Motivation: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.Results: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17 142 compounds screened against 59 cancer cell lines from the NCI60 panel  by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data . We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.Contact:terez@pasteur.fr; ab454@ac.cam.ukSupplementary information: Supplementary data are available at Bioinformatics online. Cultured cell lines have, despite their inherent limitations, served as versatile preclinical disease models for cancer drug discovery . Recent simultaneously, as the cell line profiling data and chemical information were separately used as predictive features and were not integrated into a single machine learning model. To overcome this limitation, two recent studies have pioneered the combination of drug and cell line information  on the data from the Genomics of Drug Sensitivity in Cancer project (GDSC) for drug sensitivity prediction (R2) of 0.64 on an external set. The second study   of 17 142 compounds screened against 59 cancer cell lines from the NCI60 panel. Conformal prediction was implemented to predict the confidence intervals (CI) for individual predictions. The integration of these different, yet complementary, streams of information is often termed proteochemometrics or pharmacogemonic (PGM) modelling (c(dc), and cell line, Φcl(dcl), descriptors into a single vector, namely Φpair. Although this encoding approach is widely used, other approaches exist  is encoded by the tensor product of Φc(dc) and Φcl(dcl)  . SubsequΦcl(cld) a.Fig. 1We curated cell line profiling data consisting of 59 cell lines that were characterized using various genetic and proteomic profiling methods. From this, we assembled 14 profiling datasets, denoted here as dataset views. We benchmarked their predictive signal, and demonstrated that the simultaneous modelling of the compound and cell line information improves the prediction of compound potency across the NCI60 panel compared with models trained on only compound information. Our modelling approach permits us to interpolate and extrapolate compound bioactivities to novel cell lines and tissues in the NCI60 panel, and, to a lesser extent, to chemically dissimilar compounds. Finally, we demonstrate that the predicted bioactivities can be used to estimate growth inhibition patterns across the NCI60 panel and that the predicted drug-pathway associations are consistent with the experimental data published in the literature.50 values  for 17 142 compounds were downloaded from CellMiner (database version 1.4)  was normalized with the StandardiseMolecules function of the R package camb using the default values  was calculated to delineate the clusters. The U-matrix value associated with a given neuron, The compounds were clustered using periodic two-dimensional Self-Organizing Maps (SOMs) (ensemble.RandomForestRegressor module of the python library scikit-learn (mtry): dimensionality of the input space. Radial-kernel Support Vector Machine (SVM) models were trained using the svm module of the python library scikit-learn  σ: {2−8, 2−6,…, 22}. All calculations were performed on 16 Intel Xeon E5-2670 processors and a total memory of 256 GB. The training times on the complete dataset ranged between 6 and 8 h.Random Forest (RF) models  were trait-learn ; (ii) crpairs. The interpolation power of these 10 models was evaluated on a per cell line and on a per compound cluster basis. To this end, the RMSE and R20 values were calculated on subsets of the test set grouped by cell line (cell line-averaged performance) or by the compound cluster (compound cluster-averaged performance).Assessment of the interpolation power with a 10-fold CV (10-fold CV PGM) model: a given dataset was randomly divided into (i) a training set comprising 90% of the data points and (ii) a test set comprising the remaining 10% of the data b. This p50 values for chemically novel compounds, and thus permits the assessment of the extrapolation capabilities of a PGM model in chemical space.Leave-One-Compound-Cluster-Out (LOCCO) models c: all daLeave-One-Cell-Line-Out (LOCO) models d: this vLeave-One-Tissue-Out (LOTO) models: The performance of the PGM models was further evaluated by extrapolating to cell lines whose tissue of origin was not present in the training set. This scheme is similar to LOCO, except for the fact that all of the cancer cell lines originating from the same tissue were left out from the training set each time. It was employed because cell lines from the same tissue, despite their genetic differences, often still share commonalities with regard to compound activity.To quantify the improvement of the PGM models over models exclusively trained on compound fingerprints (compound-only models), the following one-space learning strategies were explored  compound descriptors and on (ii) one cell line profiling data set view.Family Quantitative Structure-Activity Relationship (QSAR F): the models were exclusively trained using the compound fingerprints as the input features e. A QSARper cell line: one QSAR model per cell line was exclusively trained on compound descriptors, which is, therefore, not able to include information from the biological side or measurements against other cell lines. In this case, the comparison was made with the compound cluster-averaged interpolation power of the PGM models to evaluate whether the integration of the compound and cell line information leads to higher predictive ability with respect to the per cell line QSAR models.Individual QSAR models Dirac kernel  kernel  of the transcript levels (log2) of 19 965 genes  was added to each data point in A, thus defining the sample B. We define experimental uncertainty as the standard deviation of replicate pGI50 measurements. In cases where no experimental uncertainty was available for a data point, the mean of the available replicate-averaged experimental uncertainties, namely 0.27 pGI50 units, was used. Next, the RMSEtest and R20 test values were calculated for A with respect to B. These steps were repeated 1000 times, which led to the distributions of the maximum achievable RMSEtest and R20 test values.Minimum performance. The procedure was the same as in the previous case, except that sample A was randomized before calculating the RMSEtest and R20 test values.To assess the maximum and the minimum achievable RMSEε, where ε is the tolerated error. The non-conformity score, αnew, for a new data point, xnew, quantifies how distant it is with respect to the data points from the training set, xnew with an error model. The main advantage of conformal prediction is that the CI are always valid, i.e. a confidence of 0.8 means that the predicted confidence regions will contain the observed value in at least 80% of the cases  for individual predictions, 50 values (point prediction model), whereas the second predicted the errors in the prediction (error model), complete dataset. The point prediction model was generated by training an RF model on the proper training set with a 10-fold CV and with pGI50 values as the dependent variable. The generated CV predictions served to calculate the residuals (i.e. prediction errors) for the data points in the proper training set. Subsequently, the error model was generated by training an RF model on the proper training set using these residuals as the dependent variable. The pGI50 values and prediction errors were subsequently predicted for the calibration set and employed to calculate the vector of non-conformity scores for this set, which, after being sorted in increasing order, led to: α value for a given confidence level, namely ε. Finally, the prediction errors  and the expression of a given pathway, we fitted a linear model considering the tissue type, T, as a blocking factor .The average logSupplementary Material.We collected seven profiling datasets for 59 cell lines from the NCI60 panel, excluding ME.MDA-N due to the lack of gene transcript microarrays . These mcomplete dataset, comprising all available data and including 17 142 distinct compounds (Supplementary Table S8) and 941 831 data points, we assembled three additional datasets: (i) the uncorrelated bioactivities 0.5 dataset, comprising 3641 distinct compounds (199 940 data points) whose bioactivity distributions on the 59 cell lines exhibit standard deviations higher than 0.5 pGI50 units (Supplementary Table S9); (ii) the uncorrelated bioactivities 1 dataset, comprising 165 distinct compounds (9376 data points) whose bioactivity distributions on the 59 cell lines exhibit standard deviations higher than 1 pGI50 unit (Supplementary Table S10); and (iii) the high confidence dataset, exclusively comprising data points averaged over at least two experiments (304 212 data points and 5302 distinct compounds). The uncorrelated bioactivities 0.5 and 1 datasets served to assess the models’ performance on compounds displaying a dynamic range of bioactivities across the cell line panel, while the latter dataset served to evaluate whether the models’ performance improves when using replicate-averaged cell line sensitivity data (Supplementary Table S11 for details).In addition to the Supplementary Figure S1 and Section 2.3), which resulted in the definition of 31 distinct chemical clusters. Several clusters, e.g. 4 and 18, are highly homogeneous, as highlighted by the high inter-neuron similarity . In contrast, other clusters are comprised of more diverse compounds . For instance, cluster 2 (Supplementary Table S8) is composed of polycyclic aromatic compounds with diverse halogen substituents and topologies. The definition of structural clusters will be employed later to examine the extrapolation power of the PGM models to chemically dissimilar structures.To assess the chemical diversity in the data, we clustered the 17 142 compounds with SOMs (uncorrelated bioactivities 1 dataset using (i) compound fingerprints and (ii) either gene expression data or a cell line kernels as the input features (P < 0.05). The performance of these models was comparable to that of the 10-fold CV PGM models encoding cell lines with the cor. GE and MLT kernels trained with RF and SVM, respectively  requires smaller training times than the kernel methods and (ii) are robust with respect to the value of their parameters , respectively. These values were consistent with the theoretical maximum and minimum achievable performance, which were 1.42 and 0.35 pGI50 units, respectively, for the RMSEtest and 0.96 and−0.96, respectively, for the R20 test . Moreover, the model performance did not stem from chance correlations, as R20 test values became negative when 75% of the pGI50 values were randomized . Modelling the high confidence dataset led to a similar performance, with an RMSEtest of 0.45 pGI50 units and an R20 test value of 0.84. This indicates that the predictive power of RF does not decrease when we included the data points measured in only one experiment, which is in agreement with a recent benchmarking study on the noise sensitivity of machine learning algorithms in bioactivity modelling .The PGM models trained on the ndomized   the individual QSAR 10-fold CV RF models with increasingly larger training sets, whose performance was assessed on the held-out data (orange in the uncorrelated bioactivities 1 dataset and (ii) the 10-fold CV PGM RF models on the compound and cell line descriptors (‘G.t.l. 1000 genes’ dataset view). In the latter case, the training set comprised an increasingly larger percentage of the data annotated on a given cell line  was assigned to the training set, whereas the lowest difference was found when almost all of the data available on a given cell line was used for training. This indicates that integrating data across different cell lines seems more beneficial when the data are relatively scarce.To test whether the PGM models display higher predictive power than complete dataset, the 10-fold CV PGM models slightly outperformed models trained exclusively on compound descriptors, namely QSAR F, with RMSEtest and R20 test values of 0.45 pGI50 units and 0.78, respectively. These results indicate  that the pGI50 values of the compounds are largely correlated across the cell line panel. However, the 10-fold CV PGM models increased the relative performance over the QSAR F models on the uncorrelated bioactivities 0.5 dataset, with respective RMSEtest values of 0.58 versus 0.69 pGI50 units, respectively, highlighting the finding that the PGM models appear to be more suitable for modelling compounds exhibiting an uncorrelated pGI50 value on a cell line panel. Moreover, the 10-fold CV PGM models significantly outperformed individual cell line models, i.e., QSAR , trained on the data points corresponding to a given cell line that exclusively used the compound descriptors as the input features. These individual models displayed an average RMSEtest value of 0.73 ± 0.05 pGI50 units (Supplementary Table S12). Therefore, integrating the biological information from different cell lines in a single PGM model improves the compound sensitivity prediction on the NCI60 panel over the QSAR models, and even more so in cases where activities across cell lines show less of a correlation.On the uncorrelated bioactivities 0.5 dataset, as it contains the compounds displaying the least correlated pGI50 values on the cell line panel and thus is more challenging to model. For each dataset view, we trained a 10-fold CV PGM model using that dataset view and the compound fingerprints as the input features, which resulted in a total of 140 models (14 data set views × 10 CV folds). An analysis of variance (ANOVA) on the RMSEtest values (P < 1 × 10−17). Similar to previous reports , or compound cluster, compound cluster-averaged performance . Thus, one average RMSEtest value across the 10-fold models was calculated for each cell line and compound cluster. If not otherwise indicated, the results presented in the following sections correspond to models trained on the uncorrelated bioactivities 0.5 dataset with (i) compound fingerprints and (ii) the ‘G.t.l. 1000 genes’ dataset view as the input features. The cell line-averaged RMSEtest values ranged from 0.41 ± 0.01 (U251) to 0.86 ± 0.01 pGI50 units (HOP-92). We found significant differences for the tissue-averaged performance , with RMSEtest values ranging from 0.48 ± 0.01  to 0.70 ± 0.01  pGI50 units . In addition, the learning curves showed that the RMSEtest values of approximately twice the replicate-averaged experimental uncertainty (0.27 pGI50 units) and the maximum achievable performance (0.35 pGI50 units) can be obtained when <10% of the data is used as a training set . In contrast, an ANOVA analysis did not yield significant (P > 0.05) differences among the 31 chemical clusters , with observed median RMSEtest values in the 0.48–0.65 pGI50 units range. Hence, overall, it has been found that the interpolation power is constant across the 31 chemical clusters, but not across tissues.The interpolation power of the 10-fold CV PGM models was evaluated by calculating the RMSEcomplete dataset. The LOCO models exhibited mean RMSEtest values of 0.43 ± 0.08 pGI50 units (Supplementary Table S12), with the lowest and highest RMSEtest values of 0.31 and 0.61 observed for cell lines U251 and OVCAR-5, respectively. Notably, we found that the LOCO and cell line-averaged predictions are highly correlated (rs = 0.92), indicating that interpolation and extrapolation to novel cell lines are correlated. The RMSEtest values for the LOTO models ranged between 0.35 (prostate) and 0.63 pGI50 units (leukaemia). Remarkably, the prediction errors were similar across the entire bioactivity range . Overall, we did not observe significant differences in performance among the LOCO, LOTO , and compound cluster-averaged  results . We found that the RMSE values between the observed and predicted pGI50 values calculated using the LOCO and LOTO models for 47 out of 81 drugs, such as Imiquimod (NSC 369100) and Bendamustine (NSC 138783), were below 0.5 pGI50 units . High RMSE values, between 1 and 1.5 pGI50 units, were observed for 11 drugs, such as the folate antimetabolite pemetrexed (NSC 698037) and Irinotecan (NSC 728073). Together, these data indicate that the PGM models extrapolate compound bioactivities to novel cell lines and tissues at the same level of statistical significance as for interpolation within a given cell line or tissue.We further evaluated the extrapolation power to novel cell lines and tissues with the LOCO and LOTO models using the CO, LOTO b, cell lcomplete dataset. The LOCCO models exhibited mean RMSEtest values of 0.83 ± 0.17 pGI50 units (Supplementary Table S12), which differed significantly from the LOCO and LOTO results (P < 0.01), and from the compound cluster-averaged interpolation performance . Notably, the chemical diversity within the compound clusters was not correlated with model performance, as low RMSEtest values were consistently obtained for the heterogeneous and homogeneous clusters . The lowest RMSEtest value of 0.53 pGI50 units was obtained for cluster 24, which contains 485 compounds with polycyclic ring systems, generally with no more than three fused rings, as well as ring assemblies linked by sulphide, sulfinyl, secondary amine, carbonyl and alkyl groups (Supplementary Table S13). Cluster 16, which was modelled with the highest RMSEtest value of 1.23 pGI50 units, contains tri- and tetracyclic systems with hydroxybenzene, methoxybenzene and quinone functionalities. We obtained errors in prediction values below 0.5 pGI50 units for 15 out of 81 drugs in the complete dataset, and below 1 pGI50 units for 43 compounds. The worst predicted drugs were depsipeptide (NSC 630176) and the halichondrin B analogue NSC 707389, with respective errors in prediction of 4.29 and 4.35 pGI50 units . Taken together, these results indicate that the range of errors is considerably large (>4 pGI50 units), and, hence, extrapolations in the chemical space remain a challenging task, and certainly on this particular dataset.A markedly different trend was observed for the ability of the PGM models to generalize across the chemical space, as assessed by the LOCCO models using the  results b . Therefore, the combination of the Random Forest and conformal prediction estimates the compound activity as a pGI50 region associated to a user-defined confidence level, which represents an easily interpretable estimate of the reliability of individual predictions.Conformal prediction was included in the modelling framework to provide CI for the individual predictions. The percentage of data points for which the predicted value lay within the calculated CI, which were calculated with an increasing confidence level, is highly correlated with the confidence level (Spearman’s Supplementary Table S14) present in the uncorrelated bioactivities 0.5 dataset. For each pathway, we fitted a linear model controlled by the tissue source, where the average pathway expression was considered as the predictor of drug sensitivity. Overall, no significant differences  were observed between the pathway–drug associations calculated with the most predictive 10-fold CV PGM  and LOCO models, as the median Spearman’s rs values between the Supplementary Fig. S11b). These results indicate that the 10-fold CV and LOCO predictions reflect the association between gene expression summarized at the pathway level and the cell line sensitivity. However, significant differences were found among these groups and the LOCCO and LOTO models, for which the median Spearman’s rs values were 0.63 and 0.03, respectively. We obtained similar results when considering only the pathways significantly associated to drug response [false discovery rate (FDR) < 20%] .To investigate whether the bioactivities predicted by the PGM models make it possible to identify genomic markers of drug sensitivity, we evaluated the consistency between the pathway–drug associations inferred from the experimental and predicted bioactivities for the 37 FDA-approved drugs and the 19 compounds in the clinical trials . High Spearman’s rs values were obtained across the 22 distinct drug MoAs, indicating that no specific MoA is favoured. Notably, most Spearman’s rs values increased  when the calculation of the pathway–drug associations was restricted to the pathways significantly associated to drug response (FDR < 20%). Together, we can conclude that the identification of genomic markers of drug sensitivity is significantly dependent on the presence of cell lines originating from the same tissue and structurally similar compounds in the training set.Next, we analysed whether the pathway–drug associations are consistently predicted for drugs exhibiting diverse MoAs. Out of the 56 drugs considered, 26 exhibited median Spearman’s Supplementary Fig. S11e), with median Spearman’s rs values in the 0.53–0.58 range, and 32 out of the 56 drugs from the uncorrelated bioactivities 0.5 dataset had values higher than 0.5 for . Nevertheless, the LOCO, LOTO and LOCCO models  displayed a marked decrease in the rs values  with respect to the most predictive 10-fold CV PGM models.The experimental and predicted growth inhibition patterns obtained by the most predictive models were highly correlated  The predictions accounted for 55 out of 59 cases for the relative sensitivity of the cell line. For instance, the six leukaemia cell lines (green turquoise) were predicted to be sensitive to MTX. Moreover, complex inhibition patterns for renal-derived cell lines (light magenta) were accounted for by the predictions, as the TK-10, RXF-393 and A498 cell lines were predicted to be highly resistant to MTX, whereas the effect of MTX on sensitive cell lines, namely UO-31, SN12C, CAKI-1 and ACHN, was also correctly predicted . For the GDSC dataset, we obtained lower mean RMSEtest and higher R2test values, namely 0.75 ± 0.01 and 0.74 ± 0.01 pGI50 units, respectively, with the 10-fold CV models compared with test of 0.83 and an R2test of 0.72 pGI50 units, and test of 0.83 ± 1.00 and an R2test of 0.32 ± 0.37 pGI50 units. The same trend was observed for the LOTO models, with mean RMSEtest and R2test values of 0.81 ± 0.16 and 0.72 ± 0.08 pGI50 units, respectively, compared with test and R2test values of 0.99 and 0.61 pGI50 units, respectively. The results obtained here, with RMSEtest values of 1.40 ± 0.80 pGI50 units, are comparable to those of test values of 0.85 ± 0.41 pGI50 units, when extrapolating to new compounds. However, it is important to mention that We compared our results with previous studies . Although the error in prediction should ideally be close to the experimental uncertainty, this performance may still be useful for compound prioritization. Previous studies have shown that adding physicochemical descriptors or increasing the bit-string length of the Morgan fingerprints (set to 256 here) leads to higher predictive power when modelling a highly diverse set of molecules (Supplementary Text and Supplementary Table S15).A major challenge to the cell line sensitivity prediction is the extrapolation of compound bioactivities to novel cell lines and to structurally distinct compounds. We did not find significant differences in performance between the interpolation and the extrapolation to new cell lines (LOCO) and tissues (LOTO), with mean RMSEprofiles , it was R20 test values of∼0.74, which were comparable to those obtained on the GDSC and NCI60 datasets. Given that gene expression profiles for the 44 cell lines comprised in the CCLE and the NCI60 panel are highly correlated (Spearman’s rs = 0.88) , we suggest that PGM models trained on the NCI60 panel could be applied to the CCLE cell line panel, which could identify new purposes for the 17 142 compounds considered here  (red here .Emax), potency (IC50) and on the steepness of the drug response curve. Overall, this study indicates that parameters other than the potency of the drug response curve should be considered in studies comparing drug activity, as they are likely to provide crucial insights into the biology of the cell line’s response to drug treatment and into the drugs’ MoA.It is also important to consider how the cell line sensitivity is quantified for the application of the type of model presented here. in vitro cell line sensitivity data. A previous study , and we did not obtain a high correlation between the predictions calculated with a model trained on the NCI60 data for eight drugs on 44 cell lines shared by the CCLE and the subset of the NCI60 cell lines considered here. Actually, the RMSE values for these predictions against the experimental data from the CCLE and, the pIC50 values from the CCLE and the pGI50 from the NCI60 dataset for these eight drugs and 44 cell-lines was comparable, namely 0.87 log10 units, indicating that high predictive power cannot be attained given the low concordance of the sensitivity data from these two datasets. These results come as no surprise, given the different surrogates of cytotoxicity exploited by the assays used to screen the CCLE and NCI60 panels, namely metabolic activity and protein abundance, respectively. Therefore, we conclude, in accord with An additional key aspect is the consistency of the in vitro cell line sensitivity can help researchers to develop approaches for the prediction of clinical drug responses using the genomic data of tumour samples (Finally, although cultured cell lines and primary tumours differ genetically  and 2D c


In this study, we explore the effect and possible mechanism of action of zebularine on CCA cells. We demonstrate that zebularine exerts an antitumor effect on CCA cells. Zebularine treatment decreased the concentrations of DNA methyltransferase (DNMT) proteins, and DNMT1 knockdown led to apoptotic cell death in the CCA cell lines TFK-1 and HuCCT1. DNA methylation analysis demonstrated that zebularine induced DNA demethylation, and the GO Biological Process terms “hemophilic cell adhesion”, “regulation of transcription, DNA-dependent” and “Wnt signaling pathway” were found to be significantly enriched in association with demethylated genes. Furthermore, we observed that zebularine treatment decreased β-catenin protein levels in TFK-1 and HuCCT1 cells. These results suggest that zebularine alters DNA methylation status, and that some aspect of DNA demethylation by zebularine induces suppression of the Wnt signaling pathway, which leads to apoptotic cell death in CCA. We previously reported a novel mechanism of zebularine-induced cell growth arrest and apoptosis in hepatocellular carcinoma via a DNA methylation-independent pathway. Together, our present and previous studies indicate that zebularine could function as both a DNMT inhibitor and a non-DNMT inhibitor reagent, and that, while the optimal usage of zebularine may depend on cancer type, zebularine may be useful for chemotherapy against cancer.Cholangiocarcinoma (CCA) is a cancer arising from the neoplastic transformation of cholangiocytes. During tumorigenesis, tumor suppressor and cancer-related genes are commonly silenced by aberrant DNA methylation in their promoter regions. Zebularine (1-(β-D-ribofuranosyl)-1,2-dihydropyrimidin-2-one) acts as an inhibitor of DNA methylation and exhibits chemical stability and minimal cytotoxicity both  Cholangiocarcinoma (CCA) is a cancer arising from the neoplastic transformation of cholangiocytes. Several epidemiological studies show that the incidence and mortality rates of CCA have been increasing worldwide over the past few decades. In Europe, approximately 50,000 new cases of primary liver cancer are diagnosed each year. Data from the Cancer Incidence in Five Continents survey indicate that approximately 20% of those cases are attributed to CCA . AlthougIn recent years, much has been learned about epigenetic change, which has been confirmed as an important mechanism in multiple tumors. Epigenetic change is defined as any heritable modification of the genome that is not accompanied by a change in the DNA sequence . Methylade novo as well as maintenance methyltransferase activity, and DNMT3a and DNMT3b are potent de novo methyltransferases , and were subjected to the following analysis. For each assay, the delta-beta value  was calculated. As shown in http://david.abcc.ncifcrf.gov/). The numbers of CpG sites whose delta-beta values were <-0.2 were 21,214 (4.4%) and 5,297 (1.1%) in TFK1 and HuCCT1 cells, respectively. A total of 4,285 CpG sites from these two cell lines were commonly hypomethylated (delta-beta < -0.2) , and Wnt signaling-related genes (www.broadinstitute.org/igv/home). The majority of HOX cluster, homeobox and Wnt signaling-related genes that were found to be hypomethylated in TFK1 and/or HuCCT1 cell lines upon zebularine treatment were unmethylated at their promoter regions in normal tissues, and highly methylated in these CCA cell lines. Therefore, treatment of these CCA cell lines with zebularine is likely to partially reactivate any genes that were silenced during the carcinogenesis processes by demethylating their promoter regions, which may cause cancerous cells to commit to apoptotic cell death. These results suggest that zebularine reverses the promoter hypermethylation of protocadherin, transcriptional regulator, and Wnt signaling-related genes in CCA cell lines.Given that zebularine decreases DNMT1 protein levels and induces apoptotic cell death, we next set out to determine whether the apoptosis and/or growth arrest induction caused by zebularine in TFK-1 and HuCCT1 cells results from alterations in DNA methylation. We obtained the genome-wide methylation profiles of zebularine-treated and -untreated (control) TFK-1 and HuCCT1 cells using an Illumina Infinium HumanMethylation450 BeadChip (GEO accession number GSE60446). Among 482,421 assays for CpG sites, 482,143 assays fulfilled our quality control criteria [detection  < -0.2) . Further < -0.2) . in the  < -0.2) . It has cytidine . Among tll lines .. We vised genes . using IIn order to confirm that zebularine actually affects the Wnt signaling pathway, we examined β-catenin protein levels after zebularine treatment. Zebularine treatment decreased β-catenin levels in a dose-dependent manner in TFK-1 and HuCCT1 cells , indicatPrevious reports have shown that zebularine can induce DNA damage , 41. In The expression levels of DNMTs are reportedly elevated in cancers of the colon , prostatZebularine is a DNMT inhibitor with demonstrated antitumor effects against a range of cancer types . In thisWe observed that zebularine affected each of the five CCA cell lines examined in this study. There were, however, differences in sensitivity to zebularine among the five cell lines. Further studies will be needed to clarify why this is; regardless of the reason, however, these results suggest that the effect of zebularine as an antitumor reagent against CCA depends on CCA type.de novo methyltransferases [In this study, we demonstrated that zebularine decreased the levels of DNMT3a, DNMT3b, and especially DNMT1 in TFK-1 and HuCCT1 cells. These results were similar to those of other reports showing that DNMT inhibitors deplete DNMT1, 3a, and 3b proteins in human bladder, breast, and cervical cancer cells and hepatocellular carcinoma , 38, 47.sferases . The expsferases –46. In tsferases . Inhibitsferases . Hence, sferases . The balWe have previously reported that zebularine exerts antitumor activity on hepatocellular carcinoma HepG2 cells by inhibiting cell proliferation and inducing apoptosis, though it has little effect on DNA methylation in HepG2 cells . RecentlIn this study, we demonstrate that zebularine inhibits TFK-1 and HuCCT1 cell growth by inducing apoptosis. During the process of apoptosis, caspases are essential for the initiation and execution of cell death in a self-amplifying cascade in response to various stimuli . Two majAt the same time, our results also showed that DNA replication was blocked by treatment with zebularine in HuCCT1 cells, though not in TFK-1 cells. This suggests that there are some differences among CCA types in terms of the effects of zebularine. Eukaryotic cell proliferation is a highly regulated system that is controlled by CDK-cyclin complexes. The cell-cycle transition from the G1 to the S phase is the major regulatory checkpoint in this process. This transition is characterized by the phosphorylation of Rb and catalyzed by the CDK-cyclin complex , 55. In SFRP1 belongs to the secreted glycoprotein SFRP family (SFRP1 to 5), the members of which are antagonists of Wnt signaling pathways [In the present study, we demonstrated that zebularine treatments induce DNA demethylation of Wnt signaling pathway-related genes and decrease β-catenin protein levels in CCA cells. The Wnt signaling pathway regulates cell proliferation, differentiation, apoptosis and other biological processes. The activation of the Wnt signaling pathway is closely related to tumorigenesis and progression in various types of tumors , 57. Abepathways . These rin-vivo activity in preclinical murine tumor models, and oral administration of zebularine would be the most logistically expedient means of delivering regular and relatively frequent doses of zebularine, the low bioavailability of zebularine in rodents and the extremely low bioavailability of zebularine in rhesus monkeys are a cause for concern [It must be considered, however, that a number of previous studies have suggested that zebularine has limited bioavailability in humans. One of these showed that zebularine’s DNA methyltransferase inhibition effect is reversible, and also that tumors must be exposed to zebularine for long periods for inhibition of tumor growth. Holleran et al. pointed out that, given the terminal half-life of zebularine after intravenous injection into mice, rats, and monkeys, it is likely that frequent dosing or continuous intravenous infusion of zebularine will be necessary to maintain prolonged inhibition of DNA methyltransferase. Although oral administration of zebularine has been shown to have  concern . This isin-vivo anti-CCA activity of zebularine, the action and mechanisms of zebularine must be further investigated in animal models such as xenograft and orthotopic models, carcinogen-induced CCA models, and genetically engineered mouse models of CCA.The present study does not include an animal model of CCA, which will be an important step in proving that zebularine treatment can be effective against CCA in practice. In order to investigate the Our observations indicate that zebularine induces apoptotic cell death in CCA cell lines. Its effects are dependent on DNMT1 depletion and DNA demethylation, which induces the activation of certain genes including protocadherin, transcriptional regulators, and Wnt signaling-related genes. Zebularine also inhibits cell growth in certain CCA cell lines; this may contribute to its antiproliferation effects against some types of CCA cells. Our present and previous (31) reports have shown that zebularine could function as both a DNMT inhibitor and a non-DNMT inhibitor reagent, and that, when administered in the optimal manner for each cell type, zebularine may be useful for chemotherapy against cancer.S1 Figp < 0.05, compared to 0 μM.TFK-1 (A), HuCCT1 (B), KKU-100 (C), KKU-M156 (D) and KKU-M213 (E) cells were treated with zebularine at indicated concentrations for 72 h. Cell growth was measured by CellTiter-Glo Luminescent Cell Viability Assay. Data are the means ± SEM of results from at least three independent experiments. *(TIF)Click here for additional data file.S2 FigThe 31 genes included among the 2,102 genes hosting 3,309 hypomethylated CpG sites (see (PPTX)Click here for additional data file.S3 Figwww.broadinstitute.org/igv/home). The data range shown is 0 to 1 for β values and -0.5 to 0.5 for delta-beta (Δ-β) values.Visualization of the beta and delta-beta values of TFK1 and HuCCT1 cell lines after zebularine treatment together with the beta values of human normal tissues (from GEO accession numbers GSE52578 and GSE30870) for protocadherin gene cluster (A), HOXA gene cluster (B), homeobox genes (IRX2 and TLX3 as examples) , and Wnt signaling-related genes (E-J) using the Integrative Genomics Viewer Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 TableThe results of the gene ontology term analysis using DAVID for the 2,102 genes hosting 3,309 CpG sites commonly hypomethylated (delta-beta < -0.2) in TFK1 and HuCCT1 cells after zebularine (1000μM) treatment.(XLS)Click here for additional data file.S3 TableThe results of the gene ontology term analysis using DAVID for the 782 genes hosting 948 CpG sites that are located in the pTSS regions and are commonly hypomethylated (delta-beta < -0.2) after zebularine (1000μM) treatment in TFK1 and HuCCT1 cells.(XLS)Click here for additional data file.S4 Table(XLS)Click here for additional data file.


Teicoplanin is a glycopeptide antibiotic with a wide variation in human serum half-life. It is also a valuable alternative of vancomycin. There is however no study on its effect on cultured cells. The aim of the present study was to test the effect of teicoplanin on cultured cell lines CHO, Jurkat E6.1 and MCF-7. The cultured cells were exposed to teicoplanin at final concentrations of 0–11000 μg/ml for 24 hours. To determine cell viability, the 3--2,5-diphenyltetrazolium bromide (MTT) test was performed. At low concentrations of teicoplanin the numbers of cultured cells (due to cell proliferation) were increased in the three cell lines examined. The maximum cell proliferation rates were observed at concentrations of 1000, 400, and 200 μg/ml of teicoplanin for CHO, MCF-7 and Jurkat cell lines, respectively. Cell toxicity was observed at final concentrations over 2000, 6000, and 400 μg/ml of teicoplanin for CHO, MCF-7 and Jurkat cell lines, respectively. A dose-dependent manner of cell toxicity was observed. Our present findings indicated that teicoplanin at clinically used concentrations induced cell proliferation. It should therefore be used cautiously, particularly in children, pregnant women and patients with cancer. Actinoplanes teichomyceticus) is an antibiotic used in the prophylaxis and treatment of serious infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus and Enterococcus faecalis. The molecular structure of teicoplanin is related to that of vancomycin with a similar spectrum of activity .CHO, Jurkat E6.1 and MCF-7 cell lines were used in the present study. The cell lines were cultured at 37 °C in a humidified 5% CO2. After treatment, the viability of the cells was evaluated by the 3--2,5-diphenyltetrazolium bromide (MTT) assay. MTT reagent was added to each plate and after 6 h of incubation 1ml of SDS (10% in 0.01N HCl) was added to dissolve the water-insoluble formazan salt. OD570 nm was measured. Unexposed cells were regarded as 100% viable.Teicoplanin at final concentrations of 0–11000 μg/ml was added to 500 μl of the cultured cell lines onto plates. The treated cells were incubated for 24 hours at 37 °C in 5% COAll the values in this study are expressed as the mean ± SD of three independent experiments.On balance, teicoplanin exerted dual effects on the cell lines examined. First it induced cell proliferation and then it showed cell toxicity. It should be mentioned that in light of our data cell proliferation and cell toxicity effects of teicoplanin showed cell specific patterns.et al., et al., et al., et al., et al., et al., et al., The concentrations of teicoplanin which revealed cell toxicity were much higher than the clinically used concentrations (Tobin Finally it should be mentioned that teicoplanin is to be used with caution particularly in children, pregnant women and patients with cancer. Further experiments are necessary to clarify the significance of the present findings and the cancer risk associated with the use of teicoplanin.


EGFR is important in maintaining metabolic homeostasis in healthy cells, but in tumors it activates downstream signaling pathways, causing proliferation, angiogenesis, invasion and metastasis. Consequently, EGFR is targeted in cancers using reversible, irreversible or antibody inhibitors. Unfortunately, tumors develop inhibitor resistance by mutations or overexpressing EGFR, or its ligand, or activating secondary, EGFR-independent pathways.vs. -resistant cell lines, using 15 datasets comprising 274 microarrays. We also analyzed separately pairs of cell lines derived using reversible, irreversible or antibody inhibitors.Here we present a global metaanalysis comparing transcriptional profiles from matched pairs of EGFR inhibitor-sensitive The metaanalysis identifies commonalities in cell lines resistant to EGFR inhibitors: in sensitive cell lines, the ontological categories involving the ErbB receptors pathways, cell adhesion and lipid metabolism are overexpressed; however, resistance to EGFR inhibitors is associated with overexpression of genes for ErbB receptors-independent oncogenic pathways, regulation of cell motility, energy metabolism, immunity especially inflammatory cytokines biosynthesis, cell cycle and responses to exogenous and endogenous stimuli. Specifically in Gefitinib-resistant cell lines, the immunity-associated genes are overexpressed, whereas in Erlotinib-resistant ones so are the mitochondrial genes and processes. Unexpectedly, lines selected using EGFR-targeting antibodies overexpress different gene ontologies from ones selected using kinase inhibitors. Specifically, they have reduced expression of genes for proliferation, chemotaxis, immunity and angiogenesis.This metaanalysis suggests that ‘combination therapies’ can improve cancer treatment outcomes. Potentially, use of mitochondrial blockers with Erlotinib, immunity blockers with Gefitinib, tyrosine kinase inhibitors with antibody inhibitors, may have better chance of avoiding development of resistance.The online version of this article (doi:10.1186/s12885-015-1337-3) contains supplementary material, which is available to authorized users. Cancer is principally caused by changes in three types of genes i.e. oncogenes, tumor suppressor genes and DNA stability genes; common environmental factors contributing to these changes could be smoking, oncogenic viruses, occupational and environmental carcinogens and predisposing genetic polymorphisms ,2. MassiEGFR, important growth factor receptor implicated in many cancers, is one of the targets for chemotherapy. EGFR belongs to ErbB family of tyrosine kinase receptors . In tumoTo treat malignancies, EGFR activity is targeted with reversible, irreversible or antibody inhibitors and their combinations. Reversible inhibitors, e.g., Gefitinib and Erlotinib, compete for the intracellular ATP binding site of the kinase; the irreversible inhibitors, e.g., PF299804 and WZ4002, block the ATP binding site by covalent interaction with Cys773 in EGFR [vs. resistant cell lines. The cell lines included non-small cell lung cancer, head and neck cancer, and epidermoid carcinoma cell lines. The inhibitors included both reversible and irreversible kinase inhibitors, as well as antibodies.Unfortunately, after a period, tumors inexorably develop resistance to inhibitors by e.g., overexpression of EGFR ligand, activating mutations in the tyrosine kinase or the ligand-binding domain, or by mutations in downstream or parallel signaling pathways, e.g., Axl or IGFIR -14. MultWe found that in EGFR inhibitor-sensitive cell lines characteristically overexpressed gene ontologies are adhesion, negative regulation of cell proliferation, lipid metabolism and oncogenic processes involving ErbB receptors. But when cells become resistant, ontological categories associated with energy metabolism, immunity involving overexpressing inflammatory cytokines, responses to external and internal stimuli, proliferation and ErbB-independent oncogenic pathways are overexpressed. The specific resistance to Gefitinib apparently develops by overexpressing immunomodulatory genes; resistance to Erlotinib by energy producing mitochondrial pathways; resistance to irreversible inhibitors by overexpressing EGFR ligands, whereas resistance to antibody inhibitors develops differently from the resistance to tyrosine kinase inhibitors.The overall flowchart of our methodology is graphically represented in Additional file Different microarray platforms used for transcriptional profiling produced different, characteristic data files, which were worked up separately and then synchronized. The CEL or TXT files deposited in these studies were first downloaded and unzipped. For each study, data obtained from sensitive and resistant cell lines were saved in different columns of excel spread sheets. Datasets obtained from Affymetrix studies were combined and analyzed using RMAExpress for quality control ,17. For RankProd package analyses gene expression microarray data specifically to identify differentially expressed genes. RankProd uses non-parametric rank product method to detect genes that are consistently found among the most strongly upregulated ones and the most strongly downregulated ones in a number of replicate experiments, comparing two different condition . We haveWe have pooled and compared the microarray data for EGFR irreversible inhibitors from two datasets, fourteen microarrays and 21631 common genes. For studying EGFR antibody inhibitors responses we found a single study with 3 microarrays from Cetuximab-sensitive and 3 from resistant cell lines, with 48607 genes.−4. For each analysis we derived two tables, one representing the ontological categories over expressed in sensitive cell lines and second table with gene ontologies overregulated in resistant cell lines [We used the RankProd Software to find out the genes differentially expressed in EGFR inhibitor-sensitive and resistant cell lines with p-values better than 10ll lines . The resWe used online Database for Annotation, Visualization and Integrated Discovery (DAVID) software as described before ,19. We avs. resistant cancer cell lines. We searched GEO Datasets using key term “EGFR Resistance” and, limiting our choices to human cell lines, selected studies that directly compare transcriptional profiles of matched EGFR inhibitor-sensitive vs. resistant datasets (Table vs. resistant cell lines were compared without any other treatment. In one study comprising four datasets (GSE38310), two different types of Erlotinib-resistant cell lines were originated from Erlotinib-sensitive cell lines and then treated either with DMSO or Erlotinib. We compared DMSO-treated sensitive cell lines with DMSO-treated resistant cell lines and Erlotinib-treated sensitive cell lines with Erlotinib-treated resistant cell lines. In a Gefitinib study comprising three datasets [Inhibitors have been studied principally to find out their mechanism of action in different cancer types. We here aim to study the differential gene expression in EGFR inhibitor-sensitive ts Table . We founts GSE383, two difIllumina microarrays were used in majority of datasets, comprising 3 studies and 7 datasets. In four studies, comparing four datasets Affymetrix microarrays were used. Agilent microarrays were used in one study comparing four datasets. Different studies used different types of cell lines for example Non-Small Cell Lung Cancer, Head and Neck Cancer, A431 and Skin cancer cell lines; therefore, we have also individually compared datasets from individual studies (data not shown).Information about the type of sensitive and resistant cells, pretreatment and type of EGFR inhibitor, labeling accession number, platform, number of datasets, number of sensitive and resistant microarray chips in each dataset is summarized in Table vs. resistant cell lines; 2) Gefitinib- and 3) Erlotinib-sensitive vs. resistant cell lines; 4) irreversible inhibitor-sensitive vs. resistant cell lines; 5) Cetuximab-sensitive vs. resistant cell lines [vs. resistant, with results graphically presented in  datasets comparing all EGFR inhibitor-sensitive ll lines . We usedvs. Erlotinib-, Irreversible- and Cetuximab-resistant groups (see below).Ontological categories overrepresented in lists of differentially expressed genes were identified using DAVID ; completvs. resistant cell lines. Table −4.We have compared overexpressed ontological categories in all sensitive In Table Oncogenesis-related genes and pathways are up-regulated in both sensitive and resistant cell lines. As reported in previous studies, in sensitive cell lines tumor growth seems dependent on the oncogene activation through ErbB receptor kinases . In contInterestingly, we have observed that in the sensitive cell lines lipids are preferentially metabolized as the source of energy. But as cells become resistant, both carbohydrates and lipids are metabolized to provide energy. In addition, resistant cell lines are inducing the expression of energy generating genes, including the oxidoreductases. This observation suggests that energy production is an important matter for development of resistant cell lines.Genes involved in responses to various extracellular and intracellular stimuli, for example steroid hormone, hydrogen peroxide and stress, are over expressed significantly in the resistant cell lines compared to sensitive ones. These responses may be related either to cells’ protection from toxins or overall cell survival, as stress and hydrogen peroxide responses prepare the cell for a toxic environment and steroids stimulate proliferation in certain cancers ,22. ThosWe have also observed that certain processes related to immunity are overexpressed in the sensitive as well as in resistant cell lines, but we found that ontological categories for these processes have significantly better p-values in the resistant ones. Further, detailed study of the genes involved in immunity revealed that the innate immunity involving the complement component system is active in sensitive cells. In contrast, the resistant cells utilize both innate and adaptive immune systems, especially involving the cytokines and chemokines Table d.via EMT [In EGFR inhibitor-sensitive cell lines, cell death and proliferation are relatively suppressed. But in resistant cell lines proliferation is relatively increased, while differentiation is decreased, thus favoring cancer cells growth and persistence. Another major distinction is that sensitive cell lines strongly express adhesion-related genes, whereas in resistant cells the genes related to cell movement are overexpressed. This suggests that the resistant cells have increased tendency to metastasize, perhaps via EMT ,24.−21 (Table Surprisingly, protein biosynthesis was the only ontological category in the top ten differentially expressed ones in Erlotinib-sensitive cell lines Table a. In the21 Table b. These In the Table Interestingly, we have not found any ontological category related to immune system development overexpressed in the sensitive cell lines. In contrast, in the resistant cell lines immunity systems, specifically biosynthesis of cytokines genes, are boosted Table b. This sIn resistant cells the ontological categories related to responses to various stimuli and angiogenesis were robustly and consistently overexpressed. We have also found increased expression of genes responsive to reactive oxygen species, which ultimately tends to activate apoptosis and immunity pathways in these cells. Expression of genes related to hypoxia has also been seen in the resistant cell lines, which may increase their drug resistance and ultimately their growth rate .Both in Gefitinib-sensitive and in resistant cell lines proliferation and apoptosis are regulated. However, in the resistant cell lines, the cell death processes are negatively regulated and the cell division is enhanced, suggesting that resistant cell lines have higher propensity to survive. Meanwhile, responses to various endogenous and exogenous stimuli are enhanced, maybe to cope with stresses from environment. In addition, immunity genes and cytokine biosynthesis are upregulated to help cancer cells to survive and grow.Interestingly, we have observed that lipid metabolism genes are prominent in the sensitive cell lines, whereas oxidoreductases, as well as both glucose and lipid metabolism, i.e. genes related to production of energy, are more prominent in the resistant cell lines. Energy requirements for the tumor cells are met preferentially by formation of new blood vessels but also by glucose and lipid metabolism.We have combined the microarray data from the cell lines selected as resistant to irreversible EGFR inhibitors WZ4002 and PF299804. Unexpectedly, in the top ten categories from the sensitive cell lines, genes related to membrane systems are found Table a. In conSurprisingly, we found increased expression of EGFR ligands in cells selected using irreversible inhibitors Table b. PerhapFor the analysis of cells selected as resistant to antibody inhibitor Cetuximab, we could find only a single study including six microarrays , which mIn Table The charts comparing overexpressed ontological categories, although very informative, contain many redundant and overlapping ontological categories Tables ,5 and 6.In Erlotinib-sensitive cell lines, transcription, translation and protein transport processes, marked as ‘T’ in Additional file Unexpectedly, categories ‘M’, describing motion of cells, and ‘V’, denoting the vascularization and angiogenesis, were found in sensitive cell lines, whereas adhesion-related genes, ‘AD’, were found relatively overexpressed in resistant cell lines. We have also found that certain categories related to energy metabolism; marked as ‘M-c’ for metabolism of carbohydrates and ‘E’ for energy-related processes are also up regulated in sensitive cells (Additional file Although extracellular region ‘EC’ is the top regulated category in irreversible inhibitor-resistant cell lines, important EGFR-activated cellular processes, including immunity, ‘IM’, angiogenesis, ‘V’, and cytoskeleton, ‘CY’, were upregulated (Additional file In Cetuximab antibody-resistant cell lines, many ontological categories are suppressed. For example, ontological category ‘IM’, immunity, is statistically the most prominent, biosynthesis of inflammatory cytokine and responses to these cytokines are specifically downregulated. We have observed that gene ontologies related to migration of the cells, marked ‘M’, are prominent in Additional file We have also found increased expression of signaling, ‘S’, apoptosis inhibition, ‘A’, and homeostasis-related genes, ‘H’, in sensitive cell lines, but not in resistant cell lines. Some of the overregulated clusters were also related to cell cycle, ‘CC’, transcription, ‘T’, and responses to external stimuli, ‘R’. Ectoderm development category was overexpressed with higher p-value in antibody-resistant cell lines, reflecting epidermal cell origin. Other ontological categories were extracellular matrix ‘EC’ and adhesion (Additional file As observed above, Tables ,5 and 6,vs. sensitive cell lines presented here identifies the transcriptional and metabolic differences that allow tumor cells to evade EGFR-targeted therapies. Specifically, we find that the most acute problem created by EGFR inhibition is to provide a sufficient energy supply to the proliferating and metabolizing transformed cells. This metabolic predicament for tumors was one of the first ones identified, already by Warburg in 1927 [Global metaanalysis of EGFR inhibitor-resistant  in 1927 . The eneIn aerobic glycolysis, tumor cells produce lactate by glycolysis in the presence of oxygen and decrease the oxidative phosphorylation due to presence of an isoform of pyruvate kinase. If pyruvate kinase is in its native form, then increased oxygen uptake and oxidative phosphorylation occur and lactate formation is decreased . In metaAdditionally, our study proposes that EGFR tyrosine kinase inhibitors-resistant cancer cell lines express specific inflammatory cytokines and angiogenesis signals to promote vascularization and perhaps autocrine immunity stimulation as important strategy to combat cancer treatment ,30. In-dPKI3CA and BRAF [Perhaps expectedly, while the sensitive cell lines depend on the ErbB-dependent signaling, resistant ones circumvent this pathway and, at least in some cases, rely on the Ras pathway. Parenthetically, the mutations that confer EGFR inhibitor resistance generally occur in EGFR, seldom in Ras. In targeted therapy, as is case of EGFR inhibition, small molecules targeting tyrosine kinase domain of EGFR or monoclonal antibodies specifically blocking the ligand binding site are used. In some tumors, ErbB-independent signaling is sustained by activation of downstream molecules, for example Ras, and BRAF ,21,31-34Interestingly, we found that irreversible inhibitors of the EGFR signaling pathway increased in resistant cell lines the expression of multiple EGFR ligands Table b,c. ThisHypoxia stimulates various processes in cancer cells including vascularization, growth factor signaling, genetic instability, cell survival, metastasis, cell death and antitumor drug resistance, in general favoring tumor survival and propagation . The celmutatis mutandis, the same for Erlotinib. However, we note that both GSE34228 and GSE38310 compared Non-Small Cell Lung Cancer lines, so the characteristic differences between Gefitinib- vs. Erlotinib-selected resistance cannot be ascribed to different cell types. Additional data are needed to corroborate or refute inhibitor-specific characteristics of resistant cells. That said, our previous metaanalysis demonstrated that different inhibitors, although working by ostensibly same mechanisms, cause identifiably different transcriptional responses, which allows for inhibitor-specific developments of resistance as well.An important caveat in this analysis stems from the fact that few of the studies, namely GSE34228 and GSE38310 for Gefitinib and Erlotinib, respectively, dominate the metaanalysis by overwhelming numbers of microarrays. Therefore, we cannot claim at this point that it is the resistance to Gefitinib that specifically produces the described changes in all cell lines selected for resistance to Gefitinib, and vs. sensitive cell lines identifies changes that allow tumor cells to evade EGFR-inhibition. The use of different inhibitors results in different, specific strategies to resist the antitumor therapy. Common pathways are upregulated in cell lines resistant to inhibitors targeting the kinase domain of EGFR, however, there are certain processes uniquely expressed against some of the inhibitors but, apparently, not others. The development of resistance to antibody inhibitors can vary significantly.This metaanalysis of transcriptional and metabolic differences between EGFR inhibitor-resistant We found that the most acute problem created by EGFR inhibition is to provide a sufficient energy supply to the cells. The energy deficit can be overcome by several routes, e.g., by boosting mitochondrial oxidative phosphorylation, or by promoting vascularization and angiogenesis.Further study of sensitive and resistant cancer cell lines responses to additional EGFR inhibitors will improve our understanding of drug resistance development and thus lead to improved anticancer treatment strategies. For example, use of mitochondrial blockers with Erlotinib, immunity blockers with Gefitinib, or combining tyrosine kinase inhibitors with antibody inhibitors, may avoid development of resistance to EGFR inhibitors.


ALL) can be used to treat patients by inducing graft versus leukemia reaction post allogeneic hematopoietic stem cell transplantation, a feature which it was post allogeneic bone marrow transplantation, a feature which this drug was not aimed to do.Blinatumomab, a bispecific T‐cell engager monoclonal antibody used to manage Philadelphia chromosome‐negative relapsed or refractory B‐cell precursor acute lymphoblastic leukemia ( She achieved remission with 9 μg dose Blinatumomab; a grade 3 neurological toxicity is usually seen with 28 μg doses (Table 2 on days 1–5). She received Tacrolimus, Methotrexate, and Rituximab for GVHD prophylaxis (graft versus host disease). On the 100th evaluation day, she relapsed with a loss of donor chimerism to 43% without evidence of GVHD.A 61‐year‐old female was diagnosed with Pre‐B ALL  CNS negative, relapsed after HyperCVAD and was refractory to Asparaginase, Vincristine, Dexamethasone (Capizzi regimen) followed by Cytoxan/Etoposide  dose 600 mg/mμg and molecular remission was achieved. It was held after two cycles because she developed nausea, diarrhea, and elevated liver enzymes . It was noted that she had a 100% donor chimerism and the biomarkers for GVHD had increased especially REG3 alpha  that increased to 217 ng/mL. She was started on prednisone at 1 mg/Kg (25 mg daily) which resulted in resolution of her symptoms and decrease in levels of REG 3 alpha . She gained weight and her liver enzymes reduced to near normal (ALT 67U/L). Prednisone was tapered to 10 mg PO daily. She is currently day 240 post‐transplant and is in remission with a 100% donor chimerism  ALL is the most common phenotype present The CD19 antigen is expressed in almost all precursor‐B ALL patients, hence representing an interesting target for therapeutic research. Blinatumomab, a bispecific T‐cell‐engaging (BiTE) monoclonal antibody engages polyclonal T cells to CD19‐expressing B cells by binding to both CD3 and CD19. It brings them in close quarters to the malignant B cells, potentiating T‐cell‐induced cytotoxic activity Blinatumomab was initially evaluated in B‐cell non‐Hodgkin's lymphoma (NHL) and in B‐cell ALL μg/m2/24 h in week 1, and 15 μg/m2/24 h on subsequent weeks and cycles. CNS events and CRS were common at higher doses during initial treatment due to the high tumor burden In a study by Topp et al. We hypothesize that suppression of CD 19‐positive B cells may induce suppression of B regulatory cells which in turn may lead to decrease T regulatory cells, thus leading to graft versus leukemia (GVL) effect, manifesting as GVHD In summary, we would like to report a case of pre‐B ALL with aggressive course refractory to multiple lines of therapy including hematopoietic stem cell transplant that responded to blinatumumab, resulting in complete donor chimerism and induction of GVHD that responded to steroids and remains quiescent with steroid taper. From our literature search, this is the first relapsed patient reported after allogeneic HSCT that attained full donor chimerism, induction of GVL as manifested by mild GVHD, and complete remission with blinatumomab which shows potential of this strategy as maintenance therapy after allogeneic HSCT in this patient population.None declared.


However, their roles seem to be tissue-specific and they are also involved in cancer cell migration and drug resistance, paving the way to complex different scenarios. The aim of this study was to determine whether the SLRPs decorin (DCN) and lumican (LUM) are recruited in cell plasticity and microenvironmental adaptation of differentiated cancer cells induced towards stem-like phenotype. Floating neurospheres were generated by applying CSC enrichment medium  to the established SF-268 and SK-N-SH cancer cell lines, cellular models of GBM and NB, respectively. In both models, the time-dependent synergistic activation of DCN and LUM was observed. The highest DCN and LUM mRNA/protein expression was detected after cell exposure to NSC SFM for 8/12 days, considering these cells as SLRP-expressing (SLRP+) CSC-like. Ultrastructural imaging showed the cellular heterogeneity of both the GBM and NB neurospheres and identified the inner living cells. Parental cell lines of both GBM and NB grew only in soft agar + NSC SFM, whereas the secondary neurospheres (originated from SLRP+ t8 CSC-like) showed lower proliferation rates than primary neurospheres. Interestingly, the SLRP+ CSC-like from the GBM and NB neurospheres were resistant to temozolomide (TMZ) at concentrations >750 μM. Our results suggest that GBM and NB CSC-like promote the activation of huge quantities of SLRP in response to CSC enrichment, simultaneously acquiring TMZ resistance, cellular heterogeneity, and a quiescent phenotype, suggesting a novel pivotal role for SLRP in drug resistance and cell plasticity of CSC-like, allowing cell survival and ECM/niche modulation potential.The presence of cancer stem cells (CSCs) or tumor-initiating cells can lead to cancer recurrence in a permissive cell–microenvironment interplay, promoting invasion in glioblastoma (GBM) and neuroblastoma (NB). Extracellular matrix (ECM) small leucine-rich proteoglycans (SLRPs) play multiple roles in tissue homeostasis by remodeling the extracellular matrix (ECM) components and modulating intracellular signaling pathways. Due to their pan-inhibitory properties against receptor tyrosine kinases (RTKs), SLRPs are reported to exert anticancer effects  The World Health Organization considers GBM the most aggressive astrocytoma, and it can develop into secondary GBM from low-grade gliomas, or to death . In contto death . Althougto death , 4, drugto death , 6.Treatment failure in cancer patients has previously been related to cancer stem cell (CSC) subpopulations, which ensure the maintenance of cancer heterogeneity, and these CSC subpopulations are more resistant to selective drugs through multiple concerted steps of self-renewal and differentiation –9. Metasin vivo and in vitro [in vivo. DCN also enhances the evasion of the immune system and muscle invasion in prostate cancer in vivo [via the nuclear epidermal growth factor receptor (EGFR) pathway [via the protracted activation of membrane EGFR [Several microenvironmental signals, including the reorganization of the extracellular matrix (ECM), hypoxia, and autocrine/paracrine factors, can determine stem and cancer cell fates –25, and in vitro –33. Howein vitro , which c in vivo , and exe in vivo . In oral pathway , 38, wheane EGFR . Clinicaane EGFR  and relaane EGFR . Similarane EGFR  and in Gane EGFR , and a 7ane EGFR .+ NB and GBM CSC-like isolated from the neurospheres showed lower proliferation rates, less apoptosis, and greater drug resistance than the parental cell lines, suggesting pivotal and synergistic roles for DCN and LUM in the TMZ resistance, survival, and maintenance of quiescent, slow-cycling, CSC-like subpopulations.It is noteworthy that SLRPs are expressed in stem cell niches in the chick embryo , in cere2. Confluent cells were detached in 5 ml of phosphate-buffered saline (PBS)–ethylenediaminetetraacetic acid (EDTA) at 37°C for 10 min, washed twice in PBS, and subcultured as monolayers. CSC enrichment of the cancer cell lines was performed by generating neurospheres in neural stem cell serum-free medium (NSC SFM) [l-glutamine, and 1% penicillin/streptomycin. NSC SFM was replaced every 2 days after mild centrifugation of the neurospheres.In this study, two established GBM and NB cell lines were enrolled. The SK-N-SH cell line is a commercial epithelial cell line originally derived from bone marrow metastasis of a 4-years-old Caucasian female suffering with NB, and it was previously enriched in CSC-like. In contrast, SF-268 is a nonepithelial cell line derived from a high-grade anaplastic astrocytoma, which has never been used for CSC-like enrichment. The established SK-N-SH (from the American Type Culture Collection) and SF-268 cancer cell lines  were routinely maintained as adherent cultures (monolayers) in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin, in a humidified atmosphere at 37°C with 5% CONSC SFM) , 54, con0 cells and neurospheres after 4 (t4), 8 (t8), and 12 days (t12) of CSC enrichment. The parental cell lines in DMEM were used as the control (t0). RNA was extracted with the RNeasy Mini Kit  and quantified with a Nanodrop spectrophotometer . The RNA (1 μg) from each sample was reversed transcribed with M-MLV reverse transcriptase , according to manufacturer’s instructions, and SYBR Green-based amplification  was performed with the CFX96 Real-Time PCR Detection System , as previously reported [5′-GGA CCG TTT CAA CAG AGA GG-3′, reverse 5′-GAC CAC TCG AAG ATG GCA TT-3′ (DCN); forward 5′-TGG AGG TCA ATC AAC TTG AGA A-3′, reverse 5′-CAA ACG CAA ATG CTT GAT CTT-3′ (LUM); forward 5′-CAA GGA GTA AGA CCC CTG GAC-3′, reverse 5′-TCT ACA TGG CAA CTG TGA GGA G-3′ ; forward 5′-GGC ATC CTC ACC CTG AAT GA-3′, reverse 5′-AGG TGT GGT GCC AGA TTT TC-3′ . The target transcripts were independently normalized to GAPDH and ACTB (housekeeping genes), and the RNA of the t0 cells was used as the calibration control. The results were expressed on a logarithmic scale as fold changes (FCs), with the 2–ΔΔCt method.The expression of DCN and LUM mRNAs was assessed in treported . The PCRWhole protein extracts were obtained with the pulsed sonication of cellular pellets in 200 μl of extraction buffer containing 50 mM Trizma-base, 0,25 mM sucrose, 5 mM EDTA (pH 7.4), 0.5% Triton-X 100 and 1% protease inhibitor cocktail. The protein concentrations were determined with the Bradford method. The proteins (50 μg) were mixed 1:1 with 2 × Laemmli buffer, heated at 95°C for 5 min, and loaded onto a 12% denaturing SDS-PAGE gel with the Kaleidoscope prestained standards. The proteins were separated electrophoretically at a constant voltage (90 V) for 90 min and blotted onto a 0.45 μm nitrocellulose membrane under semidry conditions with the Trans-Blot SD Semi-Dry Electrophoretic Transfer Cell  for 25 min at 200 mA. The membranes were blocked with 5% skim milk in PBS, incubated at 4°C overnight with rabbit polyclonal anti-LUM antibody  or mouse monoclonal anti-DCN antibody  in blocking buffer, washed 4 times in washing solution (0.1% Tween in PBS), incubated at room temperature for 1 h with the appropriate monoclonal horseradish-peroxidase-conjugated secondary antibody , and washed again in washing solution. The blots were detected with ECL Western Blotting Detection Reagents . The Molecular Imager VersaDoc MP 4000 system  was used for chemiluminescence visualization. The blots were stripped and incubated with mouse monoclonal anti-β-actin antibody  as the loading control.+ CSC-like after CSC enrichment for 8 days (t8 CSC-like), secondary neurospheres were generated from t8 CSC-like. Briefly, StemPro Accutase Cell Dissociation Reagent (Invitrogen) was used to dissociate the t8 neurospheres. A Trypan blue exclusion test was performed and 5 × 103/ml cells were collected in 2 × DMEM or NSC SFM. The cells were mixed 1:1 with a prewarmed solution containing 0.6% ultrapure agarose in PBS , seeded in triplicate in six-well plates on 2 ml of solidified 0.6% bottom agar, and incubated in a humidified atmosphere at 37°C with 5% CO2. The soft agar cultures were photographed daily under an inverted phase-contrast microscope (Nikon Eclipse TE 2000-S).The cell lines were cultured in soft agar to assess their colony or neurosphere formation under anchorage-independent conditions, in DMEM or NSC SFM. To explore the proliferation of the SLRP8) neurospheres were collected, washed in PBS, and fixed in 2.5% glutaraldehyde in 0.1 M PBS (pH 7.4) for 2 h at 4°C. The fixed neurospheres were carefully washed four times in PBS, postfixed in 1% osmium tetroxide (OsO4) in 0.1 M PBS for 1 h at 4°C, and stored in PBS at 4°C until embedding. The samples used for transmission electron microscopy (TEM) were dehydrated in series of increasing acetone concentrations and embedded in epoxy resin for ultrathin sectioning at 60°C overnight. The ultrathin slices cut with an 8800 Ultratome  were stained with 4% uranyl acetate and lead citrate and viewed on a Zeiss EM 109–902 transmission electron microscope . For scanning electron microscopy (SEM), the postfixed neurospheres were incubated in pure hexamethyldisilazane for 1 h at 4°C, dried in a critical point dryer , and metalized in an S150A Sputter Coater  for scanning in Quanta 200  or DSM 962 SEM instruments .Eight-day . DMSO was included as the vehicle control. Each condition was tested in six wells. Three days later, the medium was replaced with fresh medium to preserve TMZ activity. The end-point of the TMZ treatment was established on day 6. Finally, a 3--2,5-diphenyltetrazolium bromide (MTT) colorimetric assay was performed (Sigma). Briefly, 20 μl of MTT/well was added and the plates were incubated at 37°C. After 4 h, the medium was carefully removed and DMSO was added to dissolve the formazan salts. The reactions were measured with a Multiskan EX microplate photometer  at 570 nm. The TMZ dose–response was expressed as the percentage (%) inhibition of the cell metabolic activity relative to that of untreated cells and adjusted to the vehicle control. The inhibition of cell growth by TMZ was evaluated in quadruplicate experiments.Single cells from the monolayers and tt test was used to determine the statistical significance of the differences in the TMZ treatment results for the SLRP+ CSC-like and the parental cell lines. The differences in qPCR gene expression were evaluated with one-way analysis of variance (ANOVA). Statistical significance was set at p<0.05.Student’s two-tailed 12, increased under CSC enrichment conditions in both cell lines, showing the highest DCN and LUM mRNA expression values after 8 days (t8). Compared with the parental cell lines (t0), and considering ACTB as housekeeping gene, GBM CSC-like were more enriched in LUM mRNA  than in DCN mRNA  (p < 0.01). Similarly, the NB CSC-like showed higher LUM mRNA levels  than DCN mRNA levels   derived from both GBM and NB cell lines were enrolled in further analysis.The protein analysis showed a clear increase in LUM protein (70-kDa) during CSC enrichment. The highest LUM expression was detected in both SF-268 and SK-N-SH tospheres . By conteres  grew as small secondary neurospheres in NSC SFM, and curiously also as small colonies in DMEM after 30 days . Higher concentrations of TMZ (750–1500 μM) induced significant differences between the SLRP+ t8 CSC-like and the parental cell line. At these concentrations, the metabolic activity of the SF-268 parental cell line ranged from 38.99% to 58.86% (51.80% ± 7.59%), whereas that of SF-268 SRLP+ t8 CSC-like ranged from 79.09% to 79.64% (78.94% ± 0.45%) (p<0.05). The SF-268 parental IC50 was around 1250 μM, and did not reach the IC50 of the SF-268 SRLP+ t8 CSC-like.Low-range TMZ concentrations (0–500 μM) did not induce significant differences in the metabolic activities of the SF-268 parental cells and SRLP+ t8 CSC-like. As shown in + t8 CSC-like (p<0.05). However, in cells treated with 750–1500 μM TMZ, the metabolic activity switched and ranged from 42.91% to 68.56% (54.96% ± 9.61%) for parental cells and from 63.92% to 70.61% (66.64% ± 2.50%) for SRLP+ t8 CSC-like (p<0.05). The IC50 in SK-N-SH parental cells was around 1250 μM, and did not reach the IC50 of the SK-N-SH t8 SRLP+ CSC-like.At low and high concentrations, TMZ induced significant differences in the metabolic activities of the SK-N-SH parental cells and the SRLPvia the EMT in an instructive microenvironment. Different CSC subpopulations have been found within the same tumor [8 neurospheres, DCN mRNA increased at t8 and DCN protein at t12. This fact could be explained by differences between DCN and LUM in intrasphere trapping and maybe by CSC specific post-transcriptional regulation mechanisms which are still unknown. In addition, the influence of growth factors and their receptors, such those of EGF and TGF-β1, in DCN modulation and trafficking has been showed in adult cells [The stem cell theory of cancer is a new understanding of cancer development that considers oncogenesis to be aberrant organogenesis . Becauseme tumor , dispellme tumor , 60. Sevme tumor , pancreame tumor , colorecme tumor , uterineme tumor , prostatme tumor , and lunme tumor , among olt cells , 67, sugin vitro cancer models useful in the CSC-like enrichment of primary [8 neurospheres, switched towards a quiescent phenotype in the soft-agar-grown secondary neurospheres, showing reduced cell proliferation and apoptosis. The SLRP+ CSC-like still partly grew as loose cell aggregates in the DMEM soft agar-grown secondary cultures, suggesting residual CSC-like evasion of cell death. Moreover, the SLRP+ CSC-like acquired TMZ resistance, as shown in an MTT-based assay. Despite the higher proliferation rate of the parental cell lines vis-à-vis the slow cycling of the CSC-like at low doses of TMZ, significant resistance to high-dose TMZ was observed in the SLRP+ CSC-like of both cell lines, which could be a microenvironment-related phenomenon and/or attributable to the overexpression of multidrug-resistance or DNA repair genes in the CSC-like [The 3D tumorsphere cultures in conditioned serum-free media, which constitute a methodological evolution of the neurosphere cultures used in neural stem and progenitor cell research –70, are  primary –75 and e primary , 77. Her primary , and celCSC-like , 79. On 8 CSC-like relative to those of the parental cell lines, suggest that GBM and NB cells can acquire a quiescent stem-like phenotype. These results indicate a putative relation between DCN and cell quiescence which has been observed in other cellular models [Interestingly, quiescent CSC-like seem to be involved in the EMT and the mesenchymal–epithelial transition (MET) and in cancer dormancy, which have been closely associated with drug resistance . Our datr models , and thar models . HoweverThe tumor mass is mainly composed of differentiated tumor cells. In metastatic cancer, the MET program fosters epithelial-like cell proliferation of homed circulating tumor cells. Previous studies have shown that DCN inhibits glioma growth and cell differentiation. In contrast, DCN has been reported to play protective and antiapoptotic roles in glioma cell lines exposed to hypoxic microenvironments. Inherent to this study, the formation of 3D neurospheres necessarily generates a hypoxic microenvironment in the inner regions, which is why some cells inside the neurospheres undergo apoptosis/necrosis. It is well known that hypoxia plays a critical role in CSCs and niche maintenance, promoting hypoxia-inducible factor (HIF)-dependent reprogramming of the differentiated tumor cells towards a CSC-like phenotype . InteresIn addition to the known ECM remodeling and soluble factors  and membrane receptors  binding activities, we propose a pivotal role for the SLRP proteoglycans in neurosphere generation, CSC niche regulation, and the maintenance of a quiescent stem-cell-like phenotype, and consequently in cell fate and drug resistance of CSC-like. The SLRP expression patterns in NB CSCs may also indicate that the developmental and oncogenic EMT programs are actively cross-linked.+ malignant cancers.Further functional and clinical studies should clarify the roles of SLRPs in CSC biology and in cancer maintenance. More accurate evaluations of the SLRPs in GBM and NB biopsy specimens in terms of the CSC niche are required to determine the clinical potential of SRLPs, which may inspire niche-targeted cancer therapies in the fight against undifferentiated SRLP