Prediction of drug response from tumor RNA sequencing data

Even when there is a promising initial response, cancer cells can easily develop resistance to monotherapy. This resistance often arises through the activation of compensatory pathways. Drug combinations are a viable strategy because they can target cancer cells at multiple sites, possibly preventing the emergence of drug resistance. Since the number of possible drug combinations vastly exceeds what could be tested clinically, several computational approaches have been developed to predict cancer drug response, trying to overcome drug resistance and improve patient outcomes. My research project focused on investigating drug combinations for breast cancer employing the deconvolution method of bulk RNA-seq and exploring the similarity of drug-specific signatures. A comprehensive panel of drugs was tested individually and in various combinations. The ultimate goal is to confirm a synergistic effect meaning that the combined action of drugs leads to a greater anti-cancer effect than the sum of their individual effects.