Skip to main content

Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma

  • Protocol
  • First Online:
Cancer Gene Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1513))

Abstract

We described here the manufacturing and implementation of two prototype perfusion culture devices designed primarily for the cultivation of difficult-to-preserve primary patient-derived multiple myeloma cells (MMC). The first device consists of an osteoblast (OSB)-derived 3D tissue scaffold constructed in a perfused microfluidic environment. The second platform is a 96-well plate-modified perfusion culture device that can be utilized to reconstruct several tissue and tumor microenvironments utilizing both primary human and murine cells. This culture device was designed and fabricated specifically to: (1) enable the preservation of primary MMC for downstream use in biological studies and chemosensitivity analyses and, (2) provide a high-throughput format that is compatible with plate readers specifically seeing that this system is built on an industry standard 96-well tissue culture plate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J (2014) Clinical development success rates for investigational drugs. Nat Biotechnol 32:40–51

    Article  CAS  PubMed  Google Scholar 

  2. 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 13:419–431

    Article  CAS  PubMed  Google Scholar 

  3. Li A, Walling J, Kotliarov Y et al (2008) Genomic changes and gene expression profiles reveal that established glioma cell lines are poorly representative of primary human gliomas. Mol Cancer Res 6:21–30

    Article  CAS  PubMed  Google Scholar 

  4. Domcke S, Sinha R, Levine DA et al (2013) Evaluating cell lines as tumour models by comparison of genomic profiles. Nat Commun 4:2126

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sandberg R, Ernberg I (2005) Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI). Proc Natl Acad Sci U S A 102:2052–2057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Aggeler J, Park CS, Bissell MJ (1988) Regulation of milk protein and basement membrane gene expression: the influence of the extracellular matrix. J Dairy Sci 71:2830–2842

    Article  CAS  PubMed  Google Scholar 

  7. Bhat R, Bissell MJ (2014) Of plasticity and specificity: dialectics of the micro- and macro-environment and the organ phenotype. Wiley Interdiscip Rev Membr Transp Signal 3:147–163

    CAS  PubMed  Google Scholar 

  8. Bissell MJ, Hall HG, Parry G (1982) How does the extracellular matrix direct gene expression? J Theor Biol 99:31–68

    Article  CAS  PubMed  Google Scholar 

  9. Zhang W, Gu Y, Sun Q et al (2015) Ex vivo maintenance of primary human multiple myeloma cells through the optimization of the osteoblastic niche. PLoS One 10:e0125995. doi:10.1371/journal.pone.0125995

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang W, Lee WY, Siegel DS et al (2014) Patient-specific 3D microfluidic tissue model for multiple myeloma. Tissue Eng Part C Methods 20:663–670

    Article  CAS  PubMed  Google Scholar 

  11. Sachs N, Clevers H (2014) Organoid cultures for the analysis of cancer phenotypes. Curr Opin Genet Dev 24:68–73

    Article  CAS  PubMed  Google Scholar 

  12. Buske P, Przybilla J, Loeffler M et al (2012) On the biomechanics of stem cell niche formation in the gut—modelling growing organoids. FEBS J 279:3475–3487

    Article  CAS  PubMed  Google Scholar 

  13. Gu Y, Zhang W, Qiaoling S et al (2015) Microbead-guided reconstruction of the 3D osteocyte network during microfluidic perfusion culture. J Mater Chem B 3:3625–3633

    Article  CAS  Google Scholar 

  14. Damiano JS (2002) Integrins as novel drug targets for overcoming innate drug resistance. Curr Cancer Drug Targets 2:37–43

    Article  CAS  PubMed  Google Scholar 

  15. Li ZW, Dalton WS (2006) Tumor microenvironment and drug resistance in hematologic malignancies. Blood Rev 20:333–342

    Article  PubMed  Google Scholar 

  16. Nakagawa Y, Nakayama H, Nagata M et al (2014) Overexpression of fibronectin confers cell adhesion-mediated drug resistance (CAM-DR) against 5-FU in oral squamous cell carcinoma cells. Int J Oncol 44:1376–1384

    CAS  PubMed  Google Scholar 

  17. Schmidmaier R, Baumann P (2008) ANTI-ADHESION evolves to a promising therapeutic concept in oncology. Curr Med Chem 15:978–990

    Article  CAS  PubMed  Google Scholar 

  18. Tolias P, Toruner GA (2014) Personalized medicine. Future Med 7:461–464

    Google Scholar 

  19. Bhatia SN, Ingber DE (2014) Microfluidic organs-on-chips. Nat Biotechnol 32:760–772

    Article  CAS  PubMed  Google Scholar 

  20. Benien P, Swami A (2014) 3D tumor models: history, advances and future perspectives. Future Oncol 10:1311–1327

    Article  CAS  PubMed  Google Scholar 

  21. Wang C, Tang Z, Zhao Y et al (2014) Three-dimensional in vitro cancer models: a short review. Biofabrication 6:022001

    Article  PubMed  Google Scholar 

  22. Hickman JA, Graeser R, de Hoogt R et al (2014) Three-dimensional models of cancer for pharmacology and cancer cell biology: capturing tumor complexity in vitro/ex vivo. Biotechnol J 9:1115–1128

    Article  CAS  PubMed  Google Scholar 

  23. Birmingham E, Kreipke TC, Dolan EB et al (2015) Mechanical stimulation of bone marrow in situ induces bone formation in trabecular explants. Ann Biomed Eng 43:1036–1050

    Article  CAS  PubMed  Google Scholar 

  24. Jaasma MJ, Plunkett NA, O'Brien FJ (2008) Design and validation of a dynamic flow perfusion bioreactor for use with compliant tissue engineering scaffolds. J Biotechnol 133:490–496

    Article  CAS  PubMed  Google Scholar 

  25. Plunkett N, O'Brien FJ (2011) Bioreactors in tissue engineering. Technol Health Care 19:55–69

    PubMed  Google Scholar 

  26. Ferrarini M, Steimberg N, Ponzoni M et al (2013) Ex-vivo dynamic 3-D culture of human tissues in the RCCS bioreactor allows the study of multiple myeloma biology and response to therapy. PLoS One 8:e71613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kwapiszewska K, Michalczuk A, Rybka M et al (2014) A microfluidic-based platform for tumour spheroid culture, monitoring and drug screening. Lab Chip 14:2096–2104

    Article  CAS  PubMed  Google Scholar 

  28. Lee JH, Gu Y, Wang H, Lee WY (2012) Microfluidic 3D bone tissue model for high-throughput evaluation of wound-healing and infection-preventing biomaterials. Biomaterials 33:999–1006

    Article  CAS  PubMed  Google Scholar 

  29. Lei KF, Wu MH, Hsu CW, Chen YD (2014) Real-time and non-invasive impedimetric monitoring of cell proliferation and chemosensitivity in a perfusion 3D cell culture microfluidic chip. Biosens Bioelectron 51:16–21

    Article  CAS  PubMed  Google Scholar 

  30. Polini A, Prodanov L, Bhise NS et al (2014) Organs-on-a-chip: a new tool for drug discovery. Expert Opin Drug Discovery 9:335–352

    Article  CAS  Google Scholar 

  31. Ocio EM, Richardson PG, Rajkumar SV et al (2014) New drugs and novel mechanisms of action in multiple myeloma in 2013: a report from the International Myeloma Working Group (IMWG). Leukemia 28:525–542

    Article  CAS  PubMed  Google Scholar 

  32. Lonial S, Anderson KC (2014) Association of response endpoints with survival outcomes in multiple myeloma. Leukemia 28:258–268

    Article  CAS  PubMed  Google Scholar 

  33. Neri P, Bahlis NJ (2012) Targeting of adhesion molecules as a therapeutic strategy in multiple myeloma. Curr Cancer Drug Targets 12:776–796

    Article  CAS  PubMed  Google Scholar 

  34. Neri P, Ren L, Azab AK et al (2011) Integrin beta7-mediated regulation of multiple myeloma cell adhesion, migration, and invasion. Blood 117:6202–6213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Damiano JS, Dalton WS (2000) Integrin-mediated drug resistance in multiple myeloma. Leuk Lymphoma 38:71–81

    CAS  PubMed  Google Scholar 

  36. Abdi J, Chen G, Chang H (2013) Drug resistance in multiple myeloma: latest findings and new concepts on molecular mechanisms. Oncotarget 4:2186–2207

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yaccoby S, Epstein J (1999) The proliferative potential of myeloma plasma cells manifest in the SCID-hu host. Blood 94:3576–3582

    CAS  PubMed  Google Scholar 

  38. Yaccoby S, Barlogie B, Epstein J (1998) Primary myeloma cells growing in SCID-hu mice: a model for studying the biology and treatment of myeloma and its manifestations. Blood 92:2908–2913

    CAS  PubMed  Google Scholar 

  39. Lawson MA, Paton-Hough JM, Evans HR et al (2015) NOD/SCID-GAMMA mice are an ideal strain to assess the efficacy of therapeutic agents used in the treatment of myeloma bone disease. PLoS One 10:e0119546

    Article  PubMed  PubMed Central  Google Scholar 

  40. Reagan MR, Mishima Y, Glavey SV et al (2014) Investigating osteogenic differentiation in multiple myeloma using a novel 3D bone marrow niche model. Blood 124:3250–3259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kirshner J, Thulien KJ, Martin LD et al (2008) A unique three-dimensional model for evaluating the impact of therapy on multiple myeloma. Blood 112:2935–2945

    Article  CAS  PubMed  Google Scholar 

  42. Zhang W, Gu Y, Hao Y et al (2015) Well plate-based perfusion culture device for tissue and tumor microenvironment replication. Lab Chip 15:2854–2863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yang X, Ogbolu KR, Wang H (2008) Multifunctional nanofibrous scaffold for tissue engineering. J Exp Nanosci 3:329–345

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Woo Y. Lee and Jenny Zilberberg are equal contributors in this work. We thank Dr. David Siegel at HUMC for providing MM patient biospecimens and Dr. Peter Tolias at Stevens for useful discussions. This work was supported in part by the Provost Office of the Stevens Institute of Technology (Stevens), the John Theurer Cancer Center at Hackensack University Medical Center (HackensackUMC), and the National Institutes of Health grants (1R21CA174543 to J.Z. and W.Y.L.).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jenny Zilberberg .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Cite this protocol

Zhang, W., Lee, W.Y., Zilberberg, J. (2017). Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma. In: Kasid, U., Clarke, R. (eds) Cancer Gene Networks. Methods in Molecular Biology, vol 1513. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6539-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6539-7_12

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6537-3

  • Online ISBN: 978-1-4939-6539-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics