ABSTRACT
Drug receptor site occupancy is a central pharmacology parameter that quantitatively relates the biochemistry of drug binding to the biology of drug action. Taxanes and epothilones bind to overlapping sites in microtubules (MTs) and stabilize them. They are used to treat cancer and are under investigation for neurodegeneration. In cells, they cause concentration-dependent inhibition of MT dynamics and perturbation of mitosis, but the degree of site occupancy required to trigger different effects has not been measured. We report a live cell assay for taxane-site occupancy, and relationships between site occupancy and biological effects across four drugs and two cell lines. By normalizing to site occupancy, we were able to quantitatively compare drug activities and cell sensitivities independent of differences in drug affinity and uptake/efflux kinetics. Across all drugs and cells tested, we found that inhibition of MT dynamics, postmitotic micronucleation, and mitotic arrest required successively higher site occupancy. We also found interesting differences between cells and drugs, for example, insensitivity of the spindle assembly checkpoint to site occupancy. By extending our assay to a mouse xenograft tumor model, we estimated the initial site occupancy required for paclitaxel to completely prevent tumor growth as 80%. The most important cellular action of taxanes for cancer treatment may be formation of micronuclei, which occurs over a broad range of site occupancies.
Subject(s)
Antineoplastic Agents/metabolism , Bridged-Ring Compounds/metabolism , Taxoids/metabolism , Antineoplastic Agents/chemistry , Antineoplastic Agents/pharmacology , Biological Transport , Bridged-Ring Compounds/chemistry , Bridged-Ring Compounds/pharmacology , Cell Line, Tumor , Epothilones/chemistry , Epothilones/metabolism , Epothilones/pharmacology , Humans , Kinetics , Microscopy , Microtubules/chemistry , Microtubules/metabolism , Taxoids/chemistry , Taxoids/pharmacologyABSTRACT
Glioblastoma multiforme (GBM) is an aggressive primary brain cancer that includes focal amplification of PDGFRα and for which there are no effective therapies. Herein, we report the development of a genetically engineered mouse model of GBM based on autocrine, chronic stimulation of overexpressed PDGFRα, and the analysis of GBM signaling pathways using proteomics. We discover the tubulin-binding protein Stathmin1 (STMN1) as a PDGFRα phospho-regulated target, and that this mis-regulation confers sensitivity to vinblastine (VB) cytotoxicity. Treatment of PDGFRα-positive mouse and a patient-derived xenograft (PDX) GBMs with VB in mice prolongs survival and is dependent on STMN1. Our work reveals a previously unconsidered link between PDGFRα activity and STMN1, and highlight an STMN1-dependent cytotoxic effect of VB in GBM.
Subject(s)
Breast Neoplasms/metabolism , Drug Resistance, Neoplasm , Glioblastoma/metabolism , Receptor, Platelet-Derived Growth Factor beta/metabolism , Stathmin/metabolism , Vinblastine/pharmacology , Animals , Antineoplastic Agents/pharmacology , Apoptosis , Cell Cycle , Cell Survival , Cells, Cultured , Computational Biology , Disease Models, Animal , Female , Gene Expression Regulation, Neoplastic , Humans , Magnetic Resonance Imaging , Male , Mice , Neoplasm Transplantation , Phosphorylation , Proteomics , Signal TransductionABSTRACT
Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.
