V600E-mutated parent cells (lines A375 and SK-MEL-28) and vemurafenib-resistant cells lines (A375R and SK-MEL-28R) in melanoma. TMB-loaded PEGylated liposomes (TPL) functionalized with nickel-chelated phospholipids were prepared using a modified hydration method. The hydrodynamic diameter and zeta potential values of optimized YTPL were 91.20 ± 12.16 nm and –0.92 ± 3.27 mV, respectively. The drug release study showed TPL did not leak or burst release in 24 h. The hemolysis observed was negligible at therapeutic concentrations of TMB. A differential scanning calorimetry (DSC) study confirmed that TMB was retained in a solubilized state within lipid bilayers. YTPL showed higher intracellular uptake in parental cell lines compared to vemurafenib-resistant cell lines. Western blot analysis and a cytotoxicity study with the EphA2 inhibitor confirmed a reduction in EphA2 expression in resistant cell lines. Thus, EphA2 receptor-targeted nanoliposomes can be useful for metastatic melanoma-specific delivery of TMB.The clinical outcomes of malignant melanoma have improved with the introduction of mitogen-activated protein kinase kinase (MEK) inhibitors. However, off-target toxicities of the MEK inhibitor trametinib (TMB) often result in dose interruption and discontinuation of therapy. The purpose of this study was to anchor a physically stable EphrinA1-mimicking peptide known as YSA (YSAYPDSVPMMS) on TMB-loaded PEGylated nanoliposomes (YTPLs), and evaluate them in BRAF Melanoma is a malignant tumor formed from melanocytes (pigment-producing cells), and is the sixth most common type of cancer in the United States . ApproxiDelivery of anticancer drugs via active and passive targeting has been studied widely using nanoparticulate systems . An enhaV600E and BRAFWT melanomas [V600E-mutated parent and resistant melanoma cell lines.Recent studies demonstrated the pivotal role of overexpressed EphA2 receptors in aggressiveness, metastatic potential, and vasculogenic mimicry in malignant melanoma ,19. EphAelanomas . Compareelanomas . Moreoveelanomas . Thus, telanomas . Moreoveelanomas . To our elanomas ,26,27. Selanomas . Moreoveelanomas . This drTrametinib was purchased from LC Laboratories ; 1,2-Dioleoyl-sn-glycero-3 phosphocholine (DOPC) was purchased from Cordenpharma ; PE 18:0/18:0-PEG2000 was obtained from Lipoid ; cholesterol, chloroform, and a Sephadex G50 column were purchased from Sigma-Aldrich ; 6-histidine tagged PEGylated (PEG) YSA (6His-PEG-YSA) was obtained from GenScript Corporation ; DOGS-NTA-Ni was obtained from Avanti ; Fetal Bovine Serum (FBS) was procured from Atlanta Biologics ; Dulbecco’s modified Eagle’s medium (DMEM) and the BCA protein estimation kit were purchased from ThermoFisher Scientific Inc. ; Penicillin–Streptomycin–Amphotericin B (PSA) was purchased from MP Biomedicals, LLC ; and ALW-II-41-27 was obtained from Cayman Chemical . Other chemicals and solvents were of analytical grade. Melanoma cell lines (A375 and SK-MEL-28) were obtained from American Type Culture Collection .Chromatographic separation of TMB was achieved using Waters e2695 separation module, a 2998 PDA detector instrument equipped with a Hypersil ODS C18 column . Acetonitrile:phosphate buffer pH 3.5 (70:30) was used as the mobile phase with a flow rate of 1 mL/min and an injection volume of 10 µL. The column temperature was kept at 25 °C and the output signal was detected using Empower 3 software. The retention time of TMB was found to be 4.7 min, detected at 248 nm. HPLC standard curve of TMB is provided as 2.Vemurafenib-resistant cell lines (A375R and SK-MEL-28R) were generated by adding 0.2 µM of vemurafenib to the media of A375 and SK-MEL-28 for 2 months. The vemurafenib resistance was confirmed prior to studies. All cell lines were maintained in DMEM, supplemented with 10% FBS and 1% PSA, and incubated at 37 °C with 5% COInitially, liposomes were prepared by different methods. Briefly, TMB:DOGS-NTA-Ni:DOPC:cholesterol:DSPE-PEG 2000 in a 1:0.75:60:16.3:2.1 molar ratio were dissolved in chloroform. DOPC, cholesterol, DSPE-PEG, and TMB were dissolved in chloroform. For thin film hydration, the solution was taken in a round bottom flask under vacuum to form a film, followed by hydration with water at 55 °C and ultrasonication (30% amplitude) for 2 min. For the modified hydration method, the same chloroform solution was added drop-wise to parenteral-grade mannitol (200 μm) with constant stirring at 45 °C and left overnight for evaporation of chloroform. Dispersion of this resultant powder was prepared in water at 55 °C and was sonicated (30% amplitude) for 2 min. For the investigation of the effect of mannitol on stability, mannitol was separated from liposomes using a Sephadex G50 column. For preparation of YTPL, first DOGS-NTA-Ni-loaded liposomes were prepared using the same composition and method described above. Liposomes were incubated with different concentrations of YSA for 30 min to obtain liposomes with varied molar ratios of DOGS-NTA-Ni:YSA.The average size, size distribution by intensity, zeta potential, and polydispersity index (PDI) were measured using a dynamic light scattering (DLS) particle size analyzer . Samples were analyzed using disposable cuvettes at 25 °C with a scattering angle of 173°. The effect of YSA concentration on particle size and zeta potential was evaluated. All the experiments were carried out in triplicates. Entrapment efficiency was estimated using ultrafiltration by Amicon ultra centrifugal filters (50K). The concentration of TMB was analyzed by HPLC. The encapsulation efficiency of TMB was expressed as the percent of drug encapsulated and calculated using the following formula:TPL at different drug loading values  was prepared for the stability study. Total drug content was measured at time zero. Samples were withdrawn at different time points to analyze the amount of precipitated drug by HPLC.TPL with 1% drug loading was used to investigate the effect of concentration of cryoprotectant  on freeze drying. TPL was prepared using the method described above (unfiltered TPL). Further, unfiltered TPL was passed through a Sephadex G50 gel column to separate mannitol. Mannitol-free TPL was refered to as filtered TPL. Briefly, 1-mL aliquots of the liposomal dispersions were filled into colorless glass vials and then stored at −80 °C overnight, followed by lyophilization  overnight in order to achieve a preservable white powder. The same protocol was used for freeze drying of YTPL. Lyophilized liposomes were reconstituted with water. Particle size, zeta potential, and YSA binding efficiency were analyzed before and after freeze drying of YTPL. For YSA binding efficiency, 400 µL of YTPL were filled into Amicon Ultra centrifugal filters (30 kDa) . Free YSA was separated by centrifuging the samples at 10,000 rpm for 10 min. Concentration of free YSA (before and after freeze drying) was analyzed using a BCA protein estimation kit. . The liposomal formulation was dried, and the semi-solid paste was weighed in an aluminum pan and hermetically sealed. The samples were equilibrated at 25 °C for 5 min and were heated at the rate of 10 °C/min from 40 °C to 350 °C. A hermetically sealed empty aluminum pan was used as a reference. TA Instruments Universal Analysis 2000 software (TA Instruments) was used to analyze the data.® 7) were soaked before use in Milli-Q water at room temperature overnight to remove the preservative, followed by rinsing thoroughly in Milli-Q water. Drug release of TMB from TPL was carried out in a dialysis sac with 200 mL of phosphate buffer saline (pH 7.4) containing 0.5% Kolliphor EL at 37 °C with constant stirring. The samples were withdrawn from the release medium at different time intervals. The amount of TMB in the release media was evaluated by HPLC.Drug release from TPL was carried out using the dialysis bag method. Before use, dialysis bags (Spectra/PorRat red blood cells (RBCs) were separated from plasma by centrifugation at 2000 rpm for 5 min. The cell pellet was re-dispersed into an appropriate volume of PBS to achieve the same hematocrit. Then, 1 mg/mL TMB of TPL was added to the RBC dispersion to achieve 100, 50, 10, and 2 μg/mL TMB concentrations. After 30 min of incubation at 37 °C, samples were centrifuged at 2000 rpm for 10 min. Supernatants were diluted with PBS and analyzed. A UV spectrophotometer was used to evaluate the hemoglobin release at 550 nm. PBS was used as the negative control, and sodium lauryl sulfate solution was used as the positive control (100% hemoglobin release). Percentage hemolysis was calculated by following formula:n = 6). Samples were incubated at 37 °C for 30 min and then samples were centrifuged. Plasma was separated from centrifuged samples. Sodium lauryl sulfate was added to half the samples for complete hemolysis. The TMB concentration in haemolyzed blood and plasma was analyzed by HPLC. The plasma-to-blood ratio was calculated by the equation given below:TPL was added to blood to prepare TMB at a concentration of 50 μg/mL (2 for 48 h before treatment. Coumarin-6 loaded PEGylated liposomes (C6PL) and YSA-anchored coumarin-6-loaded PEGylated liposomes (YC6PL) were incubated with cells for 1 h. Afterwards, cells were washed with HBSS and fixed with 3.7% formalin. Uptake of TPL and YTPL in different cell lines was observed for same exposure time using the EVOS FL Auto Cell Imaging System with 40× magnification.Cells were plated in a 96-well plate at a density of 10,000 cells/well and incubated at 37 °C and 5% CO2. Then the medium were removed from wells and MTT-formazan crystals were dissolved by the addition of 100 μL of dimethyl sulfoxide (DMSO) to each well. The quantity of MTT-formazan was determined by 570 nm absorbance as the wavelength reference.Cytotoxicity of TMB, TPL, and YTPL was evaluated in A375, SK-MEL-28, A375R, and SK-MEL-28R cell lines using 3--2,5-diphenyl tetrazolium bromide (MTT) assay. ALW-II-41-27 (an EphA2 receptor ATP-competitive inhibitor), vemurafenib, and vemurafenib with 0.1 µM ALW-II-41-27 were tested in both SK-MEL-28 and SK-MEL-28R. Cells were seeded in 96-well plates at a density of 5000 cells/well and allowed to grow for 24 h before treatments. TMB and TPL were diluted in cell culture medium at different concentrations. After 48 h treatment, cell viability was determined by the MTT assay. Briefly, MTT dye was dissolved at a final concentration of 5 mg/mL in PBS. Cells were incubated with 20 μL of 5 mg/mL MTT solution in each well for 3 h at 37 °C, 5% COv/v NP-40, 0.5% w/v deoxycholate, 0.1% w/v SDS, 10% v/v glycerol, 10 mM NaF, 0.4 mM EDTA, pH 8.0) with protease inhibitors. The lysates were cleared by centrifugation at 10,000 g for 10 min and then reduced with Laemmli buffer containing β-mercaptoethanol, separated on 4–15% MiniProtean TGX gels , transferred to a PVDF membrane, and probed with primary antibodies from Cell Signaling Technology for EphA2 (6997) and β-actin (8457) for chemiluminescence detection using the Azure Biosystems c500 imager .Whole cell protein lysates were obtained from A375, SK-MEL-28, A375R, and SK-MEL-28R cell lines. Briefly, cells were scraped in modified RIPA buffer  and Microsoft Excel. Differences between groups were considered to be significant at a p-value of <0.05. Each experiment has been performed in triplicate and carried out at least three times. All statistical analyses were performed using Student’s The results of TPL prepared using different methods are given in The hydrodynamic diameter of TPL prepared using a modified hydration method was found to be 109.45 ± 9.40 nm with a zeta potential of −35.55 ± 9.60 mV. The zeta potential of DOGS-NTA-Ni-loaded liposomes was lower than TPL. For YTPL, particle size was found to be similar at all the DOGS-NTA-Ni: YSA ratios a. A sligThe precipitation of a hydrophobic drug from liposomes is another issue with respect to long term stability. In order to evaluate the physical stability, TPL with different drug loading values  at a 0.5 mg/mL TMB concentration were prepared. As expected, we observed that lower the drug loading, the lower the percentage of TMB precipitation a. MoreovFreeze drying was carried out to covernt TPL into solid powder for reconstitution. Since TPL was prepared using a modified hydration method that contains mannitol, we evaluated the effect of mannitol on TPL stability. Unfiltered TPL are liposomes without the separation of mannitol. Filtered TPL was prepared by separating mannitol using a G50-Sephadex column while the unfiltered TPL was the liposme which had not undergone filtering of the mannitol. Particle size and zeta potential of unfiltered and filtered TPL before and after freeze drying with various concentrations of trehalose are depicted in DSC endotherms of TMB and TPL were obtained as shown in TPL showed less than an 8% drug release in 24 h at sink conditions , which iNegligible hemolysis was observed even at 100 μg/mL of TMB used , Table 3Coumarin-6 was selected as the fluorescent dye for labelling the liposomes. As shown in 50 values of each of the formulations is given in 50 values for TMB, TPL, and YTPL were similar at around 0.7 nM. Cell viability graphs of TMB, TPL, and YTPL in melanoma cell lines are given in In vitro cytotoxicity of TMB, TPL, and YTPL was evaluated in patent cell lines only. IC50 values of ALW-II-41-27 were 122.4 nM and 177.0 nM for SK-MEL-28 and SK-MEL-28R, respectively. Co-treatment with ALW-II-41-27 did not alter the cytotoxicity of vemurafenib in parent or vemurafenib-resistant SK-MEL-28 cells. Vemurafenib diluted in 0.1 µM ALW-II-41-27 showed similar viability compared to single vemurafenib treatment, which indicates that the EphA2 inhibitor did not change the resistance sensitivity. Thus, EphA2 receptor inhibition is not co-related to cytotoxicity of vemurafenib or vemurafenib resistance in melanoma.To further investigate whether the EphA2 receptor was correlated with vemurafenib-resistance, ALW-II-41-27, an EphA2 ATP-competitive inhibitor, inhibited the growth of SK-MEL-28 and SK-MEL-28R in a concentration-dependent manner . The IC5V600E-mutated melanoma parent cell lines compared to the resistant cell lines.The protein expression of EphA2 was significantly lower in the whole cell lysates from A375R and Sk-MEL-28R melanoma cell lines compared to A375 and Sk-MEL-28 . The resToxicities associated with the use of oral MEKi  severely restrict the targeted therapy of malignant melanoma. The aim of this paper was to investigate whether an EphA2 receptor-directed liposomes could be helpful in targeting parent and BRAFi-resistant melanoma. For the first time, we developed EphA2 receptor-targeted TMB-loaded PEGylated nanoliposomes to minimize the off-targeted side effects of TMB. We further characterized the liposomes for cellular internalization in parent and vemurafenib-resistant melanoma cells.Nanotechnology has had a profound effect on many areas of healthcare and scientific research, the use of tumor-targeted nanocarriers allow for the delivery of low water-soluble cancer medications to selectively bind cancer cells and minimize off-target side effects ,31. The Since there are no previous reports on formulation development of TMB, we explored four different methods for preparation of TPL, which were compared and optimized based on formulation feasibility and physical stability ,39,40. EV600E-mutated melanoma with overexpressed EphA2 receptors. PEGylated nanoliposomes of TMB prepared by a modified hydration method using a chelating lipid, DOGS-NTA-Ni, allow for the simple and one-step attachment of a 6His-PEG-YSA peptide on liposomal surfaces [The enhanced permeation and retention (EPR) effect is known as passive targeting and is effective especially for the parenteral administered nanoparticles. Drug tends to accumulate around tumor tissue due to leaky vasculature and an abnormal lymphatic drainage system . Conventsurfaces . YSA pepsurfaces .Particle size and zeta potential of nanoparticles play an important role, especially for parenteral administration. Different concentrations of 6His-PEG-YSA peptides were used to optimize particle size and zeta potential. The succinyl group of DOGS-NTA-Ni contributes to negative zeta potential of the liposome. Due to the complexation between the YSA target ligand and DOGS-NTA-Ni, the zeta potential changed from negative to positive in a YSA concentration-dependent manner. It was found that 1:2.5 M DOGS-NTA-Ni:YSA showed no further changes in zeta potential, suggesting that YTPL was saturated with YSA. Thus, a 1:2.5 molar ratio of DOGS-NTA-Ni:YSA was used as an optimized ratio for YTPL.® or Myocet®) are available in liquid form [® and Visudyne® are available as lyophilized liposomal powders [®, and Lucentis® are commercially available. Trehalose has an ability to reduce hygroscopicity so that hydrogen bonds can form easily, has low chemical reactivity, and has a high glass transition temperature (Tg) [The application of liposomes in commercial use is restricted by physical stability, even though advantages such as biocompatibility and nontoxicity are very attractive . In thisuid form , whereinuid form . AmBisom powders . Trehaloure (Tg) . Mannitoure (Tg) ,49. Surpure (Tg) . Differeure (Tg) . TMB in The release of liposomes is important to evaluate the quality of the formulation as well as to predict in vivo behaviour of a liposomal drug delivery system. In this study, drug release results did not show burst leaking of TMB from the liposomes. Therefore, we anticipate that TMB will be confined within the liposome and will not show any burst release in the blood. However, it showed promising activity in our in vitro studies, indicating that TMB will be released and act after internalization by cancer cells. We expect liposomes to accumulate at tumor site due to the EPR effect, and for complete release of TMB at the tumor site in vivo due to the active-target effect.Negligible hemolysis was observed even at the therapeutic concentrations of TMB. The blood-to-plasma ratio determines the concentration of the drug in the general circulation and the concentration of the target drug in plasma, which provides an indication of drug the binding to erythrocytes. The result for blood-to-plasma ratios of TMB is around 1 which indicates that TMB is evenly distributed in plasma and red blood cells.50 of the TMB liposome confirmed that TMB was released after internalization of TPL into cancer cells. Moreover, the IC50 values of TMB, TPL, and YSL are similar in A375 and SK-MEL-28, while in-vivo behavior may vary due to drug distribution and the microenvironment of the tumor. Coumarin-6 was used as a fluorescent probe in the uptake study. The intensity of this fluorescence dye can be correlated with the extent of liposome uptake. YTPL showed higher cell internalization compared to non-targeted liposomes (TPL) because of receptor-mediated uptake. Surprisingly, YTPL presented less uptake in vemurafenib-resistant cell lines, indicating that the expression of EphA2 receptor could be lower in vemurafenib resistant cell lines. This finding is contradictory to a previous study that found vemurafenib-resistant melanoma cell lines had higher EphA2 receptor expression compared to parent cells [The very low ICnt cells . Our WesWe have successfully developed YSA-anchored trametinib-loaded PEGylated nanoliposomes (YTPL) by a modified hydration method, with better entrapment efficiency and stability compared to the thin film hydration method. Moreover, YTPL showed compatibility with red blood cells as well as the desired cytotoxicity to melanoma cells. Metastatic melanoma-specific delivery of MEK inhibitors can be achieved by targeting overexpressed EphA2 receptors in vemurafenib-sensitive cells using nanosized EphA2-targeted PEGylated liposomes. Nevertheless, YTPL is not a viable option for the treatment of vemurafenib-resistant melanoma since TMB has cross-resistance to vemurafenib-resistant cell lines. Moreover, according to our studies, resistant cell lines do not internalize YTPL as much as parental cell lines.


BCR-ABL1+ cell lines. To investigate potential resistance mechanisms, ponatinib resistance was generated in BCR-ABL1+ cell-lines by long-term exposure to increasing concentrations of ponatinib. Two cell lines with prior dasatinib resistance demonstrated BCR-ABL1 kinase domain (KD) mutation(s) upon exposure to ponatinib. In one of these cell lines the T315I mutation had emerged during dasatinib exposure. When further cultured with ponatinib, the T315I mutation level and BCR-ABL1 mRNA expression level were increased. In the other cell line, compound mutations G250E/E255K developed with ponatinib exposure. In contrast, the ponatinib resistant cell lines that had no prior exposure to other TKIs (TKI-naïve) did not develop BCR-ABL1 KD mutations. Rather, both of these cell lines demonstrated Bcr-Abl-independent resistance via Axl overexpression. Axl, a receptor tyrosine kinase, has previously been associated with imatinib and nilotinib resistance. Ponatinib sensitivity was restored following Axl inhibition or shRNA-mediated-knockdown of Axl, suggesting that Axl was the primary driver of resistance and a potential target for therapy in this setting.TKI resistance remains a major impediment to successful treatment of CML. In this study, we investigated the emerging modes of ponatinib resistance in TKI-naïve and dasatinib resistant  BCR-ABL1 kinase domain (KD). More than 100 mutations have been identified which either prevent or significantly disrupt the binding of imatinib to Bcr-Abl [BCR-ABL1 T315I mutation (commonly referred to as the gatekeeper mutation), which accounts for approximately 15-20% of clinically observed BCR-ABL1 mutations [Since its clinical introduction over a decade ago, imatinib has revolutionized the treatment of chronic phase-chronic myeloid leukaemia (CP-CML). However, approximately 30-40% of patients fail to respond optimally to imatinib . The bes Bcr-Abl . To over®, Ariad Pharmaceuticals, Cambridge, MA, USA) a third generation TKI, is a potent Bcr-Abl inhibitor approved in the USA and Europe for treatment of CML patients with resistance to other TKIs. The medium human peak and trough plasma levels of ponatinib when dosed at 45 mg once daily are 145 nM and 64 nM respectively [T315I. However, while ponatinib is the only available TKI to target Bcr-AblT315I, the interaction of ponatinib with T315I mutant Bcr-Abl is weaker than its interaction with Bcr-Ablp210 [Ponatinib  –10, incrnd ABCG2 , 11–17, pression , 18–20. activity , suggestactivity –27. WhilM et al. .BCR-ABL1+ cell lines to increasing concentrations of ponatinib. Cell lines were either TKI-naïve, or were previously treated with dasatinib to induce dasatinib resistance to mimic the clinical use of ponatinib in TKI-resistant/refractory CML & Ph+ALL patients, many of which had been on dasatinib with or without the T315I mutation. By characterising the resultant ponatinib resistant cell lines, we identified that in the setting of prior dasatinib exposure, Bcr-Abl dependent mechanisms cause ponatinib resistance. However, in the TKI-naïve setting, Bcr-Abl-independent modes of resistance dominate, and Axl presents as a key mediator of this resistance, suggesting Axl may be a potential therapeutic target in the cases of ponatinib resistance.To investigate potential resistance mechanisms, ponatinib resistance was generated in this study by exposing K562 T315I-R  and K562 DOX 55D-R cells , as higher concentrations of TKI were required to inhibit Bcr-Abl tyrosine kinase activity  compared to the control cell line K562 (7.2 nM). In addition, the K562 DOX-R cells also demonstrated a similar ponatinib IC50  compared to the K562 DOX control (8.9 nM) (K562-R and K562 DOX-R cell lines were able to maintain high viabilities (>75%) in 200 nM ponatinib. These results suggested that Bcr-Abl independent mechanisms were likely involved in the development of ponatinib resistance in these two cell lines.However, as demonstrated in , there w(8.9 nM) . To confBCR-ABL1 mRNA can cause resistance to first and second generation TKIs [BCR-ABL1 transcript number in the four ponatinib resistant cell lines. As expected, substantial increases in the expression level of BCR-ABL1 mRNA were observed during the development of the K562 T315I-R and K562 DOX 55D-R cell lines. There was a significant increase in BCR-ABL1 mRNA from 1206% (relative to %BCR) in naïve cells to 8027% in the K562 T315I-R resistant line   revealed an increase in mRNA expression, peaking at 9034% in the 90 nM ponatinib intermediate, K562 T315I 90 nM PON    demonstrated a further reduction in BCR-ABL1 mRNA expression (1299%), which was not significantly different to the ponatinib naïve control line K562 DOX 55D (1069%)  Figure . Moreove) Figure . During ) Figure . This ov) Figure . This reBCR-ABL1 mRNA levels also increased during the development of K562-R and K562 DOX-R cell lines, however, the gene expression levels were significantly lower compared to the two ponatinib resistant cell lines which had developed prior resistance to dasatinib. As demonstrated in Figure K562 exhibited fluctuations in the BCR-ABL1 mRNA transcript level, from 487% in the K562 TKI-naïve control cells, peaking at 1408% in the 40 nM ponatinib culture . However, the K562-R final (200 nM ponatinib) resistant line did not exhibit a significantly increased BCR-ABL1 mRNA level compared to its K562 control. This result suggested that the overexpression of BCR-ABL1 mRNA may mediate early stages of ponatinib resistance, but it is not the major mechanism that caused resistance in this cell line. Conversely, the BCR-ABL1 mRNA expression level in the K562 DOX-R ponatinib resistant line increased from 266% in the control line to 871% in the final 200 nM ponatinib culture   Figure . These rK562 T315I-R resistant or K562 T315I control cell lines  sequencing was performed on the intermediate cell lines generated during the development of ponatinib resistance and revealed a slow but steady increase in T315I% from 44% in the control line peaking at 66%  and E255K (55%) were present in the K562 DOX 55D-R (200 nM) cell line, however neither the K562 DOX 55D ponatinib naïve control cell line, nor any of the intermediate cell lines, harboured a mutation detectable by Sanger sequencing (limit of detection is 10%).Conventional Sanger sequencing was unable to detect a BCR-ABL1 KD mutation in the K562 DOX 55D cell line. Interestingly, a single mutation E279K and a single mutation E255K were observed in 0.92% and 0.72% respectively in this 100 nM intermediate cell line. In the resistant line K562 DOX 55D-R (200 nM), the compound mutations G250E/E255K (54%) were also confirmed by SMCS   to invesS Figure  and therK562-R and K562 DOX-R demonstrated increased adherence in culture as observed by morphology change, while the cells in the controls K562 and K562 DOX remained in suspension ; a 1.5 fold increase in the K562 DOX-R line compared to the K562 DOX control line .Increased adherence is a hallmark of Axl overexpression which has previously been associated with TKI resistance –27. TherK562-R and K562 DOX-R resistant cell lines was dramatically increased compared to their corresponding control lines. Taken together, these data confirmed the overexpression of Axl in the two Bcr-Abl-independent resistant cell lines.Next, the Axl protein level was assessed in the two resistant cell lines by flow cytometry. As indicated in Figure K562-R  and K562 DOX-R  resistant lines was significantly reduced compared to their corresponding control lines.Since Axl was overexpressed in the two resistant lines, we next determined if Axl inhibition might restore ponatinib sensitivity. A specific pharmacological Axl inhibitor, R428 was employed. R428 is 50 to 100-fold more specific in targeting Axl than the other TAM family members (Mer and Tyro3) –37. AddiTaken together, these results indicate that Axl inhibition restored ponatinib sensitivity in the two Bcr-Abl independent resistant cell lines, suggesting that Axl is critical in mediating Bcr-Abl independent ponatinib resistance in these cell lines.AXL was knocked-down in the K562-R, and K562 DOX-R resistant cell lines by Lentiviral shRNA (with a GFP tag). Stable AXL knock-down K562-R-AXL KD, and K562 DOX-R-AXL KD cell lines were generated, as well as the scramble control lines K562-R-scramble CTL, and K562 DOX-R-scramble CTL. The positive expression of GFP in the AXL knock-down and scramble cell lines indicated the success of the transduction  in viability after incubating with 10 nM ponatinib for 72 hours. This reduction was significantly higher  compared to the reduction in the control line treated with scrambled shRNA (22% reduction). In addition, the level of this reduction was similar to the K562 parental ponatinib naïve control (n=3), suggesting that AXL knock-down restores ponatinib sensitivity in the K562-R resistant cell line to the parental control level.Next, the successfully transduced cell lines were exposed to ponatinib to determine whether AXL knock-down in the K562 DOX-R resistant cell line also demonstrated a significant decrease in viability (53% reduction) compared to the corresponding control line treated with scrambled shRNA (16% reduction)  . This result suggested that AXL knock-down is responsible for restoring ponatinib sensitivity in the K562 DOX-R resistant cell line, to a level that was comparable to that observed in the ponatinib naïve parental control.The ) Figure  after inIn vitro cell line models have been used extensively to characterise resistance to the first and second generation TKIs in CML: our group and others have previously generated imatinib, nilotinib and dasatinib resistance in human and/or murine BCR-ABL1+ cell lines by culturing with gradually increasing concentrations of TKIs in a step-wise manner [e manner , 28, 29.e manner , 28, 29.BCR-ABL1+ cell lines were chosen as in vitro models for ponatinib resistance development, with resistance successfully generated in 4 of the 5 cell lines . The ABCB1-overexpressing K562 variant, K562 DOX, was kindly provided by Prof. Leonie Ashman [et al. [K562 T315I  and K562 DOX 55D which had prior exposure to 200 nM and 55 nM dasatinib respectively  vehicle controls were maintained in parallel.To generate ponatinib resistance, all es Table  were culet al. [5 cells were incubated in the presence of TKI at 37°C (5% CO2) for 2 hours. After incubation, cells were washed and lysed in Laemmli buffer [IC50 experiments for imatinib, nilotinib, dasatinib and ponatinib were determined by measuring the inhibition of the Bcr-Abl substrate CrkL by using the method as outlined by White et al. . In briei buffer . ProteinPonatinib , dasatinib  and nilotinib (Symansis) were dissolved in DMSO (10mM). Imatinib (Symansis) was dissolved in water (10mM). Serial dilutions of all drugs were made immediately prior to use.Axl inhibitors R428 and BMS-777607  were dissolved in DMSO at 10 mM. Serial dilutions were made in DMSO immediately prior to use.BCR-ABL1 quantification was performed as previously described [BCR-ABL1 transcript numbers were reported as BCR-ABL1/BCR%.escribed , 47. In 6 cells, and cDNA synthesized as a template to amplify the KD of BCR-ABL in a long PCR reaction using the Expand Long Template PCR System . Products were purified using ExoSAP-IT  and sent to Molecular Pathology (SA Pathology) for analysis.Sanger sequencing was performed as previously described , 47. BriNext generation sequencing (NGS) termed Single Molecule Consensus Sequencing (SMCS) was performed using previous method and kindly processed by Dr. Wendy Parker .5 cells were incubated with Hanks Balanced Salt Solution (HBSS)  supplemented with 10mM Hepes (Sigma-Aldrich) and with corresponding isotype controls: IgG2aPE/IgG2bPE , IgG1PE ; or PE-conjugated anti-hBCRP1 (ABCG2-PE) , PE-conjugated anti-CD243 (ABCB1-PE) , PE-conjugated anti-hAxl  or PE-conjugated anti-hCD44  antibodies for 40 minutes on ice. Following HBSS wash, cells were resuspended in fixative and analyzed by flow cytometry . Results were analyzed by FCS Express 4 Research Flow Edition software .5 × 10AXL transcript levels were measured by RQ-PCR. GUSB was used as control gene and the expression level of AXL was calculated as a ratio of GUSB transcript levels. The primers used for AXL amplification were as follows:AXL Forward primer: 5′ TGC ATG AAG GAA TTT GAC CA 3′AXL Reverse primer: 5′ TCG TTC AGA ACC CTG GAA AC 3′Samples were amplified in triplicate in the Rotor-Gene 3000 PCR cycler machine  with the following conditions: 50°C for 2 min, 95°C for 10 min, 45 cycles of 95°C for 15 seconds and 60°C for 1 min.Cells were removed from drug-containing culture medium, washed and re-cultured in fresh media (with testing drug) for 72 hours. Cells then were stained with 7-Amino-actinomycin D (7AAD) and Annexin V-PE. Flow cytometry (BD LSRFortessaTM X-20 with high-throughput platform) was performed to determine cell viability: cells stained positive for Annexin V-PE and 7AAD were considered to be apoptotic or dead respectively. The data were analysed using FCS Express software .HEK 293T cells were transfected by lentiviral shRNA plasmid pGFP-C-shLenti comprised of either scramble control vector (sequence as below) or AXL knock-down (sequence as below) with Green Fluorescense Protein  according to the manufacturer's instructions. The GFP level and Axl expression level were measured after fluorescence-activated cell sorting (FACS) and before each experiment to ensure purity.Scramble control sequence:5′ GCA CTA CCA GAG CTA ACT CAG ATA GTA CT 3′AXL shRNA sequences:5′ GAA CAG GAT GAC TGG ATA GTG GTC AGC CA 3′t-test or Kruskal-Wallis analysis of variance to determine statistical differences. Correlation between 2 groups was compared using Pearson's correlation statistical analysis. The analysis was performed using GraphPad Prism 5 . Statistically significant differences were defined as p<0.05.Comparison between two groups was performed using unpaired 2-tailed Student's


N-glycomic phenotype of 16 colon cancer cell lines, selected for their differential CDX1 mRNA expression levels. We found that high CDX1 mRNA expression associated with a higher degree of multi-fucosylation on N-glycans, which is in line with our previous results and was supported by up-regulated gene expression of fucosyltransferases involved in antenna fucosylation. Interestingly, hepatocyte nuclear factors (HNF)4A and HNF1A were, among others, positively associated with high CDX1 mRNA expression and have been previously proven to regulate antenna fucosylation. Besides fucosylation, we found that high CDX1 mRNA expression in cancer cell lines also associated with low levels of sialylation and galactosylation and high levels of bisection on N-glycans. Altogether, our data highlight a possible role of CDX1 in altering the N-glycosylation of colorectal cancer cells, which is a hallmark of tumor development.The caudal-related homeobox protein 1 (CDX1) is a transcription factor, which is important in the development, differentiation, and homeostasis of the gut. Although the involvement of CDX genes in the regulation of the expression levels of a few glycosyltransferases has been shown, associations between glycosylation phenotypes and CDX1 mRNA expression have hitherto not been well studied. Triggered by our previous study, we here characterized the  N-glycosylation, in which oligosaccharides are attached via an N-glycosidic linkage to an asparagine (N) of the consensus sequence -N-X-S/T-  . Ch. ChN-gly cancers ,6,7 and  cancers . FUT1, 2, 3, 4, 5, 6, 7, and 9 and leads to the expression of blood group related Lewis antigens  of the antenna in α1,3- and/or α1,4-linkage, FUT1 and FUT2 are responsible for the addition of a fucose to the galactose  in α1,2-linkage, forming the H-type epitope. FUT2 is further called the secretor gene and polymorphisms leading to an inactive FUT2 lead to the absence of blood type epitopes in saliva and various epithelial cell types, the so-called non-secretor phenotype [FUT8, on the other hand, is the gene encoding for the enzyme mediating the attachment of a fucose-residue to the first GlcNAc of the N-glycan core, the so-called core-fucosylation. More detailed information on the biological role of fucosylation has been given elsewhere [By screening different colorectal cancer cell lines, we previously showed an association of caudal-related homeobox protein 1 (CDX1) mRNA expression with increased multi-fucosylation (more than one fucose), which is indicative for antenna fucosylation . Antennaantigens B, which henotype ,13. FUT8lsewhere .N-, O-, and glycosphingolipid glycans) vs. individual glycan classes. Moreover, core- and antenna-fucosylation are not always distinguished and only a few studies report on stage-dependent changes. While many groups reported a general increase of fucosylation in colon and other cancers [Fucosyltransferases are expressed in a cell type- and organ-dependent manner and altered expression of the enzymes, as well as the produced fucose-containing glycan epitopes, have been associated with various pathological conditions , includi cancers , other r cancers ,19. NotaGMDS), an important player in the fucose biosynthesis pathway, helps cancer cells to escape from natural killer (NK) cell-mediated tumor immune surveillance [GMDS gene have been identified in metastatic lesions of some colon cancers (10%). Also the colon cancer cell line HCT116 bears a GMDS mutation, resulting in almost complete absence of fucosylation. Strikingly, the parental HCT116 cells with GMDS mutation revealed a more aggressive phenotype in tumor formation and metastasis in mice, as compared to the GMDS-rescued HCT116 cells [GMDS-rescued HCT116 were more susceptible to TRAIL-induced apoptosis [The group around Miyoshi and Moriwaki hypothesize that deficiency of fucosylation via mutations of GDP-mannose-4,6-dehydratase , hitherto only very little has been described with regard to CDX1-associated glycosylation profiles. Triggered by our previous findings , which spression  with an FUT3 and FUT6, both involved in antenna fucosylation. Strikingly, other genes associated with fucosylation were likewise differentially expressed between the investigated cell lines with high vs. low CDX1 expression. A higher GMDS gene expression is positively associated with high CDX1 mRNA expression in the tested colorectal cancer cell lines and also transcription factors hepatocyte nuclear factor (HNF)1A and HNF4A, which have been previously shown to regulate antenna fucosylation of human plasma proteins [N-glycomic characterization revealed a decrease in galactosylation and sialylation in cell lines with high CDX1 expression, compared to cells with low CDX1 gene expression.We confirmed a higher degree of multi-fucosylation in cell lines with high CDX1 gene expression, which was supported by higher mRNA expression of fucosyltransferases proteins  and are proteins ,36, show3) were from Merck  and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) was from Fluorochem . Peptide N-Glycosidase F (PNGase F) was purchased from Roche Diagnostics . The peptide calibration standard was purchased from Bruker Daltonics . MultiScreen® HTS 96 multiwell plates (pore size 0.45 μm) with high protein-binding membrane (hydrophobic Immobilon-P PVDF membrane) were purchased from Millipore , while the 96-well polypropylene 0.8 mL 96-deepwell plate and 96-well PCR plate polypropylene were from Greiner Bio . All buffers were prepared using ultrapure water generated from an 18.2 MΩ-cm Purelab Ultra system from Elga . Control Visucon-F plasma pool  was obtained from Affinity Biologicals . Cell culture media were purchased from Gibco , fetal bovine serum (FBS) from Autogen Bioclear , and penicillin-streptomycin from Invitrogen . The RNeasy mini kit was purchased from Qiagen .8M guanidine hydrochloride (GuHCl), 1-hydroxybenzotriazole (HOBt) hydrate, 50% sodium hydroxide (NaOH), and super DHB matrix  were obtained from Sigma-Aldrich . HPLC SupraGradient acetonitrile (ACN) was obtained from Biosolve . Dithiothreitol (DTT), ethanol, and sodium bicarbonate , based on trypan blue staining.Details of colorectal cancer cell line origin and characteristics can be found in N-glycans were released in duplicate from three biological replicates per cell line using a PVDF-membrane based release protocol followed by linkage-specific sialic acid derivatization. Purification by cotton-HILIC-SPE and MALDI-TOF-MS analysis was performed as described previously [N-glycans were released overnight at 37 °C. Released glycans were derivatized using ethyl esterification allowing for discrimination of N-acetylneuraminic acid linkages  [eviously . Shortlym/z range of 1000 to 5000 for a total of 10 000 shots . Tandem mass spectrometry (MALDI-TOF-MS/MS) was performed for structural elucidation via fragmentation in gas-off TOF/TOF mode.Released, derivatized, and purified glycans (5 µL sample) were spotted on an anchor chip MALDI target plate  and co-crystallized with 0.5 µL of 5 mg/mL superDHB in 50% can, supplemented with 1 mM NaOH. Spectra were recorded in positive-ion reflector mode, after calibration with a Bruker peptide calibration kit, using a Bruker UltrafleXtremeTM mass spectrometer, controlled by FlexControl 3.4 software Build 119 . Mass spectra were obtained over an m/z 0.06, 4 cycles), baseline corrected , and exported to xy-files using FlexAnalysis 3.4 (Stable Build 76). Mean average spectra were generated per technical replicate, which were summed to one spectrum using the open-source software mMass  as calibrants (minimum five used), followed by peak picking in mMass, with cut-off signal-to-noise (S/N) 3. The peaklist was manually revised and analyzed in GlycoWorkbench 2.1 stable build 146 (http://www.eurocarbdb.org/) using the Glyco-Peakfinder tool (http://www.eurocarbdb.org/ms-tools/) for generation of a glycan compositions list. Our novel in-house software, developed for automated data processing, MassyTools version 0.1.8.0 [rd degree function and targeted data extraction of the area under the curve for each individual mass spectrum. To prevent over-estimation of overlapping glycan species, only the first three isotopes were extracted and the area under the curve was corrected based on the theoretical isotopic pattern. The quality of the data was assessed using several quality parameters calculated within the software. Only good quality spectra  as well as analytes  were included for analyses. Raw data after pre-processing is provided in the Supplementary Tables. Finally, the corrected area-under-the-curve values were rescaled to a total relative intensity of 100% for each spectrum. Selected glycan compositions were confirmed by MS/MS and a final peak list as well as MS/MS data is given in Spectra were smoothed . The sphttp://www.r-project.org/) with the significance level α = 0.05 to assess differences in N-glycosylation between CDX1 high and low expressing cells. Bonferroni correction was applied to p-values to adjust for multiple testing . The formulas for calculation are given in  testing . BoxplotTotal RNA was extracted by using the RNeasy mini kit according to the manufacturer’s instructions. Twenty micrograms of RNA of each sample were sent to the Molecular Biology Core Facility of the Paterson Institute for Cancer Research, Manchester, UK, for gene expression microarray analysis using the Human genome U133+2 chips, following the manufacturer’s instructions . Microarray data were analyzed using Partek Genomics Suite software. The data were log2-transformed and RMA-normalized (with GC correction) using quantile normalization with Median Polish for Probeset summarization as optional settings in the software. t-test comparing mean-expression levels of the 8 CDX1high vs. 8 CDX1low cell lines. The significant level was adjusted for multiple testing. Fold changes were calculated for CDX1high and CDX1low cell lines and for significantly different expressed glycosyltransferases, data was visualized as boxplots in Rstudio showing the median and interquartile range. To evaluate the correlation between relative abundances of N-glycans traits based on mass spectrometry data and glycosyltransferase gene expression in the 16 investigated cell lines, linear regression analysis was performed in GraphPad Prism Version 6 . Glycosyltransferase genes  as well N-glycans and CDX1 mRNA expression in a set of colorectal cancer cell lines  [N-glycosylation of an independent set of colorectal cancer cell lines with high  vs. low  expression of CDX1 mRNA. The CDX1high cell lines investigated here have, on average, 65-fold higher CDX1 mRNA expression as compared to CDX1low cell lines , especially in α2,3-linkage, while glycans from HCA46 were characterized by the presence of many fucoses , low sialylation level, and additional N-acetylhexosamines . In total, 221 individual glycan species were identified across all cell lines, from which 81 could be characterized by tandem mass spectrometry  was performed on the relative abundances of all individual N-glycans. This resulted in a model with four principle components (PC) explaining 68.9% of the variation in the data. The score plot of PC1 vs. PC2 showed clear separation of CDX1high and CDX1low cell lines along PC1 (N-glycan profile between the two groups.Our previous data suggested a positive association between the level of fucosylation on protein egative) . To valill lines . Two exetrometry . In ordelong PC1 A in thishigh and CDX1low colorectal cancer cells, derived glycan traits were calculated by grouping glycan species (direct traits) into classes according to glycosylation characteristics, such as sialylation and fucosylation. The relative abundances of derived traits as well as the calculations are given in high and CDX1low expressing cell lines and evaluated using a Mann–Whitney test. The p-values were adjusted for multiple testing (Bonferroni). Labelling of the PC1 vs. PC2 score plots using glycan-derived traits showed that multi-fucosylated glycans , indicative for antenna fucosylation , as compared to CDX1low expressing cell lines  in the eight CDX1high cell lines, compared to eight CDX1low cells  gene expression was 3.7-fold  elevated with high CDX1 expression  = 0.002; p-value (HNF4A) = 0.003; LGALS4), a target gene of HNF4A and a glycan-binding protein, was 23-fold up-regulated in CDX1high cells  to ∅ 31% in CDX1high cell lines  represent glycans with bisecting N-acetylglucosamine (GlcNAc), non-galactosylated antennae, or the addition of N-acetylgalactosamine  and showed a significantly increased expression in CDX1-positive cells in our previous study [high cell lines showed a more than 2-fold increase in the relative abundance of glycans with the feature HexNAc≥Hex, compared to CDX1low cells  was significantly correlated with MS data , and the blood group A epitope Galβ1–3/4GlcNAc). Accordingly, one of the glycosyltransferases adding a GalNAc-residue to a GlcNAc to form LacdiNAc structures on N-glycans, B4GALNT3, was significantly correlated with the MS-based trait HexNAc≥Hex  epitope, the SdA antigen Galβ1–4GlcNAc; NeuAc=xNAc≥Hex  and was,ow cells . Other gow cells .high cells than in CDX1low cell lines  LacNAc repeats , showed a trend towards higher expression with high CDX1 expression in our previous data and were higher expressed in CDX1high cell lines of the new set of cells as compared to CDX1low expressing cell lines, with ∅ 27% vs. ∅ 20% (N-acetylglucosaminyltransferase 3 (B3GNT3) and B3GNT8, both involved in the synthesis of type-1 chains and poly-LacNAc repeats, were around 2-fold up-regulated with high CDX1 expression  that fucosyltransferases FUT3, 5, and 6, all involved in antenna fucosylation, are positively regulated by HNF1A and its downstream factor HNF4A, while FUT8, initiating core-fucosylation, is inhibited [HNF1A and HNF4A resulted in reduced expression of several fucosyltransferase genes involved in antenna fucosylation in HepG2 liver cells and, partly, in pancreatic Panc1 cells, while FUT8, responsible for N-glycan core-fucosylation, was upregulated. Furthermore, GMDS and L-Fucokinase, both involved in two different pathways of fucose synthesis, were drastically down-regulated upon HNF1A or HNF4A knock-down. Publicly available ChIP-seq data of ENCODE show that HNF4A and HNF4G transcription factors bind to FUT2, 3, and 6 genes in cell lines of non-intestinal origin and, also, Lauc et al. confirmed the binding of HNF1A and HNF4A to multiple FUT genes and/or their promotors by ChiP analysis [HNF1A and HNF4A. Boyd et al. identified CDX2 binding sites on CDX1 and HNF1B, both being positively regulated by CDX2, while HNF1B is needed for activation of HNF4A, HNF1A, and HNF3G [HNF4A was also shown to positively influence transcriptional activity [HNF4A was identified as a transcriptional activator for intestinal differentiation and gene expression of HNF4A and its target genes were upregulated in colorectal cancer cell lines with a more epithelial phenotype, as compared to cells with a mesenchymal phenotype [HNF4A is galectin 4, which was described as tumor-suppressor in colon cancer [The transcription factors nhibited . The stuanalysis . At the nd HNF3G . Direct activity . HNF4A whenotype . One tarn cancer ,53, but n cancer , and wasN-glycans is a marker for the epithelial state and that CDX1 is involved in the transcriptional regulation of fucosyltransferases involved in (antenna-)fucosylation, leading to a more differentiated and less invasive phenotype in colorectal cancer cell lines. Taking our findings and the reports from literature, one may speculate that antenna fucosylation of N-glycan phenotype and glycosyltransferase expressions were less pronounced for CDX2 as compared to CDX1 (data not shown). In contrast, a direct involvement of CDX2 in the regulation of FUT2, which revealed potential binding sites for CDX1 and CDX2 and is involved in the generation of LeY/B antigens, was shown in colon cancer cell lines HT29 and DLD-1 [FUT2, could be reduced through treatment with epidermal growth factor (EGF)/bFGF [ST3GAL1/3/4 and FUT3 were transcriptionally up-regulated by c-Myc [high cell lines show a very low expression of α2,3-sialylation, thereby limiting the substrate for specific fucosyltransferases involved in the expression of sialyl Lewis epitopes . The combined expression of α2,3-sialylation and antenna fucosylation, as reflected in the trait CLFa, may be considered a proxy for sialyl Lewis epitope expression. Both groups of cell lines in the present study only showed low levels of CLFa (~4%), indicating low levels of sialyl Lewis epitopes. In the case of CDX1low expressing cell lines, the combination of α2,3-sialylation with overall fucosylation increased , which may likewise reflect increased sialyl Lewis epitope levels. Strikingly, in CDX1high expressing cell lines the expression of multi-fucosylation (indicative for antenna fucosylation) and α2,3-sialylation of N-glycans even showed opposite trends. Though FUT6 seems to have a preference for the sialylated substrate, here, the results point towards the expression of mainly non-sialylated Lewis antigens via action of fucosylytransferases FUT3 (Lewis A) and FUT4 or FUT6 (Lewis X) [high colorectal cancer cell lines analyzed here.Interestingly, in our investigated cell lines, CDX2 mRNA expression correlated with CDX1 mRNA expression (both high or both low), with the exception of the cell line RCM1. Consequently, similar observations as for CDX1-associated glycosylation were made for CDX2, but correlations with the nd DLD-1 . ExpressGF)/bFGF . On the by c-Myc . While sby c-Myc , the CDXLewis X)  in the CN-glycans from different colorectal cancer cells and could not find evidence for a fragment corresponding to an antenna with two or more sialic acids [N-glycans with terminal α2,8-linked sialic acids are expressed in cancer cells, these structures might be expressed at low levels as compared to N-glycans with terminal α2,6- and α2,3-linked sialic acids. Moreover, gene expression of ST8SIA1-5 was not significantly different between the here studied CDX1 high and CDX1 low expressing colorectal cell lines. Sialic acids can further be modified by O-acetyl-groups modulating the ligand function. O-acetylated glycans have been reported to be mainly present in the lower part of the intestinal tract [N-glycans of the same tissues [N-glycans of colorectal cancer cell lines has been shown to preserve O-acetylation [N-glycans in the investigated cell lines.Especially in the context of sialyl Lewis epitopes, the importance to distinguish α2,3- and α2,6-sialylation becomes evident. The derivatization applied here differentially modifies the sialic acids in different linkages resulting in a detectable mass shift allowing the distinction between α2,3- and α2,6-sialylation . Similaric acids . Althougal tract . Furtheral tract . In lineal tract , but nottylation . HoweverCDX1, CDX2, HNF1A, and HNF1B are involved in the transcriptional regulation of B3GALT5, the gene encoding for the enzyme responsible for type-1 chain  expression on glycolipids and glycoproteins, with preferences for O-glycans and glycosphingolipid-glycans [B3GALT5 was found to be down-regulated in colon cancer cell lines, but up-regulated upon CaCo2 cell differentiation [B3GALTs as well as B3GNTs according to observations by Isshiki et al. [N-glycans, O-glycans, glycosphingolipid-glycans, and/or others and changes on different glycan classes can be differently regulated. Therefore, it is striking that the N-glycomic data obtained by mass spectrometry are well in accordance with the transcriptomic data, suggesting that observed changes in, for example, fucosylation and sialylation are mainly attributed to N-glycans or are globally altered, but further investigations on O-glycans and glycosphingolipid-glycans are needed.We further observed a decrease in galactosylation with high CDX1 mRNA expression. Previous reports showed that -glycans . B3GALT5ntiation . Howeveri et al. , while thigh expressing cells. Glycan motifs that may contribute to this increase include LacdiNAc structures as well as the Sda antigen; the latter was shown to be expressed in normal colon tissues and decreased during colon cancer progression [MGAT3 gene expression was up-regulated with CDX1high cells. Several reports describe reduced bisection in several tumors [N-glycan branching by MGAT5 [high cells. Our results further revealed the presence of terminal HexNAc residues to be increased in CDX1gression . LacdiNAgression . Furtherl tumors  and it wby MGAT5 , as wellby MGAT5 , the latlow cells matched largely those described for cells undergoing EMT, which involves the loss of epithelial markers such as E-cadherin and gain of mesenchymal markers such as vimentin. EMT-associated glycan changes have previously been described and include the aforementioned loss of antenna-fucosylation, but also decrease in antennarity of N-glycans and bisection, whereas enhanced levels of high-mannose type glycans, core-fucosylation, and corresponding FUT8 expression, as well as increased α2,6-sialylation and ST6GAL1 gene expression, were observed [FUT3 and FUT6 has been shown to affect TGF-β receptor glycosylation, resulting in decreased fucosylation as well as FUT3/6-associated sialyl Lewis antigens and altered TGF-β-mediated EMT and invasion in colorectal cancer cells [Twist1, Zeb1, and Zeb2 was induced [HNF1A and HNF4A have been described to prevent EMT in liver cancer [Strikingly, observed glycan phenotypes for CDX1observed ,68. Inhier cells . Further induced . Finallyr cancer ,72,73. TN-glycans are still lacking and more research on the integrated regulation, as well as competition of glycosyltransferases, is needed. Also, the role of LacdiNAc structures and other terminal HexNAc epitopes in differentiation and colorectal cancer needs further investigation.While the involvement of transcription factors was shown for fucosyltransferases, reports on sialyltransferases and galactosyltransferases involved in the elongation of HNF4A and HNF1A. However, CDX genes may  influence the expression of fucose-carrying glycoproteins themselves, thereby leading to an N-glycan phenotype with enhanced multi-fucosylation. Taking into account the interaction of the two CDX-proteins with HNF1A, HNF4A, and HNF1B and together with the proven role of HNF1A and HNF4A in the regulation of fucosylation, we hypothesize a cooperation of these transcription factors being involved in the expression of FUT genes and thereby increasing fucosylation of glycoproteins with high CDX1/CDX2 expression  are involved in the regulation of multiple glycosyltransferases, especially fucosyltransferases, likely via interactions with other transcription factors, such as


Human papillomavirus (HPV) is an established risk factor for oropharyngeal squamous cell carcinoma (OSCC). The aim was to establish cell lines from HPV-positive tonsil carcinomas to be used for treatment development.TP53 status, and growth capabilities in nude mice. In vitro studies of sensitivities to radiation, cisplatin and cetuximab were performed.Fresh samples from 23 HPV-positive tonsil carcinomas were cultivated in vitro. The established cell line was analyzed for viral characteristics, cell karyotype, TP53. The ID50 for radiation was 0.90 Gy and the IC50 for cisplatin was 0.99 μmol/L. The cell line was inhibited to a maximum of 18% by cetuximab.After 19 months (eight passages), one cell line, LU-HNSCC-26, was established in vitro and also grew as xenografts. The tumor was from a 48 year old non-smoking man with non-keratinizing, p16 positive tonsil OSCC, stage T2N0M0 with HPV16. It contained 19.5 (CV% 3.7) HPV16 copies/cell (passage 8). The complete HPV16 genome sequence was obtained. Episomal HPV16 was present with an E2/E7 ratio of 1.1 (CV% 2.6). In addition, HPV16 mRNA specific for the intact E2 gene was detected. The viral expression manifested 1.0 (CV% 0.1) E7 mRNA copies per HPV16 genome. The karyotype was determined and the cell line demonstrated wild type We established an in vitro tonsil carcinoma cell line containing episomal HPV16. This is an important step towards efficient treatment development.The online version of this article (10.1186/s12885-019-5469-8) contains supplementary material, which is available to authorized users. In recent years, there has been an increase in the incidence of oropharyngeal squamous cell carcinoma (OSCC) including cancers in the tonsils and the base of the tongue , 2. In SThere is strong evidence that this rise in OSCC incidence is caused by human papillomavirus (HPV) infection  and thatIn many respects, including incidence, survival and treatment response, HPV-positive and HPV-negative OSCC behave differently, and it has become clear that the treatment strategies should differ significantly between the groups and that new regimes should be developed specifically for HPV-positive OSCC.An important tool for testing new drugs or combinations of drugs as well as for the elucidation of the biology of HPV-induced transformation is representative, in vitro and in vivo, HPV-positive OSCC cell lines. So far, a small number of HPV16-positive HNSCC cell lines have been established in vitro but none originating from tonsil tumors and all from patients with a history of smoking and alcohol use , 8. As tFor uterine cervical cancer, the integration process has been shown to be of great importance for the carcinogenic process, though not being an exclusive prerequisite as episomal HPV16 was present in 47% of cervical HPV16-positive cancers, either as pure episomal (27%) or as a mixture of integrated and episomal HPV16 (20%) . MeasureThe aim of the presented work was to establish cell lines in vitro from HPV-positive OSCC tumors that could be used for therapy development and basic research purposes.The study was approved by Lund University regional ethical review board (LU 376–01), and the samples were collected, after informed consent was obtained, at Skåne University Hospital, Lund, Sweden.2. The cells used in the described experiments were passaged less than 18 times after establishment. Mycoplasma tests were performed on a regular basis by GATC Biotech AG  and were negative.Tumor samples were rinsed consecutively in phosphate buffered saline (PBS) and R10 medium , all from GE Healthcare . The tumor samples were cut in pieces and transferred to cell culture flasks with R10 medium and left to attach and grow at 37 °C under a humidified atmosphere with 5% COFor cell doubling determination, cells were seeded in eight 96-well plates. The plates were analyzed by the sulphorhodamine B (SRB) method as previously described  consecutSingle tandem repeat (STR) analysis was performed by Uppsala Genome Center using the AmpFlSTR Identifiler polymerase chain reaction (PCR) amplification kit . Separation of DNA fragments was performed with capillary electrophoresis on the ABI3730XL DNA analyzer  and allele scoring was performed with the GeneMapper 4.0 software package.Experiments on mice were approved by the local ethics committee (488/2017). We used in-house bred, athymic 5- to 8-week-old BALB/c nude (nu/nu) mice. The mice were given food and water ad libitum and treated according to regulations issued by the Swedish Board of Agriculture (SJVFS 2017:40). Cells were injected subcutaneously into the flanks of non-anesthetized animals. To determine the doubling time, tumors were retransplanted to 10 nude mice under ketamine anesthesia, two tumors per mouse. The length and width of the tumors were measured with calipers three times per week and the relative tumor size calculated in relation to day 0 . The data points were fitted to a logarithmic equation in GraphPad Prism. After the measuring period, the mice were euthanized using cervical dislocation and the tumors removed for histological sectioning.Xenografts and cell pellets were sectioned in 3 μm slices and stained with hematoxylin and eosin, and the following antibodies: the CINtec p16-kit , anti-pan keratin, , and anti-p63  all from Ventana Medical Systems , anti-cytokeratin 5  from Leica Biosystems , and p40  from Biocare Medical , All stainings were performed using the BenchMark Ultra platform .Three HNSCC cell lines, previously established in our laboratory, were used for comparison in several experiments: LU-HNSCC-4, LU-HNSCC-5 and LU-HNSCC-6 . They weA 2–3 mm biopsy was immersed in 1 mL saline solution and immediately transferred to the laboratory. The saline was removed and the biopsy was digested in lysis buffer and the DNA extracted with the Total NA-kit using MagNA Pure LC   and stored at − 20 °C. After thawing, 200 μL was used for DNA extraction with the Total NA-kit using MagNA Pure LC (100 μL output).Briefly, the number of viral genomes per cell was quantified by carrying out two separate real-time PCR tests, including appropriate standard curves, to amplify the HPV16 E7 gene and the human beta-globin gene as previously described . The sam> 0.9. The E7 copy numbers were retrieved from the viral load measurement as described above. For quantification of the E2 copy numbers we used a modified version of the PCR described by Peitsaro et al. /44–47,idem,i(8)(q10)[cp3] t was 10–20 times lower than those of the other cell lines.The sensitivities to commonly used treatments were assessed in vitro and compared to three HPV-negative HNSCC cell lines. LU-HNSCC-26 was considerably more sensitive to cisplatin than the other HNSCC cell lines tested Fig. . The IC5max) of the drug instead of the IC50. LU-HNSCC-26 was only inhibited to a maximum of 18%  was 18–26 times lower than those of the other cell lines.As for cisplatin, LU-HNSCC-26 was considerably more sensitive to radiation compared to the other cell lines Fig. . The ID5TP53 gene was wild type and the cell line displayed a relatively simple karyotype including loss of 3p, 4q, and 7p as well as isochromosomes, which are common findings in HPV-positive HNSCC-cases, that often have less complex karyotypes with fewer signs of chromosomal instability compared to HPV-negative ones [In the current work we demonstrated the in vitro establishment of an HPV16-positive cell line, LU-HNSCC-26, derived from a tonsil carcinoma. The cell line manifested episomal HPV16 with active expression of viral mRNA. The ive ones . The douive ones .It is widely accepted that HPV infection plays an etiological role in many oropharyngeal cancers and there is ample evidence that HPV-positivity is coupled to increased survival and better prognosis. As a result of this, there are currently several clinical trials addressing the question whether it is possible to de-intensify the treatment for this patient group to reduce the toxicities while preserving the efficacy . HoweverWell characterized cellular and animal models are valuable tools for efficient treatment development. Such models can provide fast and efficient testing of novel drugs and combinations of drugs or other treatment modalities. So far, no in vitro cell line has been established from HPV-positive tonsil cancer and only few from other head and neck locations , 8. ImpoThe presence of the episomal state of the HPV16 in the LU-HNSCC-26 cell line, as demonstrated by the complete circular genome of HPV 16 and as well as the intact HPV16 E2 mRNA, is similar to that of our previously established HPV16-positive tonsil carcinoma xenograft, which manifested episomal HPV16 and integrated HPV16 simultaneously . OverallTP53, homozygous for arginine at codon 72 of the TP53. Interestingly, this arginine form of the gene product p53 has been shown to be more susceptible to HPV16 E6 mediated degradation than the proline form [The HPV16 positive LU-HNSCC-26 cell line manifested wild-type ine form . In addiine form . Furtherine form . FurtherThe p53 protein is inactivated by the HPV16 E6 protein . Here weIn this work we attempted to set up cell lines from 27 tonsil tumors of which 23 were HPV positive but only one of these actually resulted in an established cell line. This success rate is considerably lower than that for HNSCC in general, previously reported to be around 33% in our lab . In our There are around 14 tonsil cell lines described in the literature  of which50 being 10 to 20 times lower Additional file 2:TP53. The LU-HNSSC-26 is homozygous for the single nucleotide polymorphism chr17:7579472G > C (rs1042522). Partial sequencing results for TP53 mapped against GRCh37 (top) and Sanger sequencing electropherogram (bottom) with the rs1042522 indicated by arrows. (PDF 172 kb)Partial sequencing results for


Prognostic markers for glioblastoma are lacking. Both intrinsic tumour characteristics and microenvironment could influence cancer prognostic. The aim of our study was to generate a pure glioblastoma cell lines and immune classification in order to decipher the respective role of glioblastoma cell and microenvironment on prognosis.n = 481 and Rembrandt, n = 180) for which clinical data, transcriptomic profiles and outcome were recorded. Transcriptomic profiles of 129 pure glioblastoma cell lines were clustered to generate a glioblastoma cell lines classification. Presence of subtypes of glioblastoma cell lines and immune cells was determined using deconvolution.We worked on two large cohorts of patients suffering from glioblastoma (TCGA, Glioblastoma cell lines classification defined three new molecular groups called oncogenic, metabolic and neuronal communication enriched. Neuronal communication-enriched tumours were associated with poor prognosis in both cohorts. Immune cell infiltrate was more frequent in mesenchymal classical classification subgroup and metabolic-enriched tumours. A combination of age, glioblastoma cell lines classification and immune classification could be used to determine patient’s outcome in both cohorts.Our study shows that glioblastoma-bearing patients can be classified based on their age, glioblastoma cell lines classification and immune classification. The combination of these information improves the capacity to address prognosis. The current treatment relies on surgical resection of gross tumour followed by radio-chemotherapy and adjuvant therapy with temozolomide. After such therapy, most patients experiment recurrence and only a few therapeutic options are available. Despite such therapies, median survival only reaches around 15 months.2 Proneuronal tumours seem to be associated with a better outcome, whereas mesenchymal tumours are related to a poorer survival.5 However, because GBM are composed of a complex microenvironment, it is difficult to determine if such classification corresponds to a classification of the tumour cell or of the microenvironment.To better understand this pathology and define subgroups of patients with particular molecular biology and particular prognosis or response to therapy, the Cancer Genome Atlas Consortium (TCGA) performed high-dimensional profiling and molecular classification of large series of GBM tumours. Unsupervised transcriptome analysis revealed four clusters, referred to as classical, mesenchymal, neural and proneural, which were tightly associated with specific genomic abnormalities.6 GBM cells secrete numerous chemokines, cytokines and growth factors that promote infiltration of various cells: astrocytes, pericytes, endothelial cells, circulating progenitor cells and a range of immune cells such as microglia, peripheral macrophages, myeloid-derived suppressor cells (MDSC), leukocytes, CD4+ T cells and Treg into the tumour.10 Some previous work underlined that immune infiltrate could be associated with GBM prognosis. Although CD8 T cells seem to be linked with a better prognosis, some other immune components such as Th17 or myeloid cells are associated with a poorer prognosis.12 Recent computational methods as deconvolution were reported for predicting fractions of multiple cell types in gene expression profiles of admixtures.19 These methods could be used to estimate immune infiltrate in a tumour sample.The GBM microenvironment is a complex milieu that ultimately promotes tumour cell transcriptomic adaptability and disease progression.While previous classification used classical hierarchical clustering of transcriptome of bulk tumour sample, GBM contains both stromal and tumour cells. Our aim is to use pure transcriptomic data of GBM cells to isolate a tumour cells signature without the interference of transcriptomic information from the stroma microenvironment. In this article, we proposed a new classification of GBM tumours based on the estimation of GBM tumour cell heterogeneity. A new deconvolution process was proposed to estimate the quantity of immune cells present in GBM tumours. Combining this information, we were able to compute a score per patient that was associated with outcome in two independent cohorts.The first step of our analysis was to cluster transcriptome of GBM cell lines. This clustering step revealed three clusters. A signature of this three clusters was built to allow quantification of these clusters in patients’ tumours. Once the quantification was done, patients were clustered according to the quantity of cell lines clusters in their tumours. This process showed three clusters of patients. Each group were enriched in one of the three cell lines cluster built before. The results of this step were a new classification of patients’ tumour based on GBM cell lines. Once this classification is done, immune cells inside patients’ tumours were quantified using deconvolution. Finally, a multivariate Cox model was built combining the new classification and immune quantification . GSE12824 involves five GBM cell lines in duplicate from five different patients, GSE23806 involves cell lines or neurospheres or stem-like cells in duplicate in different stimulating conditions, GSE9171 involve cell lines from different patients (16 cell lines), GSE104291 involves six cell lines in triplicate from different patients and GSE8537 involves one cell line from a patient in four different conditions. All cell lines samples were obtained through Affymetrix H133plus2 platform. This dataset was used to create three groups of cell lines. We then estimated for each patient the quantity of each cell line group in their tumour.We downloaded GBM cell lines transcriptomes from different studies on GEO public repository22 Both clinical and gene expression profiles were downloaded with the TCGA2STAT R package.23 The data were already normalised using the RMA algorithm. We removed patients with a ‘G-CIMP' molecular subtype because they have a particular survival behaviour. We decided in both cohorts to exclude G-CIMP tumours because G-CIMP patients have better prognosis than other patients  of 22.7 months vs 10.5 months for other patients). This population is rare in the TCGA cohorts (39 patients among 520) and may introduce a bias in the analysis. In addition, it is known that such population is enriched in Isocitrate deshydrogenase (IDH) mutated glioma.22 All these arguments support that G-CIMP is probably a secondary GBM, which is raised from IDH mutated low-grade glioma and thus needed to be excluded because of its different biology. The number of patients after removal of these patients is 481 in this cohort. The second cohort (Rembrandt) was downloaded from GEO under the accession number ‘GSE68848'. Clinical data were downloaded from the GDOC website (https://gdoc.georgetown.edu/gdoc/). The Rembrandt study gathers patients with different types of brain tumour. We only kept the GBM type of tumours resulting in 180 patients for this cohort.24 Gene expression profiles were obtained through the H133Plus2 Affymetrix platform.We used two different cohorts for this study. The first one is the Affymetrix H133A TCGA GBM cohort.25 A batch effect was observed between the two GBM tumours datasets due to the different Affymetrix platforms. This unwanted effect was corrected using the SVA package.26 We worked on the set of genes common to the two platforms representing 51% of genes for the H133plus platform and 86% for the H133A.Raw data were normalised using the RMA method from the affy package.27 A t-test was performed for each gene testing if the average expression of the gene in one group is different compared with the other groups. Genes with a false discovery rate corrected p < 0.05 were considered as differentially expressed.A differential gene analysis was performed to find genes differentially expressed between the three groups of GBM cell lines. The differential expression analysis was performed with the limma R package.28 The most significant KEGG pathway found for each group was used to name it.Using clustering method, three types of GBM cell lines could be identified. A pathway analysis was performed on the genes differentially expressed between each of the three groups of cell lines generated. The list of differentially expressed genes for each group was uploaded on the EnrichR website.2 Cross-validation was performed on the TCGA cohort to assess the validity of the model; resulting in a well-classified rate of 90%. This model was then used to predict the molecular subtype of the Rembrandt’s tumour samples.Information about the molecular subtypes was available for the TCGA cohort but not for the Rembrandt’s one. To predict them for this latter cohort, we built a GBM molecular subtype classifier using the TCGA cohort as the training data. A Prediction Analysis for Microarrays classification model was estimated using the already published list of 840 genes that discriminate the four subtypes.29  or OS. Survival probabilities were estimated using the Kaplan–Meier method and survival curves were evaluated using the log-rank test. We censored the DFS times at 12 months and the OS times at 24 months for both cohorts.A multivariate Cox model was built with the cell line classification and the proportions of immune cells estimated. Interactions between the classification and the proportions of cells were considered in the model.32 we forced the cell line classification to be kept in the model.A LASSO survival model was performed using the glmnet R package,The resulting model was then used to estimate a linear predictor value for each patient.The multivariate Cox model combining GBM cell lines classification and immune cells quantities, estimated by deconvolution, was built as follows:Two risk groups of patients were built based on this linear predictor value, using the median value as the threshold. This model was built on the TCGA cohort and validated on the Rembrandt cohort. The validation was performed using the same coefficients on both cohorts and the same cut-offs.The last model built is composed of the linear predictor of the LASSO model and the clinical variable age. From this model, we built three groups of patients . The groups were determined using first and third quartile of the computer score with the linear predictor and the age of the patients.https://github.com/Klopfe/Glioblastoma).Version 3.3.3 of R was used for the statistical analysis. Figures were performed using GraphPad version 7.03. All data and R code can be downloaded on Github .We had the objective to generate a pure tumour cell-intrinsic transcriptomic signature of GBM. To do this, we made the hypothesis that a GBM is composed of different subgroups of GBM cell types. We downloaded 129 samples of GBM cell lines from public repository Table . Using h29 . The patients’ samples were taken from the TCGA GBM cohort (n = 481),22 used as discovery cohort, and the validation of our observations was made on the Rembrandt’s cohort (n = 180).24In order to quantify if GBM samples contained different proportions of these three tumour cell types, we used a deconvolution algorithm adapted from Cibersortn = 81), an oncogenic-enriched group (n = 202) and a neuronal communication-enriched group (n = 198), which are constituted of a mixture of neuronal, proneuronal and classical tumours with an enriched presence of neuronal tumours in oncogenic-enriched group  implying that this new classification is not related to the classical molecular classification.We then compared this GBM cell lines classification with the classical molecular classification. The new GBM cell lines classification and the classical classification were found independent  . In contrast, in Rembrandt cohort classical molecular classification is significantly (log-rank p = 0.01) associated with OS. However, in this cohort proneural tumours were not significantly associated with better OS , p = 0.11, in comparison with mesenchymal tumours)  Figure . Proneurrs) Fig. , thus sup = 0.16) and DFS log-rank p = 0.29) , p = 0.06, in comparison with oncogenic-enriched group) . Similarly to the TCGA cohort, the neural communication-enriched group is associated with poorer OS , p = 0.02 in comparison with oncogenic-enriched group)  Fig.  and  Fig. . However) Figure . In the up) Fig. .29 to obtain absolute estimations of immune cells infiltrated in tumours. Deconvolution process allowed quantification of immune cells inside GBM tumours. The estimations showed a very low infiltration in most of the tumours analysed since most of the tumours have <10% of immune cells.To estimate the different types of immune cells in GBM samples, we used a deconvolution algorithm adapted from CIBERSORTUsing transcriptomes of pure immune cells, we could estimate quantities of lymphoids cells , Th1, Th2, Th17, naive CD8, effector memory CD8, EMRA CD8 and central memory CD8) and myeloid cells  in each tumour samples from both cohorts. We observed in both cohorts that proneural and classical GBM have the weakest infiltrate of immune cells Figs. . In the Hierarchical clustering using the deconvolution estimations revealed five clusters in the TCGA cohort Fig. . In the These estimations showed differences in terms of immune infiltrations between the different molecular subgroups and between cell lines classification subgroups.We tested each immune cell as a continuous variable using univariate Cox proportional models, and only central memory CD8 T cells were associated with poorer OS in all patients belonging to TCGA cohort Fig. . In the p = 0.002, n = 479)   Fig. . The sam56) Fig. .Fig. 5A p = 0.0002, Table p = 0.0003).Finally, we wanted to check the ability of our composite score to bring new information when compared with classical clinical variables used to determine the prognosis. Clinical variables such as age, sex, 6-O-Méthylguanine-ADN méthyltransférase (MGMT) methylation and type of treatment were only available for TCGA cohort (no patients harbour IDH mutation). We built a multivariate Cox model including all these clinical variables and another one with the clinical variables and our composite score taken as continuous variable. Our composite score remains strongly significant in the model , 5.582, p Together our data underlined that a combination of the molecular signature and estimation of immune infiltrate quantities could be used to address patients’ outcome in glioblastoma patients.2 Most studies proposed that proneural tumours are associated with better outcome and could correspond to secondary GBM, which arise from low-grade tumours and mesenchymal tumours, are more common in older patients and are associated with a poor outcome. In our two cohorts, we could not confirm these observations. In the Rembrandt cohort, proneural tumours were associated with better outcome, whereas in the TCGA cohort it corresponded to the group with the worst prognosis. Similar observations were previously performed by other groups,33 thus suggesting that molecular classification have a minor role for stratification of patients in clinical practice.Although GBM tumours are morphologically identical, some patients have different clinical outcomes and different response to therapy. Gene expression profiling of GBM was used to identify several transcriptomal subgroups.34 proposed a new classification using RNA sequencing of single cells isolated from GBMs to select only specific genes expressed in GBM cells. While few information is available concerning the prognostic role of this classification, the authors observed intratumoural heterogeneity with the presence multiple subtype of GBM cells in most of tumour sample. Based on this observation, we used alternative strategies to perform both tumour-intrinsic molecular classification and to evaluate intratumoural heterogeneity. Using GBM cell lines, we defined particular clusters of GBM cells, which were enriched in genes of oncogenic pathway, metabolic pathway or neuronal communication pathway. Metabolic-enriched cluster was associated with mesenchymal tumours and oncogenic-enriched cluster was associated with neural tumours. This new classification was associated with prognosis, and in both TCGA and Rembrandt cohorts, neuronal communication-enriched tumours were associated with a poorer prognosis. Interestingly, this new classification did not perfectly mirror the classical molecular classification.A previous study from Wang et al.35 that a decrease in the level of CD8+ Tumor infiltrated lymphocytes (TILs) and a decrease in the CD8+/CD4+ ratio was observed in high-grade GBM patients in comparison with low grade. GBM have a weak CD8 immune infiltrate, which can probably not control tumour growth due to their relative low level compared with immunosuppressive cells. While a first report showed that a high CD8 T cells infiltrate was more frequently observed in longer survivors compared with shorter survivors,35 further studies suggested that CD8 alone could not be associated with clinical outcome.35 In addition, recent reports suggest that CD8 and CD4 T cells are exhausted in GBM thus suggesting their incapacity to drive an effector immune response.36 In a previous work, we observed in a series of GBM a good prognostic role of CD8 infiltrate and a deleterious role of Th17 cells; however, immune variables were not studied as continuous variables, which could induce some bias.11 Using CIBERSORT in the TCGA cohort, Wang et al.34 recently observed high proportions of macrophage, neutrophil and CD4 T cells in mesenchymal tumours. Our deconvolution strategy gives an additional information to CIBERSORT. CIBERSORT estimates the proportion of immune cells in the total number of immune cells that infiltrate the tumour and thus only gives an idea of the proportions of each cell subsets. Our deconvolution strategy could estimate the presence of cancer cells, stromal cells, as well as the presence of different immune cells, so our technic could give estimations of absolute numbers of immune cells. Our study confirmed that GBM are poorly infiltrated with the different types of CD8 T cells but also underlined the high presence of monocytic-like cells in mesenchymal tumours. These cells may be reminiscent of the MDSCs, which are strong immunosuppressive cells in various cancers.37 Treg and Th2, two CD4 populations with mainly protumoural properties, are the most frequent T cells in GBM. Together such data strongly support that GBM is mostly associated with protumoural and immunosuppressive cells and consequently may not be a good target for checkpoint inhibitor alone.38 Many addition works are required to determine if elimination of immunosuppressive cells improve outcome or favour infiltration by CD8 T cells and checkpoint efficacy. Surprisingly, when studied as continuous variables only a few immune cells were associated with outcome in the two cohorts, thus suggesting that immune reaction has a minor role in patients’ survival.Previous reports analysed the role of immune infiltrates in GBM with conflicting results. The prognosis roles of CD4 and CD8 infiltrates in GBM were previously reported in few small retrospective studies. Upon immunohistology, it was reported39 Recently, IDH mutation, TERT mutation and MGMT methylation allowed better stratification of patients.42 Currently, molecular signatures are not used to address prognosis. We proposed that our molecular and immune signature could be used to address patient prognosis. Limitations of our study include that this analysis was performed on Affymetrix Chips and further works are required to determine if such signatures could be performed using other technologies like NanoString or RNA sequencing, which would be more convenient in routine practice. An additional limitation of this strategy is that cell lines are not the original tumour cell and some transcriptional modification could occur in vitro and may limit interpretation of our results. Another issue in such work is that the process of deconvoluting the stroma in terms of cell type is dependent on the sample. Additional works are required to compare the results of deconvolution in multiple sample of a same tumour. Addition works will be required to determine if such signatures are only prognostic or predictive of efficacy of defined therapies. However, this observation was performed on a large cohort and externally validated in another cohort thus improving the strength of our observations.Finally, few variables could be used to better classify patients in function of their prognosis. The most powerful variables remained clinical variables including sex, age, the type of tumour resection.In conclusion, we demonstrated that GBM cell lines classification and immune classification could be combined to address GBM patient prognostic and could be used to stratify patients for further clinical trials. Immune classification may have some interest in the future. In addition to the determination of the prognosis such data was give some clues to select patients with T cells infiltrates which may gain benefit from checkpoint inhibitor. Such classification may also help us to find tumour, which need to target immunosuppression like Treg or TAM or tumour with immune desert, which could gain benefit from tumour vaccine.Supplementary table 1Supplementary table 2Supplementary table 3Supplementary methodsLegends supplementary figuresSupplementary Figures


BRAF-mutant melanoma, the MEK inhibitor (MEKi) trametinib has failed to show clinical benefit in KRAS-mutant colorectal cancer. To identify mechanisms of resistance to MEKi, we employed a pharmacogenomic analysis of MEKi-sensitive versus MEKi-resistant colorectal cancer cell lines. Strikingly, interferon- and inflammatory-related gene sets were enriched in cell lines exhibiting intrinsic and acquired resistance to MEK inhibition. The bromodomain inhibitor JQ1 suppressed interferon-stimulated gene (ISG) expression and in combination with MEK inhibitors displayed synergistic effects and induced apoptosis in MEKi-resistant colorectal cancer cell lines. ISG expression was confirmed in patient-derived organoid models, which displayed resistance to trametinib and were resensitized by JQ1 co-treatment. In in vivo models of colorectal cancer, combination treatment significantly suppressed tumor growth. Our findings provide a novel explanation for the limited response to MEK inhibitors in KRAS-mutant colorectal cancer, known for its inflammatory nature. Moreover, the high expression of ISGs was associated with significantly reduced survival of colorectal cancer patients. Excitingly, we have identified novel therapeutic opportunities to overcome intrinsic and acquired resistance to MEK inhibition in colorectal cancer.Despite showing clinical activity in  APC and TP53 and mutational activation of KRAS [Apc and Trp53 loss and KrasG12D expression in colonic intestinal epithelial cells demonstrated this by recapitulating the progression from adenoma to carcinoma, with a key role of KrasG12D being to accelerate tumorigenesis and increase the incidence of metastatic disease [KrasG12D in tumors caused them to revert to adenomas, underscoring their continued dependence on mutant Kras and providing further confirmation that Kras signaling remains an important driver of late-stage disease. Increasing evidence implicates oncogenic Ras in the modulation of the tumor microenvironment to support tumor growth [Common genetic alterations responsible for the development and progression of colorectal cancer (CRC) include inactivation of the tumor suppressors  of KRAS , 2. A rer growth , 5. Thisr growth , 7. Notar growth , 8, 9. HKRAS wild-type cancer [BRAF-mutant melanoma [RAS-mutant cancers, including CRC [Current approved targeted therapies for CRC include anti-angiogenic drugs, such as bevacizumab and regorafenib, as well as epidermal growth factor receptor inhibitors cetuximab and panitumumab for e cancer –13. The e cancer . This womelanoma . Howeverding CRC .KRAS-mutant CRC cell lines with differing sensitivity to pharmacologic MEK inhibition and identified transcriptional states associated with resistance. We demonstrate a striking enrichment of interferon- and inflammation-regulated genes in MEKi-resistant cell lines and importantly, we further associate these transcriptional states to the development of acquired resistance to MEK inhibition. Moreover, we describe in colorectal cell lines, organoids from metastatic patient samples and in xenograft and syngeneic models, a therapeutic strategy to suppress inflammatory gene expression, restore sensitivity to MEK inhibition and forestall the emergence of drug-resistant populations.Resistance to MEKis has been attributed to mutation of the drug-binding site of MEK , or throKRAS-mutant, CRC cell lines that were either sensitive or resistant to MEK inhibition. Utilizing the Cancer Cell Line Encyclopedia (CCLE), we classified the 13 cell lines based on their GI50 to the second-generation MEKi PD0325901 [50 > 8 µmol/L) and nine were classified as sensitive (GI50 < 250 nmol/L). We used comparative marker selection to identify genes that were differentially expressed between the two groups and focussed on the 140 genes that showed increased expression in the resistant cell lines by a factor of two fold or greater   Figure . We asse) Figure , Fig S2.) Figure . We also) Figure . GSEA an) Figure . Overall50 for trametinib compared with the parental cell line  and NFκB-driven super enhancers [Given that inflammatory gene expression appeared to associate with resistance to MEK inhibition, we hypothesized that its suppression may restore sensitivity to trametinib in resistant cell lines. The bromodomain inhibitor JQ1 inhibits inflammatory gene expression by the suppression of inflammatory gene super enhancers and via inhibition of NFκB p65  cultures from KRAS-mutant CRCs [MX1, IFI44L, IL1α, IL2, and TNFα , versus 52.5 d with trametinib alone, 42 d with JQ1 alone (both not significant) when compared with 44.5 d with vehicle  but this was not significantly better than trametinib alone (p = 0.4357). To confirm the efficacy of this combination in an immunocompetent model, we used the Kras-mutant, CT26 mouse syngeneic model in BALB/C mice. Whereas trametinib and JQ1 failed to slow tumor growth, the combination of both agents markedly suppressed tumor growth over 14 d  or by direct effects on immune cells, we assessed immune cell populations within the CT26 tumors by multi-color flow cytometry following 14 d of dosing  or the Deutsche Sammlung von Mikroorganismen und Zellkulturen  and grown in the recommended culture medium, supplemented with 10% fetal bovine serum, at 37 °C and an atmosphere of 5% COescribed . KRAS muquencing . InhibitCell lysis and immunoblotting techniques were as previously described , 49. TheCell proliferation assays were as previously described and quantified using CellTiter-Blue  . The druCells were treated with either DMSO or the indicated compounds. After 72 h, cells were stained with annexin V and propidium iodide using the Annexin V Apoptosis Detection Kit  and analyzed by flow cytometry .http://pga.mgh.harvard.edu/primerbank)  and reverse transcribed using the high capacity complementary DNA reverse-transcription kit . The PCR was performed using the Fast SYBR Green Master Mix (Applied Biosystems) on a ViiA 7 Real-Time PCR System (Applied Biosystems). Primer combinations were designed using the Harvard Primer Bank (k) Table .Total RNA was isolated using the miRNeasy kit . RNA samples were quality controlled and sequenced by the Tumor Profiling Unit of the Institute of Cancer Research . NEB  polyA kit was used to select the mRNA. Strand-specific libraries were generated from the mRNA using the NEB ultra directional kit. Illumina paired-end libraries were sequenced on a HiSeq2500  using v4 chemistry acquiring 2 × 100 bp reads. Bcl2fastq software  was used for converting the raw base calls to fastqs and to further demultiplex the sequencing data. The paired-end fastq files were used for further analysis. Tophat2 spliced alignment software was used to align reads to the reference genome (GRCH37) in combination with Bowtie2. Once the reads were aligned, HTSeq-count was used to count the number of reads mapping unambiguously to genomic features in each sample. Differential expression analysis of the count data was done in R using the DESeq2 Bioconductor package. The lists of up- and downregulated differentially expressed genes were then tested for enrichment of gene sets uniquely defining the previously defined CRC subtypes  using thBRD4 (L-004937-00-0005), NFkB p65 (L-003533-00-0005), IFIT1 (L-019616-00-0005), and MYC (L-003282-02-005)  and a non-targeting siNT-control (D-001810-01-05) were pre-incubated with Lipofectamine RNAiMAX  and Opti-MEM culture medium  according to the manufacturer’s instructions. Cells were reverse transfected with the siRNA-lipid complexes and incubated at 37 °C for the indicated time points until further analysis.siRNAs targeting 6 cells into the right flank of female NCr mice and randomly allocated into treatment groups. Treatment using published, efficacious schedules was initiated when tumors reached a mean diameter of ~6 mm (indicated as day 0). Control mice (n = 10) received vehicle (1% Hydroxypropylbetacyclodextrin (2-hydroxypropyl)-β-cyclodextrin) po, 10% DMSO in 10% w/v Hydroxypropylbetacyclodextrin ip), and treated mice (n = 10) were given 1 mg/kg trametinib orally or 50 mg/kg JQ1 administrated by intraperitoneal injection or the combination of both drugs daily for of 28 d [3, and body weights were determined three times weekly. A dosing holiday was given to all groups on day 21 to aid tolerability. CT26 tumors were established by injection of 5 × 105 cells into the right flank of female BALB/C mice and treated as above. All animal studies were approved by the local research ethics committee and carried out in accordance with the UK Animals (Scientific Procedures) Act 1986 and National Guidelines [T84 tumors were established by subcutaneous injection of 5 × 10 of 28 d , 52. Tumidelines . ApproprTumors were dissociated into a single-cell suspension using a gentleMACS Octo Dissociator with Heaters  and the Mouse Tumor Dissociation Kit (Miltenyi Biotec). Samples were run on the 37C_m_TDK_1 program, applied to a 70 μm MACS SmartStrainer and washed in phosphate-buffered saline (PBS). Erythrocytes were removed from samples by suspension in red blood cell lysis buffer for 5 min at room temperature. Samples were resuspended in PBS for flow cytometry staining.Cells were stained with a fixable viability dye (Thermo Fisher Scientific) and blocked with an anti-mouse CD16/CD32 antibody (Thermo Fisher Scientific). A panel of fluorescence-conjugated antibodies was added to cell suspensions at specified dilutions Table  and incuSupplementary materialSupplementary tables