Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.
Published in | Computational Biology and Bioinformatics (Volume 8, Issue 1) |
DOI | 10.11648/j.cbb.20200801.14 |
Page(s) | 20-28 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Computational Biology, Oncolytic Virotherapy, Bifurcation, Dynamical System
[1] | Boozari, B., Mundt, B., Woller, N., Struver, N., Gurlevik, E., Schache, P., Kloos, A., Knocke, S., Manns, M. P., Wirth, T. C. et al., Antitumoural immunity by virus-mediated immunogenic growth of hepatocellular carcinoma. Gut 2010, 59: 1416-1426. |
[2] | Diaconu, I., Cerullo, V., Hirvinen, M. L., Escutenaire, S., Ugolini, M., Pesonen, S. K., Bramante, S., Parviainen, S., Kanerva, A., Loskog, A. S. et al., Immune response is an important aspect of the antitumor effect produced by a CD40L-encoding oncolytic adenovirus. Cancer Res. 2012, 72, 2327–2338. |
[3] | Ito, H., Aoki, H., Kuhnel, F., Kondo, Y., Kubicka, S., Wirth, T., Iwado, E., Iwamaru, A., Fujiwara, K., Hess, K. R. et al., Autophagic cell death of malignant glioma cells induced by a conditionally replicating adenovirus. J. Natl. Cancer Inst. 2006, 98, 625–636. |
[4] | Chiocca, E. A., Oncolytic viruses, Nature Reviews, Cancer, 2 (2002), 938–950. |
[5] | Yu, Z., Chan, M.-K., O-charoenrat, P., Eisenberg, D. P., Shah, J. P., Singh, B., Fong, Y., Wong, R. J., Enhanced nectin-1 expression and herpes oncolytic sensitivity in highly migratory and invasive carcinoma. Clin. Cancer Res. 2005, 11, 4889–4897. |
[6] | Garber, K. China approves world’s first oncolytic virus therapy for cancer treatment. J. Natl. Cancer Inst. 2006, 98, 298–300. |
[7] | Bajzer, Z., Carr, T., Josic, K., Russel, S. J., Dingli, D., Modeling of cancer virotherapy with recombinant measles viruses, J. Theoretical Biology, 2008, 252, 109-122. |
[8] | Wodarz, D. Viruses as Antitumor Weapons$: $ Defining Conditions for Tumor Remission, Cancer Res. 2001, 61, 3501-3507. |
[9] | Tao, Y., Quo, Q., The competitive dynamics between tumor cells, a replication-competent virus and an immune response, J. Math. Biol. 2005, 51 (1), 37-78. |
[10] | Friedman A, Tian JP, Fulci G et al., Glioma virotherapy$: $ effects of innate immune suppression and increased viral replication capacity. Cancer Res, 2006, 66 (4), 2314–2319. |
[11] | Tian, J. P., The replicability of oncolytic virus: defining conditions in tumor virotherapy, Math. Biosci. Eng., 2011, 8, 841-860. |
[12] | Wu, JT, Byrne, HM, Kirn, DH et al., Modeling and analysis of a virus that replicates selectively in tumor cells. Bull Math Biol, 2001, 63 (4), 731. |
[13] | Wein, LM, Wu, JT, Kirn, DH., Validation and analysis of a mathematical model of a replication competent oncolytic virus for cancer treatment: implications for virus design and delivery. Cancer Res, 2003, 63 (6), 1317–1324. |
[14] | Wu, J. T., Kirn, D. H., Wein, L. M., Analysis of a three-way race between tumor growth, a replication-competent virus and an immune response. Bull. Math. Bio. 66 (4), 605-625. |
[15] | Burden, R. L., and Faires, J. D., Numerical Analysis. Boston: PWS Publishing Company, 1980. |
[16] | Kim PS, Crivelli JJ, Choi IK, Yun CO, Wares JR. Quantitative impact of immunomodulation versus oncolysis with cytokine-expressing virus therapeutics. Math Biosci Eng. 2015; 12 (4): 841-858. doi: 10.3934/mbe.2015.12.841 |
[17] | Strogatz, S. H., Nonlinear Dynamics and Chaos. Perseus Books Publishing, LLC, 1994. |
[18] | Lawler SE, Speranza MC, Cho CF, Chiocca EA. Oncolytic Viruses in Cancer Treatment: A Review. JAMA Oncol. 2017; 3 (6): 841-9. |
[19] | Qiao J, Wang H, Kottke T, White C, Twigger K, Diaz RM, Thompson J, Selby P, de Bono J, Melcher A, Pandha H, Coffey M, Vile R, Harrington K. Cyclophosphamide facilitates antitumor efficacy against subcutaneous tumors following intravenous delivery of reovirus. Clin Cancer Res. 2008; 14 (1): 259-69. |
[20] | Russell L, Peng KW, Russell SJ, Diaz RM. Oncolytic Viruses: Priming Time for Cancer Immunotherapy. BioDrugs. 2019; 33 (5): 485-501. |
[21] | Simpson GR, Relph K, Harrington K, Melcher A, Pandha H. Cancer immunotherapy via combining oncolytic virotherapy with chemotherapy: recent advances. Oncolytic Virother. 2016; 5: 1-13. |
APA Style
Dongwook Kim, Haeyoung Kim, Hui Wu, Dong-Hoon Shin. (2020). The Effect of the Infection Rate on Oncolytic Virotherapy. Computational Biology and Bioinformatics, 8(1), 20-28. https://doi.org/10.11648/j.cbb.20200801.14
ACS Style
Dongwook Kim; Haeyoung Kim; Hui Wu; Dong-Hoon Shin. The Effect of the Infection Rate on Oncolytic Virotherapy. Comput. Biol. Bioinform. 2020, 8(1), 20-28. doi: 10.11648/j.cbb.20200801.14
AMA Style
Dongwook Kim, Haeyoung Kim, Hui Wu, Dong-Hoon Shin. The Effect of the Infection Rate on Oncolytic Virotherapy. Comput Biol Bioinform. 2020;8(1):20-28. doi: 10.11648/j.cbb.20200801.14
@article{10.11648/j.cbb.20200801.14, author = {Dongwook Kim and Haeyoung Kim and Hui Wu and Dong-Hoon Shin}, title = {The Effect of the Infection Rate on Oncolytic Virotherapy}, journal = {Computational Biology and Bioinformatics}, volume = {8}, number = {1}, pages = {20-28}, doi = {10.11648/j.cbb.20200801.14}, url = {https://doi.org/10.11648/j.cbb.20200801.14}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20200801.14}, abstract = {Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.}, year = {2020} }
TY - JOUR T1 - The Effect of the Infection Rate on Oncolytic Virotherapy AU - Dongwook Kim AU - Haeyoung Kim AU - Hui Wu AU - Dong-Hoon Shin Y1 - 2020/07/04 PY - 2020 N1 - https://doi.org/10.11648/j.cbb.20200801.14 DO - 10.11648/j.cbb.20200801.14 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 20 EP - 28 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20200801.14 AB - Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy. VL - 8 IS - 1 ER -