| Anti-angiogenic therapies are a rapidly growing group of drugs used for the treatment of cancer. This category of drugs works by targeting the blood vessels, or vasculature, in a tumor in an effort to prevent the expansion of the tumor by shutting off the supply of nutrients and oxygen provided by the vascular tree. Vascular endothelial growth factor (VEGF) is important in the growth and maintenance of tumor vasculature. Bevacizumab, an anti-angiogenic cancer therapy, is a monoclonal antibody (mAb) that binds VEGF and blocks its activity. The FDA has recently approved bevacizumab for the treatment of non-small cell lung cancer (NSCLC). However, there has been limited clinical success with anti-VEGF therapy alone and data now suggest that some tumors may be resistant to bevacizumab treatment. The variation of tumor response to anti-angiogenic therapy suggests a complex mechanism of drug action within different tumors. Response of lung cancer to anti-VEGF therapy may be related to the expression of specific genes in tumors. We hypothesize that differences in the gene expression of lung cancer cell lines will correlate with response to anti-VEGF therapies. We will address this with the following Aims: 1) To evaluate the growth of human NSCLC cell lines in response to anti-VEGF therapy in vivo; 2) To analyze gene expression signatures of lung cancer cell lines that are resistant and sensitive to anti-VEGF therapy in vivo. Since anti-VEGF therapies primarily target the tumor vasculature, which is difficult to recapitulate outside of a living animal, the effect of anti-VEGF strategies on tumor cell gene expression must be evaluated in vivo with the use of mouse models. Therefore, a subset of human NSCLC cell lines (n=12) will be implanted into mice. Tumor-bearing mice will be treated with anti-VEGF mAbs. Gene expression in each tumor will be determined and correlated to response to anti-VEGF therapy. Genes that correspond to sensitivity or resistance to anti-VEGF therapy will be identified. The resulting gene signature will be applied to additional lung cancer cell lines to predict responsiveness to anti-VEGF therapy. The ultimate goal of the project is two-fold: 1) to be able to predict which patients are likely to respond to anti-VEGF therapy and 2) to develop effective combination therapies that will enhance the response of lung cancer patients to anti-VEGF strategies.
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