Global MS-Based Proteomics Drug Profiling

: DNA-based technologies such as RNAi, chemical-genetic profiling, or gene expression profiling by DNA microarrays combined with other biochemical methods are established strategies for surveying drug mechanisms. Such approaches can provide mechanistic information on how drugs act and affect cellular pathways. By studying how cancer cells compensate for the drug treatment, novel targets used in a combined treatment can be designed. Furthermore, toxicity effects on cells not targeted can be obtained on a molecular level. For example, drug companies are particularly interested in studying the molecular side effects of drugs in the liver. In addition, experiments with the purpose of elucidating liver toxicity can be studied using samples obtained from animal models exposed to different concentrations of a drug over time. More recently considerable advances in mass spectrometry (MS) technologies and bioinformatics tools allows informative global drug profiling experiments to be performed at a cost comparable to other large-scale technologies such as DNA-based technologies. Moreover, MS-based proteomics provides an additional layer of information on the dynamic regulation of proteins translation and particularly protein degradation. MS-based proteomics approaches combined with other biochemical methods delivers information on regulatory networks, signaling cascades, and metabolic pathways upon drug treatment. Furthermore, MS-based proteomics can provide additional information on single amino acid polymorphisms, protein isoform distribution, posttranslational modifications, and subcellular localization. In this chapter, we will share our experience using MS based proteomics as a pharmacoproteomics strategy to characterize drug mechanisms of action in single drug therapy or in multidrug combination. Finally, the emergence of integrated proteogenomics analysis, such as "The Cancer Genome Atlas" program, opened interesting perspectives to extend this approach to drug target discovery and validation.