We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.
From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.
Focal Adhesion Kinase (FAK) is a 125 kDa non-receptor kinase that plays a major role in cancer cell survival and metastasis.
We performed computer modeling of the p53 peptide containing the site of interaction with FAK, predicted the peptide structure and docked it into the three-dimensional structure of the N-terminal domain of FAK involved in the complex with p53. We screened small molecule compounds that targeted the site of the FAK-p53 interaction and identified compounds (called Roslins, or R compounds) docked in silico to this site.
By different assays in isogenic HCT116p53+/+ and HCT116 p53-/- cells we identified a small molecule compound called Roslin 2 (R2) that bound FAK, disrupted the binding of FAK and p53 and decreased cancer cell viability and clonogenicity in a p53-dependent manner. In addition, dual-luciferase assays demonstrated that the R2 compound increased p53 transcriptional activity that was inhibited by FAK using p21, Mdm-2, and Bax-promoter targets. R2 also caused increased expression of p53 targets: p21, Mdm-2 and Bax proteins. Furthermore, R2 significantly decreased tumor growth, disrupted the complex of FAK and p53, and up-regulated p21 in HCT116 p53+/+ but not in HCT116 p53-/- xenografts in vivo. In addition, R2 sensitized HCT116p53+/+ cells to doxorubicin and 5-fluorouracil.
Thus, disruption of the FAK and p53 interaction with a novel small molecule reactivated p53 in cancer cells in vitro and in vivo and can be effectively used for development of FAK-p53 targeted cancer therapy approaches.
Focal adhesion kinase; p53Cancer; Small molecule; p21; Tumor; Apoptosis
Focal adhesion kinase (FAK) is a protein tyrosine kinase that is overexpressed in most solid types of tumors and plays an important role in the survival signaling. Recently, we have developed a novel computer modeling combined with a functional assay approach to target the main autophosphorylation site of FAK (Y397). Using these approaches, we identified 1-(2-hydroxyethyl)-3, 5, 7-triaza-1-azoniatricyclo [220.127.116.11,7]decane; bromide, called Y11, a small molecule inhibitor targeting Y397 site of FAK. Y11 significantly and specifically decreased FAK autophosphorylation, directly bound to the N-terminal domain of FAK. In addition, Y11 decreased Y397-FAK autophosphorylation, inhibited viability and clonogenicity of colon SW620 and breast BT474 cancer cells and increased detachment and apoptosis in vitro. Moreover, Y11 significantly decreased tumor growth in the colon cancer cell mouse xenograft model. Finally, tumors from the Y11-treated mice demonstrated decreased Y397-FAK autophosphorylation and activation of poly (ADP ribose) polymerase and caspase-3. Thus, targeting the major autophosphorylation site of FAK with Y11 inhibitor is critical for development of cancer therapeutics and carcinogenesis field.
Focal Adhesion Kinase (FAK) is overexpressed in many types of tumors and plays an important role in survival. We developed a novel approach, targeting FAK-protein interactions by computer modeling and screening of NCI small molecule drug database. In this report we targeted FAK and Mdm-2 protein interaction to decrease tumor growth. By macromolecular modeling we found a model of FAK and Mdm-2 interaction and performed screening of >200,000 small molecule compounds from NCI database with drug-like characteristics, targeting the FAK-Mdm-2 interaction. We identified 5′-O-Tritylthymidine, called M13 compound that significantly decreased viability in different cancer cells. M13 was docked into the pocket of FAK and Mdm-2 interaction and was directly bound to the FAK-N terminal domain by ForteBio Octet assay. In addition, M13 compound affected FAK and Mdm-2 levels and decreased complex of FAK and Mdm-2 proteins in breast and colon cancer cells. M13 re-activated p53 activity inhibited by FAK with Mdm-2 promoter. M13 decreased viability, clonogenicity, increased detachment and apoptosis in a dose-dependent manner in BT474 breast and in HCT116 colon cancer cells in vitro. M13 decreased FAK, activated p53 and caspase-8 in both cell lines. In addition, M13 decreased breast and colon tumor growth in vivo. M13 activated p53 and decreased FAK in tumor samples consistent with decreased tumor growth. The data demonstrate a novel approach for targeting FAK and Mdm-2 protein interaction, provide a model of FAK and Mdm-2 interaction, identify M13 compound targeting this interaction and decreasing tumor growth that is critical for future targeted therapeutics.
Apoptosis; Focal Adhesion Kinase; Mdm-2; Small molecule compound; p53; Tumor growth
Focal Adhesion Kinase (FAK) is a non-receptor kinase that is overexpressed in many types of tumors and plays a key role in cell adhesion, spreading, motility, proliferation, invasion, angiogenesis, and survival. Recently, FAK has been proposed as a target for cancer therapy, and we performed computer modeling and screening of the National Cancer Institute (NCI) small molecule compounds database to target the ATP-binding site of FAK, K454. More than 140,000 small molecule compounds were docked into the crystal structure of the kinase domain of FAK in 100 different orientations using DOCK5.1 that identified small molecule compounds, targeting the K454 site, called A-compounds. To find the therapeutic efficacy of these compounds, we examined the effect of twenty small molecule compounds on cell viability by MTT assays in different cancer cell lines. One compound, A18 (1,4-bis(diethylamino)-5,8-dihydroxy anthraquinon) was a mitoxantrone derivative and significantly decreased viability in most of the cells comparable to the to the level of FAK kinase inhibitors TAE-226 (Novartis, Inc) and PF-573,228 (Pfizer). The A18 compound specifically blocked autophosphorylation of FAK like TAE-226 and PF-228. ForteBio Octet Binding assay demonstrated that mitoxantrone (1,4-dihydroxy-5,8-bis[2-(2-hydroxyethylamino) ethylamino] anthracene-9,10-dione directly binds the FAK-kinase domain. In addition, mitoxantrone significantly decreased the viability of breast cancer cells in a dose-dependent manner and inhibited the kinase activity of FAK and Y56/577 FAK phosphorylation at 10-20 μM. Mitoxantrone did not affect phosphorylation of EGFR, but decreased Pyk-2, c-Src, and IGF-1R kinase activities. The data demonstrate that mitotraxone decreases cancer viability, binds FAK-Kinase domain, inhibits its kinase activity, and also inhibits in vitro kinase activities of Pyk-2 and IGF-1R. Thus, this novel function of the mitoxantrone drug can be critical for future development of anti-cancer agents and FAK-targeted therapy research.
ATP; cancer; enzyme activity; FAK; Focal adhesion kinase; kinase; therapy
Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)—a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.
Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.
We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis ‘point-and-click’ simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both ‘in silico’ and ‘bench’ scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.
To gain insight into the structure of the IgCAM3 domain, the IgCAM3 domain of MLCK1 has been expressed, purified and crystallized.
Myosin light-chain kinase-dependent tight junction regulation is a critical event in inflammatory cytokine-induced increases in epithelial paracellular permeability. MLCK is expressed in human intestinal epithelium as two isoforms, long MLCK1 and long MLCK2, and MLCK1 is specifically localized to the tight junction, where it regulates paracellular permeability. The sole difference between these long MLCK splice variants is the presence of an immunoglobulin-like cell-adhesion molecule domain, IgCAM3, in MLCK1. To gain insight into the structure of the IgCAM3 domain, the IgCAM3 domain of MLCK1 has been expressed, purified and crystallized. Preliminary X-ray diffraction data were collected to 2.0 Å resolution and were consistent with the primitive trigonal space group P212121.
IgCAM3 domain; myosin light-chain kinase 1
Hyperkinetic Jak2 tyrosine kinase signaling has been implicated in several hematological disorders including the myeloproliferative neoplasms (MPNs). Effective Jak2 inhibitors can thus have significant therapeutic potential. Here, using structure based virtual screening, we identified a benzothiophene derived Jak2 inhibitor named A46. We hypothesized that this compound would inhibit Jak2-V617F mediated pathologic cell growth. To test this, A46 was analyzed for its ability to i) inhibit recombinant Jak2 protein catalysis ii) suppress Jak2-mediated pathogenic cell growth in vitro iii) inhibit the aberrant ex vivo growth of Jak2-V617F expressing primary human bone marrow cells and iv) inhibit Jak2-mediated pathogenesis in vivo. To this end, we found that A46 selectively inhibited Jak2-V617F protein when compared to wild type Jak2 protein. The drug also selectively inhibited the proliferation of Jak2-V617F expressing cells in both a time- and dose-dependent manner and this correlated with decreased Jak2 and STAT5 phosphorylation within treated cells. The Jak2-V617F cell growth inhibition correlated with an induction of cell cycle arrest and promotion of apoptosis. A46 also inhibited the pathologic growth of primary Jak2-V617F expressing bone marrow cells, ex vivo. Lastly, using a mouse model of Jak2-V617F mediated MPN, A46 significantly reduced the splenomegaly and megakaryocytic hyperplasia in the spleens of treated mice and the levels of IL-6 in the plasma. Collectively, our data demonstrate that the benzothiophene based compound, A46, suppresses Jak2-mediated pathogenesis, thereby making it a potential candidate drug against Jak2-mediated disorders.
Jak2 kinase; V617F; Small Molecule Inhibitor; Benzothiophene; Myeloproliferative Neoplasms
Relative expression algorithms such as the top-scoring pair (TSP) and the
top-scoring triplet (TST) have several strengths that distinguish them from
other classification methods, including resistance to overfitting,
invariance to most data normalization methods, and biological
interpretability. The top-scoring ‘N’ (TSN) algorithm is a
generalized form of other relative expression algorithms which uses generic
permutations and a dynamic classifier size to control both the permutation
and combination space available for classification.
TSN was tested on nine cancer datasets, showing statistically significant
differences in classification accuracy between different classifier sizes
(choices of N). TSN also performed competitively against a wide
variety of different classification methods, including artificial neural
networks, classification trees, discriminant analysis, k-Nearest neighbor,
naïve Bayes, and support vector machines, when tested on the Microarray
Quality Control II datasets. Furthermore, TSN exhibits low levels of
overfitting on training data compared to other methods, giving confidence
that results obtained during cross validation will be more generally
applicable to external validation sets.
TSN preserves the strengths of other relative expression algorithms while
allowing a much larger permutation and combination space to be explored,
potentially improving classification accuracies when fewer numbers of
measured features are available.
Classification; Top-scoring pair; Relative expression; Cross validation; Support vector machine; Graphics processing unit; Microarray
Summary: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery.
Supplementary information: Supplementary data are available at Bioinformatics online.
Somatic mutations in the Jak2 allele that lead to constitutive kinase activation of the protein have been identified in human disease conditions such as the myeloproliferative neoplasms (MPNs). The most common mutation in these patients is a V617F substitution mutation, which is believed to play a causative role in the MPN pathogenesis. As such, identifying the molecular basis for the constitutive activation of Jak2-V617F is important for understanding its clinical implications and potential treatment. Here, we hypothesized that conversion of residue 617 from Val to Phe resulted in the formation of novel π stacking interactions with neighboring Phe residues. To test this, we first examined the Jak2 structure via molecular modeling and identified a potential π stacking interaction between F594, F595 and F617. Disruption of this interaction through site directed mutagenesis impaired Jak2 auto-phosphorylation, Jak2 dependent gene transcription and in vitro kinase activity of the Jak2-V617F protein. Further, substitution of F594 and F595 with Trp did not affect Jak2 function significantly, but replacement with charged residues did, showing the conservation of aromaticity and hydropathy index at these positions. Using molecular dynamics (MD) simulations, we found that the π stacking interaction between residues 595 and 617 in the Jak2-V617F protein was of much greater energy and conformed to the properties of π stacking, relative to the Jak2-WT or Jak2-V617F/F594A/F595A. In summary, we have identified a π stacking interaction between F595 and F617 that is specific to and is critical for the constitutive activation of Jak2-V617F.
Janus kinase 2 (Jak2); Pi stacking; Constitutive Activation
Stochastic expression of genes produces heterogeneity in clonal populations of bacteria under identical conditions. We analyze and compare the behavior of the inducible lac genetic switch using well-stirred and spatially resolved simulations for Escherichia coli cells modeled under fast and slow-growth conditions. Our new kinetic model describing the switching of the lac operon from one phenotype to the other incorporates parameters obtained from recently published in vivo single-molecule fluorescence experiments along with in vitro rate constants. For the well-stirred system, investigation of the intrinsic noise in the circuit as a function of the inducer concentration and in the presence/absence of the feedback mechanism reveals that the noise peaks near the switching threshold. Applying maximum likelihood estimation, we show that the analytic two-state model of gene expression can be used to extract stochastic rates from the simulation data. The simulations also provide mRNA–protein probability landscapes, which demonstrate that switching is the result of crossing both mRNA and protein thresholds. Using cryoelectron tomography of an E. coli cell and data from proteomics studies, we construct spatial in vivo models of cells and quantify the noise contributions and effects on repressor rebinding due to cell structure and crowding in the cytoplasm. Compared to systems without spatial heterogeneity, the model for the fast-growth cells predicts a slight decrease in the overall noise and an increase in the repressors rebinding rate due to anomalous subdiffusion. The tomograms for E. coli grown under slow-growth conditions identify the positions of the ribosomes and the condensed nucleoid. The smaller slow-growth cells have increased mRNA localization and a larger internal inducer concentration, leading to a significant decrease in the lifetime of the repressor–operator complex and an increase in the frequency of transcriptional bursts.
Expressing genes in a bacterial cell is noisy and random. A colony of bacteria grown from a single cell can show remarkable differences in the copy number per cell of a given protein after only a few generations. In this work we use computer simulations to study the variation in how individual cells in a population express a set of genes in response to an environmental signal. The modeled system is the lac genetic switch that Escherichia coli uses to find, collect, and process lactose sugar from the environment. The noise inherent in the genetic circuit controlling the cell's response determines how similar the cells are to each other and we study how the different components of the circuit affect this noise. Furthermore, an estimated 30–50% of the cell volume is taken up by a wide variety of large biomolecules. To study the response of the circuit caused by crowding, we simulate the circuit inside a three-dimensional model of an E. coli cell built using data from cryoelectron tomography reconstructions of a single cell and proteomics data. Correctly including random effects of molecular crowding will be critical to developing fully dynamic models of living cells.
The interaction of focal adhesion kinase (FAK) and insulin-like growth factor-1 receptor (IGF-1R) plays an important role in cancer cell survival. Targeting this interaction with small molecule drugs could be a novel strategy in cancer therapy. By a series of pull-down assay using GST-tagged FAK fragments and His-tagged IGF-1R intracellular fragments, we showed that the FAK-NT2 (aa 127–243) domain directly interacts with the N-terminal part of the IGF-1R intracellular domain. Overexpressed FAK-NT2 domain was also shown to co-localize with IGF-1R in pancreatic cells. Computational modeling was used to predict the biding configuration of these two domains and to screen for small molecules binding to the interaction site. This strategy successfully identified a lead compound that disrupts FAK/IGF-1R interaction.
FAK is a tyrosine kinase that functions as a key orchestrator of signals leading to invasion and metastasis. Since FAK interacts directly with a number of critical proteins involved in survival signaling in tumor cells, we hypothesized that targeting a key protein-protein interface with drug-like small molecules was a feasible strategy for inhibiting tumor growth. In this study, we targeted the protein-protein interface between FAK and VEGFR-3 and identified compound C4 (chloropyramine hydrochloride) as a drug capable of 1) inhibiting the biochemical function of VEGFR-3 and FAK, 2) inhibiting proliferation of a diverse set of cancer cell types in vitro, and 3) reducing tumor growth in vivo. Chloropyramine hydrochloride reduced tumor growth as a single agent, while concomitant administration with doxorubicin had a pronounced synergistic effect. Our data demonstrate that the FAK-VEGFR-3 interaction can be targeted by small drug-like molecules and this interaction can provide the basis for highly-specific novel cancer therapeutics.
An interaction between the B2 subunit of vacuolar H+-ATPase (V-ATPase) and microfilaments is required for osteoclast bone resorption. An atomic homology model of the actin binding site on B2 was generated and molecular docking simulations were performed. Enoxacin, a fluoroquinolone antibiotic, was identified and in vitro testing demonstrated that enoxacin blocked binding between purified B2 and microfilaments. Enoxacin dose dependently reduced the number of osteoclasts differentiating in mouse marrow cultures stimulated with 1,25-dihydroxyvitamin D3, as well as markers of osteoclast activity, and the number of resorption lacunae formed on bone slices. Enoxacin inhibited osteoclast formation at concentrations where osteoblast formation was not altered. In summary, enoxacin is a novel small molecule inhibitor of osteoclast bone resorption that acts by an unique mechanism and is therefore an attractive lead molecule for the development of a new class of antiosteoclastic agents.
X-ray diffraction data from the targeting (FAT) domain of focal adhesion kinase (FAK) were collected from a single crystal that diffracted to 1.99 Å resolution.
X-ray diffraction data from the targeting (FAT) domain of focal adhesion kinase (FAK) were collected from a single crystal that diffracted to 1.99 Å resolution and reduced to the primitive orthorhombic lattice. A single molecule was predicted to be present in the asymmetric unit based on the Matthews coefficient. The data were phased using molecular-replacement methods using an existing model of the FAK FAT domain. All structures of human focal adhesion kinase FAT domains solved to date have been solved in a C-centered orthorhombic space group.
focal adhesion kinase; targeting domain
Focal Adhesion Kinase (FAK) is a non-receptor kinase that is overexpressed in many types of tumors. We developed a novel cancer-therapy approach, targeting the main autophosphorylation site of FAK, Y397 by computer modeling and screening of the National Cancer Institute (NCI) small molecule compounds database. More than 140,000 small molecule compounds were docked into the N-terminal domain of the FAK crystal structure in 100 different orientations that identified 35 compounds. One compound 14 (1,2,4,5-Benzenetetraamine tetrahydrochloride) significantly decreased viability in most of the cells to the levels equal or higher than control FAK inhibitor, 1a (2-[5-Chloro-2-[2-methoxy-4-(4-morpholinyl)phenylamino]pyrimidin-4-ylamino]-N-methylbenzamide; TAE226) from Novartis, Inc. The compound 14 specifically and directly blocked phosphorylation of Y397-FAK in a dose- and time-dependent manner. It increased cell detachment and inhibited cell adhesion in a dose-dependent manner. Furthermore, 14 effectively caused breast tumor regression in vivo. Thus, targeting the Y397 site of FAK with 14 inhibitor can be effectively used in cancer therapy.
Focal Adhesion Kinase; Y397 site; autophosphorylation; tumor; inhibitor
A highly diversified novel immune-type receptor from catfish, NITR10, was crystallized to reveal novel mechanisms of immune recognition.
X-ray diffraction data from crystals of a novel immune-type receptor (NITR10 from the catfish Ictalurus punctatus) were collected to 1.65 Å resolution and reduced to the primitive hexagonal lattice. Native and selenomethionine derivatives of NITR10 crystallized under different conditions yielded P3121 crystals. SeMet NITR10 was phased to a correlation coefficient of 0.77 by SAD methods and experimental electron-density maps were calculated to 1.65 Å. Five NITR10 molecules are predicted to be present in the asymmetric unit based on the Matthews coefficient.
immune-type receptors; NITR10; Ictalurus punctatus
Novel immune-type receptors (NITRs) comprise an exceptionally large, diversified family of activating/inhibitory receptors that has been identified in bony fish. In this study, we characterize the structure of an activating NITR that is expressed by a cytotoxic NK-like cell line and specifically binds an allogeneic B cell target. A single amino acid residue within the NITR immunoglobulin variable (V)-type domain accounts for specificity of the interaction. Structures solved by x-ray crystallography reveal: (1) the V-type domains of NITRs form homodimers resembling heterodimers formed by rearranging antigen binding receptors and (2) both subunits of NITR dimers form ligand-binding surfaces in CDR1 that determine specificity for the nonself target. In the evolution of immune function, it appears that a specific NK-type of innate recognition may be mediated by a complex germline multigene family of V structures resembling those that are somatically diversified in adaptive immune responses.
Jak2 tyrosine kinase is essential for animal development and hyper-kinetic Jak2 function has been linked to a host of human diseases. Control of this pathway using Jak2-specific inhibitors would therefore potentially serve as a useful research tool and/or therapeutic agent. Here, we used a high throughput program called DOCK, to predict the ability of 20,000 small molecules to interact with a structural pocket adjacent to the ATP binding site of murine Jak2. One small molecule, 2-methyl-1-phenyl-4-pyridin-2-yl-2-(2-pyridin-2-ylethyl)butan-1-one (herein designated as Z3) bound to Jak2 with a favorable energy score. Z3 inhibited Jak2-V617F and Jak2-WT autophosphorylation in a dose-dependent manner, but was not cytotoxic to cells at concentrations that inhibited kinase activity. Z3 selectively inhibited Jak2 kinase function with no effect of Tyk2 or c-Src kinase function. Z3 significantly inhibited proliferation of the Jak2-V617F-expressing, human erythroleukemia cell line, HEL 92.1.7. The Z3-mediated reduction in cell proliferation correlated with reduced Jak2 and STAT3 tyrosine phosphorylation levels as well as marked cell cycle arrest. Finally, Z3 inhibited the growth of hematopoietic progenitor cells isolated from the bone marrow of an essential thrombocythemia patient harboring the Jak2-V617F mutation and a polycythemia vera patient carrying a Jak2-F537I mutation. Collectively, the data suggest that Z3 is a novel specific inhibitor of Jak2 tyrosine kinase.
Small Molecule Inhibitor; Jak2; Myeloproliferative Disorders