Egeblad, M., Nakasone, E. S. & Werb, Z. Tumors as organs: complex tissues that interface with the entire organism. Dev. Cell 18, 884–901 (2010).
Google Scholar
Feig, C. et al. The pancreas cancer microenvironment. Clin. Cancer Res. 18, 4266–4276 (2012).
Google Scholar
Sahai, E. et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 6, 174–186 (2020).
Google Scholar
DeNardo, D. G. & Ruffell, B. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 7, 369–382 (2019).
Google Scholar
Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).
Google Scholar
Miller, B. W. et al. Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy. EMBO Mol. Med. 7, 1063–1076 (2015).
Google Scholar
Jiang, H. et al. Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat. Med. 22, 851–860 (2016).
Shi, Y. et al. Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring. Nature 569, 131–135 (2019).
Google Scholar
Sherman, M. H. et al. Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell 159, 80–93 (2014).
Google Scholar
Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).
Google Scholar
Tuveson, D. & Clevers, H. Cancer modeling meets human organoid technology. Science 364, 952–955 (2019).
Google Scholar
Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).
Google Scholar
Hughes, C. S., Postovit, L. M. & Lajoie, G. A. Matrigel: a complex protein mixture required for optimal growth of cell culture. Proteomics 10, 1886–1890 (2010).
Google Scholar
Brassard, J. A. & Lutolf, M. P. Engineering stem cell self-organization to build better organoids. Stem Cell 24, 860–876 (2019).
Google Scholar
Socovich, A. M. & Naba, A. The cancer matrisome: from comprehensive characterization to biomarker discovery. Semin. Cell Dev. Biol. 89, 157–166 (2019).
Google Scholar
Cook, C. D. et al. Local remodeling of synthetic extracellular matrix microenvironments by co-cultured endometrial epithelial and stromal cells enables long-term dynamic physiological function. Integr. Biol. 9, 271–289 (2017).
Google Scholar
Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560–564 (2016).
Google Scholar
Kratochvil, M. J. et al. Engineered materials for organoid systems. Nat. Rev. Mater. 4, 606–622 (2019).
Google Scholar
Valdez, J. et al. On-demand dissolution of modular, synthetic extracellular matrix reveals local epithelial-stromal communication networks. Biomaterials 130, 90–103 (2017).
Google Scholar
Naba, A., Clauser, K. R. & Hynes, R. O. Enrichment of extracellular matrix proteins from tissues and digestion into peptides for mass spectrometry analysis. J. Vis. Exp. 101, e53057 (2015).
Hingorani, S. R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).
Google Scholar
Hruban, R. H. et al. Pathology of genetically engineered mouse models of pancreatic exocrine cancer: consensus report and recommendations. Cancer Res. 66, 95–196 (2006).
Google Scholar
Schönhuber, N. et al. A next-generation dual-recombinase system for time- and host-specific targeting of pancreatic cancer. Nat. Med. 20, 1340–1347 (2014).
Google Scholar
Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteom. 11, M111.014647 (2012).
Google Scholar
Tian, C. et al. Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells. Proc. Natl Acad. Sci. USA 116, 19609–19618 (2019).
Google Scholar
Horton, E. R. et al. Definition of a consensus integrin adhesome and its dynamics during adhesion complex assembly and disassembly. Nat. Cell Biol. 17, 1577–1587 (2015).
Google Scholar
Humphries, J. D. Integrin ligands at a glance. J. Cell. Sci. 119, 3901–3903 (2006).
Google Scholar
Hamidi, H. & Ivaska, J. Every step of the way: integrins in cancer progression and metastasis. Nat. Rev. Cancer 18, 533–548 (2018).
Google Scholar
Jones, M. C. et al. Isolation of integrin-based adhesion complexes. Curr. Protoc. Cell Biol. 66, 9.8.1–9.8.15 (2015).
Google Scholar
Robertson, J. et al. Defining the phospho-adhesome through the phosphoproteomic analysis of integrin signalling. Nat. Commun. 6, 6265 (2015).
Google Scholar
Takizawa, M. et al. Mechanistic basis for the recognition of laminin-511 by α6β1 integrin. Sci. Adv. 3, e1701497 (2017).
Google Scholar
Samarelli, A. V. et al. Neuroligin 1 induces blood vessel maturation by cooperating with the α6 integrin. J. Biol. Chem. 289, 19466–19476 (2014).
Google Scholar
Aumailley, M., Timpl, R. & Sonnenberg, A. Antibody to integrin α6 subunit specifically inhibits cell-binding to laminin fragment 8. Exp. Cell Res. 188, 55–60 (1990).
Google Scholar
Lee, S. P. et al. Sickle cell adhesion to laminin: potential role for the α5 chain. Blood 92, 2951–2958 (1998).
Google Scholar
Hernandez-Gordillo, V. et al. Fully synthetic matrices for in vitro culture of primary human intestinal enteroids and endometrial organoids. Biomaterials 254, 120125 (2020).
Google Scholar
Johnson, G. & Moore, S. W. Identification of a structural site on acetylcholinesterase that promotes neurite outgrowth and binds laminin-1 and collagen IV. Biochem. Biophys. Res. Commun. 319, 448–455 (2004).
Google Scholar
Brown, A. et al. Engineering PEG-based hydrogels to foster efficient endothelial network formation in free-swelling and confined microenvironments. Biomaterials 243, 119921 (2020).
Google Scholar
Knight, C. G. et al. The collagen-binding A-domains of integrins α1β1 and α2β1 recognize the same specific amino acid sequence, GFOGER, in native (triple-helical) collagens. J. Biol. Chem. 275, 35–40 (2000).
Google Scholar
Kuhlman, W., Taniguchi, I., Griffith, L. G. & Mayes, A. M. Interplay between PEO tether length and ligand spacing governs cell spreading on RGD-modified PMMA-g-PEO comb copolymers. Biomacromolecules 8, 3206–3213 (2007).
Google Scholar
Eble, J. A., Bruckner, P. & Mayer, U. Vipera lebetina venom contains two disintegrins inhibiting laminin-binding β1 integrins. J. Biol. Chem. 278, 26488–26496 (2003).
Google Scholar
Cavaco, A. C. M. et al. The interaction between laminin-332 and α3β1 integrin determines differentiation and maintenance of CAFs, and supports invasion of pancreatic duct adenocarcinoma cells. Cancers 11, 14–20 (2019).
Google Scholar
Gasmi, A. et al. Amino acid structure and characterization of a heterodimeric disintegrin from Vipera lebetina venom. Biochim. Biophys. Acta 1547, 51–56 (2001).
Google Scholar
Arruda Macêdo, J. K., Fox, J. W. & de Souza Castro, M. Disintegrins from snake venoms and their applications in cancer research and therapy. Curr. Protein Pept. Sci. 16, 532–548 (2015).
Google Scholar
Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).
Google Scholar
Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).
Google Scholar
Laklai, H. et al. Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat. Med. 22, 497–505 (2016).
Google Scholar
Rice, A. J. et al. Matrix stiffness induces epithelial–mesenchymal transition and promotes chemoresistance in pancreatic cancer cells. Oncogenesis 6, e352 (2019).
Google Scholar
Rubiano, A. et al. Viscoelastic properties of human pancreatic tumors and in vitro constructs to mimic mechanical properties. Acta Biomaterialia 67, 331–340 (2018).
Google Scholar
Panciera, T. et al. Reprogramming normal cells into tumour precursors requires ECM stiffness and oncogene-mediated changes of cell mechanical properties. Nat. Mater. 19, 797–806 (2020).
Google Scholar
Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).
Google Scholar
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).
Google Scholar
Wu, J. et al. Generation of a pancreatic cancer model using a Pdx1-Flp recombinase knock-in allele. PLoS ONE 12, e0184984 (2017).
Google Scholar
Ouyang, H. et al. Immortal human pancreatic duct epithelial cell lines with near normal genotype and phenotype. Am. J. Pathol. 157, 1623–1631 (2010).
Google Scholar
Furukawa, T. et al. Long-term culture and immortalization of epithelial cells from normal adult human pancreatic ducts transfected by the E6E7 gene of human papilloma virus 16. Am. J. Pathol. 148, 1763–1770 (1996).
Google Scholar
Huch, M. et al. Unlimited in vitro expansion of adult bi-potent pancreas progenitors through the Lgr5/R-spondin axis. EMBO J. 32, 2708–2721 (2013).
Google Scholar
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Google Scholar
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCt method. Methods 25, 402–408 (2001).
Google Scholar
Qin, X. et al. Cell-type-specific signaling networks in heterocellular organoids. Nat. Methods 17, 335–342 (2020).
Google Scholar
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
Google Scholar
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Google Scholar
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).
Google Scholar
Naba, A. et al. The extracellular matrix: tools and insights for the ‘omics’ era. Matrix Biol. 49, 10–24 (2016).
Google Scholar
Bult, C. J. et al. Mouse genome database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).
Google Scholar
Zhang, H., Meltzer, P. & Davis, S. RCircos: an R package for Circos 2D track plots. BMC Bioinformatics 14, 244–245 (2013).
Google Scholar
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Google Scholar
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Google Scholar
Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).
Google Scholar
Pérez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).
Google Scholar
