Elucidation of Tumor-Stromal Heterogeneity and the Ligand-Receptor Interactome by Single-Cell Transcriptomics in Real-world Pancreatic Cancer Biopsies

The majority of patients with pancreatic ductal adenocarcinoma (PDAC) present with locally advanced or metastatic disease, which precludes surgical resection of their cancer (1). The gold standard for diagnosis is a tissue biopsy prior to initiation of systemic therapy, which is obtained by either endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) of the primary lesion or through interventional radiology–guided percutaneous biopsy of a metastatic site. While typically adequate for histopathologic assessment, PDAC biopsies, especially those obtained via EUS-FNA without rapid onsite cytopathology assessment, can be limited by low neoplastic cellularity (2, 3). Nonetheless, such biopsies may be the only source of tissue from patients with locally advanced or metastatic PDAC who are likely to derive the most benefit from tailored approaches.

Multiple recent studies have demonstrated the feasibility of using next-generation sequencing (NGS) platforms for mutational analysis as applied to these limited “real-world” biospecimens obtained from patients with PDAC (4, 5). Notably, although targeted NGS panels can identify somatic or germline DNA mutations in a clinically meaningful timeframe, no more than a quarter of patients with PDAC, at best, harbor such actionable mutations (6). On the other hand, transcriptomic profiling of PDAC has revealed the presence of molecular subtypes that may guide therapeutic stratification as is seen in other cancer types (7). Single-cell RNA sequencing (scRNA-seq) has also aided in elucidating intratumoral heterogeneity with implications in evolution of cancer cells, resistance to therapy, and effects of stromal cells such as immune cells and fibroblasts on tumor behavior.

We explore the feasibility of using scRNA-seq on de novo limited biopsies obtained from patients with primary and metastatic PDAC. As recently described in studies using surgically resected PDAC samples, scRNA-seq provides an unprecedented level of insight into the architecture of the neoplastic cells and their tumor microenvironment (TME), including compartment specific heterogeneity (8–11). We demonstrate that scRNA-seq on limited fine-needle and core biopsies captured nearly all of the previously reported repertoire of cell types in surgical resections, including the tumor-stromal heterogeneity inherent to this disease, and revealed putative mechanisms for immune evasion within the TME. We believe that precision medicine approaches to a near universally lethal disease such as PDAC should deploy multiple orthogonal approaches, including NGS for actionable DNA alterations and scRNA-seq on contemporaneous de novo biopsies for assessment of the tumor-stromal architecture.

A total of 9 patients were recruited at the University of Texas MD Anderson Cancer Center (Houston, TX) through informed written consent following Institutional Review Board approval (Lab00-396 and PA15-0014). The study was conducted in accordance with Good Clinical Practices concerning medical research in humans per the Declaration of Helsinki. Five primary pancreatic cancers and four metastatic lesions in liver (LiM), lung (LuM), peritoneum (PM), and vaginal apex (VM) were used. P2–P5 primary samples came from treatment-naïve patients, while P1 was a second FNA from a patient who had been treated with gemcitabine/paclitaxel. For metastatic samples, the most recent therapies prior to sample acquisition were 5-fluorouracil/liposmal irinotecan, evofosfamide/ipilimumab, and capecitabine for VM, LuM, and PM, respectively. LiM was a treatment-naïve sample. Histologic confirmation of PDAC was performed by a pathologist.

Pancreatic cancer biopsies were collected in high-glucose DMEM supplemented with GlutaMAX, HEPES buffer, and 1% BSA (all Thermo Fisher Scientific). Samples were delivered to the laboratory within 2 hours after the procedure. Single-cell dissociation was performed as described previously (8). Briefly, samples were minced with sterile surgical scalpels to 0.5–1 mm fragments in approximately 1 mL of media. After centrifugation for 5 minutes at 125 ×g, cells were resuspended in DMEM with 0.5 mg/mL Liberase TH (Sigma-Aldrich) and 1% penicillin-streptomycin (Corning) and incubated in an orbital shaker for 15 minutes at 37°C. Liberase was quenched with equal volume of 1% BSA in DMEM followed by centrifugation for 5 minutes at 125 ×g. Cells were resuspended in 2 mL Accutase (Sigma-Aldrich) and incubated in an orbital shaker for 15 minutes at 37°C. Dissociated cells were passed through a 40-μm strainer and centrifuged for 5 minutes at 125 ×g. Isolated cells were resuspended in 0.04% BSA in PBS (Corning) for subsequent viability analysis and counting.

Single-cell transcriptomic amplification and library preparation was performed using the 5′ or 3′ Library Construction Kit (10× Genomics) following the manufacturer's recommendations. Final library quality and concentration were measured on TapeStation System using High Sensitivity D5000 ScreenTape (Agilent). Libraries were sequenced on NextSeq 500 (Illumina) according to the manufacturer's instructions.

Raw base call files were demultiplexed and converted to FASTQ files, which were subsequently used to generate feature-barcode matrices using Cell Ranger RNA v3.1 (10× Genomics) and hg19 was used as a reference. Additional analytic methods are included in the Supplementary Data.

The datasets generated during the current study are available in the Gene Expression Omnibus repository under accession number GSE156405.

We explored the feasibility of conducting scRNA-seq in a panel of limited “real-world” biopsies obtained from patients with primary and metastatic PDAC. Five primary samples (P1–P5) were obtained via EUS-FNAs of the pancreas, while four metastatic samples were obtained as core needle biopsies (liver, lung) or segmental biopsies (vaginal apex, peritoneal). Pooled analysis of all samples yielded a total of 31,720 cells organized into eight clusters (Fig. 1A), and highly expressed genes in each cluster were used to identify cell types (Fig. 1B). Epithelial cells were the most commonly represented cell type (47%), followed by T cells (28%) and myeloid cells (12%; Fig. 1A). Across primary and metastatic lesions, all cell types were represented and the average cell composition was similar (Supplementary Fig. S1A; Supplementary Table S1).

To assess the heterogeneity specific to cancer cells, we extracted the epithelial cluster for further analysis, which revealed six distinct transcriptomic subclusters (Fig. 1C; Supplementary Table S1). Epithelial 1 subcluster was characterized by enrichment of antigen presentation, type I IFN response, and response to IFNγ pathways (Supplementary Fig. S1B). Along the same line, epithelial 2 subcluster was enriched for response to reactive oxygen species and intrinsic apoptosis, while epithelial subclusters 3 and 4 were enriched for epigenetic process and DNA replication/cell cycle, respectively, altogether demonstrating diverse transcriptomic programs present in PDAC tumor cells. A previous scRNA-seq analysis of PDAC demonstrated lipid processing and secretory cells within ductal epithelial cells (9), which were also present in our samples. Specifically, subcluster 5 expressed lipid processing cell markers APOA1, FABP1, and DNASE1, while subcluster 6 expressed secretory cell markers CFTR, SOD3, and COL18A1 (Fig. 1D). All epithelial subclusters were present in both primary and metastatic tissues, but lipid processing and secretory subclusters were proportionately less common in metastases (Supplementary Fig. S1C; Supplementary Table S2). Furthermore, signature genes for ductal and acinar cells were applied to the lipid processing and secretory subclusters (12). This analysis demonstrated that the former was enriched in acinar cells while the latter was enriched in ductal cells (Supplementary Table S3).

Copy-number inference from scRNA-seq showed numerous alterations in subclusters 1–4, whereas lipid processing and secretory subclusters did not contain matching alterations (Supplementary Fig. S1D), suggesting that the latter were derived from normal epithelium, as postulated previously (9). In addition, this combined inferred copy-number analysis demonstrated how limited biopsies can readily capture tumor heterogeneity of primary and metastatic PDAC.

Previous studies have classified PDAC into molecular subtypes based on their bulk transcriptome. These classifications include Bailey subtypes [pancreatic progenitor, squamous, aberrantly differentiated endocrine exocrine (ADEX), and immunogenic], Collisson subtypes (classical, quasimesenchymal or QM, and exocrine-like), and Moffitt subtypes (classical and basal-like; refs. 13–15). A subsequent study demonstrated two common epithelial subtypes, namely classical (or pancreatic progenitor) and basal-like (or squamous/QM), whereas immunogenic and ADEX or exocrine-like subtypes were associated with low tumor purity (16). To precisely delineate the cellular contributions to bulk molecular subtypes, we used nearest template prediction (17) to classify all single cells into a subtype based on the three classifiers (Supplementary Fig. S2A–S2D; Supplementary Table S4). Aligning the cell type to molecular subtypes confirmed that immunogenic, ADEX, and exocrine-like subtypes were mostly composed of non-epithelial cells (Supplementary Fig. S2E), consistent with the previous finding of low tumor purity in these subtypes (16).

To understand the molecular subtypes in the context of epithelial cells, we reapplied the classifiers on the epithelial subset (Supplementary Fig. S2A–S2D and S2F; Supplementary Table S5). Of note, while subtype prediction from pseudobulk projection yielded one molecular subtype for each biopsy sample (Supplementary Fig. S2G), all tumor samples were composed of more than one subtype at single-cell level regardless of the classifier (Fig. 2A–C; Supplementary Fig. S2H–S2J), consistent with recent reports that Moffitt classical and basal-like subtypes coexist in a single tumor (18, 19)

We performed pseudotime analysis on the epithelial cells to elucidate potential evolutionary trajectories of PDAC tumor cells, which revealed two major branches (Fig. 2D). Interestingly, cells from the liver metastasis formed their own branch in the trajectory (cell fate 1; Fig. 2E; Supplementary Fig. S3A) and showed upregulation of epithelial–mesenchymal transition and hypoxia pathways (Fig. 2F), which may contribute to their more aggressive phenotype. Cell fate 2 mostly consisted of lung and vaginal metastases, with lung metastatic cells lying towards the end of the trajectory (Fig. 2E; Supplementary Fig. S3A). Lung metastatic cells were enriched for aerobic metabolism pathways, such as oxidative phosphorylation and electron transport chain (Fig. 2F), which may be expected given the presence of ample oxygen in the microenvironment. Of note, given that the liver- and lung-metastatic cells form diverging trajectories with enrichment of distinct gene sets even though cells in both sites mostly classified as basal-like (Fig. 2C and D), our results suggest that there may be heterogeneity even within a single subtype that may be responsive to the metastatic site. In addition, we included a third molecular “hybrid” subtype which has recently been reported by Raghavan and colleagues (20) and found it to be present in every case studied (Supplementary Fig. S3B).

In the PDAC TME, functionally and transcriptomically distinct subtypes of cancer-associated fibroblasts (CAF) are present (21, 22). Inflammatory CAFs (iCAF) secrete cytokines, whereas myofibroblastic CAFs (myCAF) are characterized by the expression of extracellular matrix (ECM) components (21, 23). A third subtype of CAFs, named antigen-presenting CAFs or apCAFs, express class II MHC genes (9). Initial analysis of the CAF population from our scRNA-seq revealed two transcriptomically distinct subclusters, which were designated as iCAFs and myCAFs based on the expression of PDGFRA, CXCL12, ACTA2, and TAGLN as described previously (Supplementary Fig. S4A and S4B). To assess the presence of apCAFs, we looked for coexpression of the invariant chain of MHC II molecule CD74 with HLA-DPA1, HLA-DRA, and HLA-DRB1, which was present in a subset of CAFs (Fig. 3A). Utilizing nearest template prediction (17), we categorized each fibroblast into one of the three subtypes (Fig. 3B and C) and found no significant difference in the proportions of the CAF subtypes in primary and metastatic tumors (Supplementary Fig. S4C; Supplementary Table S6). In our samples, apCAFs did not coexpress PTPRC or MSLN (Supplementary Fig. S4D), confirming that these cells are not immune or mesothelial cells, as has been suggested in a recent study using murine PDAC (24). While apCAFs express MHC II molecules and can present antigens to CD4 T cells, they lack the costimulatory molecules required to activate an immune response. Therefore, it was hypothesized that apCAFs would contribute to the immunosuppressive environment in PDAC by leading to anergy or differentiation of CD4 T cells into regulatory T cells (Treg; ref. 9). Indeed, the proportion of apCAFs in biopsy samples was negatively correlated with the ratio of memory and effector CD8 T cells to Tregs (Fig. 3D). Our results recapitulate a potential mechanism by which stromal cells within the PDAC microenvironment may lead to immune suppression.

PDAC is characterized by immunosuppressive microenvironment (1). To understand how immune cells change from the circulation to the PDAC TME, we performed scRNA-seq on peripheral blood mononuclear cells (PBMC) from 8 of 9 patients obtained at or around the time of their tissue biopsies. Global comparison of all cell types revealed that there was a subpopulation of myeloid cells that were found only in the PDAC but not in the periphery (Fig. 4A; Supplementary Fig. S5A), and subclustering analysis revealed this unique population to be macrophages (Fig. 4B; Supplementary Fig. S5B and S5C). Given the potential polarization of tumor-associated macrophages into classical M1 and alternative M2 phenotypes (25), we performed trajectory inference analysis on monocytes and macrophages from PBMCs and tumors, which revealed a single unbranched trajectory representing the differentiation from monocytes to macrophages (Fig. 4C). The expression of immune suppressive genes such as MARCO and TREM2 increased with pseudotime (Fig. 4D). Global gene expression changes along the pseudotime trajectory revealed upregulation of macrophage activation, acute and chronic inflammation, and negative regulation of myeloid-mediated immunity pathways (Fig. 4E). Furthermore, macrophages were characterized by an upregulation of angiogenesis and hypoxia pathways (Fig. 4E), which are associated with the M2 phenotype (25), confirming the immune suppressive role of tumor-infiltrating macrophages.

To elucidate the different types of myeloid cells present in the PDAC TME, we performed subclustering analysis of tumor-infiltrating myeloid cells and found eight distinct cell types (Fig. 4F; Supplementary Table S7). Using previously published subtyping of monocytes and dendritic cells (DC) based on scRNA-seq profiles (26), we classified these subclusters as monocyte 1 (equivalent to CD14^high cells), monocyte 2 (equivalent to CD16^high cells), macrophage 1 (expressing APOE and SPP1), macrophage 2 (expressing complement molecules), DC1 (CLEC9A), DC2/3 (CD1C), plasmacytoid (p)DCs (TCF4, IRF7), and mast cells (TPSAB1, CPA3; Fig. 4G; Supplementary Fig. S5D). All subclusters were represented in primary tumors and metastases without significant differences in their proportions (Supplementary Fig. S5E). In addition to MARCO and TREM2, tumor-associated macrophages expressed PPARG and IL6 transcripts (Supplementary Fig. S5F), the products of which are also implicated in immune suppression (27). Of note, a recent study comparing the immune compartments of PDAC and lung cancer revealed reduced numbers of DC infiltration in PDAC that led to the attenuation of antitumor T-cell activity (28). Consistent with these findings, DCs represented smallest subsets of myeloid cells in our samples. Moreover, given that the DC1 subset expressed IDO1 (Fig. 4G), which inhibits T-cell proliferation (9, 29), the DC1 subset may contribute to the immune suppressive phenotype in PDAC.

Analysis of tumor-infiltrating T and natural killer (NK) cells revealed 12 distinct subclusters (Fig. 4H; Supplementary Table S8). On the basis of known markers for T and NK cells (30, 31), we identified memory (ANXA1) and naïve (SELL, CCR7) CD4 T cells, memory and effector (CX3CR1) CD8 T cells, naïve CD8 T cells, mucosal-associated invariant T (MAIT) cells (SLC4A10), tissue resident memory CD8 T (Trm) cells (ZNF683), Tregs (FOXP3, CTLA4), CD56 NK cells, CD16 NK cells, IFN-responsive T cells (IFI44L, ISG15), and NK T cells (TYMS, MKI67; Fig. 4I; Supplementary Fig. S6A). Many T cells expressed inhibitory receptors PDCD1, LAG3, and BTLA (Supplementary Fig. S6B), suggesting that T cells are present in the PDAC TME but their functions may be inhibited. Next, we compared T and NK cells in PBMCs and tumors (Supplementary Fig. S6C–S6E). There was a trend of naïve CD4 T cells decreasing in proportion from periphery to primary to metastatic tumors, whereas memory CD4 and CD8 T cells showed a reverse trend (Supplementary Fig. S6F), likely reflecting antigen exposure and creation of memory T-cell populations predating metastatic spread. Gene set enrichment analysis of tumor-infiltrating T and NK cells revealed the enrichment of hypoxia and negative regulation of immune response pathways (Supplementary Fig. S6G), demonstrating the acquisition of immune suppressive phenotype similar to tumor-associated macrophages.

scRNA-seq has been used to predict potential ligand-receptor (LR) interactions, which we utilized to reveal potential mechanisms by which the cross-talk between tumor cells and immune cells may contribute to immunosuppression in PDAC. We applied an average expression-based LR interaction prediction (32, 33) to assess potential relationships between major cell types of two primary (P3, P4) and two metastatic (liver, lung) tumors, as they contained the highest cell numbers and represented the most cellular diversity. We did not combine multiple samples for analysis to rule out potentially false interactions (e.g., T cells from liver metastasis to epithelial cells in a primary tumor). Global overview of the interactomes revealed that myeloid and DCs were the most connected, and epithelial cells interacted closely with these two cell types (Fig. 5A and B; Supplementary Fig. S7A and S7B). One of the most significant LR pairs responsible for these connections was predicted to be between macrophage migration inhibitory factor (MIF) and the transcript for the invariant HLA-DR allele, CD74 (Fig. 5C). This interaction has been implicated in promoting myeloid-derived suppressor cell–mediated immunosuppression in melanoma and breast cancer (34, 35), while tumor-cell MIF expression has been linked to a more aggressive phenotype in PDAC (36). Another prevalent interaction between tumor epithelial cells and myeloid/DCs involved amyloid precursor protein (APP) and CD74; of interest, we have previously described that APP overexpression in PDAC cells upregulates proliferation (37). The potential role of neoplastic APP in subverting antigen presentation via CD74 interaction on immune cells in the PDAC TME is uncharted. In addition to myeloid cells, CD74 expression in B cells has been linked to promote their proliferation (38) and recent work has demonstrated the role of tumor-infiltrating B cells in the initiation and proliferation of PDAC (39–41). Our LR analysis predicted a putative interaction between CD74 receptor on B cells and its cognate ligand, MIF, expressed by neoplastic epithelial cells, respectively. We were able to validate this putative interaction in a panel of archival PDAC samples by multiplex immunofluorescence, confirming the proximity of the two cell types expressing the corresponding receptor-ligand pair (Supplementary Fig. S8).

Given that scRNA-seq data can be biased by gene dropout, thereby diminishing the average expression of genes in each cell type, we next predicted LR interactions at single-cell level (42). When looking at predicted interactions involving ligands from cells in the TME and cognate receptors on epithelial cells, we found that epithelial cells were more strongly connected to fibroblasts and myeloid cells, whereas there were fewer connections with T and B cells (Fig. 6A; Supplementary Fig. S9A and S9B). Many predicted interactions between epithelial cells and fibroblasts involved components of the ECM (Fig. 6B), which is expected given that one of the major functions of fibroblasts is the production of ECM proteins. There were many pro-inflammatory interactions such as IFNG from T/NK cells and IFNGR1/2 on epithelial cells (Fig. 6B), once again pointing to inflammatory signals present in the PDAC microenvironment.

We also explored the reverse interactions in which ligands were expressed on epithelial cells and receptors on TME cells, which again revealed more connections with fibroblasts and myeloid cells (Fig. 6C; Supplementary Fig. S9C and S9D). Most prominent interactions with fibroblasts were growth factors, and interactions with immune cells involved cytokine and checkpoint genes (Fig. 6D). Of note, epithelial cells interacted with T/NK cells via IL18-IL18R1 and LGALS9-HAVCR2. IL18 has been shown to promote IFNγ production in T/NK cells (43), and the interaction between LGALS9 and HAVCR2 can suppress T cell–mediated immunity (44). Additional immunosuppressive interactions between epithelial and T cells included PVR-TIGIT and CD274-PDCD1. Interactions with myeloid and DCs included CD24-SIGLEC10, which promotes immune evasion and is a potential target in cancer immunotherapy (45). One of the most common interactions between B and epithelial cells was BTLA-TNFRSF14 (also known as herpesvirus entry mediator or HVEM), which can suppress both T cell–mediated and B cell–mediated immunity (46). Overall, these results suggest that PDAC tumor cells can drive an immunosuppressive microenvironment by affecting multiple immune-cell types.

We systematically analyzed scRNA-seq from “real-world” biopsies of primary and metastatic PDAC, demonstrating that even these scant samples recapitulate significant cellular heterogeneity. Recently, there have been multiple studies utilizing single-cell technologies in human and murine PDAC demonstrating heterogeneity in both tumor epithelial and TME compartments (8–11, 24, 47–49). Our data showed higher proportion of epithelial cells compared with recent scRNA-seq studies on surgically resected PDAC tumors (9, 10), while the most common immune-cell types were myeloid and T cells, consistent with these studies. We performed comprehensive analyses on our dataset including transcriptomics subtyping at single-cell resolution, which revealed two subtypes specific to tumor epithelial cells, namely classical (or pancreatic progenitor) and basal-like (or QM/squamous), confirming the results from a previous analysis of bulk tumors (16). Furthermore, metastatic lesions were more likely to harbor a higher proportion of aggressive basal-like subtype of cells, which was also previously reported in a separate PDAC cohort using bulk transcriptome (50). Notably, we show that although projected bulk transcriptome of each tumor sample classifies PDAC dichotomously as one of two subtypes, at single-cell resolution, even limited PDAC biopsies are comprised of more than one subtype. This finding may be of clinical importance in light of the COMPASS trial, which showed that classical PDAC is more likely to respond to first-line chemotherapy, while basal-like tumors tend to be chemoresistant (51). The existence of basal-like cells within an “apparently classical” tumor could lead to preferential enrichment of basal-like cells and emergence of resistance. Thus, future iterations of trials such as COMPASS that rely on subtyping using core biopsies may need to provide a quantitative measure of cellular subtypes present rather than a qualitative dichotomous readout. The coexistence of multiple subtypes within each tumor also raises the possibility of transcriptomic plasticity between epithelial and mesenchymal phenotypes with disease progression or response to therapy, as demonstrated previously (52).

Furthermore, the overall gene expression profile and cell-type proportions revealed several putative mechanisms of immune suppression in PDAC, such as the expression of immune-suppressive genes by myeloid cells and inhibitory receptors in T cells, polarization of macrophages toward M2 phenotype, as well as the presence of apCAFs that may inhibit T cell–mediated immune response. A recent study demonstrated the expression of immune checkpoint receptors such as TIGIT and HAVCR2 in T and NK cells, as well as the expression of their corresponding ligands PVR and LGALS9 in cancer cells, demonstrating alterations of the immune environment of PDAC (11). Prediction of the prevalent LR “interactome” in our data also revealed multiple mechanisms by which tumor–TME interactions may contribute to inflammation and immune evasion including PVR-TIGIT and LGALS9-HAVCR2. Furthermore, CAFs have been shown to drive PDAC plasticity and heterogeneity via TGFβ1 upregulation of MAPK/STAT3 (53). Accordingly, our LR interactome predicted TGFB-TGFBR axis to be upregulated between fibroblasts and cancer cells.

Through integrative analysis of scRNA-seq data from “real-world” biopsies, we uncovered implicit heterogeneity of low-input biopsy samples in PDAC that demonstrates potential mechanisms for immune evasion and provide high-resolution data correlating cell types to clinically relevant molecular subtypes. Deep characterization using a contemporaneous biopsy may provide a valid orthogonal approach to precision medicine approaches when paired with further precision strategies such as NGS for actionable DNA alterations and drug sensitivity testing on patient-derived ex vivo cultures such as organoids.

J.J. Lee reports grants from NIH/NCI during the conduct of the study. V. Bernard reports grants from NIH during the conduct of the study. A. Semaan reports grants from German Research Foundation (DFG; SE-2616/2-1) during the conduct of the study. C.L. Haymaker reports personal fees from Briacell Therapeutics and Nanobiotix, as well as other support from Mesothelioma Applied Research Foundation outside the submitted work. C. Bernatchez reports personal fees from Myst Therapeutics and grants from Iovance Biotherapeutics outside the submitted work. C.M. Taniguchi reports grants from NIHR01CA227517-01, Reaumond Family Foundation, V Foundation, Mark Foundation, and Childress Family Foundation during the conduct of the study, as well as support from Xerient Pharmaceuticals outside the submitted work. A. Maitra reports other support from Cosmos Wisdom Biotechnology and Thrive Earlier Detection, an Exact Sciences Company outside the submitted work. No disclosures were reported by the other authors.

J.J. Lee: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. V. Bernard: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. A. Semaan: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. M.E. Monberg: Data curation, formal analysis, validation, investigation, visualization, methodology, writing–review and editing. J. Huang: Data curation, formal analysis, investigation, visualization, methodology, writing–review and editing. B.M. Stephens: Data curation, validation, investigation, methodology, writing–review and editing. D. Lin: Data curation, validation, investigation, methodology, writing–review and editing. K.I. Rajapakshe: Methodology. B.R. Weston: Conceptualization, resources, data curation, supervision, validation, investigation, writing–review and editing. M.S. Bhutani: Conceptualization, resources, data curation, supervision, validation, investigation, writing–review and editing. C.L. Haymaker: Data curation, formal analysis, validation, methodology, writing–review and editing. C. Bernatchez: Data curation, formal analysis, validation, methodology, writing–review and editing. C.M. Taniguchi: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. A. Maitra: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. P.A. Guerrero: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

We thank Mark W. Hurd (The University of Texas MD Anderson Cancer Center) for assistance with sample acquisition. A. Maitra is supported by the MD Anderson Pancreatic Cancer Moon Shot Program, the Khalifa Bin Zayed Al-Nahyan Foundation, and the NIH (U01CA196403, U01CA200468, P50CA221707). C.M. Taniguchi is supported by the NIH (R01CA227518-01A1), Cancer Prevention and Research Institute of Texas (CPRIT RR140012), Mark Foundation, V Foundation (V2015-22), Kimmel Foundation, McNair Foundation, and Reaumond Family Foundation. A. Semaan is supported by the German Research Foundation (SE-2616/2-1). V. Bernard is supported by the NIH (U54CA096300, U54CA096297, T32CA217789). J.J. Lee is supported by the NIH (T32CA009599).

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