Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts . To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features. Genome characterization efforts describe an increasingly comprehensive list of genetic alterations that occur in human cancers, but their contributions to the proliferation or survival of cancers often remains obscure. Furthermore, we lack a systematic understanding of the genetic vulnerabilities of human cancer cells as a function of these genetic alterations and the context in which they occur. A complementary effort to systematically assess the genetic requirements of cancer cells in many cellular contexts will help to decipher the roles of specific mutations and the vulnerabilities that these genetic alterations induce. The identification of these context-specific cancer-cell vulnerabilities is the motivation for the generation of the data set described here.To identify genes that have a context-specific effect on cell viability, we performed short hairpin RNA (shRNA) screens in a large number of cell lines in a highly parallel manner, in an effort named Project Achilles. Although screens to identify proliferation/survival genes have been performed in individual cells, Project Achilles data sets are unique in the number of genes and cell lines screened. Genome-wide parallel pooled screens has been performed with 12 ref.  cell lin3. Briefly, a pool of 54,020 shRNA plasmids targeting ~11,000 genes was used to infect the cell lines with a minimum representation of 200 cells per shRNA in each of 4 replicates. Cells were then propagated for 16 population doublings or 40 days in culture, whichever came first. The relative levels of the shRNA plasmids represented in the cell-pool gDNA were measured using Illumina-based sequencing and compared to the initial plasmid pool . In addition to the shRNA level data, a gene-level dataset generated using the ATARiS algorithm7 is also available for download. ATARiS combines data from the multiple distinct shRNAs per target gene across many cell lines to reduce the contribution of off-target effects while reinforcing the on-target effects. Moreover, specific genes can be queried and the dependency profile for such genes across cell lines can be individually downloaded from the portal. To facilitate the discovery of molecular and cellular correlates of the dependencies profiles, we created an analytical tool that we named PARIS  and made it available to the scientific community in GenePattern (www.genepattern.org).We made Achilles 2.4 available to the scientific community in the Project Achilles portal (www.broadinstitute.org/ccle). Cell line information, including source is listed in ’Table1_screening_information.xls’ (Data Citation 1). Tumor type and growth media conditions, also used for screening, were obtained from the CCLE project. For cell lines not obtained from CCLE, media conditions used by the source laboratory were employed. Cell doubling time is calculated from the lentivirally infected cells during the course of the experiment. Days in culture, calculated from the date of infection until the date of the harvest, and passage number, based on the number of splits that occurred during the time in culture, refer to the time point of the sample that was used for data collection specific to each cell line.The majority of cell lines (179) were obtained from the Cancer Cell Line Encyclopedia .Briefly the Sequenom protocol is as follows: SNPs are amplified in PCR reactions that contain a maximum of 24 loci. The Single Base Extension reaction is then performed on the Shrimp Alkaline Phosphatase treated PCR product using iPLEX-GOLD enzyme and mass-modified terminators . A small volume of reaction is then loaded onto each position of a 384-well SpectroCHIP preloaded with matrix (3-hydroxypicolinic acid). SpectroCHIPs are analyzed in automated mode by a MassArray MALDI-TOF Compact system with a solid phase laser mass spectrometer . The resulting spectra are called by real-time SpectroCaller algorithm and analyzed by MassArray Typer v.4.0 software which combines base caller with the clustering algorithm.For Fluidigm Fingerprinting, we utilize 4 HX Fluidigm IFC chip loaders and 4 FC1 cyclers for the 96.96 dynamic array. To support this process, we use an Agilent Bravo and the BioMark HD system for the liquid handling steps of the process.3. Using a library of 54,020 shRNAs targeting 11,194 genes individual shRNAs were lentivirally delivered to the cells. The abundance of the shRNAs was measured after the cells were propagated for 16 populations doublings or 40 days in culture, whichever came first, and compared to the initial DNA plasmid pool. To generate Achilles v2.4 we used the genomic DNA from these 102 cell lines and re-measured their abundance using a next generation sequencing approach. Four lines  that were screened previously failed our new QC guidelines implemented with the new sequencing deconvolution pipeline (see below). An additional 143 cell lines were screened using a similar protocol. In total, high quality data from 216 cell lines make up the final Achilles v2.4 dataset. The media conditions used for all cell lines are listed in ‘Table1_screening_information.xls’ (Data Citation 1).We previously performed a genome-wide pooled shRNA screening of 102 cancer cell lines in quadruplicate (Achilles v2.0) to identify essential geneset al.10. Briefly, the shRNA region was PCR amplified from the purified gDNA using the following conditions: 5 μl primary PCR primer mix, 4 μl dNTP mix, 1x Ex Taq buffer, 0.75 μl of Ex TaqDNA polymerase (Takara), and up to 10 μg genomic DNA in a total reaction volume of 100 μl. A total of 140 μg gDNA was used as template from each replicate. Thermal cycler PCR conditions consisted of heating samples to 95 °C for 5 min; 15 cycles of 94 °C for 30 s, 65 °C for 30 s, and 72 °C for 20 s; and 72 °C for 5 min. PCR reactions were then pooled per sample. A secondary PCR step was performed containing 5 μM of common barcoded 3′ primer, 8 μl dNTP mix, 1x Ex Taq buffer, 1.5 μLEx TaqDNA polymerase, and 30 μl of the primary PCR mix for a total volume of 90 μl. 10 μl of independent 5′ barcoded primers are then added into each reaction, after which the 100 μl total volume is divided into two 50 μl final reactions. Thermal cycler conditions for secondary PCR are as follows: 95 °C for 5 min; 15 cycles of 94 °C for 30 s, 58 °C for 30 s, and 72 °C for 20 s; and 72 °C for 5 min. Individual 50 μl reactions are then re-pooled. Reactions are then run on a 2% agarose gel and intensity-normalized. Equal amounts of samples, based on gel intensity are then mixed and gel-purified using a 2% agarose gel. Samples were sequence using a custom sequencing primer using standard Illumina conditions.Deconvolution was performed similar to that described in Ashton Primary PCR Primers:5′: AATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCG3′: CTTTAGTTTGTATGTCTGTTGCTATTATGTCTACTATTCTTTCCCSecondary PCR Primers:5′(BC):AATGATACGGCGACCACCGAGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGANNNNACGA3′: CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTTGTGGATGAATACTGCCATTTGTCTCCustom Sequencing primer:GAGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAhttp://www.broadinstitute.org/rnai/public/resources/protocolsFor current methods please visit: 7reads per replicate.20 replicates were multiplexed into a single Ilumina sample, and run on multiple lanes to achieve a minimum of 1Raw 45-mer reads were extracted from fastq files and binned into reads containing each unique 4-mer PCR primer barcode used for each screening replicate. Within each replicate, instances of the 21-mer shRNA sequence was counted, using the TRC reference list of all 21-mer hairpin sequences expected. This generated a matrix of the counts of raw Illumina reads for each shRNA in each screening replicate. These counts were normalized to the total number of reads collected for each replicate to account for the (modestly) variable read depth of each replicate. This was performed using the following equation:2 [/ ×1e6] +1Normalized shRNA value=loghttp://genepattern.org).A GenePattern module to perform both the extraction of raw reads counts and to convert into a normalized data file named PoolQ will be available soon in GenePattern  was chosen as the threshold used to fail individual replicate samples. A measure of the overall distribution of each replicate sample was scored by calculating its 75th percentile. Those individual replicate samples that had a score less than the mean—1 standard deviation of all scores were removed from further analysis. In addition, any cell line that lacked 3 replicates passing both of these QC metrics was also removed. The GenePattern module ‘ReplicatesQC’ was used to run these metrics and identified replicate samples to be removed. Four additional cell lines were removed from the final dataset because they were engineered cell lines.2 transformed read counts per replicate sample (Data Citation 1) were processed in a GenePattern pipeline. The pipeline consisted of individual GenePattern modules (http://genepattern.org and in the GParc repository: http://www.broadinstitute.org/software/gparc/), each responsible for a specific processing step. During the period of Illumina data collection, changes in both the cluster kit used (cBot v7 for early samples to cBot v8 for later samples), and the SBS kit (SBS v2 for earlier samples and v3 for later samples) were implemented. To minimize any technical error due to the variation in Illumina chemistry, we kept the data for each subset of samples independent for those collected using the cBotV7/sbsv2 kits and those collected using the cBOTv8/sbsv3 kits. Separate files corresponding to samples processed under different sequencing chemistry and software conditions were maintained in the pipeline until the correct reference DNA pool sample was mapped to each sample. Briefly, the pipeline starts with modules that remove undesirable shRNAs and failing QC replicate samples. These modules are ‘FilterLowshRNAs’, which removes shRNAs that start at low abundance in the plasmid DNA pool , ‘shRNAremoveOverlap’, which removes one of a pair of shRNAs that have an offset of <3 basepairs and ‘removeSamples’, which removes a list of failing replicates. The pipeline then calculates fold change values per shRNA per replicate using the ‘shRNAfoldChange’ module and normalizes the shRNA depletion values per replicate cell line to the same scale, using quantile normalization with the ‘NormLines’ module. Remaining replicates of each cell line were then collapsed to a single value per cell line, per shRNA using the ‘shRNAcollapseReps’ module. The last step in the data processing pipeline maps shRNAs to gene symbols, using a mapping file ‘CP0004_20131120_19mer_trans_v1.chip’ (Data Citation 1) and the ‘shRNAmapGenes’ module. Multiple shRNAs can be mapped to the same genes in the final shRNA-level data file (Data Citation 1), depending on this transcriptome mapping.Normalized and logData files have been deposited in the FigShare data repository as one text-based README file, four.gct formatted files, 3 tables as either.xls or tab delimited text and one.chip mapping file (Data Citation 1).File 1. ‘Achilles_Analysis_README_v2.4.3.txt’This is a README file outlining the data processing steps that occur between the initial logged and normalized read count files and the final shRNA- and gene-levels files.File 2. ‘cBOTv7_sbsv2_allreps_log.gct’The logged and normalized read counts from the pooled screening of the first group of cancer cell lines, performed in quadruplicate, and the appropriate DNA reference. This is a.gct formatted file, with replicate cell lines in columns and shRNAs in rows. Sequences of shRNA barcodes are in the 1st column (labeled ‘Name’), with a blank 2nd column (labeled ‘Description’).File 3. ‘cBOTv8_sbsv3_allreps_log.gct’The logged and normalized read counts from the pooled screening of the second group of cancer cell lines, performed in quadruplicate, and the appropriate DNA reference. This is a.gct formatted file, with replicate cell lines in columns and shRNAs in rows. Sequences of shRNA barcodes are in the 1st column (labeled ‘Name’), with a blank 2nd column (labeled ‘Description’).File 4. ‘Achilles_QC_v2.4.3.rnai.gct’The final shRNA-level file obtained after processing formatted as a.gct file, with cell lines in columns and shRNAs in rows. Sequences of shRNA barcodes are in the 1st column (labeled ‘Name’) and gene names mapped to those shRNAs are in the 2nd column (labeled ‘Description’).File 5. ‘Achilles_QC_v2.4.3.rnai.Gs.gct’The final gene-level file obtained after analysis of the shRNA level file with the ATARiS algorithm. This is a.gct formatted file, with cell lines in columns and ATARiS gene solutions in rows. ATARiS gene solutions are in the 1st column (labeled ‘Name’) and gene names are in the 2nd column (labeled ‘Description’).File 6. ‘Achilles_QC_v2.4.3.shRNA.table.txtThe shRNA quality file produced after analysis of the shRNA level file with the ATARiS algorithm. This is a tab-delimited text file, with shRNAs in rows. The columns contain annotation information for each shRNA, including consistency scores and whether each shRNA was used in the resulting gene solution.File 7. ‘Table1_screening information.xls’The file of cell line information, including annotations about screening conditions, are present in this.xls formatted file. Described in more detail in the Methods section.File 8. ‘Table2_SNP_genotyping.xls’The file of SNP genotypes, per cell line, are present in this.xls formatted file. Described in more detail in the Methods section.File 9. ‘CP0004_20131120_19mer_trans_v1.chip’The shRNA to gene mapping file. This is a tab delimited text file with shRNA barcode sequences in rows, along with the mapping of each to gene transcript identifier, gene identifier and gene symbol.1  in a 1:1:1:1 ratio of concentration for the Reference pool and in a 1:4:16:64 ratio for the Dilution series pool. We show that 4-fold changes in relative shRNA abundance can be accurately shown by NGS (Illumina) sequencing, similar to that seen previously by custom Affymetrix arrays1 .3 to compare with the values obtained using sequencing to deconvolve our pooled screening data. The percentage depletion of these shRNAs in the competition assay was correlated to their fold change in the pooled screening (We used a previously generated dataset of 350 shRNAs tested in competition assays in OVCAR-8 cellscreening . The fouhttp://genepattern.org). This table also contains information about the number of SNPs used for matching and the fraction matching between the reference and post-screen fingerprints. The ability to match post-screening results back to reference cell line genotypes confirms cell line identity in the screening results and ensures the correct use of previously collected genomic information from the CCLE project.SNP fingerprinting was performed to validate the cell line identity after pooled screening. Birdseed genotypes from Affymetrix SNP6.0 arrays for 79 SNPs were primarily used as references for cell line identity and are listed by their dbSNP identifier in ‘Table 2_SNP_genotyping.xls’ (Data Citation 1), along with a designation of which fingerprinting platform was used for comparison. In some cases, a reference panel was genotyped before screening commenced. Fluidigm or Sequenom fingerprints after screening were extracted and compared to the reference, using the GenePattern module ‘FPmatching’  was performed on the quantile-normalized shRNA level data to identify systematic variation among groups of cell lines. Scatterplots of the first two principal components (variables that account for the most variation in the data) show biological diversity between lineages, as seen in As outlined in the sample quality control methods, the Pearson correlation within cell line replicates (intra-replicate) was calculated and compared with non-replicate pairs of samples (inter-replicate). The majority of cell lines have replicate correlations above the calculated quality control threshold (0.6795), highlighting the reproducibility of these screens . In addi3 is also hosted in this Portal. Project Achilles is an ongoing project and as additional data is generated, these data sets will be made available in the Portal when they become ready for public release. Moreover, we are continuously evaluating our data processing steps and re-iterations of the same data sets might be released in the Portal if they present a significant improvement.The Project Achilles Portal was developed at the Broad Institute to make the Achilles data sets more visible and easily available to the scientific community. In addition to the data set described here, our previously published data sethttp://www.broadinstitute.org/ataris/)7. We preferentially use the gene level scores in our downstream analysis since this takes into account the consistency of the different shRNA values for one gene across cell lines, and thus helps to maximize the on-target effects of shRNAs. In addition to the data at the shRNA and gene level, a sample information file, an shRNA to gene mapping file and a detailed description of the processing steps are provided which each version of the data. The data can be downloaded or launched using GENE-E (http://www.broadinstitute.org/cancer/software/GENE-E/).The datasets are organized by version and with each version, data is available for download at the shRNA level and summarized at the gene-level using the ATARiS algorithm  and a consistency score (provides a score for each shRNA that represents the confidence that its observed phenotypic effects are the result of on-target gene suppression). A sortable heatmap with the shRNA and ATARiS scores per cell line is also shown.RNAi essentiality profiles (shRNA- or gene-based) that best match the target profile in a collection of samples according to a rescaled normalized mutual information score (RNMI). As new metrics are evaluated, PARIS may be modified to support them.PARIS is a sensitive and general information-based feature selection method. A target profile of interest is identified, such as mutation status of an oncogene, and then the method selects the top H and the Mutual Information, MI between the target profile t and a given essentiality profile x are function of empirical probability distributions1112–14. The mutual information is normalized (NMI) using the joint entropy,15 that takes into account differences in entropy across essentiality profiles. We also rescale the NMI with the score of the target against itself,RNMI matching score is estimated by an empirical permutation test where the target values are randomly permuted and compared with all the essentiality profiles in order to make a global null distribution and compute nominal p-values and False Discovery Rates16. The RNMI matching score has important advantages when compared to other association metrics such as increased sensitivity to non-linear correlations and wider dynamic range at the top of the matching scale which is especially useful when comparing against multiple genomic features. The use of information-based measures of association is not new18 but it has only been applied systematically to practical inferential problems, and genomics in particular, over the last decade19–23. Recently there has been a renewed appreciation of the potential of information-based approaches25.The joint entropy http://genepattern.org) and a tutorial is available in the Project Achilles Portal. The Cancer Cell Line Encyclopedia portal (http://www.broadinstitute.org/ccle) is an excellent resource for obtaining genomic information on most of the Project Achilles-screened cell lines, to use in PARIS and other data mining tools.We made the PARIS publically available as a GenePattern module .


Inflammatory breast cancer (IBC) is an aggressive type of advanced breast cancer with a poor prognosis. We recently found that focal adhesion kinase 1 (FAK1) is upregulated and phosphorylated (active) in IBC. In this study, we investigated the effect of CEP-37440, a dual inhibitor of FAK1 and anaplastic lymphoma kinase (ALK), using human IBC cell lines and preclinical models of IBC., we tested CEP-37440 using FC-IBC02, SUM149, and SUM190 IBC xenograft mouse models.Cell proliferation assays were performed in the presence of several concentrations of CEP-37440 using IBC and triple-negative breast cancer non-IBC cell lines. In vitro, we studied the expression of total FAK1, phospho-FAK1 (Tyr 397), total ALK and phospho-ALK (Tyr 1604). In vivoCEP-37440 at low concentration decreased the proliferation of the IBC cell lines FC-IBC02, SUM190, and KPL4, while not affecting the proliferation of normal breast epithelial cells. At higher concentration, CEP-37440 was also able to inhibit the proliferation of the IBC cell line MDA-IBC03 and the triple-negative non-IBC cell lines MDA-MB-231 and MDA-MB-468; the IBC cell line SUM149 showed a slight response to the drug. CEP-37440 decreased the cell proliferation of FC-IBC02, SUM190, and KPL4 by blocking the autophosphorylation kinase activity of FAK1 (Tyr 397). None of the cells evaluated expressed ALK. In vivo, after 7 weeks of CEP-37440 treatment, the SUM190, FC-IBC02, and SUM149 breast tumor xenografts were smaller in mice treated with 55 mg/kg bid CEP-37440 compared to the controls; the tumor growth inhibition (TGI) was 79.7 %, 33 %, and 23 %, respectively. None of the FC-IBC02 breast xenografts mice treated with CEP-37440 developed brain metastasis while 20 % of the mice in the control group developed brain metastasis. Expression array analyses in FC-IBC02 cells showed that CEP-37440 affects the expression of genes related to apoptosis, interferon signaling, and cytokines.CEP-37440 is effective against some IBC cells that express phospho-FAK1 (Tyr 397), and its antiproliferative activity is related to its ability to decrease phospho-FAK1. Our results suggest that combinational therapies could be more effective than using CEP-37440 as a single agent.The online version of this article (doi:10.1186/s13058-016-0694-4) contains supplementary material, which is available to authorized users. Inflammatory breast cancer (IBC) is a very aggressive type of advanced breast cancer with a poor prognosis. IBC occurs typically in patients under the age of 50 and is often misdiagnosed as an infection since it does not present as a lump . The cliWe recently found that focal adhesion kinase 1 (FAK1 or PTK2) is amplified, upregulated and phosphorylated (active) in IBC . FAK1 is50: 2.3 nM, cellular IC50: 88 nM) and anaplastic lymphoma kinase (ALK)   = 100 × (1-exp (mean difference in log volumes at the end of the treatment), where the mean difference in log volumes at the end of the treatment was estimated from the fitted LME models. The data were analyzed using R package ‘nlme’  were studied in the IBC cell lines KPL4, MDA-IBC03, FC-IBC02, SUM190, and SUM149, and in the non-IBC triple-negative cell lines MDA-MB-231 and MDA-MB-468. All of the cell lines expressed high levels of FAK1 (between 0.28 ng to 0.7 ng FAK1/μg protein), and no differences were observed between IBC and non-IBC cell lines Fig. . HoweverThe IBC cell lines FC-IBC02, SUM190, KPL4, SUM149 and MDA-IBC03 were tested for expression of ALK by ELISA. The human T cell lymphoma cell line Karpas 299 that carries the NPM (nucleophosmin)-ALK fusion gene was used as positive controls and these cells were strongly positive for total ALK and phospho-ALK (Tyr 1604). All of the IBC cell lines tested were negative for total ALK and phospho-ALK (Tyr 1604), and the non-IBC cell lines MDA-MB-231 and MDA-MB-468 were also negative for total ALK and phospho-ALK expression (data not shown).Cell proliferation rate was studied in the IBC cell lines in the presence of several concentrations of CEP-37440. The MTS assay that measures cellular respiration was used as a surrogate of cell proliferation. In the presence of 300 nM CEP-37440, FC-IBC02 significantly decreased in proliferation compared to the control without the drug, and 1000 nM CEP-37440 completely inhibited FC-IBC02 proliferation, killing these cells after 72 h of treatment Fig. . FC-IBC0In KPL4 cells, treatment with 1000 nM CEP-37440 for 144 h reduced significantly the proliferation of cells compared to the control, and the growth rate was even lower in the presence of 2000 nM or 3000 nM CEP-37440 Fig. . The gro50) for each cell line are shown in Table In SUM190 cells, treatment with 1000 nM CEP-37440 for 144 h reduced proliferation to approximately 60 % Fig. . The res50) for each cell line are indicated in Table In conclusion, CEP-37440 at low concentration specifically reduced the proliferation of three out of five IBC cell lines; FC-IBC02, KPL4 and SUM190 cells showed decreased proliferation in the presence of 1000 nM CEP-37440, and this same concentration did not significantly reduce the proliferation of normal breast epithelial cells. The CEP-37440 concentrations required to reduced the growth rate to 50 %  and total FAK1 and phospho-FAK1 (Tyr 397) were studied Fig. , but theIn KPL4 cells, the levels of total FAK1 were similar between cells treated with CEP-37440 and the control without treatment Fig. , howeverIn conclusion, low concentration of CEP-37440 (1000 nM) was able to decreased phospho-FAK1 by half in FC-IBC02, SUM190, and KPL4 cells after 48 h of treatment compared to the controls without treatment, while in SUM149, it only decreased slightly.p = 0.05), with ten downregulated and 33 upregulated genes  was able to completely inhibit the proliferation of these cells in vitro. Cells were treated with 1000 nM CEP-37440 for 48 h, RNA was isolated from the cells, and expression studies were performed. The microarray data have been deposited into the NCBI’s gene expression omnibus (GEO) data sets GSE73285). A total of 43 genes showed changes in their expression when cells were treated with CEP-37440 compared to the control  were tested in these mice. In the animals harboring FC-IBC02 breast tumor xenografts, mice treated with 55 mg/kg bid CEP-37440 showed smaller tumor breast xenografts compared to the control group that did not receive the drug  . Furthermore, there was approximately 21 % reduction in the FC-IBC02 breast tumor xenografts size after the mice were treated with 30 mg/kg bid CEP-37440 at week 8 (40 days of treatment) (p = 0.380)  Fig. . FC-IBC080) Fig. . Importa80) Fig. , none of80) Fig.  and the 80) Fig.  .Fig. 6Ip = 0.021) and approximately 23 % reduction in the SUM149 breast tumor xenografts size after the mice were treated with 55 mg/kg bid CEP-37440 at week 7 (p = 0.302). SUM149 breast tumor xenograft mice treated with CEP-37440 showed no significant weight loss compared to the control group that was not treated with CEP-37440  . At higher concentrations, CEP-37440 also inhibited the proliferation of the IBC cell line MDA-IBC03 and the triple-negative non-IBC cell lines MDA-MB-231 and MDA-MB-468. Since CEP-37440 is a dual inhibitor of FAK1 and ALK, we studied the expression of both proteins in the IBC cell lines and found that they expressed FAK1 but not ALK. We demonstrated that CEP-37440 decreased the cell proliferation of FC-IBC02, SUM190, and KPL4 by blocking the autophosphorylation kinase activity of FAK1 (Tyr 397).We previously showed that IBC cells exhibit amplification of the chromosomal arm 8q where FAK1 is located (8q24.3) . HoweverIn vivo studies showed that CEP-37440 significantly decreased breast tumor growth in the SUM190 and FC-IBC02 mouse xenograft models, and the tumor growth inhibition was dose and time dependent. Mice harboring SUM190 and FC-IBC02 breast tumor xenografts showed 79.7 % and 33 % TGI, respectively, when treated with 55 mg/kg bid CEP-37440 for 35–40 days. The tumor breast xenografts of mice treated with CEP-37440 showed lower levels of phospho-FAK1 (Tyr 397) than the breast tumor xenografts in the control groups (data not shown). Although the IBC cell line SUM149 showed in vitro only a slight response to high concentrations of the drug, CEP-37440 reduced the size of the primary tumor in the mice harboring SUM149 breast tumor xenografts, and results were better with 30 mg/kg bid than 55 mg/kg bid CEP-37440. Since there was no total regression of the primary tumor or metastatic sites in the lungs in mice harboring IBC xenografts, our results suggest that a combination therapy approach would be more effective for IBC patients than CEP-37440 alone. Preclinical studies showed that the combination therapy approach using inhibitors of FAK1 with other signaling pathways increased the efficacy of single inhibitors , 23. TheNone of the FC-IBC02 breast xenograft mice treated with CEP-37440 developed brain metastases in contrast to 20 % in the control group. This data suggests that CEP-37440 is able to cross the blood-brain barrier. Related work by another group found that TAE226, another small molecule inhibitor targeting the ATP-binding site of FAK1, increased apoptosis of glioblastoma, an infiltrative brain tumor, and inhibited tumor growth .The overexpression and phosphorylation of FAK1 on Tyr 397 is frequently associated with tumor metastasis as well as poor patient prognosis –28, indiIn a recent study, FAK1 expression was analyzed in breast primary tumor samples from stages III–IV patients and a significant association between high FAK1 expression in the primary tumor, lymphovascular invasion, and triple-negative phenotype was found. In addition, a strong positive correlation was observed between high FAK1 expression and shorter overall survival and progression-free survival in patients with metastatic tumors ; howeverFAK1 is one of the most attractive tyrosine kinase targets in cancer therapy since it plays a role in signal transduction and in the development of numerous human tumors, including breast, colon, thyroid, prostate, pancreas, and brain cancers . Besideswww.clinicaltrials.gov NCT01922752). Based upon the preclinical studies described above and the data available for other FAK1 inhibitors, CEP-37440 may have clinical applications as part of a combinatorial therapy against IBC.Finally, our in vivo experiments demonstrated that animals treated with CEP-37440 did not experience reduced body weight or hair loss, suggesting that CEP-37440 was not toxic for mice at a dose of 30 or 55 mg/kg/bid. CEP-37440 is currently in a phase I dose-escalation clinical trial in patients with solid tumors (These results suggest that CEP-37440 could be effective against IBC cells that express phospho-FAK1 (Tyr 397) if the drug is able to decrease the level of phospho-FAK (Tyr-397). Our results also suggest that combinational therapies could be more effective than using CEP-37440 as a single agent.Additional file 1: Figure S1.FC-IBC02 cell proliferation assays: estimated time trends in response to different CEP-37440 concentrations in the triple-negative IBC cell line FC-IBC02. (DOC 54 kb)Additional file 2: Table S1.FC-IBC02 cell proliferation assays: comparisons from the LME model and time trend estimates by CEP-37440 doses. (DOC 81 kb)Additional file 3: Figure S2.KPL4 cell proliferation assays: estimated time trends in response to CEP-37440 concentrations in the ErbB2-positive IBC cell line KPL4. (DOC 76 kb)Additional file 4: Table S2.KPL4 cell proliferation assays: comparisons from the LME model for log-transformed responses and time trend estimates. (DOC 56 kb)Additional file 5: Figure S3.SUM190 cell proliferation assays: estimated time trends in response to CEP-37440 concentration in the ErbB2-positive IBC cell line SUM190. (DOC 55 kb)Additional file 6: Table S3.SUM190 cell proliferation assays: comparisons from the LME model for log-transformed responses and time trend estimates. (DOC 83 kb)Additional file 7: Figure S4.In vivo studies using FC-IBC02 xenograft model: log-transformed tumor volumes and estimated time trends in each group from the LME model. (DOC 44 kb)Additional file 8: Table S4.In vivo studies using FC-IBC02 xenograft model: results from the LME model and CEP-37440 treatment comparisons. (DOC 50 kb)Additional file 9: Figure S5.In vivo studies using SUM149 xenograft model: log-transformed tumor volumes and estimated time trends in each group from the LME model. (DOC 393 kb)Additional file 10: Table S5.In vivo studies using SUM149 xenograft model: results from the LME model and CEP-37440 treatment comparisons. (DOC 56 kb)Additional file 11: Table S6.In vivo studies using SUM190 xenograft models. (DOC 39 kb)Additional file 12:Supplementary Materials and Methods. Detail description of materials and methods. (DOCX 17 kb)


Elimination of ACKR2 greatly reduced DN in OVE26 mice, an established DN model. Albuminuria was significantly lower at 2, 4, and 6 months of age. ACKR2 deletion did not affect diabetic blood glucose levels but significantly decreased parameters of renal inflammation including leukocyte infiltration and fibrosis. Activation of pathways that increase inflammatory gene expression was attenuated. Human biopsies stained with ACKR2 antibody revealed increased staining in diabetic kidney, especially in some tubule and interstitial cells. The results demonstrate a significant interaction between diabetes and ACKR2 protein in the kidney. Unexpectedly, ACKR2 deletion reduced renal inflammation in diabetes and the ultimate response was a high degree of protection from diabetic nephropathy.In diabetic nephropathy (DN) proinflammatory chemokines and leukocyte infiltration correlate with tubulointerstitial injury and declining renal function. The atypical chemokine receptor ACKR2 is a chemokine scavenger receptor which binds and sequesters many inflammatory CC chemokines but does not transduce typical G-protein mediated signaling events. ACKR2 is known to regulate diverse inflammatory diseases but its role in DN has not been tested. In this study, we utilized ACKR2 Although hyperglycemia is the initiating and essential cause for all diabetic complications there is accumulating evidence that inflammatory processes activated by chronic elevated glucose are integral to the development of diabetic complications . DiabetiACKR2 is a chemokine decoy receptor  which caAll animal procedures followed the NIH Guide for the Care and Use of Laboratory Animals and were approved by the University of Louisville Institutional Animal Care and Use Committee. ACKR2 mice on the C57BL/6 background originally from Charles River Italia   were breμg/mg).Glucose was assayed in serum samples obtained from nonfasted mice at 6 months of age by the Glucose (HK) Assay Kit (Sigma-Aldrich). At 2 months urine glucose was evaluated with Clinistix (Bayer). Albumin was measured from spot urine samples with a mouse albumin ELISA kit  within the linear range of the assay. Urine creatinine was measured with a creatinine assay kit . Urine albumin was expressed as the ratio of albumin to creatinine . Positive staining was detected with HRP conjugated second antibody and diaminobenzidine (DAB). CD45 positive cell infiltration was evaluated by quantitating the DAB stained pixel area in 8 random, nonoverlapping 200x image fields from the cortical region per mouse with 3 mice per group. Digital images were taken by an observer blind to the identity of the section and the number of positive pixels was quantified by another observer blind to section identity. Pixel number was determined using the ability of Adobe Photoshop to select areas of matching color intensity.http://www.ingenuity.com/, Ingenuity Pathway Analysis, Redwood City, CA) for data analysis. Gene array data was uploaded to GEO and the access number is GSE51205.RNA extraction was done with the RNeasy Mini Kit  from frozen kidneys. Extracted RNA was checked for quality on Agilent 2100 Bioanalyzer . The RNA samples having RNA integrity number (RIN) above 8.8 (average 9.1) were used for probe preparation. A 100 ng aliquot of RNA from each mouse was used for probe preparation with an Ambion WT Expression kit. The kit generates sense-strand cDNA from total RNA for fragmentation and labeling was done with an Affymetrix GeneChip WT Terminal Labeling Kit (PN90067). Probes from 3 six-month-old female mice in each group were hybridized to Affymetrix mouse gene 1.0 ST exon arrays and scanned with a GCS 3000 7G scanner and signals were analyzed with Command Console software . Gene expression profiles were uploaded to Ingenuity software . The cDNA was synthesized with high-capacity cDNA archive kit  and PCR was performed on an Applied Biosystems 7300 thermocycler with commercially available Taqman reagents  for ccbp2 (ACKR2) (Mm00445551_m1), ccl2 (Mm00441242_m1), ccl5 (Mm01302428_m1), ccr2 (Mm04207877_m1), and ccr5 (Mm01216171_m1). Amplification was performed in duplicate using 40 cycles of denaturation at 95°C for 15 sec and primer annealing/extension at 60°C for 1 min. Expression data were normalized to 18s ribosomal RNA (Hs99999901-sl) or GAPDH RNA measured on the same samples. Relative expression ratio was calculated according to the 2n = 9) from diabetic patients with confirmed diabetic nephropathy and 6 nondiabetic control renal tissue samples . The research protocol was approved by our Medical Ethics Committee. Tissue was embedded in paraffin, stained with rat anti-human ACKR2 antibody , and detected with DAB. ACKR2 staining in each section was scored semiquantitatively in tubular and interstitial regions with the criteria of 0 for none, 1 for rare, 2 for some, 3 for common, and 4 for common plus intense. The scorer had no knowledge of group identification of the slides. ACKR2 expression was presented as the average score of each group. In some samples tissues were double labeled with FITC conjugated Lotus tetragonolobus lectin (Vector labs), a marker for tubule epithelial cell brush border [Immunohistochemistry with anti-human ACKR2 antibody was used for detection of ACKR2 expression in human kidneys: renal tissue biopsies . Expression of ACKR2 RNA was 80% higher in diabetic kidneys compared to normal kidneys (p = 0.11). Interestingly high levels of ACKR2 in lungs were observed. Knockout mice served as negative controls for expression analysis.Enzymatic assays, necessary for accurate measurement of blood glucose in OVE diabetic mice , 26, ind−/− mice . Urine g kidneys , though μg/mg) in all groups at 2, 4, and 6 months of age  are elevated in DN , 27, 28.−/− mice .p ≤ 0.000001) and for CCR5 the correlation coefficient was 0.6 (p ≤ 0.05).The global changes in gene expression profiles were evaluated by microarray. To confirm the reliability of the microarray results correlation coefficients were calculated between RT-PCR and microarray results for CCL2, CCL5, CCR2, and CCR5 based on the 12 samples used in both assays. For all but CCR5 the correlation was at least 0.96  software. Only 18 of 30,000 genes differed at the 0.05 level between the nondiabetic groups, FVB and ACKR2. Therefore, RNA expression of the OVE and OVE-ACKR2http://dx.doi.org/10.1155/2016/5362506, show the 50 transcripts most reduced and increased in OVE-ACKR2 relative to OVE, resp.).In OVE kidney, many protective pathways such as immune response and cytokine signaling were activated, as indicated by the high number of RNAs with significantly altered expression. The same pathways in OVE-ACKR2 contained only a few RNAs with altered expression. With few exceptions, most of the biological pathways in  Lotus tetragonolobus lectin [The effect of diabetes on kidney ACKR2 protein expression was evaluated in human DN and nondiabetic samples using a rat anti-human ACKR2 monoclonal antibody, previously evaluated on human samples , 29, 30.s lectin . ACKR2 sThis study demonstrates that the ACKR2 chemokine scavenger receptor has an unexpected important role in the development of diabetic kidney disease. Deletion of the ACKR2 gene in OVE diabetic mice produced a great reduction in albuminuria, accompanied by reduced severity of renal fibrosis, leucocyte infiltration, and inflammatory chemokine gene expression. In addition, ACKR2 protein content was elevated in several cell types in kidneys of DN patients.Chemokines and cytokines regulate the inflammatory processes and contribute to progressive kidney damage in diabetes . ChemokiTo determine if the ACKR2 RNA results indicate that diabetes alters ACKR2 protein immunohistochemistry studies were performed on human tissue since only an anti-human ACKR2 antibody has been validated , 29, 30.The primary finding of this study was that ACKR2 deletion dramatically reduced DN. The reduction of albuminuria in OVE-ACKR2 mice was significant at the earliest age tested, two months. As OVE mice aged DN progressed and the protection by ACKR2 KO became more striking. At 6 months ACKR2 deletion produced a greater reduction in diabetic albuminuria. In addition several markers demonstrated reduced inflammation in OVE-ACKR2 kidneys compared to OVE kidneys. Histologically this was indicated by decreased leukocyte infiltration and less fibrosis. Gene expression data demonstrated that absence of ACKR2 prevented activation of multiple molecular pathways involved in immune or inflammatory processes in kidneys of diabetic mice. The finding of such potent renal protection from diabetes by deletion of ACKR2 was contradictory to our expectation, which was that deletion of ACKR2 would exacerbate DN by increasing renal inflammation. This expectation was based on the damage inflammation produces in DN and the anti-inflammatory potency of ACKR2 as a scavenger of proinflammatory chemokines. In several studies manipulation of ACKR2 levels modified tissue inflammation in a manner that would be predicted based on anti-inflammatory potency of ACKR2 as a chemokine scavenger: this was shown in experimental models of colitis and psoriasis, where deletion of ACKR2 increased colon  or skin Mechanisms to explain protection from DN by deletion of ACKR2 are not obvious. Protection was not due to reduced OVE diabetes since hyperglycemia was equivalent in OVE and OVE-ACKR2 mice . In cons summary, we found that deletion of the ACKR2 gene produced a dramatic reduction in albuminuria and renal inflammation in the OVE diabetic mouse without decreasing diabetes. In human samples diabetes increased the expression of ACKR2 protein in tubule cells, leukocytes, and endothelial cells.InSupplement Tables 1 and 2 show the 50 transcripts most reduced (Table 1) and most increased (Table 2) in OVE-ACKR2 kidney compared to OVE kidney. Only OVE-ACKR2 transcripts that were significantly different from OVE p≤0.05 are included. Values are the average signal ±SEM from a sample size of 3.Supplement Figure 1 images show that most ACKR2 positive cells in diabetic human kidneys are tubule epithelial cells. In the upper panel ACKR2 staining is red and the proximal tubule brush border is labeled green with Lotus tetragonolobus lectin, a marker for kidney tubule brush border. The lower panel is a DIC image showing that the central tubule has an almost continuous brush border adjacent to nearly continuous ACKR2 stained cells. In both images most ACKR2 staining is on tubule epithelial cells with a brush border.


Chondrosarcoma is a malignant cartilage forming bone tumour for which no effective systemic treatment is available. Previous studies illustrate the need for a better understanding of the role of the IGF pathway in chondrosarcoma to determine if it can be a target for therapy, which was therefore explored in this study.Expression of mediators of IGF1R signalling and phosphorylation status of IRS1 was determined in chondrosarcoma cell lines by qRT-PCR and western blot. The effect of activation and inhibition of IGF1R signalling on downstream targets was assessed by western blot. Ten chondrosarcoma cell lines were treated with OSI-906  after which cell proliferation and migration were determined by a viability assay and the xCELLigence system, respectively. In addition, four chondrosarcoma cell lines were treated with a combination of doxorubicin and OSI-906. By immunohistochemistry, IGF1R expression levels were determined in tissue microarrays of 187 cartilage tumours and ten paraffin embedded cell lines.in vitro culturing upregulates IGF1R expression.Mediators of IGF1R signalling are heterogeneously expressed and phosphorylated IRS1 was detected in 67 % of the tested chondrosarcoma cell lines, suggesting that IGF1R signalling is active in a subset of chondrosarcoma cell lines. In the cell lines with phosphorylated IRS1, inhibition of IGF1R signalling decreased phosphorylated Akt levels and increased IGF1R expression, but it did not influence MAPK or S6 activity. In line with these findings, treatment with IGF1R/IR inhibitors did not impact proliferation or migration in any of the chondrosarcoma cell lines, even upon stimulation with IGF1. Although synergistic effects of IGF1R/IR inhibition with doxorubicin are described for other cancers, our results demonstrate that this was not the case for chondrosarcoma. In addition, we found minimal IGF1R expression in primary tumours in contrast to the high expression detected in chondrosarcoma cell lines, even if both were derived from the same tumour, suggesting that The results from this study indicate that the IGF pathway is not essential for chondrosarcoma growth, migration or chemoresistance. Furthermore, IGF1R is only minimally expressed in chondrosarcoma primary tumours. Therefore, the IGF pathway is not expected to be an effective therapeutic target for chondrosarcoma of bone.The online version of this article (doi:10.1186/s12885-016-2522-8) contains supplementary material, which is available to authorized users. Therefore, further unravelling of critical signalling pathways in chondrosarcoma is essential to identify new therapeutic targets.Chondrosarcoma is the second most common primary bone malignancy in humans  and reprOne pathway which has been implicated in chondrosarcoma proliferation is the IGF pathway. The IGF pathway has two closely related ligands: IGF1 and IGF2 . When a IGF1R can be the upstream receptor that is responsible for the well known activation of the PI3K/Akt/mTOR pathway, the Src-pathway and the Ras/Raf/MEK pathway in (a subset of) chondrosarcoma cell lines and primary cultures –17. In aThese above mentioned studies illustrate the need for a better understanding of the role of IGF1R signalling in chondrosarcoma to determine if it is a convincing target for therapy. Because chondrosarcoma is a very heterogenous disease, it is possible that the IGF1R directed therapy is only effective in a subset of patients. Hence, we used our large chondrosarcoma cell line panel, including three grade 2 and three grade 3 conventional chondrosarcomas, three dedifferentiated chondrosarcomas and one mesenchymal chondrosarcoma cell line. We analyzed expression levels of IGF1R and other important mediators of IGF1R signalling and determined the effect of IGF1R inhibitors. Our results indicate that the IGF pathway is not important for chondrosarcoma growth as IGF1R inhibition did not demonstrably impact chondrosarcoma cell line proliferation, migration and chemoresistance. In addition, IGF1R expression is low/absent in chondrosarcoma primary tumours in contrast to chondrosarcoma cell lines. This illustrates that there is limited preclinical rationale for using IGF1R inhibitors for the treatment of chondrosarcoma of bone.The IGF1R inhibitors OSI-906, NVP-ADW742 and GSK1838705A were purchased from Selleck Chemicals LCC .The conventional chondrosarcoma cell lines JJ012 , SW1353 http://bioinfo.ut.ee/primer3/). To correct for the amount of cDNA input, gene expression levels were normalized using the expression levels of CYPa and CPSF6 [RNA isolation and cDNA synthesis was performed as described . To detend CPSF6 , 36. ΔΔCWestern blotting was performed as described previously . Per samwww.graphpad.com). The results shown are representative results from at least three independent experiments.In all cell viability experiments, the cell lines were plated in triplicate at a density of 3000 to 10000 cells per well depending on the growth rate. For the positive control (Ewing sarcoma cell line TC-32) the 96 well plates were coated with gelatine. After the cells were allowed to adhere overnight, the IGF1R inhibitors were added in their corresponding concentrations. In addition, we determined the effect of OSI-906 when IGF1 (50 ng/ml) was added to the medium. For the combination treatment of doxorubicin and OSI-906, both inhibitors were added at the same time. Because JJ012 and SW1353 are relatively more sensitive to doxorubicin, JJ012 and SW1353 were treated with 0, 1 nM, 10 nM or 100 nM while CH2879 and OUMS27 were treated with 0, 10 nM, 50 nM and 100 nM doxorubicin. These concentrations of doxorubicin were combined with DMSO, 0.1 μM, 0.5 μM or 1 μM OSI-906. After 72 h of incubation, cell viability was measured using the WST-1 reagent (Roche) (single treatment with OSI-906) or PrestoBlue Cell Viability Reagent (Promega Benelux BV) (single treatment with NVP-ADW742 and GSK1838705A and combinations with IGF1 and doxorubicin) according to the manufacturer’s instructions. Colorimetric values in the plates were subsequently measured using a Wallac 1420 VICTOR2 (Perkin Elmer). Data were analysed in Graphpad Prism 5.0  based on cell-electrode subtract impedance detection technology  was usedThe specificities of two IGF1R antibodies  were compared by western blot (as described above) and immunohistochemistry on colon tissue (as described in ). The moUsing qRT-PCR analyses, we demonstrate that all cell lines express the receptors IGF1R, IGF2R and IR Fig. . HoweverTo determine IGF pathway activity, the phosphorylation status of IRS1 was determined in three chondrosarcoma cell lines. Immunoprecipitation for IRS1 followed by western blot analyses with a phospo-IRS1 antibody revealed the presence of phosphorylated IRS1 in JJ012 and SW1353, but not in CH2879 Fig. . This deTreating our full chondrosarcoma cell line panel for 72 h with concentrations from 0.01 to 1000 nM of OSI-906 revealed that chondrosarcoma cell viability was not affected by inhibition of the IGF pathway, whereas the positive control cell line TC-32 (Ewing sarcoma) showed dose-dependent decrease of cell viability Fig. . FurtherBy adding OSI-906 to the upper chamber of the CIM plates (xCELLigence), we demonstrated that IGF1R signalling was not essential for the migration of JJ012, SW1353, CH2879 and OUMS27 . IGF1R antibody #3018 was selected for these immunohistochemical stainings as both the positive immunohistochemical control and the control western blot demonstrated its high specificity compared to the IGF1R antibody #3027 . Immunohistochemistry confirmed the western blot evidence of IGF1R expression Fig. , with meThe aim of this study was to investigate whether the IGF pathway is a suitable target for therapy in chondrosarcoma. Heterogeneous expression of IGF1R, IR, IGF2R, IGF1, IGF2, IRS1 and IGFBP3 was seen, both at the mRNA and protein levels, in chondrosarcoma cell lines. This indicates that essential IGF pathway components are present in cultured chondrosarcoma cells. Furthermore, detection of phosphorylated IRS1 in two out of three chondrosarcoma cell lines demonstrates that the IGF pathway is active in a subset of chondrosarcoma cell lines. In the cell lines with phosphorylated IRS1, IGF pathway inhibition decreased phosphorylated Akt levels and increased IGF1R expression; the latter suggests activation of a feedback loop, which is further supported by the downregulation of IGF1R expression by IGF1 treatment. However, this did not influence the amount of phosphorylated S6, which is located further downstream in the PI3K/Akt/mTOR pathway. Furthermore, the activated MAPK levels were not affected by IGF pathway stimulation or inhibition, demonstrating that activity of the downstream targets is not dependent on IGF1R signalling.In line with these findings, we demonstrate that despite activity of the pathway, IGF1R signalling is not essential for chondrosarcoma cell survival. Treatment with three different IGF1R/IR inhibitors does not have an effect on chondrosarcoma cell viability, irrespective of apparent pathway activity and stimulation with IGF1. Chondrosarcoma cell line OUMS27 was previously shown to be sensitive to IGF1R/IR inhibition by Zhang et al. . It is dIGF1R signalling is involved in resistance to cytotoxic drugs in certain cancers . Since cin vivo situation. These experimental differences might explain the difference in our findings.Our study could not confirm a role for the IGF pathway in chondrosarcoma cell migration. In contrast to the study from Wu et al., showing that IGF1 induced chondrosarcoma migration was inhibited by an IGF1R antibody , we chosStrikingly, we detected high expression of the IGF1R in chondrosarcoma cell lines compared to primary tumours. Moreover, we show that each of four patients with matched cell lines and primary tumours had strong membranous IGF1R expression in the cultured cells compared to absent or very weak expression in the corresponding primary tumour. The finding that cell lines are insensitive to IGF1R inhibition despite their high IGF1R expression is in line with the results from the study by Schwartz et al., which described absence of a correlation between IGF1R expression levels and responsiveness to an IGF1R targeting antibody . This se50 values within the therapeutic range [Increased activity of the IGF pathway is implicated in several other cancers  includinic range .Clinical trials to test the safety and efficacy of IGF1R antibodies, sometimes in combination with an mTOR inhibitor, have been performed in sarcoma patients , but onlIn summary, the results of this study demonstrate that although chondrosarcoma cell lines have high IGF1R expression and activation of downstream targets, inhibition of IGF1R/IR signalling does not affect chondrosarcoma proliferation, migration and chemoresistance. Therefore, we conclude that there is no convincing preclinical rationale for using IGF1R/IR inhibitors in the treatment of chondrosarcoma.ACT, Atypical cartilage tumour; CIM, Cell Invasion and Migration; FBS, Fetal bovine serum; FFPE, formalin-fixed, paraffin-embedded; HRP, horseradish peroxidase; IGF, Insulin-like growth factor; IGF1R, Insulin-like growth factor receptor; IGFBP, Insulin-like growth factor binding protein; IP, Immunoprecipitation; IR, Insulin receptor; IRS, Insulin receptor substrate; MAPK, Mitogen-activated protein kinases; TMA, Tissue microarray


A meta-analysis of AKR1C3 mRNA expression in patient samples was also conducted, which revealed that AKR1C3 mRNA was upregulated in CRPC, but downregulated in ER-positive breast cancer. 11β-PGF2α and testosterone levels in the cell line panel correlated with AKR1C3 protein expression. SN33638 prevented 11β-PGF2α formation in cell lines that expressed AKR1C3, but partially inhibited testosterone formation and subsequently cell proliferation and/or PSA expression only in high (LAPC4 AKR1C3-overexpressing cells) or moderate (22RV1) AKR1C3-expressing cell lines. SN33638 had little effect on 17β-estradiol production or estrone-stimulated cell proliferation in ER-positive breast cancer cell lines. Although SN33638 could prevent 11β-PGF2α formation, its ability to prevent testosterone and 17β-estradiol production and their roles in CRPC and ER-positive breast cancer progression was limited due to AKR1C3-independent steroid hormone production, except in LAPC4 AKR1C3 cells where the majority of testosterone was AKR1C3-dependent. These results suggest that inhibition of AKR1C3 is unlikely to produce therapeutic benefit in CRPC and ER-positive breast cancer patients, except possibly in the small subpopulation of CRPC patients with tumors that have upregulated AKR1C3 expression and are dependent on AKR1C3 to produce the testosterone required for their growth.AKR1C3 is a novel therapeutic target in castration-resistant prostate cancer (CRPC) and estrogen receptor (ER)-positive breast cancer because of its ability to produce testosterone and 17β-estradiol intratumorally, thus promoting nuclear receptor signaling and tumor progression. A panel of CRPC, ER-positive breast cancer and high/low AKR1C3-expressing cell lines were treated with SN33638, a selective inhibitor of AKR1C3, in the presence of hormone or prostaglandin (PG) precursors, prior to evaluation of cell proliferation and levels of 11β-PG F Hormone-dependent malignancies, such as breast and prostate cancer, generally require intracellular concentrations of steroid hormones for nuclear receptor activation to trigger downstream signaling and promote tumor growth , 2. Howe4-androstene-3,17-dione  type 5, has been implicated in the development of CRPC , 6–9, an17-dione , 5α-andr17-dione , or andr17-dione . In the 17-dione , 12, 14.AKR1C3 mRNA has been reported to be upregulated in metastatic and non-metastatic CRPC compared to local prostate carcinoma (de novo synthesis of androgens in the prostate can drive androgen receptor (AR) activation and may be responsible for the development of resistance to androgen deprivation therapy in CRPC patients  F synthase to convert PGH1β-PGF2α , 23, an 1β-PGF2α , 25. Fur1β-PGF2α , 27, in 1β-PGF2α , 29, and1β-PGF2α , 31.in vitro, as determined by its catalytic efficiency, is its PGD2 11-ketoreductase activity phenyl)piperidine-1-sulfonamides as potent and isoform-selective inhibitors of AKR1C3  expression.We recently reported the synthesis of a novel series of f AKR1C3 . The leaf AKR1C3 , 37 and f AKR1C3  . Cell lines were maintained in αMEM supplemented with 5% FCS (Moregate Biotech) , 10% FCS, and 1% PSG   or 10% FCS and 0.01–0.02 mg/mL human insulin (Sigma-Aldrich) , RPMI with 10% FCS (HCC1500), IMDM with 10% FCS and 1% PSG (LAPC4), or DMEM with 10% non-heat inactivated FCS (VCaP). Transfected cell lines were further supplemented with 0.5–1.0 μM puromycin (Life Technologies). For drug treatments, cells were seeded in phenol red-free media  supplemented with 5% charcoal-stripped serum (Life Technologies). All media, except phenol red-free DMEM (Sigma-Aldrich), were purchased from Life Technologies. LAPC4, LAPC4 AKR1C3, VCaP, and HCC1500 cells were maintained in poly-d-lysine (Becton Dickinson) coated flasks and 96-well plates.The cell lines used are described in Table eviously , 41 usinAKR1C3 and NQO1 gene expression were analyzed using the publicly accessible online database Oncomine (Compendia Biosciences). mRNA expression was analyzed in all prostate cancer datasets that contained CRPC samples , all breast cancer datasets that had normal breast samples, and breast cancer samples with known hormone status  and in the Barretina cell line dataset. The AKR1C3 or NQO1 mRNA expression value for each sample was normalized to the median expressed probeset for that particular sample.® Novex 4–12% Bis–Tris pre-cast 10-well gels (Life Technologies) and separated by SDS-PAGE at 120 V for 90 min. Each gel was transferred onto a nitrocellulose membrane (Bio-Rad Laboratories) at 100 V for 70 min and incubated in blocking buffer [PBS with 0.5% Tween®-20 (Serva) and 5% BSA] for 1 h. Membranes were washed in PBS with 0.5% Tween®-20 then cut and incubated overnight at 4°C with monoclonal antibodies against either AKR1C3  at a 1:5000 dilution in 5% BSA or α-tubulin  at a dilution of 1:5000 in 5% BSA. Membranes were washed then incubated with goat anti-mouse IgG HRP-conjugated secondary antibody (Santa Cruz Biotechnology) at 1:10,000 dilutions in blocking buffer for 1 h at room temperature. After further washes, the membranes were incubated with SuperSignal® West Pico chemiluminescent substrate (Thermo Scientific) for 5 min prior to imaging on a LAS-3000 luminescent image analyzer (Fujifilm). Western blots were quantitated using ImageJ 1.45s software (NIH).Cell lysis was carried out for each cell line in modified radioimmunoprecipitation assay lysis buffer containing 1% protease inhibitor cocktail (Sigma-Aldrich) on ice for 30 min. Cells were centrifuged at 13,000 rpm for 5 min at 4°C to remove insoluble material. Protein concentration of cell lysates was determined by bicinchoninic acid assay (Sigma-Aldrich) against bovine serum albumin  standards. Twenty micrograms of each lysate was loaded onto NuPAGE2α ELISA)] to reach approximately 70% confluency at the time of sampling and were left to settle for 24 h at 37°C and 5% CO2. Inhibitors were added to the plates at single or multiple concentrations for 1 h prior to administration of equimolar (28 nM) concentrations of PGD2 (11β-PGF2α ELISA), androstenedione (testosterone ELISA), or estrone (17β-estradiol ELISA). The plates were returned to the incubator for a further 24 h before cell supernatant was removed for analysis of 11β-PGF2α, testosterone, or 17β-estradiol by ELISA as per manufacturer’s instructions , and cells were fixed for sulforhodamine B analysis as described below. Each ELISA plate was read at a wavelength of 405 nM (11β-PGF2α and testosterone) or 650 nM (17β-estradiol) on a SpectraMax M2 microplate reader (Molecular Devices) and quantitated against a standard curve generated in hormone-deprived culture media. Cross-reactivity to PGD2, androstenedione, or estrone was accounted for by adding these to cell-free wells of each ELISA plate.Cells were seeded into 96-well plates in hormone-deprived media at appropriate seeding densities [10,000–50,000 cells per well for all lines except HCC1500  at 4°C for 1 h and staining with 0.4% sulforhodamine B (Sigma-Aldrich) in 1% acetic acid for 30 min in the dark at room temperature. Plates were washed in 1% acetic acid, dried, and incubated with unbuffered Tris base  for 30 min on a plate shaker in the dark to solubilize the stain. The plates were read on a BioTek EL808 microplate reader at an absorbance of 490 nM with a reference wavelength of 450 nM.Cells were seeded into 96-well plates in hormone-deprived media at appropriate seeding densities  to reach approximately 70% confluency at the time of sampling and left to settle for 48 h at 37°C and 5% CO2. SN33638 was added to the plates 1 h prior to multiple concentrations of androstenedione. The plates were returned to the incubator for 24 h, before the cell supernatant was removed and frozen for PSA analysis. Determination of total PSA concentration was carried out at LabPLUS, Auckland City Hospital, by immunoassay on a Cobas® e601 analyzer according to manufacturer’s instructions (Roche).Cells were seeded at 20,000 cells per well  or 50,000 cells per well (22RV1) into 96-well plates in hormone-deprived media and left to settle for 24 h at 37°C and 5% COAKR1C3 mRNA expression levels in CRPC and breast cancer patients relative to normal tissue or primary disease. Meta-analysis of seven prostate cancer datasets that included normal prostate or primary prostate carcinoma samples in addition to CRPC revealed that AKR1C3 expression is significantly upregulated in CRPC relative to both normal prostate and primary prostate carcinoma  and there was no significant correlation between NQO1 mRNA and AKR1C3 mRNA expression in CRPC patients  .We generated a cell line panel of prostate and ER-positive breast cancer cell lines and evaluated AKR1C3 protein expression by western blotting in comparison to cell lines with high (NCI-H460 NSCLC and HCT116 AKR1C3-overexpressing colon cancer cells) and low (HCT116 wild-type) AKR1C3 expression. To ensure the panel included CRPC and ER-positive breast cancer cell lines with high AKR1C3 expression, we transfected LAPC4 and T47D cells with an AKR1C3 plasmid to generate AKR1C3-overexpressing cell lines. The stably transfected cell lines had high protein expression of AKR1C3, similar to NCI-H460 and HCT116 AKR1C3, in contrast to the wild-type lines (LAPC4 and T47D), which had very low AKR1C3 expression  and indomethacin and flufenamic acid were inactive at all concentrations tested up to 30 μM. Addition of PGD2 or SN33638 to cells for 24 h had no effect on cell proliferation relative to unstimulated controls in all cell lines (data not shown).The PGD2α, testosterone levels positively correlated with AKR1C3 protein expression , LAPC4 AKR1C3 , and NCI-H460 cell lines , but only in one moderate AKR1C3-expressing cell line: 22RV1 . In LNCaP cells, testosterone levels were lower than the negative control after androstenedione stimulation alone, but were significantly increased on addition of 10 μM SN33638 to levels similar to those seen in the negative control and other prostate cancer cell lines (P < 0.05).Since AKR1C3 has been implicated in the development of CRPC because of its ability to produce testosterone intratumorally from circulating androstenedione, we determined testosterone concentrations in the prostate cancer cell lines and known high and low AKR1C3-expressing cells after stimulation with androstenedione for 24 h. Similarly to 11β-PGF50 of 7.4 ± 1.2 nM , indomethacin (EC50 > 30 μM), and flufenamic acid (EC50 > 30 μM); however, only approximately 70% of the testosterone produced in these cells could be inhibited by the AKR1C3 inhibitors. When testosterone formation was limited to that which was inhibitable by SN33638, the correlation with AKR1C3 protein expression  was significantly improved  relative to the correlation between total testosterone and AKR1C3 protein expression. Since 17β-HSD3 can also convert androstenedione to testosterone, cells were treated with a non-cytotoxic dose (1 μM) of a 17β-HSD3 inhibitor (5-(3-bromo-4-hydroxybenzylidene)-3-(4-methoxyphenyl)-2-thioxothiazolidin-4-1) alone and in combination with SN33638. The 17β-HSD3 inhibitor significantly reduced testosterone levels in HCT116 AKR1C3 cells by 32.1% as a single agent  and by 97.3% in combination with SN33638 (P < 0.0001), an effect significantly greater than that achieved by SN33638  or 17β-HSD3 inhibitor (P < 0.0001 vs. combination) treatment alone  and by 39.6% in NCI-H460 cells (P < 0.05)  Figure . ApigeniP < 0.001; Student’s t-test) and 10 nM androstenedione , and in 22RV1 cells with 10 nM androstenedione , but had no significant effect on LAPC4 wild-type cells  as a biomarker of AR activity. Cells were stimulated with physiological concentrations of up to 10 nM androstenedione, with or without SN33638, for 24 h before PSA concentrations were determined. Androstenedione stimulation promoted PSA production in all three cell lines, with the highest PSA levels observed in LAPC4 AKR1C3 cells. SN33638 treatment significantly inhibited the formation of PSA in LAPC4 AKR1C3 cells that was attributable to stimulation with 1 nM , indicating it was largely non-cytotoxic at 10 μM. Stimulation of proliferation was observed with all concentrations of androstenedione in high AKR1C3-expressing LAPC4 AKR1C3 cells, but only at 1 and 10 nM in LAPC4 wild-type and 22RV1 cells. SN33638 significantly inhibited androstenedione-stimulated cell proliferation at 0.1 nM  and caused a substantial (45.9%) but non-significant reduction in stimulated cell proliferation with 1 nM androstenedione in LAPC4 AKR1C3 cells, but had no effect on androstenedione-stimulated cell proliferation in LAPC4 wild-type cells and 22RV1 cells  were used than in the ELISA assays. SN33638 had no effect on unstimulated cell proliferation in all cell lines, except for a 7.3% reduction in MCF7 cells   was used throughout these studies to ensure near-complete inhibition of AKR1C3 activity was achieved  than cell proliferation (5-day incubation) as more testosterone was produced with longer incubation times of androstenedione (data not shown). AKR1C3-independent testosterone concentrations also increased with longer incubation times and/or increased androstenedione concentrations, and are likely to account for the lack of effect of SN33638 on testosterone production or proliferation over the 5-day incubation in 22RV1 cells and on LAPC4 AKR1C3 cell proliferation with higher androstenedione concentrations. AKR1C3-independent testosterone production may also explain the observed stimulation of proliferation and PSA expression in the low AKR1C3-expressing LAPC4 cell line. These results suggest that AKR1C3 inhibition may prevent androstenedione-stimulated PSA expression or cell proliferation in CRPC cells, as previously shown for other small molecule inhibitors of AKR1C3 or RNAi , 50, butThe greatest inhibitory effects of SN33638 were observed in the LAPC4 AKR1C3 cell line, which is an artificial experimental model. Whether similar effects of SN33638 would be observed in high AKR1C3-expressing wild-type cell lines is unknown, because of the dearth of CRPC cell lines available that express high levels of AKR1C3. This is surprising given the evidence here and elsewhere that AKR1C3 is upregulated in a subset of CRPC tumors , 47, 48.AKR1C3 in this cell line has been reported to be greater than that of the other 17β-HSD isoforms  that are capable of converting estrone to 17β-estradiol (AKR1C3 has also been implicated in the development of ER-positive breast cancer because of its ability to convert estrone to 17β-estradiol , 11. Howstradiol . Minor i2 to 11β-PGF2α, its activity at preventing steroid hormone reduction and resultant CRPC and ER-positive breast cancer growth is limited due to the involvement of additional enzymes in testosterone and 17β-estradiol synthesis. AKR1C3 inhibitor therapy is therefore unlikely to be beneficial for the treatment of CRPC and ER-positive breast cancer, except possibly in the small subpopulation of CRPC patients with tumors that have upregulated AKR1C3 expression and are dependent on AKR1C3 for producing the testosterone required for their growth.In conclusion, our data suggests that while inhibition of AKR1C3 by SN33638 can prevent the conversion of PGDStephen M. F. Jamieson conceived and designed the experiments. Yarong Diana Yin and Melissa Fu performed the experimental studies. The majority of experiments in prostate cancer cell lines were conducted by Yarong Diana Yin and in breast cancer cell lines by Melissa Fu. Data was analyzed by Yarong Diana Yin, Melissa Fu, and Stephen M. F. Jamieson. Daniel M. Heinrich, Darby G. Brooke, and William A. Denny were responsible for the design of SN33638 and Daniel M. Heinrich and Darby G. Brooke for its synthesis. Stephen M. F. Jamieson drafted the figures, table, and manuscript. Yarong Diana Yin, Melissa Fu, Daniel M. Heinrich, Darby G. Brooke, and William A. Denny critically revised the paper and all authors approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We design logic circuits based on the notion of zero forcing on graphs; each gate of the circuits is a gadget in which zero forcing is performed. We show that such circuits can evaluate every monotone Boolean function. By using two vertices to encode each logical bit, we obtain universal computation. We also highlight a phenomenon of “back forcing” as a property of each function. Such a phenomenon occurs in a circuit when the input of gates which have been already used at a given time step is further modified by a computation actually performed at a later stage. Finally, we show that zero forcing can be also used to implement reversible computation. The model introduced here provides a potentially new tool in the analysis of Boolean functions, with particular attention to monotonicity. Moreover, in the light of applications of zero forcing in quantum mechanics, the link with Boolean functions may suggest a new directions in quantum control theory and in the study of engineered quantum spin systems. It is an open technical problem to verify whether there is a link between zero forcing and computation with contact circuits. This concludes the proof. delay lines may be needed to assemble a circuit such that the output produced by zero forcing in parallel gates is synchronous. However, given our choice of gadgets, exactly internal time refers to the zero forcing steps inside the gadgets/gates; external time refers to the time steps of the computation.Notice that extra vertices forming The gadgets Let us consider the gadget It is straightforward to realize the operation COPY. See Fig. It may be worth observing the following points:If each Boolean variable in the input of a circuit is set to This is a circuit computing the Boolean function back forcing, because it is induced by the color-change rule acting backwards with respect to the direction from input to output in the whole circuit. The gadget transmittal back forcing, because if something back forces its output black then the gate transmits the back force, i.e., it modifies the color of the output vertex in a gate used previously. Figure The phenomenon will be called The gadget delay lines—for example, by adding extra vertices in each gadget or between them. Alternatively, we could consider delay lines directly embedded in the structure of the gadgets implementing the logical gates.We can also slow down back forcing, by including appropriate filter. In some sense, the filter can be understood as an electronic diode allowing zero forcing only in one direction.Also, back forcing can be avoided completely by including the gadget in Fig. In relation to the circuit for the function Despite the fact that the color-change rule induces a non-reversible process (black coloring cannot be undone) a simple modification of the encoding strategy allows us to implement universal, and hence also reversible, computation (see Drechsler and Wille dual rail strategy, where two vertices are employed to encode a single logical bit. Specifically, as shown in Fig. The idea is to adopt a gluing together the copies of two types of subgraphs/gadgets corresponding to the Boolean gates AND and OR. We have briefly discussed the minimality of such gadgets in terms of vertices and edges. Even we did not give a formal proof, it seems evident that our gadgets are optimal in this respect.We have shown that all monotone Boolean functions can be realized by zero forcing in a graph constructed by We have highlighted a phenomenon of “back forcing action”. Back forcing has an effect on the coloring of gates already used, as a function of what has happened in the “future”, i.e., at a later stage of the computation. Because of the relation between zero forcing and minimum ranks, the model described here is amenable to be studied with linear algebraic tools, potentially suggesting a novel direction in the analysis of monotone Boolean functions and, speculating, the introduction of rank problems in questions relevant to parametrized complexity.Finally, we have shown that universal computation can be obtained with zero forcing by simply adopting a dual rail encoding.An open direction suggested by the paper is to understand the link between zero forcing and the dynamics at the basis of other unconventional models of computation, like, for example, the billiard ball computer—introduced as a model of reversible computing (Fredkin and Toffoli From a physics perspective, zero forcing sets describe controlling, cooling, and symmetries of quantum systems—engineered spin systems like Heisenberg and AKLT chains. While an application of monotone Boolean functions in physics is not immediate, the connection that we have highlighted is nonetheless interesting. For example, can monotone Boolean functions be used to characterize symmetries in quantum mechanics?


DKK3 was frequently silenced in breast cell lines (5/10) by promoter methylation, compared with human normal mammary epithelial cells and tissues. DKK3 methylation was detected in 78% of breast tumour samples, whereas only rarely methylated in normal breast and surgical margin tissues, suggesting tumour-specific methylation of DKK3 in breast cancer. Ectopic expression of DKK3 suppressed cell colony formation through inducing G0/G1 cell cycle arrest and apoptosis of breast tumour cells. DKK3 also induced changes of cell morphology, and inhibited breast tumour cell migration through reversing epithelial-mesenchymal transition (EMT) and down-regulating stem cell markers. DKK3 inhibited canonical Wnt/β-catenin signalling through mediating β-catenin translocation from nucleus to cytoplasm and membrane, along with reduced active-β-catenin, further activating non-canonical JNK signalling. Thus, our findings demonstrate that DKK3 could function as a tumour suppressor through inducing apoptosis and regulating Wnt signalling during breast tumorigenesis.Dickkopf-related protein 3 (DKK3) is an antagonist of Wnt ligand activity. Reduced DKK3 expression has been reported in various types of cancers, but its functions and related molecular mechanisms in breast tumorigenesis remain unclear. We examined the expression and promoter methylation of DKK3 in 10 breast cancer cell lines, 96 primary breast tumours, 43 paired surgical margin tissues and 16 normal breast tissues.  Constitutive activation of the Wnt signalling pathway is a hallmark of multiple human cancers. Genetic and epigenetic alterations contribute to the aberrant activation of Wnt signalling, which is further involved in cell survival, proliferation, metastasis and invasion, thus contributing to cancer initiation and progression. However, unlike other tumours, β-catenin mutations are uncommon in breast cancer , althougWnt signalling pathway is composed of canonical Wnt/β-catenin signalling and non-canonical Wnt signalling independent of β-catenin . SeveralDKK3 has been found to be down-regulated or silenced by promoter CpG methylation in multiple malignancies, including acute lymphoblastic leukaemia [The DKK family consists of a group of secreted glycoproteins, including DKK1, DKK2, DKK3, DKK4, and a unique DKK3-related protein, DKKL1 (soggy) . DKK1, Deukaemia , gastriceukaemia , colon [eukaemia , hepatoceukaemia , renal [eukaemia , bladdereukaemia  and cerveukaemia , 23 carceukaemia –26, its DKK3 in breast cancer. We also investigated its biological functions and molecular mechanisms relevant to breast cancer. Our findings demonstrated that DKK3 regulated Wnt/β-catenin and JNK signalling, thus acting as a tumour suppressor. Its tumour-specific promoter methylation appears to be a potential biomarker for early detection of breast cancer.In this study, we assessed the expression and promoter methylation of 2. HMEpC and HMEC were cultured as previously described [Breast tumour cell lines  were used . Human mescribed . RNA samescribed . Fresh cDNA demethylation treatment of breast cancer cell lines was performed as described previously . Cell liGenomic DNA and total RNA were isolated from cell lines and tissues using DNAzol and Trizol reagents , respectively, according to the manufacturer's recommendations . Tumour First-strand cDNA was synthesized from 1 μg of total RNA using MuLV Reverse Transcriptase  to a final volume of 20 μl. For RT-PCR , samplesMethylation-Specific PCR (MSP) was used to determine the DKK3 promoter methylation status, as described previously . Bisulfi6 cells/well) in 6-well plates and transfected with 4 μg of pMH-DKK3 or pMH empty vector using Lipofectamine™ 2000  following the manufacturer's protocol. After 48 hrs, cells were digested using 0.1% trypsin and centrifuged at 1000 r.p.m. for 5 min. Pellets were washed with PBS and fixed in ice-cold 70% ethanol for 1 hr, and treated with 100 μl of 50 mg/l propidium iodide for 30 min. at 4°C in the dark. Annexin V-FITC/PI staining was used for apoptosis analysis. Data were analysed with the CELL Quest software .To assess cell cycle status, MB231 cells and BT549 cells were seeded  were separated by sodium dodecyl sulphate/polyacrylamide gel electrophoresis (SDS-PAGE) and then electrophoretically transferred onto a PVDF membrane . After blocking the membrane in 5% skim milk powder dissolved in PBS for 1 hr at room temperature, the antibodies used were β-catenin, JNK, and phosphor-JNK , active-β-catenin (Millipore), cyclin D1, c-Myc, E-Cadherin, vimentin , and DKK3 . The samples were subsequently incubated with a 1:2,000 dilution of an HRP-conjugated goat-antimouse IgG  for 1 hr. Bands were visualized using an enhanced chemiluminescence kit . GAPDH was used as a control.t-test, non-parameter Spearman test and Fisher's exact test were used to compare experimental methylation statuses and clinical data. Differences were considered statistically significant when P < 0.05.Statistical analyses were performed using SPSS software version 13 . Chi-square test, DKK family genes in 10 breast cancer cell lines using semiquantitative RT-PCR. Result showed that DKK1 was detected in all the cell lines; DKK2 mRNA was absent in 9/10 cell lines but weakly expressed in BT549; DKK3 was silenced/down-regulated in five cell lines ; DKK4 was significantly down-regulated in all the cell lines . We further investigated the methylation status of DKK2 and DKK3 promoters. MSP analysis revealed that both DKK2 (data not shown) and DKK3 methylation were frequently detected in breast cancer lines. Specifically, DKK3 was methylated in eight of 10 (80%) breast cell lines with little or no expression, but not in HMEC and HMEpC cells with expression  of breast tumours and 12.5% of (2/16) normal breast tissues, but not in surgical margins examined (P < 0.05)  . These rDKK3 methylation with clinicopathological features. We found that DKK3 methylation was statistically correlated with clinical stage, lymph node metastasis and oestrogen receptor (ER) status (P < 0.05), but not associated with age, tumour size, progesterone receptor (PR) and hormone receptor (HR) status of breast cancer patients . To evaluate the mechanism of DKK3 in suppressing cell proliferation, flow cytometric analyses of apoptosis and cell cycle were performed. We found that MB231 cells with DKK3 expression underwent significant apoptosis compared to controls, with 37% of cells staining positive for Annexin V (P < 0.05), compared to controls  DkkCollectively, our results demonstrate that DKK3 functions as a tumour suppressor inhibiting Wnt/β-catenin signalling in breast carcinogenesis, and raises the possibility of DKK3 methylation as a potential tumour marker and future therapeutic target for breast cancer.


Approximately 20% of melanomas contain a mutation in NRAS. However no direct inhibitor of NRAS is available. One of the main signaling pathways downstream of NRAS is the MAPK pathway. In this study we investigated the possibility of blocking oncogenic signaling of NRAS by inhibiting two signaling points in the MAPK pathway.Fourteen NRAS mutated human melanoma cell lines were treated with a pan-RAF inhibitor , a MEK inhibitor  or their combination and the effects on proliferation, cell cycle progression, apoptosis, transcription profile and signaling of the cells were investigated.The majority of the cell lines showed a significant growth inhibition, with high levels of synergism of the PRi and MEKi combination. Sensitive cell lines showed induction of apoptosis by the combination treatment and there was a correlation between p-MEK levels and synergistic effect of the combination treatment. Proliferation of sensitive cell lines was blocked by the inhibition of the MAPK pathway, which also blocked expression of cyclin D1. However, in resistant cell lines, proliferation was blocked by combined inhibition of the MAPK pathway and cyclin D3, which is not regulated by the MAPK pathway. Resistant cell lines also showed higher levels of p-GSK3β and less perturbation of the apoptotic profile upon the treatment in comparison with the sensitive cell lines.The combination of PRi + MEKi can be an effective regimen for blocking proliferation of NRAS mutant melanomas when there is higher activity of the MAPK pathway and dependence of proliferation and survival on this pathway.The online version of this article (doi:10.1186/s12943-015-0293-5) contains supplementary material, which is available to authorized users. BRAF or NRAS in approximately 50% and 20% of cases, respectively [BRAFV600 mutation, the MAPK pathway, and therefore the growth of melanoma cells, can be efficiently blocked by BRAF inhibitors such as vemurafenib or dabrafenib [Over activity of the RAS/RAF/MEK/ERK mitogen-activated protein kinase (MAPK) pathway is the hallmark of the majority of melanomas, which is frequently due to mutations in ectively . In melabrafenib . HoweverIn normal cells, RAS is the critical switch that connects the signal of activated receptor tyrosine kinases (RTKs) to the downstream signaling network particularly the MAPK pathway. In the MAPK pathway, RAF isoforms  are the direct downstream proteins of RAS . Upon acIn the case of mutated RAS, the main direct effector protein is CRAF, which transfers the signal to the downstream factors in the MAPK pathway. It has been reported that CRAF also plays other roles independent of the MAPK signaling and can regulate other effectors such as MST-2 MAP3K10) and ASK-1 (MAP3K5) . There i and ASK-Immediately downstream of RAFs, MEK is one of the main signaling nodes in the MAPK pathway and MEK inhibitors have shown significant growth inhibitory effects in some BRAF and NRAS mutant melanoma cells ,13. BRAFIn BRAF mutant melanomas, over-activity of alternative pathways, such as PI3K/AKT, can induce resistance to the inhibitors of the MAPK pathway . ReasonaPrevious studies have shown that one of the main reasons for resistance to the targeted therapy approach is the reactivation of the main oncogenic driver pathway through the adjustment of feedback systems . To prevIn this study, we hypothesized that the oncogenic effects of mutated NRAS could be inhibited by agents that block proteins downstream of the MAPK pathway. However, to avoid the feedback or paradoxical activation of the MAPK pathway in NRAS mutated melanomas, the pathway was blocked at both RAF and MEK steps by using the combination of a PRi (Amgen Compd A) and a MEKi (trametinib). A panel of 14 human melanoma cell lines with various NRAS mutations was used for this study to investigate the growth inhibitory effect of this combination. Here, we report that combination of PRi and MEKi shows synergistic effects in the majority of NRAS mutated cell lines in the panel. Cell lines with higher activity of MAPK pathway were more sensitive to the combination therapy. The resistance to therapy and lack of synergism was the result of higher activity of pro-survival pathways and independence of cell cycle progression from the MAPK pathway.NRAS mutant human melanoma cell lines  and three sensitive cell lines  were investigated. Each cell line was treated with PRi (500 nM), MEKi (25 nM) and their combination for 48 hours. After harvest and fixation, cells were stained with DAPI for cell cycle analysis by flow cytometry  and three sensitive  cell lines. No band for cyclin D1 was detected in M202 and 207 confirming the RNA expression data  in all the six tested cell lines Figure C. The prIn order to identify the signaling mechanism of resistance to the combination treatment, we performed antibody array analysis in the resistant cell line M207 and the sensitive cell line M296  was induced by the MAPK inhibitors, particularly the combination treatment, in M296 but not in M207 cell line  in the sensitive cell line Figure D. MoreovNRAS mutation is detected in around 20% of melanomas; however no effective direct inhibitor of mutated NRAS is clinically available . The MAPInterestingly, we found a variable baseline activity of MAPK pathway among the cell lines in the NRAS mutant panel. Sensitive cell lines showed higher level of p-MEK and p-ERK as opposed to the more resistant cell lines that had lower baseline levels of these two phosphoproteins. Indeed, we found a significant correlation between the ranking of p-MEK levels and the synergistic effect of complete blocking of the MAPK pathway by the combination treatment. These findings suggest that perhaps higher activity of the MAPK pathway is an indication for higher dependency of the cells on this pathway and therefore higher sensitivity of these cell lines to the blockade of the MAPK pathway.In addition, upon performing cell cycle analysis and apoptosis assays we observed a distinguished pattern of response to the drugs in sensitive cell lines versus the response of resistant cell lines. In most of the cells, progression from G0/G1 to S phase is regulated by the interaction of one of the cyclin D isoforms with CDK4/CDK6. Indeed the effect of the MAPK pathway on induction of cell cycle progression is mainly through the induction of cyclin D1 expression . Considein vitro study on breast cancer cell lines, the sensitivity to the CDK4/6 inhibitor palbociclib (PD 0332991) was mainly limited to the luminal cell lines with a microarray signature of high cyclin D1, high RB and low CDKN2A; meanwhile cyclin D3 was not part of the sensitivity signature [Both cyclin D1 and D3 can bind to CDK4/6 to induce cell cycle progression from G1 to S phase. To block the function of these cyclin-CDK complexes, inhibitors of CDK4/6 have been developed. While in theory these inhibitory compounds should be effective in cancer cells that are dependent on either one of the cyclin Ds , it is not clear if in practice they show such a general effect. For instance, in an ignature . In the ignature .In addition to the cell cycle progression, our results also indicated that resistant cell lines are better equipped to support their survival independent of the MAPK pathway. The role of GSK3β in regulation of different cell functions including metabolism, cell cycle progression and cell survival has been very well studied. Phosphorylation of GSK3β inhibits its activity and promotes cell proliferation and survival . InteresUsually, p-GSK3β is considered to be downstream the PI3K/AKT pathway. In this study, although higher levels of p-GSK3β were detected in all three resistant NRAS mutant cell lines, high levels of p-AKT was not detected in two of them Figure C. In theOne of the pro-survival effects of ERK is exerted through the phosphorylation of the pro-apoptotic protein BIM and therefore targeting this protein for degradation. Interestingly, in comparison with the basal BIM levels, blockade of MAPK pathway caused relatively higher fold inductions of BIM protein in the sensitive cell lines. This is an indication for the higher perturbation of pro-apoptotic factors and the shift of balance toward apoptosis upon the blockade of the MAPK pathway in the sensitive cell lines. Indeed, in transcriptome studies, we observed the high perturbation in the pro- apoptotic profile of treated sensitive cell line, M296. As it was expected, by performing cleaved PARP apoptosis assays, we observed higher rates of apoptosis in PRi + MEKi treated sensitive cell lines as compare with the resistant ones.As it has been shown in BRAF mutant melanomas, resistance to blocking of the MAPK pathway can be the result of reactivation of the same pathway or activation of alternative pathways. Reasonably the same pattern of endogenous or developing resistance can exist in NRAS mutant melanomas. In this study, we observed the synergistic effect of PRi + MEKi combination in majority of NRAS mutant cell lines particularly in those with higher activity of the MAPK, but not in cell lines with MAPK independent pro-survival and cell cycle progression factors. In the future preclinical and clinical studies, it would be interesting to find out whether this combination shows better outcomes and delays the onset of resistance in comparison to single agent MEKi.Considering the findings of this study, our general conclusion is that level of basal p-MEK in each cell line reflects the activity and importance of the MAPK pathway in the cells. Perhaps, cell lines with higher levels of p-MEK are also more dependent on this pathway and more sensitive to the blockade of this pathway and its consequences such as down regulation of pro-survival and cell cycle progression factors such as cyclin D1. On the other hand, cell lines with lower activities of the MAPK pathway, that contain higher activities of other pro-survival pathways such as high p-GSK3β and dependency on cyclin D3 for cell cycle progression are resistant to the blockade of the MAPK pathway by the combination treatment.Trametinib was purchased from Selleck Chemicals  and the pan-RAF inhibitor (Amgen Compd A - hereafter PRi)  was obtaMelanoma cell lines were treated with serial dilutions of trametinib, PRi, their combinations or DMSO for 72 hours or 5 days depending on the growth rate of the cell lines. The assays were performed in duplicates for each concentration and repeated at least twice. Cell viability was detected by a bioluminescence assay . The IC50s were calculated from the growth inhibition assays. To determine synergistic effect of the drugs, Combination Index (CI) was calculated by CalcuSyn software .Cell lines were transfected with the cyclin D3 specific or no target siRNAs , as it has been described before  and wereWestern blotting was performed as previously described . PrimaryCell lines were treated with DMSO or 500 nM PRi, 25 nM of MEKi or their combinations for 48 hours. After the harvest, cell cycle analysis was performed as it was described before . For anaet al. [et al. [M207 and M296 cells were treated for 24 hours with 500 nM PRi, 25 nM of MEKi, or their combination. After harvest total RNA was isolated by RNeasy kit . Further processing, hybridization (Affymetrix Human U133plus2.0 Array), and slide scanning were performed by the Clinical Microarray Core at UCLA. The data was processed with R/Bioconductor  and sampet al.  and Dry  [et al. . The expDetection of a panel of phosphoproteins in M207 and M296 was carried out by treating them with DMSO or PRi (500 nM), MEKi (25 nM) or combination of the drugs for 24 hours and using Human Phospho-Kinase Array Kit . Harvest of cells, lysis and all other steps were performed according to the instruction of the manufacturer. To detect the signal levels, arrays were exposed to ECL2 reagent , scanned and signals were quantified as it was described in the Western blotting in above. The signals were normalized between the arrays by considering the reference spots. The relative signal level for each phosphoprotein was determined by calculating the ratio of each signal over the signal of M296 control which was assigned the value of 100.