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Research Abstracts Online
January 2010 - March 2011

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University of Minnesota Rochester
Graduate Program in Biomedical Informatics and Computational Biology

PI: Claudia Neuhauser

Applying Network Theory to Optimize Cancer Virotherapy

Despite the advent of novel therapies, most tumors remain incurable. Recent genome-wide studies of various tumor types show that there is considerable variability in genetic profile of tumors arising from the same tissue, reducing the prospect that small molecules can cure cancer. In contrast, many replicating viruses selectively infect and replicate in many different types of tumors, regardless of the underlying molecular pathways that drive the tumors. This cancer selectivity makes replicating viruses an attractive therapeutic option and several viruses are currently undergoing clinical trials in patients with advanced malignancy. Tumor virotherapy presents a significant departure from standard therapy since the outcome depends on the dynamic interactions between the tumor, oncolytic virus, and the immune system. Success specifically depends on amplification of the oncolytic virus, a feature that is distinctly different from all other pharmacologic approaches to cancer therapy. The dynamic aspects of therapy and their importance can only be understood in the context of mathematical and computational approaches in the same way as ecologists and evolutionary biologists have tackled ecosystems in nature. Given that the oncolytic virus has to spread from cell to cell either by diffusion or by the formation of syncytia (virus dependent), the spatial organization of the tumor is thought to play a key role on the success of therapy. These researchers are studying how the spatial relationships between cancer and normal cells within the tumor determine the outcome of therapy.

Group Member

Yaming Chen, Graduate Student