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Review
. 2023 Dec 12;24(24):17378.
doi: 10.3390/ijms242417378.

Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis

Laura Menotti  1 , Andrea Vannini  1
Affiliations
Review

Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis

Laura Menotti et al. Int J Mol Sci. .

Abstract

Oncolytic viruses (OVs) are the frontier therapy for refractory cancers, especially in integration with immunomodulation strategies. In cancer immunovirotherapy, the many available "omics" and systems biology technologies generate at a fast pace a challenging huge amount of data, where apparently clashing information mirrors the complexity of individual clinical situations and OV used. In this review, we present and discuss how currently big data analysis, on one hand and, on the other, simulation, modeling, and computational technologies, provide invaluable support to interpret and integrate "omic" information and drive novel synthetic biology and personalized OV engineering approaches for effective immunovirotherapy. Altogether, these tools, possibly aided in the future by artificial intelligence as well, will allow for the blending of the information into OV recombinants able to achieve tumor clearance in a patient-tailored way. Various endeavors to the envisioned "synthesis" of turning OVs into personalized theranostic agents are presented.

Keywords: OV combination therapy; cancer theranostics; cancer therapy; immunopeptidomics; infection computational modeling; infection mathematical modeling; oncolytic immunovirotherapy; oncolytic virus; virus synthetic biology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the integration of omics, bioinformatics, synthetic biology, and experimental, preclinical, and clinical data in the design of OVs, leading to the desired “synthesis” of engineering effective anti-tumor theranostics. Red and orange arrows indicate the flow of input information, and green and blue arrows the flow of output of data analysis and rationalization.
See this image and copyright information in PMC

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