Mathematical Biosciences and Engineering, 2015, 12(6): 1237-1256. doi: 10.3934/mbe.2015.12.1237.

Primary: 92C50, 92B05; Secondary: 37N25.

Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Treatment strategies for combining immunostimulatory oncolytic virus therapeutics with dendritic cell injections

1. Department of Mathematics and Computer Science, University of Richmond, Richmond, VA
2. Weill Cornell Medical College, New York, NY
3. Department of Bioengineering, College of Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791
4. Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ
5. School of Mathematics and Statistics, University of Sydney, Sydney, NSW

Oncolytic viruses (OVs) are used to treat cancer, as they selectively replicate inside of and lyse tumor cells. The efficacy of this process is limited and new OVs are being designed to mediate tumor cell release of cytokines and co-stimulatory molecules, which attract cytotoxic T cells to target tumor cells, thus increasing the tumor-killing effects of OVs. To further promote treatment efficacy, OVs can be combined with other treatments, such as was done by Huang et al., who showed that combining OV injections with dendritic cell (DC) injections was a more effective treatment than either treatment alone. To further investigate this combination, we built a mathematical model consisting of a system of ordinary differential equations and fit the model to the hierarchical data provided from Huang et al. We used the model to determine the effect of varying doses of OV and DC injections and to test alternative treatment strategies. We found that the DC dose given in Huang et al. was near a bifurcation point and that a slightly larger dose could cause complete eradication of the tumor. Further, the model results suggest that it is more effective to treat a tumor with immunostimulatory oncolytic viruses first and then follow-up with a sequence of DCs than to alternate OV and DC injections. This protocol, which was not considered in the experiments of Huang et al., allows the infection to initially thrive before the immune response is enhanced. Taken together, our work shows how the ordering, temporal spacing, and dosage of OV and DC can be chosen to maximize efficacy and to potentially eliminate tumors altogether.
  Figure/Table
  Supplementary
  Article Metrics

Keywords ordinary differential equations model.; co-stimulatory molecules; Oncolytic virotherapy; cytokines; mathematical model; adenovirus

Citation: Joanna R. Wares, Joseph J. Crivelli, Chae-Ok Yun, Il-Kyu Choi, Jana L. Gevertz, Peter S. Kim. Treatment strategies for combining immunostimulatory oncolytic virus therapeutics with dendritic cell injections. Mathematical Biosciences and Engineering, 2015, 12(6): 1237-1256. doi: 10.3934/mbe.2015.12.1237

References

  • 1. Surgery, 156 (2014), 263-269.
  • 2. J. Theor. Biol., 225 (2003), 257-274.
  • 3. PLoS Comput. Biol., 7 (2011), e1001085.
  • 4. J. Theor. Biol., 252 (2008), 109-122.
  • 5. Bull. Math. Biol., 72 (2010), 469-489.
  • 6. Immunity, 21 (2004), 341-347.
  • 7. Cancer Res., 61 (2001), 5453-5460.
  • 8. J. Virol., 75 (2001), 10663-10669.
  • 9. Front Oncol, 3 (2013), p56.
  • 10. Cancer Res., 67 (2007), p8420.
  • 11. Clin. Cancer Res., 13 (2007), 4677-4685.
  • 12. Cancer Gene Ther., 16 (2009), 873-882.
  • 13. Cancer Gene Ther., 18 (2011), 305-317.
  • 14. Bull. Math. Biol., 73 (2011), 2-32.
  • 15. Expert Rev Vaccines, 12 (2013), 1155-1172.
  • 16. Cancer Res., 66 (2006), 2314-2319.
  • 17. J. Virol., 74 (2000), 2895-2899.
  • 18. Mol. Ther., 18 (2010), 264-274.
  • 19. J. Virol., 80 (2006), 3549-3558.
  • 20. Biomaterials, 31 (2010), 1865-1874.
  • 21. Biomaterials, 32 (2011), 2314-2326.
  • 22. (submitted).
  • 23. Nat. Med., 7 (2001), 781-787.
  • 24. J. Theor. Biol., 263 (2010), 530-543.
  • 25. PLoS ONE, 5 (2010), e15482.
  • 26. Nat Commun, 4 (2013), p1974.
  • 27. Mol. Ther., 18 (2010), 888-895.
  • 28. Gene Ther., 15 (2008), 247-256.
  • 29. J. Theor. Biol., 239 (2006), 334-350.
  • 30. Mol. Ther., 19 (2011), 1008-1016.
  • 31. Nat. Rev. Microbiol., 12 (2014), 23-34.
  • 32. Clin. Cancer Res., 15 (2009), 2352-2360.
  • 33. Bioinformatics, 30 (2014), 1884-1891.
  • 34. J. Theor. Biol., 294 (2012), 56-73.
  • 35. Gene Ther., 19 (2012), 543-549.
  • 36. Nat. Biotechnol., 30 (2012), 658-670.
  • 37. Clin. Cancer Res., 18 (2012), 6679-6689.
  • 38. Nat Immunol., 2 (2001), 423-429.
  • 39. Nat Immunol., 1 (2000), 47-53.
  • 40. Mol. Cancer Ther., 5 (2006), 362-366.
  • 41. Cancer Res., 61 (2001), 3501-3507.
  • 42. Math Biosci Eng, 10 (2013), 939-957.
  • 43. PLoS ONE, 4 (2009), e4271.
  • 44. Hum. Gene Ther., 8 (1997), 37-44.
  • 45. Bull. Math. Biol., 66 (2004), 605-625.
  • 46. Pathol. Oncol. Res., 18 (2012), 771-781.
  • 47. Neoplasia, 15 (2013), 591-599.

 

This article has been cited by

  • 1. Jana L. Gevertz, Peter S. Kim, Joanna R. Wares, Mentoring Undergraduate Interdisciplinary Mathematics Research Students: Junior Faculty Experiences, PRIMUS, 2017, 27, 3, 352, 10.1080/10511970.2016.1191571
  • 2. Urszula Ledzewicz, Shuo Wang, Heinz Schättler, Nicolas André, Marie Amélie Heng, Eddy Pasquier, On drug resistance and metronomic chemotherapy: A mathematical modeling and optimal control approach, Mathematical Biosciences and Engineering, 2016, 14, 1, 217, 10.3934/mbe.2017014
  • 3. Daniel Santiago, Johannes Heidbuechel, Wendy Kandell, Rachel Walker, Julie Djeu, Christine Engeland, Daniel Abate-Daga, Heiko Enderling, Fighting Cancer with Mathematics and Viruses, Viruses, 2017, 9, 9, 239, 10.3390/v9090239
  • 4. Syndi Barish, Michael F. Ochs, Eduardo D. Sontag, Jana L. Gevertz, Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy, Proceedings of the National Academy of Sciences, 2017, 114, 31, E6277, 10.1073/pnas.1703355114
  • 5. Raluca Eftimie, Joseph J. Gillard, Doreen A. Cantrell, Mathematical Models for Immunology: Current State of the Art and Future Research Directions, Bulletin of Mathematical Biology, 2016, 78, 10, 2091, 10.1007/s11538-016-0214-9
  • 6. Adrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. Coster, Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment Protocols, Bulletin of Mathematical Biology, 2018, 80, 6, 1615, 10.1007/s11538-018-0424-4
  • 7. Elzbieta Ratajczyk, Urszula Ledzewicz, Heinz Schättler, Optimal Control for a Mathematical Model of Glioma Treatment with Oncolytic Therapy and TNF-$$\alpha $$α Inhibitors, Journal of Optimization Theory and Applications, 2018, 176, 2, 456, 10.1007/s10957-018-1218-4
  • 8. Hang Xie, Yang Jiao, Qihui Fan, Miaomiao Hai, Jiaen Yang, Zhijian Hu, Yue Yang, Jianwei Shuai, Guo Chen, Ruchuan Liu, Liyu Liu, Nils Cordes, Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy, PLOS ONE, 2018, 13, 10, e0206292, 10.1371/journal.pone.0206292
  • 9. Jana L. Gevertz, Joanna R. Wares, Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections, Computational and Mathematical Methods in Medicine, 2018, 2018, 1, 10.1155/2018/8760371
  • 10. Victor Cervera-Carrascon, Riikka Havunen, Akseli Hemminki, Oncolytic adenoviruses: a game changer approach in the battle between cancer and the immune system., Expert Opinion on Biological Therapy, 2019, 1, 10.1080/14712598.2019.1595582
  • 11. Jiantao Zhao, Jianjun Paul Tian, Spatial Model for Oncolytic Virotherapy with Lytic Cycle Delay, Bulletin of Mathematical Biology, 2019, 10.1007/s11538-019-00611-2
  • 12. Adrianne L. Jenner, Peter S. Kim, Federico Frascoli, Oncolytic virotherapy for tumours following a Gompertz growth law, Journal of Theoretical Biology, 2019, 10.1016/j.jtbi.2019.08.002

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, Joanna R. Wares, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved