Breast cancer is a major public health problem which treatment needs new pharmacological options. In the last decades, during the postgenomic era new theoretical and technological tools that give us novel and promising ways to address these problems have emerged. In this work, we integrate several tools that exploit disease-specific experimental transcriptomic results in addition to information from biological and pharmacological data bases obtaining a contextual prioritization of pathways and drugs in breast cancer subtypes. The usefulness of these results should be evaluated in terms of drug repurposing in each breast cancer molecular subtype therapy. In favor of breast cancer patients, this methodology could be further developed to provide personalized treatment schemes. The latter are particularly needed in those breast cancer subtypes with limited therapeutic options or those who have developed resistance to the current pharmacological schemes.