Identifying candidate drugs based on transcriptional landscape associated with triple-negative breast cancer

Abstract

Triple-negative breast cancer (TNBC) is a special type of breast cancer in which ER, PR and HER2 are all negative, characterized by high malignancy, strong invasiveness, and high recurrence rate. It is critical to develop novel drugs for improved therapies for triple-negative breast cancers. Here, we unveiled the transcriptional landscape associated with triple-negative breast cancer and identified candidate drugs using a comprehensive connectivity map. The candidate components could induce reverse expressions of the TNBC gene expression signature and trigger transcriptional reprogramming with a positive impact on modulating chemoresistance and the survival probability of breast cancer patients. Our study also provided an example of drug discovery using in silico drug screening followed by further validations, illustrating an effective computational drug discovery strategy.