Inhibition of cell growth of A549 cells treated with TGFβ-1 and dasatinib.

<p>(A) A549 cells were treated with DMSO, different concentrations of TGFβ-1 (0–10 ng/mL; white bars), different concentrations of dasatinib (0–400 nM; black bars), or a combination of TGFβ-1 and dasatinib (dashed bars) for 48 hours. Treatment of cells with both agents resulted in increase inhibition (**), as compared to when cells were treated with either agent alone (*). (B) A549 cells were treated with DMSO, 5 ng/mL TGFβ-1, 100 nM dasatinib, or a combination of 5 ng/mL TGFβ-1 and 100 nM dasatinib for 48 hours. After 48 hours, propidium iodide stained cells were analyzed to determine cell cycle distribution and analyzed as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114131#s2" target="_blank">Materials and Methods</a>.</p

Induction of apoptosis after treatment with TGFβ-1 and dasatinib.

<p>(A) A549 cells were treated with 100 nM of dasatinib, 1000 nM of AZD0530, or erlotinib with or without 5 ng/mL TGFβ-1 for 48 hours. (B) A549 cells were pre-treated with 10 µg/mL cycloheximide (CHX) for 1 hour, followed by TGFβ-1 plus or minus dasatinib. After incubation, cells were harvested, lysed, and PARP cleavage detected by Western Blot analysis. (C) A549 cells were seeded in 96-well plates at 5×10³ per well. <i>C</i>ells were treated, and Cell Player 96-Well Kinetic Caspase 3/7 Reagent was added simultaneously. Treatments were done in triplicate. Values are shown as the average number of caspase 3/7 positive cells from 3 independent experiments.</p

APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data

With continuously increasing scale and depth of coverage in affinity proteomics (AP–MS) data, the analysis and visualization is becoming more challenging. A number of tools have been developed to identify high-confidence interactions; however, a cohesive and intuitive pipeline for analysis and visualization is still needed. Here we present Automated Processing of SAINT Templated Layouts (APOSTL), a freely available Galaxy-integrated software suite and analysis pipeline for reproducible, interactive analysis of AP–MS data. APOSTL contains a number of tools woven together using Galaxy workflows, which are intuitive for the user to move from raw data to publication-quality figures within a single interface. APOSTL is an evolving software project with the potential to customize individual analyses with additional Galaxy tools and widgets using the R web application framework, Shiny. The source code, data, and documentation are freely available from GitHub (https://github.com/bornea/APOSTL) and other sources

3D Docked Pose and Interactions of dasatinib and bosutinib with TβR-I 3D rendering of the binding pose of (A) dasatinib and (B) bosutinib docked into TβR-1.

<p>Protein is represented by purple carbon cartoon representation with residues surrounding a ligand are in line representation. Bosutinib is colored with magenta carbons and dasatinib with green carbons. Hydrogen bonds represented by dashed yellow lines with distances (gray) and interacting residue (black) labeled. A π-π stacking interaction between the phenyl of TYR-219 and the pyrimidine of dasatinib is circled in red.</p

Interactions of dasatinib and bosutinib with TβR-I.

<p>Ligand interaction diagram of (A) bosutinib and (B) dasatinib docked into TβR-1. Ligand is represented in black. Hydrogen bonds are labeled by numbers next to the bond. Distances and angles of each hydrogen bond as labeled in the diagram are given in tables within each figure. (C) Site 1 IFD results. Average (black) and best (white) IFD scores of the four compounds docked into site 1 based on ∼50 reported poses for each compound. IFDScore is multiplied by -1 for clarity of presentation.</p

Combination TGFβ-1 and dasatinib treatment effect on phosphorylation of canonical and non-canonical TGFβ pathway intermediaries.

<p>A549 NSCLC cells were treated with DMSO, 5 ng/mL TGFβ-1, 100 nM dasatinib, or a combination of 5 ng/mL TGFβ-1 and 100 nM dasatinib for different amounts of time (1 hour for detection of pSmad2 and pSmad3, and 48 hours for detection of pSrc). After incubation, whole cell lysates were collected and subjected to Western blotting with the indicated antibodies.</p

Increase in BIM after combination treatment mediated by Smad3.

<p>(A) A549 cells were treated with DMSO, 5 ng/mL TGFβ-1, 100 nM dasatinib, or a combination of 5 ng/mL TGFβ-1 and 100 nM dasatinib for 48 hours. After treatment, whole cell lysates were collected and subjected to Western blotting with the indicated antibodies. (B) siRNA against Smad3, and negative control were transfected into A549 cells. After 4-hour incubation, cells were washed and media containing compounds were added to each well. Cells were harvested 48 hours post-transfection for protein extraction preparation and Western blotting analysis. Figure is representative of 3 independent experiments.</p

GSK3 Alpha and Beta Are New Functionally Relevant Targets of Tivantinib in Lung Cancer Cells

Tivantinib has been described as a potent and highly selective inhibitor of the receptor tyrosine kinase c-MET and is currently in advanced clinical development for several cancers including non-small cell lung cancer (NSCLC). However, recent studies suggest that tivantinib’s anticancer properties are unrelated to c-MET inhibition. Consistently, in determining tivantinib’s activity profile in a broad panel of NSCLC cell lines, we found that, in contrast to several more potent c-MET inhibitors, tivantinib reduces cell viability across most of these cell lines. Applying an unbiased, mass-spectrometry-based, chemical proteomics approach, we identified glycogen synthase kinase 3 (GSK3) alpha and beta as novel tivantinib targets. Subsequent validation showed that tivantinib displayed higher potency for GSK3α than for GSK3β and that pharmacological inhibition or simultaneous siRNA-mediated loss of GSK3α and GSK3β caused apoptosis. In summary, GSK3α and GSK3β are new kinase targets of tivantinib that play an important role in its cellular mechanism-of-action in NSCLC

Fragment-Based and Structure-Guided Discovery and Optimization of Rho Kinase Inhibitors

Using high concentration biochemical assays and fragment-based screening assisted by structure-guided design, we discovered a novel class of Rho-kinase inhibitors. Compound <b>18</b> was equipotent for ROCK1 (IC₅₀ = 650 nM) and ROCK2 (IC₅₀ = 670 nM), whereas compound <b>24</b> was more selective for ROCK2 (IC₅₀ = 100 nM) over ROCK1 (IC₅₀ = 1690 nM). The crystal structure of the compound <b>18</b>–ROCK1 complex revealed that <b>18</b> is a type 1 inhibitor that binds the hinge region in the ATP binding site. Compounds <b>18</b> and <b>24</b> inhibited potently the phosphorylation of the ROCK substrate MLC2 in intact human breast cancer cells

ZEB1 Mediates Acquired Resistance to the Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer

<div><p>Epithelial-mesenchymal transition (EMT) is one mechanism of acquired resistance to inhibitors of the epidermal growth factor receptor-tyrosine kinases (EGFR-TKIs) in non-small cell lung cancer (NSCLC). The precise mechanisms of EMT-related acquired resistance to EGFR-TKIs in NSCLC remain unclear. We generated erlotinib-resistant HCC4006 cells (HCC4006ER) by chronic exposure of <i>EGFR</i>-mutant HCC4006 cells to increasing concentrations of erlotinib. HCC4006ER cells acquired an EMT phenotype and activation of the TGF-β/SMAD pathway, while lacking both T790M secondary <i>EGFR</i> mutation and <i>MET</i> gene amplification. We employed gene expression microarrays in HCC4006 and HCC4006ER cells to better understand the mechanism of acquired EGFR-TKI resistance with EMT. At the mRNA level, <i>ZEB1 (TCF8)</i>, a known regulator of EMT, was >20-fold higher in HCC4006ER cells than in HCC4006 cells, and increased ZEB1 protein level was also detected. Furthermore, numerous <i>ZEB1</i> responsive genes, such as <i>CDH1 (E-cadherin)</i>, <i>ST14</i>, and <i>vimentin</i>, were coordinately regulated along with increased <i>ZEB1</i> in HCC4006ER cells. We also identified ZEB1 overexpression and an EMT phenotype in several NSCLC cells and human NSCLC samples with acquired EGFR-TKI resistance. Short-interfering RNA against <i>ZEB1</i> reversed the EMT phenotype and, importantly, restored erlotinib sensitivity in HCC4006ER cells. The level of micro-RNA-200c, which can negatively regulate ZEB1, was significantly reduced in HCC4006ER cells. Our results suggest that increased <i>ZEB1</i> can drive EMT-related acquired resistance to EGFR-TKIs in NSCLC. Attempts should be made to explore targeting <i>ZEB1</i> to resensitize TKI-resistant tumors.</p></div