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Viruses 2017, 9(9), 239; doi:10.3390/v9090239

Fighting Cancer with Mathematics and Viruses

1
Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
2
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
3
German Cancer Research Center, Heidelberg University, 69120 Heidelberg, Germany
4
Cancer Biology PhD Program, University of South Florida, Tampa, FL 33612, USA
5
National Center for Tumor Diseases Heidelberg, Department of Translational Oncology, Department of Medical Oncology, 69120 Heidelberg, Germany
6
Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
7
Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: John Murray and Ruy Ribeiro
Received: 15 July 2017 / Revised: 18 August 2017 / Accepted: 18 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Mathematical Modeling of Viral Infections)
View Full-Text   |   Download PDF [3232 KB, uploaded 23 August 2017]   |  

Abstract

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 antitumor 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 research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments. View Full-Text
Keywords: oncolytic virotherapy; combination therapy; mathematical model; immune system; cancer; immunotherapy oncolytic virotherapy; combination therapy; mathematical model; immune system; cancer; immunotherapy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Santiago, D.N.; Heidbuechel, J.P.W.; Kandell, W.M.; Walker, R.; Djeu, J.; Engeland, C.E.; Abate-Daga, D.; Enderling, H. Fighting Cancer with Mathematics and Viruses. Viruses 2017, 9, 239.

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