Skip to main content

Advertisement

SpringerLink
Log in

Chemoprotection Across the Tumor Border: Cancer Cell Response to Doxorubicin Depends on Stromal Fibroblast Ratios and Interstitial Therapeutic Transport

  • Published:
Cellular and Molecular Bioengineering Aims and scope Submit manuscript

Abstract

Introduction

Increasing evidence suggests that the tumor microenvironment reduces therapeutic delivery and may lead to chemotherapeutic resistance. At tumor borders, drug is convectively transported across a unique microenvironment composed of inverse gradients of stromal and tumor cells. These regions are particularly important to overall survival, as they are often missed through surgical intervention and contain many invading cells, often responsible for metastatic spread. An understanding of how cells in this tumor-border region respond to chemotherapy could begin to elucidate the role of transport and intercellular interactions in relation to chemoresistance. Here we examine the contribution of drug transport and stromal fibroblasts to breast cancer response to doxorubicin using in silico and in vitro models of the tumor-stroma interface.

Methods

2D culture systems were utilized to determine the effects of modulated ratios of fibroblasts and cancer cells on overall cancer cell viability. A homogenous breast mimetic in vitro 3D collagen I-based hydrogel system, with drug delivered via pressure driven flow (0.5 µm/s), was developed to determine the effects of transport and fibroblasts on doxorubicin treatment efficacy. Using a novel layered tumor bulk-to-stroma transition in vitro 3D hydrogel model, ratios of MDA-MB-231s and fibroblasts were seeded in successive layers creating cellular gradients, yielding insight into region specific cancer cell viability at the tumor border. In silico models, utilizing concentration profiles developed in COMSOL Multiphysics, were optimized for time dependent viability prediction and confirmation of in vitro findings.

Results

In general, the addition of fibroblasts increased viability of cancer cells exposed to doxorubicin, indicating a protective effect of co-culture. More specifically, however, modulating ratios of cancer cells (MDA-MB-231):fibroblasts in 2D co-cultures, to mimic the tumor-stroma transition, resulted in a linear decrease in cancer cell viability from 77% (4:1) to 44% (1:4). Similar trends were seen in the breast-mimetic in vitro 3D collagen I-based homogenous hydrogel system. Our in vitro and in silico tumor border models indicate that MDA-MB-231s at the top of the gel, indicative of the tumor bulk, receive the greatest concentration of drug for the longest time, yet cellular death is lowest in this region. This trend is reversed for MDA-MB-231s alone.

Conclusion

Together, our data indicate that fibroblasts are chemoprotective at lower density, resulting in less tumor death in regions of higher chemotherapy concentration. Additionally, chemotherapeutic agent transport properties can modulate this effect.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Abbreviations

MDA-MB-231:

Human breast triple negative adenocarcinoma cell line (luminal)

HCC38:

Human breast triple negative invasive ductal carcinoma cell line (luminal)

MCF7:

Human breast ER+/PR+ adenocarcinoma cell line (basal)

HDF:

Human dermal fibroblast

TC:

Tumor cell

Fb:

Fibroblast

ABM:

Agent-based model

TME:

Tumor microenvironment

TSTM:

Tumor to stroma transition model

IFP:

Interstitial fluid pressure

DOX:

Doxorubicin

References

  1. Bandyopadhyay, A., et al. Doxorubicin in combination with a small TGFβ inhibitor: a potential novel therapy for metastatic breast cancer in mouse models. PLoS One 5:10365, 2010.

    Article  Google Scholar 

  2. Brennen, W. N., D. M. Rosen, H. Wang, J. T. Isaacs, and S. R. Denmeade. Targeting carcinoma-associated fibroblasts within the tumor stroma with a fibroblast activation protein-activated prodrug. JNCI J. Natl. Cancer Inst. 104:1320–1334, 2012.

    Article  Google Scholar 

  3. Carey, L. A., et al. The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin. Cancer Res. 13(8):2329–2334, 2007.

    Article  Google Scholar 

  4. Chen, A. A., G. H. Underhill, and S. N. Bhatia. Multiplexed, high-throughput analysis of 3D microtissue suspensions. Integr. Biol. 2:517, 2010.

    Article  Google Scholar 

  5. Conze, D., et al. Autocrine production of interleukin 6 causes multidrug resistance in breast cancer cells. Cancer Res. 61(24):8851–8858, 2001.

    Google Scholar 

  6. Dean, M., T. Fojo, and S. Bates. Tumour stem cells and drug resistance. Nat. Rev. Cancer 5:275–284, 2005.

    Article  Google Scholar 

  7. Deisboeck, T. S., L. Zhang, J. Yoon, and J. Costa. In silico cancer modeling: is it ready for prime time? Nat. Clin. Pract. Oncol. 6:34–42, 2009.

    Article  Google Scholar 

  8. Dekker, T. J. A., et al. Prognostic significance of the tumor-stroma ratio: validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854). Breast Cancer Res. Treat. 139:371–379, 2013.

    Article  Google Scholar 

  9. Falkenberg, N., et al. Three-dimensional microtissues essentially contribute to preclinical validations of therapeutic targets in breast cancer. Cancer Med. 5:703–710, 2016.

    Article  Google Scholar 

  10. Fang, W. B., M. Yao, and N. Cheng. Priming cancer cells for drug resistance: role of the fibroblast niche. Front. Biol. 9:114–126, 2014.

    Article  Google Scholar 

  11. Farmer, P., et al. A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat. Med. 15:68–74, 2009.

    Article  Google Scholar 

  12. Friedl, P., and S. Alexander. Cancer invasion and the microenvironment: plasticity and reciprocity. Cell 147:992–1009, 2011.

    Article  Google Scholar 

  13. Fukuda, J., et al. Micropatterned cell co-cultures using layer-by-layer deposition of extracellular matrix components. Biomaterials 27:1479–1486, 2006.

    Article  Google Scholar 

  14. Gao, M.-Q., et al. Stromal fibroblasts from the interface zone of human breast carcinomas induce an epithelial-mesenchymal transition-like state in breast cancer cells in vitro. J. Cell Sci. 123:3507–3514, 2010.

    Article  Google Scholar 

  15. Gattazzo, F., A. Urciuolo, and P. Bonaldo. Extracellular matrix: a dynamic microenvironment for stem cell niche. Biochim. Biophys. Acta Gen. Subj. 2506–2519:2014, 1840.

    Google Scholar 

  16. Gottesman, M. M., T. Fojo, and S. E. Bates. Multidrug resistance in cancer: role of ATP-dependent transporters. Nat. Rev. Cancer 2:48–58, 2002.

    Article  Google Scholar 

  17. Griffith, L. G., and M. A. Swartz. Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7:211–224, 2006.

    Article  Google Scholar 

  18. Gritsenko, P. G., O. Ilina, and P. Friedl. Interstitial guidance of cancer invasion. J. Pathol. 226:185–199, 2012.

    Article  Google Scholar 

  19. Haessler, U., J. C. M. Teo, D. Foretay, P. Renaud, and M. A. Swartz. Migration dynamics of breast cancer cells in a tunable 3D interstitial flow chamber. Integr. Biol. 4:401, 2012.

    Article  Google Scholar 

  20. Hazlehurst, L. A., J. S. Damiano, I. Buyuksal, W. J. Pledger, and W. S. Dalton. Adhesion to fibronectin via beta1 integrins regulates p27kip1 levels and contributes to cell adhesion mediated drug resistance (CAM-DR). Oncogene 19:4319–4327, 2000.

    Article  Google Scholar 

  21. Heldin, C.-H., K. Rubin, K. Pietras, and A. Ostman. High interstitial fluid pressure—an obstacle in cancer therapy. Nat. Rev. Cancer 4:806–813, 2004.

    Article  Google Scholar 

  22. Hennequin, E., C. Delvincourt, C. Pourny, and J. C. Jardillier. Expression of MDR1 gene in human breast primary tumors and metastases. Breast Cancer Res. Treat. 26:267–274, 1993.

    Article  Google Scholar 

  23. Huber, S., et al. Breast tumors: computer-assisted quantitative assessment with color Doppler US. Radiology 192:797–801, 1994.

    Article  Google Scholar 

  24. Jain, R. K. Transport of molecules in the tumor interstitium: a review. Cancer Res. 47:3039–3051, 1987.

    Google Scholar 

  25. Jain, R. K. Transport of molecules, particles, and cells in solid tumors. Annu. Rev. Biomed. Eng. 1:241–263, 1999.

    Article  Google Scholar 

  26. Jain, R. K., and L. T. Baxter. Mechanisms of heterogeneous distribution of monoclonal antibodies and other macromolecules in tumors: significance of elevated interstitial pressure. Cancer Res. 48:7022–7032, 1988.

    Google Scholar 

  27. Jain, R. K., R. T. Tong, and L. L. Munn. Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis: insights from a mathematical model. Cancer Res. 67:2729–2735, 2007.

    Article  Google Scholar 

  28. Jang, S. H., M. G. Wientjes, D. Lu, and J. L. S. Au. Drug delivery and transport to solid tumors. Pharm. Res. 20:1337–1350, 2003.

    Article  Google Scholar 

  29. Kalluri, R., and M. Zeisberg. Fibroblasts in cancer. Nat. Rev. Cancer 6:392–401, 2006.

    Article  Google Scholar 

  30. Kingsmore, K. M., et al. Interstitial flow differentially increases patient-derived glioblastoma stem cell invasion via CXCR4, CXCL12, and CD44-mediated mechanisms. Integr. Biol. 8(12):1246–1260, 2016.

    Article  Google Scholar 

  31. Knowlton, S., S. Onal, C. H. Yu, J. J. Zhao, and S. Tasoglu. Bioprinting for cancer research. Trends Biotechnol. 33:504–513, 2015.

    Article  Google Scholar 

  32. Lankelma, J., et al. Doxorubicin gradients in human breast cancer. Clin. Cancer Res. 5:1703–1707, 1999.

    Google Scholar 

  33. LeBedis, C., K. Chen, L. Fallavollita, T. Boutros, and P. Brodt. Peripheral lymph node stromal cells can promote growth and tumorigenicity of breast carcinoma cells through the release of IGF-I and EGF. Int. J. Cancer 100:2–8, 2002.

    Article  Google Scholar 

  34. Liu, W., et al. Magnetically controllable 3D microtissues based on magnetic microcryogels. Lab Chip 14:2614, 2014.

    Article  Google Scholar 

  35. Ljungkvist, A. S. E., J. Bussink, P. F. J. W. Rijken, J. H. A. M. Kaanders, A. J. Van der Kogel, and J. Denekamp. Vascular architecture, hypoxia, and proliferation in first-generation xenografts of human head-and-neck squamous cell carcinomas. Int. J. Radiat. Oncol. Biol. Phys. 54:215–228, 2002.

    Article  Google Scholar 

  36. Loeffler, M., J. A. Krüger, A. G. Niethammer, and R. A. Reisfeld. Targeting tumor-associated fibroblasts improves cancer chemotherapy by increasing intratumoral drug uptake. J. Clin. Invest. 116:1955–1962, 2006.

    Article  Google Scholar 

  37. Loessner, D., K. S. Stok, M. P. Lutolf, D. W. Hutmacher, J. A. Clements, and S. C. Rizzi. Bioengineered 3D platform to explore cell–ECM interactions and drug resistance of epithelial ovarian cancer cells. Biomaterials 31:8494–8506, 2010.

    Article  Google Scholar 

  38. Lotti, F., et al. Chemotherapy activates cancer-associated fibroblasts to maintain colorectal cancer-initiating cells by IL-17A. J. Exp. Med. 210(13):2851–2872, 2013.

    Article  Google Scholar 

  39. Martin, K. S., S. S. Blemker, and S. M. Peirce. Agent-based computational model investigates muscle-specific responses to disuse-induced atrophy. J. Appl. Physiol. 118:1299–1309, 2015.

    Article  Google Scholar 

  40. Marusyk, A., et al. Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes. Cancer Res. 76:6495–6506, 2016.

    Article  Google Scholar 

  41. Moorman, A. M., R. Vink, H. J. Heijmans, J. van der Palen, and E. A. Kouwenhoven. The prognostic value of tumour-stroma ratio in triple-negative breast cancer. Eur. J. Surg. Oncol. 38:307–313, 2012.

    Article  Google Scholar 

  42. Morrison, B. J., et al. Breast cancer stem cells: implications for therapy of breast cancer. Breast Cancer Res. 10:210, 2008.

    Article  MathSciNet  Google Scholar 

  43. Munson, J. M., and A. C. Shieh. Interstitial fluid flow in cancer: implications for disease progression and treatment. Cancer Manag. Res. 6:317–328, 2014.

    Article  Google Scholar 

  44. Munson, J. M., et al. Anti-invasive adjuvant therapy with imipramine blue enhances chemotherapeutic efficacy against glioma. Sci. Transl. Med. 4:127ra36, 2012.

    Article  Google Scholar 

  45. Munson, J. M., R. V. Bellamkonda, and M. A. Swartz. Interstitial flow in a 3d microenvironment increases glioma invasion by a cxcr4-dependent mechanism. Cancer Res. 73:1536–1546, 2013.

    Article  Google Scholar 

  46. Navalitloha, Y., E. S. Schwartz, E. N. Groothuis, C. V. Allen, R. M. Levy, and D. R. Groothuis. Therapeutic implications of tumor interstitial fluid pressure in subcutaneous RG-2 tumors. Neuro. Oncol. 8:227–233, 2006.

    Article  Google Scholar 

  47. Netti, P. A., D. A. Berk, M. A. Swartz, A. J. Grodzinsky, and R. K. Jain. Role of extracellular matrix assembly in interstitial transport in solid tumors. Cancer Res. 60:2497–2503, 2000.

    Google Scholar 

  48. Olsen, M. M., and H. T. Siegelmann. multiscale agent-based model of tumor angiogenesis. Procedia Comput. Sci. 18:1016–1025, 2013.

    Article  Google Scholar 

  49. Pietras, K., and A. Östman. Hallmarks of cancer: Interactions with the tumor stroma. Exp. Cell Res. 316:1324–1331, 2010.

    Article  Google Scholar 

  50. Provenzano, P. P., and S. R. Hingorani. Hyaluronan, fluid pressure, and stromal resistance in pancreas cancer. Br. J. Cancer 108:1–8, 2013.

    Article  Google Scholar 

  51. Provenzano, P. P., C. Cuevas, A. E. Chang, V. K. Goel, D. D. Von Hoff, and S. R. Hingorani. Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell 21:418–429, 2012.

    Article  Google Scholar 

  52. Raghunand, N., et al. Enhancement of chemotherapy by manipulation of tumour pH. Br. J. Cancer 80:1005–1011, 1999.

    Article  Google Scholar 

  53. Ramanujan, S., A. Pluen, T. D. McKee, E. B. Brown, Y. Boucher, and R. K. Jain. Diffusion and convection in collagen gels: implications for transport in the tumor interstitium. Biophys. J. 83:1650–1660, 2002.

    Article  Google Scholar 

  54. Rouzier, R., et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. 11(16):5678–5685, 2005.

    Article  Google Scholar 

  55. Sethi, T., et al. Extracellular matrix proteins protect small cell lung cancer cells against apoptosis: a mechanism for small cell lung cancer growth and drug resistance in vivo. Nat. Med. 5:662–668, 1999.

    Article  Google Scholar 

  56. Shain, K. H., and W. S. Dalton. Cell adhesion is a key determinant in de novo multidrug resistance (MDR): new targets for the prevention of acquired MDR. Mol. Cancer Ther. 1:69–78, 2001.

    Google Scholar 

  57. Shannon, A. M., D. J. Bouchier-Hayes, C. M. Condron, and D. Toomey. Tumour hypoxia, chemotherapeutic resistance and hypoxia-related therapies. Cancer Treat. Rev. 29:297–307, 2003.

    Article  Google Scholar 

  58. Shen, F., et al. Quantitation of doxorubicin uptake, efflux, and modulation of multidrug resistance (MDR) in MDR human cancer cells. J. Pharmacol. Exp. Ther. 324:95–102, 2008.

    Article  Google Scholar 

  59. Shen, K., et al. Resolving cancer–stroma interfacial signalling and interventions with micropatterned tumour–stromal assays. Nat. Commun. 5:5662, 2014.

    Article  Google Scholar 

  60. Shieh, A. C. Biomechanical forces shape the tumor microenvironment. Ann. Biomed. Eng. 39:1379–1389, 2011.

    Article  Google Scholar 

  61. Shieh, A. C., H. A. Rozansky, B. Hinz, and M. A. Swartz. Tumor cell invasion is promoted by interstitial flow-induced matrix priming by stromal fibroblasts. Cancer Res. 71:790–800, 2011.

    Article  Google Scholar 

  62. Shieh, A. C., H. A. Rozansky, B. Hinz, and M. A. Swartz. Tumor cell invasion is promoted by interstitial flow-induced matrix priming by stromal fibroblasts. Cancer Res. 71:790–800, 2011.

    Article  Google Scholar 

  63. Shields, J. D., M. E. Fleury, C. Yong, A. A. Tomei, G. J. Randolph, and M. A. Swartz. Autologous chemotaxis as a mechanism of tumor cell homing to lymphatics via interstitial flow and autocrine CCR7 signaling. Cancer Cell 11:526–538, 2007.

    Article  Google Scholar 

  64. Subik, K., et al. The expression patterns of ER, PR, HER2, CK5/6, EGFR, Ki-67 and AR by immunohistochemical analysis in breast cancer cell lines. Breast Cancer (Auckl.) 4:35–41, 2010.

    Google Scholar 

  65. Tasoglu, S., and U. Demirci. Bioprinting for stem cell research. Trends Biotechnol. 31:10–19, 2013.

    Article  Google Scholar 

  66. Tchou, J., and J. Conejo-Garcia. Targeting the tumor stroma as a novel treatment strategy for breast cancer: shifting from the neoplastic cell-centric to a stroma-centric paradigm. Adv. Pharmacol. 65:45–61, 2012.

    Article  Google Scholar 

  67. Trédan, O., C. M. Galmarini, K. Patel, and I. F. Tannock. Drug resistance and the solid tumor microenvironment. JNCI J. Natl. Cancer Inst. 99:1441–1454, 2007.

    Article  Google Scholar 

  68. Ueda, K., C. Cardarelli, M. M. Gottesman, and I. Pastan. Expression of a full-length cDNA for the human “MDR1” gene confers resistance to colchicine, doxorubicin, and vinblastine. Proc. Natl. Acad. Sci. USA 84:3004–3008, 1987.

    Article  Google Scholar 

  69. Walpole, J., J. C. Chappell, J. G. Cluceru, F. Mac Gabhann, V. L. Bautch, and S. M. Peirce. Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks. Integr. Biol. (Camb.) 7:987–997, 2015.

    Article  Google Scholar 

  70. Weinstein, R. S., et al. Relationship of the expression of the multidrug resistance gene product (P-glycoprotein) in human colon carcinoma to local tumor aggressiveness and lymph node metastasis. Cancer Res. 51:2720–2726, 1991.

    Google Scholar 

  71. Whiteside, T. L. The tumor microenvironment and its role in promoting tumor growth. Oncogene 27:5904–5912, 2008.

    Article  Google Scholar 

  72. Wong, H. L., R. Bendayan, A. M. Rauth, H. Y. Xue, and K. Babakhanian. A mechanistic study of enhanced doxorubicin uptake and retention in multidrug resistant breast cancer cells using a polymer-lipid hybrid nanoparticle system. J. Pharmacol. Exp. Ther. 317:1372–1381, 2006.

    Article  Google Scholar 

  73. Zhang, L., C. G. Strouthos, Z. Wang, and T. S. Deisboeck. Simulating brain tumor heterogeneity with a multiscale agent-based model: linking molecular signatures, phenotypes and expansion rate. Math. Comput. Model. 49:307–319, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  74. Zhang, L., Z. Wang, J. A. Sagotsky, and T. S. Deisboeck. Multiscale agent-based cancer modeling. J. Math. Biol. 58:545–559, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  75. Zhao, Y., et al. Three-dimensional printing of Hela cells for cervical tumor model in vitro. Biofabrication 6:35001, 2014.

    Article  Google Scholar 

Download references

Acknowledgments

The researchers would like to acknowledge Lynette Sequeira for her technical laboratory assistance, Charles Calderwood for statistical assistance. We also thank RC Cornelison, RP Pompano, SM Peirce-Cottler, and SS Blemker for helpful discussion. We would also like to acknowledge the Janes Lab at UVa for initial contribution of cell lines, the Advanced Microscopy Facility and the Biorepository and Tissue Research Facility. This research was funded in part through funding to JM Munson from the UVa Cancer Center through the NCI Cancer Center Support Grant P30 CA44579 and support from the Snell Endowment Fund and Commonwealth of VA, the School of Medicine, and funding to GF Beeghly from the Harrison Undergraduate Research Awards Center at UVa.

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All human samples were acquired according to the ethical standards. No animal studies were performed in this work.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA

    Daniel K. Logsdon, Garrett F. Beeghly & Jennifer M. Munson

  2. Department of Biomedical Engineering and Mechanics, Virginia Tech, Kelly Hall, 325 Stanger Street, Blacksburg, VA, 24061, USA

    Jennifer M. Munson

Authors
  1. Daniel K. Logsdon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garrett F. Beeghly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennifer M. Munson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer M. Munson.

Additional information

Associate Editor Michael King oversaw the review of this article.

Jennifer M. Munson, Ph.D. is an Assistant Professor of Biomedical Engineering at the University of Virginia. Dr. Munson received her Bachelor of Science in Chemical Engineering and Neuroscience from Tulane University in 2006. She worked at Genentech in Process Engineering before pursuing graduate study at Georgia Tech with Ravi Bellamkonda, Ph.D. Supported by a National Science Foundation Graduate Research Award, she developed liposomal nanocarriers to deliver a novel anti-invasive therapeutic to glioblastoma. During her Ph.D. she was awarded a Fulbright Fellowship to Switzerland to pursue independent study on the glioma microenvironment at L’École Polytechnique Fédérale de Lausanne with Melody Swartz, Ph.D. After completing her Ph.D. in 2011, she returned to Switzerland as a Whitaker Scholar for postdoctoral training on the breast cancer microenvironment, focusing on changes that alter interstitial transport. Dr. Munson began her faculty career at the University of Virginia in 2014 and moved to Virginia Tech to the Department of Biomedical Engineering and Mechanics in 2017, pursuing research interests related to the cancer microenvironment, drug delivery, and transport in brain and breast cancers. Her work includes the development of tissue engineered systems for the study of interstitial flow and tissue transport as well as translation of these systems for patient-specific drug screening. She was awarded the Rita Schaffer Young Investigator Award by the Biomedical Engineering Society in 2016.

This article is part of the 2017 CMBE Young Innovators special issue.

figure a

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Logsdon, D.K., Beeghly, G.F. & Munson, J.M. Chemoprotection Across the Tumor Border: Cancer Cell Response to Doxorubicin Depends on Stromal Fibroblast Ratios and Interstitial Therapeutic Transport. Cel. Mol. Bioeng. 10, 463–481 (2017). https://doi.org/10.1007/s12195-017-0498-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12195-017-0498-3

Keywords

Search

Navigation