Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works , , . However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0.In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant  contains supplementary material, which is available to authorized users. Historically, drug discovery was phenotypic by nature. Small organic molecules exhibiting observable phenotypic activity (e.g. whole-cell activity) were detected, a famous example being penicillin, which was serendipitously found. Phenotypic screening, an original drug screening paradigm, is now gaining new attention given the fact that in recent years the number of approved drugs discovered through phenotypic screens has exceeded those discovered through molecular target-based approaches. The latter, despite being the main drug discovery paradigm in the past 25 years, can potentially suffer from the failure in identifying and validating the therapeutic targets. In reality, most FDA approvals of first-in-class drugs actually originated from phenotypic screening before their precise mechanisms of actions or molecular targets were elaborated. A popular example of this is aspirin , for which it took nearly a century to elucidate the mechanism of its actions and molecular targets.There are some public phenotypic screening datasets online to support the study of the pharmacological functions of drugs. Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) are the most popular datasets in the field .A pioneer work using machine-learning approaches to predict drug response on cancer cell lines was by Menden et al. . The autThe first wave of applications of deep learning in pharmaceutical research has emerged in recent years. Its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples cover bioactivity prediction , de novoConvolutional neural network (CNN) is a machine learning model that can detect relevant patterns in data and support classification and regression . CNN hasInspired by the achievements of CNN in these areas, we are interested to see if CNN, compared to conventional machine-learning techniques –4, couldthe decisive step in the model development process.Erik et al.  stated tRecently, in drug discovery, researchers started to use the molecular structure of drugs directly as features –20 inste50. Wang et al.  appears in all the 223 drugs. Meanwhile, there is only one drug containing [Au] and only one drug containing [Cl]. Suppose the rows are used to represent different symbols, and the columns are used to represent positions in the SMILES format, then each drug in SMILES format can be converted into a 72∗188 one-hot matrix which only contains 0 and 1. In the one-hot matrix for a drug, a value 1 at row The structure of the proposed model tCNNS is shown in Fig. First stage: A model with two CNN branches is built to distil features for drugs and cell lines separately. A 1D CNN is used for the cell-line branch since the input data are 1D feature vectors for cell lines. Another 1D CNN is used for the drug branch and treat different symbols as different channels in the CNN. The convolution is applied along the length of the SMILES format. The structures for the two branches are the same. For each branch, there are three similar layers: each layer with convolution width 7, convolution stride 1, max pooling width 3, and pooling stride 3. The only difference between the layers is that their number of channels are 40, 80 and 60, respectively. The choices of these parameters for the CNN are inspired by the model in , in whic50 values. There are three hidden layers in the FCN, each with 1024 neurons. The dropout probability is set to be 0.5 for the FCN during the training phase [Second stage: After the two branches of the CNN, there is a fully connected network (FCN), which aims to do the regression analysis between the output of the two branches and the ICng phase .tCNNS is implemented using TensorFlow v1.4.0 , which iR2), Pearson correlation coefficient (Rp), and root mean square error (RMSE). This is the same as that in the benchmark paper [Three metrics are adopted to measure the performance of our model: the coefficient of determination R, PearsonR2 measures variance proportion of the dependent variables that is predictable from the independent variables. Let yi be the label of a sample xi, and our label prediction on xi is fi. The error of our prediction, or residual, is defined as ei=yi−fi. Let the mean of yi be R2 is defined as: Rp measures the linear correlation between two variables. Y is used as the true label and F as the corresponding prediction for any sample. Let the mean and standard deviation of Y be σY respectively, and those for the prediction F be σF respectively. Rp is defined as: Y and F, and RMSE is defined as: Rediscovering Known Drug-Cell Line Responses. In this part, the drug-cell line interaction pairs are divided into a training set, a validation set and a testing set. tCNNS is trained on the training set and the result on the test set is reported. The validation set is used to decide when to stop training.Predicting Unknown Drug-Cell Line Responses. In this part, tCNNS is trained on the known drug-cell line interaction pairs in GDSC and is used to predict the missing pairs in GDSC.Retraining Without Extrapolated Activity Data. In this part, tCNNS is trained and tested on a subset of GDSC data. The subset is called max_conc data, and it is more accurate than the rest of the data in GDSC.Blind Test For Drugs And Cell lines. In this part, drugs and cell lines, instead of the interaction pairs, are divided into the training set, the validation set and the test set.Cell Lines Features Impacts. In this part, the performance of tCNNS is tested with respect to the different sizes of the feature vectors for the cell lines.Biological Meaning v.s Statistical Meaning. In this part, the input data are transformed in various ways to check whether tCNNS can capture the biological meaning in the data.Eliminating Outliers. The 223 drugs are visualized in different feature spaces to show that the features extracted from SMILES can solve the problem of outliers in traditional feature space.In this section, the performance of our model tCNNS is demonstrated under various data input settings. The titles and the meaning of these experiments are summarized as follows: 50 for 172,114 of them. To compare to the results of previous studies [50 values in GDSC.In the 223×948  drug-cell line interaction pairs, GDSC provides the ICExperiments were set in this way to stimulate those real situations in which the models can only be trained on known interaction pairs between drugs and cell lines, and the models will be useful only if it can predict unknown interaction pairs. The validation set was separated from the training set so that it would be possible to choose a suitable time to stop training independently and avoid the problem of over-fitting.tCNNS was tested 50 times, and an example of the regression result is displayed in Fig. R2 was increased from 0.72 to 0.826 for the mean and 0.831 for the top quartile. Rp was increased from 0.85 to 0.909 for the mean and 0.912 for the top quartile, and RMSE was reduced from 0.83 to about 0.027.In the 50 repeated experiments, R2 of around 0.65 and Rp of around 0.81, which is shown in Additional file These results clearly showed that tCNNS outperformed the previous work reported in  in many R2 was further increased to 0.92 and the top quartile Rp was further increased to 0.96. The cost of this enhancement was that the network would become unstable and diverge [Many hyper-parameters affected the performance of tCNNS, such as the number of layers and the filter size. It was found that a smaller pooling size and more numbers of channels could further enhance the performance, but with a decrease in stability. For example, when the pooling size was reduced from 3 to 2, the top quartile  diverge  during tIn this part, tCNNS was trained on all the known interaction pairs in GDSC and then it was used to predict the values for those missing pairs in GDSC. The known pairs were split into 90% as the training set, and 10% as the validation set. Again, if the RMSE on the validation set did not decrease in 10 recent epochs, the training process would stop and the trained tCNNS was used to predict the values for the missing items. The results are shown in Fig. 50 values for missing items grouped by drugs. For each drug, the box represents the distribution of the values with its related cell lines. Drugs were sorted by the median of the distribution: the 20 drugs with highest median and 20 drugs with the lowest median value were plotted. As the real values for these missing pairs were not known, the accuracy of our prediction was obtained by survey and analysis as follows.Figure Bortezomib was the best drug in our prediction. In fact, the top 40 pairs with the lowest IC50 value were all from Bortezomib with some other cell lines. The outstanding performance of Bortesomib in missing pairs was consistent with that in the existing pairs. There is some supporting information in [Bortezomib can make cell lines to be sensitive to many other anti-cancer drugs.ation in  that theAica ribonucleotide and Phenformin have the poorest performance in tCNNS prediction. Based on our survey, the former one was initially invented to stop bleeding, and the later one was initially used as an anti-diabetic drug. These two drugs have the potential to cure cancer because they can inhibit the growth of cell (Aica ribonucleotide) or inhibit the growth of Complex I (Phenformin), but their effects are limited since anti-cancer is only the side effect of them, and not their main function.50 of drug Bortezomib with cell line NCI-H2342 was 1.19∗10−4μg. The small value indicated that there may be a good therapeutic effect. This prediction was supported by the findings reported in [Bortezomib is able to control Phosphorylation that causes lung cancer and NCI-H2342 is a lung cell line. Similar evidence to support this prediction can also be found in Cell Signaling Technology’s 2011 published curation set (https://www.phosphosite.org/siteAction.action?id=3131).Based on the tCNNS predictions, the ICorted in , 49, in 50 value verified by biological experiments, and maximum screening concentration (max_conc), which is the maximum IC50 value verified by biological experiments. In GDSC, any IC50 beyond these two thresholds is extrapolated, and not verified by experiments. In general, IC50 value within min_conc and max_conc are more accurate than those outside of the thresholds.For each drug in GDSC, there are two important thresholds called minimum screening concentration (min_conc), which is the minimum IC50 values below max_conc, which is about 37% of the whole existing 172,114 IC50 values.In the GDSC data that we used in this paper, only max_conc is provided, and there are 64,440 IC50 values below the max_conc threshold, which were randomly divided into 10% data for validating, 10% data for testing. The remaining 80% data is used for training and the size is reduced to 1% while the experiment was repeated 20 times. The regression result is shown in the Additional file In this part, tCNNS was trained on the IC50 values, R2 can be almost 0.5 and Rp be around 0.7. Secondly, and more importantly, tCNNS performed better with less and more accurate data. The dash lines (results on data below max _conc) were always above the solid lines , and the final performance on max _conc data was almost as good as that on the total data, although the amount of data for the former was only 37% of the latter. To further compare the best performance on all data and max _conc data only, the distribution of the 20 times experiments are shown in Additional file From Additional file There are three experimental results shown in Additional file blind test was designed.In previous experiments, interaction pairs between drugs and cell lines were randomly selected to be in the training set, the validation set, or the testing set, which meant that a specific drug or a specific cell line can exist in training and testing at the same time. This experimental setting corresponds to the problem of predicting the effect of a certain drug on a new cell line when its effect on another cell line is given. The problem becomes more challenging if the tested drug is a brand new one, and its effect on any cell lines is not known. To evaluate the performance of tCNNS on this challenging problem, a new experimental setting called 50 values were kept for testing. For the remaining 90% drugs, 90% of their related IC50 values were randomly selected for training and 10% for validating.In the blind test for drugs, drugs were constrained from existing in training and testing at the same time. The interaction pairs were divided based on drugs. 10% (23/223) drugs were randomly selected and their related IC50 values were kept for testing. For the remaining 90% (904/948) cells, 90% of the related IC50 were used for training and 10% for validating.In the blind test for cell lines, cell lines were prevented from existing in the training set and the testing set at the same time. The interaction pairs were divided based on cell lines. Similar to the case for drugs, 10% (94/948) cell lines were randomly selected and their related ICThe blind test for drugs on all data and on the data below max _conc were repeated for 150 times respectively to check the distribution of the results. The same number of experiments for the cell lines were also conducted. The results on all data are shown in Fig. From Fig. Comparing the results of the blind tests for drugs and for cell lines, the blind test for cell lines is slightly better, and the reason is that there is more common information shared among different cell lines and less among drugs. For example, cell lines share similar genetic information, but drugs can be very diversified. To reduce the information sharing among cells lines, another experimental setting was designed in which cell lines from the same tissue cannot exist in training and testing at the same time. The result was shown in Table R2 and Rp in all tissues, which indicated that blood is the most different tissue from other tissues.In GDSC, the 948 cell lines belong to 13 tissue types and 49 sub-tissue types. The 13 tissue types were used instead of 49 sub-tissue types because it can increase the distances and reduce the similarities among different tissues. Each time one tissue type was selected as testing data. For the rest of the tissues, they were mixed together and split into 90% for training and 10% for validation. From Table In GDSC, the 735 features for cell lines after preprocessing belongs to 310 gene mutation states, and 425 copy number variations. As different laboratories may use different methods to extract the features for cell lines, in reality, it is not easy to have the complete 735 features for all cells. Besides, researchers may also have smaller and different feature groups for cell lines. It is attractive if tCNNS can have good performance with fewer features for cell lines. In this part, tCNNS performance was tested with different smaller numbers of features for cell lines to check the change of the performance with respect to the change of numbers of features for cell lines. The corresponding results in this part are shown in Fig. The order of the symbols was randomly shuffled, which equals shuffling the rows in the one-hot representation.The SMILES format was cut into two pieces, and the positions of which were switched. It is equivalent to shift the columns in the one-hot representation.The positions in the SMILES format were shuffled, which equals to shuffling the columns in the one-hot representation.tCNNS takes the one-hot representation of the SMILES format as the features for drugs. Initially, in the one-hot representation of the SMILES format, each row represents a symbol, and each column represents a position in the SMILES format, which is left aligned. For researchers, the SMILES format is a well-defined concept with biological meaning. However, tCNNS may lack the ability to comprehend the biological meaning of the SMILES format and it instead relies on the statistical pattern inside the data. To verify this hypothesis, the one-hot representation of the SMILES format was modified in three ways as follows: 50 values.The experiments were repeated 10 times in the three settings respectively and the results were compared with those obtained by using the SMILES format without any modification. The comparison is shown in Additional file In the last column of the Additional file To check the differences between the features extracted using PaDEL and the features extracted from the SMILES descriptions using CNN, the distribution of the drugs were visualized in different feature spaces. In a deep neural network, the fully connected layer is responsible for regression analysis, and CNN is used for extracting high-level features from the drug features. The input data for the fully connected network is the output of CNN tranche. Hence when drawing the distribution of drugs using CNN, the output of the last layer of CNN tranche was used for drugs.The distribution of cell lines in genetic features space of GDSC was also compared with that found in the output space of the last layer in CNN. The visualization tool used was t-SNE , which wR2, Rp respectively, and 0.831, 0.912 for top quartile of R2, Rp respectively.Rediscovering Known Drug-Cell Line Responses. In this part, tCNNS was trained on 80% data as the training set and tested on the other 10% data. The remaining 10% data were used as a validation set to decide when to stop training. The experiment was repeated 50 times and tCNNS achieves 0.826, 0.909 for mean Predicting Unknown Drug-Cell Line Responses. tCNNS was used to predict the missing interaction pairs in GDSC. A literature survey was carried out and some published works that support the predictions of tCNNS were found and discussed.50 values below max_conc are divided as 80% for training, 10% for validation and 10% for testing. Figure Retraining Without Extrapolated Activity Data. tCNNS was trained on max_conc data. Those ICRp drops to 0.2 and the mean of R2 drops to barely above 0.Blind Test For Drugs And Cell lines. Drugs and cell lines were restricted from existing in the training set and the testing set at the same time. In the blind test, the performance of tCNNS drops significantly, especially in the drug blind test where the mean of R2 remains above 0.80 when the number of features drops to 300, and it is still above 0.72 even when the number of features drops to 50.Cell Lines Features Impacts. The number of features for cell lines was reduced from about 700 to about 50. The mean of Biological Meaning v.s Statistical Meaning. As the results remain almost the same with different modifications to the input data, it can be concluded that tCNNS relies on the statistical pattern, instead of capturing the biological meaning in the data.Eliminating Outliers. 7 outliers exist in the traditional drug feature space. However, this problem does not exist in the feature space extracted from SMILES by tCNNS.To conclude, there are seven subsections in this section, and they are summarized as follows: 50 values are small but the predictions of tCNNS are incorrectly large. In the top right corner, the input IC50 values are large but the predictions of tCNNS are incorrectly small. This means that tCNNS can be further optimized if it can enhance its performance in these two areas.In Fig. 50 values and the SMILES format of drugs are stronger than those observed between the IC50 values and the features extracted using PaDEL.Based on these results, it is concluded that the connections between the ICR2 drops to 0.33 and Rp drops to 0.6. When tCNNS is trained on all data, it learns general knowledge which is useful on the whole dataset. That is why its performance remains stable when tested on data below max_conc. On the other hand, when tCNNS is trained on data below max_conc, it learns knowledge that only be applicable to this specific subset of all data. Its performance is dragged down by data above the max_conc when tested on all data. Although the performance of tCNNS on all data and data below max_conc is similar, the paths they achieve the performance are different.Although the extrapolated data cannot enhance the accuracy of the model, they can help to improve scalability. The tCNNS model trained on all data performs well when tested on data below max_conc, which is natural because the later is a subset of the former. However, the model trained on data below max_conc performs poorly when tested on all data. R2 were almost the same, and the mean of Rp on all data was only a bit better than that on the data below mac _conc. Moreover, the variations of R2 and Rp on data below max _conc was a bit bigger than those on all data. To conclude, the contribution from low quality extrapolated data was limited, and they can only reduce variation and improve Rp a bit.Comparing the result on data below max _conc with that on all data, it can be seen that the means of in silico predicting power.The results in the blind test give us some hint that with limited budgets, the in vivo experiment should be carefully arranged to cover a wider range of drugs and cells from different tissues to get better 50 value, the performance will be significantly improved. For example, NCI-H378 is a special cell line for lung cancer in GDSC, and there are only two IC50 values records for it. For other cell lines, all of them have at least 20 IC50 values. tCNNS can still make accurate predictions one of the IC50 values for NCI-H378 if the other value is used during the training. Based on the results, the best drug for NCI-H378 is Bortezomib, which has been previously recalled [It is very important to have the specific drug or cell line in the training stage before the performance is predicted. Experimental results support that even with only one or two related ICrecalled , 49.Docetaxel for them. The predicted IC50 value between Docetaxel and NCI-H378 is 0.03μg , and the predicted IC50 between Docetaxel and NCI-H250 is 0.04μg . It is reported in [APR-246 is a potential useful drug on lung cancer because of its synergy with TP53 mutations in lung cells, and there is an "additive effects" between APR-246 and Docetaxel.Moreover, tCNNS predicts another potential drug orted in  that APRThe visualization result of outliers highlights that CNN can distribute the drugs and cell lines more uniformly than features using PaDEL and features of GDSC. For drugs, those seven outliers in the PaDel space are exactly the seven drugs that are composed of multiple parts. The structures of the outliers are shown in the Additional file In this paper, a model called tCNNS has been illustrated for phenotypic screening between cancer cell lines and anti-cancer drugs. tCNNS has been tested on a new version of GDSC with more data compared to previous works. It has achieved a much better coefficient of determinant and Pearson correlation than previous works and has made predictions for missing values in GDSC with trustful evidence. tCNNS can also converge with a very small set of training data and fewer features for cancer cell lines, which is economically efficient. tCNNS can take SMILES as input data for drugs, and this can solve the outlier problem occured in previous works where drug fingerprints are used as features.Additional file 1Supplementary. Some experiments results and figures are in the supplementary file of this paper. (PDF 378 kb)


Human cancer cell lines are fundamental models for cancer research and therapeutic strategy development. However, there is no characterization of circular RNAs (circRNAs) in a large number of cancer cell lines.Here, we apply four circRNA identification algorithms to heuristically characterize the expression landscape of circRNAs across ~ 1000 human cancer cell lines from CCLE polyA-enriched RNA-seq data. By using integrative analysis and experimental approaches, we explore the expression landscape, biogenesis, functional consequences, and drug response of circRNAs across different cancer lineages.https://hanlab.uth.edu/cRic), to benefit the biomedical research community.We revealed highly lineage-specific expression patterns of circRNAs, suggesting that circRNAs may be powerful diagnostic and/or prognostic markers in cancer treatment. We also identified key genes involved in circRNA biogenesis and confirmed that TGF-β signaling may promote biogenesis of circRNAs. Strikingly, we showed that clinically actionable genes are more likely to generate circRNAs, potentially due to the enrichment of RNA-binding protein (RBP) binding sites. Among these, circMYC can promote cell proliferation. We observed strong association between the expression of circRNAs and the response to drugs, especially those targeting chromatin histone acetylation. Finally, we developed a user-friendly data portal, CircRNAs in cancer cell lines  contains supplementary material, which is available to authorized users. RNAs were isolated from the MDA-MB-231 that were transfected with either circMYC or control plasmids. Two micrograms of RNAs was incubated with RNase R (VWR) for 3 h to degrade linear RNAs, followed by TRIzol™-chloroform-based RNA purification. cDNAs were generated from the RNAs and were used for PCR reactions. No reverse transcriptase (No RT) was used as a negative control and the circMYC was used as a positive control to localize the appearance of circMYC. The treatment with RNase R and without circMYC overexpression was send out for Sanger sequencing with the primer as 5′-CATCAGCACAACTACGCAGC-3′.cDNA of MYC was amplified via PCR from the human cDNA using the following primers: F 5′-GGTCAGAGTCTGGATCACCTTC-3′ and R 5′-ACTGTCCAACTTGACCCTCTTG-3′. After the PCR amplification and purification, the cDNA part that undergoes circularization was cloned into the pcDNA3.1 circRNA mini vector which induces circularization of mRNA via the Gibson cloning . MDA-MB-The MDA-MB-231 cells were plated onto TTP culture dishes. The cells were transfected with control siRNA or siRNA specific targets the junction of circular MYC for 8 h (INTERFERin® from PolyPlus-transfection®). The siRNA sequence is 5′ ACAGUGUCAGAGUCUGGAUCACCTT 3′+ 5′ AAGGUGAUCCAGACUCUGACACUGUCC 3′−. One day after the siRNA transfection, the cells were further transfected with control or circMYC plasmids for 6 h (JetPRIME™ from PolyPlus-transfection®).g for 5 min followed by the removal of precipitates. The supernatant was subjected to BCA protein assay to determine the protein concentration. For western blot analysis, 30 μg of protein was used.After 1 day with overexpression of circMYC, the RIPA buffer with protease inhibitor (Thermo Scientific) from Boston BioProducts was used to harvest protein lysates from the transfected MBA-MD231 cells. The cell lysate was centrifuged at 13,000The transfected cells were harvested, and the RNAs were purified at day 4 via the TRIzol™-chloroform extraction. For one well in a six-well plate, 500 μL of TRIzol™ was added into the well to lyse cells and stabilize RNAs, followed by addition of chloroform. The upper layer of the phenol-chloroform was used for column-based RNA purification (RNeasy from Qiagen). One microgram of total RNA was used for cDNA synthesis by iScript™ cDNA synthesis kit from Bio-Rad, which contains both oligo dT and random hexamers. The levels of circMYC were detected by the following divergent primers which detect only circularized MYC transcript using RT-qPCR: F 5′-CATCAGCACAACTACGCAGC-3′ and R 5′-TCCAGCAGAAGGTGATCCAG-3′. 18s rRNA was used as internal control: F 5′-GTAACCCGTTGAACCCCATT-3′ and R 5′-CCATCCAATCGGTAGTAGCG-3′. For the cell proliferation assay, WST-1 from Sigma Aldrich was used from days 1, 2, 3, and 4.We analyzed matched cell lines with both circRNA profiling and drug response data. For each individual circRNA, we first classified the cell lines into two groups based on the expression pattern of the circRNAs, based on if it exceeded the median value of backsplicing reads of all cell lines. We applied the Wilcoxon rank-sum test to identify individual drugs associated with the circRNA expression profile and quantify the difference in the mean AUC between the two groups. To correct the outcome of multiple tests, we used the Benjamini-Hochberg corrections. We defined a significant association between drug sensitivity and the circRNA profile with FDR < 0.05.To systematically investigate the global circRNA landscape in cancer cell lines, we combined four well-established, user-friendly computational algorithms , 32, CIRWe further examined the expression pattern of circRNAs across cancer cell lineages. We identified 1108 circRNAs (1.20%) ubiquitously expressed in ≥ 15 cancer lineages. For example, circDNMT1 derived from DNMT1, which is a DNA methyltransferase with essential roles in mammalian development , was expRs = 0.08 and p = 0.70; Additional file To examine whether lineage-specific circRNAs are introduced by their linear mRNA genes, we obtained lineage-specific mRNA genes as previously described . We obsecis-regulatory elements and trans-acting proteins [n = 51) [n = 22) [n = 71) [n = 104) [Rs| > 0.3, FDR < 0.05), suggesting their significant roles in the biogenesis of circRNAs  , 3′ end [n = 22) , RNA hel[n = 71) , and a sn = 104) . Nine gen = 23,714) available in the gene expression matrix of CCLE are used as background for GSEA. We identified 16 significant related biological processes that may contribute to the biogenesis of circRNAs, such as EMT, which is consistent with a previous report [Rs = 0.37, p < 2.2 × 10− 16; Additional file p = 5.5 × 10− 16; Additional file t test, p = 0.022; Fig. t test, p = 0.048; Fig. To further explore other potential biological processes involved in circRNA biogenesis, we performed gene set enrichment analysis (GSEA) using 50 “hallmark” gene sets from the Molecular Signature Database that represent major biological processes . All pros report  , intron length (p < 1 × 10− 4), number of exons (p < 1 × 10− 4), or gene expression level , exon length (p < 1 × 10− 4), or number of exons  and protein level (Student’s t test p = 0.005) are significantly higher in the group with circMYC expression , which is derived from one of the most important cancer genes. Interestingly, circMYC is significantly more abundant than its paralogous genes  with 265 drugs for 644 human cancer cell lines , and Cann > 30) and examined the pharmaceutical targets of these drugs. Interestingly, we found that the top drug category is drugs that target chromatin histone acetylation. For example, 6 of 11 HDAC inhibitors are associated with circRNAs . For example, cellular sensitivity to the drug Vorinostat, a HDAC inhibitor, is positively associated with 48 circRNAs and negatively associated with 25 circRNAs . In this portal, we provide circRNA expression data and the association between circRNA expression and multi-omic data, including mRNA expression, proteomic, mutation, and drug sensitivity data. CircRiC enables users to examine the features of circRNAs in cancer cell lines in a flexible and interactive way. CircRiC provides four interactive modules: expression landscape, biogenesis, drug response, and integrative analysis  and protein levels   . Furthermore, the recent release of next-generation CCLE facilitates the development of novel therapeutic strategies . With thn = 28) [To facilitate circRNA research in the community, we constructed an interactive and user-friendly data portal, CircRiC, which provides comprehensive analysis of circRNAs with multidimensional data across ~ 1000 cancer cell lines. Our data resource is a complement to MiOncoCirc, which covered circRNA expression from more than 2000 tumor samples and a few cancer cell lines (n = 28) . We expeIn conclusion, we systematically characterized circRNAs in ~ 1000 cancer cell lines and demonstrated the lineage specificity of circRNAs across multiple human cancer lineages. We identified potential factors involved in circRNA biogenesis and confirmed that TGF-β signaling promotes circRNA formation. We observed that clinically actionable genes are likely to generate more circRNAs, potentially due to the enrichment of RBP binding sites. We further characterized the functions of circMYC in promoting cell proliferation and revealed the potential link between circRNA expression and pharmaceutical profiling. Finally, we developed a user-friendly data portal CircRic that integrates our data to benefit the research community.Additional file 1:Figure S1. Expression landscape of circRNAs across cancer cell lines. Figure S2. Potential regulation of EMT on biogenesis of circRNAs. Figure S3. Enrichment of circRNAs in clinically actionable genes. Figure S4. Characterization of functional effects and drug response of circMYC in cancer. Figure S5. Therapeutic liability of circRNAs in CTRP. Figure S6. Examples of modules in CircRiC. Table S1. Summary of circRNAs identified across different cancer cell lines. (PDF 7931 kb)


Since microRNAs  have been implicated in oncogenesis, many of them have been identified as therapeutic targets. Previously we have demonstrated that miRNA-10b acts as a master regulator of the viability of metastatic tumor cells and represents a target for therapeutic intervention. We designed and synthesized an inhibitor of miR-10b, termed MN-anti-miR10b. We showed that treatment with MN-anti-miR10b led to durable regression/elimination of established metastases in murine models of metastatic breast cancer. Since miRNA-10b has been associated with various metastatic and non-metastatic cancers, in the present study, we investigated the effect of MN-anti-miR10b in a panel of over 600 cell lines derived from a variety of human malignancies. We observed an effect on the viability of multiple cell lines within each cancer type and a mostly dichotomous response with cell lines either strongly responsive to MN-anti-miR10b or not at all even at maximum dose tested, suggesting a very high specificity of the effect. Genomic modeling of the drug response showed enrichment of genes associated with the proto-oncogene, c-Jun. Systemic treatment options for cancer often include chemotherapy, as a mainstream treatment approach. However, considering the disadvantages of standard chemotherapy, e.g. non-specific delivery, toxicity to healthy tissues, distressing side effects , and the potential for chemoresistance, we envision a future in which chemotherapy is complemented or replaced by alternative approaches to shorten the period of chemotherapy or lower the dose of anti-cancer drugs and minimize adverse symptoms while increasing the survival rate.The alternative that we propose relies on targeting oncogenic microRNAs (oncomiRs) that promote the migration, proliferation, and survival of tumor cells. In our earlier work, we identified miRNA-10b as a master regulator of the viability of metastatic tumor cells. We determined that miR-10b not only promotes the ability of tumor cells to migrate and invade surrounding tissue (become metastatic), as has been described previously  but, mosWe demonstrated that MN-anti-miR10b could completely prevent the formation of de novo metastases  and, wheIn a model of Stage IV metastatic breast cancer, we found that MN-anti-miR10b selectively accumulated in distant  metastases from breast cancer after intravenous injection. A weekly treatment protocol with MN-anti-miR-10b and low-dose doxorubicin demonstrated complete regression of pre-existing lung metastases in 65% of the animals and inhibition of multiple organ metastases in 94% of the animals. This translated into a significant reduction in cancer mortality in animals treated with MN-anti-miR10b and low-dose doxorubicin relative to control groups, including a group treated with monotherapy of standard dose doxorubicin, used to model standard-of-care.miRNA-10b has been implicated in the development of a wide range of human cancers including colorectal, gastric, bladder, pancreatic, ovarian, hepatocellular and brain cancer –8. RecenOur study revealed the association of these responses with gene expression patterns that suggested an effect on transcription, metastatic dissemination, and immune response. Finally, our data implied that the link between miR-10b inhibition and cell viability is not limited to metastatic cells, but also includes cells derived from primary tumors, which is a novel finding. We believe that this finding could have a significant impact on treating both non-metastatic and metastatic cancers.MN-anti-miR10b was synthesized as described in . The LNAAminated magnetic nanoparticles were synthesized following a protocol published previously . Nanoparticles with a size of 20.3 ± 0.6 nm were used for conjugation to the oligonucleotides. The magnetic nanoparticles were conjugated to the heterobifunctional linker N-succinimidyl 3-[2-pyridyldithio]-propionate  and activated oligos sequentially. Briefly, SPDP was dissolved in anhydrous DMSO and incubated with magnetic nanoparticles. The 5’-ThioMC6 of the oligo was activated to release the thiol via 3% TCEP treatment in nuclease-free PBS. The oligos were purified using an ammonium acetate/ethanol precipitation method. After TCEP activation and purification, the oligos were dissolved in water and incubated with the SPDP-modified magnetic nanoparticles overnight. The number of oligos per magnetic nanoparticle was determined as 8.0 ± 0.7 using the electrophoresis analysis method described previously .We have assembled a collection of 624 human tumor cell lines representing the spectrum of common and rare types of adult and childhood metastatic and non-metastatic cancers of epithelial, mesenchymal and haematopoietic origin. We have used this large panel of cell lines in order to better capture the high degree of genomic diversity in cancer and to identify rare mutant subsets with altered sensitivity. All cell lines were obtained from commercial repositories. Cell lines were expanded for less than 3 passages and frozen stocks were created. To further test the quality of the collection post expansion we performed the following: To identify cross-contaminated or synonymous lines, a panel of single nucleotide polymorphisms (SNPs) was profiled for each cell line  and a pair-wise comparison score calculated. In addition, we performed short tandem repeat (STR) analysis  and matched this to an existing STR profile generated by the providing repository. Cells from authenticated stocks were not continuously kept in culture for more than 3 months.Cells were seeded in 384-well microplates at ~15%-50% confluence in medium with 10% FBS and penicillin/streptavidin + high glucose (18–25 mM). The optimal cell number for each cell line was determined to optimize growth during treatment. For adherent cell lines, after overnight incubation, cells were treated with increasing concentrations of MN-anti-miR10b (2-fold dilution series ranging from 0.16 to 10.64 μM as oligo) using liquid handling robotics, and then returned to the incubator for assay at a 72-hr time point. For suspension cell lines, cells were treated with MN-anti-miR10b immediately following plating and returned to the incubator for 72 hrs. Cells were then stained with 55 μg/ml Resazurin (Sigma) prepared in Glutathione-free media for 4 hours. Quantitation of fluorescent signal intensity was performed using a fluorescence plate reader at excitation and emission wavelengths of 535/595 nM for Resazurin. All screened plates were subjected to stringent quality control measures that included having coefficient of variance of control well values below 20% and signal over noise  over 5-fold. Additionally, directionality of drug response was tested by comparing the average of viability values obtained with the two minimum doses and the two maximum doses and rejecting drug response if the ratio was below 1.2 (wrong direction of response). Effects on cell viability were measured and a curve-fitting algorithm was applied to this raw dataset to derive a multi-parameter description of drug response, including the half maximal inhibitory concentration (IC50). Curve fitting was performed using the drexplorer R package .The linear regression Elastic Net  was used to obtain a multivariate model of the drug response across all cell line tested. We used Tissue of origin (Tis), Copy Number (CN), RNA expression data and mutation (MUT) data as input together with IC50s. RNA expression was U219 Affymetrix chip based. To minimize the number of genomic features in the input, the CN data was restricted to 1700 genes for which copy number alterations were translated into RNA expression. Mutation data were from full Exome Seq after algorithmic filtering of germline events (no matched germline sequence available for the cell lines). The EN modeling was run 100 times with 10% of the cell lines randomly left out in each run . To identify biological mechanisms related to the genes identified as response predictor by EN we used the STRING  database  on the fThe sensitivity to MN-anti-miR10b was tested in 624 human cell lines representing metastatic and non-metastatic cancers of the following origin: Biliary Tract, Bone, Brain, Breast, Esophagus, Head & Neck, Intestine, Kidney, Leukemia, Liver, Lung, Lymphoma, Muscle, Nervous System, Ovary, Pancreas, Pleura, Skin, Stomach, Thyroid, Urinary Tract, and Uterus. Representative data are included in Figs The Genomic Modeling results are presented in Genes associated with resistance that were present at a frequency of > 0.7 included:HEXB ;ITPRIPL2 ;SEPW1 (plays a role as a glutathione (GSH)-dependent antioxidant and may be involved in a redox-related process and cell cycle progression); andRPN2 .Genes associated with sensitivity that were present at a frequency of > 0.7 included:VPREB3 (a gene involved in B cell maturation);ZBTB44 (a zinc finger protein involved in nucleic acid binding);BCL7A ;PTPN18 ;VPREB1 (a gene that belongs to the immunoglobulin superfamily and involved in early stages of B cell development);LTB ;SARNP (possibly involved in cell cycle progression);CD79B (encodes the Ig-beta protein of the B cell antigen component); SPIB ;APBB1IP ;ANKLE1 (endonuclease that probably plays a role in the DNA damage response and DNA repair); andLIMD2  .−10) This is supportive of a meaningful functional relation between the genes identified by EN modeling. The list of genes associated with sensitivity  was enriched for genes involved in chromatin organization  and overall for gene products located in the nucleus . The genes associated with resistance were also more interconnected (based on STRING) than expected by chance . The top biological processes enriched were extracellular matrix organization (FDR = 0.0002), and endomembrane system localization . Members of the lysosome pathway (KEGG 04142) were also enriched in the genes more expressed in resistant cell lines (FDR<6.10−5). A full list of enrichment terms and values can be found in Annotation of the genomic modeling output using STRING to identify functional networks and enrichment of biological processes revealed that the genes associated with sensitivity present with more connection in STRING than expected by chance , Copy Number (CN), RNA expression data and mutation (MUT) data as input together with IC50s. Genes are stratified based on sensitivity to MN-anti-miR10b.(XLSX)Click here for additional data file.S2 DatasetThe linear regression Elastic Net results were stratified according to their enrichment score and grouped by annotation to arrive at common biological processes explanatory for sensitivity to MN-anti-miR10b.(XLSX)Click here for additional data file.


PKM2 is one of 97 genes that are overexpressed in non-small-cell lung cancer (NSCLC) cell lines. Herein, we demonstrate a novel role of subcellular PKM2 expression as a biomarker of therapeutic response after targeting this gene by shRNA or small molecule inhibitor (SMI) of PKM2 enzyme activity in vitro and in vivo. We examined two established lung cancer cell lines, nine patients derived NSCLC and three normal lung fibroblast cell lines for PKM2 mRNA, protein and enzyme activity by RT-qPCR, immunocytochemistry (ICC), and Western blot analysis. All eleven NSCLC cell lines showed upregulated PKM2 enzymatic activity and protein expression mainly in their cytoplasm. Targeting PKM2 by shRNA or SMI, NSCLC cells showed significantly reduced mRNA, enzyme activity, cell viability, and colony formation, which also downregulated cytosolic PKM2 and upregulated nuclear enzyme activities. Normal lung fibroblast cell lines did not express PKM2, which served as negative controls. PKM2 targeting by SMI slowed tumor growth while gene-silencing significantly reduced growth of human NSCLC xenografts. Tumor sections from responding mice showed >70% reduction in cytoplasmic PKM2 with low or undetectable nuclear staining by immunohistochemistry (IHC). In sharp contrast, non-responding tumors showed a >38% increase in PKM2 nuclear staining with low or undetectable cytoplasmic staining. In conclusion, these results confirmed PKM2 as a target for cancer therapy and an unique function of subcellular PKM2, which may characterize therapeutic response to anti-PKM2 therapy in NSCLC.Pyruvate kinase M2 (PKM2) is an alternatively spliced variant, which mediates the conversion of glucose to lactate in cancer cells under normoxic conditions, known as the Warburg effect. Previously, we demonstrated that  Lung cancer is the most common cause of cancer related mortality worldwide, accounting for approximately 1 in 4 cancer deaths . About 8in vitro and in vivo models of colorectal cancer [in vitro [in vitro and in vivo models of human cancers. Silencing PKM2 specific RNA decreased cell viability and increased apoptosis in several cancer cell lines in vitro and regressed tumor volumes by delivering them in in vivo mouse models of hepatocellular and ovarian cancer [Targeting colorectal cancer cell lines with gefitinib (a tyrosine kinase inhibitor) or oxaliplatin revealed that nuclear PKM2 contributed to therapeutic resistance l cancer , 18. Furin vitro . As manyn cancer . Addition cancer , 21–23. n cancer . As PKM2n cancer . PKM2 isn cancer .shRNA-PKM2) might influence NSCLC growth kinetics. Here, we tested whether SMI and shRNA-PKM2 influence NSCLC growth kinetics in vitro and in vivo. Our results demonstrate that PKM2 is primarily expressed in the cytosol of NSCLC cells and xenograft tumor cells. Both approaches (SMI and shRNA-PKM2) not only inhibited the PKM2 enzyme activity and cell proliferation in NSCL cells, but also decreased PKM2 mRNA expression and ultimately led to cell killing by apoptosis. Additionally, our results showed a unique subcellular compartmentalization of PKM2 in NSCLC cells and xenografts upon treating them with anti-PKM2 therapies in vitro and in vivo indicating that PKM2 expression may serve as a biomarker of therapeutic response in NSCLC.Because patient-derived and established NSCLC cell lines showed overexpression of PKM2 and PKM2 enzyme activities, we hypothesized that inhibiting enzyme activity by small molecule inhibitor (SMI) or gene silencing by shRNA (shRNA (AGAGCCTACCTGTATGTCAAT) and control plasmids (AGAGCCTACCTGTATGTCAAT) were purchased from Qiagen  and transfected by using Attractene transfection reagent (Qiagen) as suggested by the manufacturer.We studied nine patient-derived NSCLC cell lines, which were previously established at the Earle A. Chiles Research Institute, Portland, OR. The two cell lines established at the NCI (H1299 and H358) and 3 normal lung fibroblast cell lines  were purchased from ATCC and were maintained in the ATCC recommended complete medium. These cells lines were purchased from ATCC within first two years of undertaking experiments and if not their identity was confirmed by short-tandem repeats (STR) profiling. The nine cell lines from the Earle A. Chiles Research Institute were cultured in complete medium with 10% FBS. A small molecule inhibitor (SMI) of the PKM2 enzyme inhibitor  phenyl 1–2,5,-dimethylpyrole) was purchased from ChemDiv  and dissolved in PBS for treating the NSCLC and normal lung cell lines. For PKM2 targeting in NSCLC cell lines, 2 incubator using RNA-easy mini prep kit (Qiagen) following the manufacturer’s instructions and amplified using forward; CTATCCTCTGGAGGCTGTGC and backward; CCAGACTTGGTGAGGACGAT primers for human PKM2. β-actin gene specific primers (forward: TGAGAGGGAAATCGTGCGTG and backword TGC TTG CTG ATC CAT ACC ATC TGC) were used to amplify β-actin as a house keeping gene for quantitative expression of PKM2 mRNA [Total RNA from 3 normal lung and 11 NSCLC cell lines was extracted after incubating them with different concentrations of PKM2 inhibitor (1 to 80 μM) for 48 hours at 37°C in a COKM2 mRNA , 28. BriPyruvate kinase activity in control, SMI-treated or shRNA-transfected NSCLC cells was measured by a continuous assay coupled to lactate dehydrogenase (LDH). To assess activity in cell lysates, cells were treated with SMI for the indicated time and lysed in NP40 containing 25 μM Tris/Cl pH 7.4 lysis buffer immediately before measuring pyruvate kinase activity as described previously , 29. BriCell viability of NSCLC cell lines was measured by trypan blue exclusion technique. The number of viable cells was manually counted using a hemocytometer and expressed as % of untreated cells , 31.shRNA targeted cultures was measured in vitro as described previously [shRNA-PKM2 transfected cell cultures.Colony formation of NSCLC cell lines in SMI-treated or eviously . The numshRNA-PKM2 transfected NSCLC cells using earlytox Caspase-3/7 R100 assay kit following manufacturer’s recommendations . The endpoint fluorescence was measured on a SpectraMax M5 plate reader and each value is expressed as arbitrary fluorescence units.Patient-derived and established NSCLC cell lines were seeded in 4 well glass chambered slides and incubated with 0–80 μM of SMI for 5 days. After a brief wash with 1x PBS, the TUNEL positive apoptotic cells were counted as per manufacturer’s instructions . The assay was performed in triplicate and data were shown as mean ± SD. Intracellular caspase3/7 activity was measured from control (untreated), sh-negative control and PKM2 expression in NSCLC cells and xenografts sections was analyzed by ICC and IHC techniques as described previously , 33. ForTwenty million NSCLC or normal lung cells were washed with 1x PBS before processing for subcellular fractionation. Nuclear and cytoplasmic fractions from NSCLC cells and xenografts were prepared using NE-PER nuclear and cytoplasmic extraction reagents . All cytoplasmic and nuclear fractions were stored at -80°C until analyzed for PKM2 or Caspase activity or Western blot analysis.500,000 NSCLC cells were seeded in a 10-cm tissue culture plate and treated with different concentrations of SMI when the cell confluency was between 50–60%. After 48 hours, total cell lysate, subcellular fractions (cytosol/nuclear fractions) were prepared using NE-PER reagents as described above. The supernatant from total cell lysate, nuclear and cytosol fractions were quantitated by Bio-Rad protein assay . Twenty micrograms of protein equivalent lysates were boiled in 4 x SDS sample loading buffer and run on mini-Protean TGX AnyKd gel (Bio-Rad). The proteins were transferred to nitrocellulose and Western blots were performed using rabbit monoclonal anti-PKM2 antibody  and incubated with IR Dye 800CW near-infrared fluorescent goat anti-rabbit IgG secondary antibody . The membrane was washed three times and scanned using Odyssey CLx system .Female nude nu/nu mice aged between 6 and 7 weeks were housed in a temperature-controlled, pathogen-free animal facility with 12-hour light and dark cycles. All animal studies were conducted under approved protocol #1992–42 by the CBER Institutional Animal Care and Use Committee (ACUC) in accordance with the principles and procedures outlined in the NIH Guideline for the Care and Use of Laboratory Animals. All animal experiments were performed under ABSL-2 conditions in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Animals were monitored daily with increased monitoring at a minimum of twice daily during experiments. The analgesics were not used to avoid their possible interference in PKM2 signaling and translocation to tumor cell organelles and in tumor microenvironment. Euthanasia before tissue harvest was by ketamine/xylazine overdose while euthanasia due to body weight loss or termination of study was by carbon dioxide inhalation in a chamber where the carbon dioxide was from a cylinder source delivered by Euthanex equipment .Five million H358 cells were injected subcutaneously into the right flank of each mouse. When tumors reached 5 mm in diameter, H358-tumor-bearing mice were injected with different doses of PKM2 inhibitor  once a day on alternate days for 12 days  by i.t. injection. Tumor size was measured weekly and overall sacrifice time (OST) of the animals was calculated based on the sacrifice of mice when tumors reached 2 cm in diameter, according to NIH animal guidelines. Mice were weighed at day 10 and day 25 after PKM2 inhibitor treatment. Experiments were repeated twice.6shRNA-PKM2 gene silenced or negative control plasmid transfected H358 cells were implanted in six-week-old female nu/nu mice, weighing 17-20g for developing xenografts as described above. Before implanting the cells, they were either PKM2 gene silenced, mock-transfected or negative control plasmid transfected and selected by using G148 antibiotic. The transfected NSCLC cells were tested for PKM2 RNA and protein by RT-qPCR and IFA assays. For these experimental differences of transfection and selection involved in before implanting the cells in mice, the control group of mice was different compared to SMI treated mice, which were labeled as non-silenced mice in which negative control sh-RNA plasmid was used. The therapeutic response and OST were evaluated between these two groups. The xenografts in mice, which responded to anti-PKM2 therapy (either SMI or shRNA-PKM2) were referred to regressing tumors and those not responding were termed as non-regressing tumors.Similarly, 5x10Statistical analysis was performed using Student’s t test for comparison between two groups or ANOVA among more than two groups of mice using Graph Pad Prism program . Survival curves were generated by the Kaplan-Meier method and compared by using the log-rank test. The level of statistical significance was set at a P value of ≤0.05.We observed that all patient-derived and established NSCLC cell lines significantly overexpress PKM2 mRNA ranging from 5.3 to 16.5 times greater than normal lung cell lines  and a marginal decrease in normal lung cell lines (shRNA-PKM2 cell lysates. We observed that no treatment (none) and shRNA negative control lysates showed similar values, and therefore pooled together in Based on RT-qPCR, PKM2 enzyme activities and ICC results, we hypothesized that PKM2 may have a critical role in NSCLC cell growth. As shown in ll lines  compared50 of SMI (concentration of SMI causing 50% inhibition of NSCLC cell line growth) ranged between 30–65 μM  in untreated NSCLC cell cultures. In contrast, treatment of NSCLC lines with SMI for 48 hours or sh-RNA-PKM2 resulted in a decrease in cytoplasmic enzyme activity with a concomitant rise in nuclear PKM2 enzyme activity  in SMI treated or shRNA-PKM2 NSCLC cells compared to respective controls. In contrast, three normal lung fibroblast cell lines failed to show such increase in Caspase 3/7 activities .To investigate whether PKM2 inhibition by SMI or r growth . Howeverr growth . In contshRNA-PKM2 during the course of the experiment. We observed that there was no significant difference in general appearance and body weight among four groups of animals in both tumor models (data not shown).We carefully monitored the general condition and body weight of mice to evaluate the general toxicity of SMI and To determine whether PKM2 inhibition modulates subcellular compartmentalization, we evaluated cytoplasmic and nuclear staining by performing the experiments on two different occasions resulting in four different readings to evaluate the extent of intensity for PKM2 immunostaining by viewing the specimens at 200 x magnification to score percent positive fields in a blinded fashion for statistical significance. PKM2 intensity scores of ≥ 2+ were considered positive, while ≤ 1+ were considered negative. We observed a very high concordance (exact 95% CI = 72.8–91.3) among the four sets of readings in both data sets (83.3%) obtained from each independent experiment, where scores 3+ and 4+ were combined as 3+, while scores 1+ and no score  were considered as 1+. These results demonstrate high precision in the IHC measurement for cytoplasmic and nuclear staining. For the remainder of the analysis, discordant scores were resolved by assigning the majority score and when there was an equal number of discordant scores, the lower score was used. For the rest of the analysis, all discordant cases in PKM2 were coded as negative.shRNA-PKM2 therapy demonstrated a significant decrease in cytosolic PKM2 in shRNA-PKM2 treated mice xenografts (regressing) compared to parental or non-responding tumor xenografts (non-regressing) (P≤0.01). Immunostaining for nuclear PKM2 levels was ≤1+ with ~10% of the positive fields in most of the regressing tumors  showed a statistically significant trend  compared to negative values of PKM2 (≤ 1+) (Nuclear PKM2 was identified as a merged immunostaining of PKM2 (red) with DAPI (blue) staining as described in materials and methods. As shown in g tumors . In contg tumors . But, >92 (≤ 1+) .in vitro. These treatments affected both PKM2 enzyme activity and mRNA transcription, which subsequently led to reduced levels of PKM2 protein, as demonstrated by Western blot analysis. In contrast, three normal lung cell lines, which showed lower enzyme activity and mRNA, did not show such a dramatic response to both types of anti-PKM2 strategies. Further, we observed that PKM2 targeting with SMI or shRNA-PKM2 significantly affected cell viability in all NSCLC cell lines and impaired the ability to form colonies in vitro, suggesting that PKM2 enzyme activity is important for tumor growth in NSCLC cells. The normal lung fibroblast cell lines failed to show such changes after targeting PKM2. Previously published results have shown that PKM2 can interact with mutant EGFR and HSP90 and prolonged the protein half-life of EGFR in lung cancer cells [in vitro and in vivo.We have demonstrated overexpression of PKM2 in patient-derived and established NSCLC cell lines. These cell lines not only overexpressed PKM2 protein but also high PKM2 enzyme activity in >55% of the cells with > 2+ immunostaining intensity. The overexpressed PKM2 in NSCLC is inhibitable in a concentration-dependent manner with either a drug or gene silencing by shRNA er cells . It is aer cells . Our prein situ in the tumor microenvironment [PKM2 in colon carcinoma cell lines led to a decrease in PKM2 activity and an increase in the pyruvate kinase substrate phosphoenol pyruvate, leading to an increased macromolecular biosynthesis required for rapid proliferation; suggesting that PKM2 influences the metabolic state of the cells and has a role in tumor maintenance and growth in vivo [Elevated PKM2 is also associated with disease aggressiveness and poor overall survival of patients with signet ring cell gastric cancer , gall blironment , 38, 39.ironment , 41. Cor in vivo , 43. In  in vivo . PKM2 is in vivo , 29.shRNA-PKM2 affected cell viability and clonogenicity of NSCLC cells in vitro, which confirmed our results that PKM2 is a growth supportive factor We further investigated the mechanism for decreased cell viability and clonogenicity in PKM2 targeted NSCLC cells. We observed that SMI and shRNA-PKM2 both downregulated PKM2 mRNA and decreased PKM2 activities, which correlated inversely with the number of apoptotic nuclei in targeted tumor cell populations. These changes are also associated with increased Caspase 3/7 activities in NSCLC cells upon treating them with SMI or shRNA-PKM2, which is a hallmark of apoptosis suggesting that SMI or shRNA-PKM2 affected the cell viability by inducing Caspase 3/7 mediated apoptosis. Similar observations have been made by Goldberg and Sharp, who showed that targeting PKM2 with encapsulated siRNAs resulted in decreased cell viability and increased apoptosis in 10 members of the NCI-60 panel representing multiple types of cancer cell lines while having little effect in normal fibroblasts or endothelial cells and tumor regression in vivo mouse model [shRNA-PKM2 induced apoptosis in vitro and in vivo human lung cancer xenografts treated with docetaxel, which suggested that PKM2 could, to some extent, reverse chemotherapy resistance to cancer therapy [Our data demonstrated that targeting PKM2 with SMI and se model . It was  therapy , 46.shRNA-PKM2 in vitro. We observed that PKM2 targeting by both strategies had decreased cytoplasmic and increased nuclear PKM2 enzyme activity in patient-derived established NSCLC cell lines, which also corroborated with PKM2 expression examined by ICC assay in vitro. Importantly, our in vivo mouse results also demonstrated such compartmentalization, which could differentiate animals responding to the PKM2 targeted therapy. Interestingly, the regressing xenografts had lower PKM2 in cytoplasm and nucleus while non-regressing tumor specimens showed a significant and dramatic increase in nuclear PKM2.Interestingly, we identified a distinct subcellular compartmentalization of PKM2 enzyme activity and protein expression in cytoplasm and nucleus after targeting PKM2 by SMI or in vitro, PKM2 inhibitor did not mediate significant effects on in vivo tumor growth in two NSCLC tumor xenografts and did not show statistically significant improvement of survival compared to control mice. The reason for lack of SMI activity in vivo is not clear. It is possible that PMK2 inhibitor levels, being a small molecular weight compound of 231.25 Da, were not retained in the tumor bed at a high enough level to exert a measurable anti-tumor effect. In contrast, PKM2 was silenced robustly by shRNA in tumor cells before xenograft development, which showed significant anti-tumor effects compared to non-silenced and mock-transfected xenografts. Because gene silenced and mock-transfected xenograft bearing mice were sacrificed on day 120 per our Institutional Animal Care and Use Committee (ACUC) protocols, the survival curves were found non-significantly separated in mock-transfected and PKM2 silenced group mice were not significant. The percent survival in mock-transfected and non-silenced mice was also non-significant as non-silenced group mice received transfection reagent without any DNA as additional control compared to negative control DNA in mock-transfected group mice. Similar studies of silencing PKM2 in colorectal cancer cell lines modulated PKM2 to the nucleus in response to oxaliplatin treatment sensitivity and characterized chemoresistance [Despite its anti-proliferative activities sistance . It was sistance .in vitro or in vivo models of different types of human cancers. It is likely that PKM2 can have limited role as a biomarker of therapeutic response in certain types of cancer with different pathological grade and clinical stage (TNM). Additional studies in larger sample size of cancer subjects with different pathological grade and clinical stage (TNM) can potentially establish its meaningful role as a biomarker of therapeutic response in clinical settings. Why PKM2 is compartmentalized is not clear. PKM2 has been reported to be localized in the cell nucleus as a dimer that functions as an active protein kinase; whereas, PKM2 in its tetrameric form is localized in the cytosol and is an active pyruvate kinase [The significance of variable PKM2 subcellular distribution is not known. Our results show that when PKM2 is downregulated by SMI or gene silencing, subcellular compartmentalization of PKM2 occurs. It is also not known that other types of PKM2 inhibitors critically play a similar role in subcellular compartmentalization of PKM2 in NSCLC e kinase . It is pS1 FigImmunocytochemical assay was performed to evaluate the intensity of PKM2 immunostaining in NSCLC cells using rabbit monoclonal anti-PKM2 antibody and alexa 595 conjugated goat anti rabbit IgG antibody. The immunostained cells were viewed at 200X magnification.(TIF)Click here for additional data file.S1 TableExtent of ICC was evaluated as ≤ 1+, weakly positive; 2+, strongly positive; 3+ stronger;4+, strongest. Percent positive field was counted after viewing PKM2positive cells at 200X magnification and shown in the parenthesis.(DOC)Click here for additional data file.S2 TableExtent of ICC was evaluated as ≤ 1+, weakly positive; 2+, strongly positive; 3+ stronger;4+, strongest. Percent positive field was counted after viewing PKM2positive cells at 200X magnification and shown in the parenthesis.(DOCX)Click here for additional data file.S3 TableExtent of ICC was evaluated as ≤ 1+, weakly positive; 2+, strongly positive; 3+ stronger;4+, strongest. Percent positive field was counted after viewing PKM2positive cells at 200X magnification and shown in the parenthesis.(DOC)Click here for additional data file.


In MG, highest T-cell responses were noted against recombinantly expressed Epsilon 1-221. Among the synthetic peptides Epsilon 201-215 showed the most prominent T-cell response and represented the peptide with the most remarkable difference between MG and HD. Taken together, prominent T-cell responses against the ε subunit of the human AChR indicate an important role in the pathogenesis of MG.The human acetylcholine receptor (AChR) is well characterized as the target antigen in myasthenia gravis (MG). Pathogenic antibody responses against the AChR alpha-chain have been investigated extensively and are of diagnostic and prognostic value. However, less is known on the pathogenetic relevance of T-cell responses against epitopes of the different AChR chains . Using an enzyme-linked immunospot (ELISPOT) assay we measured T-cell responses against recombinant fragments and synthetic peptides of the  Both antibody responses as well as B-lymphocyte activity have been investigated extensively in MG and are of great diagnostic and prognostic value.The human nicotinic acetylcholine receptor (AChR) composed of five subunits  autoantibody production is T helper cell-dependent. Although MG is considered a prototypic paradigm for an antibody-driven autoimmune disorder, the pathogenetic importance of T-helper cells is well appreciated. Several studies have been performed comparing T-cell responses involving the man AChR –8. The εc thymus . In accoferation , 10. Conxpressed .ε subunit while no significant differences were notable against alpha subunit epitopes.We used an enzyme-linked immunospot (ELISPOT) assay to determine T-cell responses against recombinant fragments and synthetic peptides of the human AChR. In accordance with previous observations we found prominent T-cell responses against the Peripheral blood lymphocytes (PBL) were obtained with informed consent from patients with generalized or ocular MG (n=15) or healthy donors . PBL were isolated by density centrifugation and were either frozen immediately and thawed for analysis or used directly.α and ε subunit polypeptides were synthesized by PCR on cDNA prepared from total RNA of human calf muscle as described elsewhere [ E. coli and purified by SDS/PAGE with a standard protocol [α subunit [ε subunit.Human lsewhere . Recombilsewhere . Fragmenprotocol , 12. AlpPeptides were synthesized by solid-phase Fmoc-chemistry on an automated peptide synthesizer for multiple peptide synthesis as described previously . Epsilonγ-secreting T-cells using an ELISPOT (enzyme-linked immunospot) assay as described previously [γ monoclonal antibody (mAb) . After washing and blocking the plates with culture medium , fresh or freshly thawed PBL from MG patients and HD were incubated for 20 h in duplicate in the presence or absence of human AChR antigens or controls (1 μg/ml). Concanavalin A  was used as positive control. Using biotinylated anti-human IFN-γ mAb , streptavidin-alkaline phosphatase (Mabtech), and BCIP/NBT as substrate (Sigma), antigen-specific IFN-γ secreting T lymphocytes were visualized and counted on a dissecting microscope. Results are calculated and assigned as the numbers of IFN-γ-secreting events among 106 PBL minus the corresponding numbers of events per 106 PBL without antigen.We measured frequencies of interferon (IFN)-eviously . Microti t-test was performed for statistical analysis. A p value of < 0.05 was accepted to be significant.Student'sγ secreting T-cells after stimulation with AChR fragments  were not observed see . In an a subunit . Althougjunction .γ ELISPOT method may provide a valuable tool to measure AChR-specific T-cell responses in MG. In MG patients who tested positive, T lymphocytes specific for epitopes of the AChR ε subunit may be a target for therapeutical intervention in MG.We conclude that the IFN-


Plasmablastic lymphoma (PBL) is a rare aggressive lymphoma. Although it was first described in HIV- (human immunodeficiency virus-) infected patients, PBL has been diagnosed in patients with other immunodeficiencies as well as in immunocompetent patients. PBL immunohistochemically expresses plasmacytic markers and lacks pan B-cell markers. The cells of origin of PBL are considered to be plasmablasts. MYC gene rearrangement and MYC overexpression are frequently found in PBL, but the pathogenesis of PBL is yet to be elucidated. Here, we report a case of composite lymphoma of PBL and diffuse large B-cell lymphoma (DLBCL); that is, PBL in the urinary bladder and DLBCL in the nasal cavity occurred simultaneously. We extracted DNA from the two lymphomas for polymerase chain reaction and sequenced the amplified immunoglobulin heavy variable genes and the complementarity-determining region- (CDR-) 3. The sequence of the CDR3 region of both tumors matched. MYC rearrangement was found in the bladder tumor but not in the nasal tumor. The patient was treated with R-CHOP , and durable remission had been obtained. The results of the DNA analysis indicated that both PBL and DLBCL emerged from common postgerminal B cells. This case may help to elucidate the pathogenesis of PBL. Plasmablastic lymphoma (PBL) is a rare aggressive lymphoma that was first described in 1997 as a new clinical entity related to human immunodeficiency virus infection and is recognized as a variant of diffuse large B-cell lymphoma (DLBCL) .Plasmablasts are considered to be the cells of origin of PBL, but the mechanisms of tumor evolution remain unclear . Here, wA 37-year-old previously healthy man presented with swelling of the right nasal root and frequent urination. Otolaryngologic examination revealed thickened mucosa inside the right nasal cavity. Despite the use of both antibiotics and antihistamines, the symptoms deteriorated and the patient underwent diagnostic biopsy of the nasal mucosa. Hematoxylin and eosin (H&E) staining showed diffuse proliferation of atypical lymphoid cells with a prominent nucleolus and clear cytoplasm. Immunohistochemical staining revealed that the atypical cells were positive for CD20 and negative for CD138 . These cTo evaluate the cause of frequent urination, ultrasonography of the urinary bladder was performed, and it revealed numerous irregular surfaced masses scattered on the wall of the urinary bladder. Biopsy specimens of one of the tumors showed the proliferation of atypical lymphoid cells with abundant basophilic cytoplasm and pleomorphic nucleoli. Immunohistochemical staining indicated that these atypical cells were negative for CD20 and positive for CD138 . These cTo identify the cell type of origin of the two lymphoma subtypes occurring simultaneously in one patient, we extracted DNA from frozen sections of each biopsy sample for polymerase chain reaction and sequenced the immunoglobulin heavy variable (IGHV) genes and the complementarity-determining region- (CDR-) 3, as previously described , 5 see s. The seThis case was diagnosed as composite lymphoma of PBL and DLBCL and classified as stage 4A and IPI high/intermediate . The patient underwent eight courses of R-CHOP  with four intrathecal infusions of methotrexate, cytarabine, and hydrocortisone to prevent central nervous system relapse, achieving a complete metabolic response based on PET/CT. Four years after completion of chemotherapy, he developed a soft tissue tumor on the cheek, and a biopsy specimen revealed recurrence of DLBCL.PBL is an aggressive lymphoma with the distinct morphologic features of large immunoblasts. The characteristic immunophenotype usually seen in PBL is the expression of plasmacytic markers, including CD138, CD38, and IRF4/MUM1, and the absence of pan B cell markers, including CD20 and PAX5. CD45 is weakly expressed or absent, and CD79a is often expressed in PBL . The blaThe cells of origin of PBL are thought to be plasmablasts. Plasmablasts are precursor plasma cells derived from activated B cells. Several studies, including gene expression profiling studies, have investigated the pathogenesis of PBL, but it remains unclear. PBL was first reported in HIV patients, and EBV is detected in tumor cells of 74% of patients with HIV-associated PBL . EBV, huMYC gene rearrangement is detected in up to 60% of PBL cases, and a common partner of the MYC gene is the immunoglobulin gene , 14. GenDNA sequence analysis in our case revealed that PBL in the urinary bladder and DLBCL in the nasal cavity had matched sequences at the CDR3 region of IGHV. These results indicated that both PBL and DLBCL were derived from the same clone. The clonal relation between PBL and low-grade lymphoma has been demonstrated in a case series . Marini R-CHOP is a standard treatment for patients with DLBCL, and two-thirds of nonelderly patients with advanced-stage DLBCL can be cured with 6 to 8 cycles of R-CHOP. A standard treatment for PBL has not been established due to the rarity of the disease and its aggressive clinical course. Median survival time for PBL patients is estimated to be less than 1 year. A standard-dose CHOP-like regimen is considered inadequate because it provides the least possibility of cure . The Nat


TP53 alterations, in advanced solid tumors and is associated with poor prognosis. Tetraploidization step will lead to aneuploidy and chromosomic rearrangements. The mechanism leading to tetraploid cells is important since endoreplication, abortive cytokinesis and cell fusion could have distinct consequences. Unlike processes based on duplication, cell fusion involves the merging of two different genomes, epigenomes and cellular states. Since it is involved in muscle differentiation, we hypothesized that it could play a role in the oncogenesis of myogenic cancers. Spontaneous hybrids, but not their non-fused immortalized myoblast counterparts they are generated from, induced tumors in mice. Unstable upon fusion, the hybrid genome evolved from initial mitosis to tumors with a highly rearranged genome. This genome remodeling finally produced targeted DMD deletions associated with replicative stress, isoform relocalization and metastatic spreading, exactly as observed in human myogenic sarcomas. In conclusion, these results draw a model of myogenic oncogenesis in which cell fusion and oncogene activation combine to produce pleomorphic aggressive sarcomas.Whole-genome doubling is the second most frequent genomic event, after  A century ago, Theodor Boveri formulated the hypothesis that tetraploid cells are a transient stage during oncogenesis between the diploid and aneuploid states . The mosTetraploid cells may be generated in three ways: endoreplication, cytokinesis failure and cell fusion. Endoreplication is a mechanism in which cells increase their genomic DNA content without dividing  and contThe fusion of myoblasts is the central mechanism for muscle differentiation, leading to a non-proliferating syncytium, thus ensuring the integrity of the genome. We hypothesized that this physiological mechanism could be diverted to proliferation upon activation of oncogenes. This tetraploidization state would lead to rearranged aneuploid genome that characterized pleomorphic sarcomas .CDK4 and hTERT introduction, see Material and Methods) human myoblast cell lines were transfected with plasmid CFP-Hygromycin (A8 CFP) or tdTomato-Blasticidin (A8 Tomato). After 72 h of co-culture, double antibiotic selection was added in order to select hybrids formed by spontaneous fusion. Six hybrid cell lines were established (H1 to H6) from distinct clones, each arising from one fusion event. All hybrids were mononucleated and expressed dual fluorescence, tdTomato and CFP, thus validating their hybrid nature  showed an increase in proliferation capacity compared to the parental cell lines B.Myoblasts are defined by their capacities to fuse to differentiate into myotubes under specific conditions. After 14 days of differentiation, parental cell lines and two hybrids, H1 and H5, had formed multinucleated myotubes C.The other hybrids did not survive in these culture conditions  or formed round multinucleated cells (H4). A8 Tomato, H1, H2 H4 and H6 formed colonies in soft agar. However, apart from H4, hybrids did not form more colonies than the parental cell lines in non-adherent conditions D. In regTo investigate the DNA content of hybrids, we first evaluated their ploidy  to 4N (H6) and was heterogeneous in some clones . To test whether the DNA content could evolve with passages, we also re-evaluated the ploidy at later passages  and PS100 (melanoma marker) and had a strong expression of desmin and a focal expression of myogenin . All tumResults above show that LP cell lines developed tumors faster than EP ones and that hybrid cell lines evolve in culture conditions leading to cell lines with recurrent and specific chromosome number variations. Our hypothesis was that the genome of these LP cells is fit for oncogenesis, while EP cell lines need to acquire more instability to become competent for it. To test this, we studied the genome of tumors developed from EP and LP hybrid cell lines.As expected, LP tumors did not have a different genomic index than the cell lines they derive from, so the genome did not harbor extra chromosomic rearrangements after in vivo engraftment A. In conSince chromosomic alterations tended towards the same pattern after in vivo experiments with EP and LP cell lines, we hypothesized that all the cells moved to a specific genomic remodeling, resulting in a tumorigenic transcriptomic program. To test this hypothesis, we performed RNA sequencing of parental, EP and LP hybrid cell lines and three tumors from LP cell lines. PCA analysis separates samples into four groups: parental cell lines, EP cell lines, LP hybrids and cell lines derived from three tumors . Then, wGenome remodeling upon fusion was then analyzed by whole genome sequencing (WGS) in three samples. This highlighted three types of structural variants in addition to CNVs A: (i) inDMD in 82% of cases (DMD deletions were homozygous. Since DMD inactivation by deletion has been reported to be a driver event in sarcomas with myogenic differentiation [DMD deletion was detected in 2/3 samples subjected to WGS (H2-LP-Tumor1 and H2-LP-Tumor3), so we were able to define three fusion points in DMD and to validate these deletions by PCR and Sanger sequencing  did not express Dp427, or only faintly, in proliferation conditions, whereas the expression increased in conditions of muscle differentiation. Second, tumors with the DMD deletion (H2-LP-Tumor1) did not express Dp427 in proliferation and differentiation conditions. The third group was composed of tumors that displayed a DMD deletion  and maintained a weak expression of Dp427 in differentiation conditions. In this group, a heterozygous deletion (H4-LP-Tumor3) and/or a heterogeneous representation of DMD deletion in the tumor could be an explanation for the weak expression of Dp427.Protein analysis  showed tDMD deletions did not target the Dp71 ORF and this isoform has been demonstrated to be essential for proliferation in myogenic cancers [ cancers ,35. Our DMD deletion), Dp427 (green) was detected by immunofluorescence on the FFPE tumor at the membrane of the cells as well as all the other isoforms (red)  B. In sam nucleus .DMD deletions suppressed Dp427 isoform expression and all the other isoforms, including Dp71, were detected in the nucleus (H2-LP-Tumor1).To sum up, when Dp427 was strongly expressed, it was located to the membrane with all DMD isoforms (H4-LP-Tumor1). In contrast, DMD deletion is thought to occur specifically in sarcomas with myogenic differentiation including leiomyosarcomas (LMS), we looked at the localization of the different isoforms of DMD in LMSs  and in vivo (for the early passage hybrids). It is likely that these specific losses and gains are necessary for tumor development in this specific myogenic context . All theDMD deletions, likely related to expression/replication conflict under in vivo pressure selection. This kind of alteration has been described as “replicative stress” [The third phase of genome evolution might correspond to the acquisition of punctual focus alterations, i.e., 5′- stress” ,39. The  stress” ,40. Prog stress” . Genes a stress” ,43 and cDMD structural alteration (deletions). DMD is one of the longest human genes (2.2 Mb). Spatial and temporal overlap of transcription and replication machineries in the late S phase triggers DNA break spots called common fragile sites (CFSs), particularly in long genes [DMD deletions are then detected because the locus fits these features and deletions have a tumor suppressor impact on oncogenesis [The development of myoblast hybrid tumors is associated with highly frequent ng genes . This inng genes . We showogenesis .DMD abrogates the expression of the Dp427 isoform, always preserving Dp71. Even if we cannot definitively rule out the possibility that cell cycle status could account for Dp71 delocalization this is unlikely since we observed, both in hybrid cell lines and in human tumors, that Dp427 loss is strongly associated to Dp71 nucleus relocation. Dp427 is a tumor suppressor [DMD deletion is associated with metastatic evolution [Deletion of ppressor  whereas ppressor , demonstppressor ,35. In eppressor . Dp71 isppressor  and is lppressor .One of tIn conclusion, cell fusion, the event involved in myoblast differentiation is a way to induce in a single event genomic remodeling and genetic aberrations leading to tumor development. Together with oncogene activation upon mutation, myoblast fusion generates a model reflecting the evolution of the genome following whole genome doubling and could account not only for the aneuploidy commonly found in pleomorphic sarcoma but also, and more specifically, for the targeted genomic alterations specific to histotypes.CDK4 and hTERT) were kindly provided by Bénédicte Chazaud. Immortalization of human myoblast were performed as previous described [2. Parental cell lines were generated by lentiviral infection using hygromycin-CFP and blasticidin-tdTomato plasmids (kindly provided by Dr. Richard Iggo). Tumor cell lines obtained from xenografts were obtained after 24 h of collagenase treatment and then cultured in the medium described above. Passages of cell lines described as “early passages hybrid” range from passages 13 to 20 after selection of clones; passages of cell lines described as “late passage hybrids” range from passages 25 to 32.Immortalized human myoblast cell lines  and hygromycin . To validate hybrid selection, dual fluorescence was evaluated by microscopy  and flow cytometry .Cell lines were resuspended in PBS 1X and fixed with 70% ethanol overnight at −20 °C. Then, cells were washed and a solution of propidium iodide  and RNAse A  was added for 30 min at room temperature. DNA content was quantified with propidium iodide fluorescence intensity by flow cytometry  and analyzed using FlowJo software .® VYB, Miltenyi, Bergisch Gladbach, Germany) and data were analyzed using FlowJo (BD Bioscience).In triplicate, 1000 cells were seeded in a 96-well plate. From day 1 to day 9, cells were harvested and collected in 200 µL of FACS buffer (PBS 1× supplemented with 5% FBS and 2mM EDTA). The number of viable cells was determined by flow cytometry  and transferrin . Medium was changed every two days during the first week and then every day. At 14 days, cells were fixed with 4% PFA and images were taken with Axio Vert.A1 (Zeiss). For western blot and immunofluorescence in muscular differentiation conditions, cells were cultivated for 6 days in differentiation medium.Cells were diluted in 0.35% agar medium solution in order to seed 5000 cells per well. A mix of agarose and cells was left on the top of pre-coated 0.5% agarose. Medium was changed once a week. After 6 weeks, colony number for one field was manually counted using an Axio Vert.A1  at 4x magnification. Each cell line was seeded in triplicate and two fields were counted for each well.Animals were maintained under specific pathogen-free conditions in the animal facility of Bordeaux University, and under opportunist and pathogen-free conditions in the CREFRE US006, Toulouse. Experiments were performed in conformity with the rules of the Institutional Animal Care and Use committee  and all efforts were made to minimize animal suffering.2)/2. Once tumors reached 2000 mm3, mice were sacrificed by cervical dislocation. Tumors were formalin-fixed and nitrogen-frozen, and one part was also used to establish cell lines from the tumor. Survival curves were plotted with Graph Pad Prism ) and statistical analysis was performed with the Log-rank Mantel-Cox test.One million cells in 100 µL PBS 1× were subcutaneously injected into 8-week-old NSG mice. For early passage cell lines, five mice were injected and for late passage cell lines six mice were injected. Tumors were measured every week using a digital caliper and tumor volume was calculated using the formula V =  was performed using the mouse monoclonal antibody against human protein S100 , myogenin , desmin  and pan-cytokeratin . Pictures were captured using a Zeiss Cell Observer Microscope.A 4-µm formalin-fixed paraffin-embedded tissue section was de-paraffinized in three xylene baths for 5 min and then rehydrated in successive baths of ethanol from 100% to 70%. For heat-induced epitope retrieval, slides were incubated for 20 min in a microwave oven in Dako Target Retrieval pH6. Then, they were incubated with primary antibodies for 1 h at room temperature in a humidity chamber and after 1 h with secondary antibodies. Slides were then mounted with Vectashield mounting medium plus DAPI . Images were acquired on a Zeiss Cell Observer Microscope or a LSM 780 confocal microscope (Zeiss). Primary antibodies used for Dp427 were NCL-DYS1  and ab15277 for all the dystrophin isoforms . Secondary antibodies used were Alexa Fluor 488  anti-mouse and Alexa Fluor 594 anti-rabbit .https://github.com/obigriffith/biostar-tutorials/blob/master/Heatmaps/heatmap.3.R). Finally, six groups of genes were identified and a functional enrichment analysis of gene ontology was performed using Bioconductor packages goseq and org.Hs.eg.db.Total RNA from cell lines was extracted with TRIzol reagent (Life Technologies) and purified with the RNeasy MinElute Cleanup Kit  according to the manufacturer’s instructions. RNA concentration was evaluated with Clariostar and quality-checked with an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA sequencing was performed by the Centro Nacional de Análisis Genómico  to obtain more than 20 million paired-end reads with a length of 75 bp. Subsequent CNAG RNA sequencing steps, ERCC spike-in sequencing control quality check and bioinformatics analysis pipeline have already been described . ThereafDNA extractions were performed on cell lines and frozen tumor samples. Genomic DNA was extracted using the standard phenol-chloroform method and quantified using Clariostar. DNA arrays were performed using Affymetrix CytoScan HD Arrays  according to the manufacturer’s instructions. Analysis was performed using the Chromosome Analysis Suite software (Affymetrix) with genome version GRCH37 (hg19). Genomic index was calculated as follows: GI = A²/C where A corresponds to the total number of alterations and C to the number of chromosomes affected by these alterations. To compare genomic index of the different groups, a Mann-Whitney test was performed. Losses and gains of each sample were defined with an average log2(ratio)  ≤ −0.2 or ≥ 0.2, respectively. Thus, penetrance plots were obtained for four subgroups: EP hybrids, LP hybrids, EP tumors and LP tumors.http://broadinstitute.github.io/picard/). Thus, genomic rearrangements were established with GRIDSS [https://samtools.github.io/bcftools/bcftools.html) v1.6 with RAS > 0, AS > 0 and QUAL > 0 criteria.DNA of H2 LP Tumor 1, H4 LP Tumor 1 and H4 LP Tumor 3 were sequenced at CNAG. Whole genome sequencing characteristics were paired-end sequencing with 150 bp reads and an expected coverage of 45×. Sequenced reads were aligned on Human GRCh38/hg38 genome with BWA mem v0.7.17  and PCR A Circos plot  was perfhttp://frodo.wi.mit.edu.proxy.insermbiblio.inist.fr/primer3/). The touch-down (TD) 60 °C program was used . PCR was performed on 50 ng of DNA using AmpliTaqGold® DNA polymerase (ThermoFisher Scientific).For PCR, primers were designed using the Primer 3 program  supplemented by phosphatase/protease inhibitor cocktail . Proteins were collected in supernatant after 15 min of centrifugation at 13,000g and quantified by DC protein assay kit, Biorad. 40 µg of total proteins were loaded on Mini Proteane TGX stain free 4–15% . Transfers on PVDF membrane were performed using a dry transfer system  and membranes were blocked in 5% non-fat dry milk in 0.1% PBS-Tween solution. Membranes were incubated overnight with the primary antibody: mouse anti-Dp427  or Rabbit anti-Dp71  at 4 °C overnight. After wash, membranes were incubated for 1 h at room temperature with appropriate secondary antibodies: anti-rabbit HRP  and anti-mouse HRP . After incubation with chemiluminescent substrate , signals were detected using PXi .In conclusion, cell fusion is a way to induce in a single event genomic remodeling and genetic aberrations leading to tumor development and metastasis progression. This tetraploidization mechanism, with oncogene activation, permits to reflect the evolution of myogenic sarcoma, from aneuploidy to genomic alterations specific to this histotype.