Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All figures are associated with raw data. Raw images for MIBI and NanoString GeoMx experiments are publicly available on Science Data Bank (https://doi.org/10.57760/sciencedb.01706). Raw and processed sequencing data (single-nucleus RNA-seq) for patient-derived tumors and organoids, and NanoString GeoMx reads and count matrices have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE202051 and GSE199102, respectively. Raw snRNA-seq data are also available in the controlled access repository DUOS (https://duos.broadinstitute.org/) under dataset ID 000139. Processed snRNA-seq data are also available on the Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP1089 (untreated) and https://singlecell.broadinstitute.org/single_cell/study/SCP1096 (treated)
Code availability
All code has been made available in GitHub at https://github.com/karthikj89/humanpdac116 and https://github.com/whwanglab/PDAC117.
References
Versteijne, E. et al. Neoadjuvant chemoradiotherapy versus upfront surgery for resectable and borderline resectable pancreatic cancer: long-term results of the Dutch randomized PREOPANC trial. J. Clin. Oncol. 40, 1220–1230 (2022).
Mizrahi, J. D., Surana, R., Valle, J. W. & Schroff, R. T. Pancreatic cancer. Lancet 395, 2008–2020 (2020).
Collisson, E. A., Bailey, P., Chang, D. K. & Biankin, A. V. Molecular subtypes of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 16, 207–220 (2019).
Guo, J. A. et al. Refining the molecular framework for pancreatic cancer with single-cell and spatial technologies. Clin. Cancer Res. 27, 3825–3833 (2021).
Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).
Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).
Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).
Raphael, B. J. et al. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203 (2017).
Aung, K. L. et al. Genomics-driven precision medicine for advanced pancreatic cancer: early results from the COMPASS trial. Clin. Cancer Res. 24, 1344–1354 (2018).
Hosein, A. N., Brekken, R. A. & Maitra, A. Pancreatic cancer stroma: an update on therapeutic targeting strategies. Nat. Rev. Gastroenterol. Hepatol. 17, 487–505 (2020).
Murphy, J. E. et al. Total neoadjuvant therapy with FOLFIRINOX in combination with losartan followed by chemoradiotherapy for locally advanced pancreatic cancer: a phase 2 clinical trial. JAMA Oncol. 5, 1020–1027 (2019).
Liu, H. et al. Use of angiotensin system inhibitors is associated with immune activation and longer survival in nonmetastatic pancreatic ductal adenocarcinoma. Clin. Cancer Res. 23, 5959–5969 (2017).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).
Steele, N. G. et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nat. Cancer 1, 1097–1112 (2020).
Azevedo-Pouly, A. C. P. et al. RNA isolation from mouse pancreas: a ribonuclease-rich tissue. J. Vis. Exp. (90), e51779 (2014).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
Drokhlyansky, E. et al. The human and mouse enteric nervous system at single-cell resolution. Cell 182, 1606–1622 (2020).
Tosti, L. et al. Single-nucleus and in situ RNA-sequencing reveal cell topographies in the human pancreas. Gastroenterology 160, 1330–1344 (2021).
Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).
Ligorio, M. et al. Stromal microenvironment shapes the intratumoral architecture of pancreatic cancer. Cell 178, 160–175 (2019).
Väyrynen, S. A. et al. Composition, spatial characteristics, and prognostic significance of myeloid cell infiltration in pancreatic cancer. Clin. Cancer Res. 27, 1069–1081 (2021).
Liudahl, S. M. et al. Leukocyte heterogeneity in pancreatic ductal adenocarcinoma: phenotypic and spatial features associated with clinical outcome. Cancer Discov. 11, 2014–2031 (2021).
Grünwald, B. T. et al. Spatially confined sub-tumor microenvironments in pancreatic cancer. Cell 184, 1–16 (2021).
Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).
Li, Q. et al. Reg proteins promote acinar-to-ductal metaplasia and act as novel diagnostic and prognostic markers in pancreatic ductal adenocarcinoma. Oncotarget 7, 77838–77853 (2016).
Liu, X. et al. REG3A accelerates pancreatic cancer cell growth under IL-6-associated inflammatory condition: involvement of a REG3A-JAK2/STAT3 positive feedback loop. Cancer Lett. 362, 45–60 (2015).
Eberlein, J. et al. Chemokine signatures of pathogen-specific T cells I: effector T cells. J. Immunol. 205, 2169–2187 (2020).
Johnston, R. J. et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8+ T cell effector function. Cancer Cell 26, 923–937 (2014).
Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).
Carlson, C. M. et al. Kruppel-like factor 2 regulates thymocyte and T-cell migration. Nature 442, 299–302 (2006).
De La O, J. P. et al. Notch and Kras reprogram pancreatic acinar cells to ductal intraepithelial neoplasia. Proc. Natl Acad. Sci. USA 105, 18907–18912 (2008).
Guerra, C. et al. Chronic pancreatitis is essential for induction of pancreatic ductal adenocarcinoma by K-Ras oncogenes in adult mice. Cancer Cell 11, 291–302 (2007).
Morris, J. P.IV et al. β-Catenin blocks Kras-dependent reprogramming of acini into pancreatic cancer precursor lesions in mice. J. Clin. Invest. 120, 508–520 (2010).
Liberzon, A. et al. The molecular signatures database Hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Roa-Peña, L. et al. Keratin 17 identifies the most lethal molecular subtype of pancreatic cancer. Sci. Rep. 9, 11239 (2019).
Juiz, N. et al. Basal-like and classical cells coexist in pancreatic cancer revealed by single-cell analysis on biopsy-derived pancreatic cancer organoids from the classical subtype. FASEB J. 34, 12214–12228 (2020).
Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat. Genet. 52, 231–240 (2020).
Raghavan, S. et al. Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell 184, 6119–6137 (2021).
Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 9, 1102–1123 (2019).
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
Dardenne, E. et al. N-Myc induces an EZH2-mediated transcriptional program driving neuroendocrine prostate cancer. Cancer Cell 30, 563–577 (2016).
Qiu, H. et al. MEN1 deficiency leads to neuroendocrine differentiation of lung cancer and disrupts the DNA damage response. Nat. Commun. 11, 1009 (2020).
He, H. et al. Krüppel-like factor 4 promotes esophageal squamous cell carcinoma differentiation by up-regulating keratin 13 expression. J. Biol. Chem. 290, 13567–13577 (2015).
Rorke, E. A. et al. Structural and biochemical changes underlying a keratoderma-like phenotype in mice lacking suprabasal AP1 transcription factor function. Cell Death Dis. 6, e1647 (2015).
Champliaud, M. F. et al. Gene characterization of sciellin (SCEL) and protein localization in vertebrate epithelia displaying barrier properties. Genomics 70, 264–268 (2000).
Zhang, D. et al. Stem cell and neurogenic gene-expression profiles link prostate basal cells to aggressive prostate cancer. Nat. Commun. 7, 10798 (2016).
Karasawa, M. et al. Localization of metallothionein in hair follicles of normal skin and the basal cell layer of hyperplastic epidermis: possible association with cell proliferation. J. Invest. Dermatol. 97, 97–100 (1991).
Aiello, N. M. et al. EMT subtype influences epithelial plasticity and mode of cell migration. Dev. Cell 45, 681–695 (2018).
Chen, Y.-A., Lu, I.-L. & Tsai, J.-W. Contactin-1/F3 regulates neuronal migration and morphogenesis through modulating RhoA activity. Front. Mol. Neurosci. 11, 422 (2018).
Di Donato, V. et al. An attractive Reelin gradient establishes synaptic lamination in the vertebrate visual system. Neuron 97, 1049–1062 (2018).
Kantor, D. B. et al. Semaphorin 5A is a bifunctional axon guidance cue regulated by heparan and chondroitin sulfate proteoglycans. Neuron 44, 961–975 (2004).
Hori, K. & Hoshino, M. Neuronal migration and AUTS2 syndrome. Brain Sci. 7, 54 (2017).
Sakurai, T. The role of NrCAM in neural development and disorders: beyond a simple glue in the brain. Mol. Cell. Neurosci. 49, 351–363 (2012).
Lau, H. H., Ng, N. H. J., Loo, L. S. W., Jasmen, J. B. & Teo, A. K. K. The molecular functions of hepatocyte nuclear factors: in and beyond the liver. J. Hepatol. 68, 1033–1048 (2018).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556 (2017).
Shi, D. D. et al. Therapeutic avenues for cancer neuroscience: translational frontiers and clinical opportunities. Lancet Oncol. 23, e62–e74 (2022).
Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).
Guo, J. A. et al. Pan-cancer transcriptomic predictors of perineural invasion improve occult histopathologic detection. Clin. Cancer Res. 27, 2807–2815 (2021).
Farrell, A. S. et al. MYC regulates ductal-neuroendocrine lineage plasticity in pancreatic ductal adenocarcinoma associated with poor outcome and chemoresistance. Nat. Commun. 8, 1–12 (2017).
Komori, T. Runx2, a multifunctional transcription factor in skeletal development. J. Cell. Biochem. 87, 1–8 (2002).
Roel, G., Gent, Y. Y. J., Peterson-Maduro, J., Verbeek, F. J. & Destrée, O. Lef1 plays a role in patterning the mesoderm and ectoderm in Xenopus tropicalis. Int. J. Dev. Biol. 53, 81–89 (2009).
Tahara, N. et al. Sall4 regulates neuromesodermal progenitors and their descendants during body elongation in mouse embryos. Development 146, dev177659 (2019).
Andre, P., Song, H., Kim, W., Kispert, A. & Yang, Y. Wnt5a and Wnt11 regulate mammalian anterior-posterior axis elongation. Development 142, 1516–1527 (2015).
Cherubini, A. et al. FOXP1 circular RNA sustains mesenchymal stem cell identity via microRNA inhibition. Nucleic Acids Res. 47, 5325–5340 (2019).
Sleightholm, R. L. et al. Emerging roles of the CXCL12/CXCR4 axis in pancreatic cancer progression and therapy. Pharmacol. Ther. 179, 158–170 (2017).
Cheng, H. W. et al. CCL19-producing fibroblastic stromal cells restrain lung carcinoma growth by promoting local antitumor T-cell responses. J. Allergy Clin. Immunol. 142, 1257–1271 (2018).
Hirth, M. et al. CXCL10 and CCL21 promote migration of pancreatic cancer cells toward sensory neurons and neural remodeling in tumors in mice, associated with pain in patients. Gastroenterology 159, 665–681 (2020).
Boulakirba, S. et al. IL-34 and CSF-1 display an equivalent macrophage differentiation ability but a different polarization potential. Sci Rep. 8, 256 (2018).
Bettac, L., Denk, S., Seufferlein, T. & Huber-Lang, M. Complement in pancreatic disease: perpetrator or savior? Front. Immunol. 8, 15 (2017).
Peng, J. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 29, 725–738 (2019).
Maji, S. et al. Bcl-2 antiapoptotic family proteins and chemoresistance in cancer. Adv. Cancer Res. 137, 37–75 (2018).
Zhang, M. et al. Platelet-derived growth factor D is a prognostic biomarker and is associated with platinum resistance in epithelial ovarian cancer. Int. J. Gynecol. Cancer 28, 323–331 (2018).
Chen, X. et al. SPP1 inhibition improves the cisplatin chemo-sensitivity of cervical cancer cell lines. Cancer Chemother. Pharmacol. 83, 603–613 (2019).
Christie, E. L. et al. Multiple ABCB1 transcriptional fusions in drug resistant high-grade serous ovarian and breast cancer. Nat. Commun. 10, 1295 (2019).
Chauvet, S. et al. Gating of Sema3E/PlexinD1 signaling by neuropilin-1 switches axonal repulsion to attraction during brain development. Neuron 56, 807–822 (2007).
Wasser, C. R. & Herz, J. Reelin: neurodevelopmental architect and homeostatic regulator of excitatory synapses. J. Biol. Chem. 292, 1330–1338 (2017).
Denny, S. K. et al. Nfib promotes metastasis through a widespread increase in chromatin accessibility. Cell 166, 328–342 (2016).
Biasci, D. et al. CXCR4 inhibition in human pancreatic and colorectal cancers induces an integrated immune response. Proc. Natl Acad. Sci. USA 117, 28960–28970 (2020).
Fearon, D. T. & Janowitz, T. AMD3100/Plerixafor overcomes immune inhibition by the CXCL12-KRT19 coating on pancreatic and colorectal cancer cells. Br. J. Cancer 125, 149–151 (2021).
Skrypek, N. et al. The oncogenic receptor ErbB2 modulates gemcitabine and irinotecan/SN-38 chemoresistance of human pancreatic cancer cells via hCNT1 transporter and multidrug-resistance associated protein MRP-2. Oncotarget 6, 10853–10867 (2015).
Harder, J. et al. Multicentre phase II trial of trastuzumab and capecitabine in patients with HER2 overexpressing metastatic pancreatic cancer. Br. J. Cancer 106, 1033–1038 (2012).
Sjöstedt, E. et al. An atlas of the protein-coding genes in the human, pig, and mouse brain. Science 367, eaay5947 (2020).
VanDussen, K. L., Sonnek, N. M. & Stappenbeck, T. S. L-WRN conditioned medium for gastrointestinal epithelial stem cell culture shows replicable batch-to-batch activity levels across multiple research teams. Stem Cell Res. 37, 101430 (2019).
Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).
Freed-Pastor, W. A. et al. The CD155/TIGIT axis promotes and maintains immune evasion in neoantigen-expressing pancreatic cancer. Cancer Cell 39, 1342–1360 (2021).
de Man, F. et al. Individualization of irinotecan treatment: a review of pharmacokinetics, pharmacodynamics, and pharmacogenetics. Clin. Pharmacokinet. 57, 1229–1254 (2018).
& Conroy, T. et al. FOLFIRINOX or gemcitabine as adjuvant therapy for pancreatic cancer. N. Engl. J. Med. 379, 2395–2406 (2018).
Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/791699 (2019).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 9, 1–12 (2019).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426 (2018).
Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).
Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334 (2019).
Schupp, J. C. et al. Integrated single cell atlas of endothelial cells of the human lung. Circulation 144, 286–302 (2021).
Bernard, V. et al. Single-cell transcriptomics of pancreatic cancer precursors demonstrates epithelial and microenvironmental heterogeneity as an early event in neoplastic progression. Clin. Cancer Res. 25, 2194–2205 (2019).
Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).
Büttner, M., Ostner, J., Müller, C., Theis, F. & Schubert, B. scCODA: a Bayesian model for compositional single-cell data analysis. Preprint at bioRxiv https://doi.org/10.1101/2020.12.14.422688 (2020).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
Schapiro, D. et al. HistoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
Zanotelli, V. R. R. T. & Bodenmiller, B. ImcSegmentationPipeline: A Pixelclassification Based Multiplexed Image Segmentation Pipeline (Zenodo, 2017).
Zanotelli, V., Ndamond & Strotton, M. BodenmillerGroup/ImcSegmentationPipeline: IMC Segmentation Pipeline (Zenodo, 2020).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).
Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. Preprint at arXiv https://arxiv.org/abs/1905.02269 (2019).
Muus, C. et al. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nat. Med. 27, 546–559 (2021).
Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with. Elife 8, e43803 (2019).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinf. 12, 323 (2011).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013).
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Jagadeesh, K. (2022). karthikj89/humanpdac: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6496927
Shiau, C. & Yadollahpour, P. (2022). whwanglab/PDAC: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6496573
Acknowledgements
We are grateful to the patients and families who contributed their time and surgical specimens to this study. We thank L. Gaffney for assistance with preparing figures; E. Rueckert from NanoString for assistance with instrumentation; J. Ptacek from Ionpath for assistance with MIBI; and K. Yee, J. Teixeira, K. Anderson, M. Magendantz, K. Mercer, W. Rideout, B. Li, P. Westcott, C. McCabe, N. Sharif and J. Pfiffner-Borges for administrative and technical support. This work was supported in part by the Ludwig Institute for Cancer Research (A.R.), Klarman Cell Observatory (A.R. and R.X.), Lustgarten Foundation (T.J.), American Society for Clinical Oncology/Conquer Cancer Foundation Young Investigator Award (W.L.H), Hopper-Belmont Foundation Inspiration Award (W.L.H.), American Cancer Society/Massachusetts General Hospital Institutional Research Grant (W.L.H.), UCSF Dean’s Yearlong Fellowship (J.A.G.), Early Postdoc Mobility Fellowship (no. P2ZHP3 181475) from the Swiss National Science Foundation (D.S.), SU2C-Lustgarten Foundation (T.S.H. and D.T.T.) and the Robert L. Fine Cancer Research Foundation (D.T.T.). This study was also conducted with support of the Ontario Institute for Cancer Research (PanCuRx Translational Research Initiative) through funding provided by the Government of Ontario, the Wallace McCain Centre for Pancreatic Cancer supported by the Princess Margaret Cancer Foundation, the Terry Fox Research Institute, the Canadian Cancer Society Research Institute and the Pancreatic Cancer Canada Foundation. W.L.H. is an Andrew L. Warshaw, M.D. Institute for Pancreatic Cancer Research Fellow. D.S. is a Damon Runyon Cancer Research Fellow (DRQ-03-20). R.K.J. receives support from an NCI Cancer Moonshot grant (U01-CA224348) and the Ludwig Cancer Center at Harvard. T.J. and A.R. were investigators of the Howard Hughes Medical Institute (HHMI) during the time this work was performed but are no longer affiliated with HHMI. T.J. is David H. Koch Professor of Biology and a Daniel K. Ludwig Scholar. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Author information
Authors and Affiliations
Contributions
W.L.H., K.A.J., J.A.G., H.I.H., C.F.C., A.S.L., O.R.R., T.J. and A.R. developed the study concept and designed the experiments. W.L.H., E.D. and N.V.W. developed and optimized the snRNA-seq method for pancreatic cancer specimens. W.L.H., J.A.G., H.I.H., E.D., N.V.W., D.D., J.W., M.C., L.N., T.D. and D.P. performed snRNA-seq experiments, with guidance from O.R.R. K.A.J., W.L.H., J.A.G., H.I.H., C.S. and A.R. analyzed the snRNA-seq data with computational assistance from P.Y., R.M., O.A., J.G., C.L., C.P., A.T. and Z.E. and scientific insights from J.M.S., W.A.F-P., A.M., D.T.T., A.J.A. and R.K.J. W.L.H., G.E., J.A.G., H.I.H., C.R., D.C., J.R., P.Z., C.F.C. and A.S.L. acquired patient-derived tumor specimens with clinical input from M.M-K., A.J.A., T.S.H., J.Y.W., C.R.F., M.Q., D.T.T., C.D.W., D.P.R and J.S.L. W.L.H., C.F.C. and A.S.L. curated clinical data. K.A.J., W.L.H. and H.I.H. performed survival analyses on bulk RNA-seq data from TCGA and PanCuRx/ICGC with statistical assistance from D.S.N. S.L.F. and D.A. performed MIBI experiments, and D.S., P.Y., K.A.J., W.L.H., J.A.G. and H.I.H. analyzed the data. H.I.H., W.L.H., J.A.G., P.D., E.M., D.R.Z., K.F., J.M.B. and R.F. performed the DSP experiments with assistance from V.J. and J.H.C. P.Y., J.W.R., D.R.Z., W.L.H, H.I.H. and P.D. analyzed the DSP data with input from M.J.A. J.L.B., M.K. and M.M.-K. provided histological sections for MIBI and DSP experiments. M.M-K. performed histopathological analyses. J.S., W.L.H. and J.A.G. performed the multiplexed immunofluorescence. J.A.G., W.L.H., G.E., C.S. and P.Y. performed and analyzed the patient-derived organoid experiments. K.A.J., H.I.H., J.W.R., P.Y., J.A.G., C.S. and W.L.H. generated the tables and figures. Funding for the work was provided by A.R., T.J., R.X., D.T.T. and W.L.H. W.L.H., K.A.J., J.A.G., H.I.H., P.Y., J.W.R., D.T.T., T.J. and A.R. wrote the manuscript, and all authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
A.R. is a cofounder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. From 1 August 2020, A.R. is an employee of Genentech. T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific. He is also a cofounder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics. He is President of Break Through Cancer. None of these affiliations represent a conflict of interest with respect to the design or execution of this study or interpretation of data presented in this manuscript. T.J.’s laboratory also currently receives funding from Johnson & Johnson Lung Cancer Initiative, but this funding did not support the research described in this manuscript. D.T.T. has received consulting fees from NanoString Technologies, which was used in this work. D.T.T. has received consulting fees from ROME Therapeutics, Foundation Medicine, EMD Millipore Sigma and Pfizer that are not related to this work. D.T.T. is a founder and has equity in ROME Therapeutics, PanTher Therapeutics and TellBio, which is not related to this work. D.T.T. receives research support from ACD-Biotechne, PureTech Health and Ribon Therapeutics, which was not used in this work. M.M.-K. has served as a compensated consultant for H3 Biomedicine and AstraZeneca and received a research grant (to institution) from Novartis that is not related to this work. R.K.J. received consultant fees from Elpis, Pfizer, SPARC and SynDevRx; owns equity in Accurius, Enlight and SynDevRx; serves on the Board of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors and Tekla World Healthcare Fund; and received a Research Grant from Boehringer Ingelheim (all not related to this work). The interests of D.T.T., M.M.-K. and R.K.J. were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. O.R.R. is a co-inventor on patent applications filed by the Broad Institute for inventions related to single-cell genomics. O.R.R. is an employee of Genentech since 19 October 2020. W.L.H., K.A.J., J.A.G., H.I.H., T.J. and A.R. are co-inventors on U.S. Provisional Patent Application no. 63/313,596 (related to this work). All other authors declare no competing interests.
Peer review
Peer review information
Nature Genetics thanks Eric Collisson, Francisco X. Real, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Cell type and inferred CNA composition across PDAC tumors.
a, UMAP embeddings of single-nucleus profiles (dots) from individual tumors (panels) from untreated (left) and treated (right) patients colored by post hoc cell-type annotations (color legend). b, Example inferCNV analysis of the epithelial subset from a study specimen. Inferred amplifications (red) and deletions (blue) based on expression (color bar) of sliding 100-gene window in each chromosomal locus (columns) from each cell (rows) labeled by its annotated cell type (color code). c, Inferred CNA frequencies in the snRNA-seq cohort have similar distribution as those derived from TCGA genomic study1. Frequency (y axis) of CNAs on each chromosome arm (x axis) as inferred across the patients in the snRNA-seq cohort (light green bars) and from genome analysis of PDAC (dark green bars) from the TCGA cohort. d, Proportion of cells (y axis) in each of the four major compartments (color legend, top) or immune cell subsets (color legend, bottom) as estimated by snRNA-seq or MIBI (x axis) in each matched untreated (left; n = 5) or treated (right; n = 2) tumor.
Extended Data Fig. 2 Treatment associated with distinct cell type proportions across compartments.
Proportions (y axis) of cell types (x axis) in untreated (n = 18), CRT (n = 14), or CRTL (n = 5) tumors out of all nonmalignant cells (top left) or in specific nonmalignant cell compartments in the tumor. The boxes indicate upper and lower quartiles, with the horizontal lines marking the means. The lines extending vertically from the boxes (whiskers) indicate the maximum and minimum values excluding outliers. Data points are plotted as circles. * Bonferroni adjusted p < 0.05, two-sided Mann–Whitney U test. Exact p-values for significant comparisons were nonmalignant myeloid CRT-CRTL = 0.016344, epithelial (nonmalignant) ductal CRT-CRTL = 0.031683, lymphoid Treg untreated-CRT = 0.033156, immune CD8+ T CRT-CRTL = 0.03507, immune Treg untreated-CRT = 0.022686.
Extended Data Fig. 3 Impact of treatment on differential gene expression in immune cells, malignant cells, and CAFs.
a, Differential expression (β-value, x axis, Poisson mixed-effect linear regression model, lme4 R package) and its significance (-log10(adjusted p-value), y axis) for CD8+ T cells (top row), dendritic cells (second row), Tregs (third row) and macrophages (bottom row, color legend) in CRT vs. untreated (left), CRTL vs. untreated (middle), and CRTL vs. CRT (right) tumors. Selected enriched or depleted genes are labeled. Bonferroni adjusted p-value < 0.05 is indicated with a dotted horizontal line. b, Differential expression (β-value, x axis, Poisson mixed-effect linear regression model, lme4 R package) and its significance (-log10(adjusted p-value), y axis) for malignant cells (top row) and CAFs (bottom row, color legend) in CRT vs. untreated (left), CRTL vs. untreated (middle), and CRTL vs. CRT (right) tumors. Selected enriched or depleted genes are labeled. Bonferroni adjusted p-value < 0.05 is indicated with a dotted horizontal line.
Extended Data Fig. 4 Prior signatures derived primarily from the bulk setting insufficiently delineate cells from snRNA-seq.
a, Malignant cell signatures. UMAP embeddings of single nucleus profiles (dots) from all tumor nuclei (top panels) or only malignant cells (bottom panels) colored by expression score (color bar, Methods) of signatures derived from the Bailey2, Collisson3, Moffitt4, and Chan-Seng-Yue5 studies. b, CAF signatures. UMAP embeddings of single nucleus profiles (dots) from all fibroblast nuclei colored by normalized expression score (color bar, Methods) of myCAF, apCAF, and iCAF signatures6 and well as cross-tissue fibroblast lineage signatures (COL3A1+ myofibroblast, LRRC15+ myofibroblast, CCL19+ colitis, ADAMDEC1+ colitis, NPNT+ alveolar, and PI16+ adventitial)7.
Extended Data Fig. 5 Stability, error and power in selection of programs in consensus NMF.
a, Estimated stability (blue, left y axis) and error (red, right y axis) in the cNMF solution learned with different numbers of programs (k, x axis) for malignant cells (left) and CAFs (right). b, Number of malignant (out of 14; left) and CAF (out of 4; right) programs recovered in the cNMF solution learned with a different proportion of samples (x axis) subsampled from our cohort.
Extended Data Fig. 6 Correlation among malignant cell or CAF expression programs.
Correlation (color bar) among expression scores of malignant state and lineage programs across all malignant nuclei (a) or fibroblast programs across all fibroblast nuclei (b).
Extended Data Fig. 7 Enrichment of malignant cell and CAF programs in genes differentially expressed with treatment regimen.
Fold enrichment of overlap (x axis) between gene program signatures (top 200 genes; rows) and genes differentially expressed (q < 0.05) in CRT (n = 14) vs. untreated (n = 18) (left), CRTL (n = 5) vs. untreated (middle), or CRTL vs. CRT (right). * Bonferroni adjusted p < 0.05, two-sided hypergeometric test. Exact p-values for significant comparisons were untreated-CRT: CYS-untreated = 8.54 × 10−6, CYS-CRT = 7.36 × 10−4, CYG-untreated = 9.53 × 10−7, CYG-CRT = 5.96 × 10−4, MYC-CRT = 3.72 × 10−2, ADH-M-CRT = 2.95 × 10−2, RBS-untreated = 2.25 × 10−2, TNF-CRT = 5.61 × 10−3, ACN-untreated = 2.87 × 10−2, ACN-CRT = 1.51 × 10−6, CLS-untreated = 4.77 × 10−5, CLS-CRT = 3.72 × 10−2, BSL-untreated = 2.44 × 10−2, BSL-CRT = 2.65 × 10−5, SQM-CRT = 5.10 × 10−3, MES-untreated = 2.16 × 10−3, MES-CRT = 1.09 × 10−4, NEN-untreated = 4.83 × 10−3, NRP-untreated = 2.58×10−3, NRP-CRT = 6.06 × 10−10, ADH-F-untreated = 1.03 × 10−8, ADH-F-CRT = 3.68 × 10−43, IMM-untreated = 6.12 × 10−6, IMM-CRT = 5.24 × 10−8, MYO-untreated = 3.66 × 10−72, MYO-CRT = 5.60 × 10−4, NRT-untreated = 4.56 × 10−4, NRT-CRT = 5.60 × 10−7; untreated-CRTL: CYS-untreated = 9.45 × 10−19, CYS-CRT = 4.91 × 10−3, CYG-untreated = 1.04 × 10−13, ADH-M-CRT = 1.10 × 10−21, RBS-untreated = 7.60 × 10−3, RBS-CRT = 4.91 × 10−3, IFN-CRT = 4.49 × 10−2, ACN-untreated = 9.62 × 10−3, ACN-CRT = 4.14 × 10−6, SQM-untreated = 2.65 × 10−16, MES-CRT = 9.59 × 10−6, NEN-untreated = 1.43 × 10−2, NRP-untreated = 6.79 × 10−3, NRP-CRT = 2.74 × 10−20, ADH-F-untreated = 7.60 × 10−21, ADH-F-CRT = 1.54 × 10−151, IMM-untreated = 2.58 × 10−9, MYO-untreated = 4.80 × 10−6, MYO-CRT = 2.09 × 10−5, NRT-untreated = 2.57 × 10−10; CRT-CRTL: MYC-CRT = 3.07 × 10−2, ADH-M-CRTL = 4.17 × 10−8, IFN-CRTL = 1.99 × 10−2, ADH-F-CRT = 7.16 × 10−7, ADH-F-CRTL = 5.88 × 10−60, IMM-CRTL = 2.56 × 10−2.
Extended Data Fig. 8 Multivariable Cox regression analysis for overall survival in TCGA and PanCuRx/ICGC PDAC cohorts.
Hazard ratios ± 95% confidence interval (middle) and p-values (right) for each variable (clinicopathologic and program expression score in bulk RNA-seq, rows) in multivariable Cox regression model for overall survival (OS), based on a cohort of 269 patients with untreated, resected primary PDAC profiled by RNA-seq in TCGA and PanCuRx/ICGC.
Extended Data Fig. 9 Digital spatial profiling with whole-transcriptome atlas (WTA) enables accurate mapping of cell type signatures in space as a complement to snRNA-seq.
a, Immunofluorescence images of FFPE sections from all PDAC specimens analyzed using whole-transcriptome DSP (n = 21 independent tumors) separated by treatment status (top, untreated; bottom, treated). Color legend indicates target of fluorophore-conjugated antibodies. b, Expression (z-score of normalized counts across segments; purple/yellow color bar) of signature genes (rows) from different cell types (color legend 3 and left color bar 3) across segments (columns, color legend 2 and horizontal color bar 2) and treatment regimens (columns, grayscale legend 1 and horizontal grayscale bar 1) profiled by WTA, capturing epithelial (green), fibroblasts (blue) and immune (red) cells. Columns and rows are clustered by unsupervised hierarchical clustering. c, Pearson correlation coefficient (color bar) of the scores of each CAF, malignant, and immune feature in snRNA-seq (rows, columns) across patient tumors. Rows and columns are ordered by hierarchical clustering.
Extended Data Fig. 10 snRNA-seq captures a greater diversity and abundance of cell types relative to prior single-cell approaches.
Number of nuclei/cells per untreated tumor that passed quality control filters (y axis) in our study (n = 18) vs. Peng et al. study (n = 24)69 (grayscale legend), in total (left) and partitioned by cell type (right). The boxes indicate upper and lower quartiles, with the horizontal lines marking the means. The lines extending vertically from the boxes (whiskers) indicate the maximum and minimum values excluding outliers. Data points are plotted as solid circles. *Bonferroni adjusted p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, two-sided Mann–Whitney U test. Exact p-values for significant comparisons were all cell types = 5.76 × 10−9, lymphoid = 5.54 × 10−5, myeloid = 7.25 × 10−3, CAF = 3.70 × 10−3, pericyte = 5.22 × 10−11, vascular smooth muscle = 1.09 × 10−9, endocrine = 1.84 × 10−2, endothelial = 4.00 × 10−2.
Supplementary information
Supplementary Information
Supplementary Note
Supplementary Table 1
Supplementary Tables 1–8 (in separate tabs).
Rights and permissions
About this article
Cite this article
Hwang, W.L., Jagadeesh, K.A., Guo, J.A. et al. Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat Genet 54, 1178–1191 (2022). https://doi.org/10.1038/s41588-022-01134-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-022-01134-8
This article is cited by
-
Tumor immune microenvironment-based therapies in pancreatic ductal adenocarcinoma: time to update the concept
Journal of Experimental & Clinical Cancer Research (2024)
-
Gene panel selection for targeted spatial transcriptomics
Genome Biology (2024)
-
The impacts of active and self-supervised learning on efficient annotation of single-cell expression data
Nature Communications (2024)
-
Spatial genomics: mapping human steatotic liver disease
Nature Reviews Gastroenterology & Hepatology (2024)
-
HMGA2 alleviates ferroptosis by promoting GPX4 expression in pancreatic cancer cells
Cell Death & Disease (2024)
