Vaccinia virus injected human tumors: oncolytic virus efficiency predicted by antigen profiling analysis fitted boolean models
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-200507
- Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculateVirotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculate differences in the tumor growth signaling network by tumor type and gender. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, the T-killer cell mediated cell death, Interferon and Interleukin signaling networks. The in silico findings conform very well with in vivo findings of tumor growth. Similar to a previously published analysis of vaccinia virus-injected canine tumors, we were able to confirm the suitability of our boolean modeling for prediction of human tumor growth after virus infection in the current study as well. In summary, these findings indicate that our boolean models could be a useful tool for testing of the efficacy of VACV-mediated cancer therapy already before its use in human patients.…
Autor(en): | Alexander CecilORCiD, Ivaylo Gentschev, Marion Adelfinger, Thomas Dandekar, Aladar A. Szalay |
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URN: | urn:nbn:de:bvb:20-opus-200507 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften |
Fakultät für Chemie und Pharmazie / Lehrstuhl für Biochemie | |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Bioengineered |
Erscheinungsjahr: | 2019 |
Band / Jahrgang: | 10 |
Heft / Ausgabe: | 1 |
Seitenangabe: | 190-196 |
Originalveröffentlichung / Quelle: | Bioengineered 2019, Vol. 10, No. 1, 190-196. DOI: 10.1080/21655979.2019.1622220 |
DOI: | https://doi.org/10.1080/21655979.2019.1622220 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Freie Schlagwort(e): | boolean modeling; cancer therapy; human xenografted mouse models; oncolytic virus |
Datum der Freischaltung: | 30.03.2020 |
Sammlungen: | Open-Access-Publikationsfonds / Förderzeitraum 2019 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |