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Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy
Published on: April 3, 2018
Spatiotemporal spread of oncolytic virus in a heterogeneous cell population
Sabrina Glaschke1, Hana M Dobrovolny2
1Institute of Physics, Universitat Kassel, Kassel, Germany; Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
Abstract:
Oncolytic (cancer-killing) virus treatment is a promising new therapy for cancer, with many viruses currently being tested for their ability to eradicate tumors. One of the major stumbling blocks to the development of this treatment modality has been preventing spread of the virus to non-cancerous cells. Our recent ability to manipulate RNA and DNA now allows for the possibility of creating designer viruses specifically targeted to cancer cells, thereby significantly reducing unwanted side effects in patients. In this study, we use a partial differential equation model to determine the characteristics of a virus needed to contain spread of an oncolytic virus within a spherical tumor and prevent it from spreading to non-cancerous cells outside the tumor. We find that oncolytic viruses that have different infection rates or different cell death rates in cancer and non-cancerous cells can be made to stay within the tumor. We find that there is a minimum difference in infection rates or cell death rates that will contain the virus and that this threshold value depends on the growth rate of the cancer. Identification of these types of thresholds can help researchers develop safer strains of oncolytic viruses allowing further development of this promising treatment.
Insights
Researchers developed a mathematical model to create safer oncolytic (cancer-killing) viruses. By adjusting viral infection and cell death rates, these designer viruses can be contained within tumors, minimizing side effects for cancer patients.
Area of Science:
- Oncolytic virotherapy
- Mathematical modeling in oncology
- Cancer cell targeting
Background:
- Oncolytic virus therapy shows promise for cancer treatment but faces challenges in preventing off-target spread.
- Genetic engineering offers potential for creating cancer-specific viruses to improve safety and efficacy.
- Controlling viral dissemination to healthy tissues is crucial for advancing this therapeutic modality.
Purpose of the Study:
- To determine the viral characteristics necessary for containing oncolytic virus spread within a tumor.
- To model the prevention of oncolytic virus dissemination to non-cancerous tissues.
- To identify thresholds for viral infection and cell death rates that ensure tumor confinement.
Main Methods:
- Utilized a partial differential equation model to simulate viral spread dynamics.
- Analyzed the impact of differential infection and cell death rates in cancerous versus non-cancerous cells.
- Investigated the relationship between these rates, tumor growth, and viral containment.
Main Results:
- Oncolytic viruses can be contained within tumors by exploiting differences in infection or cell death rates between cancer and healthy cells.
- A minimum threshold difference in these rates is required for effective viral containment.
- This containment threshold is dependent on the tumor's cancer cell growth rate.
Conclusions:
- Mathematical modeling can guide the development of safer, targeted oncolytic viruses.
- Identifying specific thresholds for viral infection and cell death rates is key to designing effective and safe cancer therapies.
- This research facilitates the creation of improved oncolytic virus strains for clinical application.
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