Mining high‐throughput screens for cancer drug targets—lessons from yeast chemical‐genomic profiling and synthetic lethality

The recent decrease in the rate that new cancer therapies are being translated into clinical use is mainly due to the lack of therapeutic efficacy and clinical safety or toxicology of the candidate drug compounds. An important prerequisite for the development of safe and effective chemical compounds is the identification of their cellular targets. High‐throughput screening is increasingly being used to test new drug compounds and to infer their cellular targets, but these quantitative screens result in high‐dimensional datasets with many inherent sources of noise. We review here the state‐of‐the‐art statistical scoring approaches used in the prediction of drug–target interactions, and illustrate their operation using publicly available data from yeast chemical‐genomic profiling studies. The real data examples underscore the need for developing more advanced data mining approaches for extracting the full information from the high‐throughput screens. A particular medical application stems from the concept of synthetic lethality in cancer and how it could open up new opportunities for personalized cancer therapies. © 2012 Wiley Periodicals, Inc.