Qualitative optimization of oncolytic virotherapy and immune therapy combination treatments

Negar Mohammadnejad; Thomas Hillen; Negar Mohammadnejad; Thomas Hillen

doi:10.3934/mbe.2026016

Mathematical Biosciences and Engineering

2026, Volume 23, Issue 2: 388-420. doi: 10.3934/mbe.2026016

Previous Article Next Article

Research article Special Issues

Qualitative optimization of oncolytic virotherapy and immune therapy combination treatments

Negar Mohammadnejad ,
Thomas Hillen ^,

Department of Mathematical and Statistical Sciences, University of Alberta, 116 St & 85 Ave, Edmonton, T6G 2R3, Canada

Received: 16 September 2025 Revised: 22 October 2025 Accepted: 28 October 2025 Published: 07 January 2026

Oncolytic viruses (OVs) are designed to selectively target and destroy cancer cells while sparing normal, healthy tissue. Several viruses for oncolytic virotherapy are currently developed. In this paper, we will use mathematical modeling to consider key strategies that can improve the efficacy of oncolytic virotherapy. These include the integration of immunotherapy approaches with virotherapy to amplify anti-tumor immune responses, as well as optimizing the timing, dosage, and sequencing of viral administrations. Specifically, we consider strategies that increase the burst size of the virus, immunostimulation and immunosuppression, we optimize for different weekly virus injection schedules, and we consider the combination of OV therapy with chimeric antigen receptor (CAR) T-cell therapy. A limiting factor is the availability of data. We parametrize the model using several different data sets. These, however, correspond to different cancers and experimental setups. Hence our model cannot be considered to be validated. Consequently, our results are qualitative. Our results highlight the critical importance of timing for virotherapy's efficacy and overall success. They outline strong evidence for promising treatment scenarios that needs to be further tested experimentally in the future.
- virotherapy,
- immunotherapy,
- cancer modelling,
- optimized schedule,
- oncolytic virus
Citation: Negar Mohammadnejad, Thomas Hillen. Qualitative optimization of oncolytic virotherapy and immune therapy combination treatments[J]. Mathematical Biosciences and Engineering, 2026, 23(2): 388-420. doi: 10.3934/mbe.2026016

Related Papers:

Abstract

Oncolytic viruses (OVs) are designed to selectively target and destroy cancer cells while sparing normal, healthy tissue. Several viruses for oncolytic virotherapy are currently developed. In this paper, we will use mathematical modeling to consider key strategies that can improve the efficacy of oncolytic virotherapy. These include the integration of immunotherapy approaches with virotherapy to amplify anti-tumor immune responses, as well as optimizing the timing, dosage, and sequencing of viral administrations. Specifically, we consider strategies that increase the burst size of the virus, immunostimulation and immunosuppression, we optimize for different weekly virus injection schedules, and we consider the combination of OV therapy with chimeric antigen receptor (CAR) T-cell therapy. A limiting factor is the availability of data. We parametrize the model using several different data sets. These, however, correspond to different cancers and experimental setups. Hence our model cannot be considered to be validated. Consequently, our results are qualitative. Our results highlight the critical importance of timing for virotherapy's efficacy and overall success. They outline strong evidence for promising treatment scenarios that needs to be further tested experimentally in the future.

References

[1]	C. E. Engeland, J. C. Bell, Introduction to oncolytic virotherapy, in Oncolytic Viruses (Ed. C. E. Engeland), Springer, New York, NY, (2020), 1–6. https://doi.org/10.1007/978-1-4939-9794-7_1
[2]	D. Lin, Y. Shen, T. Liang, Oncolytic virotherapy: Basic principles, recent advances and future directions, Signal Transduction Targeted Ther., 8 (2023), 156. https://doi.org/10.1038/s41392-023-01407-6 doi: 10.1038/s41392-023-01407-6
[3]	F. Maryam, S. Gul, Mechanisms, challenges and future prospects of oncolytic virotherapy: A comprehensive review, J. Curr. Oncol. Med. Sci., 4 (2024), 813–827.
[4]	G. Marelli, A. Howells, N. R. Lemoine, Y. Wang, Oncolytic viral therapy and the immune system: A double-edged sword against cancer, Front. Immunol., 9 (2018), 866. https://doi.org/10.3389/fimmu.2018.00 doi: 10.3389/fimmu.2018.00
[5]	N. Mohammadnejad, T. Hillen, Stacked waves and qualitative analysis of an oncolytic virus–immune model, bioRxiv, 2025. submitted for publication. https://doi.org/10.1101/2025.09.04.674336
[6]	Y. Kim, D. R. Clements, A. M. Sterea, H. W. Jang, S. A. Gujar, P. W. K. Lee, Dendritic cells in oncolytic virus-based anti-cancer therapy, Viruses, 7 (2015), 6506–6525. https://doi.org/10.3390/v7122953 doi: 10.3390/v7122953
[7]	M. Jafari, M. Kadkhodazadeh, M. B. Shapourabadi, N. H. Goradel, M. A. Shokrgozar, A. Arashkia, et al., Immunovirotherapy: The role of antibody-based therapeutics in combination with oncolytic viruses, Front. Immunol., 13 (2022), 1012806. https://doi.org/10.3389/fimmu.2022.1012806 doi: 10.3389/fimmu.2022.1012806
[8]	R. L. Martuza, A. Malick, J. M. Markert, K. L. Ruffner, D. M. Coen, Experimental therapy of human glioma by means of a genetically engineered virus mutant, Science, 252 (1991), 854–856. https://doi.org/10.1126/science.1851332 doi: 10.1126/science.1851332
[9]	A. A. Baabdulla, T. Hillen, Oscillations in a spatial oncolytic virus model, Bull. Math. Biol., 86 (2024), 93. https://doi.org/10.1007/s11538-024-01322-z doi: 10.1007/s11538-024-01322-z
[10]	S. M. Al-Tuwairqi, N. O. Al-Johani, E. A. Simbawa, Modeling dynamics of cancer virotherapy with immune response, Adv. Differ. Equations, 2020 (2020), 438. https://doi.org/10.1186/s13662-020-02893-6 doi: 10.1186/s13662-020-02893-6
[11]	D. Wodarz, Viruses as antitumor weapons: Defining conditions for tumor remission, Cancer Res., 61 (2001), 3501–3507.
[12]	D. Wodarz, N. Komarova, Towards predictive computational models of oncolytic virus therapy: Basis for experimental validation and model selection, PLoS One, 4 (2009), e4271. https://doi.org/10.1371/journal.pone.0004271 doi: 10.1371/journal.pone.0004271
[13]	N. L. Komarova, D. Wodarz, ODE models for oncolytic virus dynamics, J. Theor. Biol., 263 (2010), 530–543. https://doi.org/10.1016/j.jtbi.2010.01.009 doi: 10.1016/j.jtbi.2010.01.009
[14]	J. P. Tian, The replicability of oncolytic virus: Defining conditions in tumor virotherapy, Math. Biosci. Eng., 8 (2011), 841–860. https://doi.org/10.3934/mbe.2011.8.841 doi: 10.3934/mbe.2011.8.841
[15]	R. Eftimie, J. Dushoff, B. W. Bridle, J. L. Bramson, D. J. D. Earn, Multi-stability and multi-instability phenomena in a mathematical model of tumor–immune–virus interactions, Bull. Math. Biol., 73 (2011), 2932–2961. https://doi.org/10.1007/s11538-011-9653-5 doi: 10.1007/s11538-011-9653-5
[16]	K. M. Storey, S. E. Lawler, T. L. Jackson, Modeling oncolytic viral therapy, immune checkpoint inhibition, and the complex dynamics of innate and adaptive immunity in glioblastoma treatment, Front. Physiol., 11 (2020), 151. https://doi.org/10.3389/fphys.2020.00151 doi: 10.3389/fphys.2020.00151
[17]	P. Pooladvand, C. O. Yun, A. R. Yoon, P. S. Kim, F. Frascoli, The role of viral infectivity in oncolytic virotherapy outcomes: A mathematical study, Math. Biosci., 334 (2021), 108520. https://doi.org/10.1016/j.mbs.2020.108520 doi: 10.1016/j.mbs.2020.108520
[18]	J. T. Wu, H. M. Byrne, D. H. Kirn, L. M. Wein, Modeling and analysis of a virus that replicates selectively in tumor cells, Bull. Math. Biol., 63 (2001), 731–768. https://doi.org/10.1006/bulm.2001.0245 doi: 10.1006/bulm.2001.0245
[19]	H. F. Lodish, Molecular Cell Biology, Macmillan, 2008.
[20]	J. H. Kim, Y. S. Lee, H. Kim, J. H. Huang, A. R. Yoon, C. O. Yun, Relaxin expression from tumor-targeting adenoviruses and its intratumoral spread, apoptosis induction, and efficacy, JNCI J. Natl. Cancer Inst., 98 (2006), 1482–1493. https://doi.org/10.1093/jnci/djj397 doi: 10.1093/jnci/djj397
[21]	A. Friedman, J. P. Tian, G. Fulci, E. A. Chiocca, J. Wang, Glioma virotherapy: Effects of innate immune suppression and increased viral replication capacity, Cancer Res., 66 (2006), 2314–2319. https://doi.org/10.1158/0008-5472.CAN-05-2661 doi: 10.1158/0008-5472.CAN-05-2661
[22]	S. Gujar, J. G. Pol, G. Kroemer, Heating it up: Oncolytic viruses make tumors "hot" and suitable for checkpoint blockade immunotherapies, OncoImmunology, 7 (2018), e1442169. https://doi.org/10.1080/2162402X.2018.1442169 doi: 10.1080/2162402X.2018.1442169
[23]	J. Gong, M. M. D. Santos, C. Finlay, T. Hillen, Are more complicated tumour control probability models better, Math. Med. Biol., 30 (2013), 1–19. https://doi.org/10.1093/imammb/dqr023 doi: 10.1093/imammb/dqr023
[24]	R. D. Yates, Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, John Wiley & Sons, Inc., 2014.
[25]	E. V. Shashkova, S. M. May, M. A. Barry, Characterization of human adenovirus serotypes 5, 6, 11, and 35 as anticancer agents, Virology, 394 (2009), 311–320. https://doi.org/10.1016/j.virol.2009.08.038 doi: 10.1016/j.virol.2009.08.038
[26]	M. S. C. Ornella, J. J. Kim, E. Cho, M. Cho, T. H. Hwang, Dose considerations for vaccinia oncolytic virus based on retrospective reanalysis of early and late clinical trials, Vaccines, 12 (2024), 1010. https://doi.org/10.3390/vaccines12091010 doi: 10.3390/vaccines12091010
[27]	V. Naumenko, J. Rajwani, M. Turk, C. Zhang, M. Tse, R. P. Davis, et al., Repeated dosing improves oncolytic rhabdovirus therapy in mice via interactions with intravascular monocytes, Commun. Biol., 5 (2022), 1–14. https://doi.org/10.1038/s42003-022-04254-3 doi: 10.1038/s42003-022-04254-3
[28]	M. A. Currier, L. C. Adams, Y. Y. Mahller, T. P. Cripe, Widespread intratumoral virus distribution with fractionated injection enables local control of large human rhabdomyosarcoma xenografts by oncolytic herpes simplex viruses, Cancer Gene Ther., 12 (2005), 407–416. https://doi.org/10.1038/sj.cgt.7700799 doi: 10.1038/sj.cgt.7700799
[29]	International Virotherapy Center, RIGVIR^® Guidelines on the Use in Oncology (Public Version for Informational Purposes), 2024. Available from: https://www.virotherapy.com/doc/IVC-Rigvir-Guidelines-in-Oncology_10.2024.pdf. [Accessed: 19 Oct 2025]
[30]	M. Garofalo, K. W. Pancer, M. Wieczorek, M. Staniszewska, M. Wieczorek, L. Kuryk, et al., From immunosuppression to immunomodulation – turning cold tumours into hot, J. Cancer, 13 (2022), 2884–2892. https://doi.org/10.7150/jca.71992 doi: 10.7150/jca.71992
[31]	R. Burchett, S. Walsh, Y. Wan, J. L. Bramson, A rational relationship: Oncolytic virus vaccines as functional partners for adoptive T cell therapy, Cytokine Growth Factor Rev., 56 (2020), 149–159. https://doi.org/10.1016/j.cytogfr.2020.07.003 doi: 10.1016/j.cytogfr.2020.07.003
[32]	A. Ageenko, N. Vasileva, V. Richter, E. Kuligina, Combination of oncolytic virotherapy with different antitumor approaches against glioblastoma, Int. J. Mol. Sci., 25 (2024), 2042. https://doi.org/10.3390/ijms25042042 doi: 10.3390/ijms25042042
[33]	A. L. De Matos, L. S. Franco, G. McFadden, Oncolytic viruses and the immune system: The dynamic duo, Mol. Ther. Methods Clin. Dev., 17 (2020), 349–358. https://doi.org/10.1016/j.omtm.2020.01.001 doi: 10.1016/j.omtm.2020.01.001
[34]	G. Fulci, L. Breymann, D. Gianni, K. Kurozomi, S. S. Rhee, J. Yu, et al., Cyclophosphamide enhances glioma virotherapy by inhibiting innate immune responses, Proc. Natl. Acad. Sci. U.S.A., 103 (2006), 12873–12878. https://doi.org/10.1073/pnas.0605496103 doi: 10.1073/pnas.0605496103
[35]	M. S. Koch, M. Zdioruk, M. O. Nowicki, A. M. Griffith, E. Aguilar, L. K. Aguilar, et al., Systemic high-dose dexamethasone treatment may modulate the efficacy of intratumoral viral oncolytic immunotherapy in glioblastoma models, J. Immunother. Cancer, 10 (2022), e003368. https://doi.org/10.1136/jitc-2021-003368 doi: 10.1136/jitc-2021-003368
[36]	A. Tison, G. Quéré, L. Misery, E. Funck‐Brentano, F. X. Danlos, E. Routier, et al., Safety and efficacy of immune checkpoint inhibitors in patients with cancer and preexisting autoimmune disease: A nationwide, multicenter cohort study, Arthritis Rheumatol., 71 (2019), 2100–2111. https://doi.org/10.1002/art.41068 doi: 10.1002/art.41068
[37]	Cancer Research Institute, Immunomodulators: Checkpoint Inhibitors, Cytokines, Agonists, and Adjuvants, Available from: https://www.cancerresearch.org/treatment-types/immunomodulators. [Accessed: 14-Feb-2025]
[38]	L. R. C. Barros, E. A. Paixão, A. M. P. Valli, G. T. Naozuka, A. C. Fassoni, R. C. Almeida, CART math: A mathematical model of CAR-T immunotherapy in preclinical studies of hematological cancers, Cancers, 13 (2021), 2934. https://doi.org/10.3390/cancers13122934 doi: 10.3390/cancers13122934
[39]	S. Zuo, M. Wei, B. He, A. Chen, S. Wang, L. Kong, et al., Enhanced antitumor efficacy of a novel oncolytic vaccinia virus encoding a fully monoclonal antibody against T-cell immunoglobulin and ITIM domain (TIGIT), EBioMedicine, 64 (2021), 103240. https://doi.org/10.1016/j.ebiom.2021.103240 doi: 10.1016/j.ebiom.2021.103240
[40]	G. V. Kochneva, G. F. Sivolobova, A. V. Tkacheva, A. A. Gorchakov, S. V. Kulemzin, Combination of oncolytic virotherapy and CAR T/NK cell therapy for the treatment of cancer, Mol. Biol., 54 (2020), 1–12. https://doi.org/10.1134/S0026893320010100 doi: 10.1134/S0026893320010100
[41]	K. J. Mahasa, R. Ouifki, A. Eladdadi, L. de Pillis, A combination therapy of oncolytic viruses and chimeric antigen receptor T cells: A mathematical model proof-of-concept, Math. Biosci. Eng., 19 (2022), 4429–4457. https://doi.org/10.3934/mbe.2022205 doi: 10.3934/mbe.2022205
[42]	E. Ponterio, T. L. Haas, R. De Maria, Oncolytic virus and CAR-T cell therapy in solid tumors, Front. Immunol., 15 (2024), 1455163. https://doi.org/10.3389/fimmu.2024.1455163 doi: 10.3389/fimmu.2024.1455163
[43]	M. Conte, A. Xella, R. T. Woodall, K. A. Cassady, S. Branciamore, C. E. Brown, et al., CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma, Cold Spring Harbor Laboratory Press, United States, bioRxiv, 2025.
[44]	J. He, F. Munir, D. Ragoonanan, W. Zaky, S. J. Khazal, P. Tewari, et al., Combining CAR T cell therapy and oncolytic virotherapy for pediatric solid tumors: A promising option, Immuno, 3 (2023), 37–56. https://doi.org/10.3390/immuno3010004 doi: 10.3390/immuno3010004
[45]	S. L. Greig, Talimogene laherparepvec: First global approval, Drugs, 76 (2016), 147–154. https://doi.org/10.1007/s40265-015-0522-7 doi: 10.1007/s40265-015-0522-7
[46]	S. J. Russell, G. N. Barber, Oncolytic viruses as antigen-agnostic cancer vaccines, Cancer Cell, 33 (2018), 599–605.
[47]	A. A. Baabdulla, F. Cristi, M. Shmulevitz, T. Hillen, Mathematical modelling of reoviruses in cancer cell cultures, PLoS One, 20 (2025), e0318078. https://doi.org/10.1371/journal.pone.0318078 doi: 10.1371/journal.pone.0318078
[48]	C. M. Oh, H. J. Chon, C. Kim, Combination immunotherapy using oncolytic virus for the treatment of advanced solid tumors, Int. J. Mol. Sci., 21 (2020), 7743. https://doi.org/10.3390/ijms21207743 doi: 10.3390/ijms21207743
[49]	T. Bansod, Spiral Waves in Oncolytic Virotherapy and Patch Formation in Gene Networks, Master's Thesis, University of Alberta, 2025.
[50]	J. D. Murray, Mathematical Biology, Biomathematics, Springer, 19 (1989). https://doi.org/10.1007/978-3-662-08539-4
[51]	K. J. Mahasa, A. Eladdadi, L. De Pillis, R. Ouifki, Oncolytic potency and reduced virus tumor-specificity in oncolytic virotherapy: A mathematical modelling approach, PLoS One, 12 (2017), e0184347. https://doi.org/10.1371/journal.pone.0184347 doi: 10.1371/journal.pone.0184347
[52]	R. R. Rich, T. A. Fleisher, W. T. Shearer, H. W. Schroeder Jr, C. M. Weyand, D. B. Corry, et al., Clinical Immunology: Principles and Practice, 6th edition, Elsevier, 2022.

Reader Comments

Your name:*

Email:*
© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)