Connecting clusters of patient to drug responses of cell lines to suggest personalized therapeutics for breast cancer

Recent surge in genome-wide expression data from patient tumors and cell-lines in breast cancer, as well as response data of breast cancer cell-lines to many drugs, opens the opportunity for data integration approaches that can lead to better personalized therapeutics. Here we integrated such data to generate a tripartite network that connects clusters of patients to cell-lines, and cell-lines to drugs to suggest which drugs may work best for each cluster of patients. We combined gene expression profiles from 400 patient tumor samples from two independent publicly-available studies, with gene expression and drug growth-inhibition-response profiles of 31 breast cancer cell-lines to build this tripartite network.