204969, P01 CA117696 to AM and grants from the NIH and Welch Foundation to EO.

37 Author contributions 38 39 ANH: study concept and design, acquisition of data, analysis and interpretation of data, drafting 40 of the manuscript. HH: study concept and design, acquisition of data, analysis and interpretation 41 of data, drafting of the manuscript. ZW: bioinformatics analyses, interpretation of data. KP: 42 pathologic interpretation and scoring of human tissue samples. WD: acquisition of data. JH: 43 bioinformatics analyses, critical review of the manuscript. AM: bioinformatics analyses, 44 interpretation of data, critical review of the manuscript. EO: bioinformatics analyses, 45 interpretation of data, critical revision of the manuscript. UV: study concept and design, critical 46 revision of the manuscript. RAB: study concept and design, interpretation of data, drafting of 47 the manuscript. 48 49 Abbreviations 50 ADM, acinar-to-ductal metaplasia 51 BET, bromodomain and extraterminal 52 CAF, cancer-associated fibroblast 53 FB, fibroblast population 54 GEM, gel bead in emulsion 55 GEMM, genetically engineered mouse model 56 GO, gene ontology 57 PDA, pancreatic ductal adenocarcinoma 58 PSC, pancreatic stellate cell 59 scRNA-seq, single-cell RNA sequencing 60 UMI, unique molecular identifiers 61

62 Abstract: 63 Background & Aims 64 Pancreatic ductal adenocarcinoma (PDA) is a major cause of cancer-related death with limited 65 therapeutic options available. This highlights the need for improved understanding of the biology 66 of PDA progression. The progression of PDA is a highly complex and dynamic process featuring 67 changes in cancer cells and stromal cells; however, a comprehensive characterization of PDA 68 cancer cell and stromal cell heterogeneity during disease progression is lacking. In this study, 69 we aimed to profile cell populations and understand their phenotypic changes during PDA 70 progression. 71 72 Methods 73 We employed single-cell RNA sequencing technology to agnostically profile cell heterogeneity 74 during different stages of PDA progression in genetically engineered mouse models. 75 76 Results 77 Our data indicate that an epithelial-to-mesenchymal transition of cancer cells accompanies 78 tumor progression. We also found distinct populations of macrophages with increasing 79 inflammatory features during PDA progression. In addition, we noted the existence of three 80 distinct molecular subtypes of fibroblasts in the normal mouse pancreas, which ultimately gave 81 rise to two distinct populations of fibroblasts in advanced PDA, supporting recent reports on 82 intratumoral fibroblast heterogeneity. Our data also suggest that cancer cells and fibroblasts are 83 dynamically regulated by epigenetic mechanisms. 84 85 Conclusion 86 This study systematically outlines the landscape of cellular heterogeneity during the progression 87 of PDA. It strongly improves our understanding of the PDA biology and has the potential to aid 88 in the development of therapeutic strategies against specific cell populations of the disease. 89 90 Key words: single-cell RNA sequencing; pancreatic cancer; cellular heterogeneity; fibroblasts; 91 macrophages 92

93 Introduction 94 Pancreatic ductal adenocarcinoma (PDA) carries the highest mortality rate of all major 95 malignancies in industrialized countries, with a 5-year survival of 8.5%. Patients are faced with 96 limited treatment options that achieve poor durable response rates, highlighting the need for an 97 improved understanding of PDA disease biology [1]. PDA progression is a complex and 98 dynamic process that requires interaction between cancer cells and stromal cells [2]. It is 99 characterized by the formation of a unique microenvironment consisting of heterogeneous 100 stromal cell populations that include fibroblasts, macrophages, lymphocytes, and endothelial 101 cells. These stromal compartments are critical in driving PDA biology [3]. 102 103 The dynamic phenotypic changes in different cell populations during PDA progression is not 104 fully understood. Gene expression profiling of bulk tissues provides a limited picture of the 105 cellular complexity of the heterogeneous cell populations in PDA. In contrast, single-cell RNA 106 sequencing (scRNA-seq) has the potential to enable gene expression profiling at the level of the 107 individual cell [4] and provides a powerful tool to understand the cellular heterogeneity of PDA. 108 We applied scRNA-seq to investigate gene expression changes of cancer cells and stromal 109 cells during PDA progression in genetically engineered mouse models (GEMMs). This unbiased 110 approach provided evidence of considerable intratumoral cellular heterogeneity, including 111 molecular insights into epithelial and mesenchymal populations of cancer cells and distinct 112 molecular subtypes of macrophages and cancer-associated fibroblasts (CAFs). 113 114 Methods 115 Animal studies 116 KIC, KPC and KPfC mice were generated as previously described [5-7]. Mice were sacrificed 117 when they were moribund: 60 days old for the KIC (n = 3, late PDA) and KPfC (n = 1) or 6 118 months old for the KPC (n = 1). The 2 KIC mice were sacrificed at 40 days old (early PDA) and 119 “normal pancreas” mice (n = 2) were sacrificed at 60 days old. In experiments using more than 120 one mouse, tissues were pooled prior to enzymatic digestion. The KPfC mouse had a pure 121 C57BL/6 genetic background and all others had a mixed background (C57BL/6 with FVB). 122 Ultrasound imaging was carried out under general anesthesia with isoflurane. Mice were 123 euthanized by cervical dislocation under anesthesia. AVMA Guidelines for the Euthanasia of 124 Animals were strictly followed. Tissues were either fixed in 10% formalin for 125 immunohistochemistry or enzymatically digested for single-cell analysis. 126

127 Tissue digestion 128 A 10x digestion buffer was prepared in PBS: collagenase type I (450 units/ml, Worthington 129 Biochemical, Lakewood, NJ), collagenase type II (150 units/ml, Worthington), collagenase type 130 III (450 units/ml, Worthington), collagenase type IV (450 units/ml, Gibco/Thermo Fisher, 131 Waltham, MA), elastase (0.8 units/ml, Worthington), hyaluronidase (300 units/ml, Sigma-Aldrich, 132 St. Louis, MO), and DNase type I (250 units/ml, Sigma-Aldrich). Tumors and pancreas were 133 enzymatically digested into a single-cell suspension. Briefly, freshly dissected tissue was placed 134 into a 10-cm tissue culture dish and a sterile razor blade was used to cut the tissue into fine 135 pieces. Samples were resuspended in PBS and washed twice by centrifuge at 2000 rpm for 3 136 minutes and added to a 50 ml tube containing 1x digestion buffer containing 1% FBS. The tube 137 was incubated on a shaker at 37°C for 60 minutes. Then 35 ml of PBS was added and cells 138 were washed three times prior to filtering out debris using a 70 μm mesh filter. Single cells were 139 resuspended in 100 μl of PBS in preparation for single-cell library creation. Cell viability was 140 measured by trypan blue. Viability was 80% for the normal pancreas and late KIC samples, 75% 141 for the early KIC and KPfC, and 90% for the KPC. 142 143 Single-cell cDNA library preparation and sequencing 144 Library generation was performed using the 10x Chromium System (10X Genomics Inc., 145 Pleasanton, CA). Single-cell suspensions were washed in 1x PBS (calcium- and magnesium- 146 free) containing 0.04% weight/volume bovine serum albumin (400 μg/ml) and brought to a 147 concentration of 200-700 cells/μl. The appropriate volume of cells was loaded with Single Cell 3’ 148 gel beads into a Single Cell A Chip and run on the Chromium Controller. Gel bead in emulsion 149 (GEM) was incubated and then broken. Silane magnetic beads were used to clean up the GEM 150 reaction mixture. Read 1 primer sequence was added during incubation and full-length, 151 barcoded cDNA was amplified by PCR after cleanup. Sample size was checked on an Agilent 152 Tapestation 4200 (Agilent, Santa Clara, CA) using DNAHS 5000 tape and concentration 153 determined by a Qubit 4 Fluorometer (Thermo Fisher) using the DNA HS assay. Samples were 154 enzymatically fragmented and underwent size selection before proceeding to library 155 construction. During library preparation, Read 2 primer sequence, sample index, and both 156 Illumina adapter sequences were added. Samples were cleaned up using AMPure XP beads 157 (Beckman Coulter, Brea, CA) and post-library preparation quality control was performed using 158 DNA 1000 tape on the Agilent Tapestation 4200. The final concentration was ascertained using 159 the Qubit 4 Fluorometer DNA HS assay. Samples were loaded at 1.5 pM and run on the

160 Illumina NextSeq500 High Output Flowcell (Illumina, San Diego, CA) using V2.5 chemistry. The 161 run configuration was 26 x 98 x 8. 162 163 Bioinformatic analyses 164 We used Cell Ranger version 1.3.1 (10x Genomics) to process raw sequencing data and the R- 165 package Seurat version 2.0 [8] for downstream analyses. Cell clusters were identified via the 166 FindClusters function using a resolution of 0.6 for all samples, using a graph-based clustering 167 algorithm implemented in Seurat. Marker genes for each cluster were computed, and 168 expression levels of several known marker genes were examined. Different clusters expressing 169 known marker genes for a given cell type were selected and combined as one for each cell 170 type. Gene ontology and pathway analysis were performed using the DAVID bioinformatics 171 suite, version 6.8 [9]. 172 173 Histological analysis 174 Formalin-fixed tissues were embedded in paraffin and cut in 5 μm sections. Sections were 175 evaluated by H&E and immunohistochemical analysis using antibodies specific for vimentin 176 (5741, Cell Signaling Technology, Danvers, MA), BRD4 (AB128874, Abcam, Cambridge, MA), 177 Sox9 (AB5535, EMD Millipore, Burlington, MA), CDH11 (NBP2-15661, Novus Biologicals, 178 Centennial, CO), and H3K27ac (AB4729, Abcam). Following an initial antigen retrieval with Tris- 179 EDTA-glycerol (10%) buffer and inhibition of endogenous peroxidase activity, the slides were 180 incubated with primary antibody overnight at 4°C. Slides were then incubated with horseradish 181 peroxidase or alkaline phosphatase conjugated secondary antibody (Vector Laboratories, 182 Beringame, CA) for 1 hour at 25°C. This was followed by development using the appropriate 183 chromogenic substrate: DAB, Warp Red or Ferangi Blue (Biocare Medical, Pacheco, CA). In the 184 case of multichannel immunohistochemistry, slides were subsequently stripped using a sodium 185 citrate buffer and by boiling at 110°C for 3 minutes. The procedure was then repeated as above 186 using a different-colored chromogen for development. All human PDA samples were provided 187 by the UT Southwestern Tissue Management Shared Resource and their use was approved by 188 the UT Southwestern institutional review board for the purpose of research. All patient samples 189 were de-identified and interpreted by a board-certified pathologist (KP). 190 191 Results 192 Cellular heterogeneity during PDA progression

193 We sought to determine the composition of single pancreatic cancer cells during progression in 194 GEMMs. Normal mouse pancreas, 40-day-old KIC (KrasLSL-G12D; Cdkn2aflox/flox; Ptf1aCre/+) mouse 195 pancreas, termed “early KIC” (with early neoplastic changes confirmed by ultrasound; 196 Supplementary Fig. 1), and 60-day-old KIC pancreas, termed “late KIC” (Fig. 1A) were freshly 197 isolated and enzymatically digested followed by single-cell cDNA library generation using the 198 10x Genomics platform [10]. Libraries were subsequently sequenced at a depth of more than 199 105 reads per cell. We performed stringent filtering, normalization, and graph-based clustering, 200 which identified distinct cell populations in the normal pancreas and each stage of PDA. 201 202 In the normal mouse pancreas, 2354 cells were sequenced and classified into appropriate cell 203 types based on the gene expression of known markers: acinar cells, islet cells, macrophages, T 204 cells, and B cells, as well as three distinct populations of fibroblasts. Fibroblasts-1, fibroblasts-2, 205 and fibroblasts-3 (Fig. 1B and E) were noted. In the early KIC pancreas (3524 cells sequenced), 206 the emergence of a cancer cell population was observed (9.9% of cells), expressing known PDA 207 markers such as Krt18 and Sox9 [11] (Fig. 1C and F). The acinar cell population was 208 substantially reduced, while there was a marked increase in total macrophages and fibroblasts. 209 Of note, the same three populations of fibroblasts seen in the normal pancreas were identified in 210 the early KIC lesion. Additionally, endothelial cells were observed at this stage. This indicates 211 that the expansion of fibroblasts and macrophages is an early event during PDA development, 212 accompanying tumor initiation. We next characterized the late KIC pancreas (804 cells 213 sequenced) and noted the absence of normal exocrine (acinar) and endocrine (islet) cells (Fig. 214 1D and G). Instead, two distinct populations of cancer cells were present, suggesting 215 phenotypic cancer cell heterogeneity as a late event in the course of the disease. We also 216 observed the presence of only two distinct fibroblast populations, which had a similar 217 percentage in relation to total cells. Noticeably, macrophages became a predominant cell 218 population in the late KIC tumor. Moreover, we observed lymphocytes at this stage. The cellular 219 heterogeneity in cancer cells and stromal cells in the early and late KIC lesions highlighted the 220 dynamic cellular changes that occur during PDA progression. 221 222 Mesenchymal cancer cells emerge in advanced PDA 223 Gene expression analysis of cancer cell epithelial markers (Cdh1, Epcam, Gjb1, and Cldn3) and 224 mesenchymal markers (Cdh2, Cd44, Axl, Vim, and S100a4) revealed that early KIC cancer cell 225 populations assumed an epithelial expression profile (Fig. 2A and C). This is in contrast to tumor 226 cell populations in the late KIC tumors, where we identified two distinct cancer cell populations:

227 one enriched for epithelial markers and the other, more abundant population, enriched for 228 mesenchymal markers (Fig. 2B and C). These data support that tumor cell epithelial plasticity 229 contributes to cancer cell heterogeneity during the progression of KIC tumors. 230 231 The hierarchical clustering of the top significant genes in each of the three cancer cell 232 populations (epithelial cancer cells in early KIC, epithelial and mesenchymal cancer cell 233 populations in late KIC) was performed (Fig. 2D). In addition, gene clusters from the cancer cell 234 populations were subjected to pathway and gene ontology (GO) analysis. First, we compared 235 cancer cells of the early KIC population to the total cancer cells of the late KIC and found that 236 the most downregulated genes in late KIC cancer cells were associated with normal pancreatic 237 function such as pancreatic secretion, digestion and absorption, and insulin secretion (Fig. 2E 238 and F). Moreover, normal pancreatic acinar genes such as Try4, Try5, Cela2a, Cela3b, Reg2, 239 and Rnase1 were expressed at higher levels in early KIC cancer cells, while late KIC cancer 240 cells expressed a higher level of the pancreatic ductal gene Muc1 (Fig. 2D). This is suggestive 241 of an ongoing acinar-to-ductal metaplasia (ADM) during tumor progression in this GEMM. In 242 contrast, the most upregulated genes in late KIC cancer cells were associated with ribosome, 243 glycolysis/gluconeogenesis, and amino acid biosynthesis, which is highly suggestive of 244 increased translation and metabolically active cancer cells in established KIC tumors. 245 Interestingly, pathways previously reported to be closely associated with the stroma and 246 progression of PDA were also highlighted, such as ECM-receptor interaction [12], TGFβ [13], 247 and hippo signaling pathways [14]. We then compared early KIC cancer cells with the late KIC 248 epithelial cancer cell population to understand the mechanisms that promoted the progression 249 of PDA in the epithelial cancer cell compartment. Interestingly, similar cell functions/signaling 250 pathways were identified by comparing the two epithelial cancer cell populations (Fig. 2G and 251 H). Taken together, these analyses objectively demonstrate an ADM state during the 252 progression of KIC tumors and suggest that stroma-cancer cell interaction promotes the 253 progression of PDA and cancer cell heterogeneity. 254 255 Mesenchymal cancer cells exist in advanced PDA GEMMs with different diverse mutations 256 In addition to KRAS mutations, additional driver events are required for PDA progression [8], 257 with TP53 and INK4A being the second- and third-most commonly mutated genes in human 258 PDA, respectively. As such, we sought to understand the effect of different secondary driver 259 mutations on the phenotypes and heterogeneity of cancer cells. We performed scRNA-seq in 260 another PDA GEMM, KPfC (KrasLSL-G12D; Trp53Flox/Flox; Pdx1Cre/+) (Fig. 3A). Consistent with late

261 KIC tumors, two distinct cancer cell populations expressing Krt18 and Sox9 were noted in late 262 KPfC (60-day-old) tumors (Fig. 3A and B), one marked by epithelial markers such as Gjb1, Tjb1, 263 Ocln, and Cldn3, while the other was marked by mesenchymal markers such as Vim, Cd44, Axl, 264 S100a4, and Fbln2 (Fig. 3C and D). Epithelial and mesenchymal cancer cell populations in 265 KPfC mice shared many genes in common with the corresponding populations in KIC; however, 266 they also expressed unique gene signatures (Fig. 3F). 267 268 We then compared the total cancer cell gene signatures between late KIC and late KPfC mice 269 by KEGG and Biocarta pathway analysis methods, in an attempt to identify potential differences 270 in cancer cell signaling pathways caused by the different secondary driver mutations. As 271 expected, the p53 signaling pathway was upregulated in the KIC model by comparison to the 272 KPfC model (Fig. 3E). The analyses of late KIC and late KPfC mice suggests that cancer cell 273 heterogeneity is a late-stage tumor event that occurs in the setting of multiple secondary driver 274 mutations. However, under the same oncogenic Kras mutation, different secondary driver 275 mutations can potentially lead to different signaling pathways that drive PDA progression. 276 277 Macrophage heterogeneity during PDA progression 278 We found a marked increase in the size of the macrophage population as PDA progressed from 279 normal pancreas to early KIC and eventually late KIC tumors (Fig. 1B-D). We further 280 characterized the macrophage compartment during PDA progression by subclustering 281 macrophages in early and late KIC tumors, which revealed three transcriptionally distinct 282 macrophage clusters in early KIC and two in late KIC (Fig. 4A and C). 283 284 Macrophage population 1 in early KIC tumors was characterized by the expression of Fn1, 285 Lyz1, Lyz2, Ear1, and Ear2 as well as Cd14 (Fig. 4B). Moreover, these macrophages 286 specifically expressed high levels of the IL1 receptor ligands: Il1a, Il1b, and Il1rn. GO analysis 287 suggested that this macrophage population was involved in healing during inflammation, the 288 regulation of type I and III hypersensitivities, and antigen processing and presentation (Fig. 4E). 289 In contrast, macrophage population 2 was noted to express an abundance of chemokines, 290 including Ccl2, Ccl4, Ccl7, Ccl8, and Ccl12, as well as many complement-associated genes 291 (Fig. 4B). Indeed, leukocyte activation, complement activation, and humoral response genes 292 were the most significantly enriched GO categories in this macrophage population (Fig. 4E). 293 The third macrophage population expressed Ccl17 and Ccr7 and was enriched in ribosomal 294 small-unit biogenesis, translation, and antigen-processing functions (Fig. 4B and E). Importantly,

295 macrophages in normal mouse pancreas weakly expressed genes found in macrophage 296 population 2 and 3 from early KIC mice, suggesting that the normal pancreas macrophages 297 could be noncommitted macrophages residing in tissue in the normal organ that are induced to 298 adopt a distinct phenotype upon tumor initiation (Fig. 4B). 299 300 The late KIC tumor featured two macrophage subpopulations (Fig. 4C). Macrophage population 301 1 highly expressed genes such as S100a8 and Saa3, which have been shown to be expressed 302 in lipopolysaccharide-treated monocytes [15]. Moreover, numerous chemokines were elevated 303 in this population such as Ccl2, Ccl7, Ccl9, Ccl6, Cxcl3, and Pf4 (Fig. 4D). GO analysis revealed 304 this population is likely associated with Stat3 activation, leukocyte chemotaxis, and response to 305 lipopolysaccharide and inflammatory stimuli (Fig. 4F). These data suggest that macrophage 306 population 1 was inflammatory in nature. Macrophage population 2 of late KIC tumors was rich 307 in MHC-II antigen presentation molecules: Cd74, H2-Aa, H1-Ab1, H2-Dma, H2-Dmb1, H2- 308 Dmb2, and H2-Eb1 (Fig. 4D), and GO analysis highlighted antigen presentation and adaptive 309 immune response pathways as being elevated (Fig. 4F). Consistently, in late KPfC tumors, we 310 also observed two distinct populations of macrophages with similar features (Supplementary 311 Fig. 3). Interestingly, we did not observe a macrophage population in late tumors that correlated 312 with macrophage population 1 from the early tumors, suggesting that this population might 313 undergo negative selection or a differentiation into inflammatory and/or MHC-II–rich 314 macrophages during tumor progression. 315 316 We also compared the features of the total macrophage clusters between early and late KIC 317 tumors and observed a substantially enhanced macrophage inflammatory signature as the 318 tumor progressed (Fig. 4G). A wide variety of inflammatory genes increased, including Il1a, Il1b, 319 Il1r2, and Il6. GO analysis of this gene list highlighted leukocyte chemotaxis and inflammatory 320 response functions as increased in advanced KIC tumors (Fig. 4H). These data suggest that 321 PDA progression is characterized by an increase in inflammatory features in macrophages. 322 323 Fibroblast heterogeneity during PDA progression 324 In normal pancreas and early KIC tumors, we had identified three distinct populations of 325 fibroblasts, while in late KIC only two fibroblast populations were noted (Fig. 1B-D). To ascertain 326 the relationship between these fibroblast populations and the dynamics of their phenotypic 327 changes during PDA progression, we projected fibroblasts from the three analyses onto a single 328 tSNE plot and applied a graph-based clustering algorithm (Fig. 5A) which revealed three distinct

329 molecular subtypes of fibroblasts in the normal pancreas, early KIC tumors, and late KIC 330 tumors. The overlay demonstrates that the normal pancreas and early KIC tumors contained all 331 three fibroblast subtypes while the late KIC contained only two (Fig. 5A), confirming our initial 332 analysis (Fig. 1B-D). Specifically, this analysis demonstrated that fibroblast population 1 (FB1) 333 and fibroblast population 3 (FB3) found in normal and early KIC pancreas were present in the 334 late KIC tumor whereas fibroblast population 2 (FB2) was absent. 335 336 In the normal pancreas, FB1, FB2, and FB3 made up 35.4%, 56.9% and 7.7% of the total 337 fibroblasts, respectively (Supplementary Fig.4A). In early KIC tumors, although the total 338 fibroblasts expanded (Fig. 1C), the ratios of each fibroblast population remained similar. 339 Furthermore, in the late KIC tumors, FB1 and FB3 were present in nearly equal proportions of 340 46.5% and 53.5%, respectively (Supplementary Fig.4A). Each fibroblast population was 341 characterized by distinct marker genes. For example, FB1 markedly expressed Cxcl14, Ptn, and 342 several genes mediating insulin-like growth factor signaling such as Igf1, Igfbp7, and Igfbp4. 343 FB2 specifically expressed Nov, a member of the CCN family of secreted matricellular proteins 344 [16] as well as Pi16, which has been shown to be expressed in fibroblast populations in various 345 tissue types [17], in addition to Ly6a and Ly6c1. FB3 showed distinct expression of mesothelial 346 markers such as Lrrn4, Gpm6a, Nkain4, Lgals7, and Msln [18] in addition to other genes 347 previously shown to be expressed in fibroblasts such as Cav1, Cdh11, and Gas6 [19-21]. 348 349 Hierarchical clustering of the most significant genes for each fibroblast subtype confirmed the 350 persistence of FB1 and FB3 during the progression of PDA (Fig. 5B) and that they exist across 351 different advanced-stage PDA GEMMs (KPC and KPfC), suggesting a consistent cell of origin. 352 Interestingly, the gene expression heatmap also indicated that the FB2 population started to 353 move toward an FB1-like expression profile in early KIC tumors, suggesting FB1 and FB2 might 354 converge into a single CAF population with FB1 features by late invasive disease. Of note, Il6, 355 Ccl2, Ccl7, Cxcl12, and Pdgfra were expressed in FB1 and FB2 in the normal pancreas and 356 early KIC tumors, and showed greater expression in FB1 of late KIC (Fig. 5C). In contrast, the 357 myofibroblast markers Acta2 and Tagln were expressed by a portion of FB3. These data 358 support the presence of previously described, mutually exclusive, inflammatory (FB1) and 359 myofibroblastic (FB3) CAF subtypes [22-24]. Interestingly, FB3 also expressed numerous MHC- 360 II–associated genes (Fig. 5C). GO analysis suggested that FB1 was involved in an acute phase 361 response and inflammatory response, FB2 was more associated with physiological functions of 362 fibroblasts, while FB3 had antigen processing and presentation through the MHC-II pathway

363 and had complement activation functions (Fig. 5D). Furthermore, we analyzed genes that 364 increased in FB1 and FB3 during PDA progression, and found that FB1 showed a progressive 365 increase in the expression of genes associated with inflammatory response and chemotaxis 366 (Fig. 5E and Supplementary Fig. 4B) while FB3 genes displayed increased function on 367 translation during disease progression, possibly due to enhanced antigen processing activity. 368 These data suggest that FB1 is an inflammatory population and the inflammatory feature 369 increases during PDA progression, while FB3 consists of the well-studied myofibroblast 370 population, and displays an enrichment for class 2 MHC genes. 371 372 We also found that some genes essentially exclusive to FB3 in the normal and early KIC 373 pancreas became expressed in FB1 and FB3 populations in late KIC, marking these genes as 374 potential global fibroblast markers in advanced PDA. One such gene was Cdh11 (Fig. 5C). We 375 validated these data by immunohistochemistry. We found in late KIC tumors, stromal staining 376 for αSMA and PDGFRα were nearly mutually exclusive, whereas CDH11 showed uniform 377 staining across all morphologically discernable fibroblasts (Fig. 5F). Taken together, these data 378 provide the first in vivo description of all CAF populations during PDA progression. 379 380 Mesenchymal cancer cells and CAFs show evidence of increased epigenetic regulation and 381 super-enhancer activity in advanced PDA 382 Unique molecular identifiers (UMI) serve to barcode each input mRNA molecule during cDNA 383 library generation, enabling the determination of initial transcript number even after cDNA library 384 amplification [25]. We compared UMI counts across all cell types between early and late KIC 385 tumors (Fig. 6A and B). In early lesions, there was a marked increase in UMI in the beta islet 386 cells (median: 2849, range: 1322-12,857), which might indicate that increased transcriptional 387 activity is a means by which the endocrine requirements of these cells are met. No other cell 388 population in the early KIC tumor displayed this level of UMI. The early KIC cancer cells 389 displayed a relatively low UMI count (median: 1979, range: 1163-7735). In contrast, the 390 mesenchymal cancer cell population in the late KIC tumor displayed a marked increase in total 391 UMI count with a median count of 18,334 and range of 4433-50,061 (Fig. 6C). The epithelial 392 cancer cells in the late KIC also displayed an increased UMI, albeit to a far lesser degree than 393 the mesenchymal cancer cell population (median: 10,368, range: 4940-30,440). 394 395 We reasoned that the increased transcriptional activity may be associated with increased 396 activity of epigenetic regulation as well as super-enhancer [26]. BRD4 belongs to the

397 bromodomain family of transcriptional regulators and is a key regulator of super-enhancer 398 activity [27]. Prior studies have shown that MYC activity is promoted by super-enhancer activity 399 in PDA [28]. We found that in late KIC and KPfC tumors, Brd4 was expressed highly in epithelial 400 and mesenchymal cancer cells while Myc was expressed mainly in the mesenchymal cancer 401 cell population (Fig. 6C, Supplementary Fig. 6). In addition, several genes encoding high- 402 mobility group A proteins (Hmga1, Hmga1-rs, Hmga2) were markedly expressed in late KIC and 403 KPfC mesenchymal cancer cells. HMGA proteins are chromatin-associated proteins that 404 regulate transcriptional activity, including enhancesome formation [29]. Lastly, critical 405 components of the SWI/SNF complex (Smarcb1, Arid1a, Arid2), which are essential in 406 nucleosome remodeling and transcriptional regulation [30], were also expressed highly in 407 epithelial and mesenchymal cancer cells of the late KIC, but not cancer cells in the early KIC 408 lesion. Taken together, these data provide multiple lines of evidence to suggest that the 409 transcript load of a more aggressive mesenchymal cancer cell population is increased relative to 410 cancer cells in early lesions or epithelial cancer cells in advanced PDA. 411 412 Interestingly, we also noted that fibroblasts in late KIC tumors also showed an increased UMI 413 (median: 14,538, range: 4461-37,497). They also displayed an increased expression of super- 414 enhancer and other epigenetic transcriptional regulator genes in contrast to fibroblasts from 415 normal mouse pancreas or early KIC pancreas (Fig. 6D). These data are suggestive of 416 increased super-enhancer and transcriptional activity as normal pancreas fibroblasts become 417 CAFs. 418 419 We validated these single-cell RNA expression data using three-color immunohistochemical 420 analysis of late KIC tumors: SOX9 was used as a pan-cancer cell marker, vimentin as a 421 mesenchymal marker, and BRD4 was a surrogate marker for super-enhancer activity. We 422 identified positive co-staining for vimentin and Brd4 in CAFs, positive triple-staining 423 (vimentin+/Sox9+/Brd4+) in mesenchymal cancer cells, and single staining of Sox9 in epithelial 424 cancer cells that localized to more differentiated, duct-like structures in the advanced tumors 425 (Fig. 6E). Next, we performed immunohistochemical analysis on 16 whole tumor human 426 pancreatic cancer sections using an antibody against H3K27ac, a commonly accepted marker 427 of increased gene regulatory element activity [26, 31]. The malignant epithelium and stromal 428 fibroblasts were scored separately. These analyses showed markedly positive 3+/3+ staining in 429 the stromal fibroblasts of all whole tumor sections (Fig. 6F). In 6/16 cancer epithelia the score 430 was 1+ and 10/16 scored 2+, with no samples showing a cancer epithelial scoring of 3+. Taken

431 together, these are the first data indicating differential super-enhancer activity in distinct tissue 432 compartments of PDA. 433 434 Discussion 435 We have carried out an scRNA-seq of different stages of the KIC GEMM, in addition to late 436 KPfC and KPC tumors in an effort to agnostically profile the phenotypic changes of cancer and 437 stromal cells during PDA progression. We have established the emergence of a mesenchymal 438 cancer cell population as a late-stage tumor event and have identified novel features of different 439 macrophage and fibroblast populations. This significantly improves our understanding of PDA 440 progression and lays the foundation for the development of novel therapeutic approaches. 441 442 PDA pathogenesis involves metaplasia of normal acinar cells to ductal epithelium, which in turn 443 undergo neoplastic transformation in a KRAS-driven manner [32]. Malignant ductal epithelium 444 may then assume more aggressive, mesenchymal features as the disease progresses. In this 445 study, mesenchymal cancer cell populations were noted in late-stage tumors. Our data support 446 a model in which mesenchymal features of cancer cells are acquired later in the disease 447 process, although others have argued that this can be one of the earliest events in PDA [33]. 448 Mesenchymal cancer cell populations have been studied extensively in pancreatic cancer 449 mouse models and has been shown to be critical to chemotherapeutic resistance while their 450 contribution to metastasis has been more controversial [34, 35]. Mesenchymal cancer cells 451 have previously demonstrated an increased protein anabolism and activation of the 452 endoplasmic reticulum–stress-induced survival pathways in a PDA GEMM [36]. 453 454 Indeed, in the late KIC model, ribosomal pathways were the most significantly upregulated 455 pathways in cancer cells (Fig. 2E-H). It is likely that the demand for increased ribosomal activity 456 stems from high transcriptional activity governed by epigenetic mechanisms in the 457 mesenchymal cancer cells, as we also saw markedly increased UMI counts in this population 458 (Fig. 6B). The bromodomain and extraterminal (BET) family of proteins such as BRD4, which is 459 markedly upregulated in cancer cells and fibroblasts of late-stage PDA (Fig. 6C and D), serve to 460 recruit regulatory complexes to acetylated histones at enhancer sites, resulting in increased 461 transcription [37]. Previously, a combination approach using a BET protein inhibitor and a 462 histone deacetylase inhibitor led to near-complete tumor regression and improved animal 463 survival in a PDA GEMM [28]. Super-enhancer activation has recently been shown to be 464 fundamental in the pathophysiology of a variety of neoplasms [38] and is intimately associated

465 with Hmg2a in PDA, as super-enhancer attenuation has been demonstrated to downregulate 466 Hmg2a expression and the growth of PDA cells in a three-dimensional in vitro model [39]. Our 467 study is the first to demonstrate that these epigenetic regulatory mechanisms in PDA are 468 present in specific tissue compartments, namely mesenchymal cancer cells and CAFs. Future 469 efforts to target super-enhancer activity in PDA should consider distinct tissue compartments 470 governing the sensitivity and resistance to novel therapeutics. 471 472 Our data revealed two molecular subtypes of macrophages in advanced PDA (Fig. 4). One 473 expressed numerous chemokine and inflammation-associated genes while the other was rich in 474 MHC-II–associated genes. In a previous study, MHC-II–positive macrophages were isolated 475 from orthotopic breast tumors and highly expressed CCL17, consistent with our data [40]. In 476 parallel with our study, MHC-IIlow macrophages were found to be highly enriched for numerous 477 chemokines. Moreover, in an orthotopic hepatoma mouse model, an early MHC-II+ macrophage 478 population appeared to suppress tumor growth but an MHC-IIlow macrophage population 479 became the predominant macrophage population as the tumor progressed, resulting in a 480 protumor phenotype [41]. Nonetheless, to confirm their pathophysiological significance, 481 functional studies are required in which inducible selective ablation [42] is performed on the two 482 late-stage PDA macrophage subpopulations using specific markers we have identified in this 483 study. Zhu and colleagues [43] have shown that bone marrow-derived monocytes make up 484 approximately 80% of MHC-II–positive macrophages in a PDA GEMM whereas MHC-II– 485 negative macrophages in normal pancreas and PDA were shown to be maintained 486 independently of monocyte contributions. Monocyte-independent MHC-IIlow tissue resident 487 macrophages expanded during tumor progression and contributed to PDA growth and survival. 488 Conversely, Sanford and colleagues [44] have shown that monocytes can give rise to a pro- 489 inflammatory macrophage population in a PDA mouse model, which when antagonized with 490 neutralizing antibodies against CCR2, resulted in decreased tumor growth and reduced 491 metastases in vivo [44]. These data highlight the need for an scRNA-seq study on macrophage 492 populations in PDA GEMMs with labelled bone marrow replacement to reconcile these 493 discrepancies. 494 495 More importantly, in the studies of tumor-associated macrophages, inflammatory chemokines 496 are commonly used to indicate an M1 type of macrophage, which are normally associated with 497 immune-stimulatory functions. Nevertheless, our study indicates that a distinct M1/M2 498 macrophage phenotype is not readily discernable at the single-cell level. Instead, as PDA

499 progresses, an inflammatory feature is substantially increased, and this accompanies an 500 increase of an important M2 macrophage marker, ARG1 (Fig. 4F and H). This raises questions 501 on the M1/M2 classification system, as the inflammatory feature is associated with the 502 progression of PDA. Future studies should focus on the function of these inflammatory 503 macrophages in PDA in addition to validating markers for macrophage classification. 504 505 While numerous studies have generally shown that CAFs are tumor-promoting in the biology of 506 PDA and other carcinomas [45, 46], recent studies have found that the function of CAFs in PDA 507 biology are more varied. Özdemir and colleagues [42] demonstrated that the depletion of 508 αSMA+ cells from the microenvironment in a PDA GEMM resulted in shortened survival and 509 poorly differentiated tumors [42], and low myofibroblast tumor content was shown to be 510 associated with worse survival in human PDA sections. These data prompted a paradigm shift 511 whereby certain CAFs may function to constrain, rather than promote, PDA. Moreover, until 512 recently, the molecular heterogeneity of CAFs in PDA has not been well-appreciated. The 513 primary attempt to characterize fibroblast heterogeneity in PDA demonstrated that mouse 514 pancreatic stellate cells (PSCs) could be induced to express αSMA in vitro when directly co- 515 cultured with primary mouse PDA cells in an organoid co-culture system [22]. These 516 myofibroblastic CAFs were designated as “myCAFs.” This was distinct from IL6+ fibroblasts that 517 were produced in vitro when PSCs were indirectly co-cultured with mouse PDA organoids 518 through a semi-permeable membrane. The IL6+ fibroblasts were also positive for PDGFRα and 519 numerous other cytokines and therefore termed inflammatory CAFs or “iCAFs.” The 520 immunohistochemistry of human and mouse PDA tissue showed distal IL6+ stroma as a distinct 521 population from the peritumoral αSMA+ stroma. Subsequent studies in PDA GEMMs 522 demonstrated that the iCAF population can mediate pro-tumorigenic properties and is a 523 potential therapeutic target in an attempt to sensitize PDA to immunotherapeutic strategies [23, 524 24]. 525 526 Our current study is the first to demonstrate the existence of three distinct molecular subtypes of 527 fibroblasts in the normal mouse pancreas, which in turn gave rise to two distinct subtypes of 528 CAFs that were largely conserved across three different PDA GEMMs. We noted that FB1 529 expressed insulin-like growth factor signaling genes (Igfbp7, Igfbp4, and Igf1) in addition to 530 Pdgfra, Cxcl12, Il6, and several other cytokines (Ccl11, Ccl7, Ccl2, and Csf1). We propose that 531 our FB1 population is the previously described iCAF population and hence likely pro- 532 tumorigenic. Conversely, the FB3 population was positive for the myofibroblast markers Acta2

533 and Tagln, and therefore most closely represents the previously described myCAF population. 534 Importantly, our agnostic approach did not identify any further putative CAF populations and so 535 we support the two-CAF model proposed by Öhlund and colleagues [22]. 536 537 In summary, this report systematically outlines the cellular landscape during the progression of 538 PDA and highlights the cellular heterogeneity in PDA pathogenesis. As such, future targeted 539 therapeutic strategies should be developed with their intended target subpopulation in mind. 540 541 Address requests for reprints to: Rolf A Brekken, 6000 Harry Hines Blvd., Dallas, TX 75390- 542 8593. Email: [email protected]. 543 544 Acknowledgements 545 We thank the McDermott Center Next-Generation Sequencing Core at UT Southwestern for 546 preparing and sequencing the single-cell RNA-seq libraries. We thank Dr Jeon Lee (UT 547 Southwestern) for assistance in pre-processing of scRNA-seq data. We also thank Dave Primm 548 of the UT Southwestern Department of Surgery for help in editing this article. 549 550 Conflicts of interest 551 The authors have no conflicts of interest to report. 552 553 Ethics approval 554 Mice were used in this study according to UT Southwestern institutional guidelines and 555 approved by the institutional animal care and use committee at UT Southwestern Medical 556 Center. All human samples were procured through the UT Southwestern tissue management 557 shared resource and approved through the UT Southwestern institutional review board. 558 559 Data sharing statement 560 There are no additional unpublished data from this study. 561 562 References:

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664 Figure Legends 665 666 Figure 1. Cellular heterogeneity during PDA progression. A) Representative H&E sections 667 of the normal pancreas, early KIC lesion, and late KIC lesion (magnification: 20x). B) tSNE plot 668 of normal pancreas displaying 2354 cells comprising 8 distinct cell populations. C) tSNE plot of 669 the early KIC lesion displaying 3524 cells containing 9 cells types with the emergence of the 670 cancer cell population. D) tSNE plot of the late KIC tumor showing 804 cells and 7 distinct 671 populations. Stacked violin plots of representative marker gene expression for each of the cell 672 populations seen in the E) normal pancreas, F) early KIC lesions, and G) late KIC lesions. 673 674 Figure 2. Analysis of early and late KIC cancer cell populations demonstrate the 675 emergence of the mesenchymal cancer cell population as a late event. A) tSNE plots of the 676 early KIC lesion demonstrated the expression of known epithelial markers in the sole cancer 677 population (black outline). Mesenchymal markers were absent in this population. B) tSNE plots 678 demonstrating the emergence of two cancer cell populations in the late KIC tumor. One cancer 679 cell population expressed the epithelial markers (smaller population outlined in black) and a 680 second expressed the mesenchymal markers (larger population outlined in black). C) Violin 681 plots showing the high expression of epithelial markers in the early KIC cancer cell population 682 and late KIC epithelial cancer cell population but not in the mesenchymal population. 683 Mesenchymal markers were overexpressed in the mesenchymal cancer cell population but not 684 in the early KIC or late KIC epithelial cancer cell populations. D) Single-cell profiling heatmap of 685 all early and late KIC cancer cells displaying differentially expressed genes between the three 686 cell populations. Gene names are listed in the boxes on the far right of the heatmap. Each 687 column represents an individual cell and each row is the gene expression value for a single 688 gene. E) KEGG pathway analysis and F) gene ontology analysis comparing all early KIC cancer 689 cells against all late KIC cancer cells. Red bars are increased categories and blue bars are 690 decreased categories. G) KEGG and BIOCARTA pathway analysis and H) gene ontology 691 analysis comparing all early KIC cancer cells against only the late KIC epithelial cells. Red bars 692 are categories increased in the late KIC and blue bars are decreased in the late KIC. (****P < 693 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 694 695 Figure 3. Comparison between cancer cells of KIC and KPfC tumors. A) tSNE plot of the 696 late KPfC lesion displaying 2893 cells and 8 distinct cell populations. B) Stacked violin plots 697 showing representative marker gene expression for each of the cell populations seen in the late

698 KPfC lesion. C) Single-gene tSNE plots of the KPfC tumor displaying the presence of epithelial 699 markers (Ocln, Gjb1, and Tjp1) in the epithelial cancer cell population (upper black outlined 700 population) and mesenchymal markers in the mesenchymal cancer cell population (lower black 701 outlined population). D) Violin plots showing the overexpression of epithelial markers in the 702 epithelial cancer cell population and mesenchymal markers in the mesenchymal cancer cell 703 population. E) KEGG and BIOCARTA pathway analysis comparing all late KIC to all late KPfC 704 cancer cells. Red bars are categories increased in the late KIC and blue bars are increased in 705 KPfC. F) Single-cell profiling heatmap comparing all cancer cells in the KIC versus all cancer 706 cells in the KPfC. Each column represents an individual cell and each row is the gene 707 expression value for a single gene. (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 708 709 Figure 4. scRNA-seq analysis of KIC tumor progression reveals multiple subpopulations 710 of macrophages. A) tSNE plot of three macrophage subpopulations in the early KIC tumor. B) 711 Heatmap depicting the 30 top significantly overexpressed genes in each of the three early KIC 712 macrophage subpopulations. Macrophages from the normal pancreas are displayed (far left 713 group). Each column represents an individual cell and each row is the gene expression value for 714 a single gene. C) tSNE plot representation of two macrophage subpopulations in the late KIC. 715 D) Heatmap depicting the 30 top significantly overexpressed genes in each of the two late KIC 716 macrophage subpopulations. Each column represents an individual cell and each row is the 717 gene expression value for a single gene. E) GO analysis of biological processes in the three 718 macrophage subpopulations seen in the early KIC. F) GO analysis of biological processes in the 719 two macrophage subpopulations of the late KIC. G) Violin plots of the expression of 720 inflammatory genes and Arg1 comparing the macrophages in early and late KIC. H) GO 721 analysis of biological processes that are upregulated in the late KIC macrophages relative to 722 early KIC macrophages. (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 723 724 Figure 5. Analysis of fibroblasts during PDA progression reveals multiple molecular 725 subtypes. A) All fibroblasts from the normal pancreas, early KIC tumors, and late KIC tumors 726 were projected onto a single tSNE plot with the FB1, FB2, and FB3 populations distinguished by 727 pink, orange, and brown, respectively (upper left panel). Normal pancreas fibroblasts were 728 highlighted in red (upper right panel), early KIC fibroblasts in green (lower left panel) and late 729 KIC fibroblasts in blue (lower right panel). Normal pancreas and early KIC contained fibroblasts 730 in all three groups whereas the late KIC had only FB1 and FB3. B) Heatmap displaying the top 731 significant genes (cutoff: P < 10-40) for each of the three fibroblast populations. Thirty random

732 cells from each fibroblast population are displayed. All three late-cancer GEMMs (late KIC, 733 KPfC, and KPC) display only FB1 and FB3 fibroblast populations. C) Violin plots demonstrating 734 representative marker genes for each fibroblast subtype: FB1 overexpressed cytokines and 735 Pdgfra. FB3 overexpressed mesothelial markers, myofibroblast markers, MHC-II molecules and 736 Cdh11. D) Gene ontology analysis of the top biological processes in each of the three fibroblast 737 subtypes. E) GO analysis of genes upregulated in late FB1 and FB3 compared to early FB1 and 738 FB3 in KIC, respectively. F) Immunohistochemical analysis of PDGFRα, αSMA, and CDH11 739 stained serially on the same slide. Colors were deconvoluted into a single color layer. PDGFRα 740 and αSMA staining were mutually exclusive, whereas CDH11 was a pan-CAF marker 741 (magnification: 20x). (****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05). 742 743 Figure 6. Analysis of transcriptional activity in different stages of PDA reveals differential 744 epigenetic and super-enhancer activity in distinct tissue compartments. A) tSNE plot of 745 UMI counts in the early and B) late KIC. C) Violin plots of epigenetic regulatory genes in the 746 cancer cell populations of normal pancreas, early KIC, and late KIC. D) Violin plots of epigenetic 747 regulator genes in the normal, early fibroblast, and late fibroblast populations showing their 748 upregulation in CAFs. E) Sequential triple immunohistochemical staining on the same late KIC 749 tumor section for cancer cells (SOX9, pink), mesenchymal cells (vimentin, brown) and super- 750 enhancer activity (BRD4, blue). Well-differentiated ductal epithelium stained solely for SOX9 751 (green outline). Mesenchymal cancer cells (blue arrows) and CAFs (brown arrows) both show 752 co-staining with BRD4. F) Immunohistochemical analysis of human PDA whole tissue sections 753 using the H3K27ac antibody. These representative figures from four different human PDAs 754 demonstrate the 3+/3+ staining in the stromal fibroblasts (red arrows) with 1-2+ staining in the 755 cancer epithelium (magnification: 20x). 756 757

758 Supplementary Figure 1. Ultrasound image of early KIC mouse (39 days old), 1 day prior to 759 sacrifice. Two-dimensional measurement of the neoplastic lesion is denoted in teal (1.10 mm x 760 0.82 mm). V = ventral surface, D = dorsal surface, S = spleen. 761 762 Supplementary Figure 2. A) tSNE plot of KPC (6 months) displaying 1007 cells making up 8 763 distinct cell populations as indicated. B) Stacked violin plots of marker genes and UMI for each 764 of the 8 KPC populations. 765 766 Supplementary Figure 3. Heatmap depicting gene expression levels (horizontal) of single cells 767 (vertical) in the macrophage populations of the KPfC GEMMs. Proinflammatory (Macrophage 1) 768 and MHC-II–rich (Macrophage 2) subtypes are noted below the heatmap. 769 770 Supplementary Figure 4. A) The relative proportions of FB1, FB2, and FB3 in the normal 771 mouse pancreas, early KIC lesions, and late KIC lesions. B) Genes increased most significantly 772 in the FB1 and FB3 populations as the normal pancreas progressed to early KIC and then to 773 late KIC. 774 775 Supplementary Figure 5. Violin plots depicting gene expression of Myc and epigenetic 776 regulatory genes in the KPfC epithelial and mesenchymal cancer cell populations. 777 778

B normal pancreas 2354 cells C early KIC 3524 cells D late KIC 804 cells Acinar cells Cancer cells 9.90% Fibroblasts-2 Fibroblasts-3 1.36% 10.11% 13.22% Lymphocytes Islet cells Islet cells Acinar cells 61.68% 7.71% 3.83% 1.15% Fibroblasts-2 Red blood Beta cells 1.50% 26.65% cells 7.59% Cancer cells-2 Fibroblasts-1 27.86% 6.29%

Macrophages 42.41% Fibroblasts-3 Cancer cells-1 T cells 5.72% Macrophages Fibroblasts-1 11.01% 3.73% 10.07% 27.70% B cells Macrophages Endothelial cells 5.52% 3.82% 5.16% Fibroblasts-3 1.02% Fibroblasts-1 4.98%

E F G Amy1 Amy1 Sox9

Amy2a2 Amy2a2 Krt18

Pyy Pyy Col1a1

Sst Sst Col1a2

Col1a1 Ins1 Adgre1

Col1a2 Ins2 Cd14

Cd14 Sox9 Cd3d

Krt18 Adgre1 Cd79b

Cd3d Col1a1 Hba-a1

Cd3e Col1a2 Hba-a2

Cd19 Cd52

Cd79b Cd14

Cdh5

Pecam1

Figure 1 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Cdh1 Epcam Gjb1 B Cdh1 Epcam Gjb1 Cancer cells (epithelial)

Cancer cells-2 (mesenchymal) Cancer cells-1 (epithelial) KIC KIC

Axl Axl Cdh2 Cd44 Late Cdh2 Cd44 Early Early

Late Epithelial Cancer Cells C KIC Cancer Cells D Late Mesenchymal Early Cancer Cells Cancer Cells Ggct Ctla2a Cd44 Anxa2 Gsto1 Cdh1 Cdh2 Ctgf Sox11 Plaur S100a10 S100a11 Meg3 Igfbp4 Pmepa1 Id1 Tmsb4x Serpine1 F2r Sdc1 Plac8 Rpl29 Inhba Axl Vim Msln Tpt1 Igfbp3 Hmga2 Ldha Rbp1 Rbm3 Npy Prkg2 Pgk1 S100a6 Basp1 Areg Timp1 Lgals3 Tspan8 Mcpt2 Lgals1 Ecm1 Pkm Onecut2 Cldn2 Epcam Vim Fxyd5 Cpe Anxa1 Anxa3 S100a4 Emp1 Nbl1 2200002D01Rik Ly6e Tff2 Krt19 Gsta4 Gcnt3 Epcam Krt7 Pglyrp1 Lgals4 Spint2 Cldn7 Sfn Fxyd3 Tff1 Aqp5 Krtcap3 Krt20 Muc1 Ly6d Mal2 Cldn4 Ly6a Dmbt1 Mmp7 Wfdc2 S100a14 Slpi Cldn3 S100a4 Serpinb1a 2210407C18Rik Cdh1 Apoe Ins1 Ins2 Anxa5 Aplp2 Ces1d Ctrl Cpb1 Ifitm3 Sepp1 Mgst1 Rnase1 Try4 Sparc Gcg Ambp Reg1 Cpa1 Wfdc18 Spp1 Try5 Pnliprp1 Zbtb20 Spink1 Cela1 Pnlip Prox1 Cel Ctrb1 Cldn10 Cp Klk1 Clps Wfdc15b Cldn3 Zg16 Prss2 Tmem176a Tm4sf4 Reg2 Cela2a Tmem176b Ttr Iapp Cela3b Tmem45a Dcdc2a Gstm1 Sycn 2210010C04Rik low high relative expression

KIC Cancer Cell Progression KIC Cancer Cell Progression E KEGG Pathway Analysis F Gene Ontology Analysis Biological Biological Process

Top Down-regulated

Top Up-regulated Cellular Component

Top Down-regulated Top Up-regulated

Fold Enrichment Fold Enrichment Early and Late KIC Early and Late KIC Epithelial Cancer Cell Comparison H Epithelial Cancer Cell Comparison G Pathway Analysis Gene Ontology Analysis Biological Biological Process KEGG Cellular Component

Top Down-regulated Top Down-regulated Top Up-regulated

Top Up-regulated BIOCARTA

Figure 2 Fold Enrichment Fold Enrichment bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Ocln Gjb1 Tjp1 A late KPfC 2893 cells C Cancer cells-1 (epithelial) Cancer cells-1 Lymphocytes 2.45% 13.07% Macrophages 65.64% Cancer cells-2 Granulocytes (mesenchymal) 3.94% KPfC

Late Late Vim Cd44 Axl Cancer cells-2 5.22%

Fibroblasts-3 Fibroblasts-1 4.42% 1.76% Red blood cells 3.49%

B D KPfC Cancer Cells Sox9 Gjb1 Vim

Krt18

Col1a1 Tjp1 S100a4 Col1a2

Adgre1

Cldn3 Fbln2 Fcgr2b

Itgax

S100a9

Cd3d

Cd79

Epithelial Cancer Cells Hba-a1 Mesenchymal Epithelial Mesenchymal Cancer Cells Cancer Cells Cancer Cells F Mt1 Rps27rt Gm10260 Rps18−ps3 Hba-a2 Mt2 Gm8797 Gm10709 Gm8730 Ggct Tuba1a Igfbp4 Prkg2 Ctla2a Nrn1 Timp1 Msn Serpine1 Mcpt8 Igfbp3 Cpe Fbln2 Inhba Ctgf Meg3 Hmga2 F2r Id3 Ankrd1 Igfbp7 Lgals1 Axl S100a4 Tnc Itgb1 Vim Prkg2 Fxyd5

Npy Prl2c3 Lgals3 Ldha Pgk1

Cdkn2a Cldn6 Lars2 Mcpt1 Mcpt2

E Pathway Comparison between AA467197 Wfdc17 Ccl6 Cxcl2 Cancer Cells in Late KIC and Late KPfC Uba52 Lyz2 Saa3 Cd74 mt-Co1 Reg1 Gpx2 Gsta4 Tff2 Phgr1 Reg3b Ltf Lgals4 Pdzk1ip1 Prap1 Reg3g Prom1 Muc1 Sftpd Gkn1 Agr2 Pglyrp1 Car2 Sepp1 Tff1 Gkn3 Fxyd3 Dmbt1 Aqp5 KEGG Cldn4 Ly6a Cldn7 Cdh1 Mmp7 Sfn Ly6e Spint2 Krtcap3 Rbp1 Cldn3 Wfdc2 Krt7 Mal2 Serpinb1a S100a14 Epcam Krt19 2210407C18Rik

Apoe Cp Wfdc18 Ttr Tmem45a Top Down-regulated in KIC Top Up-regulated in KIC BIOCARTA

KIC KPfC Fold Enrichment low high Figure 3 relative expression bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved.Early No KIC reuse allowed without permission. Macrophage2 A B Normal Pancreas Early KIC Early KIC Sod2 Fabp5 Ctsd Clec4e Macrophage Macrophage1 Macrophage3 H2−DMb1 Timp1 Cd14 Ptgs2 Early KIC Il1a Rnf149 Ctss Ear1 Ctsz Il1rn Thbs1 Ear2 Macrophage2 Prdx5 Trem2 Lgals3 Lyz1 Bcl2a1b Il1b Spp1 Fn1 Capg Tgm2 Lyz2 Ccrl2 Tnfaip2 Slc7a11 Timp2 Ltc4s Gas6 Ccl12 Macrophage1 Ifi27l2a Pltp Fcgrt Folr2 Emp1 Mrc1 Sepp1 F13a1 Glul C4b Cd163 Ccl7 Ednrb Wfdc17 Lyve1 Cd209f Cfh Hmox1 Cd209g Fcna Macrophage3 Hbegf Ccl4 Cbr2 Pf4 Ccl8 Ccl2 Etv3 Jak2 Pkib Il1r2 Gm2a Tnpo3 Sub1 Tbc1d4 H2afy Tmsb10 Cytip Napsa Rpl4 Tmem123Plbd1 Ccr7 Gpr132 Mkrn1 Cst3 Ccl17 Crem Psmb8 Ramp3 H2afz low high Ccnd2 Syngr2 Plac8 relative expression C D Vps37b Lsp1 Tspan13 Late KIC Late KIC Late KIC Macrophage1 Macrophage2 Anxa1 Cxcl3 Ccl7 Lrg1 Emp3 Ier3 Hbb−bs Ccl6 Cd44 F13a1 Lcn2 Ccl9 Sdc4 Ear10 Ear1 Wfdc17 Itgam Osm Ccl2 Slpi S100a6 Ifitm6 Ear2 S100a8 Arg1 Pf4 Thbs1 Wfdc21 Chil3 Saa3 Macrophage1 C1qb Rgs1 Plet1 Ccr7 Aif1 Tspan13C1qc Ifitm1 Macrophage2 Il23a Cst3 Mgl2 Ccl17 H2−Aa C1qa Cxcl16 Tbc1d4 H2−Eb1 Acp5 Mmp12 Apoe H2−Ab1 Spp1 Mmp13 Il1r2 H2−Dma Cd74 low high H2−DMb1 AW112010 relative expression H2−DMb2 Adamdec1

KIC Macrophages E Early KIC Macrophage Signatures G Gene Ontology Biological Process Analysis Macrophage Macrophage 1 Arg1

Il1a Macrophage Macrophage 2

Il1b Macrophage Macrophage 3 Il1r2

Il6

Fold Enrichment

F Late KIC Macrophage Signatures Gene Ontology Biological Process Analysis H Macrophage Macrophage 1

Early and Late KIC Macrophages Comparison Gene Ontology Biological Process Analysis Macrophage Macrophage 2

Top Up-regulated

Fold Enrichment

Fold Enrichment Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Top genes Other genes A Fibroblast Clusters Normal Pancreas B in the heatmap in the clusters Ptn FB1 FB1 Apoe Jun Ifitm2 Thbs1 Sfrp1 Egr1 Mt1 Bgn Smoc2 Gdf10 Serpinh1 FB3 FB1 Cygb Jund Gpm6b Junb FB3 FB3 Cxcl14 Abca8a Ifitm1 Mgp Pcolce Steap4 Cxcl1 Il6 Malat1 Srpx Cyp1b1 Ccl7 Gpx3 Fosb G0s2 Ccl11 Itm2a Igf1 Emp1 Cxcl2 FB2 FB2 Pi16 Pcolce2 Slco3a1 Rnase4 FB2 Sema3c Efemp1 Rpl35a Rpl17 Ly6c1 Creb5 Gpc6 Gstm1 Early KIC Late KIC Timp2 Nov Scara3 Ptgs1 Clec3b Cd248 Serpinb6a FB1 FB1 Lrrn4 Rpp25 Cd74 Pkm Gpm6a Hdc C1rb Atp9a FB3 Nkain4 Plxna4 C1d Mgat4b FB3 FB3 Msln Cd38 C3 Ltbp3 Lgals7 Clu Tpm1 Ces2g FB3 Krt19 Cd82 Anxa11 Hist1h1c Upk1b Cmbl Mdh2 Dkk3 Slpi C2 Mdk Cbx5 FB2 FB2 Bcam Ndufaf1 Lpcat3 Sfxn3 Myrf Csrp2 H2-Aa Nxn Bst1 Spint2 H2-Ab1 Daglb Stmn2 H2-Eb1 FB1 FB2 FB3 Cav1 low high F11r Cdh11 H2-DMb1 relative expression Arhgdib Cxadr H2-D1 C H2-DMb1 Cyb5r3 Il6 PDA Fibroblast Signatures D Gene Ontology Biological Process Analysis Ccl2 FB1

Ccl7

Cxcl12 FB2

Pdgfra FB3

Msln

Fold Enrichment

Lrrn4

Gene Ontology Analysis of KIC Fibroblast E Increasing Signature Upk3b FB1 FB3 Cd74

Fold Enrichment

H2-Aa Late KIC F Cadherin-11/ PDGFRα/αSMA Cadherin-11 H2-Dmb1

Acta2

Tagln

Cdh11

PDGFRα αSMA Figure 5 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Early KIC nUMI B Late KIC nUMI Acinar cells Cancer cells Macrophages Lymphocytes

Islet cells Red blood Fibroblasts-2 cells Beta cells

Cancer cells-2 (mesenchymal) Cancer cells-1 Fibroblasts-3 (epithelial) Macrophages Fibroblasts-1

Fibroblasts-1 Endothelial cells Fibroblasts-3

C D

nUMI nUMI

Brd4 Brd4

Myc Arid1a

Arid1a Smarcb1

Arid2

Smarcb1 E Late KIC Hmga1 BRD4 / Hmga1-rs Vimentin Hmga2 / Epithelial Cancer Cells Mesenchymal Cancer Cells

SOX9 Fibroblasts F Human PDA H3K27ac Human PDA Figure 6 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplementary Figure 1 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A late KPC 1007 cells Macrophages 64.45%

Fibroblasts-3 4.37% B lymphocytes 9.33%

Fibroblasts-1 Red blood 5.26% Cancer cells 2.88% cells 2.48%

Endothelial cells 1.99% T lymphocytes 9.24%

B Sox9

Krt18

Col1a1

Col1a2

Fcgr2b

Cd14

Cd3d

Cd79a

Peacam

Cdh5

Hba-a1

Supplementary Figure 2 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

KPfC Macrophages

Macrophage 1 Macrophage 2

Supplementary Figure 3 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

KIC Fibroblast Populations A during PDA Progression FB3 FB3 100% 7.66% 2.64% 80% FB3 53.49% FB2 56.94% 60% FB2 68.89%

40%

FB1 46.51% 20% FB1 35.41% FB1 28.47% 0% Normal Early KIC Late KIC Pancreas

B KIC FB1 Increasing Signature KIC FB3 Increasing Signature Early KIC Normal Late KIC Normal Pancreas Early KIC Pancreas Late KIC

Supplementary Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/539874; this version posted February 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Myc

Brd4

Hgma1

Hgma2

Supplementary Figure 5