The use of patient-specific data in drug and dose selection is becoming an increasingly important component in cancer therapy. Basing drug choice on molecular aspects of the tumor is consistent with the identification of cancer as a molecular disease or diseases, even within the same histological type, and its treatment specific to the background from which it arose and exists. Multiple examples exist of single-gene mutations, and over- or underexpression that convey either sensitivity or resistance to a given agent. What about the picture that global gene expression in a cancer presents regarding drug sensitivity or resistance? Coexpression extrapolation (COXEN) is a methodology that acts as a Rosetta stone between patterns of gene expression that correlate to drug responses in vitro and those in tumors of untreated patients to predict chemosensitivity in such tumors even to drugs that are not specifically indicated for that histotype. Further applications of COXEN in drug discovery allow for in silico screens correlating drug response and gene expression in a genetically diverse cell panel to gene expression patterns in a target tumor with the potential for identifying and repurposing compounds. Here we discuss how COXEN is being developed and tested for application in drug selection, repositioning, and repurposing in oncology.