Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and  The human genome encodes thousands of regulatory DNA sequence elements that control spatial and temporal patterns of gene expression . A majorApproaches that integrate diverse regulatory genomic data sets have served as useful complements to experimental approaches in gene regulation studies, e.g. for identifying enhancers –25 and fThe first issue is to determine the most informative measurements to predict such interactions in a new cell type or time point, where such interactions might be difficult to measure experimentally. Recent comparison of experimentally detected enhancer-promoter interactions across multiple cell lines has shown that the interactions tend to be cell line-specific ,18,31. OIn this paper, we make two contributions. First, we present a new computational method RIPPLE (Regulatory Interaction Prediction for Promoters and Long-range Enhancers) to predict interactions between distal enhancer elements and target genes in multiple cell lines. RIPPLE is based on a supervised machine learning framework that uses interactions detected from 5C experiments in a particular cell line as a training set and a small number of genomic data sets selected from the ENCODE collection of regulatory genomic data sets . To seleUsing our ensemble approach we generated genome-wide enhancer-promoter interactions for four cell lines with 5C data and a new cell line (HepG2) for which we did not have 5C data. Examination of our predictions from multiple cell lines shows that cell line-specific interactions are between enhancers and promoters that are active in two cell lines, but they interact in only one of the cell lines. Our genome-wide interactions are significantly enriched for experimentally measured interactions (e.g. from ChIA-PET and high resolution Hi-C) providing external validation of our predictions. In addition to being associated with known regulatory proteins  and activating chromatin marks , our predictions also implicate novel factors  that have not been studied extensively for establishing long-range interactions. Overall our work provides a systematic way to predict cell line-specific enhancer-promoter interactions using a minimal set of data sets in multiple cell lines and offers important insights into the interplay between architectural proteins, general transcription factors and histone modifications for establishing these interactions.There are three important components to RIPPLE: (i) Learning a single cell line-specific classifier, (ii) Determining the minimal number of data sets for predicting interactions in multiple cell lines, (iii) Ensemble learning to predict interactions in new cell lines.RIPPLE is based on a predictive learning framework where the goal is to train a binary classifier on examples of interactions and non-interactions using features derived from various regulatory genomic data sets . SMC3 ChIP-seq data for the H1hesc cell line was obtained from the Gene expression omnibus (GSM897119) and the MOSAiCS peak calling algorithm was applied to call peaks  and H1 Embryonic Stem cell (H1hesc), learning a separate classifier for each cell line. Because our end goal was to build a classifier that works across multiple cell lines, we focused on 23 data sets that were measured in all four cell lines Figure ,37  of the signal enrichment. If multiple peaks with different values overlapped a region we took the larger value. Expression levels were always represented as continuous features.We represented an enhancer or promoter region as a real or binary vector, each dimension corresponding to one of the 23 genome-wide data sets Figure . A promon-dimensional feature vectors of the enhancer and promoter regions to obtain a feature vector of size 2n, where n includes everything other than the RNA-seq data set. In the PRODUCT case, each enhancer-promoter pair was represented using an n-dimensional vector, each dimension representing the product of the corresponding values in the enhancer or the promoter feature vector. For the case of real feature values, we added a small correction of 0.01 to each dimension to avoid setting a feature value of the pair to zero, if one of the regions did not have a feature value. Finally, for each pair, we included two additional features, the Pearson's correlation of the n signals  associated with an enhancer to signals associated with the promoter of a pair; and the RPKM expression level of the gene associated with the promoter. To assess the performance of a specific feature encoding we used the Area Under the Precision-Recall curve (AUPR), which measures the tradeoff in the precision and recall of predictions as function of classification threshold, estimated with 10-fold cross validation . AUPR was computed using AUCCalculator , while forward primers were for non-TSS regions. In our approach, we defined promoters as a genomic region that had a TSS (Gencode v10) or a TSS was <2500 bp away. We defined distal elements as those that are at least 2500 bp away from a TSS, thus being necessarily distal from the target promoter. In addition we filtered the enhancer promoter regions such that they had at least one non-zero feature values. To generate the negative set, we sampled from the non-interacting primer pairs while controlling for the distance between interacting pairs. This was important because the tendency for an enhancer to interact with a promoter depends upon the distance between them. To ensure that the positive and negative examples exhibited the same distance distribution, we binned all measured primer pairs into 10 Kbp bins. For any pair of interactions that is in the positive set that is assigned to a bin, ib , we sample uniformly at random from the set of non-interacting pairs from the same bin ib. In both the positive and negative set we considered pairs of enhancers and promoters that were in <1 MB apart and that had features associated with them. We trained classifiers on a balanced data set but evaluated performance on the same sized negative set (1 times) as well as on a larger negative sets that was 10 times the size of the positive set . In all cases the classifiers are significantly better than random , however for the 10 times negative set the AUPR was lower. Here a random classification prediction was obtained by shuffling the probabilities of interactions among the pairs. Many of the ‘false positives’ that are not detected in the 5C data set can be true interactions. To provide additional support to these predictions we compared the difference in contact counts in the positive and negative sets in the 5C data, and compared this to predicted positives and negatives in the 1 times and 10 times negative sets and found that at all classification probability thresholds of >0.5 the contact counts of our predicted positives are significantly higher than negatives .RIPPLE uses Carbon Copy Chromosome Capture Conformation (5C) derived interactions as a positive data set from Sanyal et al. . The 5C We considered two types of approaches as the main predictive learning algorithm for RIPPLE: Random Forests approach and regularized regression approaches. Random Forests are powerful ensemble learning approaches that have been shown to have very good generalization performance . We trailasso) and regularized logistic regression (glmlasso). Prior to using the linear regression approach we converted the 0-1 labels to −1 and +1, although the linear regression classifier on the 0-1 labels had similar AUPRs. As with the Random Forests, we performed 10-fold cross-validation to assess the performance of each classifier. In each fold of cross validation (CV) we performed another round of 5-fold cross validation to select λ that minimized prediction error in the test set of that CV fold. Once the best λ is selected for a fold, we re-learned the regression weights using this λ.In addition to Random Forests we also applied two regularized regression based classification approaches: (i) L1-penalized linear regression (LASSO), and (ii) L1-penalized logistic regression (GLMLASSO). These L1-regularized regression approaches are powerful predictive models while at the same time perform feature selection to learn a ‘sparse’ model  where thn features to a RF classifier, it will learn a predictive model that uses all n features. On the other hand, sparse learning approaches such as those based on Lasso can do model selection by setting some coefficients of features to 0. However, such a model does not perform as well as a Random Forests approach . Our motivation for using this strategy is as follows: Random Forests are powerful classifiers for the task at hand, however, they do not offer model selection. That is, given h Figure . Furtherc ranges from 1 to 4 for our four cell lines and our multi-task learning approach aims to select features that are useful for all cell lines. c represents the regression weights for the cth cell line. In order to select the features important for all cell lines, we use the Group Lasso framework , ⋅⋅⋅, βK(j)} for the jth feature. In Group Lasso, the regularization takes the form of an L1/L2 norm, and the objective function is defined asWe used a multi-task learning framework because we had four classification problems, one for each cell line, and we needed a feature to be selected based on its utility across all four classification problems. Multi-task learning is a popular machine learning approach that aims to simultaneously solve multiple learning problems to share information between the learning problems . In our ramework . Group Ljth row of The L1/L2 norm for http://www.yelab.net/software/SLEP/). Specifically we used the mtLogisticR function with 10-fold cross-validation. In each fold we applied the mtLogisticR function of the multi-task classification problem. Final features were selected such that they were in at least 8 of the 10 folds of cross-validation. Such features were ranked based on the sum of the absolute value of the regression weights across all cell lines followed by selecting the top 20 features.To perform multi-task Group Lasso for our problem we used the Sparse Learning with Efficient Projections (SLEP) package  would be included if it was important either at the enhancer or at the promoter region.After obtaining important features from both the Random Forests classifier and multi-task Group LASSO, we performed an additional step of feature refinement starting with the intersection set of both Group Lasso and Random Forests based approach. Our feature refinement procedure was guided by the extent to which the features were correlated . We considered subsets of data set combinations based on their correlation structure to first remove additional features that would not significantly reduce performance, followed by another iteration of adding data sets . For example, CTCF, SMC3 and RAD21 were highly correlated in all four cell lines, and we evaluated the performance when removing any of these individually or in combination . The feature refinement step resulted in a minimal set of data sets that were used to train an RF classifier that served as the main predictive model of RIPPLE.et al. . We defined ‘distal’ (putative enhancer) regions as those that were further than 2500 bp from a TSS. We then removed any regions that had no feature data (using the same feature data sources as for 5C). Next, we used the significant interactions from the original publication to create a set of true positive interactions. Specifically, each true interaction consisted of a promoter region and a distal region that appeared on opposite sides of a significant interaction at any of the three available resolutions.Using their Hi-CCUPS method, Rao, Huntley To define a balanced set of negative pairs , we employed a similar procedure as for 5C data (described above). In addition to controlling for distance between the regions, we also controlled for expression of the promoter in the cell line. This step was important as the space of possible negative pairs was dominated by non-expressed promoters, while most promoters in positive pairs had non-zero expression. Specifically, a negative drawn to match a positive for which the promoter region had non-zero expression was also required to have non-zero expression. This procedure identified a total of 2761 positive promoter-distal pairs for Gm12878 and 6649 for K562.We assessed the performance of RIPPLE based on AUPR on Hi-C data in three experiments. First, we performed ten-fold cross-validation within each cell line (Gm12878 and K562). For each fold, we trained a Random Forests classifier of 500 trees on training data from 9 out of 10 folds and tested on the hold out fold. Second, we assessed the performance of RIPPLE to predict Hi-C interactions between cell lines by training on examples of one cell line and testing on examples of the other cell line. Third, we measured the ability of RIPPLE to generalize across the Hi-C and 5C platform on the same cell line. For each of K562 and Gm12878 cell lines, we trained a Random Forests classifier on all the data from one platform and tested on data from the other platform.K different classifiers, K = 4 in our setup. For the ith test pair of interest, we obtain classification probabilities from each classifier, ijp, 1 ≤ j ≤ K. We convert each ijp, into percentile ranks and take an average of these ranks. The Spectral Meta Learner approach uses the correlation between predictions of different classifiers  interacted significantly with a promoter cluster (PCj), we used a Hypergeometric test of enrichment and a fold-enrichment. Let the total number of enhancer-promoter interactions be the background j, and i and i to PCj. We ask using the Hypergeometric test the probability of observing i given that there are To test whether enhancer cluster P-value <0.05). We tested each set of genes (either from a subnetwork or promoter cluster) for enrichment of curated gene sets from Gene Ontology, MSigDB curated pathways . C++ program for feature set generation and MATLAB scripts used to train the Random forests on 5C and Hi-C and to apply RIPPLE on new enhancer promoter pairs are available at our supplemental website http://pages.discovery.wisc.edu/∼sroy/ripple/index.html. The code is also available at https://bitbucket.org/roygroup/ripple/downloads. RIPPLE classifier predicts the probability whether an enhancer interacts with a promoter. The higher the probability the more likely is an enhancer to interact with the promoter. Predicted genome-wide interactions with their probabilities for each of the five cell lines studied in this work are available online at our supplemental website http://pages.discovery.wisc.edu/∼sroy/ripple/index.html. These interactions can be downloaded as tab-delimited files as well as queried using a region or gene of interest.Feature set generation was done using a custom C++ program. Regularized linear and logistic regression and Random Forests were applied using the MATLAB statistics toolbox. Multi-task Group Lasso was performed using the SLEP learning package  classification algorithm as its predictive model, which performs significantly better than linear or logistic regression-based algorithms Figure . RIPPLE A common way to link enhancers to promoters is to use canonical enhancer  and promoter marks (H3K4me3) together with mRNA levels of a candidate gene . We askeHaving determined the key data sets needed for building RIPPLE, we next turned our attention to cell line-specific interactions. For two cell lines A and B, an interaction is said to be specific to cell line A if the interaction is detected only in cell line A, but not in B. There are four possible states for an interaction seen in cell line A and not in B Figure : (i) BotWe next examined these configurations among the predicted interactions. We defined a predicted set of interactions for these 5C regions using a classification probability of interaction >0.5 . We categorized predicted interactions from each pair of cell lines into the five categories in Figure To examine whether our enhancer-promoter interaction prediction strategy and the data sets selected using 5C data generalize to interactions detected using other 3C experimental techniques, we applied our RIPPLE approach to high-resolution Hi-C data recently made available for two cell lines Gm12878 and K562 . These HFirst, we performed 10-fold cross-validation within each cell line (Gm12878 and K562). Based on AUPR from 10-fold cross-validation within the K562 (0.776) and Gm12878 (0.845) cell lines, the RIPPLE features perform well on the Hi-C data . These values are comparable to what we obtained on the 5C data suggesting our prediction framework is applicable to Hi-C interactions as well. Next, we measured the ability of RIPPLE to generalize between the Hi-C and 5C platform on the same cell line. For each of K562 and Gm12878, we trained a Random Forests classifier on all data from one platform and tested on the other. The classifiers perform comparably in both directions on both cell lines . This suggests that the features used in RIPPLE and our general predictive learning approach can generalize between different 3C platforms.Our analysis so far demonstrates the feasibility of predicting new regulatory interactions among enhancers and promoters in cell lines with available 5C data. We next asked if we could extend this approach to predict interactions in new cell lines where such 5C data does not exist. This would require us to train a classifier on one cell line and predict interactions in a different cell line. To address this we applied the RIPPLE classifier trained on one cell line to enhancer-promoter pairs from a different test cell line. We evaluated the quality of the predictions in the test cell line by comparing to the AUPR obtained under cross-validation when trained on the test cell line , (i) a commonly used average of percentile ranks of predictions  than the interactions predicted by the classified trained only on H1hesc  demonstrating the advantage of an ensemble based approach. Predictions from IM-PET have a significantly lower contact counts than RIPPLE . The margin of contact count difference between PRESTIGE and RIPPLE is not significant . RIPPLE detected interactions at different distance ranges whereas IM-PET and PRESTIGE had a tendency to detect interactions that were short range  enrichment of regulatory signals on enhancers and promoters in the high confidence networks, (ii) enhancer-promoter subnetworks, (iii) the extent of cell line-specificity in our genome-wide maps and possible regulatory mechanisms by which cell line-specificity of interactions is established.The high confidence interactions analyzed in Figure i in one cell line would have as close as possible a pattern as cluster i in another cell line. The majority of the patterns in a cluster were observed in other cell lines , were primarily defined by combinations of CTCF, DNase I, H3K27ac, H3K9ac and RAD21 Figure .We next asked whether the enhancers in each cluster had a tendency to interact with promoters in another cluster , and for every gene, the number of enhancers that are predicted to be connected to it (in degree). On average an enhancer was predicted to regulate 3–4 target genes, while a gene was predicted to be regulated by 10–21 enhancers suggesting that enhancers and promoters form combinatorial interaction patterns with sets of enhancers regulating sets of promoters  together with additional components of the transcription machinery , many of which have not been thoroughly characterized in the context of enhancer-promoter interactions. This was important to study the contribution of one-dimensional signals to three-dimensional organization of the genome, in which enhancer-promoter interactions are critically important.Our feature analysis identified that architectural proteins, activating histone modification marks and a general transcription factor (e.g. TBP) are important for establishing these interactions. The importance of a general transcription factor in establishing interactions was a surprising discovery, but is consistent with the interplay of CTCF and cohesin components with transcription factors to trigger cell-type specific gene expression programs . The preAmong the marks that we found as important for long range regulatory interactions were known marks such as H3K9ac, H3K7ac and H3K4me2, as well as marks that have thus far not been associated with enhancer-promoter interactions, namely H3K36me3 and H4K20me1. H4K20me1 has been associated with gene repression as well as activation . More reOur cross-cell line prediction shows that a classifier trained on one cell line can predict interactions in other cell lines. However the accuracy of classifiers can vary from one test cell line to another, making the choice of the classifier non-trivial. Ensemble approaches that aggregate predictions from multiple classifiers are a natural way to approach this problem. We examined three different ways of combining predictions from multiple cell lines and found that the overall performance of different ensembles was similar. We used the percentile rankings of predictions which has the advantage of being easily extensible to classifiers trained on more cell lines. While the ensemble predictor was close to the best performing cell line more systematic approaches to combine shared information between different cell lines, (e.g. by considering different weighting strategies of different cell lines) will be an important direction of future work.Our analysis of both small-scale 5C and our genome-wide predictions showed that cell line-specific interactions are of two major categories. In one, cell line specificity of interactions is mediated by the loss of activity of an enhancer, and in another, cell line-specificity of interactions can occur even if the enhancer is active . We found such enhancers tend to have a subset of the regulatory signals needed to establish an interaction. For an enhancer to interact it must have a combination of activating marks, and binding by one or both of the architectural proteins. CTCF was by far the most important determinant of these cell line-specific interactions.We analyzed our global maps to characterize the network properties of interactions among enhancers and promoters. At a coarse scale we identified different classes of interacting enhancers and promoters, that were discriminated by the presence of different combinations of chromatin marks and architectural proteins. Promoter clusters were additionally associated with elongation specific marks namely H3K36me3 and H4K20me1. A finer scale analysis identified connected subnetworks of enhancers and promoters forming multi-input multi-output subnetworks that were significantly enriched for Gene Ontology biological processes.http://pages.discovery.wisc.edu/∼sroy/ripple/queryg.php) that users can query using their regions of interest. Our associated predictions will serve as a useful resource to query potential interacting partners for a new gene or locus of interest and prioritize enhancers for follow up studies using targeted validation.In conclusion, we have developed a systematic framework to predict interactions between enhancer and promoter regions across multiple cell lines by integrating 3C data sets with one-dimensional regulatory signals measured in chromatin marks and TF ChIP-seq data sets. Our approach identified that a combination of architectural proteins, transcription factors and histone modifications are needed for establishing long-range regulation. Our approach can be easily applied to new cell lines to predict interactions among new genomic loci in the same cell line, or be used to generalize to new cell lines where we do not have any training data. We provide our genome wide interactions as a web-based resource (Supplementary Data are available at NAR Online.


Smyrnium olusatrum, was evaluated in human breast adenocarcinomas MDA-MB 231 and BT 474, and Caucasian prostate adenocarcinoma PC 3 cell lines by MTS assay. MTS assay showed a dose-dependent growth inhibition in the tumor cell lines after isofuranodiene treatment. The best antiproliferative activity of the isofuranodiene was found on PC 3 cells with an IC50 value of 29 μM, which was slightly less than the inhibition against the two breast adenocarcinoma cell lines with IC50 values of 59 and 55 μM on MDA-MB 231 and BT 474, respectively. Hoechst 33258 assay was performed in order to study the growth inhibition mechanism in prostate cancer cell line; the results indicate that isofuranodiene induces apoptosis. Overall, the understudy compound has a good anticancer activity especially towards the PC 3. On the contrary, it is less active on Chinese hamster ovary cells (CHO) and human embryonic kidney (HEK 293) appearing as a good candidate as a potential natural anticancer drug with low side effects.The anticancer activity of isofuranodiene, extracted from  Cancer is the leading cause of death worldwide and its relative importance continues to increase. In 2008, it caused 7.6 million deaths and the incidence of cancer continues to increase with an estimated 13.1 million deaths in 2030  and thei Smyrnium olusatrum L. (Apiaceae), well known as wild celery or Alexanders, representing a pot-herb that was cultivated in gardens for many centuries owing to its culinary properties and afterwards it was superseded by the improved form of celery (Apium graveolens L.).Nonetheless, also plants which were historically used for other purposes may deserve attention of scientists. This is the case of Smyrnium , an age old anticancer drug, was used as positive control [ in vitro antiproliferative activity against the tested cancer cell lines and a lower cytotoxicity against the two nontumorigenic cell lines (CHO and HEK 293). MTS assay showed a dose-dependent growth inhibition in the tumor cells after isofuranodiene treatment. As it is observed in μM and increased up to 120 μM . The effective isofuranodiene concentration for 50% inhibition (IC50) in these cells was 59 μM. As shown in μM to 19 ± 8.05% at 120 μM versus control 100%, resp.; P < 0.05) showing an IC50 of 55 μM. In addition, isofuranodiene induced a significant inhibition of PC 3 cell proliferation  with an IC50 of 29 μM . Results indicate that the molecule shows antiproliferative activity against all the three cell lines suggesting that isofuranodiene could be considered an anticancer agent such as cisplatin. Notably the major effect is exerted against the PC3 prostate adenocarcinoma cells.Human breast adenocarcinomas MDA-MB 231 and BT 474, Caucasian prostate adenocarcinoma PC 3, and two nontumorigenic, CHO and HEK 293, cell lines were treated with various concentrations  among the tumor cell lines and the two noncancer cell lines is significant. This suggests that since the toxicity of the isofuranodiene is being less in noncancer cells it could be a potential anticancer drug with low side effects.Moreover, data reported in Figures ectively . The conμM) of isofuranodiene at different incubation times . Cells were then subsequently washed with buffer (PBS), fresh drug-free medium was added, and residual inhibitory activity was evaluated at 72 h from incubation. As shown in Figures μM) and with time of treatment up to 72 h; (2) after washing, the inhibitory activity is partially reversed after low incubation times but not after 48 and 72 h; (3) in general, the dose that produced the 50% of antiproliferative activity, after washing, is shifted approximately to 80–100 μM except for 2 h, in which the antiproliferative effect is very low, and 48–72 h in which the IC50 is very similar before and after washing.In order to understand the kinetic of the compound and the reversibility of the antiproliferative effect, the isofuranodiene was evaluated in drug washout experiments. For this purpose, the human cancer cell lines were treated with various concentrations (10–150 μM (IC50) of isofuranodiene for 24 h, and apoptotic cell death was analyzed by Hoechst 33258 staining and quantified by using fluorescence microscopy.Since antiproliferative effect of isofuranodiene in PC 3 cell line was noteworthy, an additional study was carried out to explore the cell growth inhibition mechanism. For this reason, the Hoechst 33258 assay was performed in order to investigate whether the compound induced cell growth inhibition by cell apoptosis. In this experiment, PC 3 cells were treated with 29 P < 0.05) suggests that isofuranodiene is able to induce apoptosis in prostate cancer cell lines.In the control, most cells contained intact genomic DNA ; howeverThe antiproliferative effect of isofuranodiene at MDA-MB 231, BT 474, and PC 3 cells is comparable to that of cisplatin. This effect is dose dependent increasing with the isofuranodiene concentrations. The best antiproliferative activity was exhibited on PC 3 prostate cancer cells, where the death is induced with an apoptotic mechanism. The lower cytotoxicity against the two nontumorigenic CHO and HEK 293 cells with respect to the cancer ones allows to hypothesize that isofuranodiene could be an anticancer agent endowed with low side effects.


However, the underlying pharmacological mechanisms of most natural anticancer compounds remain elusive, which has become one of the major obstacles in developing novel effective anticancer agents. Here, to address these unmet needs, we developed an anticancer herbs database of systems pharmacology (CancerHSP), which records anticancer herbs related information through manual curation. Currently, CancerHSP contains 2439 anticancer herbal medicines with 3575 anticancer ingredients. For each ingredient, the molecular structure and nine key ADME parameters are provided. Moreover, we also provide the anticancer activities of these compounds based on 492 different cancer cell lines. Further, the protein targets of the compounds are predicted by state-of-art methods or collected from literatures. CancerHSP will help reveal the molecular mechanisms of natural anticancer products and accelerate anticancer drug development, especially facilitate future investigations on drug repositioning and drug discovery. CancerHSP is freely available on the web at  Cancer has been declared to be one of the major global public health problems, and there is a disturbing increase in both incidence and mortality rates in recent year23479Although tremendous efforts have been made in the past for providing databases containing cancer related information, to our knowledge, no such dedicated comprehensive repository of anticancer herbs and anticancer herb-originating natural products has been developed currently as yet. Some databases like CancerDRCancerHSP records anticancer herbal medicines with anticancer compounds. Further more, holistic evaluation were performed for each compound. At molecular level, protein targets of the ingredients are obtained through computational predictions and literatures. At cellular level, anticancer activities of these compounds based on cancer cell lines were obtained from literatures. After mapping cell lines to primary organ or tissue sites, the therapeutic effects of each compound were emerging at the organ or tissue level. For whole body level, pharmacokinetic ADME parameters are provided by a series of computational methods. CancerHSP will not only provide more effective new therapies, but also accelerate the process of drug resistance study, drug repositioning and even anticancer drug discovery.As a comprehensive anticancer herbs database of systems pharmacology, CancerHSP were consisted of six major parts of data: (1) anticancer herbs, (2) anticancer ingredients for each herb, (3) targets with action mode for ingredients, (4) anticancer activities for ingredients based on cancer cell lines, (5) primary sites (organ or tissue) of cell lines, and (6) pharmacokinetic properties (ADME) of ingredients . All antThe current release of CancerHSP has recorded 2439 anticancer herbal medicines and 3575 anticancer ingredients coupled with anticancer activities based on 492 different cancer cell lines. These cell lines are distributed in 21 tissues and organs . Furtherhttp://lsp.nwsuaf.edu.cn/load_intro.php?site=CancerHSP&id=60).In our web server, we have provided a user-friendly search tool for exploring CancerHSP. Users can search the information by herbal name, chemical name, InChIKey, CAS number, target name, and bioactivity in the search box at the CancerHSP homepage . The resWe have also introduced a powerful browsing option, which can provide an overall view on cell line data. Similar to result page of searching, the browsing page also allows users to sort and filter the result in every column of table. The primary organ or tissue sites of cell lines were provided in this page. In addition, the cell lines in browsing page are internal linked to result page of searching and partially external linked to Cancer Cell Line Encyclopedia (CCLE) database , where tAs a public-facing database, CancerHSP provides a “Download” page where the entire database can be downloaded for further analysis. To maintain CancerHSP comprehensive and up to date, a submission interface was provided where users can submit his/her own data concerning to natural products for cancer treatment. However, to ensure accuracy of the submission, we will scrutinize the authentication of them.To date, increasing herbal anticancer products were identified, isolated and characterized from herbs in last decades and constitute a substantial percentage of today’s new drugs. Unfortunately, most of these drugs were impeded by the major problems including narrow therapeutic index, strong side effects and drug resistance, which could reduce life quality of cancer survivors14CancerHSP integrates large scale experimental studies and computational analyses to provide a mechanistic understanding of drug action across multiple scales of complexity ranging from molecular and cellular levels to tissue and organism levels. It can be used to identify targets for new drugs, study drug repositioning, and evaluate drug side effects and adverse events. Availability of natural products in the database for development of innovative combination cancer therapy regimens will provide a benchmark for the resolution of the cancer therapy translational research enterprise.In all cancer treatment cases, the effectiveness of the treatment is directly related to the therapeutic ability to targets and to kill the cancer cells while affecting as few healthy cells as possibleth 10, 2014). After careful reading these articles, information related to herbs, herbal ingredients and anticancer activities based on cancer cell lines were manually extracted and compiled. Further, the primary sites of cell lines and external links to CCLEIn order to develop a comprehensive information resource of anticancer herbs and ingredients, research articles providing information related to keywords such as “herb”, “traditional Chinese medicine”, etc., coupled with keywords like “anticancer”, “cytotoxicity”, “anti-proliferation” and “apoptosis” were extracted from PubMed and Google scholar  and Caco-2 permeability (Caco-2), blood-brain barrier (BBB) and Lipinski’s rule of five  were performed as previously describedCancerHSP is designed as a relational database on an apache server. All data were organized in a publicly available MySQL database as the back end , with a How to cite this article: Tao, W. et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci. Rep. 5, 11481; doi: 10.1038/srep11481 (2015).


There are errors in the “Oncomine data analysis” section of the Materials and Methods. It should read:Oncomine and CCLE data analysisUsmg5 copy number profiles in a large subset of cancer cell lines . In the dataset, the log2  values were analyzed. The number of DNA copies ) were calculated as advised in Oncomine instructions.We used the Oncomine Cancer Genomics Data Analysis tool [30] and Cancer Cell Line Encyclopedia, CCLE [35] to mine Usmg5 copy number in cancers” section of the Results. It should read:There are errors in the “Usmg5 copy number in cancer cell linesUsmg5 copy number in a large panel of cell lines  of ll lines  Several There are errors in Usmg5 copy number in various cancer cell lines :R215. doi: 10.1158/1541-7786.MCR-08-010766. Hu X, Stern HM, Ge L, O'Brien C, Haydu L, Honchell CD, Haverty PM et al. Genetic alterations and oncogenic pathways associated with breast cancer subtypes. Mol Cancer Res. 2009 Apr;7(4):511–22. doi: 10.1186/1755-8794-3-2367. Lu X, Zhang K, Van Sant C, Coon J, Semizarov D. An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models. BMC Med Genomics. 2010 Jun 22;3:23. doi: 10.1158/1541-778668.Olejniczak ET, Van Sant C, Anderson MG, Wang G, Tahir SK, Sauter G et al. Integrative genomic analysis of small-cell lung carcinoma reveals correlates of sensitivity to bcl-2 antagonists and uncovers novel chromosomal gains. Mol Cancer Res. 2007 Apr;5(4):331–9. Doi: 10.1172/JCI3712769. Sos ML, Michel K, Zander T, Weiss J, Frommolt P, Peifer M et al. Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J Clin Invest. 2009; Jun;119(6):1727–40. doi:


A previous genome-wide screening analysis identified a panel of genes that sensitize the human non-small-cell lung carcinoma cell line NCI-H1155 to taxol. However, whether the identified genes sensitize other cancer cells to taxol has not been examined. Here, we silenced the taxol-sensitizer genes identified  in nine other cancer cell types  that showed reduced cell viability in the presence of a sub-lethal concentration of taxol. Surprisingly, none of the genes studied increased sensitivity to taxol in the tested panel of cell lines. As observed in H1155 cells, SKOV3 cells displayed induction of five of the six genes studied in response to a cell killing dose of taxol. The other cell types were much less responsive to taxol. Notably, four of the five inducible taxol-sensitizer genes tested  were upregulated in a taxol-resistant ovarian cancer cell line. These results indicate that the previously identified taxol-sensitizer loci are not conserved genetic targets involved in inhibiting cell proliferation in response to taxol. Our findings also suggest that regulation of taxol-sensitizer genes by taxol may be critical for acquired cell resistance to the drug. Paclitaxel (taxol) and taxanes are microtubule-stabilizing agents that act primarily by interfering with spindle microtubules, causing cell cycle arrest and apoptosis. These drugs are used for the treatment of non-small-cell lung cancer (NSCLC) and ovarian cancer, among others. However, their therapeutic usefulness is limited by the phenomenon of acquired chemoresistance ,2. Drug Combining a high-throughput cell-based screening platform (one-well/one-gene) with a genome-wide library of chemically synthesized small interfering RNAs, Whitehurst and colleagues identified a panel of 87 genes that represent major focal points of the response of the human non-small-cell lung cancer (NSCLC) line NCI-H1155 to taxol , PSMA6 : H1155 (neuroendocrine), H1299 (large cell), H520 (squamous cell), and H661 (large cell). Given the existence of several upregulated and downregulated pathways shared between cancer cells originating from different organs, we hypothesized that sensitizer genes identified in lung cancer cells may also produce sensitizing effects in other cancer cells. Our results indicate that the previously identified taxol-sensitizer genes are not conserved genetic targets that modulate cell proliferation in all taxol-treated cancer cells. We analyzed the regulation of these genes in various cell types and discuss possible reasons for the lack of sensitization effects observed in the cancer cell lines tested. Our results highlight possible pitfalls associated with the extrapolation of experimental data to other cell line models in chemoresistance studies.w/v) penicillin/streptomycin and 10% (v/v) fetal bovine serum (FBS) at 37 °C in a humidified atmosphere containing 5% CO2. The chemotherapeutic drugs used included taxol, vincristine, and cisplatin . Unless indicated otherwise, chemicals were purchased from Sigma-Aldrich . All reagents were used according to the instructions provided by the supplier.NSCLC , hepatocellular carcinoma , cervical (HeLa), ovarian carcinoma , and lymphoblastoma (HOB1) cell lines  were gro50) confirmed that the drug-resistance phenotype was stable for at least two months in a drug-free medium.The SKOV3-derived taxol-resistant ovarian cancer (SKOV3/Tx600) , HeLa-dein vitro assay as previously described  colorimetric escribed . One hun260/A280 ratio between 1.9 and 2.2 were used. Real-time quantitative PCR was performed on total RNA as before [∆Ct(X) − ∆Ct(GAPDH).Total RNA was extracted with the Trizol reagent (Life Technologies) as previously described . RNA cons before . All unkThe primer pairs for PCR were as follows: acrbp , atp6v0d2 , fgd4 , hs6st2 , psma6 , and tubgcp2 .Knockdown of candidate genes was performed using commercially-available pLKO.1 plasmids expressing shRNA  as described before . Luciferp value) was calculated using a two-tailed Student’s t test for single comparison.Data were reported as mean values ± standard deviation (SD). Three independent experiments were performed unless indicated otherwise. Statistical significance , liver , cervical (HeLa), neuroblastoma (HOB1) and ovarian cancer cells (SKOV3). Silencing of the taxol-sensitizer genes produced no sensitization to taxol in these cell lines. In fact, silencing 5 of the 6 genes induced taxol resistance in H1299 cells B–G. ThesThe reason why the sensitization effects were only found in H1155 cells remains unclear. Based on our observations, the genes that are overexpressed in taxol-resistant cells were shown to be inducible by a low cytotoxic dose of the drug. We attempted to correlate the inducibility of the six taxol senitizer genes following taxol treatment with the overexpression of these genes in taxol-resistant cells. The RNA level of taxol-sensitizer genes in the ten cell lines was measured using qPCR following treatment with 1 nM of taxol . High inTo assess whether the chemosensitizer genes are important for taxol resistance, we monitored their expression levels in drug-resistant cells. The level of acrbp and atp6v0d2 expression was upregulated in cisplatin-resistant HeLa, vincristine-resistant HOB1, and taxol-resistant SKOV3 cells A,B. fgd4In this study, we assessed the effects of silencing of taxol-sensitizer genes on the response of cancer cell lines to taxol. These genes were described earlier as being able to sensitize the H1155 cancer cell line to taxol, with sensitization levels at times reaching over 1000 fold [P = 0.02 for both genes). Based on this information, it appears that no solid clinical data has been obtained to support the function of these genes in taxol chemoresistance. Based on these results, we conclude that the effects of the taxol-sensitizer loci are not conserved throughout cancer cells.Silencing of these genes also sensitized cancer cells to vincristine in our experiments. On the other hand, the genes did not modify cell response to cisplatin, supporting the notion that the taxol-sensitizer genes are specific for cell response to microtubule-damaging agents . HoweverAmong the six taxol-sensitizer genes tested, four of them  were upregulated in response to taxol in both H1155 and SKOV3 cells. Except for tubgcp6, these genes were not upregulated in the other cancer cells tested. The tubgcp6 gene could be induced in six of ten cell lines, including five cell lines which were not sensitized to taxol following silencing of the gene. These results suggest that specific gene response to taxol may be required but not sufficient to produce a cell response to the drug. Previous studies by others have indicated that silencing of ACRBP (testis cancer antigen) or TUBGCP2 (microtubule-associated protein) did not affect cell viability but did enhance taxol-induced mitotic arrest in H1299 and H2126 cells  . In addiExpression of certain genes such as the taxol-sensitizers may be critical for the cells to overcome mitotic arrest following treatment with mitotoxins such as taxol. Differential response in the expression of these genes following treatment with taxol may contribute to differential mitotic progression by enhancing the robustness of the mitotic spindle apparatus. Prolonged activation of mitotic checkpoint is important for taxol-based therapy in various cancer cells. Lack of response of taxol-sensitizer genes to taxol in the non-H1155 cells tested is likely to be due to aberrant mitotic arrest and/or inefficient reduction of checkpoint and apoptotic activation. Silencing of the genes may sensitize cancer cells like H1155 to the drug only when the mitotic spindle apparatus is appropriately regulated and reduction of the apoptotic apparatus is successful. Whether these pathways are defective in non-H1155 cells is worthy of further investigation.Notably, we found enhanced expression of five of six taxol-sensitizer genes in taxol-resistant ovarian cancer cells (SKOV3/Tx600). The only gene that was not upregulated in the resistant cells, hs6st2, was the same that was not inducible in the parental, sensitive SKOV3 cells. Two of the six genes studied (acrbp and atp6v0d2) were also upregulated in vincristine-resistant HOB1 cells. However, except for abcb1, the genes that were upregulated in vincristine-selected SKOV3 cells  were difRational approaches to target genes that modulate drug response are now being considered in the pipeline of drug development for cancer therapy. Accumulating evidence suggests that many of these genes reflect states of dependency that are unique to cancer cells. Such dependency states can arise due to a strict dependency on a single oncogene in specific cancer cell line, or even to a non-oncogene. Identification of these “Achilles’ heels” within individual tumors remains an important advancement for the development of targeted therapies. High-throughput genome-wide RNA interference screening of cancer cell lines has started to reveal novel candidates for context-dependent therapeutic targets. Here, we examined the role of taxol-sensitizer genes identified previously using high-throughput screening and genome-wide RNA interference. Our results suggest that these genes are not consensus targets involved in chemoresistance in various cancer cells and do not function in modulating cancer cell proliferation in general. The reason for this apparent contradiction could be due to different cellular responses to the drug in the cell lines tested. Nevertheless, some taxol-sensitizer genes are associated with a sensitive response to the drug in an ovarian cancer cell line and are up-regulated in taxol-resistant variants. Findings concerning the various forms of cancer cell dependency to specific genes and the relevance of these processes in resistance to chemotherapeutic drugs may still lead to the development of more effective cancer therapies in the near future.


In order to search for new anticancer agents with a better pharmaco-toxicological profile, we investigated hybrid compounds obtained by inserting isothiocyanate group(s) on a naphthalenetetracarboxylic diimide scaffold. Moreover, since water-soluble fullerene derivatives can cross cell membranes thus favoring the delivery of anticancer therapeutics, we explored the cytostatic and cytotoxic activity of hybrid compounds conjugated with fullerene. We studied their cytostatic and cytotoxic effects on a human T-lymphoblastoid cell line by using different flow cytometric assays. In order to better understand their pharmaco-toxicological potential, we also analyzed their genotoxicity. Our global results show that the synthesized compounds reduced significantly the viability of leukemia cells. However, the conjugation with a non-toxic vector did not increase their anticancer potential. This opens an interesting research pattern for certain fullerene properties.One important strategy to develop effective anticancer agents is based on natural products. Many active phytochemicals are in human clinical trials and have been used for a long time, alone and in association with conventional anticancer drugs, for the treatment of various types of cancers. A great number of  Increasing recurrence of tumors and severe side-effects of chemotherapeutic agents represent the main causes of the reduced clinical efficacy of several anticancer agents that are currently used. Thus, there is a constant need for more effective and less toxic anticancer therapies. One important strategy to develop effective anticancer agents is based on natural products. Many active phytochemicals are in human clinical trials and have been used for a long time, alone and in association with conventional anticancer drugs, for the treatment of various types of cancers.in vitro, in vivo and clinical reports document the anticancer activities of isothiocyanates (ITCs). They are effective in the prevention and treatment of different cancer types -naphthalene-1,4,5,8-tetracarboxylic diimide (N-BDMPrNDI), and the second one characterized by the ITC group    through The pharmacological and genotoxic effects of MC725 were compared with those of MC705, CM9 and the NDI derivative (N-BDMPrNDI) .in vitro anticancer effects of its fullerene conjugate.This study aimed to identify the molecular mechanisms responsible for the cytotoxic effectiveness of a new synthetic ITC mounted on an NDI scaffold against human Jurkat acute lymphoid leukemia cells and to investigate the vs. 11.2%), thus confirming caspase-3 activation following CM9 treatment . Furthermore, it is also possible to observe an increase in the ratio Bax/Bcl-2 at the different concentrations analyzed in The stimulation of the intrinsic pathway is characterized by changes in the inner mitochondrial membrane, the opening of the mitochondrial permeability transition pore, the loss of the mitochondrial transmembrane potential, and the release of pro-apoptotic proteins from the mitochondria into the cytosol. Proteins of the Bcl-2 family regulate those apoptotic mitochondrial events . The BclP53 activation controls cell fate outcomes, including apoptosis and cell cycle arrest , throughAs CM9 was previously found to inhibit cell proliferation mediated by an accumulation of cells in the G1 phase and a parallel decrease in the fraction of cells in the S phase , we evalThe cell cycle is regulated by several proteins and it is known that cyclin E is a key transition protein between G1 and S cell cycle phases. A complex balance between its timed synthesis and rapid degradation by the ubiquitin-proteasome system allows the maintenance of the oscillating level of cyclin E during the cell cycle and therefore the unidirectional transition of cells through the G1-S checkpoint . Treatmein vivo and in vitro in many tumor cell types xx and [B]x are the concentrations of drugs A and B used in combination to obtain X% of drug effect. [IA]x and [IB]x are the concentrations of individual agents to achieve the same effect. CI = 1 indicates additive effect, CI < 1 synergistic effect, CI > 1 antagonism.[A]50 for two well-known natural ITCs like sulforaphane and phenethyl isothiocyanate in the same cell line was about 8-fold and 4-fold higher, respectively, than that detected for CM9 [Our results together show that the synthesized ITC CM9 reduced significantly the viability of Jurkat leukemia cells in a concentration-dependent manner. The mechanism of cell death induced by CM9 involves the modulation of Bcl-2 and Bax expression and increase in active caspase-3, activating the intrinsic pathway and execution phase of apoptosis. Thus, CM9 is a bioactive compound in leukemia cells and could represent a promising prototype of drugs for acute leukemia treatment. Notably in this context, the concentration required to reach IC for CM9 ,39. Furt for CM9 .The conjugation of CM9 with a non-toxic vector, e.g., MC705, allowed the obtention of a new derivative (MC725), through the formation of a thiourea. Thiourea is stable in cells, thus preventing the release of CM9. Accordingly, the anticancer potential of CM9 is only partly maintained and the conjugation does not increase its anticancer potential. This opens an interesting research pattern on fullerene properties. The conjugation of MC725 with a fluorescent marker allows the definition of its intracellular concentration and therefore the understanding as to whether the presence of fullerene increases the delivery and the intracellular concentration of CM9. Furthermore, future experiments could be addressed to investigate the cytotoxic and cytostatic activity of a mixture of fullerene plus CM9. Effectively, it has been shown that a mixture of metallofullerene nanoparticles plus anticancer drugs can favor accumulation of intracellular chemotherapeutic drugs such as cisplatin . In this


MYC family members are among the most frequently deregulated oncogenes in human cancers, yet direct therapeutic targeting of MYC in cancer has been challenging thus far. Synthetic lethality provides an opportunity for therapeutic intervention of MYC-driven cancers.t test (Mann–Whitney test) or ANOVA.A pooled kinase shRNA library screen was performed and next-generation deep sequencing efforts identified that PRKDC was synthetically lethal in cells overexpressing MYC. Genes and proteins of interest were knocked down or inhibited using RNAi technology and small molecule inhibitors, respectively. Quantitative RT-PCR using TaqMan probes examined mRNA expression levels and cell viability was assessed using CellTiter-Glo (Promega). Western blotting was performed to monitor different protein levels in the presence or absence of RNAi or compound treatment. Statistical significance of differences among data sets were determined using unpaired Inhibition of PRKDC using RNAi (RNA interference) or small molecular inhibitors preferentially killed MYC-overexpressing human lung fibroblasts. Moreover, inducible PRKDC knockdown decreased cell viability selectively in high MYC-expressing human small cell lung cancer cell lines. At the molecular level, we found that inhibition of PRKDC downregulated MYC mRNA and protein expression in multiple cancer cell lines. In addition, we confirmed that overexpression of MYC family proteins induced DNA double-strand breaks; our results also revealed that PRKDC inhibition in these cells led to an increase in DNA damage levels.Our data suggest that the synthetic lethality between PRKDC and MYC may in part be due to PRKDC dependent modulation of MYC expression, as well as MYC-induced DNA damage where PRKDC plays a key role in DNA damage repair.The online version of this article (doi:10.1186/1471-2407-14-944) contains supplementary material, which is available to authorized users. Targeted therapies inhibiting druggable gain-of-function oncogenes such as BCR-ABL, EGFR, HER2, ALK, and mutant BRAF have shown striking benefits in cancer patients in the clinic. However, many challenges remain for targets which lack druggable domains, such as RAS and MYC, two of the most frequently deregulated human oncogenes –4.MYC family members  are ubiquitously expressed in mammals, exhibit similar biological functions, and are under tight regulation throughout development and adulthood. MYC is found downstream of a number of growth factor receptors, acting as a central hub directing signals that favor various aspects of cell growth, such as cell proliferation and resistance to apoptosis . Rather Due to the lack of a druggable domain, it has been challenging to develop small molecule inhibitors that target MYC itself, or disrupt MYC-mediated protein-protein or protein-DNA interactions , 7. AddiBRCA1/2 mutations [BRCA1/BRCA2 mutations [For non-druggable targets like MYC, synthetic lethality offers a unique opportunity for therapeutic intervention. The principle of synthetic lethality is that mutation of either gene ‘A’ or ‘B’ alone is non-detrimental to the cell, while mutation of both genes leads to cell death , 17. Forutations –20. In putations , 22. Towutations .In our quest to reveal novel synthetic lethal genes in the context of MYC-deregulated cancers, we conducted a pooled shRNA screen using isogenic cell lines. We identified and confirmed PRKDC , a protein kinase with a major role in non-homologous end joining (NHEJ) DNA repair , 25), asThe following primary antibodies were used in western blot: γH2AX , GAPDH , gamma-tubulin , c-MYC , N-MYC , L-MYC  and PRKDC . For immunoblots, cells were lyzed with either CelLytic M cell lysis buffer (Sigma) or RIPA buffer (Sigma cat#89900) and equal amounts of protein lysates were mixed with XT sample buffer and reducing agent (Bio-Rad), separated by SDS Criterion precast gels (Bio-Rad), and transferred to a PVDF membrane (Bio-Rad). Proteins were detected with primary antibodies and horseradish peroxidase-conjugated secondary antibodies by using SuperSignal West Dura Extended Duration Substrate (Thermo Scientific).shRNAs pLKO.1 lentiviral plasmids used for non-targeting shRNA control (SHC002) and human PRKDC knockdown were purchased from Sigma. The shRNA clones used for targeting PRKDC were TRCN0000197152 (shPRKDC#1), TRNC0000196328 (shPRKDC#2), TRCN0000195491 (shPRKDC#3), TRCN0000194985 (shPRKDC#4) and TRCN0000194719 (shPRKDC#5). Non-targeting control shRNA and three PRKDC shRNAs  were cloned into the inducible pLKO-Tet-On puromycin vector as previously described . pCDH-CM2. The following cell lines were obtained from ATCC: HEK 293 T, WI-38, WI-38 VA 13, Daudi, EB1, HS604T, HS616T, HuT 102, MC116, Namalwa, H1963, H196, H209, H526, H524, H82, H69, Raji, SW1271, and TO175T. DEL, L428, SU-DHL-10, WSU-DLCL2, Jurkat, DND41 and SR768 were purchased from DSMZ. A4/Fuk was obtained from JCRB Cell Line Bank. OCI-Ly3 was a kind gift from Dr. Mark Minden . The human lung fibroblast cell lines WI-38 and WI-38 VA13 were routinely cultured in EMEM supplemented with 10% Fetal Bovine Serum (FBS). SCLC cell lines H1963, H196, SW1271, H209, H526, H524, H82 and H69 were maintained in RPMI 1640 with 10% FBS. The lymphoma cell lines EB1, Daudi, Raji, A4/Fukuda, Jurkat, DND41, WSU-DLCL2, DEL, HUT102, Namalwa and L428 were cultured in RPMI 1640 with 10% FBS. MC116 and SU-DHL-10 cells were maintained in RPMI 1640 with 20% FBS. OCI-Ly3 cells were cultured in IMDM with 20% FBS. The SR786 cell line was cultured in RPMI 1640 with 15% FBS. HS604T cells were maintained in DMEM with 10%FBS and 2 mM Glutamine. HS616T and TO175T cell lines were cultured in DMEM with 10% FBS. HEK 293 T cells were maintained in DMEM with 10% FBS. All cell lines were maintained with a cocktail of penicillin and streptomycin (Gibco).All cell lines were cultured in a humidified incubator at 37°C with 5% COhttp://www.broadinstitute.org/rnai/public/resources/protocols). Briefly, shRNA-encoding plasmids were transfected into 293 T cells with packaging plasmids encoding gag-pol-rev and vesicular stomatitis virus envelope glycoprotein using Fugene6 (Roche). Growth media was changed the following day and lentivirus-containing supernatants were harvested 2–3 days after transfection, filtered and used to infect cells in the presence of 8 μg/ml polybrene (Sigma).Lentivirus packaging and infection was performed according to the established protocols from the RNAi consortium . Medium was changed the following day and cells were selected by puromycin and expanded for at least one week before performing experiments. Induction of shRNA expression was performed by addition of 100 ng/ml doxycycline (Clontech) to the cell culture medium.Cell viability assay was performed using CellTiter-Glo (Promega) according to manufacturer’s instructions. Briefly, cells were seeded at 3000 cells/well in a 96-well plate (6 wells/sample) in the presence and absence of doxycycline. CellTiter-Glo measurements were taken at several time points to track cell proliferation.Total RNA was isolated using the Qiagen RNeasy kit according to the manufacturer’s instructions. cDNA was generated from 0.5 μg total RNA using High Capacity cDNA Reverse Transcription kit (ABI). TaqMan probes include c-MYC (Hs00153408_m1), N-MYC (Hs00232074_m1), L-MYC1 (Hs00420495_m1), L-MYC2 (Hs01921478_s1) and PRKDC (Hs00179161_m1) (ABI). TaqMan PCR was performed by using the ABI PRISM 7900 HT Sequence Detection System. All experiments were performed in triplicate and normalized to GAPDH.6 cells were harvested as day 1 sample. 2×106 cells were further cultured for 14 days (day 14 sample). Genomic DNA from day 1 and day 14 samples was isolated by using a DNeasy blood & tissue kit (Qiagen). Deep-sequencing template libraries were generated by PCR amplification of shRNA hairpin from genomic DNA. PCR products were purified by using a QIAquick PCR purification kit (Qiagen). After purification, PCR products from each sample were quantified, pooled at equal proportions and analyzed by high-throughput sequencing (Eureka).pGW-LentLox3.7 lentiviral-based human kinome shRNA library containing 1300 shRNAs  was designed and constructed by Genomics Institute of the Novartis Research Foundation (GNF). 1300 lentiviral kinase shRNA plasmids and non-targeting control shRNA plasmids were combined at equal concentration in one pool and used to generate pooled lentivirus. For screening, three WI-38 isogenic cell lines  were infected with the pooled lentivirus using a MOI of 0.5. Medium was changed the following day and twenty-four hours later, 2×10t test (Mann–Whitney test) or one-way ANOVA using PRISM 6 . P values are indicated with asterisks .All numerical data are shown as mean ± SD or SEM. Error bars on all graphs represent the standard deviation or SEM between measurements. Statistical significance of differences among data sets were determined using unpaired In our pursuit to uncover novel synthetic lethality targets of the MYC signaling pathway, we conducted a large scale loss-of-function RNAi screen in L-MYC-overexpressing lung fibroblasts and their isogenic controls. Here, we used an in-house pooled kinase shRNA library (1279 shRNA constructs targeting 486 human kinase genes). We screened the lung fibroblast cell line, WI-38, stably expressing empty vector, L-MYC isoform 1 (L-MYC1) or L-MYC isoform 2 (L-MYC2) , with our lentiviral shRNA library. Genomic DNA was isolated from each sample and subjected to next generation sequencing. The change in relative abundance of each shRNA among the three WI-38 cell lines was analyzed.Our primary screening hits include both novel candidates, and previously reported genes functioning as synthetic lethal partners with MYC, such as CDK2 and GSK3B; CDK2 and GSK3B served as positive controls in this screen , 28  are correlated with their gene amplification status , c-MYC (H524) or N-MYC (H526) overexpression, compared to cell lines without MYC family gene overexpression (H196 and SW1271)  cell lines with differential levels of MYC overexpression, we used shRNAs to knockdown PRKDC. Since many SCLC cell lines grow in suspension and form big clumps, evaluation of PRKDC knockdown effects on cell growth using constitutive shRNAs was difficult. We therefore decided to use doxycycline-regulated inducible shRNAs for PRKDC downregulation for these experiments. Briefly, cancer cells were stably infected with inducible TetOn lentiviruses where the expression of PRKDC shRNA was under the control of the doxycycline promoter. In vitro, PRKDC knockdown was confirmed via PRKDC-specific TaqMan and Western blot analysis in each SCLC cell line tested in response to doxycycline treatment  had minimal effect on growth inhibition  greatly inhibited cell proliferation  and H69 (N-MYC gene amplification) cell lines upon PRKDC knockdown . The IC50 of the two PRKDC inhibitors were measured in a cell proliferation assay in these cell lines   pathway, such as ATR, are also implicated in MYC synthetic lethality . In geneAside from its involvement in NHEJ DNA repair, PRKDC has also been implicated in gene regulation and protein abundance. It was reported that RNAi-mediated knockdown of PRKDC decreased the abundance of MYC protein in Hela cells . MechaniA number of studies have suggested that PRKDC acts as a modulator of gene transcription. PRKDC phosphorylates a variety of transcription factors including FOS, JUN, SP1, OCT-1, TFIID, E2F, the estrogen receptor, and the large subunit of RNA polymerase II , 68. PRKTargeting cancer with synthetic lethal partners offers a unique advantage to leave normal tissues unscathed and destroying only the cancer cells. Our study demonstrates that inhibition of PRKDC may offer a therapeutic strategy in MYC-driven cancers.6). Minimal requirement for the reads in pCDH-L-MYC1 sample on day 1 is 500. The table is sorted on RPM in pCDH-L-MYC1 sample on day 14. The top nine genes from this table with less than three-fold reduction in control Day1 versus Day14 samples were selected for follow-up. (PDF 682 KB)Additional file 1: Table S1: Summary of deep sequencing results for the screen using WI-38 cell lines stably-expressing empty vector (pCDH), L-MYC1 or L-MYC2, after infection with a pooled kinase shRNA library. The read counts were normalized to RPM / × 10Additional file 2: Figure S1: Stable WI-38 cell lines were subject to a cell viability assay at days 1, 3, 5 and 7 and growth curves were analyzed. Data are shown as mean ± SD. Statistical analysis using one-way ANOVA; ****P ≤0.0001; ***P ≤ 0.001; **P ≤ 0.01. (PDF 99 KB)50 values of 2.09 + 0.56 μM and 13.76 + 5.18 μM, respectively. The two groups followed a similar trend in terms of IC50 when cells were treated with KU0060648 . Data are shown as mean ± SEM. Statistical analysis using unpaired t test (Mann-Whitney test); **P ≤ 0.01. (PDF 128 KB)Additional file 3: Figure S2: Human lymphoma cell lines were divided into two groups, high MYC and low MYC gene expression , and treated with PRKDC inhibitors, NU-7441 and KU0060648, for 3 days. Cells were subject to cell viability assays and IC50 of the two PRKDC inhibitors were measured in these cell lines. High MYC and low MYC expression cell lines treated with NU-7441 displayed ICAdditional file 4: Figure S3: Different lymphoma cell lines were treated with increasing concentrations of the PRKDC inhibitor, NU-7441, and a proteasome inhibitor, MG132. After a 4 h drug exposure time, c-MYC protein levels were analyzed via immunoblotting with an anti-MYC antibody. PRKDC and GADPH protein levels were also monitored with anti-PRKDC and anti-GADPH antibodies, respectively. (PDF 122 KB)


Metastatic melanoma represents the most deadly form of skin cancer. The poor response to chemotherapy and the brief response to the anti-BRAF vemurafenib in selected population of patients, make the identification of new therapeutic approaches an urgent need. Our goal is the evaluation of the efficacy of barasertib, an aurora B kinase inhibitor impairing cytokinesis, in both mutated and non-mutated melanoma cell lines.Panel of melanoma cells: BRAFV600E mutated cells (MBA72 and Hmel1), the same cell in which the resistance to vemurafenib was induced by chronic exposure to it (MBA72R and Hmel1R) and BRAF wt (HBL and LND1).Cells were characterized for vemurafenib and barasertib effectiveness on cell growth by MTT assay after 3 and 6 days of continuous exposure.Cell cycle was determined by flowcytometry and migration was evaluated by wound-healing assay.Cells with BRAFV600E mutation are sensitive to vemurafenib conversely, those with BRAF wt and the resistant ones showed an IC50 of at least 10 folds higher.3days-barasertib exposure strongly reduced cell growth  in all cell lines; when the drug was given together with vemurafenib, no gain in effectiveness was evident. Prolonged exposure to barasertib (6 days) showed a progressive increase of effectiveness particularly in cells BRAF wt.The analysis of cell death mechanisms involved in determining the effectiveness of barasertib and vemurafenib showed that the first drug induced both apoptosis and necrosis conversely, the latter mainly apoptosis.Cell cycle analysis demonstrated that barasertib induced an increase in cell size and in polyploidia, suggesting also the mitotic catastrophe as a further cell death mechanism.Moreover, the anti-metastatic behaviour of this agent has been evaluated in function of drug concentration and time exposure. Preliminary results showed a strong reduction of cell migration after drug exposure.The sensitivity of melanoma cells to barasertib is irrespective to BRAF mutational status; however, cells BRAF wt show an higher nuclear modification. In conclusion, our results suggest barasertib as a novel therapeutic approach in melanoma treatment irrespective of BRAFV600E mutation.


Proteomic analysis by reverse-phase protein array revealed that 8-chloroadenosine treatment leads to inhibition of the mTOR pathway. In time-course experiments, 8-chloroadenosine treatment rapidly activated AMPK, measured by AMPK and ACC phosphorylation, and subsequently caused dephosphorylation of p70S6K and ribosomal protein RPS6 in the sensitive cell lines. However, in the resistant cell lines, AMPK activity and the mTOR pathway were unaffected by the treatment. We also noted that the resistant cell lines had elevated basal levels of phospho RPS6 and AKT. Inhibition of PI3K pathway enhanced the efficacy of 8-chloroadenosine across all cell lines. Our observations indicate that 8-chloroadenosine activity is associated with inhibition of the mTOR pathway, and that phospho RPS6 and PI3K pathway activation status may determine resistance. Among solid tumors, RCC is one of the few susceptible to mTOR inhibition. We thus infer that 8-chloroadenosine may be effective in RCC by activating AMPK and inhibiting the mTOR pathway.The adenosine analog 8-chloroadenosine has been shown to deplete ATP and inhibit tumor growth in hematological malignancies as well as in lung and breast cancer cell lines. We investigated effects of 8-chloroadenosine on clear cell (cc) renal cell carcinoma (RCC) cell lines. 8-chloroadenosine was effective against ccRCC cell viability in vitro, with IC The adenosine analog 8-chloroadenosine is a novel anticancer drug candidate. In vitro, it is potently cytotoxic against cancer cell lines from different tumor types, including leukemia , mantle Treatment with 8-chloroadenosine in vitro results in induction of cell cycle arrest and apoptosis , 12; howRCC is a common malignancy, with >65,000 patients diagnosed annually in the US. The clear cell (ccRCC) is the most frequent RCC type. There are limited established treatment options for ccRCC patients, including surgical resection, VEGF pathway inhibitors, and high-dose interleukin-2 (IL-2), although novel immunotherapies will likely join this list soon. The rapalogs everolimus and temsirolimus are also approved and in clinical use for advanced ccRCC. Indeed, a limited number of ccRCC tumors are profoundly sensitive to mTOR inhibition therapy, but a larger proportion, >25%, is refractory . MoreoveHere, we present our findings on the effects of the novel candidate drug, 8-chloroadenosine, in ccRCC. We show that 8-chloroadenosine is effective on ccRCC cells lines and leads to AMPK activation and mTOR pathway inhibition. Furthermore, we provide evidence that PI3K pathway activation is associated with resistance to 8-chloroadenosine, and that PI3K inhibition synergizes with 8-chloroadenosine treatment.2. Bright field images were taken at 10x and 40x magnification.Human ccRCC cell lines A498, ACHN, CAKI1, RXF393, SN12C, TK10, and UO31 were obtained from the National Cancer Institute (NCI60 collection); 786-O cell line was obtained from American Type Culture Collection (ATCC), and RCC4 cell line  was a kiTo assess phases of cell cycle, cells treated with 8-chloroadenosine  or vehicle and incubated for 48 hours. The drug was added only once at the beginning of the experiment. The cells were washed with PBS, fixed in cold 70% ethanol, washed again with PBS, treated with RNase and stained with 50 μg/ml propidium iodide solution. Cell cycle data was collected on FACS Canto II flow cytometer  and analyzed with FlowJo software . Two-tailed paired t-test was used to obtain p-values.50 and synergy  values. Synergy between 8-chloroadenosine and A66 was measured by Chou-Talalay method in the Calcusyn software.Cells were plated in 96-well plates and treated once with vehicle, 8-chloroadenosine , the PI3K p110α inhibitor A66 , or 1:1 combination of the latter two. Cell TiterGlo  and Optima plate reader were used for assessing cell viability after 96 hours, and the Calcusyn software  for calculating ICCells were plated in 96-well plates and treated once with vehicle or 40 μM 8-chloroadenosine. ATP levels in the cells were measured after 6 hours using CellTiter Luminescent assay  per instructions of the manufacturer. ATP levels in each cell line treated with vehicle vs. 8-Cl-adenosine and ATP levels between sensitive and resistant cell lines were compared by two-tailed t-test.Cells were grown in RPMI, treated once with 40 μM 8-chloroadenosine or vehicle for 24 hours. Cells were washed with cold PBS prior to lysis, and lysates were prepared on ice. Protein concentration was quantified with the DC Protein Assay . RPPA was performed as previously described . The RPPCells were lysed in RPPA lysis buffer supplemented with NaF, NaVO4, PMSF, protease inhibitor tablet complete , phosphatase inhibitor tablet PhosSTOP . Lysates were mixed with sample buffer supplemented with beta-mercaptoethanol and boiled on heatblock for 5 minutes and then cooled on ice prior to running at 70V-120V on 10% polyacrylamide or 5%-18% gradient  gels. Proteins were wet-transferred to nitrocellulose membrane, and probed with primary and HRP-conjugated secondary antibodies in 5% skim milk in TBS-T, and signal was developed using substrate solution . Antibodies against phospho S6 S235/236 (2211), AMPKα T172 (2532), ACC S79 (3661), p70 S6K T389 (9205), AKT S473 (9271), ERK T202/204 (9101), GSK3α.S21/GSK3β.S9, and antibodies against total S6 (2217), AKT (9272), GSK3α/β (SC7291), beta catenin (9562), vinculin (V9131), and beta actin were purchased from Cell Signaling Technology (USA), Santa Cruz Biotechnology (USA), and Sigma-Aldrich (USA). Western blot bands were quantified for total intensity using ImageJ software v1.48 . Band intensities were corrected for background and loading control. For each blot, the values were standardized with the highest value set to 100.50 values to range from 2 μM to 36 μM. CAKI-1 and ACHN cell lines were the most sensitive, while RCC4 and RXF-393 were the most resistant.To our knowledge, 8-chloroadenosine has not been previously tested in ccRCC cell lines. First, in a pilot experiment, we determined that 8-chloroadenosine treatment leads to reduced proliferation and/or cell death by 7 days in SN12C, TK10, and UO31 cells . We also8-chloroadenosine treatment has been reported to decrease ATP in cells by converting ATP to 8-chloro-ATP via ATP synthase . Hence, ATP depletion is known to activate AMP-activated kinase (AMPK), which gauges the AMP and ATP levels of the cell and orchestrates responses to energy crises. AMPK is activated when its threonine 172 residue (T172) in the alpha catalytic subunit is phosphorylated . T172 ph50 values included decrease in phospho S6.S235/236 and phospho 4EBP1.S65 in the sensitive, but not resistant, cell lines, and increase in MYC in the resistant cell lines . We analyzed levels of total and phosphorylated proteins in various signaling pathways, including MAPK, PI3K, and mTOR. The protein changes that correlated with the ICll lines . We valill lines .AMPK, as the energy sensor of the cell, can shut down energy expending processes, such as protein and lipid syntheses. AMPK decreases synthesis of acetyl-coA into fatty acids by inhibiting the enzyme acetyl-coA carboxylase (ACC) through phosphorylation of residue S79. AMPK activation also decreases protein synthesis by phosphorylating TSC1/2 TOR and resulting in mTOR pathway inhibition. To establish whether AMPK activation by 8-chloroadenosine precedes mTOR pathway inhibition, we performed a time-course experiment. We found rapid increase in AMPK activity, measured by ACC phosphorylation, within the first 10 minutes of 8-chloroadenosine treatment, followed by mTOR pathway inhibition, measured by p70 S6K dephosphorylation, between 30 minutes and 2 hours . FinallyThe role of the PI3K pathway in mTOR pathway activation and resistance to mTOR inhibition is well established. Indeed, through RPPA, we found greater baseline levels of phospho AKT S473 and phospho AKT T308 in the resistant cell lines . Using wResistant cell lines, where AKT is constitutively active, avoid AMPK phosphorylation at T172 Figs . A recenAnother pathway known to regulate mTOR activity is the MAPK pathway. Hence, we wondered whether the resistant cell lines would also have higher levels of MAPK activity. However, we did not find a consistent difference in phospho ERK T202/204 levels before treatment between the sensitive and resistant cell lines by RPPA and Western blot . These rTo determine whether activated PI3K contributes to 8-chloroadenosine resistance, we inhibited the PI3K pathway using the PI3K p110α inhibitor A66. We found the combined treatment more effective than either 8-chloroadenosine or A66 alone . MoreoveThe adenosine nucleoside analog 8-chloroadenosine is a novel drug candidate with inhibitory activity against malignant hematological and epithelial cells. 8-chloroadenosine cytotoxicity is thought to be mediated through ATP depletion, but there is no consensus on the molecular pathways critical to its effects. In ccRCC cell lines, we found that 8-chloroadenosine is cytotoxic in vitro, and sensitivity to 8-chloroadenosine is associated with AMPK activation and mTOR pathway inhibition. Moreover, cells with constitutive PI3K pathway activation were more resistant to 8-chloroadenosine and pharmacological inhibition of PI3K pathway synergized with 8-chloroadenosine treatment.50 values ranging from 2 μM to 36 μM  and sequestration of translation initiation factor 4E (eIF4E). To downregulate lipid synthesis, AMPK phosphorylates and inactivates ACC, halting production of malonyl-CoA for fatty acid biosynthesis. We think that 8-chloroadenosine, through AMPK activation, tricks cells into an energy-saving starvation mode, and consequently results in the shutdown of protein and fatty acid syntheses, jeopardizing proliferation and survival of cells . In contAMPK activation may have therapeutic potential. However, despite promising results from AMPK activation studies in vitro, it is not clear whether AMPK activation will have anti-cancer efficacy , 22. AMPFurther evidence for anti-tumorigenic effect of AMPK activation in ccRCC comes from The Cancer Genome Atlas (TCGA) ccRCC patient data set (KIRC), which revealed that functional AMPK (phosphorylated at T172) is associated with prolonged survival . It is tChallenges remain in moving 8-chloroadenosine to the oncology clinic despite the observed activity. In mice, the half-life of parenteral 8-chloroadenosine is approximately one hour, and attempts at improving its pharmacokinetic properties have achieved limited success . On the Emerging ATP depletion therapies, such as 8-chloroadenosine, have potential to provide a new class of cancer drugs. Energy starvation of cancer cells not only downregulates the mTOR pathway, but also likely inhibits fatty acid synthesis and perturbs redox balance, leading to broad changes in cellular metabolism. Here we have described how 8-chloroadenosine activates AMPK and subsequently inhibits the mTOR pathway in sensitive RCC cells. We found that resistance to 8-chloroadenosine is associated with an activated PI3K pathway. Because of the importance of the mTOR pathway in ccRCC and the role AMPK, the energy sensor of the cell, plays in the regulation of the mTOR pathway, ccRCC may be an appropriate model for developing ATP depletion therapies. Ultimately, we believe our results will give impetus to future studies developing ATP depletion therapies in ccRCC.


Breast cancer is classified into three subtypes by the expression of biomarker receptors such as hormone receptors and human epidermal growth factor receptor 2. Triple-negative breast cancer (TNBC) expresses none of these receptors and has an aggressive phenotype with a poor prognosis, which is insensitive to the drugs that target the hormone receptors and human epidermal growth factor receptor 2. It is, thus, required to develop an effective therapeutic reagent to treat TNBC.in vitro, and that this reagent induces apoptosis accompanying accumulation of 4N and 8N DNA cells in TNBC cells.The study using a panel of 19 breast cancer cell lines revealed that midostaurin, a multi-target protein kinase inhibitor, suppresses preferentially the growth of TNBC cells comparing with non-TNBC cells. Clustering analysis of the drug activity data for the panel of cancer cell lines predicted that midostaurin shares the target with Aurora kinase inhibitors. Following studies indicated that midostaurin attenuates the phosphorylation reaction mediated by Aurora kinase in the cells and directly inhibits this protein kinase Midostaurin suppresses the proliferation of TNBC cells among the breast cancer cell lines presumably through the inhibition of the Aurora kinase family. The precise study of midostaurin on cell growth will contribute to the development of the drug for the treatment of TNBC.The online version of this article (doi:10.1186/s12929-015-0150-2) contains supplementary material, which is available to authorized users. The human genome encodes more than 500 protein kinases, which are divided into two major groups recognizing the hydroxyl side chains of serine/threonine and tyrosine, respectively –3. It isBreast cancer is one of the most common malignancies in women, nowadays, which is classified into the subtypes according to the expression of biomarkers: the hormone and growth factor receptors. They include the subtype expressing human epidermal growth factor receptor 2 (HER2), the subtype expressing endocrine receptors such as estrogen and progesterone receptors without expression of HER2 (ER+), and the subtype with none of these receptors called triple-negative breast cancer (TNBC) . TNBC, hWe examined the effect of several protein kinase inhibitors on a panel of 19 breast cancer cell lines covering these three subtypes, and found that midostaurin preferentially suppresses cell growth of the TNBC cell lines. The breast cancer cells do not express FLT-3 , and thuAurora kinase is a family of serine/threonine protein kinases consisting of three members of A, B, and C, which have crucial roles for cell division by controlling chromatid segregation and cytokinesis . Aurora Breast cancer cell lines used in this study were purchased from American Type Culture Collection : ER+ includes HCC1428, MCF7, and ZR-75-1; HER2 includes AU565, BT-474, HCC1419, HCC1954, MDA-MB-453, SK-BR-3, and ZR-75-30; TNBC includes BT-20, BT-549, HCC1806, HS578T, MDA-MB-157, MDA-MB-231, MDA-MB-435S, MDA-MB-436, and MDA-MB-468 . Protein concentrations of cell lysates were measured with DC protein assay kit , and equal amounts of the protein samples for each experiment (1–20 μg) were separated by SDS–PAGE and transferred to Immobilon P (Millipore). Blots were incubated with respective primary and secondary antibodies at room temperature for 1 h. Proteins were detected using chemiluminescent substrate  according to the manufacturer’s protocol and either LAS-4000  or CEPROS P2 (Fujifilm). The experiments including Aurora kinases were carried out after calibration by using GAPDH as an internal control. Where indicated, the signals were semi-quantitated by using Image J [The cells with or without the treatment were subjected to Western blot analysis as described  with min Image J .To find the target molecules responsible for the anti-proliferative effect of midostaurin in breast cancer, a hierarchal clustering method based on the drug efficacy value was applied with minor modifications , 23. BriGST-tagged Aurora kinase A and Aurora kinase B were purchased from Carna Biosciences . The kinase activity of Aurora kinases A and B in the presence of either midostaurin or VX-680 was evaluated using Aurora Family Kinase Assay Kit  according to manufacturer’s protocol.Cells grown on coverslip were arrested by a single thymidine block with 2 mM thymidine for 24 h as described  with minCells were cultured with each inhibitor for various periods, harvested with trypsin, and fixed with 70 % ethanol at −20 °C overnight. Thereafter, the cells were incubated in phosphate-buffered saline containing 0.25 mg/ml DNase-free RNase  at 37 °C for 15 min. Subsequently, an equal volume of propidium iodide solution (50 μg/ml) was added. Samples were analyzed with FACS Verse .Statistical analyses were performed with R version 3.0.2 , 26. NumA panel of 19 cell lines, representing three subtypes of human breast cancer, 3 of ER+, 7 of HER2, and 9 of TNBC, were treated with different concentrations of midostaurin, and cell viability was measured  of midostaurin responsible for its anti-proliferative effect, the drug efficacy pattern of midostaurin against the panel of cancer cell lines was compared with that of the drugs having known targets by clustering analysis. The dataset of drug sensitivity of 45 breast cancer cell lines against various known target  were utiIn vitro kinase assay showed that midostaurin at 1 μM inhibited the kinase activity of GST-tagged Aurora kinases A and B by 16 and 34 %, respectively . Most of the TNBC cell lines showed a high level of phosphorylation of Histone H3 Ser10 with low cell viability. The phosphorylation level of Histone H3 was higher in TNBC cell lines than that in non-TNBC cell lines (Fig. To determine the role of Aurora kinases in TNBC, the expression level of Aurora kinases A and B was examined in the cancer cell lines Fig. . Expressely Fig. b. FurtheIt has been reported that Aurora kinases are expressed in M phase and the activity is required for the proper mitotic progression . Previouin vitro kinase reaction of Aurora kinases A and B, and dysregulation of spindle formation, and attenuation of cell cycle progression. Aurora kinase inhibitors are regarded as therapeutic reagents for various cancers [in vitro assay [In this study, it was revealed that midostaurin preferentially suppresses proliferation of TNBC cells among the breast cancer cell lines. Clustering analysis herein predicted that midostaurin shares the target with Aurora kinase inhibitors in the breast cancer cell lines. The Aurora kinase family has three members, Aurora kinases A, B, and C, and the inhibitors identified by the clustering analysis, VX-680 and GSK1070916, are so-called pan-Aurora kinase inhibitors, which suppress all Aurora kinases . Among t cancers , 31, andro assay . It is lAurora kinases A and B play central roles in mitotic process: Aurora kinase A is essential in maturation of centromere and spindle assembly, and Aurora kinase B is required for spindle assembly checkpoint function and in cytokinesis process . VX-680 Currently, midostaurin has been evaluated as the drug for the treatment of AML by targeting FLT-3, which is expressed exclusively in hematopoietic cells . The preMidostaurin has been developed as the drug for the treatment of AML, which has constitutive active FLT-3. The results obtained indicate that midostaurin preferentially inhibits growth of the TNBC cell lines through inhibition of the Aurora kinase family. It is plausible that midostaurin has a potential to be a drug for the treatment of different types of cancer by inhibiting respective target kinases.


One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.  •Kinome-wide (714 gene) siRNA screens in 117 cell lines from ten cancer histotypes•Integrating genotype data reveals cancer driver gene dependencies•Integrating protein interaction data aids the interpretation of genetic dependencies•Identified dependencies enable prediction of mutant cell line responses to drugs Campbell et al. use parallel siRNA screens to identify the kinase dependencies of 117 cancer cell lines from ten cancer types. They use this resource to identify kinase dependencies associated with specific cancer types or driver genes and show that the integration of protein interaction networks facilitates the interpretation of these dependencies. ABL addiction, and therapeutic approaches that exploit synthetic lethal effects, such as PARP inhibitors for BRCA-deficient cancers  represented KGDs in >70% of the cell lines screened and four of these  are known to be involved in the mitotic cell-cycle -checkpoint.To allow data to be compared between different cell lines, the viability data from each screen were standardized by the use of a robust tatistic B. We conectively C. On aveZ score data  of 0.1 . Taken together, these results suggested that FGFR inhibitors might show some utility in osteosarcoma, but that factors in addition to FGFR1 and FGFR2 amplification might explain drug sensitivity in this setting.Using average linkage hierarchical clustering to cluster the siRNA ore data D, we fouore data D. Using ) of 0.1 D. As expistology . In contnd FGFR2   to be KGDs in OCC models, compared to other ovarian cancer histologies such as serous ovarian cancer , JAK2 (p = 1 × 10−2), and the downstream effector of ERBB2, PIK3CA , a kinase involved in inositol phosphate metabolism , a regulator of cytokinesis  and lung cancer models  in ovarian cancer models was associated with an increased dependency upon MARK2  amplified esophageal models had an increased dependency upon MAPK1  were identified as ERBB2 dependencies. In total, 163 dependencies not supported by a direct link could be reached by adding one intermediate connection .For KGDs where a direct relationship between the driver gene and the kinase was not known, we used a simple information-flow type analysis to identify the shortest known molecular paths between driver gene and the kinase dependency I and S1Knd SRP72 C. We foun of MYC , which iC , we found that the dependencies associated with each driver gene were significantly more connected on the STRING interaction network than would be expected by chance , but also a network of genes involved in MAPK signaling  and inositol phosphate metabolism   were SMAD4 mutation associated KGDs. Consistent with this, loss of SMAD4 has been shown to be associated with increased AKT activation in colorectal and pancreatic tumor cell lines  and epothilone B  A. Similalorectal , pancrealorectal , and esolorectal  cancers lorectal B. The inll lines . Furthernhibitor . In addi network B, suggesprofiles  and founr VX-680   were associated with dependencies that were more functionally connected than would be expected by chance. This suggested that mutation of one pathway may induce dependency on a second pathway, consistent with observations from yeast where it has been shown genetic dependencies can often be best explained as occurring between pairs of pathways . The depTSC1 OR TSC2 OR STK11) was associated with an increased dependency upon the signal recognition particle SRP72 gene (rho = −0.40), but mutation of STK11 alone better explained the relative sensitivity of mutant and non-mutant cell lines in this regard (rho = −0.44). We therefore filtered these associations to identify 175 across-histotype  and then carrying out MP tests on the siRNA data as before, we identified ten KGDs including TWF2  as a pathway and found a CDK6 KGD (KRAS mutant tumor cell lines upon CDK6 was readily apparent (rho = −0.38), but was stronger when KRAS, HRAS, NRAS, or BRAF mutant tumor cell line models were combined as a group (rho = −0.43). CDKs, including CDK6, have been identified by a number of groups as putative non-oncogene addictions for KRAS mutant cancers . We tested this hypothesis using published drug screening results for a CDK4/6 inhibitor (PD0332991) in 628 cell lines . None of the individual members of this pathway showed as strong an association with this inhibitor .We also investigated MAPK gene alterations  or small molecule inhibitors (as we have shown for the FGFR sensitivity of osteosarcoma cell lines) might be used as a form of validation. Alternatively, methods such as CRISPR-Cas9 mediated gene targeting RNA mediated RNAi. With this in mind, we used a relatively short cell culture period between transfection and cell viability assessment (a 4 to 7 day period). Nevertheless, we cannot predict whether longer-term cell culture or longer-term gene silencing might result in a somewhat different profile of genetic dependencies. Furthermore, we used an ATP-based assay of cell viability in the screens. Some modes of cell inhibition exist that might have been missed using this method. As with any high-throughput technique, siRNA screens also have inherent false-positive and false-negative effects. Addressing false positives is especially important given the well-documented off-target effects associated with RNAi reagents . Consequargeting  might beIn carrying out functional screens in cancer cell lines, we have tried to use some of the lessons learned from studies in model organisms to aid the interpretation of our identified dependencies. For example, integrating protein-protein interaction data with functional data  was an ahttps://cansar.icr.ac.uk/), alongside the computational scripts used to integrate data .Finally, to make our resource as useful to the community as possible, we have made all of the data described in this manuscript available (Cell lines were transfected with a plate-arrayed siRNA library targeting 714 kinases and kinase-related genes (Dharmacon SMARTpools). Positive control (siPLK1) and multiple negative controls  and AllStar  were included on every plate. 20 breast cancer models were screened in a 96-well-plate format while the remaining cell lines were screened in a 384-well-plate format A. All scZ score of the interest group and the median Z score of the “other” group to that expected based on random permutation. There were one million random samples that were created with the same sample sizes as the interest and other groups and the difference in the medians of the two groups calculated, allowing an empirically determined p value to be calculated. Correction for multiple testing was performed using the Benjamini and Hochberg FDR (To identify associations between specific features (histotype or driver gene mutation) and sensitivity to specific siRNAs, a one-sided MP test was used. For each siRNA, we compared the observed difference between the median berg FDR  and onlyZ score data can be found in https://cansar.icr.ac.uk/.All siRNA Data from HINT , BioGRIDC.J.L., A.A., C.J.R., and J.C. designed the experiments and wrote the manuscript. J.C. and C.J.R. performed statistical analyses and data integration. H.H., H.N.P., I.B., I.Y.C., J.F., R.B., R.M., R.R., S.C.-C., and S.P.-V. performed siRNA screens. C.T.W., H.H., H.N.P., and R.R. performed drug profiling. H.H. and R.B. performed protein quantitation. H.N.P. performed RT quantitative (q)PCR. A.G., J.C., and W.W. processed the siRNA and small molecule inhibitor screen data. J.T., B.A.-L., R.N., S.J.S., T.F., and D.A.Q. contributed materials, reagents, and analysis tools. A.A. and C.J.L. secured funding. All authors read and approved the final manuscript.