Abstract
The emergence of acquired drug resistance results from multiple compensatory mechanisms acting to prevent cell death. Simultaneous monitoring of proteins involved in drug resistance is a major challenge for both elucidation of the underlying biology and development of candidate biomarkers for assessment of personalized cancer therapy. Here, we have utilized an integrated analytical platform based on SDS-PAGE protein fractionation prior to liquid chromatography coupled to multiple reaction monitoring mass spectrometry, a versatile and powerful tool for targeted quantification of proteins in complex matrices, to evaluate a well-characterized model system of melphalan resistance in multiple myeloma (MM). Quantitative assays were developed to measure protein expression related to signaling events and biological processes relevant to melphalan resistance in multiple myeloma, specifically: nuclear factor-κB subunits, members of the Bcl-2 family of apoptosis-regulating proteins, and Fanconi Anemia DNA repair components. SDS-PAGE protein fractionation prior to liquid chromatography coupled to multiple reaction monitoring methods were developed for quantification of these selected target proteins in amounts of material compatible with direct translation to clinical specimens (i.e. less than 50,000 cells). As proof of principle, both relative and absolute quantification were performed on cell line models of MM to compare protein expression before and after drug treatment in naïve cells and in drug resistant cells; these liquid chromatography-multiple reaction monitoring results are compared with existing literature and Western blots. The initial stage of a systems biology platform for examining drug resistance in MM has been implemented in cell line models and has been translated to MM cells isolated from a patient. The ultimate application of this platform could assist in clinical decision-making for individualized patient treatment. Although these specific assays have been developed to monitor MM, these techniques are expected to have broad applicability in cancer and other types of disease.