Dynamics of multiple myeloma tumor therapy with a recombinant measles virus

Dingli, David; Offord, Chetan P.; Myers, Rae; Kw, Peng; Tw, Carr; Josić, Krešimir; Russell, Stephen J.; Bajzer, Željko

doi:10.1038/cgt.2009.40

Cited by 64 publications

(65 citation statements)

References 31 publications

(44 reference statements)

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“…This model complements other oncolytic virotherapy models in the literature, see Bajzer et al (2008), Berezovskaya et al (2007), Dingli et al (2006, 2009), Karev et al (2006), Komarova and Wodarz (2010), Novozhilov et al (2006), and is very close to the model by Titze et al (2017). It differs to that of Titze et al (2017), as tumour cell death due to factors unrelated to treatment are neglected.…”

Section: Model Descriptionsupporting

confidence: 57%

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Jenner

Coster

Kim

et al. 2018

Letters in Biomathematics

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In recent years, interest in the capability of virus particles as a treatment for cancer has increased. In this work, we present a mathematical model embodying the interaction between tumour cells and virus particles engineered to infect and destroy cancerous tissue. To quantify the effectiveness of oncolytic virotherapy, we conduct a local stability analysis and bifurcation analysis of our model. In the absence of tumour growth or viral decay, the model predicts that oncolytic virotherapy will successfully eliminate the tumour cell population for a large proportion of initial conditions. In comparison, for growing tumours and decaying viral particles there are no stable equilibria in the model; however, oscillations emerge for certain regions in our parameter space. We investigate how the period and amplitude of oscillations depend on tumour growth and viral decay. We find that higher tumour replication rates result in longer periods between oscillations and lower amplitudes for uninfected tumour cells. From our analysis, we conclude that oncolytic viruses can reduce growing tumours into a stable oscillatory state, but are insufficient to completely eradicate them. We propose that it is only with the addition of other anti-cancer agents that tumour eradication may be achieved by oncolytic virus. ARTICLE HISTORY

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Section: Model Descriptionsupporting

confidence: 57%

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Jenner

Coster

Kim

et al. 2018

Letters in Biomathematics

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“…Once its value is fixed, the lower is the initial dose of virus administered, smaller is the chance of tumor eradication. Experimental [23] and mathematical [25] results support this direct correlation between MOI and therapeutic success. Also, our simulations indicate that virus replication within the tumor tissue, modelled by the viral burst size b s , is the next main trait in the hierarchy of factors determining the virotherapeutic success.…”

Section: Discussionmentioning

confidence: 68%

“…Furthermore, the last behavior can be either a monotonic or an oscillating growth. It is worthy to notice that such oscillations were observed in human myeloma xenografts induced in mice treated with measles virus (MV) [23], in an ovarian cancer xenograft model [24], and in a mathematical approach used by Dingli et al for modeling MV virotherapy [25]. The therapeutic outcome depends on both viral characteristics and tumor dynamics.…”

Section: Discussionmentioning

confidence: 99%

Questing for an optimal, universal viral agent for oncolytic virotherapy

Paiva

Martins²,

Ferreira³

2011

Phys. Rev. E

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One of the most promising strategies to treat cancer is attacking it with viruses designed to exploit specific altered pathways. Here, the effects of oncolytic virotherapy on tumors having compact, papillary and disconnected morphologies are investigated through computer simulations of a multiscale model coupling macroscopic reaction diffusion equations for the nutrients with microscopic stochastic rules for the actions of individual cells and viruses. The interaction among viruses and tumor cells involves cell infection, intracellular virus replication and release of new viruses in the tissue after cell lysis. The evolution in time of both viral load and cancer cell population, as well as the probabilities for tumor eradication were evaluated for a range of multiplicities of infection, viral entries and burst sizes. It was found that in immunosuppressed hosts, the antitumor efficacy of a virus is primarily determined by its entry efficiency, its replicative capacity within the tumor, and its ability to spread over the tissue. However, the optimal traits for oncolytic viruses depends critically on the tumor growth dynamics and do not necessarily include rapid replication, cytolysis and spreading currently assumed as necessary conditions to a successful therapeutic outcome. Our findings have potential implications on the design of new vectors for the viral therapy of cancer.

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“…Bajzer and Dingli as well as Jacobsen and Pilyugin added a syncytia-forming fusion and budding mechanisms to lysis for a mathematical model (Figure 4) that may be tailored to a particular viral mode of action [126][127][128][129]. These models only allowed budding as a mechanism for viral particle production from syncytia, assuming that no apoptosis occurs from fused cells [130].…”

Section: Modeling Specific Mechanisms Of Actionmentioning

confidence: 99%

Fighting Cancer with Mathematics and Viruses

Santiago¹,

Heidbuechel²,

Kandell³

et al. 2017

Preprint

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After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex anti-tumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these fields of research for more efficient development and effective treatments.

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Dynamics of multiple myeloma tumor therapy with a recombinant measles virus

Cited by 64 publications

References 31 publications

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Questing for an optimal, universal viral agent for oncolytic virotherapy

Fighting Cancer with Mathematics and Viruses

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