Predicting drug efficacy based on the integrated breast cancer pathway model

This study is based on a simple hypothesis - “ideal” drugs for a patient can cure the patient´s disease by modulating the gene expression profile of the patient to a similar level with those in healthy people, on the pathway level. To verify this hypothesis, we present a computational framework to evaluate drug effects on gene expression profiles in breast cancer. First, a breast cancer pathway model has been constructed by utilizing a computational connectivity maps (C-Maps) approach. This model includes important protein and drug information. In this pathway, specific drug-protein interactions (i.e. activation/inhibition) are annotated as edge attributes. Thus, we get a novel Pharmacology Effect Network, or PEN. We then develop a ranking algorithm called PET (i.e. Pharmacological Effect on Target) to combine gene expression information and our constructed PEN to evaluate specific drugs´ efficacies. Finally, we applied PET and PEN to evaluate 23 breast cancer drugs. The ranking results clearly show the validity of our framework.