Abstract
The high attrition rate of oncology drug candidates can be in part explained by the disconnect between the standard preclinical models (e.g., 2D culture, xenograft tumors) commonly employed for drug discovery and the complex multicellular microenvironment of human cancers. As such, significant focus has recently shifted to the establishment of preclinical models that more closely recapitulate human tumors, such as patient-derived xenografts, 3D spheroids, humanized mice, and mixed-culture models. For these models to be suited to drug discovery, they should optimally exhibit reproducibility, high-throughput, and robust and simple assay readouts. In this article, we describe a protocol for the generation of an in vitro 3D co-culture spheroid model that recapitulates the interaction of tumor cells with stromal fibroblasts in pancreatic adenocarcinoma. We additionally describe protocols relevant to the analysis of these spheroids in high-throughput drug discovery campaigns such as the assessment of spheroid proliferation, immunofluorescence and immunohistochemistry staining of spheroids, live-cell and confocal imaging and analysis of cell surface markers.
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References
Hutchinson L, Kirk R (2011) High drug attrition rates—where are we going wrong? Nat Rev Clin Oncol 8(4):189–190
Cook D, Brown D, Alexander R et al (2014) Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13(6):419–431
Pammolli F, Magazzini L, Riccaboni M (2011) The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 10(6):428–438
Paul SM, Mytelka DS, Dunwiddie CT et al (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214
Wilding JL, Bodmer WF (2014) Cancer cell lines for drug discovery and development. Cancer Res 74(9):2377–2384
Selby M, Delosh R, Laudeman J et al (2017) 3D models of the NCI60 cell lines for screening oncology compounds. SLAS Discov 22(5):473–483
Benien P, Swami A (2014) 3D tumor models: history, advances and future perspectives. Future Oncol 10(7):1311–1327
Byrne AT, Alferez DG, Amant F et al (2017) Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat Rev Cancer 17(4):254–268
Malaney P, Nicosia SV, Dave V (2014) One mouse, one patient paradigm: new avatars of personalized cancer therapy. Cancer Lett 344(1):1–12
Clevers H (2016) Modeling development and disease with organoids. Cell 165(7):1586–1597
Miller KD, Siegel RL, Lin CC et al (2016) Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin 66(4):271–289
Rahib L, Smith BD, Aizenberg R et al (2014) Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res 74(11):2913–2921
Hwang CI, Boj SF, Clevers H et al (2016) Preclinical models of pancreatic ductal adenocarcinoma. J Pathol 238(2):197–204
Heinemann V, Reni M, Ychou M et al (2014) Tumour-stroma interactions in pancreatic ductal adenocarcinoma: rationale and current evidence for new therapeutic strategies. Cancer Treat Rev 40(1):118–128
Amrutkar M, Gladhaug IP (2017) Pancreatic cancer chemoresistance to gemcitabine. Cancers 9(11):E157
Lakiotaki E, Sakellariou S, Evangelou K et al (2016) Vascular and ductal elastotic changes in pancreatic cancer. APMIS 124(3):181–187
Olive KP, Jacobetz MA, Davidson CJ et al (2009) Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 324(5933):1457–1461
Provenzano PP, Cuevas C, Chang AE et al (2012) Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell 21(3):418–429
Heid I, Steiger K, Trajkovic-Arsic M et al (2017) Co-clinical assessment of tumor cellularity in pancreatic cancer. Clin Cancer Res 23(6):1461–1470
Janes MR, Zhang J, Li LS et al (2018) Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor. Cell 172(3):578–589 e517
Hirt UA, Waizenegger IC, Schweifer N et al (2018) Efficacy of the highly selective focal adhesion kinase inhibitor BI 853520 in adenocarcinoma xenograft models is linked to a mesenchymal tumor phenotype. Oncogene 7(2):21
Luca AC, Mersch S, Deenen R et al (2013) Impact of the 3D microenvironment on phenotype, gene expression, and EGFR inhibition of colorectal cancer cell lines. PLoS One 8(3):e59689
Pampaloni F, Reynaud EG, Stelzer EH (2007) The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol 8(10):839–845
Tsunoda T, Takashima Y, Fujimoto T et al (2010) Three-dimensionally specific inhibition of DNA repair-related genes by activated KRAS in colon crypt model. Neoplasia 12(5):397–404
Zanoni M, Piccinini F, Arienti C et al (2016) 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained. Sci Rep 6:19103
Dhurjati R, Krishnan V, Shuman LA et al (2008) Metastatic breast cancer cells colonize and degrade three-dimensional osteoblastic tissue in vitro. Clin Exp Metastasis 25(7):741–752
Price KJ, Tsykin A, Giles KM et al (2012) Matrigel basement membrane matrix influences expression of microRNAs in cancer cell lines. Biochem Biophys Res Commun 427(2):343–348
Roife D, Dai B, Kang Y et al (2016) Ex vivo testing of patient-derived xenografts mirrors the clinical outcome of patients with pancreatic ductal adenocarcinoma. Clin Cancer Res 22(24):6021–6030
Ahonen I, Akerfelt M, Toriseva M et al (2017) A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues. Sci Rep 7(1):6600
Francone TD, Landmann RG, Chen CT et al (2007) Novel xenograft model expressing human hepatocyte growth factor shows ligand-dependent growth of c-Met-expressing tumors. Mol Cancer Ther 6(4):1460–1466
Finnberg NK, Gokare P, Lev A et al (2017) Application of 3D tumoroid systems to define immune and cytotoxic therapeutic responses based on tumoroid and tissue slice culture molecular signatures. Oncotarget 8(40):66747–66757
Koerfer J, Kallendrusch S, Merz F et al (2016) Organotypic slice cultures of human gastric and esophagogastric junction cancer. Cancer Med 5(7):1444–1453
Hirakawa T, Yashiro M, Doi Y et al (2016) Pancreatic fibroblasts stimulate the motility of pancreatic cancer cells through IGF1/IGF1R signaling under hypoxia. PLoS One 11(8):e0159912
Stockert JC, Horobin RW, Colombo LL et al (2018) Tetrazolium salts and formazan products in Cell Biology: viability assessment, fluorescence imaging, and labeling perspectives. Acta Histochem 120(3):159–167
Jacobi N, Seeboeck R, Hofmann E et al (2017) Organotypic three-dimensional cancer cell cultures mirror drug responses in vivo: lessons learned from the inhibition of EGFR signaling. Oncotarget 8(64):107423–107440
Murphy KC, Fang SY, Leach JK (2014) Human mesenchymal stem cell spheroids in fibrin hydrogels exhibit improved cell survival and potential for bone healing. Cell Tissue Res 357(1):91–99
Magidson V, Khodjakov A (2013) Circumventing photodamage in live-cell microscopy. Methods Cell Biol 114:545–560
Renier N, Wu Z, Simon DJ et al (2014) iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159(4):896–910
Costa EC, Moreira AF, de Melo-Diogo D et al (2018) Polyethylene glycol molecular weight influences the ClearT2 optical clearing method for spheroids imaging by confocal laser scanning microscopy. J Biomed Opt 23(5):1–11
Greenbaum A, Jang MJ, Challis C et al (2017) Q&A: How can advances in tissue clearing and optogenetics contribute to our understanding of normal and diseased biology. BMC Biol 15(1):87
Carpenter AE, Jones TR, Lamprecht MR et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10):R100
Verveer PJ, Swoger J, Pampaloni F et al (2007) High-resolution three-dimensional imaging of large specimens with light sheet-based microscopy. Nat Methods 4(4):311–313
Lorenzo C, Frongia C, Jorand R et al (2011) Live cell division dynamics monitoring in 3D large spheroid tumor models using light sheet microscopy. Cell Div 6:22
Kuen J, Darowski D, Kluge T et al (2017) Pancreatic cancer cell/fibroblast co-culture induces M2 like macrophages that influence therapeutic response in a 3D model. PLoS One 12(7):e0182039
Rodenhizer D, Gaude E, Cojocari D et al (2016) A three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients. Nat Mater 15(2):227–234
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Meier-Hubberten, J.C., Sanderson, M.P. (2019). Establishment and Analysis of a 3D Co-Culture Spheroid Model of Pancreatic Adenocarcinoma for Application in Drug Discovery. In: Moll, J., Carotta, S. (eds) Target Identification and Validation in Drug Discovery. Methods in Molecular Biology, vol 1953. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9145-7_11
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DOI: https://doi.org/10.1007/978-1-4939-9145-7_11
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