However, the mechanisms by which miR34a is regulated in these cancers is largely unknown. In this study, we find that a long noncoding RNA transcribed antisense to the miR34a host gene, is critical for miR34a expression and mediation of its cellular functions in multiple types of human cancer. We name this long noncoding RNA lncTAM34a, and characterize its ability to facilitate miR34a expression under different types of cellular stress in both TP53-deficient and wild-type settings.The microRNA-34a is a well-studied tumor suppressor microRNA (miRNA) and a direct downstream target of TP53 with roles in several pathways associated with oncogenesis, such as proliferation, cellular growth, and differentiation. Due to its broad tumor suppressive activity, it is not surprising that  Of these recently identified elements, long noncoding (lnc) RNAs are defined as transcripts exceeding 200 base pairs (bp) in length with a lack of a functional open reading frame. Some lncRNAs are dually classified as antisense (as) RNAs that are expressed from the same locus as a sense transcript in the opposite orientation. Current estimates using high-throughput transcriptome sequencing, indicate that up to 20–40% of the approximately 20,000 protein-coding genes exhibit antisense transcription5.In recent years advances in functional genomics have revolutionized our understanding of the human genome. Evidence now points to the fact that approximately 75% of the genome is transcribed but only ~1.2% of this is responsible for encoding proteins6 and, although characterization of several of these has identified asRNA-mediated regulation of multiple well-known tumorigenic factors8, the vast majority of potential tumor-associated asRNAs have not yet been characterized. The known mechanisms by which asRNAs accomplish their regulatory functions are diverse, and include recruitment of chromatin modifying factors9, acting as microRNA (miRNA) sponges10, and causing transcriptional interference11.Systematic large-scale studies have shown aberrant expression of asRNAs to be associated with tumorigenesismiR34a12. Upon TP53 activation miR34a expression is increased allowing it to down-regulate target genes involved in cellular pathways such as growth factor signaling, apoptosis, differentiation, and cellular senescence14. Thus, miR34a is a crucial factor in mediating activated TP53 response and, the fact that it is often deleted or down-regulated in human cancers indicates its tumor suppressive effect and makes it a valuable prognostic marker19. Reduced miR34a transcription is mediated via epigenetic regulation in many solid tumors, including colorectal-, pancreatic-, and ovarian cancer20, as well as numerous types of hematological malignancies21. In addition, miR34a has been shown to be transcriptionally regulated via TP53 homologs, TP63 and TP73, other transcription factors, e.g., STAT3 and MYC, and, in addition, posttranscriptionally through miRNA sponging by the NEAT1 lncRNA26. Despite these findings, the mechanisms underlying miR34a regulation in the context of oncogenesis have not yet been fully elucidated.Responses to cellular stress, e.g., DNA damage, sustained oncogene expression, and nutrient deprivation, are all tightly controlled cellular pathways that are almost universally dysregulated in cancer. Cellular signaling, in response to these types of stresses, often converges on the transcription factor TP53 that regulates transcription of coding and noncoding downstream targets. One important noncoding target of TP53 is the tumor suppressor miRNA known as miR34a expression is induced in a TP53-null background, although endogenous mechanisms for achieving this have not yet been discovered30. In addition, previous reports from large-scale studies interrogating global TP53-mediated regulation of lncRNAs have identified a lncRNA (known as RP3-510D11.2 and LINC01759) originating in the antisense orientation from the miR34a locus that is induced upon numerous forms of cellular stress35. Despite this, none of these studies have functionally characterized this transcript, which we name Long-Non-Coding Transcriptional Activator of MiR34a (lncTAM34a). In this study we functionally characterize the lncTAM34a transcript, and find that it positively regulates miR34a expression resulting in a decrease of several tumorigenic phenotypes. Furthermore, we find that lncTAM34a-mediated up-regulation of miR34a is sufficient to induce endogenous cellular mechanisms counteracting several types of stress stimuli in a TP53-deficient background. Finally, similar to the functional roles of antisense transcription at protein-coding genes, we identify a rare example of an antisense RNA capable of regulating a cancer-associated miRNA.Studies across multiple cancer types have reported a decrease in oncogenic phenotypes when lncTAM34a is transcribed in a “head-to-head” orientation with approximately 100 bp overlap with the miR34a host gene (HG)  Fig. . Due to 34a Fig. . We usedlncTAM34a and miR34a expression levels could be identified. We utilized RNA sequencing data from The Cancer Genome Atlas (TCGA) after stratifying patients by cancer type, TP53 status, and, in the case of breast cancer, cancer subtypes. The results indicate that lncTAM34a and miR34a expression are strongly correlated in the vast majority of cancer types examined, both in the presence and absence of wild-type TP53  using the U2OS osteosarcoma cell line that exhibited high endogenous levels of lncTAM34a in the cell panel screening. Sequencing the cloned cDNA indicated that the transcripts 3′ transcription termination site is 525 bp upstream of the lncTAM34a transcript’s annotated termination site –220 bp (F12 primer) upstream of the annotated start site  length, ORF coverage, Fickett score, and hexamer score37. Results indicated that lncTAM34a has a similar low coding capacity to known noncoding transcripts such as HOTAIR and XIST  RNA-mediated knockdown of lncTAM34a and examining the effects on miR34a HG. The results show that a decrease in lncTAM34a levels causes a concordant decrease in miR34a HG  RNAs targeting renilla into HEK293T cells and subsequently measured luciferase and renilla expression. The results indicated that shRNA-mediated knockdown of the p1-renilla transcript (corresponding to lncTAM34a) caused p1-luciferase (corresponding to miR34a HG) levels to concomitantly decrease  and subsequently treated with increasing doses of doxorubicin. Again, the results showed a concomitant reduction in luciferase levels upon knockdown of p1-renilla, i.e., the lncTAM34a corresponding segment of the p1 transcript , Saos2 (osteogenic sarcoma), and Skov3 (ovarian adenocarcinoma). We first analyzed the levels of lncTAM34a in these stable cell lines, compared to HEK293T cells, which have high endogenous levels of lncTAM34a. On average, the overexpression was approximately 30-fold higher in the overexpression cell lines than in HEK293T cells, roughly corresponding to physiologically relevant levels in cells encountering a stress stimulus, such as DNA damage 41. Quantification of both CCND1 RNA expression  for phosphorylated polymerase II (polII) at the miR34a HG promoter in both lncTAM34a overexpressing and mock control cell lines. Indeed, we found a clear increase in phosphorylated polII binding at the miR34a promoter upon lncTAM34a overexpression indicating the ability of lncTAM34a to transcriptionally regulate miR34a levels  to control samples in 17 cancer types from TCGA  and TP53 mutations  across the different cancer types  of the lncTAM34a transcript indicates that this portion is sufficient to give rise to at least a partial miR34a inducing response and therefore, that lncTAM34a may be able to facilitate miR34a expression independent of additional factors , HCT116 and U2OS cells in McCoy’s 5a , and PC3 cells in RPMI  and 2 mM l-glutamine . All growth mediums were supplemented with 10% heat-inactivated FBS  and 50 μg/ml of streptomycin  and 50 μg/ml of penicillin . All cell lines were purchased from ATCC, tested negative for mycoplasma, and their identity was verified via STR profiling.All cell lines were cultured at 5% CO49 was utilized for the bioinformatic evaluation of antisense transcription utilizing the RefSeq50 gene annotation track.The USCS genome browser51 and are provided as an R package. All analysis took place using the R statistical programming language52 using external packages that are documented in the package associated with this article64. The package facilitates replication of the operating system and package versions used for the original analysis, reproduction of each individual figure and figure supplement included in the article, and easy review of the code used for all steps of the analysis, from raw-data to figure.All raw experimental data, code used for analysis, and supplementary methods are available for review att test unless otherwise specified. Data were either approximated to be normally distributed or transformed to be so in cases where a parametric test was utilized. In addition, variance was not assumed to be equal between groups and, therefore, the Welch (or Satterthwaite) approximation to the degrees of freedom was used.The significance threshold  in this study was set to 0.05. Statistical testing was performed using an unpaired two sample Student’s two-sided 37 and Coding-Potential Calculator38 with default settings. Transcript sequences for use with Coding-Potential Assessment Tool were downloaded from the UCSC genome browser using the Ensembl accessions: HOTAIR (ENST00000455246), XIST (ENST00000429829), β-actin (ENST00000331789), Tubulin (ENST00000427480), and MYC (ENST00000377970). Transcript sequences for use with Coding-Potential Calculator were downloaded from the UCSC genome browser using the following IDs: HOTAIR (uc031qho.1) and β-actin (uc003soq.4).Protein-coding capacity was evaluated using the Coding-Potential Assessment Tool39) converted to mzML format using msConvert from the ProteoWizard tool suite65. Spectra were then searched using MSGF + (v10072)66 and Percolator (v2.08)67. All searches were done against the human protein subset of Ensembl 75 in the Galaxy platform68 supplemented with the 6 frame translation of both the annotated  and PCR cloned sequence of lncTAM34a (51). MSGF + settings included precursor mass tolerance of 10 ppm, fully tryptic peptides, maximum peptide length of 50 amino acids and a maximum charge of 6. Fixed modification was carbamidomethylation on cysteine residues; a variable modification was used for oxidation on methionine residues. Peptide Spectral Matches found at 1% FDR  were used to infer peptide identities. The output from all searches are available in Ref. Orbitrap raw MS/MS files for 11 human cell lines were downloaded from the PRIDE repository . The sequences targeting renilla is as follows: shRenilla 1.1 (AAT ACA CCG CGC TAC TGG C) and shRenilla 2.1 (TAA CGG GAT TTC ACG AGG C). si-lncTAM34a  and si-Control  were transfected using Lipofektamine 3000 at 10 nM.shRNA-expressing constructs were cloned into the U6M2 construct using the BglII and KpnI restriction sites as previously described12 which we synthesized, cloned into the pLucRluc construct70, and sequenced to verify its identity.The overlapping region (p1) corresponds with the sequence previously published as the TP53 binding site in70 and GFP by using Lipofectamine 2000 . The expression of GFP and luminescence was measured 24 h posttransfection by using the Dual-Glo Luciferase Assay System  and detected by the GloMax-Multi + Detection System . The expression of luminescence was normalized to GFP.Cells were cotransfected with the p1 renilla/firefly bidirectional promoter construct69, and ligated into the Not1 restriction site of the pHIV7-IMPDH2 vector71. lncTAM34a was PCR amplified and subsequently cloned into the Nhe1 and Pac1 restriction sites in the pHIV7-IMPDH2-U6 plasmid.The U6 promoter was amplified from the U6M2 cloning plasmidLentivirus production was performed as previously described in Ref. lncTAM34a overexpression and miR34a was performed in biological quintuplet for all cell lines.Stable lines were generated by infecting cells with a multiplicity of infection of 1 and subsequently initiating 1–2 μM mycophenolic acid-based  selection 48–72 h postinfection. Cells were expanded as the selection process was monitored via flow cytometry analysis  of GFP and selection was terminated once >90% of the cells were GFP positive. Quantification of 72.Samples were lysed in 50 mM Tris-HCl , pH 7.4, 1% NP-40 , 150 mM NaCl , 1 mM EDTA , 1% glycerol , 100 μM vanadate , protease inhibitor cocktail , and PhosSTOP . Lysates were subjected to SDS-PAGE and transferred to PVDF membranes. The proteins were detected by western blot analysis by using an enhanced chemiluminescence system . Antibodies used were specific for CCND1 1:1000 , and GAPDH 1:5000 . All western blot quantifications were performed using ImageJFor downstream SYBR green applications, RNA was extracted using the RNeasy mini kit  and subsequently treated with DNase . 500 ng RNA was used for cDNA synthesis using MuMLV  and a 1:1 mix of oligo(dT) and random nanomers.For analysis of miRNA expression with Taqman, samples were isolated with TRIzol reagent  and further processed with the miRNeasy kit . cDNA synthesis was performed using the TaqMan MicroRNA Reverse Transcription Kit  using the corresponding oligos according to the manufacturer’s recommendations.PCR was performed using the KAPA2G Fast HotStart ReadyMix PCR Kit  with corresponding primers. Quantitative polymerase chain reaction (qPCR) was carried out using KAPA 2G SYBRGreen  using the Applied Biosystems 7900HT machine with the cycling conditions: 95 °C for 3 min, 95 °C for 3 s, and 60 °C for 30 s.® MicroRNA Assay, hsa-miR-34a, human, Cat# 4440887, Assay ID: 000426 and Control miRNA Assay, RNU48, human, Cat# 4440887, Assay ID: 001006).QPCR for miRNA expression analysis was performed according to the primer probe set manufacturers recommendations (ThermoFisher Scientific) and using the TaqMan Universal PCR Master Mix  with the same cycling scheme as above. Primer and probe sets for TaqMan were also purchased from ThermoFisher Scientific  machine. Resulting cell-cycle phases were quantified using the ModFit software . Experiments were performed in biological quadruplet (PC3) or triplicate (Skov3). The log2 fraction of cell-cycle phase was calculated for each replicate and a two sample lncTAM34a exon 1 and a tailed oligo(dT) primer followed by a second PCR using an alternate lncTAM34a exon 1 primer and a reverse primer binding to the tail of the previously used oligo(dT) primer. PCR products were gel purified and cloned the Strata Clone Kit , and sequenced.3′-RACE was performed as described as previously in Ref. The ChIP was performed as previously described in Ref. 2 in a humidity chamber in either normal medium or HBSS . Confluency was measured every hour using bright-field microscopy and the percentage of confluency was reported via the plate reader’s inbuilt algorithm. Fold confluency was then calculated as % confluency/% confluency time 0 for each condition and cell line and the mean of the three technical replicates was subsequently calculated for each of the three biological replicates. A polynomial regression model was then constructed modeling the fold confluency as the dependent variable and time and cell line as independent variables. Reported p values are derived from the t test, testing the null hypothesis that the coefficient estimate of the cell line covariate is equal to 0.Cells were incubated in the Spark Multimode Microplate  reader for 48 h at 37 °C with 5% CO4 cells per well. Time 0 measurements were taken once cells had attached and treatments with RPMI  10% FBS, RPMI 0.1% FBS, or HBSS were simultaneously initiated in the remaining cells. RPMI mediums were all additionally supplemented with 2 mM l-glutamine and 50 µg/ml Penicillin–Streptomycin. Cells were incubated for the indicated times before harvesting and CellTrace Violet was quantified via flow cytometry (Sony SH800S Cell Sorter). Time 0 was performed in biological triplicate and technical duplicate whereas all other time points were performed in biological triplicates with one technical replicate. Analysis was performed by first, subsampling each replicate and condition so that each had a total of 10,000 cells. The mean of each technical replicate for time point 0 was calculated and, subsequently, the time 0 fluorescence intensity was subtracted from each sample. The mean difference in fluorescence intensity was then calculated for each biological replicate and condition and used to build a polynomial regression model per condition  where difference in fluorescence intensity was modeled as a function of time and cell line. Reported P values are derived from the t test, testing the null hypothesis that the coefficient estimate of the cell line covariate is equal to 0.Analysis of the proliferation rate of PC3 stable cell lines was performed using the CellTrace Violet assay . CellTrace Violet is a fluorescent dye that binds covalently to all free amines on the surface and inside of cells. As cells divide the dye is diluted and hence fluorescence intensity is decreased. PC3 stable cell lines, either miR34a asRNA overexpressing or mock, were harvested and stained in 1 ml PBS with 5 µM CellTrace Violet for 20 min and subsequently seeded in 12 well plates at 2 × 10Doxorubicin was purchased from Teva .lncTAM34a (ENSG00000234546), GAPDH (ENSG00000111640), and MALAT1 (ENSG00000251562) were extracted. Cell lines for which data was downloaded include: A549, GM12878, HeLa-S3, HepG2, HT1080, K562 MCF-7, NCI-H460, SK-MEL-5, SK-N-DZ, and SK-N-SH. Initial exploratory analysis revealed that several cell lines should be removed from the analysis due to (a) a larger proportion of GAPDH in the nucleus than cytoplasm, (b) variation of lncTAM34a expression is too large to draw conclusions, or (c) they have no or low (<6 TPM) lncTAM34a expression. Furthermore, only polyadenylated libraries were used in the final analysis, due to the fact that the cellular compartment enrichment was improved in these samples. All analyzed genes are reported to be polyadenylated. In addition, only samples with 2 biological replicates were retained. For each cell type, gene, and biological replicate the fraction of transcripts per million (TPM) in each cellular compartment was calculated as the fraction of TPM in the specific compartment by the total TPM. The mean and standard deviation for the fraction was subsequently calculated for each cell type and cellular compartment and this information was represented in the final figure.Quantified RNAseq data from 11 cell lines from the GRCh38 assembly was downloaded from the ENCODE project database and quantifications for 36 for 36 cell lines was downloaded from the UCSC genome browser49 for genome version hg19. Of these, 28 cell lines had CAGE transcription start sites (TSS) mapping to the plus strand of chromosome 1 and in regions corresponding to 200 base pairs upstream of the Lnc34a start site (9241796 − 200) and 200 base pairs upstream of the GENCODE annotated lncTAM34a start site (9242263 + 200). These cell lines included: HFDPC, H1-hESC, HMEpC, HAoEC, HPIEpC, HSaVEC, GM12878, hMSC-BM, HUVEC, AG04450, hMSC-UC, IMR90, NHDF, SK-N-SH_RA, BJ, HOB, HPC-PL, HAoAF, NHEK, HVMF, HWP, MCF-7, HepG2, hMSC-AT, NHEM.f_M2, SkMC, NHEM_M2, and HCH. In total 74 samples were included. Seventeen samples were polyA−, 47 samples were polyA+, and 10 samples were total RNA. In addition, 34 samples were whole cell, 15 enriched for the cytosolic fraction, 15 enriched for the nucleolus, and 15 enriched for the nucleus. All CAGE transcription start sites were plotted and the RPKM of the individual reads was used to color each read to indicate their relative abundance. In cases where CAGE TSS spanned identical regions, the RPMKs of the regions were summed and represented as one CAGE TSS in the figure. In addition, a density plot shows the distribution of the CAGE reads in the specified interval.All available CAGE data from the ENCODE project36 for 38 cell lines was downloaded from the UCSC genome browser49. Of these cell lines, 36 had spliced reads mapping to the plus strand of chromosome 1 and in the region between the Lnc34a start (9241796) and transcription termination (9257102) site . Splice junctions from the following cell lines were included in the final figure: A549, Ag04450, Bj, CD20, CD34 mobilized, Gm12878, H1-hesc, Haoaf, Haoec, Hch, Helas3, Hepg2, Hfdpc, Hmec, Hmepc, Hmscat, Hmscbm, Hmscuc, Hob, Hpcpl, Hpiepc, Hsavec, Hsmm, Huvec, Hvmf, Hwp, Imr90, Mcf7, Monocd14, Nhdf, Nhek, Nhemfm2, Nhemm2, Nhlf, Skmc, and Sknsh. All splice junctions were included in the figure and colored according to the number of reads corresponding to each. In cases where identical reads were detected multiple times, the read count was summed and represented as one read in the figure.All available whole cell  spliced read data originating from the Cold Spring Harbor Lab in the ENCODE project34. Briefly, RNAseq data were aligned to the human hg19 assembly and quantified using GENCODE (v19) annotated HTSeq-counts and FPKM normalizations. Expression data from miR34a and lncTAM34a (identified as RP3-510D11.2) were used for further analysis. Copy number amplitudes for GENCODE genes were determined from segmented copy number data. Samples that were diploid for lncTAM34a were identified as those samples that had copy number amplitudes between −0.1 and 0.1.RNAseq data and copy number data were downloaded from TCGA and processed as described previously73.Somatic mutation data were downloaded from the Genomics Data Commons data portal (GDC) as mutation annotation format (maf) files, called using Mutect2 on 30/10/2017 (v7)64.Survival analysis was performed on TCGA vital state and follow-up data, downloaded from GDC on 27/10/2017 using the R survival packageSupplementary Document 1Supplementary Document 2


Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a  Benefiting from the development of high-throughput sequencing technology and the improvement of bioinformatics, the precision medicine has become a novel and burgeoning concept.Because experiments are expensive and time-consuming, computational approaches have been developed for tumorous drug response study, which are attracting more and more researchers’ attentions.As is known, the training dataset and the feature of samples are vitally important to machine learning models. To solve the problem of different source domains of the training and test datasets, Turki et al.k-nearest neighbor (KNN) for inference of drug response in cancer cell lines. The results revealed the ability of the ensemble models to improve the prediction accuracy.Ensemble learning has been widely used in prediction problems of bioinformatics such as disease-specific risk variant predictionWithin machine learning methods, matrix factorization is an efficient class of models for drug response inference. For example, Ammad-ud-din et al.Machine learning models could infer drug response in cell lines in large scale, which often neglected the topological information of drug similarity and cell line similarity. The network-based models could remedy the limitation. Shivakumar et al.p-nearest neighbor graphs that were constructed for drug and cell line, respectively, based on the KNN algorithm. The sparsified similarity matrices were then used to regularize the latent matrices learnt from matrix factorization together with Tikhonov regularization. Consequently, the latent drug vectors and latent cell line vectors could be obtained, respectively, through an alternative update operation. The predicted response matrix was finally computed via multiplying the two low-rank latent matrices. The performance of our model was evaluated in the 10-fold cross-validation through calculating the PCC and root-mean-square error (RMSE) between predicted values and observed values in two datasets, CCLE and GDSC. The results demonstrated the superiority of WGRMF compared with SRMF, which directly used the drug similarity and cell line similarity as regularization terms in the matrix factorization model.Previous computational methods often have their own limitations. For instance, some machine learning-based models predict only binary results; i.e., they classify cell line-drug pairs into sensitive or resistant groups. Some network-based models could be used to predict new drug-cell line associations, but cannot give the precision response values. And some other models have the need to improve their prediction accuracy. In this paper, we presented a novel approach to infer drug response in cancer cell lines using the weighted graph regularized matrix factorization (WGRMF) algorithm. For drug similarity and cell line similarity, a sparsification technique was operated using the r and nd is the total number of known response values for the query drug. The larger the PCC value is, the more accurate the prediction is. The RMSE stands for the deviation of predicted values from observed values, which is expected to be small. The RMSE for a drug could be calculated asWe utilized 10-fold cross-validation to evaluate the performance of the proposed method in the CCLE dataset and GDSC dataset, respectively. Specifically, the known response values were randomly divided into 10 subsets with equal size. Then, one subset was left in turn as the test set and the other nine were combined as the training set. We can get the predicted response values of the test set through implementing WGRMF learnt from the training set. As soon as the loop ends, each of the 10 subsets was considered as the test sample one after another, and we could obtain the predicted value as the counterpart of each known response value. To improve the reliability of the results, the whole process of cross-validation was repeated a hundred times to estimate the average performance of the model. Based on the predicted and observed response data, we calculated the PCC and RMSE for each drug to estimate the capability of WGRMF on predicting drug response in cell lines. The PCC value indicates the extent of correlation between the predicted and observed response profiles of a drug, which could be formulated asIn further estimation of the WGRMF performance, we focused on the sensitive and resistant cell lines for each drug. First, we ranked cell lines for each drug according to the response values and split them into four equal parts. Then the first and last parts were selected to compose the sensitive and resistant cell line set of each drug. Consequently, we could obtain the PCC and RMSE of sensitive and resistant cell lines for each drug, as well as the average values of PCC and RMSE.For convenience, we used PCC and RMSE to indicate the results from all cell lines, and PCCsr and RMSEsr to indicate the results from sensitive and resistant cell lines. As a result, the WGRMF got PCC, RMSE, PCCsr, and RMSEsr averaged over 23 drugs in CCLE as 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. For data in GDSC, the results of WGRMF are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr averaged over 135 drugs. To make a comparison with SRMF, the same process of 10-fold cross-validation was carried out using SRMF on both CCLE and GDSC datasets. It is worth noting that all of the above results refer to the average values and the corresponding SDs for a hundred experiments of cross-validation. The overall results of comparing WGRMF with SRMF can be seen from Furthermore, in order to inspect how the WGRMF performed on individual drugs, we selected drugs targeting genes in the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway from the GDSC dataset as examples. PI3K is well known as a signaling component downstream of receptor tyrosine kinases (RTKs), which plays important roles in various biological responses.In this work, we have used all existing response values in the GDSC dataset to train the model, which was sequentially adopted to predict the response values for those drug-cell line pairs without known response values. Then, we ranked the cell lines for each drug in GDSC according to the predicted response values, and the top 20 most likely sensitive cell lines were selected for each drug. The ranked and selected result could be obtained from In the first case of consistency identification, we made a comparative analysis between the predicted and the observed responses of four drugs, based on several gene mutation profiles of cell lines. Lapatinib, known as a small-molecule kinase inhibitor, could target the EGFR gene and Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) gene, which was approved by the US Food and Drug Administration (FDA) in 2007 for the therapy of breast cancer patients.In the second case study, we used the predicted responses of WGRMF integrated with the known data in GDSC to infer potential drug-cancer gene associations. For instance, PHA-665752 was such a small molecule that it could act as the ATP competitive inhibitor of the catalytic activity of c-Met kinase.Furthermore, as the third case study, we used the predicted responses of WGRMF to reposition drugs on specific cancer through combining the existing responses. For a given drug, we compared the responses of cell lines with a specific tissue type with the responses of other cell lines, which would help to find some useful information of the drug sensitivity on the specific cancer type. Lung cancer is one of the most common malignant tumors in the world, of which about 80% is NSCLC.p-nearest neighbor graphs for drug and cell line, respectively. Then based on the low-rank approximation (LRA), together with Tikhonov regularization, drug graph regularization, and cell line graph regularization, the objective function of WGRMF was constructed, which could be solved through an alternative update rule to obtain the latent feature matrices for drug and cell line. The predicted response matrix was finally composed by the two latent matrices. To evaluate the performance of WGRMF, we implemented 10-fold cross-validation on two different datasets, GDSC and CCLE. The averaged PCC, RMSE, PCCsr, and RMSEsr over all drugs in each dataset were calculated as the measurement of model performance. In addition, several case studies based on the GDSC dataset were carried out. Just like its good performance on other issues, the WGRMF method has shown its excellent activity in predicting novel drug responses in cancer cell lines, which is better than SRMF in comparison.The work to investigate drug response in cancer cell lines is crucial for the precision therapy of cancer patients. In this work, we presented a novel method to utilize WGRMF for inferring anticancer drug response in cell lines, which combined the information of drug similarity, cell line similarity, and known drug responses in cell lines. A sparsification technique was employed on the similarity matrices to construct the p-nearest neighbor graphs of drug and cell line. Besides, WGRMF took full advantage of neighborhood information via performing graph regularization. Furthermore, the sparsification process could help to eliminate the redundant and noisy information in the similarity matrices. Finally, the introduction of weight matrix into the objective function made it possible to consider the contribution from known responses only rather than from those unknown ones.There are several factors that could contribute to the superior performance of WGRMF. First of all, WGRMF was constructed on the local invariance assumption; that is, two close points in the original space will be close in the learnt manifolds in latent space. The postulate was guaranteed by the graph regularization with There are still some limitations existing in the current model. For example, the sparsification process and the LRA-based graph regularization have many parameters to choose or confirm. It is still difficult to optimize these parameters, which limits the application of this method. In addition, the cell line similarity was constructed only on the information of gene expression. If more genomic information of cell lines is incorporated in the cell line similarity, the prediction efficiency will be improved. In fact, some studies have demonstrated the effectiveness and robustness of the network framework based on diverse cancer hallmarks in cancer researches.https://www.cancerrxgene.org/downloads), including 652 cancer cell lines, 135 drugs, and 70,676 known response values.50 value that indicates the drug concentration for 50% inhibition in vitro. The more sensitive cell lines will get lower IC50 values to a drug. Furthermore, for the involved drugs and cell lines, we need to characterize them with some features. Thus, we adopted the PubChem fingerprint descriptors as the features of the 135 drugs, which could be acquired from https://pubchem.ncbi.nlm.nih.gov. For those 652 cell lines, the gene expression profiles were taken to characterize them. Then, based on the features of drugs and cell lines, we can compute the drug similarity matrix and the cell line similarity matrix, respectively. Motivated by the method used in SRMF,The first dataset we used was collected from Genomic of Drug Sensitivity in Cancer project , which contains 23 drugs and 491 cell lines with 10,870 known responses.The second dataset was collected from the CCLE , W = 1, otherwise W = 0; ith and jth rows of latent matrices A and B, respectively. Each term in the objective function reflects a different goal of the problem. The first term is to make the predicted result close to known response data. The second one, called Tikhonov regularization, is to minimize the norms of latent matrices A and B. The third one indicates the graph regularization of drugs, with the purpose to make two neighbor drugs nearest in the latent space. The last term has the similar meaning to the third one, which is the graph regularization of cell lines. According to some previous studies,L denotes the objective function in A and B row by row when the updates are converged. The predicted response matrix can be correspondingly computed as follows:In order to eliminate the effect of overfitting and exclude the contribution from unknown responses in matrix x W into min A,B‖W studies,min A,B‖W follows:min A,B‖Wp was set to p = 20 for both drug and cell line. The dimension parameter of latent matrix k was chosen from {50, 100, min}. The three parameters, p in the sparsification process was set as p = 10 for both drug and cell line. The latent space dimension k was set as k = min, because the value of min was <50.We now show how to set the hyper-parameters used in the method in cross-validation and prediction. For the GDSC dataset, the sparsification parameter N.-N.G. implemented the experiments, analyzed the result, and wrote the paper. X.C. conceived the project, developed the prediction method, designed the experiments, analyzed the result, revised the paper, and supervised the project. X.P., J.-Q.L., Y.Z., and C.-C.W. analyzed the result and revised the paper.The authors declare no competing interests.


Anthracycline-containing regimens, namely cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) or CHOEP (CHOP + etoposide), represent the current standard of care for patients with newly diagnosed peripheral T-cell lymphomas (PTCLs), although responses are unsatisfactory. In this study, we investigated the pathways able to mitigate the sensitivity to CHOP-based regimens in preclinical models of T-cell lymphoma (TCL) to select agents for the development of CHOP-based drug combinations.We performed gene expression profiling of malignant T-cell lines exposed to CHOEP; flow cytometry was employed to study the effects of drug combinations on cell viability, cell cycle distribution, apoptosis and mitochondrial membrane depolarisation. Western blot analyses were performed to study cell signalling downstream of the T-cell receptor and apoptosis. The in vivo effect of the drug combination was tested in xenograft models.We highlighted a modulation of tyrosine kinases belonging to the T-cell receptor pathway upon chemotherapy that provided the rationale for combining the tyrosine kinase inhibitor dasatinib with CHOEP. Dasatinib improves CHOEP activity and reduces viability in vitro. Furthermore, combination treatment results in tumour growth inhibition in in vivo xenograft mouse models.Our data provide the rationale for clinical testing of the dasatinib–CHOEP combination in patients with T-cell lymphoma. Nonetheless, ~30% of PTCL cases fail to be classified into the known subtypes and, based on exclusion criteria, they are categorised as PTCL not otherwise specified (PTCL-NOS).2 Cases of PTCL tend to have an aggressive clinical course, with poor responses to conventional chemotherapy and poor long-term survival.3 Anthracycline-based chemotherapy programs, predominantly cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) or CHOEP (CHOP + etoposide), are widely used in the upfront treatment of PTCLs, despite suboptimal results.6 Neither intensified/escalated chemotherapeutic approaches8 nor the addition of monoclonal antibodies9 have demonstrated a clear advantage in response rates and survival.Mature T-cell lymphomas (TCLs) are a phenotypically heterogeneous group of malignancies representing 10–15% of non-Hodgkin lymphomas (NHL). They comprise different cutaneous, extranodal and nodal entities that are often referred to as peripheral T-cell lymphomas (PTCL) to distinguish them from immature T-cell neoplasms.10 However, 25–30% of patients do not become transplant eligible due to primary refractory or early progressive disease, have very poor prognosis with few therapeutic options. Alternative approaches as well as new molecular druggable targets need to be identified to induce CR after first-line therapy. We speculated that the identification of pathways able to mitigate the sensitivity to CHOP-based regimens, might help selecting agents to develop rational CHOP-based drug combinations to be rapidly translated to the clinical setting.We and others have recently demonstrated that the achievement of complete response (CR) before stem cell transplantation is a prerequisite for long-term disease control.11 was combined with CHOEP in vitro and in vivo. Given dasatinib efficacy and tolerability when administered for the treatment of chronic myeloid leukaemia (CML) and acute lymphoblastic leukaemia (ALL),12 our results suggest that dasatinib could be used in combination with anthracycline-based programs in the upfront treatment of TCLs to improve outcome.To address this issue, we performed gene and protein expression analyses in a panel of malignant T-cell lines treated with CHOEP and we found that chemotherapy modulates the T-cell receptor (TCR) pathway, resulting in enhanced expression of genes encoding for pro-survival proteins and the upregulation of tyrosine kinase activity. In line with this finding, we found a cooperative effect when the tyrosine kinase inhibitor (TKI) dasatinib2 at 37 °C, in the RPMI 1640 medium  supplemented with 10% FBS, 1% L-glutamine and 100 U/ml pen/strep. Healthy T lymphocytes were obtained from the peripheral blood of healthy volunteer donors isolated upon density-gradient centrifugation and separation with the autoMACS Pro separator  using CD3 Micro Beads (Miltenyi Biotec) following the manufacturer’s instructions. CHOP was prepared as described in ref. 13: cyclophosphamide monohydrate 5.84 pM (C), doxorubicin hydrochloride 1.5 pM (H), vincristine sulfate 260 pM (O) and prednisone 1 µM (P). All drugs, including dasatinib, sorafenib and ruxolitinib, were obtained from Selleck Chemicals .HH, HD-MAR-2, JURKAT, SUP-T1 and KARPAS-299 were obtained from the German Collection of Microorganisms and Cell Cultures . OCI-Ly12 cell line was a generous gift from Dr Leandro Cerchietti . Cells were grown in a humidified chamber with 5% CO20 to fivefold IC50). The dose was increased after at least three passages. Simultaneously, the parental cell line was grown without treatment to preserve the sensitivity to CHOEP. Experiments reported were performed using cells resistant to twofold IC50.JURKAT cells resistant to CHOEP were established by culturing the JURKAT cell line in the appropriate medium supplemented with increasing concentrations of CHOEP  solution (2 µg/ml)  and the count of viable cells (PI negative) was performed using the flow cytometer MACSQuant Analyzer (Miltenyi Biotec).To test cell viability, cells were plated at a density of 5 × 10For cell cycle analyses, cells were fixed in 70% ethanol and stained with 2 µg/ml PI. Cell cycle distribution was measured by flow cytometry .For assessment of apoptosis cells were stained with Annexin V and PI  following the manufacturer’s instructions and analysed by flow cytometry . Mitochondrial membrane depolarisation was assayed using the fluorescent probe tetramethylrhodamine ethyl ester-TMRE  following the manufacturer’s instructions and measured by flow cytometry .All data were analysed using the MACSQuantify software version 2.6 (Miltenyi Biotec).50 CHOEP (CHOP + etoposide) for 24 h. Time and dose were selected to better appreciate possible modulations of genes’ expression. Upon harvesting, RNA was extracted using the RNeasy Mini kit , quantified using Qubit 2.0 (Thermo Fisher Scientific) and assessed for integrity with Bioanalyzer 2100 . Biotinylated complementary RNA  was hybridised to HumanHT-12 v4 Expression BeadChips , and raw data were retrieved using Illumina BeadStudio software version 3.8. The data were then processed using R/Bioconductor software (version 2.13). The lumi package14 was employed for robust spline normalisation and log2 transformation. The limma package15 was used for differential expression analysis. Genes with a fold change (FC) ≤ −2 or ≥ 2 and a Benjamini–Hochberg false discovery rate (FDR) < 0.05 were selected as differentially expressed. Functional annotation of differentially expressed genes was performed using DAVID Bioinformatics Resources version 6.817 (https://david.ncifcrf.gov). An enrichment p-value < 0.05 was considered statistically significant. Gene expression data have been deposited in the NCBI Gene Expression Omnibus database, accession number GSE129289.HH, HD-MAR-2, JURKAT, SUP-T1 and KARPAS-299 cells were exposed to ICCells were lysed with Cell Lysis Buffer  following the manufacturer’s instructions. Proteins were quantified using the Qubit 2.0 fluorimeter and the Qubit Protein Assay kit (Thermo Fisher Scientific). Equal amounts of proteins were subjected to gel electrophoresis using the NuPAGE system (Thermo Fisher Scientific). Proteins were transferred to a nitrocellulose membrane, which was blocked with 4% western milk in TBS-TWEEN 0.1% to saturate the non-specific sites. Incubation with primary antibodies was performed overnight at 4 °C, following the manufacturer’s instructions. Antibodies used were phospho-tyrosine (P-Tyr-100) #9411, phospho-SRC family (Tyr416) #2101, SRC family #2108, phospho-AKT (Ser473) #9271, phospho-ERK1/2 (Thr202/Tyr204) #9101, cleaved caspase 3 (Asp175) #9661; cleaved caspase 9 (Asp315) #9505 , β-actin #A2066, Vinculin #V9131 (Millipore Sigma). Proteins were visualised with ECL reagent .Proteome Profiler Human Phospho-Kinase Array Kit  was used following the manufacturer’s instructions. Briefly, the array membranes (A and B), spotted with duplicates of antibodies which recognise different phospho-proteins, were incubated with 500 μg of cell lysates. A cocktail of biotinylated antibodies was added, then streptavidin–HRP incubation was performed. Chemiluminescent detection reagents provided were used to reveal the amount of phosphorylated protein bound to the membrane. Signals detected were quantified using the open source software ImageJ. The signal of negative control was subtracted from each spot, and values were normalised on positive control present in each membrane. Then, a ratio of signal intensity (treated/untreated) was calculated.RHOA and FYN sequences containing the nucleotides of interest were amplified by PCR using the following primers: RHOA_for GCCCCATGGTTACCAAAGCA; RHOA_rev GCTTTCCATCCACCTCGATA; FYN-SH2_for ACAGGACTCCACTCACAAGG; FYN-SH2_rev ACTTGGCCGAAAAGATGCTG; FYN-CT_for TGAGCTCATGATCCACTGCT; FYN-CT_rev CTGGCTACGGAATTGAAAGC. FYN-SH2 amplicons contain the coding sequences of Leu174 and Arg176, FYN-CT amplicons of Tyr531. Oligonucleotides were purchased from Metabion . Amplified DNA was subjected to Sanger sequencing to verify the presence of missense mutations.Genomic DNA was extracted from TCL cell lines using the Nucleospin Tissue kit  and quantified using Qubit 2.0 and the Qubit DNA HS Assay kit (Thermo Fisher Scientific). 7 cells (HD-MAR-2 or OCI-Ly12) in 50% Matrigel  were subcutaneously injected into the flanks of 5- to 7-week-old NOD/SCID mice . Treatment was initiated when tumour volume reached 150–200 mm3.15 × 102O per OS. (ii) Dasatinib (DA) cohort: 30 mg/kg per OS, 5 days for 2 weeks. (iii) CHOEP cohort: one 5-days single cycle of CHOEP. Cyclophosphamide day 1, 40 mg/kg i.v.; doxorubicin day 1, 3.3 mg/kg i.v.; vincristine day 1, 0.5 mg/kg i.v.; prednisone from day 1 to day 5, 0.2 mg/kg per OS; etoposide day 1 and day 3, 3.3 mg/kg i.v. (iv) DA + CHOEP cohort: cyclophosphamide day 1, 40 mg/kg i.v.; doxorubicin day 1, 3.3 mg/kg i.v.; vincristine day 1, 0.5 mg/kg i.v.; prednisone from day 1 to day 5, 0.2 mg/kg per OS; etoposide day 1 and day 3, 3.3 mg/kg i.v., dasatinib from day 1 to day 5 and from day 8 to day 12, 30 mg/kg per OS.Mice were divided into four cohorts of 8–10 mice per cohort and treated as follows: (i) control cohort (ctrl): 0.2 ml of saline i.v. e 0.2 ml of H20 The doses of etoposide and dasatinib used are lower than the MTD.23 The treatment scheme was designed to mimic the one used in patients.The doses of cyclophosphamide, doxorubicin, vincristine and prednisone used were reported to be the maximum tolerated doses (MTD) in mice.n = 8, DA n = 8, CHOEP n = 7, DA + CHOEP n = 9. HD-MAR-2-injected mice per cohort: ctrl n = 8, DA n = 8, CHOEP n = 8, DA + CHOEP n = 8.OCI-Ly12-injected mice per cohort: ctrl 2)/2, where a and b are the two largest perpendicular axes of the tumour nodule. At the end of the experiments, mice were euthanized by cervical dislocation. After sacrifice, tumour nodules were collected and formalin-fixed/paraffin-embedded (FFPE) for immunohistochemistry (IHC) analyses. Tumour growth inhibition (TGI) was defined as (1-(T/C)) × 100, where T and C represent the mean tumour weight in the treated and control groups, respectively. The study was conducted according to the Animal Research Reporting In Vivo Experiments (ARRIVE) requirements (Supplementary Methods).Cyclophosphamide, doxorubicin, vincristine and etoposide were diluted in saline and intravenously injected. Prednisone and dasatinib were diluted in water and administered per OS using an oral gavage. Tumour size and body weight were measured every 2 or 3 days. Tumour weights were calculated as g =  in an automated immunostainer (Dako Autostainer System), following the manufacturers’ instructions. Glass slides were then digitalised using the Aperio ScanScope XT  slide scanner. Image files were subjected to analyses, using the Aperio ImageScope software, version 11.1.2.760.P < 0.05. Analyses of gene expression were performed as described.All values were expressed as the mean ± standard deviation (SD), unless otherwise indicated. Graphs and statistical analyses were performed using GraphPad Prism 5 software (GraphPad Inc) and R software version 3.3.3. One-way ANOVA test was used for cell viability, Annexin V, TMRE assays and to assess significance of IHC staining. Tukey post hoc test was applied. Two-way ANOVA test was applied to cell cycle analyses and in vivo experiments. Comparisons were performed using Bonferroni’s multiple comparison test. The differences were considered significant at 13 Escalating doses of CHOP were used, and viable cell counting performed by flow cytometry. CHOP significantly inhibited the proliferation of the cell lines in a dose-dependent and time-dependent manner with the best activity seen after 48 h of incubation and comparable half-maximal inhibitory concentration values (IC50) at this time point for all cell lines (not shown). Thereafter sensitivity to etoposide was determined: IC50 values were comparable for all cell lines (range 0.299 µM – 0.412 µM), with the exception of HH (0.089 µM) (not shown).We first assessed the sensitivity of a panel of malignant T-cell lines  to CHOP, a combination of drugs reflecting the regimen used in patients.50 for all cell lines) to CHOP (CHOEP 1 × ) strongly improves the anti-proliferative activity , with HH cells being the most sensitive and HD-MAR-2 the least sensitive , its effects on cell proliferation were observed only at micromolar concentrations . However, when we combined cyclophosphamide (C) with doxorubicin (H), vincristine (O) or prednisone (P), all combinations  were slightly more active than the single agents in reducing the cell growth (p:ns), with the best efficacy achieved by combining all drugs . The effect was further augmented by the addition of etoposide (CHOP vs CHOEP p < 0.001)  Fig. S.p < 0.001) and decreased the S-phase in SUP-T1  and KARPAS-299 cells . Exposure to CHOEP for up to 48 h gave more remarkable effects in all cell lines: G0/G1 cell cycle arrest was induced in HD-MAR-2, SUP-T1 and HH, at the expense of the S and G2/M phases , while a decrease was observed in JURKAT cells (mean of 17.4%). As expected, CHOEP modulation of cell cycle was associated with increased apoptotic and necrotic cell death that was more significant at 48 h of CHOEP exposure in HD-MAR-2, SUP-T1, JURKAT and HH , but not in KARPAS-299 , protein kinase C zeta (PRKCZ), proline-rich protein 5 (PRR5) and GRB2-related adapter protein 2 (GRAP2). Of note, GRAP2 encodes for a member of the Grb2 family of adaptor proteins that recruits signalling molecules leading to TCR activation.27 In addition, among genes of the “cell surface receptor signalling” pathway, CD53 and CD160 are upregulated by CHOEP treatment. CD160 plays a role in regulating the activation of TCR proximal signalling interacting with the tyrosine kinase Lck and promoting the PI3K/AKT pathway.30To gain insights into the molecular mechanisms underlying CHOEP effects and to unveil specific genes and pathways modified by CHOEP exposure, the gene expression profiles of untreated controls and CHOEP-treated cell lines were compared. After differential expression analysis, two lists of 73 significantly upregulated genes and 17 significantly downregulated genes were obtained . Representative phospho-array analysis of HD-MAR-2 treated cells (the least sensitive cell line to CHOEP), identified several pathways upregulated by CHOEP. These include the AKT/mTOR pathway (TOR and AKT kinases), MAPK signalling pathway , SRC family kinase pathway (Lck and Fgr) and components of the β-catenin pathway . In addition, several key transcription factors were upregulated, including STAT1, STAT3, STAT4, STAT5A/B, STAT6 and p53. Other signalling molecules that were upregulated include proteins of the DNA damage signalling pathway (Chk2 and p27) and the phospholipase C γ1 (PLCγ1), a key enzyme which links proximal and distal TCR signalling pathways32 CHOEP significantly inhibited the proliferation of OCI-Ly12  and induced tyrosine phosphorylation as detected by western blot analysis of treated cells .Collectively, these data suggest that survival signals from the TCR might contribute to determine chemotherapy sensitivity. To further investigate tyrosine kinase activity required for signal transduction through the TCR upon CHOEP treatment, western blot analysis using a pan phospho-tyrosine antibody was conducted. In all cell lines, CHOEP increased the level of tyrosine phosphorylation with different kinetics Fig. . This walls Fig. . Of notells Fig. . The modlls Fig. . No SFK lls Fig. . Nonethells Fig.  upon trells Fig. . These r33As all the above reported findings prompt to a role of the TCR signalling pathway activation in promoting pro-survival signals mainly mediated by SFKs in response to chemotherapy in TCL preclinical models, we hypothesise that the addition of a tyrosine kinase inhibitor to CHOEP might improve response rates of TCLs. To test this hypothesis, we first exposed cells to increasing concentrations of the TKI dasatinib (1.5–100 µM) for 48 h. A significant dose-dependent decrease in cell proliferation was detected in all cell lines Fig. , and dasp < 0.001), but not in KARPAS-299 cell line  compared with the exposure to single agents.To define the effects of dasatinib combined with CHOEP at the cellular level, we assessed the cell proliferation and cell death in cell lines treated with the dasatinib–CHOEP (DA + CHOEP) combination. As shown in Fig. Cell cycle analysis Fig.  indicate34 with potent activity against several receptor tyrosine kinases involved in tumour progression and tumour angiogenesis.35 Ruxolitinib is instead a selective JAK inhibitor recently approved for the treatment of primary and secondary myelofibrosis.36 Cell lines were exposed to escalating concentrations of sorafenib and ruxolitinib for 48 h, and relative ICs were obtained (Table p < 0.001). On the other hand, the addition of ruxolitinib (RU) to CHOEP did not significantly reduce cell proliferation in malignant T-cell lines with the exception of JURKAT cells   Fig. S that arep < 0.001) of HD-MAR-2, SUP-T1, JURKAT and OCI-Ly12 cell but not of KARPAS-299 and HH, as detected by staining cells with FITC–Annexin V and propidium iodide (PI) and flow cytometry analysis  but not in KARPAS-299 and HH. Interestingly, treatment with either DA or CHOEP alone reduced the percentage of TMRE-positive cells only in HD-MAR-2 and OCI-Ly12 cell lines. In addition, in KARPAS-299 and JURKAT cell lines dasatinib used either alone or in combination, induced caspase activation , whereas in SUP-T1 and OCI-Ly12 only the combination DA+CHOEP-induced caspase 9 and 3, respectively. In HH cell line, no caspase activation was detected when CHOEP and dasatinib were either used alone or combined  but not by dasatinib used alone  , the use of dasatinib alone  and the use of CHOEP , with tumour growth inhibition values of 10.2% (DA), 21.3% (CHOEP) and 75.7% (DA+CHOEP).The in vivo anti-tumour activity of the combination DA+CHOEP was investigated in two localised human tumour xenografts models in NOD/SCID mice developed using subcutaneous injection of HD-MAR-2 (the least sensitive cell line) and OCI-Ly12, the PTCL cell line, which in in vitro experiments was among the most sensitive to CHOEP. The in vivo growth of HD-MAR-2 tumour nodules was significantly affected by CHOEP and by the combined DA+CHOEP treatment  Fig. . The relns) Fig. . DespiteIn addition, immunohistochemical detection of CD31 in the tumours generated by OCI-Ly12 injection showed a significantly low microvessel density in the DA+CHOEP-treated group, as compared with the other cohorts of mice  Fig. .In an attempt to identify possible pro-survival mechanisms activated by TCLs to counteract the cell death pathways triggered by chemotherapy, we analysed the expression levels of genes induced by CHOEP treatment in a panel of TCL cell lines. Among others, CHOEP induced the upregulation of genes encoding for pro-survival proteins and for signalling molecules downstream of the TCR that was also associated with an increase in protein tyrosine phosphorylation. These results are in line with the idea that malignant T cells use growth and survival signals provided by the TCR.39 and by the presence of gene fusions, translocations, point mutations, small indels, as well as amplifications and deletions that lead to the constitutive activation of the TCR.44 In addition, the mutational status of TCR-related genes and the engagement of TCR on tumour T cells seem to have clinical implications as they have been identified as distinguishing features of poor prognosis subgroups of PTCLs.46 Thus, pharmacologic inhibition of the TCR and/or of its associated kinases using multi-tyrosine kinase inhibitors (TKIs) might represent a strategy to augment response rates and decrease chemotherapy resistance.Consistent with this, several lines of evidence suggest that TCR activation might be as relevant to TCL pathogenesis as the constitutive B-cell receptor (BCR) signalling is known to promote neoplastic B-cell survival and expansion in B-cell lymphomas. This view is supported by the widespread expression of the TCR and of its associated kinases in different subtypes of PTCLs11 used for the treatment of chronic myeloid leukaemia (CML) and acute lymphoblastic leukaemia (ALL) has been tested as a single agent in a phase I/II study for relapsed/refractory NHL.12 Of note, the only complete responses were observed in two PTCL-NOS patients.47 Despite these encouraging results, dasatinib treatment for PTCL has not moved forward. This might be due to the fact that monotherapy with selective targeted agents is unlikely to prove fully effective when used alone and most anti-tumoural therapies are based on combinations.Dasatinib, an orally administered well-tolerated small molecule inhibitor of multiple tyrosine kinases33 SRC inhibition affects TCR ability to interact with the PIK3/AKT, RAS/MAPK and STAT3 pathways, and to modulate cell migration and invasion through interaction with integrins, the focal adhesion kinase (FAK), and regulators of the family of Rho-GTPases.49 Accordingly, sorafenib another oral inhibitor of several receptor tyrosine kinases involved in tumour progression and tumour angiogenesis approved for the treatment of advanced renal cell carcinoma and hepatocellular carcinoma,50 exerts similar effects as dasatinib on malignant T cells, whereas ruxolitinib a selective inhibitor of JAK1 and JAK2 does not. Collectively data presented clearly indicate that dasatinib warrants testing in combination with anthracycline-based programs for the upfront treatment of TCLs to improve outcome.Here, we provide preclinical evidence that the anti-tumour efficacy of the combination of dasatinib and the anthracycline-based regimen CHOEP is augmented when compared with either chemotherapy or dasatinib as monotherapy. The effect is mediated by the induction of cell cycle perturbations, mitochondrial injury and caspase-dependent apoptosis in malignant T-cell lines. These findings were also confirmed in animal models where no toxic effects were observed. Of interest, the addition of dasatinib to CHOEP improves efficacy in any T-cell lymphoma and leukaemia model used irrespective of: (i) the biological differences among histologies; (ii) the response to CHOEP administered alone; (iii) the presence of activating mutations in genes of the TCR signalling pathway. This broad activity demonstrated by the DA+CHOEP combination can be explained by the fact that dasatinib targets many of the signalling pathways subverted in T-cell oncogenesis such as ABL, the SRC family kinases (SFKs), PDGFR alpha and beta.Supplementary Files


Artemisia aucheri extracts were reported to have various biological activities including a cytotoxic effect on some cancer cell lines. We investigated the antiproliferative activity of isolated sesquiterpenoids from petroleum ether extract of Artemisia aucheri (A. aucheri) aerial parts on SK-N-MC, MCF-7, and A2780 cell lines. Phytochemicals from the petroleum ether cold macerated extract were isolated using normal phase vacuum liquid chromatography and high pressure liquid chromatography (VLC and HPLC) and the structures of the components were determined by spectroscopic means. Cell viability was determined by 3--2,5-diphenyltetrazolium bromide assay. Activation of caspases-3 and -9 was evaluated using a spectrophotometer. Mitochondrial membrane potential (MMP) was measured using rhodamine 123 fluorescent dye. Two tetrahydrofuran- type sesquiterpenoids, hydroperoxide of davanone (1) and hydroxydavanone (2) were isolated and characterized. Between these compounds, compound 1 exhibited more potent activity against the MCF-7, SK-N-MC and A2780 cell lines with IC50 values of 8.45 ± 0.81 µg/mL, 9.60 ± 1.32 µg/mL and 10.9 ± 2.03 µg/mL in A2780, MCF-7 and SK-N-MC cells, respectively. Compound 1 inhibited the growth of human cancer cells by induction of apoptosis. To the best of our knowledge, this is the first comprehensive study on cytotoxic and apoptotic mechanism of two davanone derivatives isolated from A. aucheri in human cancer cells. Overall, our data suggest that hydroperoxide of davanone (1) should be further studied in-vivo as a potential antitumor agent.Different types of  Artemisia aucheri Boiss. (Asteraceae) with Persian name of “Dermaneye koohi” is regarded as a wildly growing species in Iran . Silica gel 60 F-254  was used for TLC analyses. Spots were detected by spraying cerium sulfate/molybdate reagent followed by heating (110 °C for 5 min). The chromatographic system for semi preparative HPLC consisted of a YL9111S binary pump, a YL9160 PDA detector and a Eurospher II 100-10 Si  column . NMR spectra were recorded on a Bruker Avance 500 MHz spectrometer and chemical shifts are given in δ (ppm). Mass analyses were performed with an Agilent 6410 Series mass spectrometer equipped with an electrospray ionization source. Reagents and chemicalsAll solvents used for extraction (petroleum ether 40-60), fractionation and further purification  were purchased from Caledon (Canada) and Scharlau (Spain). 3--2,5 – diphenyltetrazolium bromide (MTT), rhodamine 123, and caspases activity detection kit were procured from Sigma Aldrich (USA). Cell culture medium, penicillin–streptomycin solution, and fetal bovine serum (FBS) were purchased from Gibco (USA). Biorad protein assay kit was obtained from Sigma (USA). All tissue culture wells were from Becton Dickinson (USA).Cell culture conditions 2. The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM-F12) with 10% (v/v) heat-inactivated fetal bovine serum, 100 UmL−1 penicillin and 100 mgmL−1 streptomycin.MCF-7 (Human breast carcinoma), SK-N-MC (Human neuroblastoma), and A2780 (human ovarian carcinoma) cell lines were obtained from Pasteur Institute  and maintained at 37 ºC in a humidified atmosphere (90%) containing 5% COPlant materialA. aucheri were collected from Chahar-Bagh region, Golestan province, Iran, in September 2011. The plant was identified by Mr. S. A. Hosseini , Gorgan, Iran. A morphological comparison between collected sample and deposited voucher specimen (No. 2383) was accomplished.Aerial parts of Extraction and isolationA. aucheri (330 g) were exhaustively extracted with Petroleum ether (40-60) at room temperature. After removal of solvent in-vacuo, the residue (3.51 g) was partitioned using vacuum liquid chromatography (VLC) with increasing polarity of solvent from n- heptane to ethyl acetate (EtoAc). Aliquots with similar TLC profile were combined to give 9 fractions. The air dried aerial parts of The most potent cytotoxic fractions  were subCytotoxicity assay 50 values were calculated by plotting log10 of the percentage of proliferation versus drug concentration. Cytotoxic effects of the isolated compounds were studied in MCF-7, SK-N-MC and A2780 cell lines by MTT assay. Exponentially growing mammalian cells (on 96-well plates) were exposed for 24 h to different concentrations (0-100 µg/mL) of compounds 1 and 2. The control cells were kept in medium containing 0.1% DMSO. After 24 h of incubation, the medium was removed and 0.1 mg/well of MTT were added to the cells, and the plates were further incubated for 3 h at 37 °C. The formazan crystals were solubilized in 0.1 mL of dimethyl sulfoxide and the optical density (OD570) was measured using a microplate reader . ICMeasurement of mitochondrial membrane potentialMitochondrial membrane potential (MMP) was evaluated by rhodamine 123 fluorescent dye. Depolarization of MMP during cell apoptosis results in the loss of rhodamine 123 from the mitochondria and a decrease in intracellular fluorescence intensity , 28. AftDetermination of Caspase-3 and -9 activities p-nitroaniline (pNA) moiety. Briefly, 1 × 106 cells were collected and lysed with 50 μL of chilled lysis buffer and incubated on ice for 10 min. The cell lysates were centrifuged at maximum speed for 5 min at 4 °C. Then 10 μL of cell lysate was combined with an equal amount of substrate reaction buffer containing a caspase-3 and 9 colorimetric substrates. After 2 h of incubation at 37 °C, the pNA light emission was quantified using a microplate reader at 405 nm . Comparison of the absorbance of pNA from an apoptotic sample with an un-induced control allowed determination of the fold increase in caspase-3 and 9 activities. The protein content was determined by the Bradford method using the bovine serum albumin as a standard.The caspase-3 and -9 activities were measured by commercial caspase assay kit , according to the manufacturer’s protocol. The kit is based on the hydrolysis of the peptide substrate Ac-DEVD-pNA (for caspase-3), and Ac-LEHD-pNA (for caspase-9) resulting in the release of the Isolation and structure elucidation of compounds 1 and 2A. aucheri two terpenoids including hydroperoxide and hydroxyl derivatives of davanone were isolated. Isolated compounds  (1)1H-NMR : δ 8.02 , 6.86 , 6.40 , 5.90 , 5.18 , 4.99 , 4.16 , 2.96 , 2.03 , 1.88 , 1.76 , 1.68 ,1.39 , 1.39 , 1.25 , 1.05 . ESIMS: m/z 269 [M + H]+, 291 [M+ Na]+Hydroxydavanone (2)1H-NMR : δ 6.92 , 6.42 , 5.90 , 5.18 , 4.97 , 4.22 , 2.94 , 2.01 , 1.89 , 1.75 , 1.66 , 1.38 , 1.38 , 1.26 , 1.04 . ESIMS: m/z 253 [M + H]+, 275 [M+ Na]+ Cytotoxic activities of the compoundsin-vitro anti-cancer efficacy of the compounds, the potency of isolated compounds to induce cell death was determined on SK-N-MC, MCF-7, and A2780 cell lines under MTT method. As shown in 50 values of 8.45 ± 0.81 µg/mL, 9.60 ± 1.32 µg/mL and 10.9 ± 2.03 µg/mL in A2780, MCF-7, and SK-N-MC cells, respectively. These values are below 20 µg/mL, which indicate that compound 1 potentially presents an interesting cytotoxic activity towards three human carcinoma cell lines (31). Compound 2 can be accepted as potent cytotoxic agents against SK-N-MC because its IC50 value (IC50 = 10 ± 0.91 µg/mL) is below 20 μg/mL. Moreover, it showed moderate cytotoxic effect against MCF- 7 (IC50 = 25.4 ± 4.21 µg/mL) and A2780 (IC50 = 41.69 ± 2.87 µg/mL) cell lines. To examine the Effect of isolated compounds on mitochondrial membrane potential (MMP)To characterize the changes in mitochondrial events induced by treatments with isolated compounds, the collapse of MMP in SK-N-MC, A2780 and MCF-7 cell lines were monitored with the rhodamine 123.Decrease of mitochondrial membrane potential caused by the compound 1- induced damage of the outer membrane resulted in the loss of the dye from the mitochondria and a decrease in intracellular fluorescence, so that compound 1 signiﬁcantly decreased MMP in three human carcinoma cell lines. Moreover, the result indicated that compound 2 was not able to change MMP in any studied cell lines .Effect of isolated compounds on caspase 3 and 9 activities 50 concentration of compound 2, caspase-3 activity in A2780 and MCF-7 cells was significantly increased. To determine which apoptotic pathway is activated by this compound, we evaluated the activation of caspase 9, the apical protease in intrinsic pathways. Compound 2 was not able to increase the activity of caspase 9 in above mentioned cell lines. In addition, the obtained results indicated that compound 1 considerably increases caspase-3 and -9 enzyme activities in A2780 and SK-N-MC cell lines while no increase in caspase -3 activity was shown after exposure to compound 1 in MCF-7 cell line. Activity of caspase-9 was found to increase significantly after 24 h exposure to IC50 concentration of compound 1 in MCF-7 cell line.As shown in A. aucheri. We first reported a cell growth inhibitory effect for isolates in human carcinoma cell lines. Exposure to the isolated sesquiterpenoids for 24 h resulted in dose-dependent inhibition of carcinoma cell growth, as demonstrated by MTT assay. Between the isolates, compound 1 showed higher inhibitory potency against the growth of human carcinoma cell lines. When exposed to cytotoxic agents, the cells might suffer death or a stop in their cell cycle, which in turn can trigger death by apoptosis if cells cannot overcome the damage (50 concentration of compound 2 increased caspase-3 activation in A2780 and MCF-7 cell lines. Additionally, compounds 1 increased activity of caspase 3 in A2780 and SK-N-MC cell lines, significantly. Mitochondria are double membrane-bound organelles that play significant roles in activating apoptosis through intrinsic pathway in mammalian cell (In this study, two tetrahydrofuran- type sesquiterpenoids (hydroperoxide of davanone  and hydre damage . Apoptose damage . Here, we damage . Our resian cell . During ian cell . The resArtemisia, petroleum ether extracts of A. ciniformis and A. aucheri have been reported to be the most potent cytotoxic samples against human erythroleukemic (Artemisia have been shown against different cell lines which human cervix adenocarcinoma (Artemisia genus (Artemisia genus, as well (A. aucheri and on cytotoxicity of the purified compounds against human cancerous cell lines. Among different extracts of various species of the genus leukemic , human nleukemic , respectarcinoma , human larcinoma , human carcinoma  are somearcinoma  and isolia genus , 42-48 aia genus  had been as well -50. To tA. aucheri. Our collective data suggest that hydroperoxide of davanone strongly inhibits cell growth of MCF-7, SK-N-MC, and A2780 cancer cells through induction of apoptosis. Hydroperoxide of davanone induces apoptosis by attenuation of MMP. This process subsequently leads to activate caspase cascade. The findings in this report indicate potential therapeutic value of hydroperoxide of davanone and further research in animal models is necessary to confirm its activity in-vivo.Two tetrahydrofuran- type sesquiterpenoids (hydroperoxide of davanone  and hydr


Gangliosides act as a surface marker at the outer cellular membrane and play key roles in cancer cell invasion and metastasis. Despite the biological importance of gangliosides, they have been still poorly characterized due to the lack of effective analytical tools. Herein, we performed molecular profiling and structural elucidation of intact gangliosides in various cell lines including CFPAC1, A549, NCI-H358, MCF7, and Caski. We identified and quantified a total of 76 gangliosides on cell membrane using C18 LC-MS/MS. Gangliosides found in each cell line exhibited high complexity and diversity both qualitatively and quantitatively. The most abundant species was GM3(d34:1) in CFPAC1, NCI-H358, and MCF7, while GM2(d34:1) and GM1(d34:1) were major components in A549 and Caski, respectively. Notably, glycan moieties showed more diversity between cancer cell lines than ceramide moieties. In addition, noncancerous pancreatic cell line (hTERT/HPNE) could be distinguished by gangliosides containing different levels of sialic acid compared with cancerous pancreatic cell line (CFPAC1). These results clearly demonstrated the feasibility of our analytical platform to comprehensive profile of cell surface gangliosides for identifying cell types and subgrouping cancer cell types. Cell membrane consists of a lipid bilayer including cholesterols, phospholipids, sphingolipids, and integral and peripheral proteins, where a substantial number of sphingolipids and proteins have been glycosylated . Cell suGanglioside is a type of glycosphingolipids, which is composed of a ceramide backbone and a glycan head with one or more sialic acids. The ceramide backbone moiety is anchored in the plasma membrane of cells, whereas the glycan head moiety is exposed on the extracellular surface. The gangliosides have been reported to play a key role in cancer cell invasion and metastasis in several types of cancer such as lung cancer ,9, melanNonetheless, of the biological importance of gangliosides, their characterization has been analytically challenging. Conventional approaches to characterize gangliosides which employ thin-layer chromatography (TLC) and immunoassay with antibodies and lectins have several limitations . In addiHerein, the present study combines cell membrane extraction method with nano-LC/MS analysis for comprehensive and comparative identification and quantitation of cell surface gangliosides from various cancer cell lines, CFPAC1, A549, NCI-H358, MCF7, and Caski. We identified over 70 gangliosides from cell lines in total with great confidence by considering accurate mass, tandem MS, and retention time. Using our approach, we determined compositional and structural diversity of surface gangliosides between different cancer cells. Interestingly, lung cancer subtypes, A549 and NCI-H358 were completely distinguished by ganglioside profile. Furthermore, gangliosides were regrouped into glycan and ceramide moieties respectively, and validated to determine the major effect on ganglioside differences between cell lines. Low correlation between cell lines based on glycan moiety compared with ceramide moiety clearly indicates that glycan moiety exhibits higher heterogeneity depending on cell types than ceramide moiety in ganglioside. We have no doubt that our result on comprehensive profiling of surface ganglioside in various cell types suggest that given analytical platform would provide valuable implications on discovery of cancer cell type-specific molecular targets for cancer biomarker and therapeutics.Cancer cell lines of A549 (lung cancer), NCI-H358 (lung cancer), MCF7 (breast cancer), Caski , and CFPAC1 (pancreatic cancer) and a normal pancreatic cell line, hTERT/HPNE were obtained from American Type Culture Collection . Cell cultures were performed using the ATCC-recommended growth media, subcultivation ratios, and medium renewal interval from ATCC animal cell culture guide which was downloaded from ATCC Web site . During 2CO3 (pH 11), and finally pelleted once more. The supernatant was removed, and the membrane fractions were frozen at −80 °C.Ultracentrifugation was performed for membrane extraction from the cells as followed. The cells were interrupted in a homogenization buffer consisting of 0.25 M sucrose, 20 mM HEPES-KOH (pH 7.4), and 1:100 protease inhibitor mixture . Then the samples were sonicated on ice. The nuclear fraction of the lysates was pelleted by centrifugation at 1000 g for 10 min, and the supernatant was collected. Following ultracentrifugation at 60,000 rpm for 45 min at 4 °C, the membrane fraction was pelleted. The membrane pellet was washed and re-suspended in 0.2 M Na2O/CHCl3/MeOH . The mixture was incubated in a bath sonicator for 30 min at 10 °C and centrifuged at 8800 g for 2 min at 4 °C. After centrifugation, the supernatants were transferred into a new vial. The pellet was re-extracted in the same volume of H2O/CHCl3/MeOH , CHCl3/MeOH , and CHCl3/MeOH . Each supernatant was pooled with that from first extraction, dried by a SpeedVac, and re-suspended in 1 mL of CHCl3/MeOH . For Folch partitioning − corresponding to sialic acid (NeuAc). On the other hand, we could obtain more abundant fragment ions via positive ion detection mode tandem MS/MS. As shown in m/z 520.50 corresponding to [d34:1]) including sphingosine base (m/z 264.26 corresponding to [d18:1]) were apparently obtained from positive ion detection mode. Cancer cell gangliosides were initially assigned based on the exact masses of possible combinations of glycan and ceramide compositions. Furthermore, we confirmed the assignment by identifying gangliosides using both CID MS/MS and their corresponding retention times on C18 column. To figure out the structure of gangliosides, all fragment ions including glycan such as hexose , fucose, N-acetylhexosamine , and N-acetylneuraminic acid, ceramide including fragmented sphingosine were considered. 2R = 0.9993) among GM1 compounds with one double bond in NCI-H358. A relative retention time was calculated by dividing RT of each ganglioside species by RT of GM3 (d34:1), which was selected as reference species due to it’s the highest relative abundance in NCI-H358. Mathematical models of relative retention time (RRT) versus the number of carbons in the ceramide for different glycan headgroups established from each cell line were shown in R2 ≤ 1.000). Furthermore, duplicate technical experiments with biological duplicates were performed to examine the reproducibility of the method on a run-to-run basis. Correlation between the relative intensity of ganglioside peaks presented high similarity (R2 = 0.901–0.996)  d. Finall1–0.996) . Qualitative and quantitative diversity of gangliosides found in each cell line such as hTERT/HPNE, CFPAC1, A549, NCI-H358, MCF7, and Caski could be observed solely in chromatograms . Major aThe relative abundance of individual ganglioside compositions was quantified in quadruplicates using the chromatographic peak area for overall quantitation of ganglioside from each cancer cell line. Then, we performed cluster analysis to investigate statistical relationship between the cell lines based on ganglioside profile a,b. All r = 0.084–1.000, r = 0.661–1.000, Gangliosides consist of a hydrophobic ceramide portion, which anchors the ganglioside into the plasma membrane, and a hydrophilic glycan portion containing one or more sialic acids . GanglioR2 = 0.214) showed that the membrane ganglioside profiles of these two cell lines were apparently different  using an identical method in order to compare with cancerous pancreatic cell line (CFPAC1). Regardless of identical origin of the cell, pancreas, ganglioside distribution between hTERT/HPNE and CFPAC1 were quite different . Three mifferent . In partAlthough refined classification and characterization of cancer cell types are essential to enhanced prediction of treatment responsiveness and development of targeted therapies , many caGangliosides readily change with physiological and pathophysiological processes such as cell growth, viral transformation, oncogenesis, or tumor progression ,45. PartThus, comprehensive structural profile of gangliosides using effective analytical platform should be the first step bridging the gap between descriptive analysis and mechanistic inquiry. However, many studies on gangliosides using conventional approach such as TLC could not provide comprehensive molecular profiling to date by being applicable to specific glycan epitopes. In this study, we utilized LC/MS/MS-based analytical platform to qualitatively and quantitatively identify unique cell surface, gangliosides at individual molecular level. We achieved the comprehensive profiling of intact gangliosides from membranes of various cell lines including different cancer cell subtypes and normal cell line and clearly demonstrated their differences for the first time using a highly reproducible analytical platform combining negative ion mode nano-LC/MS method with membrane enrichment technique. C18 based LC-MS enables separation of gangliosides with different degrees of unsaturation in their lipid portion . Thus, c