Computational Models of Anti-VEGF Therapies in Prostate Cancer.

摘要

The vascular endothelial growth factor (VEGF) family of cytokines promotes vascularization, tumorigenesis and metastasis in many cancers. Our goal is to develop computational models that combine mechanistic topological data on the VEGF protein interaction network with gene expression datasets for a large population of prostate cancers. We have assembled databases of prostate cancer gene expression data, and analyzed the data using bioinformatic techniques, identifying key VEGF-based subgroups of prostate cancer plus biomarkers that identify these groups. This year, we have submitted and revised a manuscript based on this analysis, we expect it to be accepted soon. We have also created new computational models to simulate prostate cancer, based on the individualized gene expression data. We have used these models to simulate therapies that target the VEGF pathway, including anti-ligands such as bevacizumab and anti-receptors. Among are predictions are estimates of the high degree of variability in response to drugs that is predicted by the model, which is reflected in the results of clinical trials targeting VEGF in prostate cancer. This progress will continue into Year 3, where we will expand the VEGF family members included in our models, and extract key biomarkers indicative of likely success of VEGF- or VEGFR-targeting therapies.