Genetically modified adenoviruses that replicate only in cancer cells containing certain mutation have shown promise as a new treatment for cancer. The adenovirus mutant ONYX-015 in particular has shown some clinical anti-tumor activity, although to a lesser extent than expected. Improvements of this promising therapeutic approach are therefore needed and can be achieved through a better understanding of viral and host factors determining therapeutic efficacy. A crucial step in viral life cycle is successful entry of the viral particle into target cells, which is strongly dependent on the presence of the main receptor for adenovirus, the coxsackievirus and adenovirus receptor (CAR). This receptor is frequently down-regulated in highly malignant cells, rendering this population less vulnerable to viral attack. We have shown that disruption of signaling through the RAF-MEK-ERK pathway by inhibition of MEK can up-regulate CAR expression, resulting in enhanced adenovirus entry into the cells. This pharmacological intervention, however, can also negatively interfere with the replication of ONYX-015 through its effects on the cell cycle. We hypothesize that efficacy of this treatment can be increased by optimizing the overall rate of virus spread: that is, the viral infectivity needs to be increased via the up-regulation of CAR, while maintaining the ability of the virus to replicate in cells. We will achieve this by using a combination of computational and experimental approaches that will be applied to cell line models of pancreatic cancer. Computational models provide an essential tool to search for optimal treatment regimes. We will use ordinary differential equations, based on previous work, to describe the dynamics between replicating viruses and growing tumors; in addition, we will employ control theory (commonly used in engineering) to search for optimal treatment regimes. Computational modeling will be closely tied to experiments, which will involve measurements of crucial parameters, discovery of potential new factors influencing adenovirus infection of cancer cells, model validation, and tests of predictions. This research will be beneficial for public health through improving cancer treatments that make use of viruses that are capable of killing cancer cells. The proposed studies will be performed using pancreatic cancer as a model disease. However, the results of these studies will be applicable to other types of cancer, thus potentially contribute to improving the treatment outcome for many cancer patients in the future. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA118545-02
Application #
7288393
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Couch, Jennifer A
Project Start
2006-09-18
Project End
2010-07-31
Budget Start
2007-09-01
Budget End
2008-07-31
Support Year
2
Fiscal Year
2007
Total Cost
$211,621
Indirect Cost
Name
University of California San Francisco
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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Bagheri, Neda; Shiina, Marisa; Lauffenburger, Douglas A et al. (2011) A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition. PLoS Comput Biol 7:e1001085
Shiina, M; Lacher, M D; Christian, C et al. (2009) RNA interference-mediated knockdown of p21(WAF1) enhances anti-tumor cell activity of oncolytic adenoviruses. Cancer Gene Ther 16:810-9
Komarova, Natalia L; Sadovsky, Alexander V; Wan, Frederic Y M (2008) Selective pressures for and against genetic instability in cancer: a time-dependent problem. J R Soc Interface 5:105-21
Komarova, Natalia L (2007) Viral reproductive strategies: How can lytic viruses be evolutionarily competitive? J Theor Biol 249:766-84