TP53-wild-type TCLs. ALRN-6924, a stapled peptide that blocks interactions between p53 and both MDM2 and MDMX has potent in vitro activity and superior in vivo activity across 8 different PDX models compared to the standard-of-care agent romidepsin. ALRN-6924 induced a complete remission in a patient with TP53-wild-type angioimmunoblastic T-cell lymphoma, demonstrating the potential for rapid translation of discoveries from subtype-specific preclinical models.T- and NK-cell lymphomas (TCL) are a heterogenous group of lymphoid malignancies with poor prognosis. In contrast to B-cell and myeloid malignancies, there are few preclinical models of TCLs, which has hampered the development of effective therapeutics. Here we establish and characterize preclinical models of TCL. We identify multiple vulnerabilities that are targetable with currently available agents  and demonstrate proof-of-principle for biomarker-driven therapies using patient-derived xenografts (PDXs). We show that MDM2 and MDMX are targetable vulnerabilities within  T- and NK-cell lymphomas (TCL) are a group of lymphoid malignancies characterized by poor prognosis, but the absence of appropriate pre-clinical models has hampered the development of effective therapies. Here the authors establish several pre-clinical models and identify vulnerabilities that could be further exploited to treat patients afflicted by these diseases. Emblematic of our poor understanding of PTCL biology, the most common subtype of PTCL is PTCL-NOS (not otherwise specified).T-cell lymphomas (TCLs) comprise approximately 10% of all non-Hodgkin lymphomas (NHLs) in Western countries. Two-thirds of these lymphomas are peripheral TCLs (PTCLs) and the remainder are cutaneous TCLs (CTCLs). Many patients with CTCL have an indolent course that can span decades. In contrast, nearly all PTCLs are aggressive lymphomas and include both αβ and γδ T-cell lymphomas2. Long-term survivors of some like hepatosplenic T-cell lymphoma (HS-TCL) are extremely uncommon2.Treatment of PTCLs to date has been largely derivative or empiric. CHOP-based regimens, which were adopted based on their activity in B-cell lymphomas, remain the standard for first-line therapy. Except for ALK-rearranged anaplastic large-cell lymphoma (ALK+ ALCL), over 75% of PTCLs fail to respond or relapse after first-line therapy in <2 years. PTCL-NOS, angioimmunoblastic T-cell lymphoma (AITL), ALK-negative (ALK−) ALCL, and NK-TCL, the most common subtypes of PTCL, are each associated with <35% 5-year overall survivalTP53-wild-type TCL, MDM2, and MDMX are prominent therapeutic targets. ALRN-6924 shows promising pre-clinical activity in TP53-wt cell lines and PDX models and induced a complete remission in a patient with AITL. Thus, we demonstrate the potential of pre-clinical target validation and drug testing in models of rare cancers with direct clinical translation.Current approaches to treat PTCL are simply inadequate. The National Comprehensive Cancer Network guidelines list clinical trial as the top choice for all patients with PTCL (other than ALK+ ALCL) in both the first-line and relapsed/refractory settings. Improved understanding of the pathobiology of TCLs could highlight therapeutic targets, but there is a severe lack of well-characterized cellular and in vivo models of these lymphomas. To address this, we here report a comprehensive characterization of a newly established panel of patient-derived xenografts (PDXs) from multiple subtypes of TCL and available TCL cell lines. We highlight multiple potentially targetable genetic alterations and functional vulnerabilities. In 3, we utilized PDXs in passage ≤2 and made these passages and affiliated data available through the Public Repository of Xenografts (www.PRoXe.org)4.We collected a panel of previously established cell lines of T- and NK-cell lymphomas and validated their immunophenotype and STR-profiles . We also sequenced 17 TCL PDX models  and 13 additional cell lines using RNA-seq and targeted sequencing panels for recurrently mutated genes  of 20 TCL lines using the algorithm established for the Cancer Cell Line Encyclopedia   were previously available. We created two T-PLL PDXs, and confirmed ATM mutations and TCL1 rearrangement that characterize this disease  Fig. 6, 7. Simase Fig. .CDKN2A and 17p13.1 that include TP53  TCL lines, whereas TP53 alterations occur in <20% of patients with untreated PTCL or CTCL8. Of note, none of the 17 PDX models had TP53 mutations .To identify copy number alterations, we applied the GISTIC (Genomic Identification of Significant Targets in Cancer) algorithm to whole exome sequencing data from 20 cell lines. We identified recurrent amplifications and deletions, including deletions of 9p21.3 that include DUSP22 or TP63 as recurrent events of prognostic significance in ALCL lacking ALK rearrangements9. RNA-Seq identified a DUSP22 fusion in the FEPD cell line and fusions of TP63 in 4 lines  variant  genes in AITL models  resulted in a list of 30 genes as top vulnerabilities  were adequately transducible with sufficient Cas9 activity for screening. We ranked T-cell lymphoma-specific dependencies by comparing to 383 additional lines screened using the same platformies Fig. . IRF4, S16 and STAT317. Our data demonstrate that cell lines from both PTCL-NOS and CTCL subtypes are also dependent on IRF4. In B-cell malignancies, lenalidomide is known to induce IKZF1 and IKZF3 degradation by acting as a proteolysis-targeting chimera (PROTAC)18. Indeed, lenalidomide treatment reduced proliferation in 4 of 7 tested cell lines  and in MTA cells, which harbor a heterozygous S215G mutation that is believed to confer loss-of-function. In contrast, all 11 lines with homozygous, hemizygous or dominant negative (p.R273H22 in SUDHL1) TP53 mutations had IC50 values >10 μM  for the standard-of-care agent romidepsin of 1 mg per kg given intraperitoneally on days 1, 4, and 7  in mice to the AUC in patients (831 µg x h x mL−1) after a single dose of 3.1 mg per kg21, which is the recommended phase II dose.Because of the broad activity of ALRN-6924, we assessed its efficacy in eight Mice were xenografted with tumor, allowed to develop significant disease burden and then randomized to treatment with vehicle, romidepsin or ALRN-6924 on days 1, 4 and 7. All mice were sacrificed on day 8 to assess disease burden across compartments. ALRN-6924 treatment induced p21 expression by IHC and apoptosis in treated tumors , the first patient treated for TCL experienced a complete remission Fig. . She wasWe established and comprehensively characterized preclinical models of TCL to facilitate both mechanistic discovery and biomarker-driven therapy. Immunophenotyping, DNA sequencing and RNA-based clustering all suggest that these models recapitulate important aspects of TCL biology. Importantly, the PDX and cell line models provide a pre-clinical toolbox for interrogating genetic, epigenetic, metabolic and other alterations of potential clinical significance.24, involve the degradation of IKZF1, as shown in B-cell malignancies18. Further studies are needed to clarify whether responses to lenalidomide among patients with PTCL result from tumor cell-autonomous responses and, if so, to identify the relevant targets that are degraded in the presence of lenalidomide.Our CRISPR-CAS9 screen identified multiple new targets but was limited by the transducibility of available cell lines. Clearly, additional efforts are needed to extend genome-wide vulnerability screening to additional lines from other subtypes. In the 8 lines we screened, we rediscovered multiple known targets, most notably IRF4. It remains unclear whether clinical responses to lenalidomide, which occur in approximately 22% of patients with relapsed/refractory PTCL26. PTPN2 can negatively regulate JAK/STAT signaling in CD8+ T-cells27. In fact, deletion of PTPN2 in mature T cells results in autoimmunity in aged mice, possibly due to a decreased threshold for T-cell receptor (TCR) signaling28. A previous analysis identified biallelic frameshift mutations in one of 69 PTCLs29, suggesting that PTPN2 could also function as a tumor suppressor in mature T cell malignancies. An alternate possibility is that hyperactivation of TCR or JAK/STAT signaling resulting from PTPN2 loss (or inhibition) is toxic to some or all mature T cells. This concept was previously demonstrated for multiple phosphatases that buffer B-cell receptor signaling in B-cell malignancies30. Further studies are needed to determine the role of PTPN2 in PTCLs and whether loss of this phosphatase can induce a central tolerance checkpoint in lymphoma cells31.We were surprised that PTPN2 scored as a vulnerability in the CRISPR-CAS9 screen in 7 of 8 cell lines. PTPN2 was previously described as a tumor suppressor in T-ALL32. The in vivo response that we observed suggests that identifying JAK2 fusions in this disease could offer significant clinical benefit, especially in a disease like T-PLL with no standard cytotoxic or targeted regimens for second-line therapy.We validated JAK2 fusions and wild-type p53 as targets for small molecule inhibitors using in vivo PDX models. The presence of a JAK2 fusion in a T-PLL PDX was unexpected, but aberrant activation of this pathway is consistent with the activating JAK/STAT pathway mutations that have been described in T-PLLWe showed that ALRN-6924 has broad activity against multiple TCL lines and PDXs. It is extremely complicated to compare the activity of multiple drugs across multiple models, especially because the pharmacokinetics of each drug differ between mouse and human. Nonetheless, we used a maximum tolerated dose of romidepsin as a comparator to provide some relevance to current clinical treatment. The data across all 8 lines suggest that romidepsin has impressive activity in the bone marrow but ALRN-6924 was active across nearly all involved sites. Further studies are needed to understand whether these differences in compartment-specific activity are related to drug distribution or other microenvironmental determinants of efficacy.Our study demonstrates that data from genome-wide vulnerability screens and in vivo PDX trials are highly complementary and can form the basis for clinical testing in rare cancers. We continue to generate models, including two additional AITL PDXs and one additional PTCL-NOS PDX that engrafted during the review of this manuscript. All models are available to both academic and industry investigators and will hopefully facilitate the development of more effective treatment strategies for patients with TCL.33, KI-JK34, SUP-M235, SU-DHL-136, L8237, DERL238, and MOTN-139 cells were obtained from DSMZ, Karpas 29940, Karpas 38441, MyLa42 cells from Sigma-Aldrich, HUT7843, MJ44 from ATCC, and DL-4045, MTA46, and KHYG-147 cells from JCRB. TLBR148, TLBR249 and TLBR349 were kindly provided by Alan L. Epstein . FEPD50, MAC2A51, OCI-Ly13.252 and OCI-Ly1252 were kindly provided by Leandro Cerchietti  and NKL53 cells were kindly provided by Jerome Ritz . HH54, H943, and SeAx55 were kindly provided by Anthony G. Letai . HUT10256, MT257, and MT458 were kindly provided by Koichi Ohshima . KOB59 and ST159 were kindly provided by Yasuaki Yamada , Su9T0160 was kindly provided by Naomichi Arima , and SMZ-161 cells were kindly provided by Hitoshi Ohno .SR-786Cells were cultured according to the vendor’s recommendation. Cell lines were routinely tested for mycoplasma  and authenticity was validated by short tandem repeat (STR)-profiling.Ruxolitinib, RG-7112, RG-7388, and romidepsin were purchased from Selleckchem. ALRN-6924 was provided by Aileron Therapeutics.www.grcalculator.org. Proliferation assays with lenalidomide were performed in triplicate and performed twice, with a concentration of 1 μM vs DMSO, cells were stained with PI, counted and medium was replaced every 48 h. Statistical significance was assessed by two way ANOVA and Bonferroni post-test. Detection of apoptosis was performed by Annexin V staining using the biolegend® FITC Annexin V Apoptosis Detection Kit with 7-AAD. Cell cycle analysis were performed with Hoechst33342 from SigmaAldrich® according to the manufacturer’s protocol.Proliferation assays with ruxolitinib and ALRN-6924 were performed in a 384-well format, using a JANUS Automated Workstation (PerkinElmer) and CellTiter-Glo Luminescent Cell Viability reagent (Promega) at 0 h and 72 h according to the manufacturer’s protocol. Each data point represents quadruplicates and experiments were repeated at least twice. IC50 values were calculated, using the GR calculator at https://media.addgene.org/data/plasmids/21/21915/21915-attachment_Jws3xzJOO5Cu.pdf). We induced each plasmid into the target cells by lentivirus. psPAX2 and pCMV-VSV-G were used for lentiviral transduction. psPAX2 and pCMV-VSV-G were gifts from Didier Trono and Bob Weinberg . Four days after the transduction, shRNA was induced by administration of doxycycline . Fractions of GFP were measured over time by flow cytometry .Tet-pLKO-puro was a gift from Dmitri Wiederschain (Addgene plasmid # 21915). We exchanged the sequence of IRES-PuroR in the plasmid with that of P2A-EGFP using gBlocks® from Integrated DNA Technologies. Each shRNA was cloned into the Tet-pLKO-EGFP vector according to the protocol provided on the webpage shMDM4; CACCTAGAAGTAATGGCTCAA (TRCN0000003857)TBL1XR1 at 3q26.32 and TP63 at 3q28 was designed using BAC probes RP11-1148M8 and RP11-1065H24 (surrounding TBL1XR1 and labeled in green) and RP11-24F1 and RP11-53D15 (surrounding TP63 and labeled in red). The probe set was hybridized to SMZ-1, DL40, MTA, OCI-Ly12, and OCI-Ly13.2. For the HH cell line, a custom two-color breakapart FISH probe was used to detect translocation between TP63 at 3q28 and any other partner gene using BAC probes surrounding TP63: RP11-24F1 labeled in green and RP11-53D15 labeled in red. To detect TCL1 rearrangements, we utilized the Cytocell TCL1 Breakapart probe LPH 046.A custom two-color dual-fusion FISH probe designed for the detection of inversion or translocation between PDX specimens were fixed in 10% neutral-buffered formalin for 48 h and stored in 70% ethanol for up to 2 weeks before paraffin embedding. Immunohistochemical studies were performed on 4 μm paraffin sections using the following antibodies: CD3 , CD4 , p21 , CD30 , CD10 , PD1 , CD20 , CD7 , CD8 , CD57 , pJAK2 , and pSTAT5 . In situ hybridization for Epstein–Barr virus encoded RNA (EBER) was performed using the Pan Path EBER Kit (#A500K.9901).SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) was used to prepare cDNA per manufacturer’s protocol. Primers for TBL1XR1-TP63 were F, ttgcaagagtcaggattttctca, and R, gaggagccgttctgaatctg. Primers for FOXK2-TP63 were F, tcttcagggtacaaggtggg, and R, gaggagccgttctgaatctg. Primers for ACTB were F, gctcgtcgtcgacaacggctc and R, caaacatgatctgggtcatcttctc.Sample preparation of whole cell lysates, SDS-PAGE, membrane transfer and blotting were performed according to standard protocols. Antibodies to p21 (#2947), PTPN2 (TCPTP) (#58935), Jak2 (D2E12) (#3230), Phospho-Jak2 (Tyr1007/1008) (#3771), Stat3 (D1B2J) (#30835), phospho-Stat3 (Tyr705) (D3A7) (#9145), Stat5 (D3N2B) (#25656), phospho-Stat5 (Tyr694) (D47E7) (#4322), MDM2 (D1V2Z) (#86934), α-Tubulin (#2144) and GAPDH (#5174) were purchased from Cell Signaling Technologies. Antibody to TP53 (sc-47698) and MDMX (G-10) (sc-74467) were purchased from Santa Cruz Biotechnology. Antibodies were used at 1:1000 dilution  or at 1:500 dilution (Santa Cruz Biotechnology).Uncropped western blots from the main figures are provided in Supplementary Figure http://cancer.sanger.ac.uk/cell_lines, EGA accession number: EGAD00001001039) GDSC dataset62. For two additional cell lines (SUP-M2 and MJ), CCLE WES bam files were used . For the remaining cell lines, Whole exome sequencing was performed by the Cancer Cell Line Encyclopedia Platform at the Broad Institute according to standard protocols of the Cancer Cell Line Encyclopedia. Briefly, DNA was extracted using the QIAGEN® QIAamp DNA Mini Kit according to the manufacturer’s protocol. Prior to library preparation, DNA was fragmented (Covaris sonication) to 250 bp and further purified using Agentcourt AMPure XP beads. Size-selected DNA was then ligated to specific adapters during library preparation . Each library was made with sample-specific barcodes. Libraries were pooled and sequenced over a HiSeq3000.Whole exome sequencing (WES) bam files for four cell lines  were downloaded from the Sanger Institute (http://archive.broadinstitute.org/cancer/cga/indelocator) in single sample mode with default parameters. To ensure high quality variant calls, we required minimum coverage of 4 reads with minimum two reads supporting the alternate allele. Moreover, variants with low allelic fraction (AF < 0.1) and variants outside protein coding region were excluded. To remove germline-like variants, any variant with allelic frequency greater than 1E-5 in Exome Aggregation Consortium (ExAC) project63 were excluded with the exception of any cancer recurrent variant defined by minimum TCGA frequency of 3 or COSMIC frequency of 10.All the raw sequencing data was processed using CCLE variant calling pipeline. Mutation analysis for single nucleotide variants (SNVs) was performed using MuTect v1.1.6 in single sample mode with default parameters. Short indels were detected using Indelocator  with STAR 2.4.2a. Transcript expressions were estimated with Cufflinks 2.2.1 without transcript assembly65. Gene expressions were calculated as sums of transcript FPKM values. Genes with transcripts smaller than 300 bp were ignored. All expression values were log2 transformed. For gene fusion detection, we use STAR-Fusion (https://github.com/STAR-Fusion/STAR-Fusion66). It is a method that accurately identifies fusion transcripts from RNA-Seq data and outputs all supporting data discovered during alignment. We used a cutoff of 5 reads (either spanning or crossing the fusion) to call the presence of a translocation.Analysis of RNASeq data was implemented as a Snakemake67. All snoRNA and miRNA genes were first filtered out as were all genes that were only significantly expressed in 3 or fewer samples based on a FPKM threshold of 5. A custom R script then calculates the Pearson correlation between all of the samples on a pairwise basis and generates the heatmap.For the Sample–Sample Correlation heatmap . The false discovery rate (FDR—Benjamini and Hochberg) method was used to adjust for multiple comparisons.68.To permit analysis of the aggregated cell line and PDX data, we first performed quantile normalization to adjust for library depth and platform-specific differences. Principal components analysis revealed that the primary source of post-normalization variation derived from batch effects. These batch effects within PDX and Cell Line were successfully removed using the ComBat approach from SVA 3.18.0069. A laboratory-developed algorithm was used for fusion transcript detection and annotation. The integrity of the input nucleic acid and the technical performance of the assay were assessed with a qualitative reverse transcription qPCR assay and assessing the DNA/RNA content in the sequencing results. Although this assay may detect several potential fusion variants, only the most prevalent one is reported. The assay is validated for samples showing 6% or higher tumor cellularity.An Anchored Multiplex PCR (AMP) assay was used for targeted fusion transcript detection using next generation sequencing (NGS). Briefly, total nucleic acid was isolated from cell pellets, blood, bone marrow aspirate or smears. The total nucleic acid was reverse transcribed with random hexamers, followed by second strand synthesis to create double-stranded complementary DNA (cDNA). The double-stranded cDNA was end-repaired, adenylated, and ligated with a half-functional adapter. Two hemi-nested PCR reactions using the ArcherDx Heme Fusion kit primers were performed to create a fully functional sequencing library that targets specific genes  and an average of 4 guides per gene with approximately 1000 guides that do not target any location in the reference genome as negative controls. A pool of guides was transduced into a population of cells. The cells were cultured for approximately 21 days in vitro, and at the end of the assay, barcodes for each guide were sequenced for each cell line in replicate. Reads per kilobase were calculated for each replicate and then the log2 fold change compared to the initial plasmid pool was calculated for each guide. Samples with poor replicate reproducibility (<0.7 Pearson coefficient), total read counts less than 15 million, or those that failed to match the fingerprint of parent lines were removed from the analysis. Additionally, guides that have low representation in the initial plasmid pool and those with suspected off-target activity were removed from analysis. Dependency scores were generated for each gene in each cell line using the CERES algorithm70. Briefly, CERES estimates gene dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy-number-specific effect, as well as variable sgRNA activity. The gene dependency scores generated by CERES are absolute dependency scores with each cell line scaled to have an average dependency score of 0 for negative control sgRNAs and an average dependency score of −1 for a set of positive control core essential genes as defined by Hart et al.Genome-scale CRISPR-Cas9 loss-of-function screening was performed on 391 cancer cell lines including 8 T-cell lymphoma cell lines using the Avana libraryTo calculate the probability and false-discovery rate (FDR) that a gene dependency score represents a true dependency in a given cell line, we fit a two-component mixture model in each cell line. The two components were 1 the empirically determined distribution of true dependent scores, identified using the pan-essential gene scores in that cell line, and 2 the empirically determined distribution of true non-dependent scores, identified as genes that were not expressed in that line. We defined as pan-essential genes 1871 genes whose dependency scores falls in the bottom 24% of gene scores in at least 90% of the cell lines. The probability of dependency for each gene score is the probability that it was generated from the distribution of true dependent gene scores. To correct for noise in the tails of the distributions, all gene scores below −1.5 were assigned probability 1 of being dependencies and all gene scores above 0.25 were assigned probability 0. A Gaussian smoothing kernel with width 0.15 was applied to the final probability scores to further reduce noise.Z-scores were calculated for each gene across all cancer cell lines using the median dependency score for each gene and the median average deviation. Genes with Z-scores <−4 in at least 2 T-cell lymphoma lines with an FDR < 0.05 in at least 1 T-cell lymphoma line with expression of the gene >1 RPKM were considered candidate T-cell lymphoma dependencies.In order to identify outlier dependencies in T-cell lymphoma lines compared to the other cancer cell lines screened, KI-JK and SMZ-1 were transduced with Cas9 as described in “CRISPR-Cas9-screen”. Each sgRNA targeting PTPN2  was cloned into the pLKO5-U6-EF1a-RFP657 and the Control sgRNA was cloned into pLKO5-U6-EF1a-GFP, which were gifts from Benjamin Ebert (Addgene plasmid # 57822 and # 57824). We introduced each sgRNA into the target cells with lentiviral transduction. Three days after infection, Cas9-target sgRNA-transfected cells with RFP657 and Cas9-control sgRNA-transfected cells with GFP were mixed. Culture media was changed every 3–4 days and the fractions of RFP657- and GFP-expressing cells were quantified on day 9 by flow cytometry (BD FACSCanto II).The sequences used for sgRNAs were:PTPN2 #1:CCATGACTATCCTCATAGAGPTPN2 #2:CCGCGACTCACCAAGTACAGControl:GCACTACCAGAGCTAACTCA6 cells per mouse. For subcutaneous models, tumor fragments were implanted in the right flank under isoflurane anesthesia. Tumor burden was monitored periodically based on engraftment kinetics of each model by flow cytometry of peripheral blood, analysis of target organ involvement from sentinel animals for non-circulating models, or tumor size by caliper measurement. Blood was processed with Red Blood Cell Lysis Buffer (Qiagen) before staining with antibodies against human CD45  and human CD2  or human CD56  in 1 × PBS with 1 mM EDTA plus 1% fetal bovine serum. All antibodies were used at a dilution of 1:20. Flow cytometry data was analyzed with FlowJo.Viably frozen patient-derived xenograft cells were thawed and washed in 1 × PBS before tail-vein injection at 0.7–1.7 × 10Upon engraftment, mice were randomized to vehicle or treatment arms. Ruxolitinib was given at 90 mg per kg in PBS + 0.1% Tween 20, orally by gavage twice per day; romidepsin at 1 mg per kg diluted in PBS, given intraperitoneally on days 1, 4, and 7; ALRN-6924 at 20 mg per kg diluted in 20 mM sodium phosphate, 240 mM trehalose and 0.03% (w per v) polysorbate 20, pH 7.5 given intravenously on days 1, 4, and 7. The vehicle for romidepsin and ALRN-6924 was PBS intraperitoneally and ALRN6924 vehicle intravenously on the same schedule as the drugs.scidIL2rgtm1Wjl/SzJ (NSG) mice purchased from Jackson Laboratories and handled according to Dana-Farber Cancer Institute’s Institutional Animal Care and Use Committee approved protocol #13–034.All drugs were dosed in 10 mL per kg volume based on body weight. Mice were humanely sacrificed 16 h after the final dose; blood and target organs were collected for complete blood count, immunohistochemistry, and flow cytometry to determine tumor burden using the same antibodies as above. All animal work was performed in Nod.Cg-PrkdcFor PDX generation, de-identified patient samples were obtained with informed consent and xenografted under Dana-Farber/Harvard Cancer Center Institutional Review Board (IRB)-approved protocol #13–351. The patient reported in Fig. https://portal.gdc.cancer.gov/legacy-archive/search/f and have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE11408573. Mutation and RNASeq data for PDXs are also available at www.PRoXe.org. All other remaining data are available within the article and supplementary files, or available from the authors upon request.All primary data from RNAseq, exome sequencing, and CRISPR screening are available in Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


Their structures have been elucidated by means of NMR and HRESIMS spectroscopic data. The new alkaloids were evaluated for their cytotoxicity against glioma initiating cell lines (GITC-3# and GITC-18#), glioblastoma cell lines (U-87MG and T98G), and lung cancer cell line A-549, but none of them was active at 20 μg/mL concentration.Three new pyridine type alkaloids, (−)-vinmajpyridines A–C (The online version of this article (doi:10.1007/s13659-017-0137-7) contains supplementary material, which is available to authorized users. Vinca (Apocynaceae), distributed through Europe, Northwest Africa, and South-west Asia, represents a group of species which are rich in indole alkaloids of diverse structural patterns, many of which are of considerable therapeutic value +  in the positive HRESIMS. Its IR spectrum revealed a characteristic absorption band at 1724 (C=O). The 1H and 13C NMR spectral data of 1  and 8.53 (s)] assigned to be pyridine aromatic protons + .(−)-vincapyridine A : C13H17N2): C12H15NO4. white amorphous powder; c 0.04, MeOH); UV (MeOH) λmax (log ε): 204 (4.44), 219 (4.13), 269 (3.57); IR (KBr) νmax 3419, 2926, 1724, 1606, 1383, 1042 cm−1; 1H and 13C NMR data, see Table m/z 238.1072 [M+H]+ .(−)-vincapyridine B (3): C11H15NO2. white amorphous powder; c 0.02, MeOH); UV (MeOH) λmax (log ε): 204 (3.35), 279 (2.64); IR (KBr) νmax 3425, 2926, 1720, 1623, 1383, 1035 cm−1; 1H and 13C NMR data, see Table m/z 194.1172 [M+H]+ .(−)-vincapyridine C (# and GITC-18# (glioma initiating cell lines) were established previously in Kunming Institute of Zoology from three different human glioblastoma multiform samples. These cell lines were cultured in serum-free medium supplemented with 1XB27 (Life 12587-010), 50 ng/mL EGF (PeproTech AF-100-15), and bFGF (PeproTech AF-100-18B). GITCs were cultured in laminin (Gibco 1725712) pre-mdish. The cells could adhere and normally grow without differentiation. Culture dishes were pre-coated with laminin for 4-6 h at 10 mg/mL concentration. The T98G, U-87MG, and A549 cell lines were purchased from the American Type Culture Collection (ATCC), the cells were cultured in DMEM basic medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin in a humidified incubator at 37 °C and an atmosphere of 5% CO2. Cells were digested with TryplE express (Gibco 12604-021) for 3–5 min at 37 °C in cell incubator and centrifuged at 1000 rpm/min for 3 min.GITC-3#G3581] assay  assay , 21. The1–3 are available as Supplementary Information.1D and 2D NMR spectra, HRESIMS, and UV spectra of compounds Supplementary material 1 (PDF 1903 kb)Below is the link to the electronic supplementary material.


Taken together, these results suggest that AIMP2-DX2 plays an important role in the regulation of NPC and could be a potential therapeutic target and prognostic indicator for the treatment of NPC.Nasopharyngeal carcinoma (NPC) is a head and neck tumor with high degree of malignancy and with high incidence especially in southern China. AIMP2-DX2, one isoform of the aminoacyl-tRNA synthetase interacting multifunctional proteins (AIMPs), is shown to be a potential target in many cancers. However, the detailed mechanisms of AIMP2-DX2 in NPC development remain to be elucidated. Here, we found that the mRNA expression level of AIMP2-DX2 was significantly increased in NPC specimens, compared with normal nasopharyngeal tissues. Microarray immunohistochemical analysis of NPC specimens and Kaplan–Meier analysis showed that patients with high AIMP2-DX2 protein expression had shorter overall survival than those with low AIMP2-DX2 level. Furthermore, mRNA and protein expression levels of AIMP2-DX2 were both increased in cultured NPC cell lines , by being compared with normal nasopharyngeal cell line NP69. Overexpression of AIMP2-DX2 remarkably promoted the cell viability, cell migration, and invasion of cultured NPC cells. Genetic knockdown of AIMP2-DX2 by shRNA lentiviruses significantly suppressed the proliferation, migration, and invasion and induced apoptosis of NPC cells. Inhibition of AIMP2-DX2 decreased the highly expressed level of matrix metalloproteinase- (MMP-) 2 and MMP-9, further suppressed proliferation, migration, and invasion in cultured NPC cells Nasopharyngeal carcinoma (NPC) is a rare tumor derived from nasopharyngeal epithelium , most ofAIMP2 , one isoform of the three AIMPs, is involved in cancer development. AIMP2 functions in a variety of biological processes, such as cell differentiation and apoptosis , 11. AIMHowever, the role of AIMP2-DX2 in NPC remains unknown. Here, in the current study, we detected the expression level of AIMP2-DX2 in NPC specimens and analyzed its correlation with overall survival. We compared the mRNA and protein expression levels of AIMP2-DX2 in cultured NPC cell lines with normal nasopharyngeal epithelial cells. AIMP2-DX2 was overexpressed and silenced to determine the impact on proliferation, migration, and invasion in NPC cells. Further, we determined the effect of AIMP2-DX2 inhibition on tumor formation in nude mice.This study was approved by the Medical Ethical Committee for Clinical Research and Animal Trials of the First Affiliated Hospital of Jinan University, in accordance with the Declaration of Helsinki. Clinical specimens were collected under the informed consent from all participants. Of animal experiments, all efforts were made to minimize suffering.NPC samples were obtained from 77 patients  between January 2008 and December 2012. Normal nasopharyngeal tissues  were obtained from 56 cases (average age: 49.6 ± 3.2). The NPC specimens were confirmed as nasopharyngeal squamous cell carcinomas by the Department of Pathology. Samples were fixed with 10% formaldehyde when obtained and then paraffin-embedded.The rabbit anti-AIMP2-DX2 antibody was prepared as described . Immunohμg/ml polybrene; 48 h later, 2 μg/ml puromycin was added to the culture medium for selection, to generate NPC shRNA stable cell lines.Immortalized nasopharyngeal epithelial cell line (NP69) and NPC cell lines  were achieved from Cell Bank of Shanghai Institute of Cell Biology, Chinese Academy of Sciences  and cultured in accordance with the protocol. Vectors of pGag/Pol, pRev, and pLL3.7  were used for the construction of recombinant shAIMP2-DX2 lentiviral plasmid. The sequence designed for shRNA construction was 5′-AGAAATTCTCGAATGTTCTTT-3′. HEK 293T cells were cotransfected for virus packaging. 48 and 72 h after transfection, the supernatant was collected for virus enrichment. Viruses of shAIMP2-DX2 and shControl (negative control) were infected in CNE-1 cells, with 5–10 Cell lysates were separated in sodium dodecyl sulfate-polyacrylamide (SDS-PAGE) gel electrophoresis and transferred to a nitrocellulose membrane. Western blot membranes were probed with primary antibodies against full-length AIMP2-DX2 (AIMP2-F) and AIMP2-DX2, matrix metalloproteinase (MMP)-2, MMP-9, and Actin . Horseradish peroxidase-conjugated secondary antibodies  were incubated for 1 h, and bands were detected by enhanced chemiluminescence . Densitometric values were normalized to Actin levels, by the analysis of ImageJ software. aimp2-F, aimp2-DX2, mmp-2, and mmp-9. Primers used were aimp2-F , aimp2-F , aimp2-DX2 , and aimp2-F . The statistical results were normalized to the expression of Actin.Total RNA was extracted with Trizol reagent  according to the manufacturer's instruction. cDNAs were extracted and used for amplification ofμl medium with 20 μl of CellTiter 96 AQueous One Solution reagent for 3-4 h. The cell viability was determined. The growth rate was calculated from the absorbance and was normalized.Cell proliferation was detected by the MTS assay  as reported in . BrieflyIn vitro cell migration and invasion assays were examined by transwell system . Briefly, cells were seeded on a polycarbonate membrane insert incubated for 24 h. Cells migrated into the lower surface were stained with 2% crystal violet for quantification. For the invasion assay, the procedure was similar except that the transwell membranes were precoated with Matrigel .Cells plated in six-well plate were washed twice with PBS and fixed in 70% ethanol at 4°C overnight. Then, cells were stained with propidium iodide and Annexin and analyzed by flow cytometry using a FACScan flow cytometer .6) of the shAIMP2-DX2 and shControl stable CNE-1 lines were injected subcutaneously into the flanks of 4-week-old nude mice . Tumor formation in nude mice was administrated for over 4 weeks. Tumor volumes were measured during the period and calculated using the formula V = (4/3)πa2b [Cells (2 × 104/3)πa2b . Followit-tests or one-way ANOVAs. To define the cutoff score for high expression of AIMP2-DX2, ROC curve analysis was used. Survival curve was analyzed by Kaplan–Meier method, using the log-rank test. p < 0.05 was considered statistically significant.The data are presented as the mean ± SD and statistical analysis was performed by SPSS 20.0 software, using Student's p < 0.05) was significantly higher than that in normal nasopharyngeal tissues, while that of AIMP2-F showed no significant changes  were detected via qPCR assay. The results showed that the expression of AIMP2-DX2 in NPC tissues  and NPC cell lines . AIMP2-DX2 mRNA levels were significantly higher in NPC cell lines than that in the epithelial cell line NP69, while the levels of AIMP2-F showed no changes . FurtherBecause of the relatively high expression of AIMP2-DX2, CNE-1 cell line was selected to further verify the impact of AIMP2-DX2 on NPC cell behavior. We first constructed an AIMP2-DX2 overexpression encoding plasmid. The expression efficacy was confirmed by western blotting with His-Tag antibody when overexpressed in CNE-1 cells . Then CN in vitro.Further, we wondered about the impact of AIMP2-DX2 inhibition on NPC cells. Genetic silencing sequences against AIMP2-DX2 were designed and synthesized as described in the method section; then an AIMP2-DX2 shRNA lentivirus was constructed , while t in vivo. Cells of shAIMP2-DX2 and shControl CNE-1 stable cell lines were inoculated into the flanks of nude mice. Tumor volumes were assessed every week and mice were sacrificed 4 week later. The data showed that AIMP2-DX2 knockdown significantly suppressed tumor growth, compared with that in control mice . Recent studies show that AIMP2 can be departed from the complex to inhibit the development of cancers, via interacting with damaged DNA or mediating ubiquitin of substrates , 26. Durβ . Also, Af TRAPF2 . All thif TRAPF2 , 17–19. Cancer metastasis is a complex multistep process, generally including four steps: reduction of adhesion, degradation of extracellular matrix, invasion of neovascularization, and formation of proliferative lesion in the secondary part . Invasio in vitro and in vivo, via the downstream of MMP proteins. High expression of AIMP2-DX2 is associated with poor prognosis in NPC patients. The data suggest that AIMP2-DX2 may be a potential prognostic biomarker for the detection of NPC.In conclusion, the present study describes the relevance of AIMP2-DX2 in NPC development. AIMP2-DX2 is significantly upregulated in NPC specimens and cell lines. Inhibition of AIMP2-DX2 suppresses the proliferation of NPC cells


However, around 15–30% of cell lines used in research have been misidentified or cross-contaminated generating concerns about the results obtained from experiments that use them. Here we described how old aliquots of an equine macrophage cell line (e-CAS) stored at the Animal Health Trust did not contain equine cells but macrophages of murine origin (m-CAS).The 3Rs principles  are focused on finding alternatives to the use of animals in research. In this regard, cell lines are popular and useful tools for the replacement of primary cells in  In this sense, cell lines are the most cost effective and popular replacement for primary cells thanks to their unlimited life span and ability to grow indefinitely without compromising reliable results. However, the use of cell lines comes with associated challenges like genomic variations and alteration of phenotypic characteristics of the cells over time3. Also, the possibility of misidentification through contamination by other cells cannot be discarded and could lead to the use of the wrong cell line4. Several studies have shown that around 15–30% of cell lines used in research have been misidentified6, creating problems and concerns about the results obtained from them. Therefore, it is essential to appropriately characterise cell lines before their use9.Scientists are looking for non-sentient alternatives to support the Replacement, Reduction and Refinement (3Rs) principles and minimise the use of animal’s samples in research (Streptococcus equi (S. equi), a horse restricted pathogen, and how the equine innate immune cells, essentially equine macrophages and equine neutrophils, respond to S. equi infection in vitro.As scientists working in equine immunology, our objective is to obtain reliable and relevant results whilst minimising the use of samples obtained from animals. Our current research is focused on the innate immune response to The availability of equine cell lines in the market is quite limited. The ATCC only provides one horse fibroblast cell line (ATCC® CCL-57™); it also offers horse tissue of unknown origin (ATCC® CRL-6583™) for which they are not responsible for its production and characterisation. Therefore, the use of equine cell lines to meet the 3Rs principles is limited and relies on aliquots kindly provided by other scientists.10. As equine macrophages, this cell line is ideal for evaluating the innate immune response to S. equi, and aliquots of e-CAS cells have been available at the Animal Health Trust (AHT) since 2003. Our aim was to detect variations in the expression of Toll-Like Receptors (TLR) in these cells by reverse transcriptase-quantitative PCR (RT-qPCR) after their stimulation with different mitogens or S. equi strains.e-CAS is an equine macrophage cell line developed to study equine-specific pathogenic pathways and therapeutic modulation of macrophage response to defined stimuliTLR6 we realised that the aliquots of e-CAS cells at the AHT did not contain an equine macrophage cell line, but macrophages of murine origin.Here we describe how during the development of a PCR reaction specific for equine TLR1 and TLR6 was determined by PCR-SSP in DNA samples obtained from e-CAS and eqT8888 cell lines. Previously developed TLR2 and TLR4 reactions  in the sequence obtained from e-CAS cells when compared with the results obtained from eqT8888 cells or archived DNA from ponies . However, the sequence obtained from e-CAS DNA sample shared a 99% identity with TLR6 from Mus musculus  horse comparison test (Table Short tandem repeats (STRs) are short DNA sequences of normally 2–6 bp in length that are tandemly repeated and can be easily amplified by PCR. Currently, STR profiling is the standard tool for cross-contamination and authentication of human and cell lines from other specieset al. in 201413. STRs in 9 murine loci were positively identified  using the mouse DNA profiling STR test described by Almeida TLRs in response to different stimulatory conditions. e-CAS cells were derived from bone marrow of two 10-year-old horses to generate an equine macrophage cell line suitable for the study of equine-specific pathogenic pathways of macrophage response to defined stimuli10 and, therefore, the ideal candidates for our work. However, DNA samples isolated from e-CAS cells stored at the AHT did not show amplification for any of the equine TLR reactions considered in our study except for equine TLR6  to clearly establish the identity of e-CAS cells as macrophages10. It could be plausible that the fitter and faster growing murine cell line took over the cell culture replacing the original equine macrophages. However, the DNA profile obtained from e-CAS does not exactly match the one described for RAW264.7 in 2014 by Almeida et al.13, discrepancy that could be explained by alterations in their fingerprint profile with prolonged culture, as has been described for other cell lines2.Cross-contamination between cell lines is quite frequentet al. published a study in which they evaluated the cross-reactivity of anti-human leukocyte monoclonal antibodies using e-CAS and eqT8888 cell lines16. The authors concluded that e-CAS cells represented an early stage of differentiation in which the characteristic macrophage surface antigens could not be detected. Nonetheless, they were clearly positive when they used an anti-mouse CD11b antibody, result that could also support the fact that the e-CAS cells used in their study were of murine origin. We have demonstrated that e-CAS cell express CD14 on their surface  inhibitor17. Lankveld et al. investigated the effect of ketamine on TNF-α and IL-6 release and observed that ketamine suppress LPS-induced TNF-α concentration in both e-CAS and U937 cells in a dose dependent manner and the effect of ketamine was also evident on the concentration of IL-6 released by e-CAS cells18. The assays used by both groups were developed for their use in different species; they were not specific methods to measure equine cytokines20. Three other publications23 have referenced Werners et al.10, but in all cases the citation was related to the method used during the development and differentiation of e-CAS cells, and was not associated with the characteristics of these cells as equine macrophages. To our knowledge, there is no other publication in which e-CAS cells have been used in equine-specific experiments.To our knowledge, e-CAS cells have been used in two other publicationsIn summary, this work shows that the aliquots of e-CAS cells stored at the AHT are of murine origin rather than equine macrophages. However, this established identity was only possible after performing molecular assays that are specific for the equine species.In conclusion, the data presented here highlight the importance of cell line authentication prior to their use in research.10; eqT8888 are equine cells of lymphoid origin but not completely characterised24.Two cell lines were used in this study. e-CAS cell line is described as equine macrophages derived from bone marrow of two 10-year-old horsesDNA from e-CAS and eqT8888 cell lines was extracted using a QIAamp DNA minikit (QIAGEN) following manufacturer’s instructions. Archived DNA aliquots from two ponies (P1 and P2) were used as controls.TLR2 and TLR4  database and did not fall within polymorphic areas of any of the genes. PCR was performed using 2 μM of each primer, 1.5 µM of MgCl2, 200 µM dNTP, 10 ng DNA template and 0.25 µl Hotstar taq polymerase. Cycling conditions were: enzyme activation at 95 °C for 15 min, 35 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min, and a final extension at 72 °C for 10 min. After the PCR process, amplified DNA fragments were resolved at 115 V for 45 minutes using a 1.5% agarose gel stained with GelRed (Biotium).Previously published gene specific oligonucleotide primers were used to amplify equine R4 Table 11. SpeciTLR6 PCR-SSP reaction were used for sequencing. The amplicons were purified using a Monarch® PCR & DNA Cleanup Kit (New England Biolabs) following manufacturer’s instructions. One reaction for each primer (forward and reverse) was prepared in an Axygen 96 well plate. Each reaction contained 5 µl sequencing buffer, 5 µl water, 1 µl BigDye Terminator v3.1 (Thermofisher), 1 µM forward or reverse primers and 2.5 µl DNA. Cycling conditions were: 25 cycles at 96 °C for 15 s, 50 °C for 10 s and 60 °C for 2 min, followed by 10 °C indefinitely. The sequencing PCR reactions were purified by ethanol precipitation. 10 µl Hi-di was then added to each well and samples were denatured in a PCR machine at 95 °C for 1 min. The sequencing was performed on the ABI 3130XL genetic analyser (Applied Biosystems). Archived DNA samples from two Welsh Mountain ponies  were used as equine controls.Specific amplified products from the 27.Results were analysed using Chromas (Technelysium) and aligned using Mega softwareA short tandem repeat (STR) analysis was performed by the Equine Genetic Service at the AHT, an ISAG  registered laboratory. Genomic DNA samples from eCAS, eqT8888 and pony P1 were tested. The equine STR marker panel consisted of alleles at 18 different microsatellite loci: AHT4, AHT5, ASB2, ASB17, ASB23, CA425, HMS1, HMS2, HMS3, HMS6, HMS7, HTG4, HTG6, HTG7, HTG10, LEX3, LEX33 and VHL20. PCR were performed in a G-Storm GS4 Multi Block Thermal Cycler (G-storm). PCR products were separated in a capillary electrophoresis system on the 3130XL Genetic Analyser (Applied Biosystems). The GeneScan-500 LIZ Size Standard was used in each sample run for an application of automated DNA fragments analysis with five fluorescent dyes. Analysis of DNA profiles for 18 STR loci was conducted using GeneMapper 4.0 software (Applied Biosystems).et al.13 for the authentication of mouse cell lines.e-CAS cell line DNA profiling was performed by Mycrosynth AG (Switzerland) following the STR analysis developed by Almeida No datasets were generated or analysed during the current study.Supplementary information


Withania somnifera (L.) Dunal  is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts). The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides  and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas  was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.  Ayurveda (Sankskrit: knowledge of life) may have molecular targets different from those of standard anti-cancer drugs, they are attractive candidates in the search for novel drugs suitable to treat otherwise drug-resistant tumors. These natural compounds may also show reduced side effects on normal organs [Drug resistance and severe adverse side-effects are major obstacles to cancer chemotherapy. Therefore, new therapy options with improved efficacy are urgently required. Nutritional sources such as marine and terrestrial plants are fertile grounds in which to find bioactive constituents with anti-tumor activity. The long-lasting experience of traditional phytotherapy may facilitate the identification of novel treatment strategies. In India, herbs have been used as foods and medicine for millennia. In recent years, the active principles of food and medicinal herbs have been increasingly elucidated, making the active chemical compounds accessible to molecular biological and biochemical research ,5,6,7,8.l organs .Withania somnifera (L.) Dunal  belongs to the family of Solanaceae (nightshade plants). In Ayurveda, roots of the plant are either chewed or used to prepare beverages (aqueous decocts). Withania is widely known for its aphrodisiacal, liver tonic, anti-inflammatory, immune-stimulatory, and astringent activities. Furthermore, Withania somnifera is used to treat asthma, ulcers, emaciation, insomnia, senile dementia, tumors, diabetes, neurodegenerative disorders and numerous other symptoms and disorders [isorders ,12,13,14Withania somnifera and other Withania species are the withanolides, C-28-steroidal lactone triterpenoids built on an intact or rearranged ergostane framework. Within this framework, C-22 and C-26 are appropriately oxidized forming a six-membered lactone ring [Withania somnifera are alkaloids, such as isopelletierine and anaferine [The major phytochemicals in one ring . Other bnaferine .Withania somnifera’s use in traditional medicine, Withania somnifera extracts as well as isolated withaferin A can exert chemopreventive effects [in vitro, in mouse tumors in vivo, and in human xenograft tumors transplanted into nude mice [In addition,  effects ,18,19,20ude mice ,24,25,262M phase [As is frequently observed with natural products in general, withaferin A exerts its anti-cancer effect by targeting multiple pathways rather than a single target in tumor cells. Withaferin A induces cell cycle arrest in the G2M phase ,28,29,302M phase ,32. The 2M phase ,37,38,3950 concentrations of withanolides and the most frequently used standard clinical anticancer drugs, we analyzed the cross-resistance profile of 60 cell lines of the National Cancer Institute (NCI), USA [The aims of the present study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells to withanolides. Using ICCI), USA . Next, w−8 to 10−4 M in 60 cell lines of the NCI, and log10IC50 values were calculated for each withanolide for each cell line. The mean log10IC50 values for cell lines of each tumor type are depicted in Eight withanolides were analyzed. Their chemical structures are shown in 10IC50 values to each other for each cell line. As shown in 10IC50 values of the NCI cell line panel for the eight withanolides were correlated with those for established anticancer agents. Representative drugs from several major cancer drug classes  were chosen. Significant correlations were consistently observed between withanolides and carmustine (BCNU), lomustine (CCNU), and semustine (methyl-CCNU), indicating that many of the NCI cell lines demonstrate cross-resistance between withanoloides and these nitrosoureas. The correlations between withanolides and other established anticancer drugs were weak (R < 0.6)  .50 values for the withanolides. We performed COMPARE analyses of log10IC50 values for the three most cytotoxic withanolides  and the transcriptomic mRNA-based expression profiles of the NCI cell lines to produce scale indices of correlation coefficients. The microarray data from the NCI website [in silico analysis. The mRNA expression levels were determined by microarray analysis [10IC50 values) were correlated with the lowest mRNA expression levels of genes. These genes can be considered possible candidate genes in determining cellular resistance to withanolides. Furthermore, reverse COMPARE analysis was carried out, correlating the most inhibited cell lines with the highest gene expression levels. Considering a COMPARE coefficient of R > 0.6 as cut-off value, only two genes each fulfilled this criterion in connection with withafastuosin D diacetate and 4-B-hydroxy-withanolide E. These two compounds were, therefore, excluded from further analyses. The genes whose mRNA expression correlated with withaferin A diacetate are shown in COMPARE analyses were performed to obtain a gene expression profile and identify the most up- or down-regulated genes correlated with the IC website  was usedanalysis ,42,43. WRAD54L), stress response , cell growth regulation , extracellular matrix components, cell adhesion and migration , ribosomal proteins , cytoskeletal organization and regulation , signal transduction , transcription factors ,and others.Among the genes were genes from diverse functional groups, such as DNA damage response and repair  was less frequent or not observed. This suggests that withanolides and nitrosoureas may share similar mechanisms of action, e.g., DNA damage of tumor cells. Interestingly, withaferin A has been described as exerting chemo- and radiosensitizing effects on tumors  in vivo ,50,51,5250 values of withaferin A diacetate on 60 tumor cell lines with the microarray-based transcriptomic mRNA expression levels of the cell line panel [50 values for investigational drugs to identify candidate genes causing drug resistance [As a next step, we correlated the ICne panel  by COMPAne panel . Clustersistance  and to isistance . We identified genes from diverse functional groups that were significantly associated with the response of tumor cells to withaferin A diacetate. These genes were related to DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others. The fact that genes associated with sensitivity or resistance against withaferin A diacetate were from diverse functional groups speaks for the multiplicity of mechanisms by which withaferin A diacetate inhibits cancer cells. This so-called “multiplicity of mechanisms” can refer to multiple targets that together lead to multiple effects or it can refer to one target leading to activation or inactivation of multiple downstream pathways. Multi-specificity is a general feature of many natural products; rather than acting on one single target, they affect multiple targets and pathways .The multifaceted nature of withanolides has been previously recognized, e.g., induction of G2M cell cycle arrest  and apopFor example, RAD54L is involved in recombinatorial DNA repair (via the RAD52 pathway) and dissociates RAD51 from nucleoprotein filaments formed on double-stranded DNA ,60. The 10IC50 values for withanolides indicates that cell lines with high expression of these genes better resist the cytotoxic effects of withaferin A diacetate than cell lines with low expression levels. ANXA2 is a phospholipase inhibitor, which is involved in the heat stress response [The significant correlation of the expression of stress response genes to the logresponse . Heat shresponse . The posresponse ,63. PPIHresponse . PPIH anCD53, BCAR3, TRIM3, and NASP. Although these genes do not belong to the set of classical cell cycle genes, they suggest that withaferin A diacetate may play a role in inhibiting cancer cell proliferation. Many established anticancer drugs also act against cell proliferation. However, most classical anticancer drugs damage not only cancer cells, but normal proliferating cells, which leads to the severe side effects often observed with chemotherapy, e.g., myelosuppression, sterility, gastrointestinal mucosa damage, and alopecia. Whether withaferin A diacetate also exerts detrimental effects on normal tissues is not known and requires further exploration. Several genes involved in cell growth regulation were significant in our analysis, namely, 10IC50 values for withaferin A diacetate including ADAM9, LAMB1, ITGB1, ASAP2. The ECM, cell adhesion and migration are important components in cancer metastasis and progression. Withanolides are known to inhibit metastasis [Interestingly, a number of genes encoding extracellular matrix (ECM) components and genes involved in cell adhesion and migration correlated with logtastasis . We suggRPS23 and RPL5 and the still poorly characterized ribosome-associated genes LOC440055 and LOC729362 affect resistance towards withaferin A diacetate. Ribosomal proteins are often involved in antibiotic resistance. For example, Streptomycin resistance is based on the modification of an aspartic acid moiety in the ribosomal protein S12 [Another interesting finding is that cellular response to withaferin A diacetate was correlated with the expression of genes encoding several constituents of the ribosome. Whereas the role of ribosomal proteins in resistance towards established anticancer drugs has not yet been intensively investigated , our stutein S12 . RPS6 istein S12 . The RPLtein S12 ,67. In ptein S12 ,69. Statistical Analyses: The panel of human tumor cell lines of the Developmental Therapeutics Program of NCI and their testing by sulforhodamine B assay and mRNA microarray hybridization have been described [escribed ,70,71. Tescribed . For hieescribed . Hierarc10IC50 values for the withanolides were selected from the NCI database [10IC50 concentration for the corresponding compound. To derive COMPARE rankings, a scale index of correlation coefficients  was created. In the standard COMPARE approach, greater mRNA expression in cell lines correlates with enhanced drug resistance, whereas in reverse COMPARE analyses, greater mRNA expression in cell lines indicates drug sensitivity. For COMPARE analysis, the mRNA expression values of genes of interest and logdatabase . mRNA exdatabase . COMPAREdatabase . BrieflyPearson’s correlation test was used to calculate significance values and rank correlation coefficients as a relative measure of the linear dependency of two variables. This test was implemented into the WinSTAT Program . The Pearson correlation test was used as a measure for interval-scaled linear correlations. We used the Pearson test rather than the Spearman’s Rank correlation test because Spearman’s test is based on the equidistance of values, and the values used for our analysis were not equidistant.2-test was applied to bivariate frequency distributions of pairs of nominal scaled variables. This test was also implemented into the WinSTAT program . The χ2-test determines the difference between each observed and theoretical frequency for each possible outcome, squares them, divides each by the theoretical frequency, and takes the sum of the results. Performing the χ2-test necessitated defining cell lines as being sensitive or resistant to withaferin A diacetate. This was done by taking the median IC50 value log10 = -6.5 M for withaferin A diacetate as a cut-off threshold.The χIn summary, our microarray-based investigation delivered novel candidate genes that were associated with the response of cancer cells to withaferin A diacetate. These results merit further investigation to prove the causative contribution of these genes to withaferin A diacetate resistance and sensitivity.


Human tumor cell lines are extremely important tools for cancer research, but a significant percentage is cross-contaminated with other cells. Short tandem repeat (STR) profiling is the prevailing standard for authenticating cell lines that originate from human tissues. Based on the analysis of 482 different human tumor cell lines used in China by STR, up to 96 cell lines were misidentified. More importantly, the study has found that STR profiling alone is insufficient to exclude inter-species cross-contamination of human cell lines. Among the 386 cell lines which had a correct STR profile, 3 of them were inter-species cross-contaminated. Careful microscopic examination may be helpful in some cases to detect changes in morphology but additional testing is needed. Additionally, species verification by PCR could easily identify the contaminants, even with a low percentage of contaminating cells. Combining STR profiling with species identification by PCR, more than 20.5% (99/482) of tumor cell lines were revealed as having been incorrectly identified, including intra-species (14.5%), inter-species (4.4%) cross-contamination and contaminating cell lines (1.7%). Therefore, quality control of cell lines is a systemic issue. Each cell line should undergo a full QA  assessment before it is used for research. In vitro-cultured human tumor cell lines are extremely important tools for unraveling the mechanisms of tumor development, progression and metastasis, and discovering new therapies. The use of authenticated tumor cell lines holds important translational value for their model character and their reproducibility in the clinical area. However, most of currently used tumor cells are continuous cell lines, and a foreign cell line or microorganism is easily introduced without the handler’s knowledge during continuous passage culture. So each cell line should undergo a full QA assessment before it is used for research. The quality analysis of cell lines typically includes microorganism detection and species verification; also, to double-check their identity, STR profiling of human-derived cell lines should be combined with routine examination of their morphological and specific properties3. Over the past 10 years, we have been performing all QAs for quality control of the cell lines in our center . We also provide QA services for the cell lines of other laboratories. Most customers only chose STR profiling for human cell lines, considering it to be the gold standard7. However, STR profiling is occasionally insufficient. In this paper, we share our experience and our view.We obtained data for the species verification and STR profiles of 482 different human tumor cell lines which derived from patients with a variety of tumors Fig. . Among tAmong the 383 authenticated human cells, the STR profiles of 340 cell lines were consistent with those published in the DSMZ STR database. However, there were no published data for the remaining 43 cell lines that could be used for comparison , esophageal carcinoma cell line KYSE410 (identified as KEYSE150), glioma cell line U251 (identified as GOS-3), lung cancer cell lines NCI-H3122 (identified as NCI-H1299) and so on. More cell lines belong to the latter case, such as the human pancreatic cancer SW1990  , the human small cell lung cancer NCI-H446 and glioma cell line S4 . Among the cells established by Chinese scholars, more than 65.5% (19/29) were replaced by HeLa cells, concluding human liver cancer cell lines BEL7402, QGY7701, QGY7703 and SMMC7721, esophageal squamous Cell Carcinoma cell EC9706, gastric cancer cells BGC823, MGC803 and SGC7901, lung cancer cells SPC-A1 and GLC-82, oral squamous cell carcinoma Tca-8113, ovarian carcinoma cells Ho8910 and Ho8910PM, nasopharyngeal carcinoma tumor lines CNE-1, CNE-2Z and 4 derivatives. However, MGC803 obtained from another laboratory showed different STR profile with Hela and other cells in DSMZ database, so this cell is considered as correct MGC803. Additionally, 2 (THP-21 and K562-21) of the 29 cross-contaminated cell lines may be caused by mislabeling. There was also cross-contamination among cell lines from the same depositor. Five lung cancer cell lines 18 were reportedly sampled from 5 different patients; however, only 2 STR profiles were detected. Five human uveal melanoma cell lines  were supplied by a single depositor. However, as reported, OCM-1 cells were originally cultured from a human choroidal melanoma specimen in 198519, C918 and M619 cells were derived from patient tumors at the University of Iowa in the 1990s20 and Mum-2B and Mum-2C were derived from a heterogeneous tissue explant MUM-221. These 5 cell lines should thus show 4 different STR DNA types, but only 2 STR profiles were detected. These cell lines are no longer suitable for research.Although they had the correct species (human) of origin, 70 (14.5%) cases were found to have the wrong STR profile, indicating that they were misidentified and had been replaced by another cell line. Detailed data are shown in Supplementary Table When species identification was performed by PCR, 21 human cell lines were found to be fully substituted by another species such as rat, monkey, Chinese hamster, Syrian hamster or bovine  when reported in 1990 by Japanese scholars. However, in 1999, Dirks et al. reported that ECV304 was substituted with T24 human bladder carcinoma cells31. From 2004 to the present, more than 400 articles have used ECV304 as an endothelial cell; nearly 70% of these were published by Chinese scholars. In 2009, Nature announced: “It is time for all involved to tackle the chronic scandal of cell line contamination”. Today, most notable publications require that all cells lines used in a paper are verified before use, but this QA step has not been required in China. The government began taking measures to standardize research materials and resources more than 15 years ago by establishing the National Science and Technology Infrastructure (NSTI). As part of the NSTI, the China Infrastructure of Cell Line Resource (CICR) has focused on the integration, standardization and sharing of cell lines. Of all quality control measures in the system, the quality analysis of cell lines is our priority. In this paper, we report the authentication of cell lines. Among 482 human tumor cell lines tested in our study, there were 20.5% (99/482) of misidentified cell lines, which is lower incidence than that reported by Ye et al. 5 and Huang et al. 15 because of different statistical range and calculating method, as mentioned in “Samples and genomic DNA extraction” in the Methods. Some were misidentified when introduced to China long ago, such as the famous HeLa contaminants KB and Hep-2, while some cells may have been cross-contaminated during culture in China. For example, Molt-4 (human leukemia cell line) and SK-OV-3 (human ovarian carcinoma cell line) have been accepted as authentic32 and their STR profiles are included in the DSMZ database. Meanwhile, authenticated Molt-4 and SK-OV-3 can be found in our center or other laboratories. So the problem is likely to be confined to the lab that supplied the sample to us for testing, or more broadly to Chinese labs who are sharing a particular stock that is misidentified. Some cell lines established by Chinese scholars were cross-contaminated at the beginning of culture, such as BCA4, which STR profile is different from that of donor tissue. For researchers in China, it is of the utmost importance to ensure that the cell lines in use have a well-defined origin and are routinely re-analyzed to identify possible areas of contamination. Researchers can find well-authenticated cell lines from the China Infrastructure of Cell Resource (CICR).Tumor cell lines are often fundamental materials in life science and basic medical research, and the use of unverified cell lines may result in invalid data, leading to unreliable results1. Good lab practices (GLP) for tissue culture, including strict aseptic technique and vigilant observation of cellular morphology, are essential for preventing cross-contamination. When one cell line (termed A) is contaminated by another cell line (termed B), if B cells grow more quickly, A will be replaced by B in a few generations. If A and B have similar growth rates, GLP can help maintain the original A cells, and single-cell cloning can ensure the preservation of the A cell line. In the same way, if A cells are contaminated by B cells that are sensitive to trypsin digestion and easy to detach from the plate, A will be substituted by B cells. In addition, differences in morphology can arise from multiple clones in the original mass culture that evolve with passaging33, and can occur by culture conditions over time that could be related to causing differentiation of cancer stem cells within the population. These different populations may be more or less evident depending on the core stock used, number of passages, and the culture conditions prior to each vial preservation. Furthermore, heterogeneity occurs when cells are cultured over extended periods of time, subjected to differing culture conditions or are genetically unstable32. Therefore, to avoid phenotypic or genotypic drifting, it is better to adhere to the original way of nurturing the cells. With careful daily morphological examination, if two distinct morphologies are observed, the cell identity must be re-authenticated to eliminate cross-contamination. Notably, loss of heterozygosity, microsatellite instability, aneuploidy in tumor cell lines and cross-contamination make authentication problematic. It is reported that a cutoff of 70% identity for 16-locus STRs (85% for 8-loci) is needed to confirm cell line identity32.The risk of contamination by unrelated cells is a potential and often recurrent problem. In this study, we detected more than one case of cells from one depositor that were cross-contaminated with each other. Cross-contamination may arise due to several causes, including use of unchanged tips, sharing media and reagents among cell lines and use of mitotically inactivated feeder layers or conditioned medium, and mislabeling40. In our center, species-specific primers were used to easily distinguish among human, mouse, rat, Chinese hamster, Syrian hamster, bovine, dog, monkey, pig and rabbit DNA using agarose gel electrophoresis. Some of the primers targeted at the conserved sequences of the cytochrome C oxidase I gene (COI), described as “barcode region” for animal species36. A PCR-based assay is a simple, quick, accurate and cost-effective way to authenticate the species origin of a cell line and evaluate for inter-species cell line contamination in a single test. To confirm or distinguish a more broad range of animal species, chromosome analysis or sequencing of “DNA barcode” regions can be employed34. In this study we found that cross-contamination of human and murine cells was the most common type of inter-species contamination because human and murine cell lines are the most commonly used types of cell lines. In recent years, STR profiling has been suggested as a method for authenticating human cell lines. By STR typing, it is easy to identify human cells that have intra-species contamination or that have been fully replaced by animal cells. However, human cells that were partially contaminated by animal cells always showed correct STR profiles. Therefore, we recommend first performing species verification and then STR profiling for human cell identification.Cell cross-contamination includes inter-species and intra-species contamination. Today, isoenzyme electrophoresis, chromosome analysis, DNA barcoding and PCR-based assays are used for detecting inter-species contamination42. It was once the most widely used cell line due to its hardiness and rapid growth rate. However, it has been demonstrated that HeLa cells have contaminated many other cell lines; they have overgrown and completely replaced such original cell lines as WISH, KB, Hep-2, Chang Liver, INT407 and the adenoid cystic carcinoma cell lines ACC-2, ACC-3 and ACC-M11. From 1969 to 2004, 220 publications in the PubMed database were found to have used HeLa-contaminated cell lines42. In this study, we found 25 HeLa-contaminated tumor cell lines, 19 of which were established by Chinese scholars and include the gastric cancer cell lines BGC823, MGC803 and SGC791; the liver cancer cell lines BEL7402, QGY7701, QGY7703 and SMMC7721; and the ovarian cancer cell lines Ho8910 and Ho8910PM, which are widely used in anti-cancer research in the corresponding cancer fields. In addition, HCT-8, HCT116 and NCI-H157 contaminants were also common in cases of human intra-species cross-contamination.The HeLa cell line was the first human cell line; it was established in 1951 from a cervical tumor biopsy taken from Henrietta Lacks, a working-class African-American woman living near BaltimoreIn conclusion, species identification followed by STR profiling is a valuable way to authenticate cell identity. Among 482 tested tumor cell lines used in China, we detected 99 misidentified cell lines, corresponding to an incidence of 20.5%. We recommend that scholars employ only authenticated cell lines for research and perform GLP during the process. We also recommend that scholars re-examine previously stored laboratory stocks of cell lines, especially the contaminated cell lines listed here. In Chinese cell resource centers, more vigorous efforts should be taken to propagate the importance and benefits of cell line authentication.Cell pellets from 482 human tumor cell lines were collected from 2008 to 2016 by the Cell Resource Center of Peking Union Medical College (PUMC) or from customers’ laboratories. Cells with the same name and same STR profiles are considered as one cell line, although they were obtained from different laboratories, and cells with the same name but distinct testing results (STR profiles or species) were considered as different cells. These cell lines were established from patients with a variety of tumors, including lung carcinoma, breast carcinoma, colorectal carcinoma, liver carcinoma, leukemia, melanoma, glioma, and so on. Total genomic DNA was extracted using the PureLink® Genomic DNA Mini Kit , and DNA yields were assessed using a NanoDrop 2000 . DNAs were first tested cell species by PCR method and then examined by STR profiling.43. In brief, genomic DNA samples were separately amplified using multiple species-specific primers according to the manufacturer’s instructions in a GeneAmp®PCR system 9700. Electrophoretic analysis was performed using a 3730/3130xl DNA Analyzer . After electrophoresis, the data were analyzed with Gene Mapper® ID-X Software v1.2  to categorize peaks by size in relation to an internal standard allelic ladder.STR loci and the amelogenin sex-determining marker were amplified using the AmpFlSTR™ Identifiler® Plus PCR Amplification Kit   or the DNA Typerhttp://www.dsmz.de/fp/cgi-bin/str.html), which includes STR data sets for 2,455 human cell lines from ATCC, DSMZ, JCRB and RIKEN. After registration, our STR data were entered for matching with the authenticated cell lines listed in the database44. At the same time, STR profiles of cell lines were compared to additional reference where available, such as that of donor tissue. Furthermore, all cell lines were checked against database of cross-contaminated or misidentified cell lines on ICLAC  .STR data were analyzed using the DSMZ (German Collection of Microorganisms and Cell Cultures) online STR database .Supplementary informationSupplementary table S1-S5


TP53, NOTCH1 and DNMT3A as the most frequently mutated genes. We also found common alterations in JAK/STAT and epigenetic pathways. Immunohistochemical analysis showed nuclear accumulation of MYC (in 85% of the cases), NFKB (62%), p-STAT (44%) and p-MAPK (30%). This panel of cell lines captures the complexity of T/NK-cell lymphoproliferative processes samples, with the partial exception of AITL cases. Integrated mutational and immunohistochemical analysis shows that mutational changes cannot fully explain the activation of key survival pathways and the resulting phenotypes. The combined integration of mutational/expression changes forms a useful tool with which new compounds may be assayed.T and NK-cell lymphoma is a collection of aggressive disorders with unfavorable outcome, in which targeted treatments are still at a preliminary phase. To gain deeper insights into the deregulated mechanisms promoting this disease, we searched a panel of 31 representative T-cell and 2 NK-cell lymphoma/leukemia cell lines for predictive markers of response to targeted therapy. To this end, targeted sequencing was performed alongside the expression of specific biomarkers corresponding to potentially activated survival pathways. The study identified  T and NK-cell leukemia/lymphoma is a collection of aggressive disorders with unfavorable outcome accounting for 10–15% of non-Hodgkin lymphomas. The most recent WHO Classification established 23 subtypes grouped by clinical presentation [Nowadays, PTCL diagnosis requires the integration of information about clinical status, morphology, immunohistochemistry, flow cytometry, cytogenetics and molecular biology ,4. The tThus, it is possible that, having molecularly characterized the individual TCL cases, we could identify potential candidates for targeted therapy. In this study, we integrated targeted deep sequencing with immunohistochemical analysis in a large cohort of 33 well-characterized T/NK-cell lymphoma/leukemia cell lines. This has provided insights into the specific molecular mechanisms underlying the pathogenesis of TCL and into the potential implications for future diagnosis and targeted therapy of TCL patients.33 T/NK-cell lymphoma/leukemia cell lines were obtained from various sources . These iwww.broadinstitute.org/igv/). Variants were annotated with Variant Effect Predictor . Known SNPs with an allelic frequency greater than 1% in public databases  were filtered out. In order to avoid false-positive calls, we performed duplicates with separate library preparation and sequencing in independent runs. Only variants called by both runs were considered.16 genes were selected for sequencing. This set consisted of genes that are known potentially to play a role in tumorigenesis –20 S2 T. The genTissue microarrays (TMAs) were designed as described previously using twowww.broadinstitute.org/cancer/software/GENE-E). The Mann-Whitney U or Kruskal-Wallis tests were used to determine group differences. The chi-square or Fisher exact test was used as appropriate to determine associations between the presence or absence of markers. Statistical analyses were carried out using SPSS for Windows version 15 .Unsupervised hierarchical clustering with an average linkage algorithm was performed using Gene-E software v3.0.206 (http://www.broadinstitute.org/ccle), the COSMIC Cell Lines Project (http://cancer.sanger.ac.uk/cell_lines) [https://www.ebi.ac.uk/ega/), and data from four exomes produced by our group in HH, HUT-78, MJ and Myla cell lines  , EGAS000l_lines)  from thell lines . SequencCCR4, CD28 or IDH2.33 T/NK-cell lymphoma/leukemia cell lines were subjected to target amplicon-based enrichment and sequencing of the 16 selected genes  and truncating mutations (7/33)  Figs . ResidueNOTCH1 mainly harbored missense and truncating mutations . We found more than one variant of NOTCH1 per TCL cell line in six cell lines, with up to eight variants in MOLT4. NOTCH1 SNVs were distributed throughout the whole gene. We found only truncating mutations in the PEST domain; these are known to lead to aberrantly prolonged signaling in the nucleus in this domain [s domain .JAK3 and JAK1 harbored seven and five variants, five and four of them being missense mutations, respectively. Three and two variants were found in STAT5B and STAT3, respectively. Interestingly, Jurkat harbored the three STAT5B and the one STAT3 variants.We detected 17 SNVs associated with the JAK/STAT pathway. DNMT3A was the most frequently mutated gene with high diversity: we found seven variants, four of which were missense, two were truncating variants and one was located in the 5´UTR region. TET2 had three missense variants and one truncating variant, whereas IDH2 harbored no SNVs.With respect to epigenetic-related genes, TNFRSF1B gene. Two mutations were detected in PLCG1 (both in the DND-41 cell line), DDX3X and RHOA and one was found in SYK (We found little variation in the other genes. We detected the same mutation (p.V385M) in HPB-ALL and MHH-TALL-2 in the d in SYK .TP53 and NOTCH1 mutations co-occurred in the T-ALL (11/20) and T-LGL cell lines (1/1), but not in any other subtype. NK cell lines featured solely TP53 mutations. Mutations in genes involved in the JAK/STAT pathway were most frequently mutated in T-ALL. In this respect, four JAK1 mutations, six JAK3 mutations, and all STAT3 and STAT5B mutations occurred in T-ALL cell lines. Only one mutation in the JAK1 and in JAK3 genes was detected in CTCL, which co-occurred in HUT-78. Similarly, the epigenetic genes DNMT3A and TET2, most of which were related to T-ALL, were found to be altered in these subtypes. Furthermore, DNMT3A was mutated in one ALCL and ATLL case each. Two novel PLCG1 mutations were found in a single case of T-ALL (p.Q152H and p.D1199N).Among the cell lines, the T-ALL subtype carried the greatest frequency of SNVs . ATLL and CTCL both harbored 4/102 variants  . We deteIn order to identify a number of potentially deregulated disease actionable mechanisms, we used a set of 26 immunomarkers chosen not only on the basis of their biological and clinical relevance to clinical classification and pathogenesis of TCL, but also for their pharmacological implications . Hence, In order to classify our cases by specific immunohistochemical biomarkers, and to identify their potential association with pathogenesis, an unsupervised hierarchical clustering analysis (average linkage method) of the TMAs was undertaken. This produced a dendrogram with six well-defined clusters .Most of the groups were defined by specific biomarkers. All groups clearly showed positive MYC and TCRBF1 expression, with the possible exception of group 6, which had limited TCRBF1 expression. Group 1 had differential positive PD1 expression alongside activated MAPK-ERK, GATA-3 and ROR-gamma-T. In group 2, the cluster featured broad RB staining (12/14) and heterogeneous expression of TP53, MAPK-ERK, NFAT and CD30. Group 3 showed the strongest activation of both canonical and non-canonical NFKB pathways, with positive expression of CD30 and NFAT in three of five cases. Group 4 showed characteristic constitutive activation of STAT 1, 3 and 5, with positive CD30 expression in all cases, along with the heterogeneous activation of the NFKB pathway in three of the four of cell lines. Group 5, formed exclusively of ALCL-ALK+ cell lines, was defined by strongly positive ALK, BCL6, CD30 Granzyme B and TIA-1 expression, together with STAT 3 activation. Group 6, comprised the two NK cell lines included in the study. They showed a typical NK signature positive for the expression of CD56, Granzyme B and TIA-1. This small group also showed activation of STAT 3 and 5. It is worth noting that under these circumstances, CTCL cell lines were dispersed into different groups.Immunohistochemistry (IHC) scores were dichotomized to enable associations between markers to be determined  in all cell lines . OverallTP53 status into wild type, and missense and truncating mutation group. The expression of p53 was strongly associated with the presence of missense mutations compared with wild type and truncating mutations (p<0.001),  cell lines with mutated JAK showed STAT activation, as defined by nuclear staining. By contrast, ten out of 23 (43.5%) cell lines with JAK wild type showed STAT activation , . Howeverd Notch1  or MYC etivation .Our growing knowledge about the molecular basis of T- and NK-cell lymphoma is leading to a better understanding of their pathogenesis and is helping refine the subclassification of TCL. Nevertheless, despite this progress, targeted therapy is still in a preliminary phase. The results presented here can help identify the more commonly deregulated mechanisms driving tumorigenesis in TCL, and provide a useful tool for analyzing the interaction between gene mutations and the activation of key survival pathways.However, the panel of TCL cell lines tested has some limitations inherent to the difficulty of generating cell lines derived from particular T-cell lymphoma subtypes, notably AITL and PTCL-NOS. The panel is more representative for T-ALL, ATLL, CTCL, ALCL-ALK+ and NK subtypes. Despite these limitations, our results show that most T-cell lymphoma subtypes share mutations and activation of some essential pathways, such as JAK-STAT, NFKB, NFAT, chromatin regulation and others.In this study we have examined 16 genes related to TCL pathogenesis –20, seleTP53 was the mutated gene in our cell lines (72.7%). Truncating and missense mutations were correlated with low and high levels of p53 expression, respectively. The NOTCH1 gene was also frequently mutated, with five truncating mutations located in the PEST domain. Only the MOLT-4 cell line showed a high level of expression of Notch1; it was not expressed in the other cell lines . Whereas KE-37 cell line harbored only one mutation in the PEST domain, the DND-41, HPB-ALL, MOLT-4 and PF-382 cell lines were also found to be mutated in the HD domain. The MOLT-4 cell line harbored eight mutations in NOTCH1, localized in different domains from the PEST and HD domain. It has been reported that truncating mutations in the PEST domain lead to aberrantly prolonged signaling in the nucleus, but are only functional in the presence of Notch ligands [ ligands . Mutatio ligands . MutatioJAK1 and JAK3 genes in 27.3% of the cell lines analyzed. HUT-78 showed mutations in JAK1 and JAK3 pseudokinase domains [JAK1 kinase domain were found in three cases . It is important to note that the HPB-ALL and MHH-TALL-2 cell lines shared the same mutation (p.Q966V), but STAT was activated only in the MHH-TALL-2 cell line. The molecular significance of these mutations is not easy to interpret, since they could act in a receptor-dependent or independent manner with respect to activation. Therefore, although JAK inhibitors (JAKis) constitute a new therapeutic option for the treatment of PTCL patients [Mutations in the JAK/STAT pathway have been reported in PTCL patients ,12,20. W domains . Mutatiopatients , furtherDNMT3A and TET2 were found in 18.2% and 9.1% of our panel of cell lines, respectively. DNMT3A encodes a protein that catalyzes methylation and demethylation of DNA, depending on the microenvironment conditions [DNMT3A mutations to the cancer phenotype has not been explored, except for p.R882 mutations, which predict poor prognosis in acute myeloid leukemia [TET2 that is mutated at notably high frequencies  [Mutations of epigenetic regulators are so common in PTCL that they constitute one of the largest groups of mutation, including those affecting the splicing machinery, signaling pathways and transcription factors . Mutationditions . The speleukemia ,29. TET leukemia . Mutatioleukemia . In fact 40–50%) –37. SomePLCG1 (encoding p.Gln152His and p.Asp1199Asn), both of which were present in a T-ALL cell line, DND-41. Recently, two hot-spot PLCG1 mutations (encoding p.Ser345Phe and p.Ser520Phe) that enhance PLCƔ activity have been reported in T-cell lymphoma [PLCG1 encodes phospholipase CƔ1 (PLCƔ1), a key regulator of proximal TCR signaling [PLCG1 mutations, suggesting that these mutations may promote deregulated activation of downstream PLCƔ1 signaling. This activation may support the idea that specific targeting of PLC downstream signaling, like tacrolimus, which acts as a calcineurin inhibitor, could be a therapeutic option for the treatment of patients with mutations in PLCG1.This study found two mutations in lymphoma . PLCG1 eignaling . InteresRHOA mutations were detected, both of them in T-ALL. Several research groups have found frequent RHOA mutations, specifically the p.G17V mutation, in AITL and PTCL patients [RHOA, rather than as an activating mutation. Although none of the mutations found in our study corresponds to the p.G17V variant, it is important to note that both cells lines in our study that harbor RHOA mutations showed robust MYC expression. In this context, it has been reported that there is cross-regulation between MYC and RhoA activation [Two patients ,39. Intetivation .From a therapeutic perspective, our results highlight important disease mechanisms that have the potential to serve as targets for therapy. In this regard, the immunohistochemical analysis identified an activated NFKB pathway in about 62% of TCL cell lines . Recentlwww.clinicaltrials.gov; accessed September 2016) in T-cell lymphomas and other hematological malignancies. Tofacitinib has been shown to inhibit JAK3 in CTCL [The second most frequently activated pathway in cell lines was JAK/STAT (42.4%), making the blockade of this pathway a promising means of treating TCL patients. Ruxolitinib has been demonstrated to inhibit CTCL cell line proliferation at micromolar concentrations  and clin in CTCL  but othe in CTCL , are othUnsupervised hierarchical clustering identified six groups on the basis of their expression profile. We can propose a targeted therapy that takes into account the mutational background of each group . Group 1In conclusion, the study identifies commonly deregulated pathways and genes in TCL, including JAK/STAT, NOTCH, NFKB and chromatin conformation. Activation of these pathways is somehow the consequence of somatic mutation and other causes. Our findings may help in the development of preclinical models for the evaluation of new targeted drugs.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 TableProbabilities of chi-square or Fisher exact tests for tests of association between pairs of the 26 immunomarkers used. Green and orange indicate positive and negative associations, respectively.(XLSX)Click here for additional data file.S1 FigTP53 was defined as Nonsense/Frameshift , wild type  and missense . Mutational status of NOTCH1 was defined as wild type  and mutated . Mutational status of JAK was defined to be JAK1 and/or JAK3 wild type  or mutated . The immunomarkers p53, NOTCH1 and STATs, are indicated in color as in a) Mutational status of (TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.