We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( Understanding why some tumor cells respond to therapy and others do not is essential for advancing precision cancer care. Here, the authors perform large-scale transcriptomic profiling of breast cancer cell lines treated with anti-cancer drugs and find that certain drug classes induce cell line specific responses. This approach has proven most effective when oncogenic drivers are themselves targeted by drugs. For example, the presence of EGFRL858R (and related mutations) in non-small cell lung cancer (NSLC) is predictive of responsiveness to gefitinib, a drug that binds with high affinity to mutant EFGR6; the presence of an EML4-ALK fusion protein in NSLC is predictive of responsiveness to crizotinib, which inhibits the ALK4 kinase domain7; and the presence of a mutant BRAFV600E kinase in melanoma is predictive of responsiveness to the BRAF inhibitors vemurafenib and dabrafenib9. The Cancer Genome Atlas (TCGA) project and similar efforts are attempting to identify other druggable cancer mutations through molecular profiling of human cancers11, but there is growing evidence that, for many types of tumors and drugs, there exists no simple genetic predictor of response. For example, genes encoding members of the Akt/PI3K/mTOR pathway are commonly mutated in breast cancer, but the presence of these mutations is a poor predictor of responsiveness to inhibitors of Akt/PI3K/mTOR kinases12.Understanding why some tumor cells respond to therapy and others do not is essential for advancing precision cancer care. Pre-clinical cell line studies typically investigate the connection between pre-treatment cell state or genotype and drug sensitivity and resistance13 and currently being extended by the NIH LINCS Program, involves collecting molecular data from cells following exposure to drugs and other perturbations and then mining this information for insight into response mechanism. In this paper we report the collection of ~8000 gene expression signatures (in triplicate) from a genetically diverse set of six breast cancer cells exposed to ~100 small molecule drugs by using the low-cost, second generation, CMap technology L1000 transcriptomic profiling (https://clue.io/lincs)15; in parallel, we measured drug sensitivity at a phenotypic level using growth rate (GR) inhibition17, a method that corrects for the confounding effects of variability in cell division rates, plating density, and media composition. This data set differs from previous data sets of this type by including transcript data for each drug/cell line pair across dose and time, as well as six-point GR-based dose–response curves based on measurement of viable cell number; GR metrics have higher information content than conventional IC50 or Emax metrics, and increase the reproducibility of drug-response data19.A complementary approach, pioneered by the Connectivity Map (CMap)4. The key question was therefore whether cell lines that respond phenotypically to a particular drug do so in a similar way at a molecular level. We found that this was true for some classes of drug, such as inhibitors of cell-cycle kinases: cell lines had very similar sensitivities to these drugs at the phenotypic level and their L1000 signatures were also similar. In contrast, L1000 profiles for drugs such as inhibitors of MAPK or PI3K/Akt signaling, or receptor tyrosine kinases (RTKs) were cell-type specific, even among cell lines in which phenotypic responses were strong. We also identified sets of drug/cell line pairs in which significant changes in transcription were detected without any apparent effect on cell growth. To understand how this might arise we performed a follow-on study showing that BT-20 cells are responsive to PI3K inhibition at a molecular level but that this does not induce cell arrest or death due to the operation of an adaptive resistance pathway. The adaptive pathway can be blocked by several different drugs whose L1000 signatures co-cluster. Thus, the application of inexpensive, high-throughput transcript profiling combined with cellular phenotypic measurements reveals similarities and differences in responsiveness to anti-cancer drugs depending on genotype and, at least in some cases, can guide the design of effective drug combinations.On the basis of previously published information, we expected that each cell line would exhibit a significant phenotypic response  to only a subset of drugs in our test setamp, HR+, and triple-negative/TNBC12) plus non-malignant MCF 10A cells. A set of 109 investigational and clinically approved drugs enriched in kinase inhibitors was selected to represent “targeted” anti-cancer agents . Differential expression analysis was performed on the landmark genes themselves using the “Characteristic Direction” method22; a multivariate geometrical approach in which each perturbation is associated with a vector in the 978 dimensions of the L1000 landmark genes. When L1000 profiles differ, characteristic direction vectors point in different directions and the magnitude of the difference can be quantified by the angle between vectors. With large sets of expression profiles, the characteristic direction method outperforms more conventional univariate approaches23. To account for experimental noise, L1000 signatures were collected in triplicate for each condition and a signature consistency score (SCS) was computed by assessing the degree of alignment of characteristic direction vectors for replicates relative to randomly chosen vectors. We found the SCS to be an effective means for quantifying the reliability of a transcriptional response normalized by background experimental noise (see Methods for details). SCS values correlated to a modest degree with the amplitude of the characteristic direction vector .Breast cancer cell lines were selected from the three major subtypes (HER2https://clue.io/lincs), averaging the results across all perturbations in the cluster and applying the gene set enrichment tool Enrichr24 to find terms enriched in up and down regulated genes. To facilitate data exploration, we developed an interactive, on-line tool (http://amp.pharm.mssm.edu/LJP).; in this tool, signatures are tagged with user-selected metadata, drug identity for example, and external transcript signatures are projected onto the network.Clustering characteristic directions based on the cosine distance (with k = 20 clusters) distinguished responses by drug class, cell line, and time point Fig. . Out of t = 3 h signatures from all six cell lines exposed to drugs targeting protein chaperones, and clusters 19 and 20 comprised cell lines exposed to inhibitors of cell cycle kinases or components of the DNA repair machinery ; signatures from all six cell lines were once again found in these clusters. Enrichment analysis showed that components of the MAPK and GSK3β signaling cascades were downregulated in the t = 3 h (clusters 19/20), whereas cyclin-dependent kinases and genes involved in mitosis were downregulated at t = 24 h (clusters 17/18), across all six cell lines. Inhibitors of chaperones and cell cycle kinases were also broadly active at a phenotypic level , whereas for trametinib, maximum effect (GRmax) varied across cell lines. Even among cell lines that responded similarly at a phenotypic level , the characteristic directions of L1000 signatures clustered the perturbations differently . We conclude that responses of cell lines to signal transduction kinase inhibitors differ at a molecular level.A second pattern of response was observed for drugs targeting signaling kinases, such as the PI3K inhibitor alpelisib, MEK inhibitor trametinib, or ErbB inhibitor neratinib. In these cases, clusters frequently comprised (or were dominated by) signatures from one or two cell lines and multiple drugs having closely related targets (clusters 2–11). Phenotypically, responses to such drugs varied with cell line: for alpelisib, this involved differences in potency . These data suggest that responses to high and low doses of RTK and ECM inhibitors differ at a molecular level and we hypothesize that the selectivity of such drugs is lost at high doses, presumably because they bind to multiple targets.The effects of exposure to drugs at high concentrations might either represent an intensification of responses observed at low concentrations or, alternatively, they might differ qualitatively. Across all drugs and cell lines we observed that with rising dose, the amplitude of the characteristic direction vector weakly but significantly correlated with rising drug concentration (the Spearman correlation factor ranged from 0.13 to 0.32), while the angle of the characteristic direction vector changed only modestly , which captures differences in both potency and efficacy  of the GRacy Fig. , and varacy Fig.  revealedacy Fig. . This suρ=–0.65, p<10−30). Whole-cell shotgun mass spectrometry showed that many intracellular signaling kinases were present at relatively high levels in their active, phosphorylated state in all cell lines, but in contrast, RTKs had variable abundance and degree of phosphorylation across lines  cell line/perturbation pairs with no effect by either measure, representing drug resistance; (II) pairs in which responses were observed at both molecular and phenotypic levels, representing drug sensitivity; (III) pairs with measurable phenotypic response, but weak or noisy L1000 signature; and (IV) pairs with substantial changes in L1000 signature, but little or no discernable effect on cell growth. To divide the landscape of SCS and GR values  into quadrants, we set GR = 0.66 and SCS = 1.3 Fig. . Under tClass III responses, representing ~16% of cell line-perturbation pairs, represent cases in which no significant change was detected by L1000 assay, even when significant changes in phenotype were observed. MDA-MB-231 and MCF7 cells were enriched in Class III responses, but when we re-examined the underlying L1000 data, we observed a high level of sample-to-sample variability. We therefore suspect that many Class III responses in our data set arise from poor experimental repeatability  elicited substantial changes in L1000 signatures . These responses were the rarest, all, representing only ~3% of drug/cell line pairs. This class was enriched in BT-20 and Hs 578T cell lines and drugs targeting RTKs and the MEK/ERK pathways, which are known to play an important role in the proliferation of breast cancer cells Fig. . BT-20 cres Fig. . The eff26. However, BT-20 cells exhibited only a partial cytostatic response to the PI3K inhibitor alpelisib with no cell death observed even at the highest drug concentration tested. We therefore asked whether MAPK signaling might play a role in rescuing BT-20 cells from PI3K inhibitors and vice versa. This is precisely what we observed: when BT-20 cells were treated with a combination of alpelisib and trametinib the effect of the two drugs was synergistic  and a switch from partial growth arrest to cell death . From these data we conclude that co-clustering of characteristic directions highlights compounds that lie within an equivalence class, making it possible to identify multiple combinations of drugs having similar synergistic effects on cell growth , but such non-overlapping or “orthogonal” transcriptional signatures did not represent the only way to obtain synergy. For example, in Hs 578T cells, the Akt inhibitor A443654 synergized with both the ErbB inhibitor neratinib and cMET/VEGFR inhibitor foretinib, both of which clustered away from A443654 with respect to L1000 signatures and therefore represent examples of potential “synergistic orthogonality.” However, the combination of neratinib and foretinib was also synergistic, even though the L1000 signatures associated with these drugs co-clustered and were therefore similar at a molecular level  is regulated by PI3K, MAPK, and ErbB signaling cascades, (ii) serves as measure of drug response at a single-cell level, and (iii) regulates key aspects of breast cancer physiology such as cell cycle arrest and apoptosisned Fig.  and cytoned Fig. . Thus, i38, but L1000 data suggest that exposure of BT-20 cells to alpelisib results in significant upregulation of the lapatinib targets ErbB1 and ErbB3  and direct download (http://www.lincsproject.org/LINCS/data/overview).In this paper we describe the parallel acquisition of data on drug response at a phenotypic level, as quantified using growth rate inhibition (GR) dataOn the basis of a large body of prior literature we expected that only a subset of cell lines would respond to a specific drug, but it was not known how molecular changes and phenotypic responses would relate to each other. Overall, we find that the occurrence of drug-induced changes in transcription is generally well correlated with the strength of the phenotypic response. Inhibitors of CDKs or chaperones induced similar changes in intracellular signaling across all cell lines as measured by L1000 signatures, whereas inhibitors of intracellular MAPK and PI3K/Akt/mTOR kinases induced cell-type specific changes. Cell line-specific responses are likely a reflection of differential pathway activity or connectivity, and we speculate that deeper understanding of these differences will improve our ability to use kinase inhibitors in disease. For example, high variability in the effects of PI3K/Akt/mTOR kinase inhibitors in different cells lines might explain the current difficulty in predicting which tumors will respond to these drugs.4, as is expression of RTKs themselves12. High expression of a receptor appears to be a good, but not perfect, predictor of responsiveness to drugs targeting that receptor; we have previously found that this is also true of ligand responsiveness25. When a receptor is present, L1000 signatures elicited by inhibition of a specific RTK were generally similar to signatures elicited by inhibition of either PI3K or MAPK, suggesting preferential pathway engagement downstream of RTK signaling. For example, EGFR/ErbB2 inhibitors clustered with MAPK inhibitors in five out of the six cell lines, but with PI3K/AKT inhibitors in SK-BR-3 cells. The number of cell lines in the current data set is insufficient to determine the statistical significance if such correlations, but future analysis might be directed to larger numbers of cell lines and drugs representative of each cluster in Fig. Responsiveness to RTK inhibitors was also highly variable across cell linesAmong the most interesting drug/cell line pairs in our data set are those in which a significant change was observed in transcription with no significant effect on cell growth. In the case of the MEK inhibitor trametinib, for example, we found that drug exposure altered transcription in all cell lines, but cell division in only a subset. Thus, MEK is active on its target in all cells, but essential for proliferation only in some. MDA-MB-231 cells, which carry a KRAS mutation, are the most trametinib sensitive, implying possible RAS-MAPK addiction. In contrast, BT-20 cells are the most resistant to MEK inhibition, even though trametinib induces a significant change in transcription. Reasoning that this might reflect the presence in BT-20 cells of an activating PI3KCA mutation, we studied MEK and PI3K/AKT inhibitors in combination and observed a synergistic effect on cell growth. Synergy between MEK and PI3K/AKT has previously been reported, but we have found this to be true only in a subset of breast cancer cells lines. Synergy in BT-20 cells appears to involve trametinib-dependent inhibition of an adaptive pathway that makes PI3K inhibitors progressively less effective over a 48 h period. Thus, L1000 profiling can uncover relatively subtle mechanisms of drug sensitivity and resistance based on a comparison of transcriptional and phenotypic responses.27. The question arises how best to identify combinations that are effective on specific tumors. One approach is to inhibit the same protein or pathway with multiple drugs to achieve better target coverage and functional inactivation of oncogenic signaling. Examples of this strategy include combining pertuzumab and trastuzumab to treat HER2amp metastatic breast cancer39 and dabrafenib with trametinib to treat BRAFV600E melanoma41. In other cases, it appears that concurrent inhibition of parallel pathways that drive proliferation or mediate adaptive resistance is the best strategy46. Attempts to distinguish between these possibilities using existing data and sophisticated computational approaches have proven only marginally effective31. Our data show that both “orthogonal” and “non-orthogonal” approaches to drug synergy can be detected in cultured cells. Transcriptional and phenotypic profiling also make it possible to place drugs in equivalence classes based on co-clustering. In the case of BT-20 cells, drugs in an equivalence class can substitute for each other in a combination . We have not yet studied enough combinations to propose a general rule for the construction of efficacious drug combinations, but the ability of L1000 profiling to identify equivalence classes potentially provides a means for making drug substitutions within a cluster to increase tolerability or effectiveness.Combination therapy is increasingly regarded as essential for treatment of cancer using targeted drugs12. All cells were free of mycoplasma and their identity was verified by short tandem repeat (STR) profiling at the Dana-Farber Cancer Institute47.Six cell lines  were obtained from the ATCC and grown as published previouslyhttp://lincs.hms.harvard.edu/). Drugs in this collection are sourced from commercial vendors and subjected to quality control by liquid chromatography–mass spectrometry. Quality control data are available via the HMS LINCS website. For the L1000 assay, supernatant was aspirated 3 and 24 h after drug addition until only 15 µl remained. Cells were then lysed for 30 min at room temperature by adding 30 µl of Buffer TCL (Qiagen). Plates were sealed and stored at −80 °C until processing for L1000 transcriptional profiling. For cell counts, cells were stained with a 1:1000 dilution of Fixable Far Red Dead Cell Stain (Thermo Fisher Scientific) and 2 µM Hoechst 33342 (Thermo Fisher Scientific) for 30 min at room temperature and subsequently fixed with 3% formaldehyde (Sigma Aldrich).For drug response screening cells were plated at 2000 cells/well (except Hs 578T which was plated at 1000 cells/well) in 384 well plates. After 24 h cells were treated with the indicated doses of small molecule inhibitors obtained from the HMS LINCS drug collection . For immunofluorescence experiments, cells were fixed at the indicated time points for 30 min in 3% formaldehyde, permeabilized for 30 min in phosphate buffered saline (PBS) with 0.3% Triton X-100 (Sigma-Aldrich), washed twice in PBS with 0.1% Tween 20 , and blocked for 60 min with Odyssey blocking buffer (LI-COR Biosciences). Cells were incubated with antibodies against FoxO3a , phospho-Erk1/2 , or phospho-Akt  in Odyssey blocking buffer and incubated for 16 h at 4 °C. Cells were then washed three times in PBS-T for 5 min and incubated with Alexa Fluor 647 conjugated donkey anti-rabbit secondary antibody for 60 min at room temperature. Finally, cells were washed two times in PBS-T, once with PBS, and stained for 30 min with whole cell stain (Thermo Fisher Scientific) and Hoechst (Thermo Fisher Scientific), and washed three times in PBS.All fixed cells were imaged on an Operetta high content imaging system (Perkin Elmer) and analyzed using the Columbus image data storage and analysis system (Perkin Elmer) to determine the number of viable cells.6 cells per dish, except for Hs 578T which was plated at 4 × 106 cells per dish, and grown for 24 h. Cells were washed twice with cold PBS and scraped off plates in the presence of 1 ml of PBS containing 1:100 Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific) and then pooled in a one tube per cell line. Cells were pelleted at 900 g, the remaining PBS was removed, and the pellets frozen in liquid nitrogen and stored at −80 °C until lysis. Pellets were lysed in 5 ml of 2% SDS, 150 mM NaCl, 50 mM Tris (pH 8.5), 5 mM DTT, 1:100 Halt Protease and Phosphatase Inhibitor Cocktail. Shotgun proteomic and phospho-proteomic measurements were performed as described48 and the resulting data is reported in Supplementary Data The six cell lines listed above were plated in eight 15 cm dishes at 10 × 10https://clue.io/sop-L1000.pdf. Briefly, the L1000 assay is performed by amplifying mRNAs from cell lysates by ligation mediated amplification14. Probes containing gene-specific sequences are annealed to reverse-transcribed cDNAs, ligated with Taq ligase, amplified by PCR and then hybridized to Luminex beads. The unique fluorescence properties of these beads serve as bar codes. Beads with hybridized PCR products are detected and quantified using a Luminex FLEXMAP 3D reader. 80 transcripts are used to calibrate and normalize data.Extensive information about the L1000 method used in this paper can be found at 22. L1000 data is provided at five levels in the data processing pipeline:Level 1: Raw unprocessed flow cytometry data from Luminex (LXB)Level 2: Gene expression values per 1000 genes after deconvolution (GEX)Level 3: Quantile-normalized gene expression profiles of landmark genes and imputed transcripts (QNORM or INF)Level 4: Gene signatures computed using z-scores relative to the plate population as control (ZSPCINF) or relative to the plate vehicle control (ZSVCINF)Level 5: Differential gene expression signaturesFor each technical replicate of each drug-induced perturbation, the characteristic direction (CD) was evaluated by comparing its L1000 QNORM vector to vectors for DMSO-treated controls on the same platehttp://www.lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data). Characteristic direction signatures were calculated per batch. A batch is a group of experimental conditions measured at the same time point and cell-line but on multiple plates as described using the following notation:M, the number of experimental conditions.N, the number of control replicates.J, the number of plates.Xi,j, a vector of length 978 representing the jth replicate of the ith experimental condition. Note that since the replicates of an experimental condition are measured on different plates, j also, typically, denotes the plate.Cj,k, a vector of length 978 representing the kth control replicate on the jth plate.The normalized values for landmark genes used in the current work correspond in L1000 data sets to “level 3a” data  is the negative log of the one-tail comparison (on the lower end) of Clustering is based on the cosine distance between the CD signatures with SCS > 1.3 and relies on the algorithm fcm (fuzzy c-means clustering) from MATLAB with an exponent for the membership function matrix of 1.22. Perturbations with a membership of less than 55% in a single cluster were set aside from the clusters  and xctrl are the cell counts in drug-treated and, respectively, DMSO-treated control wells, and x0 is the 50%-trimmed mean of the cell count from a day 0 untreated plate grown in parallel until the time of treatment21. Within each experiment, technical replicates  were averaged to yield the mean normalized growth rate inhibition for each cell line, drug, concentration and condition for a given biological replicate.Cell counts were normalized to DMSO-treated controls on the same plate to yield the normalized growth rate inhibition (GR) for each plate  of each cell line at each drug and concentration. Normalized growth rate inhibition was calculated according to the formula: GR):Synergy of the combination of drugs A and B is evaluated on the GR value using the following formula for the excess over Bliss independence  was performed as described in Alvarez et al.The main data supporting the findings of this study are available in the Supplementary Data files or by following the links listed in this article. For additional data contact the corresponding author.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 9


BRAF and NRAS. We studied whether CM as well as conjunctival benign and premalignant melanocytic lesions express targets in the mitogen-activated protein kinase (MAPK) and AKT pathways, and whether specific inhibitors can suppress CM growth in vitro.Conjunctival melanoma (CM) is a rare but lethal form of cancer. Similar to cutaneous melanoma, CM frequently carries activating mutations in BRAF V600E mutation and expression of phosphorylated (p)-ERK and p-AKT were assessed by immunohistochemistry. We studied cell proliferation, phosphorylation, cell cycling and apoptosis in three CM cell lines using two BRAF inhibitors (Vemurafenib and Dabrafenib), a MEK inhibitor (MEK162) and an AKT inhibitor (MK2206).131 conjunctival lesions obtained from 129 patients were collected. The presence of BRAF V600E mutation was present in 19% of nevi and 26% of melanomas, but not in primary acquired melanosis (PAM). Nuclear and cytoplasmic p-ERK and p-AKT were expressed in all conjunctival lesions. Both BRAF inhibitors suppressed growth of both BRAF mutant CM cell lines, but only one induced cell death. MEK162 and MK2206 inhibited proliferation of CM cells in a dose-dependent manner, and the combination of these two drugs led to synergistic growth inhibition and cell death in all CM cell lines.The ERK and AKT are constitutively activated in conjunctival nevi, PAM and melanoma. While BRAF inhibitors prohibited cell growth, they were not always cytotoxic. Combining MEK and AKT inhibitors led to more growth inhibition and cell death in CM cells. The combination may benefit patients suffering from metastatic conjunctival melanoma. Conjunctival melanoma (CM) is a rare malignant ocular surface tumor, arising from melanocytes in the conjunctiva. Although CM accounts for 5% of all ocular melanoma, over the past decades, an increase in occurrence has been reported from Finland , Sweden Most CM originate in primary acquired melanosis (PAM) with atypia, while a small proportion arises from a preexisting nevus or de novo , 9. The BRAF mutation [GNAQ/GNA11 mutations which are found in most cases of uveal melanoma. In a recent study, a BRAF V600E mutation was found in 29% of CM, and an NRAS mutation in 18% [-KIT mutations are seldom found in CM [BRAF and NRAS are both known to activate the downstream kinases MEK1/2 and ERK1/2, thereby promoting tumor proliferation [NRAS. A number of clinical trials using PI3K or AKT inhibitors (AKTi) are ongoing in patients with BRAF wild type (WT), BRAFi-resistant and NRAS-mutant cutaneous melanoma, colon cancer and ovarian carcinoma [CM shares more similarities with cutaneous melanoma than with intraocular uveal melanoma. Like cutaneous melanoma, CM frequently harbors a mutation –16, as on in 18% . C-KIT mnd in CM . Mutant feration . BRAF inferation , 23. In feration , 25. MEKferation , 27. Ovearcinoma .BRAF or NRAS mutation. We furthermore evaluated the phosphorylation of ERK and AKT in a substantial series of conjunctival nevi, PAM without atypia, PAM with atypia and primary CM tissues.To determine whether the above-mentioned inhibitors are of use in CM, we tested the effect of several potentially useful drugs on three CM cell lines, each of which has either a BRAF V600E mutation in 131 pigmented conjunctival lesions from 129 patients and analyzed the expression of phosphorylated (p)-ERK and p-AKT by immunohistochemistry (Table BRAF V600E mutation in 19% of nevi (n=51) and 26% of melanoma (n=42)  or PAM with atypia (n=18). One of the BRAF mutated melanomas evolved from a background of PAM.We determined the presence of ) Figure . No BRAFP = 0.027) or CM (P = 0.046). Similarly, p-ERK nuclear expression was seen more often in PAM with atypia than in nevi (P = 0.004), PAM without atypia (P = 0.028) or CM (P = 0.014). In groups of nevi and CM, neither cytoplasmic nor nuclear expression was associated with the presence of a BRAF mutation.The presence of p-ERK and p-AKT was studied by immunohistochemistry. AKT activation is associated with phosphorylation of two residues: serine 473 (Ser473) and threonine 308 (Thr308) . Using tBRAF-mutant CM cell lines CRMM1 and CM2005.1 and the NRAS-mutant cell line CRMM2 , MEKi (MEK162) or AKTi (MK2206) for 24 hours and analyzed the effects on p-ERK and p-AKT levels by western blotting Table . In CRMMs Figure . This phll lines . The effBRAF-mutant cell lines CRMM1 and CM2005.1, but not of the NRAS-mutant cell line CRMM2. MEK162 and MK2206 inhibited growth of all CM cell lines in a dose-dependent manner, although only high concentrations of MK2206 were able to suppress the proliferation of CRMM2 and CM2005.1. Table 50 values of each agent for the three CM cell lines. Although CRMM1 and CM2005.1 both harbor the BRAF V600E mutation, their sensitivity to BRAFi differed very much. No indications for PTEN loss of function were found in the BRAF V600E mutated cell lines that could explain this difference. However, we did find a deletion in exon 2 of PTEN in the NRAS mutant cell line CRMM2.Cells were treated with each drug at 5 different concentrations and survival was determined 72 hours later Figure . As showTo obtain more insight into the effects of kinase inhibitors on cell growth and survival, we investigated their impact on cell cycle profiles using flow cytometry Figure . TreatmePoly (ADP-ribose) polymerase (PARP) cleavage is often associated with apoptotic cell death and has served as a marker for apoptosis and caspase activity . CRMM1 sBRAF-mutant, BRAF-WT and NRAS-mutant cutaneous melanoma patients [Treatment of MEKi combined with AKTi has been used to treat patients . We testpatients . Figure To investigate whether combination treatments would inhibit cell cycle progression and induce more cell death than single treatments, we performed flow cytometry. In all cell lines, the combination of MEK162 and MK2206 at low concentrations caused slightly stronger G1 arrest and depletion of S-phase compared to single treatments Figure .BRAF or NRAS mutations but frequently have mutations in GNAQ, GNA11, BAP1, SF3B1 or EIF1AX [BRAF V600E mutations occur in 19% of nevi and 26% of CM, but not in PAM, which confirms previous results [BRAF V600E mutations have been described to be less frequent in lentigo maligna lesions of the skin which show a comparable lentiginous histopathology as PAM [BRAF mutations do not determine the tumor's ERK phosphorylation status. It is unlikely that technical problems such as tissue fixation and the high temperature used for antigen retrieval impaired the staining of p-ERK, because positive p-ERK cells were abundant in positive control tissues (data not shown). Recently, loss of function mutations in NF1 have been described in a high percentage of cutaneous melanoma. NF1 suppression can lead to increased RAS activation in melanoma, which may explain the lack of correlation between ERK phosphorylation and BRAF mutational status [Uveal melanomas, in contrast to conjunctival melanomas, lack r EIF1AX . To date results . Likewisy as PAM . Furthery as PAM , 36, we l status , 38.Although p-ERK and p-AKT are regarded as being predominantly situated in the cytoplasm, the phosphorylation of ERK and AKT in the nucleus is essential for nuclear translocation and activation of transcription factors , 40; we BRAF V600E mutation, their sensitivity to BRAFi differed greatly: CRMM1 required a much higher dose of Dabrafenib to inhibit cell growth compared to CM2005.1. This could not be explained by loss of function in PTEN. Additionally, we demonstrate that BRAFi induced apoptosis of CM2005.1 cells. The cell cycle analysis indicates that BRAFi inhibited cell growth of CRMM1 by inducing a G1 arrest. In contrast, Vemurafenib and Dabrafenib did not inhibit cell proliferation of CRMM2, and they led to an increased p-ERK expression. This finding is in agreement with other preclinical studies in other malignancies, in which selective BRAFi stimulated cell growth and ERK phosphorylation in BRAF WT and NRAS mutant cutaneous melanoma cell lines [To investigate the effect of different drugs on cell proliferation and cell death, we tested BRAF, MEK and AKT inhibitors on three CM cell lines. Vemurafenib and Dabrafenib are selective BRAF inhibitors, and have been approved by the FDA as single agents for treatment of metastatic cutaneous melanoma. MEK162 and MK2206 target MEK and AKT respectively. Although CRMM1 and CM2005.1 both harbor a ll lines –43. The ll lines .BRAF or NRAS mutations. Importantly, IC50 values for cell culture are much lower than known plasma levels of MEK162 in patients (0.6-1 μM). However, similar to BRAFi, the disadvantage is that constitutive activation of the MAPK pathway can frequently lead to cross-talk with other signal transduction pathways, enabling melanoma cells to escape from MEK inhibition [MEK162 inhibits the downstream RAS/RAF pathway target MEK, thereby decreasing p-ERK levels and suppressing cell growth. We did not find other studies that have treated CM cells with this novel drug. Proliferation of all three cell lines was inhibited through downregulation of p-ERK upon MEK162 treatment. Furthermore, the data of cell cycle profiles and PARP cleavage show that in addition to cytostatic effects, MEK162 alone can prompt apoptosis, regardless of hibition . CurrentBRAF and NRAS mutant cutaneous melanoma [in vitro. Our results show that MK2206 inhibited proliferation in all CM cell lines, but at variable doses, and independent of their effect on p-AKT and p-PRAS40, indicating that the growth inhibitory effect induced by MK2206 is not specific or at least partly caused by off-target effects.The PI3K/AKT signal transduction pathway is considered a potential co-target for melanoma . To the GNAQ and GNA11 that occur in 91% of uveal melanoma also activate MAPK and PI3K/AKT pathways, which implies that the combination of MEK and AKT inhibitors may also be profitable for this patient group. Future studies on cutaneous and uveal melanoma cell lines are therefore warranted to evaluate the efficacy of combination treatment of MEK and AKT inhibitors. In clinical treatment, one may argue that multi-signal pathway blockades generate more side effects than individual kinase inhibitor. On the other hand, we have shown that combined treatment could be effective at considerably lower drug dosages and potentially ameliorate detrimental side effects.With the clinical availability of PI3K, AKT and mTOR inhibitors, a number of trials are now ongoing in cutaneous melanoma. Our studies show that combining MEK162 with MK2206 has a synergistic inhibitory effect on proliferation of three CM cell lines, regardless of their sensitivity to the individual agents. Mutations in the G-α-proteins in vitro study shows some growth inhibition and cytotoxic effect of kinase inhibitors and provides a rationale for the clinical application of these compounds in CM. Our results indicate that co-inhibition of the MAPK and AKT pathways may improve their efficacy. Clinical studies are now indicated to test the efficacy of MAPK and AKT inhibitors in metastatic CM.In summary, our data suggest that the ERK and AKT are constitutively activated in all conjunctival melanocytic lesions. Our n=25), de novo  and nevi . All melanomas described were invasive. The metastatic rate was 21.4% (n=9). Out of 18 PAM with atypia, 8 had severe atypia and 10 moderate atypia. No cases with minor atypia were included. The diagnosis was reviewed and the original slides were assessed according to the AJCC criteria. Because of the high recurrence rate, some patients underwent multiple surgeries.Paraffin-embedded tissue specimens were retrieved from the archive of the Department of Pathology of the Erasmus University Medical Center from patients with a conjunctival melanocytic lesion diagnosed between 1987 and 2013. The Institutional Review Board of the Erasmus University Medical Center waived the need for approval because of the retrospective and non-interventional character of the study. The study adhered to the tenets of the Declaration of Helsinki. Two hundred and seventy-eight samples obtained from 131 conjunctival lesions of 129 patients contained enough material for the study. In our patient cohort, CM were derived from PAM , and anti-Melan A to confirm the presence of the expected tissue histology within each tissue core. Additional sections were cut for further immunohistochemical analysis.We assessed the expression of p-ERK, p-AKT Ser473, p-AKT Thr 308 and BRAF V600E. Immunohistochemistry was performed with an automated staining system  using the alkaline-phosphatase method for all antibodies and a red chromogen. Briefly, following deparaffinization and heat-induced antigen retrieval for 60 minutes, the tissue sections were incubated with primary antibody anti-MAP kinase diphosphorylated ERK 1/2 , p-AKT Ser473 , p-AKT Thr 308 , BRAF-V600E  or Melan A  for 1 hour at 36°C. A subsequent amplification step was followed by incubation with hematoxylin II counter stain for 8 minutes and then bluing reagent for 8 minutes according to the manufacturer's instructions (Ventana). The immunostained TMAs were analyzed with light microscopy  by two different observers at different time points. The samples were scored using the immunoreactive scoring (IRS) method, described by Remmele and Stegner . The IRSin vitro tests, the stock solution was diluted in the indicated fresh medium.Vemurafenib , Dabrafenib , MEK162  and MK2206 (S1078) were purchased from Selleck Chemicals . All drugs were dissolved in dimethyl sulfoxide (DMSO) to reach a stock concentration of 10 mM. For BRAF V600E mutation, and CRMM2 contains an NRAS Q61L mutation. These mutations were confirmed at Erasmus University Medical Center, Rotterdam in May 2016 by next generation sequencing (NGS). Inactivating molecular changes in PTEN were tested by NGS as decribed before [CM cell lines CRMM1 and CRMM2 were established by G. Nareyeck, Essen, Germany , and kind before .The International Cell Line Authentication Committee (ICLAC) in Baseclear  in April, 2014, setting the standard for these three cell lines [Short tandem repeat analysis was performed using the AmpFLSTR® Identifiler™ PCR Amplification Kit  based on the procedure recommended by ll lines . They weCells were lysed in M-PER Mammalian Protein Extraction Reagent , supplemented with protease and phosphatase inhibitors . Proteins  were separated on Mini-PROTEAN TGX Precast Gels  using the Mini-PROTEAN Tetra system . Proteins were blotted onto polyvinylidene difluoride (PVDF) membranes  using the Trans-Blot Turbo System (Bio-Rad). Subsequently, the membranes were incubated with the appropriate primary antibodies and IRdye-680 or IRdye-800 dyes (LI-COR), and analyzed with the Odyssey Infrared Imaging System (LI-COR). Signal intensity was analyzed with Odyssey 3.0 software. Alternatively, membranes were incubated with HRP-conjugated secondary antibodies, and the bands were visualized by chemoluminescence  and exposed to X-ray films . Antibodies are listed below: BRAF ; anti-MAP kinase diphosphorylated ERK 1/2 ; p44/42 MAPK (ERK1/2) , Leiden, The Netherlands); p-AKT (Ser473) ; AKT (pan) ; PARP ; p-PRAS40 (Thr246) , PRAS40  and Vinculin .50 values were calculated on the basis of the growth inhibition curves and determined with CompuSyn software [Cells were seeded in triplicate in 96-well plates at a density of 2500 (CRMM1 and CRMM2) or 5000 (CM2005.1) cells per well, in a total volume of 100 μl medium. The next day, the media were refreshed and cells were treated with drugs at various concentrations. Cell survival was determined 72 hours later by an In-cell western assay : after rsoftware . For synsoftware . Dose-efCells were harvested, washed in PBS and fixed in ice-cold 70% ethanol and stored at -20°C. Subsequently, cells were washed with 2%FBS/PBS and suspended in 2%FBS/PBS containing 50 μg/ml propidium iodide (PI) and 50 μg/ml RNase. Flow cytometry was performed in the BD LSR II system  and the data were analyzed with FACSDiva software (BD).in vivo. Two-tailed P values equal to or below 0.05 were considered to be statistically significant. The plots of cell proliferation and cell cycle profiles were drawn with GraphPad Prism 5 software .Statistical analyses were performed with SPSS version 22.0 software . The Pearson chi-square test was used to compare the protein expression


ANRIL, antisense to the CDKN2B locus, is transcribed from a gene that encompasses multiple disease-associated polymorphisms. Despite the identification of multiple isoforms of ANRIL, expression of certain transcripts has been found to be tissue-specific and the characterisation of ANRIL transcripts remains incomplete. Several functions have been associated with ANRIL. In our judgement, studies on ANRIL functionality are premature pending a more complete appreciation of the profusion of isoforms. We found differential expression of ANRIL exons, which indicates that multiple isoforms exist in melanoma cells. In addition to linear isoforms, we identified circular forms of ANRIL (circANRIL). Further characterisation of circANRIL in two patient-derived metastatic melanoma cell lines (NZM7 and NZM37) revealed the existence of a rich assortment of circular isoforms. Moreover, in the two melanoma cell lines investigated, the complements of circANRIL isoforms were almost completely different. Novel exons were also discovered. We also found the family of linear ANRIL was enriched in the nucleus, whilst the circular isoforms were enriched in the cytoplasm and they differed markedly in stability. With respect to the variable processing of circANRIL species, bioinformatic analysis indicated that intronic Arthrobacter luteus (Alu) restriction endonuclease inverted repeats and exon skipping were not involved in selection of back-spliced exon junctions. Based on our findings, we hypothesise that “ANRIL” has wholly distinct dual sets of functions in melanoma. This reveals the dynamic nature of the locus and constitutes a basis for investigating the functions of ANRIL in melanoma.The long non-coding RNA  ANRIL (antisense non-coding RNA in the INK4A locus) is a long non-coding RNA (lncRNA) that was originally identified in familial melanoma patients with a large germline deletion in the CDKN2A/B  gene cluster and is reported to be deregulated in several malignancies such as gastric, breast, lung and bladder cancer [r cancer ,2,3,4,5.r cancer .ANRIL is that it mediates repression of the CDKN2A/B locus, by association with polycomb repressor complexes (PRC1 and PRC2) [CDKN2A or CDKN2B and ANRIL, again in different tissues and cell types [CDKN2A/B by ANRIL via PRC1 and PRC2 is under re-evaluation due to the promiscuous nature of binding of the PRC components to RNA molecules [The most commonly accepted function of nd PRC2) . Howeverlexes PRC and PRC2ll types ,10,11,12olecules .ANRIL (circANRIL) isoforms has been reported by several groups [circANRIL was found to be associated with ribosome biogenesis and nucleolar stress [The existence of multiple linear and circular l groups ,10,14. Rr stress . Despitetrans-regulation was found to be dependent on Alu motifs, which marked the promoters of ANRIL target genes and which were mirrored in ANRIL transcripts [ANRIL transcripts containing the Alu repeats were predicted to form a stem-loop structure, suggesting RNA-chromatin interactions as potential effector mechanisms [In some contexts, nscripts . ANRIL tchanisms .ANRIL present in melanoma cells. We chose this cell type because of the importance of the chromosome 9p21 locus including CDKN2A/B and ANRIL in familial and sporadic melanoma, and the availability of a large panel of New Zealand melanoma (NZM)-derived cell lines. We chose two cell lines, one producing CDKN2A (p16) protein and one not, which were being characterised as part of a separate study, but were selected for this study to ascertain whether their complement of ANRIL isoforms evinced the same specificity of processing, and whether their production conformed to patterns described by others. We found that these melanoma lines expressed a profusion of both linear and circANRIL isoform variants. Characterisation of the large number of circular isoforms, which appeared to be largely different in the two cell lines, further revealed the true complexity of this locus. We observed variability in the localisation pattern of these isoforms with linear ANRIL enriched in the nucleus and circANRIL enriched in the cytoplasm. Additionally, we found that the processing of circANRIL did not fit in with the published models of circRNA biogenesis that invoke either duplex formation of inverted Alu repeats located in long introns that flank exons, or “exon skipping” [Considering this, we set out to characterise the isoforms of  CDKN2A p protein ANRIL transcript. However, due to the length of the ANRIL transcript, quantitative PCR (qPCR) was performed using five primer sets  cases in TCGA database . Spearman correlation was also determined for each pair of samples. No correlation was found for the expression of linear and circANRIL variants (p > 0.05).Expression levels of circular (containing exon 14-5 junction) and linear ANRIL isoforms was also determined after actinomycin D treatment of melanoma cells (circANRIL and linear ANRIL. CircANRIL (p = 0.063) was found to be more stable than its linear counterpart (p = 0.0156) in case of NZM7 (circANRIL (p = 0.66) comparatively more stable than linear ANRIL (p = 0.327).The stability of circular and linear ma cells D,E. Line of NZM7 D. Even tcircANRIL isoforms was undertaken by employing a unbiased, systematic approach using outward-facing primers against exons 2, 4, 6, 7, 8, 10, 12, 19, 14, 16, 17 and 18 , 6-4-5-6 (6-4), 6-14-5-6 (14-5) and 14-4-5-6-7-14 (14-4), circANRIL variants. We found new exons upstream of exon 4, designated E4 (1) and E4 (2), that have not been reported previously . Analysis confirmed the length of exons and introns in circular and linear RNA to be similar .We examined the length of exons and introns from publicly available data for circRNA  in seven cell lines  (ANRIL in the nucleus.We sought to ascertain whether the diversity of linear and circractions . We perfd NZM87) . The rescircANRIL was also considered in the same set of melanoma lines. Quantitative RT-PCR was performed using the primer set targeted towards the exon 14-5 junction. Unlike linear ANRIL isoforms, most circANRIL transcripts localised to the cytoplasm as compared to the nucleus for almost all cell lines  has revealed differences in the abundance of nscripts ,14.ANRIL species was evident through qPCR and RNA-seq data in the panel of melanoma cell lines tested here and in melanoma samples in TCGA database respectively , diabetes, and cancers are highly associated with ANRIL expression and splicing [Diverse populations of splicing . The preANRIL may vary with the type of transcript variant expressed in each cell or tissue type, and hence secondary structure. Following this, further characterisation of various isoforms of circANRIL using outward primers revealed several novel isoforms, junctions, exons and splice variants of exons when compared to the previous study [circANRIL. Previous studies have also confirmed the presence of exons 5 and 6, and junction 14-5 as the predominant exons and junction, respectively. However, the diversity of isoforms is very variable in different tissues examined so far [circANRIL manifests a marked tissue specificity.Identification of isoforms is critical as the function of us study   [LncRNAs must localise to their specific and diverse sites of action. Their location within cells is an important clue to assigning function. For instance, finding a lncRNA primarily in the nucleus near its site of transcription may suggest that it regulates transcription of a proximal gene  . We atteensions) .ANRIL, our results confirmed that both proximal and distal exons  are predominantly found in the nucleus , an essential 60S preribosomal assembly factor [circANRIL induced nucleolar stress and p53 activation resulting in induction of apoptosis and inhibition of the cell cycle, which would be expected to confer protection against the development of atheroma, and also tumour development [circANRIL in melanoma cell lines suggests that expression of isoforms is tissue specific, implicating circANRIL in alternative functions.A major proportion of ll lines . This suy factor . Consequelopment . HoweverSRPK1 is overexpressed [MALAT-1 has been associated with splicing [Additionally, based on the heterogeneity of isoforms found in our preliminary study, we propose that our cell lines possess divergent RNA processing activities. In uveal ,27 and bxpressed . These esplicing , binds tsplicing , and is splicing ,33. The ANRIL isoforms in multiple cell lines of the same and different tumour types, including representatives of normal, noncancerous cells, will be needed. Future studies will combine with this study to provide a picture of how the isoforms are produced in different tumour cell lines. Only then will the function of “ANRIL”  be disclosed.Systematic work to catalogue 2) in order to mimic physiologically low oxygen levels in tumours. NZM cell lines were grown in α-modified minimal essential medium  supplemented with insulin (5 µg/mL), transferrin (5 µg/mL) and sodium selenite , 100 U/mL of penicillin, 100 µg/mL of streptomycin (PS) and 5% fetal bovine serum (FBS).The New Zealand melanoma (NZM) cell lines were generated from surgical samples of metastatic melanoma as previously described . WrittenTotal RNA from cultured cells or RNA from cell fractions was purified using Trizol  and treated with DNase I  according to the manufacturer’s instructions. Random hexamers was used to make cDNA using 1 μg of RNA with M-MLV-Reverse Transcriptase (Sigma-Aldrich) according to manufacturer’s instructions.2. The cycling conditions used for NEAT1 and GAPDH were as follows: 95 °C for 10 min, 30 × 95 °C for 30 s, 60 °C for 45 s, and 72 °C for 1 min, and 72 °C for 15 min. Amplified products were visualized after electrophoresis on a 0.5–1% agarose gel.PCR primers  were des−1/slope. For normalisation of transcript expression levels, 18S and 28S ribosomal RNA (rRNA), or GAPDH and HPRT were used as reference transcripts. The average expression of these reference genes was used to calculate the relative expression of the genes of interest.All primers  used for qPCR were designed using Primer3 v. 0.4.0 software. qPCR was performed with a final 1 in 10 dilution of cDNA, 0.8 µM primers and 1x Sybr Green MasterMix (Life Technologies). Primer efficiencies were calculated using the equation: Efficiency = 10The RNAseq and exon expression data from TCGA Skin Cutaneous Melanoma (SKCM) gene expression (Illumina Hi-Seq) and exon expression (Illumina Hi-Seq) were used to examine the gene expression and exon expression respectively. Statistical analysis was performed using Sigma-Plot. Correlation analysis was performed with Pearsons Product Moment Correlation (R) and statistical significance (p). To examine the exon expression, we derived the expression level of each exon based on its chromosomal location and the average value for each exon used in bar graph .circANRIL, primers pointing in opposite directions on exon 14-5 were designed, as described in [For qPCR and RT-PCR of ribed in , using PANRIL respectively, relative to the expression of GAPDH. Linear regression analysis was performed to determine stability for both NZM7 and NZM37.Melanoma cell lines NZM7 and NZM37 were treated with 10 µg/mL actinomycin D (Sigma-Aldrich) for 2, 6, 8 and 12 h. Total RNA from the cells was isolated for each point and cDNA synthesised as described before. Quantitative PCR was then done using primer sets for exon 1 and the exon 14-5 junction indicative of the presence of linear and circular isoforms of ®-T Vector cloning kit . Sequencing of the resulting clones using T7 and SP6 primers was performed in two directions using ABI Prism 3730xl Genetic Analyzer as above.Outward facing primers were used against exons 2, 4, 6, 7, 8, 14 and 16 with forward primer against the 3′ end of each exon and reverse primer against 5′ end of the same exons. RT-PCR conditions used were the same as mentioned before. When multiple bands were detected, cloning of the PCR products was conducted using a pGEMhg19) downloaded from University of California, Santa Cruz (UCSC) genome browser. We also extracted the circRNA genomic regions from circBase [For the Alu elements enrichment analysis we extracted the DNA repetitive Alu elements from the rmsk table for human genome  for 5 min. Cells were lysed using a Wheaton Dounce tissue homogeniser . Lysed cells were then centrifuged at 1000 rpm at 4 °C for 5 min and the supernatant was collected as the cytoplasmic fraction and RNA from cytoplasmic fractions was isolated using Trizol (Life Technology) as described above. The cell pellet was applied to a sucrose cushion followed by centrifugation at 600× g for 15 min. The supernatant was discarded and the pellet was stored as the nuclear fraction and nuclear RNA was isolated using Trizol (Life Technology) as described above. The purity of the cell fractions was examined by RT-PCR using NEAT1 as a nuclear marker, and the quality of RNA obtained from each fraction was assessed using GAPDH. qPCR was done for the cell fractions using different primer sets for linear and circANRIL isoforms.The Lamond lab protocol was used for cell fractionation, as described in ANRIL isoforms in melanoma cell lines. The results led us to the discovery of multiple linear and circular ANRIL species. We also characterized the different isoform of circANRIL in these cell lines and revealed the presence of an assortment of circular isoforms with varying lengths, exon-exon junctions and exon combinations. By analysing the length of introns and presence of Alu elements in introns, we found that formation of non-canonical back-spliced junctions was not mainly determined by these factors. We also observed that linear and circANRIL isoforms differ in their subcellular localisation, with linear isoforms enriched in the nucleus and circANRIL enriched in the cytoplasm. Based on our findings on subcellular localization of linear and circular ANRIL and lack of correlation between their expression, we hypothesise that “ANRIL” has wholly distinct dual sets of functions in melanoma cell lines. This reveals the dynamic nature of the locus and constitutes a basis for investigating the functions of ANRIL in melanoma.In this study we investigated the differential expression of


Human gallbladder cancer (GBC) is an aggressive malignant neoplasm with a poor prognosis. The development of ideal tools for example tumor cell lines for investigating biological behavior, metastatic mechanism and potential treatment in GBCs is essential. In present study, we established and characterized a GBC cell line derived from primary tumor.Primary culture method was used to establish this cell line from a primary GBC. Light and electron microscopes, flow cytometry, chromosome analysis, heterotransplantation and immunohistochemistry were used to characterize the epidemic tumor characteristics and phenotypes of this cell line.A novel GBC cell line, named TJ-GBC2, was successfully established from primary GBC. This cell line had characteristic epithelial tumor morphology and phenotypes in consistent with primary GBC, such as polygon and irregular cell shape, increased CA19-9 and AFP levels, and positive expression of CK7, CK8, CK19 and E-cadherin with negative vimentin. Moreover, about 25% of the cells were in the S-G2/M phase; abnormity in structure and number of chromosome with a peak number of 90–105 and 80% hypertetraploid were observed. Furthermore, this cell line had higher invasion and highest migration abilities compared to other GBC cell lines; and metastatic-related marker MMP9 and nm23 were positively expressed.A novel highly aggressive GBC cell line TJ-GBC2 was successfully established from primary GBC. TJ-GBC2 cell line may be efficient tool for further investigating the biological behaviors, metastatic mechanism and potential targeted therapy of human GBC. Human gallbladder cancer (GBC) is the most common malignancy of the biliary tract and the leading cause of cancer-related deaths in China, and is a lethal aggressive malignant neoplasm with special malignant biological characteristics, high early local invasion, extensive liver and lymph node metastases, low surgical resection rate (about 10% of GBC patients have a chance to receive surgery in the early stage), high postoperative recurrence rate, less sensitive to chemoradiotherapy, and unfavorable survival . DespiteThis study was carried out in accordance with the Declaration of Helsinki and the official recommendations of Chinese Community Guidelines, and was approved by the Ethics Committee and the Institutional Review Board at the Tongji Hospital. Written informed consent was obtained from this patient and his relation.A 67-year-old Chinese man with symptoms of acute cholecystitis was referred to our hospital. High levels of CA19-9 (>1000 U/ml), CA242 (58.4 U/ml), CA50 (428.4 U/ml) and CEA (7.8 ng/ml) were detected in the patient’s serum by radioimmunoassay, whereas serum AFP showed in a normal range. Abdominal CT revealed a thickened, irregular gallbladder wall (1.5 cm) with involvement of the liver bed (6.0 cm) and hepatic bile duct dilatation. A radical GBC resection with partial hepatectomy was done. The postoperative pathological examination of the en bloc resected specimen showed that the GBC represented a poor differentiated adenocarcinoma forming nest-streak like arranged structures with atypical hyperplasia and caryokinesis, and most cells were of mucous epidermoid carcinoma differentiation  containing 20% FBS and 100 U/ml antibiotics along the edge of culture bottle slowly. After 5-day incubating, a small amount of cells climbing out around the tissue fragment, and a large number of lymphocytes and other miscellaneous cells were observed. The growth medium was renewed and replaced every 3 days, and the bottles were regularly checked for epithelial cells and fibroblast outgrowth. If fibroblast growth was observed during primary cultures, differential trypsinisation was used to obtain a pure tumor-cell population. After 5–6 passages tumor cells were basically purified. The cell line was cultured for >60 passages.The cell line was established from a primary tumor, which was surgically obtained from above GBC patient. After rinsing thrice with sterile PBS containing antibiotics, the tumor was minced into small fragment having a diameter of 1 mm using a scalpel, and completely eliminated subcutaneous fat and submucosa. The fragment was rinsed with PBS for 3 min, wet with 20% FBS , then seeded into 25 ml culture bottle . And, the culture bottle was inversionally incubated in a humidified incubator  at 37 °C in a 5% CO7/ml) were harvested, washed, suspended in 0.1 ml of PBS, were then injected subcutaneously into the right flanks of 4-week-old athymic female nude mice . Animals were examined every week for the development of tumors. Tumor-bearing mice were sacrificed. And, tumor tissue was excised, fixed in 10% formalin, and processed for histopathology and immunohistochemistry.This study was carried out in accordance with ARRIVE  guidelines , and wasMorphologic observation included morphologic structure and ultrastructure of TJ-GBC2 cells in vitro and morphologic structure of the xenograft of TJ-GBC2 cell lines in nude mice in vivo. For microscopy, the cultured TJ-GBC2 cells were photographed directly without staining, and histomorphologic structure of the xenograft in vivo was observed with H&E staining under a phase contrast microscope . For electron microscopy, the monolayer cells cultured in the flasks were fixed with 2.5% glutaraldehyde in 1 ml PBS (pH7.2), and post-fixed in a solution of 1% osmium tetraoxide. After dehydration in graded ethanol, the samples were then embedded in Epon resin. Ultrathin sections were stained with 2.3% uranyl acetate and lead citrate, and examined under a TEM (Jeol-1230) or SEM .4 cells/100 μl/well) in DMEM/F12 medium with 10% FBS. The cell numbers were measured by a MTT assay according to the protocol provided by the MTT manufacturer. The doubling times were determined from the growth curve.Cultured TJ-GBC2 cells  and SGC996 cells derived from another primary GBC (control group) were used in this experiment. Cells were grown in a 96-well plate (5 × 106) in an exponential growth phase were harvested and fixed with cold 70% alcohol after rinsing with cold PBS twice, incubated at 4 °C environment for 24 h. After being centrifuged at 1000 r/min for 5 min, the cells were rinsed with cold PBS once, suspended in 500 μl PBS with 5 μl RNAase  and incubated at 37 °C for 30 min, then stained with 5 μl propidium iodide . This cell cycle analysis was performed in triplicate.Cell cycle analysis was performed using a FCM (FlowJo software). Cells  and NOZ (gifted from Professor Liu YB) cell lines were maintained in DMEM  supplemented with 10% FBS; whereas the OCUG-1 (gifted from Professor Liu YB) and SGC-996  cell lines were maintained in RPMI-1640 medium  supplemented with 10% FBS, respectively.4/well) were seeded onto the upper chamber, 600 μl fresh growth medium with 10% FBS were placed into the lower chamber. After 24-h in a humidified incubator at 37 °C with 5% CO2, cells that invaded through the basement membrane were stained with Giemsa , and counted under an inverted light microscope (Caikang XDS-100) in 5 independent fields at ×200 magnification. Three independent experiments were performed.Cell invasion in vitro was assessed using the Transwell chambers . 200 μl cell suspensions (5 × 105/well) were seeded in a 96-well plate  for 24 h. When cultured cells reached 50% confluence in a single layer, a wound was scratched at the center of the cell monolayer using a sterile scratch tester. Then, cells were washed with sterile PBS to remove floating cellular debris, and added with growth medium with FBS for 24 h. The cell migrating area was scanned and analyzed at 0 h, 8 h and 24 h using a Cellomocs , and was observed under an inverted light microscope (Caikang XDS-100) at × 50 magnifications. Cell migration area (pixel area) = (S3 + S4) − (S1 + S2). All experiments were performed in triplicate.Cell migration in vitro was determined using a wound-healing assay. 200 μl cell suspensions (5 × 105) were collected and centrifuged at 1000 r/min for 5 min. The supernatant was collected for CEA, CA19-9 and AFP. Pure growth medium was selected for a negative control.Epithelial tumor markers including CEA, CA19-9 and AFP in the supernatant from the cell culture were detected using an electrochemistry luminescence immunity analyzer . The cultured cells  were dehydrated in xylene and graded ethanol series, were added in order with primary antibody , biotinylated secondary antibody, SABC reagents and DAB solution , respectively; i.e., the samples were stained with Santa Cruz ABC kit according to the protocol provided by the manufacturer, and observed under an optical microscope  with ×100–400 objectives. Light brown or tan particles in cytoplast were regarded as positive. For negative control, the slides were treated with PBS in place of primary antibody.t test and F test. P < 0.05 was considered statistically significant.All data were expressed as mean ± SD and analyzed using SPSS . Statistical analyses to determine significance were tested with Student’s This present study, a cell line was in vitro successfully established from a primary tumor, which was derived from a surgically resected specimen of primary GBC, using primary culture of tissue fragment and differential adherent purified method; and the cell line was successfully frozen, resuscitated and cultured in DMEM/F12 medium supplemented with 10–20% FBS for >60 generations. In June 1999, our Tongji University established first human GBC cell line SGC-996, which was derived from primary GBC. Thus, this novel GBC cell line is currently denominated as TJ-GBC2 .Here, the epithelial tumor morphological characteristics of the TJ-GBC2 cells in vitro and the xenograft of TJ-GBC2 in nude mice in vivo were observed, and compared with the morphological characteristic of primary GBC. As showed in Fig. Growth characteristics of TJ-GBC2 cell line composed of the proliferation-related properties including proliferation capability, cell cycle and karyotype of TJ-GBC2 cells in vitro and the tumor growth of xenograft e.g. heterotransplantation in vivo. The proliferation capability of TJ-GBC2 cells was assayed using the MTT method. Cell growth curve of TJ-GBC2 cell line was showed in Fig. P = 0.000); but CEA showed normal levels . Characteristic epithelial marker CK7, CK8, CK19 and E-cadherin were positively expressed in the xenografts of nude mice, with positive p53 expression in few cells and negative mesenchymal marker vimentin expression ; whereas no difference on the number of invaded cells among NOZ, GBC-SD, OCUG-1 and TJ-GBC2 cell lines was observed ; of them, TJ-GBC2 cell line had a highest migration ability compared to GBC-SD, NOZ and OCUG-1 cell lines (all ¶P < 0.01; Fig. In order to identify the aggressive capability of TJ-GBC2 cell line, the invasion and migration assays for TJ-GBC2 cell line were performed; and, the two GBC cell lines derived from primary GBC: GBC-SD and SGC996, and two GBC cell lines derived from ascites of GBC patients: NOZ and OCUG-1 were selected for positive controls. As showed in Fig. Human GBC is a highly aggressive malignant tumor with special biological behavior and poor prognosis. Surgical resection, chemotherapy and radiotherapy for the disease are disappointing –6. So, tHuman GBC cell lines are relatively scarce. Nowadays, more than a dozen of GBC cell lines were available, including G-415, GBK-1, KMG-A, FU-GBC-1, FU-GBC-2, NOZ, PTHrP-GBK, GB-d1, TGBC1TKB, TGBC2TKB, OCUG-1, TYGBK-1, HAG-1, GBC-SD, SGC-996, EH-GB1 and EH-GB2 –24. Of tEpithelial tumor characteristics include epithelial morphological features, positive expression of epithelial markers with negative mesenchymal expression, and positive expression of epithelial tumor markers. As showed in Figs. Metastasis, the spread of malignant cells from a primary tumor to distant sites and forms a tumor of same nature , 28, is Collectively, in this study, we firstly established a novel highly aggressive TJ-GBC2 cell line derived from a Chinese patient with primary GBC. This GBC cell line has characteristic epithelial tumor morphology and phenotypes in consistent with primary GBC and highly aggressive potential, and reflects more accurately the characteristics of the primary GBC cells. Thus, TJ-GBC2 cell line may provide an efficient tool for further investigating the metastasis mechanism, early diagnosis and potential targeted therapy of human GBC.


Immortalized cell lines are widely used as in vitro models to evaluate novel therapeutic agents and characterize their molecular mechanisms. However, it is known that long-term cultivation leads to clonal selection, genetic instability, and loss of the initial heterogenic character, limiting the usefulness of cell lines as preclinical models. Herein, we present a systematic characterization and comparison of the transcriptomic landscape of canine primary B- and T-cell lymphomas, five lymphoid cell lines  and four non-neoplastic control samples. We found that lymphomas and cell lines exhibit a common “differentiation and proliferation signature”. However, our analysis also showed that, independently of the cell of origin, the transcriptional signatures of lymphomas are more similar to each other than they are to those of cell lines. In particular, we observed that not all common therapeutic targets are similarly expressed between lymphomas and lymphoid cell lines, and provide evidence that different lymphoid cell-lines should be used to model distinct aspects of lymphoma dysregulation.In dogs as well as humans, lymphoma is one of the most common hematopoietic malignancies. Furthermore, due to its characteristics, canine lymphoma is recognized as a clinically relevant  Similarly to human NHLs, canine lymphomas (CLs) account for 83% of all hematopoietic malignancies, and approximately 6% of all canine neoplasias3. Additionally, there is a remarkable resemblance between CLs and NHLs, including clinical presentation, tumor biology, and response to therapeutic agents, such as conventional chemotherapy. Akin to their human counterparts, CLs are classified into several subgroups, with B- and T-cell lymphomas being the most prominent. B-cell lymphomas represent approximately 70% of the reported cases, while T-cell lymphomas are seen in around 20%4. Equally important, lymphomas occur naturally in dogs in the presence of an active immune system. Further, dog physiology and drug metabolism are more similar to those of humans than of rodents, making CL an invaluable model for NHL research.Lymphoma is one of the most common hematopoietic malignancies. Approximately 89% of human lymphomas are non-Hodgkin’s lymphomas (NHLs), and they represent about 5% of all newly diagnosed cancer cases per year in the United States . In canine patients, advanced directed therapeutic interventions is hampered by limited detailed information on molecular markers for dysregulated pathways, tumor character, and consequential chemotherapeutic responsiveness and tolerance.The usual therapeutic management of CLs is similar to that of NHLs and consists of a CHOP -based chemotherapy. A complete remission is habitually achieved within three weeks, but the majority of canine patients relapse within the following twelve months. Therapeutic response and survival are highly correlated with lymphoma subtype, which is normally determined by immunophenotypic analysisin vitro cancer models. Indeed, cell lines permit measurements under controlled conditions, and novel drugs and drug combinations are primarily evaluated in suitable cell line sets. Moreover, long-term cultivation of primary lymphoma material is infeasible. Hence, lymphoma therapy response at phenotypic, cytomorphologic, and other molecular levels are studied in lymphoid cell lines, which are widely accepted as a relatively stable in vitro system for these purposes. Commonly used CL cell lines include CLBL-1, CLBL-1M, GL-1, CL-1 and OSW10. In particular, CLBL-1 represents a canine B-cell lymphoma cell line comprehensively characterized and compared to primary neoplastic samples14. Nevertheless, despite the indisputable value of cell lines, clonal selection and long-term passages in vitro are known to lead to the loss of many attributes of the initial tumor, e.g. its heterogeneity15, and thus, well established cancer cell lines often show distinctive expression profiles from their clinical counterparts16. A comprehensive comparative molecular analysis of lymphoid cell lines and primary lymphomas is crucial to identify concordant and discordant biological functions, processes, and/or pathways. Additionally, this knowledge should enhance the efficiency of drug screening.Immortalized cancer cell lines are frequently used as 20 as well as in veterinary oncology26. Microarray-based analyses of different CL subtypes allowed Frantz et al. (2012) to define three major prognostically significant groups and propose a robust real-time PCR-based screening assay that relies only on four genes . Furthermore, comparative microarray-based studies in canine and human diffuse large B-cell lymphomas (DLBCL) revealed the activation of the NF-KB pathway25 as well as the dysregulation of pathways potentially bearing therapeutic and prognostic value, such as the PI3K/AKT, Notch and JAK/STAT pathways24, in both species. Gene profiling has also found noticeable differences between human and canine lymphomas. Thus, in contrast to human B-cell lymphomas, canine B-cell lymphomas have been shown to rarely express BCL6 and MUM1/IRF425. Compared to conventional molecular approaches, massive parallel sequencing technologies offer numerous advantages: (i) they are not limited to known transcripts; (ii) they have a broader dynamic range of expression levels; (iii) they are more specific and sensitive; and (iv) can be used in a variety of applications, including the detection of transcript fusions, differential exon usage, and alternative splicing27. In particular, we have recently used mass parallel sequencing in order to characterize and evaluate experimental drug modes of action in the CL cell lines CLBL-1 and CLBL-1M as a model for human neoplasias11. Here, we comparatively characterize the transcriptional signatures of 16 primary CL samples, five CL cell lines and four non-neoplastic control samples by massive parallel sequencing (RNA-seq).High-throughput gene expression analysis has proven to bear major potential in human2 fold-changes between −2 and 2  up-regulated genes were not associated with any specific functions, extremely down-regulated genes (log2 fold-change ≤ 5) were associated with lymphocyte differentiation and apoptosis, among others, evidencing impaired lymphocyte activity.To investigate how representative lymphoid cell lines are of lymphomas, we systematically compared the transcriptomic profiles among primary canine lymphomas, five canine lymphoid cell lines, and four non-neoplastic lymph nodes, which were used as controls (see Methods). Specifically, we considered three lymphoid cell lines derived from B-cells  and two cell lines derived from T-cells (CL-1 and OSW). Twelve samples were classified as B-cell lymphomas and one sample was classified as T-cell lymphoma using flow cytometry (see Methods). Three additional lymphoma samples presented an uncertain phenotype, but were identified herein as probable T-cell lymphomas based on their transcriptomic profiles  of the moderated logsee Fig. . Thus, Csee Fig. .2 fold-changes between −2.6 and 2.4 with respect to control, and no two samples groups exhibiting opposite differential expression trends. Nevertheless, these differences were enough to distinguish between the lymphomas and lymphoid cell lines  in lymphomas and lymphoid cell lines relative to controls (see Methods). These miRNAs showed relatively small differences in expression, with moderated logOverall, our results indicate that whole genome transcriptomics can be used to reliably subtype CLs.28.Most of the 5,712 differentially expressed genes are most likely downstream effectors, that is, genes at the bottom hierarchical layers of the perturbed transcriptional and signaling networks. 1,703 genes were differentially expressed in lymphomas relative to controls, with 439 of these genes being differentially expressed in both B-cell and probable T-cell lymphomas  exhibited discordant differential expression: SHCBP1 is down-regulated in GL-1 cell lines and up-regulated in all remaining sample groups, whereas FLT3 is up-regulated in GL-1 cell lines and down-regulated in all remaining sample groups. While FLT3 mutations are not directly associated with lymphomas, FLT3 mutations are frequently observed in acute myeloid leukemia and considered a negative prognostic factor for this disease29. The 166 differentially expressed genes were associated with molecular functions and biological processes such as cell cycle, mitotic cell cycle and nuclear division, chromosome segregation, organelle fission, DNA replication, nuclear and cell division, cytoskeleton organization, nucleotide binding, and ATPase activity, among others  proteins is known to have a crucial role in T-cell activity and B-cell development32.Eighty-four percent of the 1,703 genes that were differentially expressed in lymphomas relative to controls were also differentially expressed in at least one lymphoid cell line. Moreover, 95%  of these genes were consistently up- and down-regulated; specifically, 481 genes were up-regulated in lymphomas and either up-regulated or unchanged in all lymphoid cell lines, while 881 genes were down-regulated in lymphomas and either down-regulated or unchanged in all lymphoid cell lines  of the genes that were up-regulated in B-cell lymphomas were also up-regulated in all B-cell-derived lymphoid cell lines see Fig. . Similar36 . To identify common dysregulated pathways among the different sample groups we examined pathway net expressions, computed as the number of up-regulated minus the number of down-regulated genes within a sample group in relation to the total number of genes in a given pathway (see Methods). Net expression values varied between −0.58  and 0.52 , with a median of −0.06. In particular, several of the genes downregulated in the pathway with the most negative net expression values, “tuberculosis”, belong to the major histocompatibility complex class II  and/or are complement factors (11), which are key in immune response and antigen presentation. An impaired antigen presentation is essential for tumor cells to evade an immune response37. Consistent with the overall numbers of up- and down-regulated genes we found that a large number of pathways  exhibited negative net expressions. Also, a total of 219 pathways had net expression values close to zero in at least one sample group, in the sense their absolute net expression values were in the range −0.05 to 0.05. Pathways with similar net expression profiles across all lymphoma and lymphoid cell line sample groups were clustered together using self-organizing maps  are encoded by PIK3CA, PIK3CB, PIK3CD and PIK3CG. Consequently, PIK3 inhibitors are used in targeted therapeutic approaches for several human hematopoietic neoplasias and are in clinical trials for lymphoma types such as indolent non-Hodgkin’s lymphoma and DLBCL44. None of the aforementioned genes were differentially expressed in B-cell lymphomas relative to controls and most were not differentially expressed in probable T-cell lymphomas either. Only SYK was down-regulated (log2 fold-change −2.7) and BTK showed a borderline non-significant tendency towards down-regulation (log2 fold-change −2.0) in probable T-cell lymphomas. Among the cell lines, we found that GSK3B expression was slightly up-regulated in OSW cells (log2 fold-change 1.0), while AKT1 was not differentially expressed. SYK and BTK were down-regulated in OSW and CL-1 cell lines . Lastly, PIK3CD was down-regulated (log2 fold-change −3.2) in CL-1 cell lines. Independently of their efficacy as therapeutic agents in the canine model, AKT1, SYK and BTK expression is consistent between B-cell lymphomas and B-cell-derived cell lines as well as between probable T-cell lymphomas and T-cell-derived cell lines, indicating the suitability of the cell lines to evaluate AKT1, SYK and BTK inhibitors. In contrast, the divergence in expression observed for GSK3B and PIK3CD between lymphomas and cell lines is likely due to clonal selection or cultivation induced artifacts, hence limiting the value of the cell lines as drug testing system. It is important to note, though, that since kinase inhibitors show major effects in protein phosphorylation patterns, further evaluation at the protein level using canine specific antibodies should be warranted to evaluate the relevance of these findings.22. Our results confirm that massive parallel sequencing (RNA-seq) can also be used for classification of B- and T-cell lymphomas in dogs, enabling a depth of analysis that was not feasible with conventional PARR (PCR for Antigen Receptor Rearrangement) and flow cytometry.Historically, classification of lymphomas has been dominated by histological and histopathological methods, but has recently evolved with the application of high-throughput transcriptomics and proteomics technologies. Thus, in humans, genome-wide gene expression profiling has led to the definition of different molecular subtypes with distinct prognosis46. Our comparative analyses of primary lymphoma and lymphoid cell line samples at the transcriptome level indicate that in vitro cultured lymphoid cell lines at least partially retain the transcriptomic landscapes of primary lymphomas. Specifically, we found that the majority of the differentially expressed genes are down-regulated compared to the controls. This suggests that an important mechanism in both lymphoid neoplasms and lymphoid cell lines involves turning off, rather than activating the expression of genes. Consistently, extremely down-regulated genes were highly enriched for normal lymphocyte functions, most likely indicating a dedifferentiation of the cells. In addition, genes that were differentially expressed in both primary lymphomas and lymphoid cell lines relative to controls were strongly associated with cell differentiation and proliferation. Distinct examples of proliferation signatures have been identified in many different tumor and tumor-derived cell types33. Basically, such proliferation signatures consist of a core set of genes whose expressions correlate with rapid cell proliferation and, they have been proposed as a component of clinical diagnostics for cancer patients, for example, to distinguish between low and high grade breast cancers46. We also observed a large number of genes whose expression was discordant between matched primary lymphomas and lymphoid cell lines. These alterations indicate cell population selection during primary and long-term culture. Moreover, the number of genes significantly differentially expressed in lymphoid cell line samples is many-fold larger than that in primary lymphoma samples. Many of these changes appear to represent a more extensive dysregulation of biological functions, processes, and/or pathways that were already dysregulated in the primary lymphomas. Despite the possibility of cross contamination of the primary tumors with non-neoplastic tissue, the fact that we found evidence for pathway- and cell-line-specific dysregulation suggests that in vitro culture leads to loss of tumor heterogeneity and clonal selection. Remarkably, we noted that not all common therapeutic targets in the B-cell receptor signaling pathway are similarly expressed between lymphomas and lymphoid cell lines, a finding that may have implications for the assessment of novel lymphoma drugs by cell line experiments and warrants further investigation.Cell lines are among the most used models in lymphoma studiesin vitro models for primary lymphomas, but also point out their limitations.In general, targeted therapies combining conventional chemotherapy with pathway-specific inhibitors improve treatment outcome. Although, in contrast to humans, dogs are normally not subjected to targeted therapeutic protocols, the similarities in lymphoma presentation in both species suggest that pathway-specific inhibition should enhance treatment outcome in dogs as well. Consequently, experimental therapeutic protocols in dogs bare significant value for human patients. Our systematic comparison between the transcriptome of primary lymphomas and lymphoid cell lines provides a solid rationale for the use of lymphoid cell lines as 2.Canine B-cell lymphoma cell lines CLBL-1 (5 replicates), CLBL-1M (5 replicates), GL-1 (1 replicate), and canine T-cell lines CL-1 (2 replicates) and OSW (4 replicates) were cultured routinely in RPMI 1640//20% FBS/2% penicillin-streptomycin  medium. All cells were maintained in a humidified incubator at 37 °C and 5% COSamples from dogs with naturally occurring lymphomas  were collected from 16 patients at the Small Animal Clinic, University of Veterinary Medicine Hannover, Foundation, Germany according to the respective legislation of the state of Lower Saxony, Germany. Four non-neoplastic lymph nodes were used as controls.Total RNA was isolated from primary lymphoma samples and lymphoid cell lines using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. On-column DNase digestion was performed with the RNase-Free DNase Set (Qiagen) to avoid genomic DNA contamination. RNA quality was characterized by RIN analyses on an Agilent 2100 Bioanalyzer  using the Nano Kit according to manufacturer’s instructions .6 reads per sample.For the preparation of sequencing libraries, 2 µg total RNA with RIN > 8 were used. Poly-A RNA was enriched and ligated to sequencing adapter using the NEBNext Ultra RNA preparation Kit (New England Biolabs) according to manufacturer’s protocols. Sequencing was conducted on an Illumina Hiseq. 2500. 100bp-long paired-end reads were generated, with approximately 30 × 1047. Duplicate reads and low quality mappings were removed using Picard (http://broadinstitute.github.io/picard) and samtools48. Raw read counts for each of the Ensembl49  genes were obtained using the Python package HTSeq50. (version 0.6.1). Quality control was assessed using FastQCThe reads were subsequently mapped to the canine genome  using BWAOf the 19,856 annotated canine protein-coding genes in Ensembl, 17,950 genes (90.4%) were represented with at least one sequence read count  in at least 1 of the 37 individual sample RNA-seq libraries. These 17,950 genes were further filtered by removing lowly expressed genes, whereby only genes displaying a number of reads normalized by the library size above the 40th percentile for at least one sample were used for subsequent analyses, yielding 10,771 genes (54% of annotated canine genes). Of the 846 annotated canine microRNA genes (miRNAs) in Ensembl, 401 (48%) had at least one sequence read count  in at least 1 of the 37 individual sample RNA-seq libraries. These miRNAs were further filtered by removing lowly expressed miRNAs , resulting in 246 miRNAs. Finally, of the 8,124 annotated canine long non-coding RNA genes (lncRNAs) in Ensembl, 4,729 (58%) had at least one sequence read count  in at least 1 of the 37 individual sample RNA-seq libraries. 1,889 where considered lowly expressed and excluded from the differential expression analysis, which included then a total of 2,840 lncRNAs.52 (version 1.10.1) was used to process read counts from HTSeq and determine statistically differentially expressed genes. Our experimental design included cell lines and primary B- and T-cell lymphoma samples . Dispersion was estimated for these 10,771 genes using the function “estimateDispersions” with default options. The function rlogTransformation was used to transform the original count data to the log2 scale. For each of the primary lymphoma or cell line sample groups, the expression value of each gene was compared to the controls using pairwise comparison . Genes were considered differentially expressed if the Benjamini-Hochberg adjusted P-value  was ≤1 × 10−5. The rlogTransformation function in the DESeq2 R/Bioconductor package was used to transform the count data to the log2 scale; this transformation minimizes differences between samples for genes with small counts and normalizes the counts with respect to library size.The DESeq2 R/Bioconductor package54 in conjunction with TOMTOM55 and JASPAR56 to identify putative transcription factor binding sites (TFBFs) in gene promoters. Gene promoters were defined as 1000-bp-long sequences starting 750 bp upstream and ending 250 bp downstream of the annotated transcription start site (TSS).We used DREME58  was used to test for functional and pathway statistical enrichment analysis. In particular, we focused on the categories: GOTERM_MF_ALL, GOTERM_BP_ALL, GOTERM_CC_ALL, GOTERM_MF_DIRECT, GOTERM_MF_FAT, GOTERM_CC_FAT, UP_KEYWORDS, and KEGG_PATHWAY, and applied and identified enriched categories based on the FDR.The Database for Annotation, Visualization and Integrated Discovery59) were used to extract prominent patterns of pathway activity. Specifically, SOMs were used to cluster and visualize the “net expression” of KEGG pathways36 across seven sample groups . The net expression of a pathway was defined as the number of significantly up-regulated genes minus the number of significantly down-regulated genes within each sample group in relation to the total number of genes in the pathway. Only genes whose expression was detected with at least one sequence read count in at least one RNA-seq library were considered. Definitions for 309 KEGG pathways were downloaded using the KEGGRest R/Bioconductor package60. The net expressions of 257 KEGG pathways comprising a minimum of 20 genes detected at the level of at least one sequence read count in at least one RNA-seq library were used to train the SOMs. Calculations were conducted with the kohonen R package61 with a 4 × 4 hexagonal SOM grid and additional default parameters.Kohonen’s self-organizing maps (SOMsThe RNA-seq data presented in this manuscript has been deposited in the NCBI GEO database (entry GSE112474).Supplementary Figures and LegendsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7