In vivo, the combination of pevonedistat and ibrutinib or pevonedistat and cytarabine prolonged survival in SCID mice xenograft models when compared with monotherapy controls. Our data suggest that targeting the neddylation pathway in DLBCL is a viable therapeutic strategy and support further clinical studies of pevonedistat as a single agent or in combination with chemotherapy or novel targeted agents.We studied the biological activity of pevonedistat, a first‐in‐class NEDD8‐activating enzyme (NAE) inhibitor, in combination with various cytotoxic chemotherapy agents and small molecule inhibitors in lymphoma preclinical models. Pevonedistat induced cell death in activated B‐cell (ABC) diffuse large B‐cell lymphoma (DLBCL) cell lines and to a lesser degree in germinal center B‐cell (GCB) DLBCL cell lines. In pevonedistat sensitive cells, we observed inhibition of NF‐κB activity by p65 co‐localization studies, decreased expression of BCL‐2/Bcl‐XL, and upregulation of BAK levels. Pevonedistat enhanced the activity of cytarabine, cisplatin, doxorubicin, and etoposide in ABC‐, but not in the GCB‐DLBCL cell lines. It also exhibited synergy with ibrutinib, selinexor, venetoclax, and A‐1331852 (a novel BCL‐XL inhibitor).  The 201The ubiquitin proteasome system (UPS) regulates BCL‐2 family members and hence the apoptotic cascade, either indirectly by altering function of the NF‐κB pathway  or by degrading pro‐apoptotic BCL‐2‐related proteins (such as BAK) , 6. InhiIn vitro exposure of cancer cell lines to pevonedistat was shown to induce apoptosis, cellular senescence, or autophagy  0.001%) for 24, 48 and 72 h.DLBCL cell lines  for 48 h. Pevonedistat‐ or vehicle control‐exposed cells were harvested, washed with PBS, and fixed with ice cold 70% ethanol at 4°C for 30 min. Then, cells were incubated with 5 µg/mL RNase for 30 minutes at room temperature and stained with propidium iodide  for 1 h. Differences in the cell cycle distribution following pevonedistat exposure were determined by flow cytometry on a FACSCalibur flow cytometer . Apoptosis induction was confirmed by flow cytometry using Annexin‐V PE Cyanine7/Sytox Blue staining and by Western blotting (PARP cleavage).We selected DLBCL cell lines with high (U2932 and OCI‐LY2) or low (TMD8 and SUDHL4) IC2.5To study the mechanisms responsible for pevonedistat antitumor activity in DLBCL, cells were exposed to pevonedistat  or vehicle for 48 h. Subsequently, changes in the expression of cell cycle regulatory proteins or BCL‐2 family members were determined by Western blotting.2.66 viable cells), were exposed to pevonedistat (0.5‐5 µM) for 1 and 4 h. NF‐κB activity was determined using a nuclear translocation assay using ImageStream technology, as previously described [To assess the effects of NAE inhibition in NF‐κB activity, DLBCL cell lines, SUDHL4, TMD8, U2932, and OCI‐LY2 (2 × 10escribed . PMA/ion2.76 cells/ml) were treated with pevonedistat (0.5‐1 µM) and/or cisplatin (1.563‐50 µM), cytarabine (1.563‐50 µM), doxorubicin (0.125–4 µM), or etoposide (1.563–50 µM) for 48 h. Cell viability was determined using the CellTiterGlo assay. Alternately, the same cell lines (0.25 × 106/mL) were exposed to pevonedistat (0.977–4 µM) and/or ibrutinib (0.391–250 nM), selinexor (0.0625‐10 µM), A‐1331852 (0.0977‐6.25 µM), and venetoclax (0.1‐1000 nM) for 72 h and cell viability was assessed by PrestoBlue®. Synergistic activity between pevonedistat and other agents was evaluated using the CalcuSyn Software Version 2.11 .To investigate if pevonedistat could potentiate the antitumor activity of chemotherapy agents, U2932, OCI‐LY2, TMD8, and SUDHL4 cells (0.25 × 102.8in vitro to pevonedistat (0.5‐1 µM) or DMSO (0.001%) and incubated at 37°C and 5% CO2 for 48 h. Subsequently, 2 × 106 viable cells were labeled with 51Cr at 37°C, 5% CO2 for 2 h. 51Cr‐labeled DLBCL cell lines were then plated at a cell concentration of 1 × 105 cells/well (CMC assay) or 1 × 104 cells/well (ADCC assay). Cells were then exposed to rituximab (10 µg/mL), obinutuzumab (10 µg/mL), ofatumumab (10 µg/mL) or isotype (10 µg/mL) and human serum  or PBMCs  for 6 h at 37°C and 5% CO2. 51Cr release was measured, and percentage of cell‐lysis was calculated as previously described [DLBCL cell lines were exposed escribed .2.9In vivo studies utilized a disseminated human lymphoma‐bearing SCID mouse xenograft model. Experiments were conducted using 6‐8‐week‐old severe combined immune‐deficient (SCID) mice (SCID C.B‐Igh‐1 b/lcrTac‐Prkdcscid /Ros), bred, and maintained at facilities certified by the American Association for Accreditation of Laboratory Animal Care (AAALAC). SCID mice were inoculated on day zero with 10 × 106 TMD8 cells through tail vein injection. After 72 h, the animals were then divided into eight cohorts of 15 animals each. The first cohort (group A) was used as control without any treatment. Group B was treated with pevonedistat at 180 mg/kg/dose given subcutaneously (SQ) on days +3, +6, +10, +13, +17, and +20. Group C was treated with ibrutinib at 12.5 mg/kg/dose given via gavage daily on days +3 to +21. Group D was treated with rituximab at 10 mg/kg on days +5, +12, and +19 administered via tail vein injection (IV). Group E was treated with cytarabine at 20 mg/mouse intraperitoneally (IP) on days +3, +10, and +17. Groups F, G, and H were treated with a combination of pevonedistat SQ and either ibrutinib via gavage, or rituximab IV, or cytarabine IP, respectively, at doses mentioned above. The endpoint of the studies was survival, defined as the time to development of limb paralysis.2.10in vitro and ex vivo studies, statistical differences between treatment groups and controls were determined by Student's t‐test. For in vitro studies combining pevonedistat and chemotherapy drugs, the coefficient of synergy was calculated using the CalcuSyn software. In addition, differences in survival between treatment groups were calculated using Kaplan–Meier curves. P‐value of less than .05 was defined as having statistical significance.All experiments were performed in triplicates on three separate occasions. Data were plotted and analyzed using SPSS 21.0 software. For 33.150 concentrations varied between 0.56 µM for the most sensitive (TMD8) and 4.05 µM for the most resistant (U2932) ABC‐DLBCL cell lines, and between 0.75 µM for the most sensitive (DB) and 14.94 µM for the most resistant (OCI‐LY2) GCB‐DLBCL cell lines  induced a dose‐dependent decrease in the expression of BCL‐2 and BCL‐XL in all cell lines and induced a dose‐dependent upregulation of the pro‐apoptotic protein, BAK in TMD‐8, DHL4 and OCI‐LY2  or low (TMD8 and SUDHL4) ICs Figure . Pevoneds Figure . In all 2 Figure . Downreg3.3NF‐κB activity is known to be tightly regulated by the UPS, and translocation of p65 into the nucleus leads to the increased transcription of anti‐apoptotic Bcl‐2 family members. Similar to observations with other UPS inhibitors , pevonedistat decreased NF‐κB activity in the cell lines tested, as demonstrated by p65 co‐localization studies Figure . NF‐κB p3.4Pre‐treatment with pevonedistat for 48 h exhibited synergistic/additive effects when combined with cisplatin, cytarabine, doxorubicin and etoposide Figure –D. The mFIGURE 5In vitro effects on pevonedistat on the anti‐tumor activity of small molecule inhibitors. Pevonedistat demonstrated significant synergy with A‐1331852 (A), venetoclax (B), ibrutinib (C) and selinexor (D). DLBCL cells (0.25 × 106/mL) were exposed to pevonedistat (0.977‐4 µM) and/or ibrutinib (0.39‐250 nM), selinexor (0.0625‐10 µM), A‐1331852 (0.0977‐6.25 µM) and venetoclax (0.1‐1000 nM) for 72 h and cell viability assessed by PrestoBlue®. Synergistic activity between pevonedistat and other agents was evaluated using the CalcuSyn Software Version 2.11 In vivo, pevonedistat alone or in combination with cytarabine or ibrutinib improved the median survival of lymphoma‐bearing SCID mice (not reached) when compared to animals treated with cytarabine (41 days) or ibrutinib (47 days) monotherapy (P < .001)  Figure .3.5in vitro and in vivo [in vivo when pevonedistat was combined with rituximab in a lymphoma mouse model. TMD8‐bearing SCID mice treated with pevonedistat and rituximab resulted in a 100% long‐term disease control when compared with animals treated with pevonedistat (P = .048). However, there was no improvement in survival when the combination was compared to rituximab monotherapy (P = .118)  Figure .4In vitro exposure of DLBCL cell lines to pevonedistat resulted in changes in the expression of BCL‐2 family members suggesting a pro‐apoptotic micro‐environment, including the downregulation of BCL‐2/BCL‐XL and increased expression of BAK. Although we demonstrated a marked upregulation of NOXA levels, in contrast to other studies [In DLBCL preclinical models, pevonedistat demonstrated a dose‐ and time‐dependent reduction in cell viability with varying sensitivity between ABC‐ and GCB‐DLBCL cell lines and primary tumor cells.  studies  we did n studies , pevonedin vitro synergy was noted with a variety of chemotherapeutic agents and small molecule inhibitors, we could not replicate similar results in vivo as our model incorporating TMD8 ABC‐DLBCL cell lines into SCID mice was quite sensitive to pevonedistat, even as a single agent. Further experiments at varying doses will be needed to demonstrate synergy between these agents in in vivo mouse models. It is also interesting to note the variability of responses between different cell lines. The next line of investigation will be to identify factors causing primary resistance to pevonedistat in cell lines and translate that data to identify patients who stand to benefit the most from this therapy and find strategies to overcome both primary and acquired resistance.Pevonedistat induces a pro‐apoptotic milieu and downregulates NF‐κB activity, which makes it an attractive partner for drug combinations. Prior studies have demonstrated that in CLL and DLBCL, pevonedistat restored sensitivity to death receptor‐mediated apoptosis through the mitochondrial pathway . In CLL,The molecular events observed in lymphoma or CLL pre‐clinical models using pevonedistat are similar to what has been demonstrated with reversible (bortezomib or ixazomib) or irreversible (carfilzomib) proteasome inhibitors , 27. In Pevonedistat has been studied as a single agent in a phase 1 trial in patients with relapsed/refractory multiple myeloma and lymphoma . The druin vitro synergy with small molecule inhibitors such as the SINE compound, selinexor, BCL‐XL inhibitor, A‐1331852 and BCL2 inhibitor, venetoclax, the mechanisms of synergy between these agents have to be further elucidated before these combinations can be studied in patients. While pevonedistat is active in many disease settings, relapsed DLBCL is a promising area of interest as adding pevonedistat to cytarabine‐containing salvage regimens may lead to higher response rates without compromising safety.In the future, combinations of pevonedistat and second generation BTK inhibitors such as acalabrutinib, zanubrutinib and LOXO‐305 merit investigation due to their enhanced safety profile. While pevonedistat did demonstrate This work was supported, in part, by grants from the National Cancer Institute (5RO1CA136907‐02), the Eugene and Connie Corasanti Lymphoma Research Fund, the Czech Ministry of Health grant IGA‐MZ NT13201‐4/2012 and institutional research grants PRVOUK‐27/LF1/1 and PRVOUK P24/LF1/3.P.T., S.K., C.M., S.B., and J.G. performed research; P.T., J.G., F.J.H.I. designed the research study; P.T., S.K., C.M., J.G., and F.J.H.I. analyzed the data; P.T., S.K., C.M. J.G., S.S., M.B, and F.J.H.I., wrote and edited the paper. All authors give their approval for the final manuscript.F.J.H.I. has served on advisory boards for Millennium Pharmaceuticals, Pharmacyclics, Celgene, Amgen, Genentech, and Seattle Genetics. The remaining authors declare no competing interest.Figure S1: Effect of pevonedistat on activity of anti‐CD20 monoclonal antibodies. (A) Pevonedistat at  had an additive effect on apoptosis induced by rituximab in ABC‐DLBCL cell lines. Pre‐incubation of DLBCL cell lines for 48 hours with pevonedistat decreased rituximab, ofatumumab or obinutuzumab‐associated cell mediated cytotoxicity (CMC) (B) and antibody dependent cellular cytotoxicity (ADCC) (C)in vitro. Both, ABC‐DLBCL and GCB‐cell lines were evaluated. DLBCL cell lines were exposed in vitro to pevonedistat (0.5‐1µM) or DMSO (0.001%) and incubated at 37°C and 5% CO2 for 48 hours. Subsequently, 2 × 106 viable cells were labeled with 51Cr at 37°C, 5% CO2 for 2 hours. 51Cr‐labeled DLBCL cell lines were then plated at a cell concentration of 1 × 105 cells/well (CMC assay) or 1 × 104 cells/well (ADCC assay). Cells were then exposed to rituximab (10 µg/ml), obinutuzumab (10 µg/ml), or isotype (10 µg/ml) and human serum  or PBMCs  for six hours at 37°C and 5% CO2. 51Cr release was measured and percentage of cell‐lysis was calculated as previously described. Each experiment was done in triplicate.Click here for additional data file.


Glucocorticoids, such as dexamethasone and prednisolone, are widely used in cancer treatment. Different hematological malignancies respond differently to this treatment which, as could be expected, correlates with treatment outcome. In this study, we have used a glucocorticoid-induced gene signature to develop a deep learning model that can predict dexamethasone sensitivity. By combining gene expression data from cell lines and patients with acute lymphoblastic leukemia, we observed that the model is useful for the classification of patients. Predicted samples have been used to detect deregulated pathways that lead to dexamethasone resistance. Gene set enrichment analysis, peptide substrate-based kinase profiling assay, and western blot analysis identified Aurora kinase, S6K, p38, and β-catenin as key signaling proteins involved in dexamethasone resistance. Deep learning-enabled drug synergy prediction followed by in vitro drug synergy analysis identified kinase inhibitors against Aurora kinase, JAK, S6K, and mTOR that displayed synergy with dexamethasone. Combining pathway enrichment, kinase regulation, and kinase inhibition data, we propose that Aurora kinase or its several direct or indirect downstream kinase effectors such as mTOR, S6K, p38, and JAK may be involved in β-catenin stabilization through phosphorylation-dependent inactivation of GSK-3β. Collectively, our data suggest that activation of the Aurora kinase/β-catenin axis during dexamethasone treatment may contribute to cell survival signaling which is possibly maintained in patients who are resistant to dexamethasone. Glucocorticoids such as dexamethasone and prednisolone are important drugs in the chemotherapeutic regimen for the treatment of ALL1. Apart from their strong anti-inflammatory and immune-suppressive actions, glucocorticoids induce growth arrest and apoptosis in ALL. Their action is exerted by binding to the glucocorticoid receptor (GR), a member of the nuclear hormone receptor superfamily that also acts as a transcription factor2. Sensitivity to glucocorticoids serves as a positive prognostic indicator, and patients unresponsive to glucocorticoids often relapse, which leads to poor prognosis. Multiple mechanisms of glucocorticoid resistance have been identified such as alterations in expression and function of GR or of GR-associated proteins like chaperones and co-chaperones, thereby affecting their function, and causing defects in the target genes, leading to the inhibition of apoptosis and defective metabolism, cross-talk with other cell signaling pathways and transcription factors, as well as changes in chromatin accessibility5.Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy of lymphoid progenitor cells, with ~80% of cases occurring in children and the remainder in adults6. Despite these positive statistics, the highest cancer-related mortality in children is associated with ALL due to relapse or treatment-related toxic effects. This shows a great need for improving the existing treatments by not only identifying new molecular targets to develop better and less toxic therapeutic agents but also by identifying patients who require less intensive therapy7.The current chemotherapeutic regimen used for the treatment of ALL, which consists of corticosteroids in conjunction with chemotherapeutic drugs, has resulted in the long-term survival of 80–90% in children and of 40% in adults, though the complete remission (CR) rates are similar in both groups8. A landmark study compared 44 drug sensitivity prediction algorithms in which genomic, epigenomic, and proteomic profiling data for human breast cancer cell lines were used9. In that study, models developed using Bayesian multitask multiple kernel learning and random forests were found to perform better than other algorithms. Another large-scale study compared multiple machine learning algorithms using multi-omics data and reported elastic net and ridge regression as the top-performing algorithms10. These studies used several thousand features for a limited number of samples to develop the model; therefore, the selection of features might have affected the performance of the algorithm. Besides those low complexity models, deep learning models have been implicated in monotherapy prediction11. Because deep learning models are highly sensitive to the feature-to-sample ratio, the feature selection approach was found to be an effective method in which only features highly conserved with the phenotype were used to build the model14. Such methods are also useful in identifying a potential molecular marker and driver of sensitivity to the drugs12.The heterogeneity in the patient’s responses to different treatments has resulted in great advancements in the bioinformatics area, where different machine learning approaches are used to better match patients to drugs. Several studies have compared machine learning approaches for monotherapy prediction. The ridge regression model built using baseline gene expression data from cell lines was shown to outperform several machine learning algorithms including random forests, nearest shrunken centroids, principal component regression, lasso, and elastic net to predict in vivo chemotherapy responseIn this study, we employed transcriptomics, peptide substrate-based kinase profiling, deep learning models, and drug synergy studies to identify the deregulated signaling pathways in dexamethasone-resistant ALL. We show that the short-term dexamethasone treatment induces the Aurora kinase/β-catenin signaling axis which is also found to be enriched in ALL patients that are predicted to be dexamethasone resistant.15. To understand how genes are regulated in ALL during glucocorticoid treatment, we used the SUP-B15 cell line as a model system. SUP-B15 cells are highly sensitive to glucocorticoids such as dexamethasone and prednisolone  as the cut-off. Most of the deregulated genes were common between dexamethasone- and prednisolone-treated cells  or DMSO for 6 h before lysis. Lysates were applied to the Pamgene protein tyrosine and protein serine/threonine chips following the manufacturer’s guidelines. While comparing kinase activity enrichment between DMSO and dexamethasone-treated cells, we observed that the activity of protein tyrosine kinases was completely downregulated in dexamethasone-treated cells , p70S6K, and PIM2, seems to be interesting. Further, using a panel of 378 kinase inhibitors, we observed that ALL cell lines that were either sensitive (NALM6 and 697) or resistant (TANOUE and JURKAT) to dexamethasone were highly responsive to inhibitors targeting Aurora kinases, PLKs, and PI3K/mTOR pathway components . Both the Cancer Cell Line Encyclopedia (CCLE)31 and the Genomics of Drug Sensitivity in Cancer (GDSC)32 provide drug sensitivity scores and various genetic features for >1000 cell lines. The GDSC dataset lacks drug sensitivity scores for dexamethasone; therefore, we used the CCLE dataset to predict dexamethasone sensitivity. The CCLE dataset contains dexamethasone sensitivity scores for 708 cell lines from different cancers, where 138 cell lines are from hematological malignancies. The use of RNA expression remains the most popular modality for drug sensitivity prediction10, and therefore, we used RNA expression data to serve the purpose. The CCLE RNA expression dataset contains >18,000 genes for each sample. The use of >18,000 genes with drug sensitivity data for 138 cell lines can perhaps be used to build a prediction model but the model will suffer from poor generalization performance when tested on new data. A high-dimensional dataset with too many features can lead to overfitting such that the model captures both the real and random effects11. Low complexity models such as logistic regression, principal component regression, partial least square regression, or support vector machine can handle higher dimensional data but, due to the linear nature of the algorithm, predictions introduce significant modeling bias34. Deep learning models provide superior prediction accuracy over linear models where the number of samples is comparable to the number of features11. To increase the ratio between sample and feature, we attempted to reduce the number of genes. We used a combined gene signature , NALM6 (R2 = 0.9581), and TANOUE (R2 = 0.9929), while RS4;11 displayed a comparatively lower correlation (R2 = 0.5977) , copy number variations (CNVs), mutations, DNA methylation, protein expression, and RNA expression  reduced event-free survival  from 205 ALL patients was used to predict dexamethasone sensitivity. The model predicted 96 patient samples as dexamethasone sensitive and 109 samples as dexamethasone-resistant Fig. . Becauseval Fig. . The TARval Fig. , but notval Fig. . HoweverFLT3, BCL2, SOCS2, etc. in samples from dexamethasone-resistant ALL patients  was found to be highly enriched in samples from dexamethasone-resistant patients Fig. , which wnts Fig. . Additionts Fig. , which iLT3 Fig.  and SOCSLT3 Fig.  were dow45, therefore, we tested whether β-catenin inhibition synergizes with dexamethasone. We observed that several inhibitors targeting β-catenin activity display synergy with dexamethasone in TANOUE cells , and a BLISS score <2 was considered no synergy  cut-off of 5% was used to define significantly deregulated genes. Gene Set Enrichment Analysis (GSEA) software64 (Broad Institute) with the gene sets database MSigDB v7.0  was used for pathway enrichment analysis. ALL patient data were downloaded from the TARGET database63. CCLE cell line data were collected from GSE3613365.ALL cell lines SUP-B15, JURKAT, MOLT-4, and CCRF-CEM were treated with 1 µM dexamethasone or 2 µM prednisolone for 6 h before lysis. All cell lines were collected from DSMZ . Cells were maintained in the recommended medium by DSMZ and were regularly tested for mycoplasma contamination. Total RNA was extracted using the RNeasy mini kit (Qiagen) and the quality of total RNA was checked by Bioanalyzer. Affymetrix Human Gene 2.0 ST Array was used for expression analysis. Raw data were processed for robust multi-array average (RMA) normalization. Significance Analysis of Microarrays (SAM) was used to identify the deregulated genesKinase activity in dexamethasone-treated SUP-B15 cells was measured using the peptide substrate-based kinase profiling  method. SUP-B15 cells were treated with 1 µM dexamethasone for 6 h before lysis. Tyrosine kinase profiling and serine/threonine kinase profiling were performed separately using standard protocols provided by the manufacturer.3VO4, Trasylol, and PMSF). The protein concentration of the total cell lysates was determined by the bicinchoninic acid (BCA) assay method . The lysates were then mixed with a 1:1 volume of SDS sample loading buffer, and 10 µg proteins from each lysate were separated on SDS-PAGE gels; they were then transferred to polyvinylidene difluoride (PVDF) membranes. The membranes were immunoblotted with different primary antibodies. The anti-phospho-ERK1/2 (1:2000), anti-phospho-S6K (1:1000), anti-phospho-AKT (1:1000), and anti-phospho-p38 (1:2500) antibodies were obtained from Cell Signaling Technologies, USA. The anti-ERK (1:200), anti-AKT (1:1000), and anti-β-actin-HRP (1:2000) antibodies were from Santa Cruz Biotechnology, USA. The anti-β-catenin (1:1000) and anti-p38 (1 µg/ml) antibodies were from BD Biosciences, USA. The anti-FLT3 (1 µg/ml)66 and anti-SOCS2 (1 µg/ml)67 antibodies were described previously. The anti-phospho-serine 9-GSK-3β (1:1000) antibody was from ThermoFisher Scientific, USA. For immunodetection, the blots were incubated with the respective horseradish peroxidase-conjugated secondary antibodies and developed with the Luminata Forte Western HRP Substrate (Millipore) and Amersham Imager 600 . Where applicable, all blots were derived from the same experiment and were processed in parallel.Cells were lysed in 1% Triton X-100 lysis buffer, supplemented with protease/phosphatase inhibitors  and 30 cell lines were classified as sensitive (defined as 0). Because the number of resistant cell lines was higher than the number of sensitive cell lines, we used linear combinations of gene expression for sensitive cell lines to increase the number of samples in the sensitive cell line group. The sequential model from Keras36 in TensorFlow68 backend was used to develop the deep learning model. The model was trained using a 500-gene signature after Min-Max normalization. For prediction, a 500-gene signature from unknown samples was collected and normalized using Min-Max normalization. Further, to develop a model that can predict synergy between dexamethasone and other drugs, we retrieved synergy data from the DrugComb database69. Considering the amount of data, we used binary classification to build the prediction model. We applied BLISS score >3 as synergy (defined as 1) and BLISS score <2 as no synergy (defined as 0). We used data from 529 combinations (29 cell lines and 23 drugs) following the classification (263 samples with BLISS score >3 and 266 samples with BLISS score <2). PyChem70 and DeepChem71 were used to define features for drugs  and 500-gene signature for cell lines. Data were normalized twice: first using tanh normalization and then by Min-Max normalization. The sequential model from Keras in the TensorFlow backend was used to develop the model. Due to the limited amount of data available, we used 421 samples as training samples and 108 samples as validation/test samples. Then we applied this model to predict synergy between dexamethasone and 1454 kinase inhibitors (information collected from Selleckchem) for 40 ALL patient data from the TARGET dataset. Similarly, we used 488 features to describe each kinase inhibitor and a 500-gene signature for each patient sample for the prediction.To develop the binary classification model, 138 cell lines related to hematological malignancies were selected from the CCLE database72 to predict the full combination (8 × 8). Synergy was calculated using the SynergyFinder web application73. We applied a similar method (6 doses) to measure synergy between dexamethasone and β-catenin. Cells were seeded in a 384-well plate and treated with the drug(s) for 48 h. Cell Titer-Glo  was used to measure cell viability following the manufacturer’s protocol.For in vitro synergy analysis, 38 kinase inhibitors and a negative control  were used. A fivefold dilution (10000-0.128 nM) of the single drugs was used. We used a 1:1 combination of 8 doses for synergy and then used DecreaseCells were seeded in a 96-well plate and treated with the drug or DMSO for 48 h. PrestoBlue  was used to measure cell viability following the manufacturer’s protocol. For apoptosis assay, cells were seeded in a 24-well plate and treated with the drug or DMSO for different time points. Apoptotic cells were quantified using the annexin-V/7-AAD kit  following the manufacturer’s protocol.Further information on research design is available in the Supplementary figures and tablesReporting summary


Glioblastoma is frequently associated with TP53 mutation, which is linked to a worse prognosis and response to conventional treatments (chemoradiotherapy). Therefore, targeting TP53 is a promising strategy to overcome this poor therapeutic response. Tumor-treating fields (TTFields) are a recently approved treatment for newly diagnosed glioblastoma, which involves direct application of low-intensity, intermediate-frequency alternating electric fields to the tumor, thereby offering a local tumor-killing effect. However, the influence of TP53 mutation status on the effectiveness of TTFields is controversial. Here, we identified the key gene signatures and pathways associated with TTFields in four glioblastoma cell lines varying in TP53 mutation status using gene profiling and functional annotation. Overall, genes associated with the cell cycle, cell death, and immune response were significantly altered by TTFields regardless of TP53 status. TTFields appeared to exert enhanced anti-cancer effects by altering the immune system in the inflammatory environment and regulating cell cycle- and cell death-related genes, but the precise genes influenced vary according to TP53 status. These results should facilitate detailed mechanistic studies on the molecular basis of TTFields to further develop this modality as combination therapy, which can improve the therapeutic effect and minimize side effects of chemoradiotherapy. In primary and secondary GBM, TP53 mutation is observed in up to 30% and 70% of cases, respectively, which results in a common molecular abnormality2.Glioblastoma (GBM) a histological subtype of glioma in which most patients survive for an average of 12–15 months3, reduces the proliferation of cancer cells, and prevents the malignant transformation of normal cells4. Moreover, TP53 regulates transcriptional target genes involved in many cellular responses including apoptosis5, senescence6, DNA repair7, and cell cycle8, among others. Several decades of research of glioma has shown that not only does TP53 serve a central role in the regulatory network of tumorigenesis, but also that the TP53 status is closely associated with the disease progression and survival of patients with GBM during radio- and chemotherapy10. Several researchers have suggested that TP53-based targeted therapy is a promising approach for treating GBM, but its value as a prognostic marker in the clinical field is unclear.TP53 is a major tumor suppressor that selectively eliminates mutated or damaged cells12. TP53 has functional effects on the transcriptional profiles of genes in several cancer cell lines13, but the impact of tumor-treating fields (TTFields) on GBM according to the TP53 status remains unknown. TTFields has been proposed as an effective cancer treatment in combination with other therapies14. Alternating electric fields are applied for at low intensity (< 1–3 V/cm) and intermediate frequency (100–500 kHz) to treat GBM; this method has been approved by the Food and Drug Administration as combinatorial treatment for patients newly diagnosed with GBM15. In addition, TTFields is well-known to interfere with mitotic spindle assembly, resulting in inhibiting cell proliferation and inducing cell death in a cancer-specific manner17. Gera et al. demonstrated that the apoptotic effects of TTFields were influenced by the TP53 status in cancer cells18. However, several researchers reported that TTFields induced cell death in both TP53-dependent and TP-independent manners19. In this study, we performed gene expression profiling during TTFields treatment in four GBM cell lines to explore effects of TTFields on diverse cellular responses in accordance with the TP53 status. Our results demonstrate that TTFields has the potential to be a targeted therapy and valuable clinical approach for treating GBM.Microarray analysis is a useful method for evaluating therapies for GBM to detect differential expression between normal and cancer cells following treatment with specific drugs or physical proceduresTo verify the correlation between gliomas and TP53, we conducted Kaplan–Meier survival analysis. This data set includes total 512 patients with TP53 alterations (248 cases) and TP53 without alterations (264 cases). As shown in Fig. We detected 10,338 differentially expressed genes (DEGs) significantly altered by TTFields in the four GBM cell lines (≥ 1.3-fold) Fig. a. To anaNext, to determine whether TTFields changed specific genes in GBM cells regardless of the TP53 status, we drew a Venn diagram showing genes for which expression was altered by more than 1.3-fold by TTFields. Fig. c,d. The Four genes associated with cell death were altered by TTFields treatment in all four GBM cell lines (≥ 1.5-fold) Fig. a,b. Amon135 genes were regulated by TTFields in WT TP53 cells and 444 genes were induced in MT TP53 cells (≥ 1.5-fold) . Among twofold changed genes in each WT and MT TP53 cell lines Table a. AlthouNext, qRT-PCR was performed for the GPNMB, KRT19, and KRT15 genes to verify the general tendencies in the gene expression results between microarray and in vitro analysis 23. Therefore, several researchers have suggested that the TP53 status can be used to establish management strategies for patient-specific therapy25. The impact of TP53 in glioma has been largely studied, and TP53 serves a central role in the regulatory network in tumorigenesis which is functionally divided into cell metabolism, stemness, tumor microenvironment, inflammatory responses, and immune response. The differential gene expression in tumorigenesis induced by TP53 status results in various cellular responses.Numerous studies have shown that TP53 is a valuable prognostic biomarker in cancer and TP53 mutations alter the expression of various genes through point mutations18. However, several researchers suggested that TTFields independently affected cell proliferation and survival in various cancer types, such as ovarian carcinoma, lung adenocarcinoma, and mesothelioma cell lines, etc.27. The influence of TTFields in patients with a different TP53 status appears to differ depending on the cancer type. In GBM cell lines, Schneiderman et al. showed that TTFields altered apoptosis by in a p53-dependent and p53-independent manner19. The results revealed that the number of cells and colony-forming potential were significantly reduced by TTFields in GBM cell lines, regardless of the TP53 status. However, caspase activity differed between WT and MT TP53 cells, although both cells eventually died. The reason for this difference appears to be that the mechanism of action depends on the TP53 status. Therefore, to understand the underlying mechanism of action of TTFields and establish an efficient strategy for combating cancer, we analysed gene expression according to the TP53 status on the variable cell response of GBM cell lines.However, the effect of TTFields depending on the TP53 status in various cancer types remains controversial. Gera et al. observed that apoptosis in colon cancer cells was dependent on the TP53 status28. In addition, immune cell infiltration and immunogenic cell death by TTFields treatment have been previously reported30. Moreover, clinical trials of combinatorial treatment with immune checkpoint inhibitors are currently underway31. As expected, Gene expression profiling revealed that the expression of genes related to the cell cycle and apoptosis was greatly affected by TTFields , protein oligomerization , rheumatoid arthritis , ATP biosynthetic process , and regulation of microtubule polymerization, mitotic spindle assembly, inclusion body assembly. An in-depth analysis of these genes related to TTFIields treatment with TP53 status should be carried out in the future.The regulatory mechanism of the cell cycle and apoptosis by TTFields is well-known in cancer biology32. Even now, ATCC U87MG cells is still one of widely used cell lines for study of glioma34. In future studies, we will verify the effect of TTF on other WT TP53 cell lines such as C6, D54 MG, EFC-2, and U343, further consolidating our research results. Furthermore, the effects of TTFields on gene expression and the potential of TTFields as an alternative therapy to alter various biological responses should be confirmed in primary cell lines such and patient specimens. Also, to increase the applicability and efficiency of TTFields treatment, further studies of secreted molecules after TTFields treatment and their function and relationship with other cellular responses are needed.Amid the ongoing controversy over the origin of the ATCC U87MG cell line, ATCC reported commercial U87MG is of central nerve system origin, with an unknown glioma patient derived originIn conclusion, identifying genes with altered expression following TTFields treatment using diverse analytical approaches may be useful in other type of cancers and related clinical fields for cancer therapy. These results provide insight for the development of combinatorial treatments or clinical approaches to maximise effective cancer treatment by identifying genes differentially expressed following TTFields in diverse cellular responses.2. TTFields were generated using the positive and negative ends of two pairs of insulated wires connected to a high-voltage amplifier and electric field generator as described previously35.U87, U373, and T98G cells were obtained from the Korean Cell Line Bank  and U251 cell lines were kindly provided by Dr. Myung-Jin Park from the Korea Institute of Radiological and Medical Sciences (KIRAMS). TP53 status and short tandem repeat (STR) analysis of four GBM cell lines were examined by Macrogen, Inc.  and Cosmogenetech, Ltd.  (data not shown). The cells were cultured in Dulbecco`s Modified Eagle Medium  containing 10% foetal bovine serum (Welgene) and 1% penicillin streptomycin  at 37℃, 5% CO®  after 0.9 V TTFields treatment for 48 h and experiments were performed according to the manufacturer’s protocol. RNA was isolated from GBM cells using an RNA extraction kit , and cDNA was synthesised. Total cDNA was measured by quantitative PCR using SYBRGreen . The primer sequences are shown in Table The lysates were solubilised with lysis buffer  according to the manufacturer’s instructions. Briefly, 25 ng total RNA was and T7 promoter primer mix and incubated at 65℃ for 10 min. cDNA master mix  was prepared and added to the reaction mixer. The samples were incubated at 40 ℃ for 2 h and then RT and dsDNA synthesis were terminated by incubation of the samples at 70 ℃ for 10 min.The transcription master mix was prepared according to the manufacturer’s protocol . Transcription of dsDNA was performed by adding the transcription master mix to the dsDNA reaction samples and incubating the samples at 40 °C for 2 h. Amplified and labelled cRNA was purified with an RNase mini column (Qiagen) according to the manufacturer’s protocol. Labelled cRNA target was quantified using ND-1000 spectrophotometer .After checking the labelling efficiency, 1,650 ng of cyanine 3-labelled cRNA target was fragmented by adding 10 × blocking agent and 25 × fragmentation buffer and incubation at 60 ℃ for 30 min. The fragmented cRNA was resuspended in 2 × hybridization buffer and directly pipetted onto an assembled Agilent Gene Expression Microarray. The arrays were hybridised at 65 ℃ for 17 h in an Agilent Hybridization oven (Agilent Technologies). The hybridised microarrays were washed according to the manufacturer’s washing protocol (Agilent Technologies).https://www.geneontology.org/index.shtml) using GeneSpringGX 7.3.1 (Agilent Technologies).Hybridization images were analysed with an Agilent DNA microarray Scanner (Agilent Technologies) and the data were quantified using Agilent Feature Extraction software 10.7 (Agilent Technologies). The average fluorescence intensity of each spot was calculated, and local background was subtracted. All data were normalised and fold-changes in gene expression were determined using GeneSpringGX 7.3.1 (Agilent Technologies). Normalization for Agilent one-color method was performed for data transformation: Set measurements less than 5.0 to 5.0 and Per Chip: Normalise to 50th percentage. The averages of normalised ratios were calculated by dividing the average of control normalised signal intensity by the average of test normalised signal intensity. Functional annotation of the genes was performed according to Gene Ontology Consortium (https://string-db.org) was used (confidence score > 0.4). Cytoscape software (Version 3.7.2)  was used to visualise the PPI network using the molecular complex detection plugin .To predict protein interactions, STRING . Overall survival curves were plotted according to the Kaplan–Meier method and the log-rank test. p < 0.05 was considered as statistically significant . We used databases of brain lower grade glioma  and glioblastoma .Overall survival and mRNA expression analyses was performed using the cBioPortal online platform  Glioblastoma Project)Supplementary Dataset 1.Supplementary Dataset 2.Supplementary Dataset 3.Supplementary Dataset 4.Supplementary Dataset 5.


The increase in the zeta potential of CMC–nZVI reached one- to four-fold of that for bare nZVI, and was greater at pH values close to PZC, high IS, and larger particle concentration. The stability of CMC–nZVI was increased by 61.8, 93.1, and 57.5% as compared to that of bare nZVI at IS of 1, 50 and 100 mM, respectively. Calculations of Derjaguin, Landau, Verwey and Overbeek (DLVO) interaction energy were in agreement with stability results, and showed the formation of substantial energy barriers at low IS indicating greater nZVI stability. Our results suggest that at IS above 50 mM and nZVI particle concentration larger than 0.1 g L−1, the likelihood of nZVI aggregation is high. Nevertheless, CMC polymer stabilizer would enhance the stability and transport of nZVI even under these unfavorable solution chemistry conditions. Surface modification of nanoscale zero-valent iron (nZVI) using polymer stabilizers  is usually used to minimize aggregation, increase stability, and enhance transport of nZVI. We investigated the stability and dynamic aggregation of bare and CMC–nZVI as affected by variations in pH, ionic strength (IS), and nZVI particle concentration. CMC coating of nZVI resulted in smaller hydrodynamic size and larger zeta potential. The largest hydrodynamic size of nZVI was associated with bare nZVI at high IS (100 mM), pH close to the point of zero charge , and larger particle concentration (1.0 g L Nanoscale zero-valent iron (nZVI) has been widely used for the remediation and detoxification of groundwater and soil resources contaminated by a variety of environmental contaminants, including chlorinated organic solvents, organochlorine pesticides, polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and metal ions ,2,3. DuePolymers and surfactants are among the main types of materials used as coatings for nZVI, including starch , xanthanProlonged nZVI nanoparticle stability in aqueous suspensions has led to greater mobility in subsurface layers, and consequently enhanced remediation activities. The extent of the stability of coated nZVI nanoparticles is dependent on polymer characteristics, thickness of the polymer layer, and solution chemistry . The amoThe stability of nZVI also depends on solution chemistry . Variations in the pH of nZVI suspensions affect the type and amount of charges on the surfaces of nZVI. The electrostatic properties of nZVI in aqueous suspensions are controlled by the pH of the suspension, with minimum nZVI stability occurring when pH approaches the point of zero charge (PZC) . The staThe influence of ionic strength (IS) on the stability and mobility of nZVI in water has been extensively examined ,23. In g−1 [The stability of nZVI is also affected by the volume fraction of nZVI particles. At larger nZVI concentrations, the range of the interaction forces between particles is closer, and the interaction is dominated by the van der Waals and magnetic attractive forces, which increase aggregation and sedimentation of particles . Researc−1 . The overall goal of this research was to assess best synthesizing conditions and solution chemistry to ensure greater nZVI stability in aqueous environments. The specific objectives were to: (1) investigate the effect of CMC surface coating and nZVI particle concentration on the dynamic aggregation of nZVI; (2) evaluate the effect of pH (5–11) and IS (1–100 mM) on the hydrodynamic size, surface charge, and stability of nZVI in aqueous suspensions; and (3) apply the classical Derjaguin, Landau, Verwey and Overbeek (DLVO) theory to qualitatively assess nZVI particle interaction forces, and help to interpret the behavior and fate of nZVI in aqueous suspensions.3.6H2O, NaHCO3, NaBH4, and Carboxymethyl cellulose  were purchased from Sigma Aldrich. All solutions were prepared in nanopure water, which was purged with N2 for 1 h prior to usage. nZVI–CMC were prepared freshly by adding 0.1 M NaBH4 aqueous solution drop wise to a 0.1 M FeCl3.6H2O, and adding 200 mL CMC with 2% concentration at ambient temperature and under atmospheric conditions. The preparation of solutions was carried out as follows: 2.5406 g of FeCl3.6H2O was dissolved in 24 mL ethanol + 6 mL nanopure water (0.1 M FeCl3.6H2O). The CMC solution was prepared by dissolving 0.4 g in 200 mL nanopure water; the solution was stirred with a mechanical stir at 750 rpm for 30 min. The sodium borohydride  powder was dissolved in 100 mL nanopure water (0.1 M NaBH4), and was added to the mixture drop wise (1 drop per 2 s). Stirring was continued for another 30 min. The suspension was filtered (Whatman 42) under vacuum, washed with 25 mL ethanol three times, and transported to storage bottle. The preparation of bare nZVI was carried out following the same previous steps without the addition of CMC. nZVI were synthesized by the reduction of ferric chloride by sodium borohydride . All che−1), and stored as a stock suspension. Two final nZVI concentrations of 0.1 and 1.0 g L−1 were prepared in NaHCO3 background electrolyte solution. nZVI suspensions were prepared in different IS concentrations of 1, 50, and 100 mM, and the pH of the suspensions was adjusted at 5, 7, 9, and 11 using 0.01 M HCl or 0.01 M NaOH. All suspensions were sonicated for 10 min prior to use.After the completion of the nZVI preparatory phase, the concentration of the prepared nZVI was determined ,  was used to characterize the shape of the nZVI particles. The specific surface area of the nZVI particles was determined by the Brunauer, Emmett and Teller (BET) method with nitrogen (N2) at 77 K using surface area and microporosity analyzer .X-ray diffraction (XRD) was used to analyze the crystalline structure of the prepared nZVI particles. XRD analysis was carried out immediately after preparation and after an aging period of one month. The XRD analysis was conducted with an Altima IV X-ray diffractometer . Iron nanoparticles were placed in a glass holder and scanned from 20° to 90° at a scanning rate of 2.0° minThe hydrodynamic size of nZVI particles in aqueous suspensions was measured using laser Doppler velocimetery . Zeta potential of nZVI nanoparticles was determined by dynamic light scattering techniques by measuring the electrophoretic mobility for the different particle suspensions using Zetasizer Nano ZS with MPT2 titrator. The point of zero charge (PZC) was determined over the pH range of 2–12. Averaged values for the hydrodynamic size and zeta potential of nZVI were obtained from 10 measurements, and were presented along with standard deviation (±1 SD).3 electrolyte solution, was quantitatively determined by time-resolved optical absorbance methodology. The change in optical absorbance of nZVI at a wavelength of 508 nm was monitored over time, in suspensions maintained under quiescent conditions, using a UV/VIS Spectrophotometer . The optical absorbance was measured every 3 min for 3 h; all measurements were made at room temperature. All measurements were replicated three times and average values were recorded along with the associated standard deviation.Colloidal stability of iron nanoparticles has been defined as the tendency to disperse in solvent against aggregation during specific time period . Dynamic−1); The classical DLVO theory was used to calculate the total interaction energy, determined as the sum of van der Waals (VDW) attractive and electric double layer (EDL) repulsive forces that exist between nZVI particles. The interaction between two nZVI particles was considered to be a sphere–sphere interaction. Calculation of the EDL repulsion interaction was carried out according to Bhattacharjee and Elimelech  as:(1)ΦEThe VDW attraction interaction energies for nZVI–nZVI nanoparticles was calculated according to Gregory  as:(2)ΦV0 was the dominant form in the prepared iron powder. This was confirmed by the characteristic diffraction peaks 2θ = 45.05° and 65.93°; and 2θ = 44.84° and 65.64° (JCPDS No. 06-0696) for the bare and CMC–nZVI, respectively  considers the size of any particle (or aggregate of particles) with certain equivalent diameter to be similar in size. Therefore, size average diameters determined by DLS cannot be directly compared to TEM diameters . The hydectively . The hyd−1, much larger hydrodynamic sizes for nZVI nanoparticles were observed at all pH and IS values. The hydrodynamic size of the nZVI nanoparticle ranged between 160.2–1939.1 and 132.7–267.3 nm for the bare and CMC–nZVI nanoparticles, respectively , and –26.4, –15.3 and –9.3 mV (at nZVI particle concentration 1.0 g L−1) for IS 1, 50, and 100 mM, respectively  and negative at high pH (>7.2–7.9). The average PZC of bare nZVI was at pH 7.6 and 7.3 for the 0.1 and 1.0 g Lectively . Increasectively .−1), and –30.1, –18.6 and –14.3 mV (at nZVI particle concentration 1.0 g L−1) for IS increased 1, 50, and 100 mM, respectively . The average PZC for the CMC–nZVI was at pH 1–2 . Zeta poectively . At pH vectively . At largSedimentation tests showed the occurrence of a decrease in absorbance over time. This was mainly attributed to the reduction in the number of particles in the suspension due to aggregation, as well as the settlement of heavier aggregates out of solution over time. The stability of nZVI was highly determined by the hydrodynamic size and zeta potential of the particles. Factors favoring smaller particle size and larger zeta potential enhanced nZVI stability. Relative absorbance of bare nZVI after 3 h was 0.69, 0.61, 0.54, and 0.36 at pH values of 11, 9, 5, and 7, respectively , which i3, and reported similar enhancement in the stability of nZVI with several polymer-coating materials.The same trend was also observed for CMC–nZVI, where relative absorbance after 3 h was 0.83, 0.62, 0.57, and 0.55 at pH values of 11, 9, 7, and 5, respectively . Under o−1 , substantial energy barriers between nZVI particles were formed indicating unfavorable attachment conditions . The same behavior was observed for both the 0.1 g L−1 A,B and 1−1  and particle concentration (0.1 and 1.0 g L−1). At IS 50 and 100 mM, interaction energy at almost all pH and particle concentrations was dominated by attractive forces, favoring irreversible aggregation of the nZVI particles in the primary energy minimum. Only two exceptions were observed. The first was with bare nZVI at IS 50 mM, 0.1 g L−1 particle concentration, and pH 11, where a small energy barrier was formed around 6 −1 particle concentration, and pH 9 and 11, where small energy barriers were formed around 2 and 7 The effect of IS on the dynamic stability of nZVI was also supported by the results of the DLVO calculations. ectively A,B. Acco−1 the likelihood of nZVI aggregation is high. Nevertheless, polymer stabilizer would enhance the stability of nZVI even under these unfavorable solution chemistry. This is clearly visible in Our results suggest that at IS above 50 mM and nZVI particle concentration larger than 0.1 g L0 peaks over time with bare nZVI, whereas Fe0 in CMC–nZVI remained without significant change for 30 d. The hydrodynamic size of nZVI was always smaller in the presence of CMC, particularly at larger IS and nZVI particle concentrations. The average PZC for bare and CMC–nZVI was at pH 7.3–7.6, and 1–2, respectively. The increase in the zeta potential of CMC–nZVI reached one- to four-fold of that for bare nZVI, and was greater at pH values close to PZC, high IS, and larger particle concentration. CMC increased nZVI stability and relative absorbance remained above 0.95 after 1 h of sedimentation, while it was reduced to 0.65 with bare nZVI. Our results suggest that at IS above 50 mM and nZVI particle concentration larger than 0.1 g L−1, the likelihood of nZVI aggregation is high. Under these conditions, maximum particle aggregation is expected, therefore, it is extremely important to use polymer stabilizer to enhance the stability and transport of nZVI particles, and subsequently increase their efficiency in environmental applications.In this research, we investigated the effect of pH, IS, nZVI particle concentration, and CMC polymer stabilizer on the stability and dynamic aggregation of nZVI suspensions. Increased surface area and smaller pore size were observed in the presence of CMC polymer stabilizer. XRD patterns revealed a decrease in Fe


The molecular structure of the Cu(II) complex revealed a mononuclear compound in which the Cu(II) ion was bonded to two morpholinyl dithiocarbamate ligands to form a four-coordinate distorted square planar geometry. The molecular structure of the Zn(II) complex was revealed to be dinuclear, and each metal ion was bonded to two morpholinyl dithiocarbamate bidentate anions, one acting as chelating ligand, the other as a bridge between the two Zn(II) ions. The anticancer activity of the morpholinyldithiocarbamate ligand, Cu(II) and Zn(II) complexes were evaluated against renal (TK10), melanoma (UACC62) and breast (MCF7) cancer cells by a Sulforhodamine B (SRB) assay. Morpholinyldithiocarbamate was more active than the standard drug parthenolide against renal and breast cancer cell lines, and [Zn(μ-MphDTC)2(MphDTC)2] was the most active complex against breast cancer. The copper(II) complex had a comparable activity with the standard against renal and breast cancer cell lines but showed an enhanced potency against melanoma when compared to parthenolide.Cu(II) and Zn(II) morpholinyldithiocarbamato complexes, formulated as [Cu(MphDTC) The Cu(II) and Zn(II) complexes were obtained from the reaction of the aqueous solution of the metal salt and the morpholinyl dithiocarbamate ligands . Single −1 shifted to 1478 cm−1 and 1431 cm−1 in the Cu(II) and Zn(II) complexes, respectively. These shifts could be ascribed to the coordination of the ligand to the metal ions, causing an electron delocalization within the dithiocarbamate moiety , was comparable to that of the standard against renal and breast cancer cell lines, but it was more active against melanoma, with an IC50 of 4.47 μM. The zinc(II) complex, [Zn(μ-MphDTC)2(MphDTC)2], was more active against breast cancer cell lines, with an IC50 of 3.17 μM, which was slightly higher than that of parthenolide, which had an IC50 of 3.52 μM. The activity of the Zn(II) complex was 8.70 and 16.54 μM against renal and melanoma cancer cell lines, respectively.The anticancer activities of morpholinyldithiocarbamate (Mphdtc) and the Cu(II) and Zn(II) complexes were evaluated against renal (TK10), melanoma (UACC62) and breast (MCF7) cancer cell lines using a Sulforhodamine B (SRB) assay. The results are presented in The starting materials, copper(II) chloride dihydrate and zinc(II) chloride, were purchased from Sigma Aldrich and used without further purification. The synthesis of morpholine dithiocarbamate ligand (MphDTC) was done using the reported method .1H- and 13C-NMR spectra were obtained on a Bruker EMX 400 MHz spectrometer . The chemical shift values were reported in parts per million (ppm) relative to tetramethylsilane (TMS) as an internal standard. The FTIR spectra of all compounds were recorded on Cary 630 FTIR spectrophotometer  in the 4000–650 cm−1 region. Electronic spectra were characterized in DMSO using Cary100 Series UV-Vis spectrophotometer . Elemental analysis was carried out using Thermoscientific Flash 2000 . The melting points were recorded using the StuartTM melting point apparatus .2] and [Zn2(μ-MphDTC)2(MphDTC)2] were obtained by slow evaporation of the dichloromethane solution of each complex. Suitable crystals (0.34 × 0.22 × 0.14 and 0.33 × 0.22 × 0.14 mm3) of Cu(II) and Zn(II) were respectively selected and mounted on a MITIGEN holder in paratone oil on a Bruker APEX-II CCD diffractometer . The crystal was kept at T = 100(2) K during data collection. Using Olex2 [Single crystals of the complexes [Cu(MphDTC)ng Olex2 , the strng Olex2  structurng Olex2  using Le−1): 1007 υ (C-S) cm−1 and 1478 υ (C-N) cm−1. Anal.Calc (%) for C10H16CuN2O2S4 (418.89): C, 30.95; H, 4.16; N, 7.22; S, 33.08. Found: C, 30.86; H, 4.13; N, 7.70; S, 32.67.Copper(II) chloride dihydrate  was dissolved in 20 mL of distilled water and added dropwise into a 30 mL aqueous solution of morpholine dithiocarbamate . A dark brown precipitate formed immediately and was further stirred for 1 h at room temperature, filtered, rinsed with methanol several times and dried in a desiccator. Yield 1.632 g, 67%. m.p 307–309 °C. FTIR , 4.04 ppm . 13C-NMR δd6DMSO-: 51.59 ppm, 66.08 ppm and 204.04 ppm. FTIR υ (cm−1): 992 υ (C-S) cm−1 and 1431 υ (C-N) cm−1. Anal. calc (%) for C10H16N2O2S4Zn (389.89): C, 30.81; H, 4.14; N, 7.19; S, 32.90. Found: C, 30.18; H, 4.14; N, 7.02; S, 33.06.An aqueous solution of zinc chloride dihydrate  was added dropwise into a morpholine dithiocarbamate aqueous solution  and stirred for 1 h at room temperature. The white precipitate that formed was filtered and rinsed with methanol and was dried in a desiccator to obtain a white solid. Yield 0.4117 g, 53%, m.p 344–346 °C. 2, 95% air and 100% relative humidity in RPMI containing 5% fetal bovine serum, 2 mM L-glutamine and 50 µg/mL gentamicin. For the screening experiment, the cells (3–19 passages) were inoculated in 96-well microtiter plates at plating densities of 7–10,000 cells/well and were incubated for 24 h. After 24 h, the cells were treated with the experimental drugs, which were previously dissolved in DMSO and diluted in medium to produce five concentrations. Cells without drugs served as controls. The blank contained complete medium without cells. Parthenolide was used as a standard. The plates were incubated for 48 h after addition of the compounds. Viable cells were fixed to the bottom of each well with cold 50% trichloroacetic acid, washed, dried and dyed by SRB. Unbound dye was removed, and protein-bound dye was extracted with a 10 mM Tris base for an optical density determination at a wavelength of 540 nm using a multiwell spectrophotometer . Data analysis was performed using GraphPad Prism software. 50% of cell growth inhibition (IC50) was determined by non-linear regression.The growth inhibitory effects of the compounds were tested in the 3-cell line panel consisting of TK10 , UACC62 (melanoma) and MCF7 (breast) cancer cells by a Sulforhodamine B (SRB) assay. The SRB assay was developed by Skehan and colleagues to measure drug-induced cytotoxicity and cell proliferation. The human cell lines TK10, UACC62 and MCF7 were obtained from the National Cancer Institute  in the framework of a collaborative research program between CSIR and NCI. Cell lines were routinely maintained as monolayer cell cultures at 37 °C, 5% COP21/n with a Cu(II) ion located on an inversion center, so that the four sulphur donors of the morpholinyl dithiocarbamate ligands were coplanar in a slightly distorted square planar geometry around the copper(II) ion. The Zn(II) complex crystallized in a triclinic crystal system and space group P-1, and only half a molecule was crystallographically independent within the unit cell (Copper(II) and zinc(II) complexes of morpholinyldithiocarbamate were synthesized and characterized by single crystal X-ray crystallography. The copper(II) complex crystallized in a monoclinic space group nit cell . The mol


KLK3, the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1. Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression.  Prostate cancer is largely driven by androgens acting through the androgen receptor to give rise to proliferative and invasive cells3. As a result, therapies aimed at inhibiting the activity of the androgen receptor have remained the primary treatment modality for men with prostate cancer for the past eighty years5.Prostate cancer is one of the most commonly diagnosed and leading causes of cancer related death in North American men7 apalutamide9, and darolutamide10. These second generation, non-steroidal anti-androgens (NSAA) antagonize AR by tightly binding the receptor and preventing its translocation to the nucleus. Clinical trials have demonstrated strong efficacy of these NSAA, improving time to progression and extending overall survival. Androgen synthesis inhibitors, such as abiraterone12, work by inhibition of the metabolic machinery that produces androgens, and have also shown significant efficacy in patients. Unfortunately, none of these drugs are curative in patients with advanced metastatic disease, as resistance will eventually develop leading to progression5. The mechanisms by which resistance occurs remains an active area of study, with mutations or splice variants in AR15, loss of AR and activation of AR-independent bypass mechanisms16, glucocorticoid receptor (GR) activation18, activation of other signaling pathways19, and metabolic changes all implicated in resistance19.Recent advances in anti-androgen therapy have seen the introduction of more potent AR inhibitors such as enzalutamide22. We have now collected a panel of 20 prostate cancer cell lines, which represents one of the largest panels of prostate cancer cell lines available to the research community. We had three initial research objectives that we wished to complete. First, we wanted to describe the characteristics of the cell line panel at the molecular level, including expression and copy number analysis and assessment of key proteins such as AR and ERG. Second, we wanted to determine the enzalutamide response in these cells. Our finding with enzalutamide response led us the third area of research, regarding the subset of samples that showed de novo over-expression of GR and estrogen receptor (ER). These studies have identified prostate cancer cell lines in which GR over-expression may compensate for the loss of AR. Furthermore, we show that a targeted therapy against GR is effective in inhibiting the growth of these cells. These results suggest that GR-targeting in prostate cancer may represent a novel therapeutic approach in men with resistance to AR-targeted drugs and identifies model cell lines with which to study these agents in prostate cancer.Effective study of NSAA resistance requires strong model systems with which to study prostate cancer in response to therapy. Our group has previously utilized large panels of breast and pancreatic cancer cell lines to gain insight into the behavior and drug response of breast and pancreatic cancersWe sought to establish a panel of prostate cancer cell lines that could capture some of the clinical heterogeneity observed in prostate cancers. The full panel of cell lines that we obtained including the provider and growth media conditions are shown in Table AR and target genes like KLK3 and TMPRSS2, while those in group II had high levels of expression of these genes. There was also a minor cluster consisting of the cell line NCI-H660, which was derived from a small cell cancer of the prostate, and the two related cell lines CWR-R1 and 22Rv1, which were both derived from the same patient and were initially grown as xenografts in immune-deficient mice . The related kallikrein gene KLK2 had expression that was highly correlated with KLK3, as did TMPRSS2, one of the genes involved in the TMPRSS2:ERG translocation that is common in prostate cancer. All three genes are indicated by black arrows in Fig. AR was not highly correlated with expression of KLK3  were also found associated with this gene cluster.Other gene clusters were also evident in the cell lines. One of the most prominent clusters was related to expression of the cancer/testis family of G-antigens  on the samples in the two major clusters to determine what biological hallmarks distinguished the groups. Although some of the lines are subclones of parental lines, we used them all in the GSEA due to the small sample numbers. There were 31 gene sets that were upregulated with a false discovery rate (FDR) q-value less than 0.25 in the first group  method to identify copy number alterations. An example of an ASCAT segmented cell line sample is shown in Fig. 24 to identify significant regions of gain  and tumors. In contrast to the 4q13.3 peak in tumors, the 4q13.2 region was the peak in the cell lines, due to the presence of small focal amplifications in 4q13.2 in 22Rv1 and CWR-R1, which contains the testosterone metabolizing genes UGT2B17 and UGSTB215. In both the prostate cell lines and tumor samples, 8q24.21, which contains the MYC gene, were also a major amplification peak. A final significantly amplified region in common was 14q.32.33. We also observed several regions that were significant in the cell lines that were not identified in the TCGA tumor samples. Regions that showed significant gains in only the cell lines included 11q13.1, 12p11.22 and 17q21.31.We next used GISTIC (Genomic Identification of Significant Targets in Cancer) 2.0RB), 16q24.1, and 17q21 (containing TP53). Unique regions of loss in the cell lines included 1p36, 2p21, 4q22, 6q25, 9p21.3 (containing CDKN2A).We also examined regions that showed significant focal losses by GISTIC 2.0  were in the µM range Fig. A. The rep value of 0.12 . Neither of these were significant after FDR or FWER corrections. Finally, we compared the AR-null responders to non-responders, and found that there were no significant hallmarks enriched in these cells, even at nominal p value levels.We performed GSEA comparing various subsets of responders to non-responders. Our initial GSEA comparing all responders  and glucocorticoid receptor  were inversely expressed compared to AR  subfamily might be responding to enzalutamide at the high levels that are required to see activity in short-term in vitro assays. We hypothesized that at high concentrations, enzalutamide may bind to these structurally-related proteins. We did observe that both the estrogen receptor  and HH870, PC3, MDAPCa1, and DU145  or better  growth inhibition compared to enzalutamide in every line tested. The GR50 values for mifepristone were fivefold lower for HH870, PC3, and MDAPCa1 than for enzalutamide. We summarized the expression of AR, GR, and the response to enzalutamide and mifepristone in Table We decided to further investigate the potential role for GR in AR-resistant prostate cell lines, since it had previously been implicated in castrate resistant disease, it had higher levels of expression than We next tested the ability of mifepristone to inhibit the growth of prostate cancer cell lines in clonogenic assays. We plated a set of 5 cell lines  at low density and grew them for 7–14 days in the presence of 25 µM enzalutamide or mifepristone compared to vehicle-treated control cells. The plating efficiency of LNCaP and HH870 was low (4.3% and 1.9% respectively), so they gave limited information about drug response. The other three cell lines all had higher plating efficiencies , allowing assessment of drug efficacy at 7 days . AR expression was not correlated strongly with KLK3 gene expression, consistent with previous reports that show a complex relationship between AR and KLK3 expression33. The TCGA study found that AR activity, as inferred by expression of AR target genes including KLK3, was not correlated with levels of AR expression, but instead varied by the molecular subtype23. Furthermore, c-MYC overexpression has been shown to oppose the action of AR on several AR-target genes like KLK333, and thus cell lines that have high levels of MYC and AR might have lower levels of KLK3 than would be expected. Finally, the presence of AR splice variants in some cell lines which may alter the function of the protein without changing AR expression. The cell lines CWR-R1 and 22RV-1 both express high levels of the AR splice variant, but do not express KLK3 at a significant level.The cell lines expressed many of the hallmark genes that would be expected in prostate cancer. For example, GAGE10 and NXF2 antigens, although this was typically at lower levels than those observed in the cell lines. The presence of these antigens in LNCaP cells was recognized more than 20 years ago34, but their relevance remains unclear. However, these cancer/testes antigen genes remain under study as potential targets for immunotherapy (reviewed in Ref35).Interestingly, we also observed that a subset of the cell lines expressed high levels of MAGE and GAGE antigens. Although this was mainly seen in LNCaP and derivatives of LNCaP, we also saw significant expression in some non-LNCaP cell lines such as LAPC4, indicating it could be a more general phenomenon. We assessed expression of GAGE genes in TCGA samples and found that ~ 10% of patients showed detectable levels of expression of 23. Interestingly, two of the definitive subtype specific genes found by the TCGA were not expressed above background levels in any of the prostate cancer cell lines (PCOTH and GATA4). The lack of expression of these two genes suggests one of three possibilities. First, these genes could be expressed in non-epithelial prostate cells associated with the tumor and thus are not present in the cell lines that are derived from epithelial cells. Second, the genes may be expressed in a subset of prostate tumors that are not represented by current cell lines in the panel, perhaps reflecting the difficulty in culturing cells of a specific subtype. Third, it may be that their expression is lost during adaptation to culture conditions. Immunohistochemical staining of prostate cancer tissues would be the best approach to determine if expression is confined to non-epithelial cells. Answering the other possibilities would likely require attempts to generate new cell lines with characterization of the primary tumor for comparison of expression levels of these genes.We also examined expression and subtyping of the prostate cell lines compared to the genes and subtypes identified by the TCGA prostate study7. We observed that response largely tracked with AR expression status: cell lines with high levels of wild-type AR were more responsive to enzalutamide than cell lines with low levels of AR or AR splice variants. However, we did note that some cells that were AR null were still amongst the most responsive to enzalutamide. GSEA analysis suggested that androgen and estrogen signaling were high in these cells. We hypothesized that other NR3 family receptors  could mimic expression of AR, and that the relatively high concentrations of enzalutamide that were used in vitro might result in targeting of other NR3 family receptors. Thus, we examined their expression in the prostate cancer cell lines. We found that NR3C1 (glucocorticoid receptor) mRNA and GR protein was expressed at moderate to high levels in a subset of the prostate cancer cell lines. In contrast, PGR expression was very low according to the RNAseq data. ESR1 expression was lower than NR3C1 but higher than PGR. We tested the expression of GR at the protein level and found moderate to high levels of GR in multiple cell lines. Treatment of cells with dexamethasone lowered the expression of GR as expected25, resulting in increased sensitivity to enzalutamide in 2 out of the three cell lines we tested. Importantly, PC3 was the cell line that did not show any response to dexamethasone, and it was the only one of the three cell lines that did not respond at all to enzalutamide in our initial screens, suggesting that there may be alternative survival mechanisms present in these cells when enzalutamide is used. Together, these data are consistent with the possibility that GR activity may be impacted in these cell lines when treated with high doses of enzalutamide.We next tested the ability of enzalutamide to inhibit the growth of all the cell lines in the panel. The doses required to inhibit the growth of even sensitive cells were in the µM range, as has previously been reported36. Interestingly, our expression profiling and Western analysis shows that LAPC4 and MDAPCa1 have moderate levels of NR3C1/GR expression. This suggests that even moderate levels of GR protein may be sufficient to signal efficiently, allowing escape from AR inhibition.We thus tested the ability of the GR/PGR inhibitor mifepristone to inhibit the growth of prostate cancer cell lines compared to enzalutamide. We selected five lines for screening, with LNCaP serving as a control. The remaining four cell lines all had low levels of AR but moderate to high levels of GR. Mifepristone was highly effective against the low expressing AR lines, with GR50 values lower than those observed for enzalutamide in the same lines. Clonogenic assays demonstrated the strong ability of mifepristone to inhibit the growth of PC3, DU145, and MDAPCa1 . This agrees with recently published data that shows that both CWRR1 and LAPC4 as well as PC3 and DU145 prostate cells can also be inhibited by mifepristone or by knockdown of GR using inducible shRNA36. Our cell line data shows that treatment of prostate cells expressing high levels of AR with mifepristone results in potent inhibition of growth. The levels of mifepristone that we used to inhibit the growth of cells in the clonogenic assay are comparable to those that are achievable in the serum of patients37. Our data also identifies multiple cell lines with low levels of AR expression that instead appear to use GR mediated signaling as a resistance mechanism. Importantly, other groups have demonstrated that GR upregulation can result from long-term exposure to anti-androgens such as enzalutamide or abiraterone38. However, cell lines in the study like HH870 that have low AR expression but high GR expression were established from patients prior to treatment with any anti-androgens39. The absence of added androgens in the growth medium suggests that up-regulation of GR could be an event that occurs during selection for cells that grow in vitro.Multiple studies have now implicated GR signaling as an alternative method of activating growth signaling in castration-resistant prostate cancer40. Ideally, we would knock out GR and determine whether this alters response to enzalutamide. However, it has been shown in a recent publication using shRNA knockdown that the same prostate cancer cell lines require GR expression for viability36. Thus, it would require engineering a mutant GR that no longer binds to enzalutamide to demonstrate that enzalutamide is effective in these AR-null lines through off-target binding to related NR3 family members. A separate approach, which we plan to assess in follow up studies, would be to identify the proteins that bind to enzalutamide to determine if GR is bound to enzalutamide at high concentrations.An important question that remains incompletely answered by our study is why enzalutamide was effective in some of the AR-null cell lines. We have shown that these lines express high levels of GR. We hypothesize that the high concentrations of enzalutamide utilized in the in vitro assays may result in binding of the drug to related NR3C family members such as GR. Surprisingly, we could not find any information about enzalutamide affinity for other NR3C family, although the ligand binding domain of GR and AR are reported to have 51% sequence identityTo our knowledge, this panel of prostate cancer cell lines represents one of the largest collections of molecularly characterized prostate cancer cell lines in the world. Our profiling data and molecular analyses in concert with drug screening studies strongly implicate GR-mediated signaling in cell lines that have high GR but low AR expression. Analysis of TCGA patient samples suggests the presence of patients with prostate cancer who have similar GR/AR expression profiles. In summary, these data strongly support the role for GR inhibition in patients and demonstrate the efficacy of mifepristone in suppressing the growth of AR-null, GR-expressing prostate cancer cells in vitro. Ongoing studies using mifepristone alone or in combination with enzalutamide should determine the efficacy of GR-based inhibition in patients with castration-resistant prostate cancer.2 in the media conditions recommended by the supplier . Genotypes for all of the cell lines are listed in supplementary Table We obtained cell lines from the sources indicated in Table Cells were grown on 60-mm tissue culture treated dishes to 70–90% confluence in a humidified incubator running at 37 °C/5% CO2. For harvest, dishes were placed in the biosafety cabinet on ice. Media was aspirated and cells were rinsed with sterile 1 × PBS two times. RIPA buffer (Sigma #R0278) containing protease-phosphatase inhibitor  was added. Slurry was scraped and collected into pre-chilled and labeled microtubes. Protein was then placed in the -80 °C for a minimum of 16 h. Slurries were thawed on ice then spun down at 4 °C, 15 K rpm for 10 min. The supernatant was transferred to a clean, pre-chilled and labeled microtube on ice. Protein concentration was determined using a 96-well format BCA assay (ThermoFisher Scientific #23225) with a Promega Glomax.20 µg protein samples including 4 × loading dye (Invitrogen #NP0007) w/ β-mercaptoethanol and RIPA buffer were boiled at 90 °C for 5 min. Contents were collected with a quick spin in the centrifuge. Samples were added to NuPAGE 4–12% Bis–Tris gels  and run in MOPS SDS running buffer (Invitrogen #NP0001-02) at 120 V for ~ 1 h on ice. Proteins from the gel were transferred at 30 V for 1.5 h to Immobilion membranes (Millipore #IPFL00010) in tris–glycine transfer buffer (Fisher Scientific #BP13064) containing methanol and insulated with ice water. Membranes were briefly rinsed in TBS after protein transfer, then placed in 5% BSA in TBS-T to block at room temperature for 1 h. Membranes were either probed with 1:1000 androgen receptor (GTX #62599) in 5% BSA in TBS-T or 1:2000 ERG (GTX #62386) at 4 °C overnight. Membranes were rinsed in 1 × TBS-T 3 times quickly, then 3 times for 5 min on a rocking platform at room temperature. They were then probed with 1:10,000 donkey anti-rabbit IgG-HRP conjugate (Jackson Laboratories #711–035-152) in 5% BSA in TBS-T for 45 min at room temperature on a rocking platform and rinsed as previously described. Membranes were placed on clear plastic and a chemiluminescent substrate (ThermoFisher Scientific #34080) added. Membranes were visualized on a Syngene PXi imaging system. Membranes were then rinsed and re-probed for the loading control at 1:1000 with GAPDH  in 5% BSA in TBS-T for 2 h room temperature, rinsed, probed with secondary and imaged as previously described.https://github.com/danielderrick/prost_RNAseq_reprocessing. Data was visualized using standard clustering approaches42. Gene set enrichment analysis was performed using the online GenePattern tool from the Broad Institute and the Hallmarks gene set44. The log transformed FKPM values for the cell lines are available in supplementary Table RNA was isolated from subconfluent cell lines using an RNeasy minikit (Qiagen) and submitted to the OHSU massively parallel sequencing core for RNAseq analysis. We used the Kallisto software to determine RNA expression as Fragments per Kilobase Million (FKPM). The scripts used to process the data are available at: 21. Samples were submitted to either the Lawrence Berkeley National Laboratory HTA microarray facility or at the OHSU genome core facility for analysis on the SNP6.0 chip platform. The resulting data was process using a Python3 wrapper (https://gitlab.com/biotransistor/myascat) that ran the R based ASCAT  SNP analysis pipeline (version 2.4.4) to generate copy number data45. Briefly, we used 1258 SNP6 cell files, available from the HapMap project (phase3 2009–04-02), as the normal reference samples. The 13 prostate cell line samples from 2009 and 8 prostate cell line samples run in 2015 were processed in separate runs then combined. SNP6.0 cel files were transformed into the BAF and LogR illummina file format, utilizing apt-probeset-genotype and apt-probeset-summarize commands from affymetrix command line tools (version 1.18.0) and the normalize_affy_geno_cluster.pl script form PennCSV (version 1.0.3), as described in the ASCAT documentation. ASCAT was run on the BAF and LogR files to generate major minor allele track files (segemnet.txt) and PCF (piecewise constant fitting) segmented BAF and LogR track files. These track files were then transformed into seg files to be able to study the minor and major allele, the B allele frequency and the total copy number of each genomic locus tracks in the IGV genome browser (http://igv.org/). Results for PCF gamma segmentation setting 25 (ASCAT default) and 40 (PennCSV default) were produced, but all GISTIC analyses were performed on segmentation settings of 40.DNA was isolated from the cell lines as previously described22, with minor modifications. Briefly, cells were plated into 96 well plates and allowed to attach overnight. The next day, cells were treated with triplicates of nine concentrations for each of three different drugs. One plate of cells was fixed, stained with DAPI, and imaged to establish cell number at time 0. Drug was left in the medium for 72 h at which point they were fixed and imaged for assessment of response. Drug response was calculated as one of two closely related metrics . Clonogenic assays were performed using standard methodology. Briefly, cells were plated at low density (500 cells/well) into 6 cm2 plates and after overnight attachment, were treated with drug or PBS as a control. Medium and drug were replenished every 2 days. Cells were fixed and stained with crystal violet 10–14 days post-plating and scanned using a STEMvision system (Stem Cell Technologies) and assessed using manual counting in Fiji46.Response to drug was assessed as described previouslySupplementary Figure S1.Supplementary Table S1A.Supplementary Table S1B.Supplementary Table S2.Supplementary Table S3.


Several signaling pathways are aberrantly activated in head and neck squamous cell carcinoma (HNSCC), including the Hedgehog-Gli (HH-GLI), WNT, EGFR, and NOTCH pathways. The HH-GLI pathway has mostly been investigated in the context of canonical signal transduction and the inhibition of the membrane components of the pathway. In this work we investigated the role of downstream inhibitors GANT61 and lithium chloride (LiCl) on cell viability, wound closure, and colony forming ability of HNSCC cell lines. Five HNSCC cell lines were treated with HH-GLI pathway inhibitors affecting different levels of signal transduction. GANT61 and LiCl reduce the proliferation and colony formation capabilities of HNSCC cell lines, and LiCl has an additional effect on wound closure. The major effector of the HH-GLI signaling pathway in HNSCC is the GLI3 protein, which is expressed in its full-length form and is functionally regulated by GSK3β. LiCl treatment increases the inhibitory Ser9 phosphorylation of the GSK3β protein, leading to increased processing of GLI3 from full-length to repressor form, thus inhibiting HH-GLI pathway activity. Therefore, downstream inhibition of HH-GLI signaling may be a promising therapeutic strategy for HNSCC. Head and neck squamous cell carcinoma (HNSCC) encompasses tumors arising in the oral cavity, larynx, nasopharynx, oropharynx, hypopharynx, and salivary glands, with an annual incidence of around 880,000 new cases worldwide and around 450,000 deaths . HNSCC hThe HH-GLI signaling pathway is a developmental pathway, mostly inactive in the adult organism except in stem cell maintenance and wound healing. The main components are the hedgehog (HH) ligands . They bind to the transmembrane receptor patched (PTCH1), which releases its inhibitory effect on the co-receptor smoothened (SMO). This triggers a phosphorylation and ubiquitination cascade in the cytoplasm through several proteins, including beta-transducin repeat containing protein 1 (βTrCP1), glycogen synthase kinase 3 beta (GSK3β), protein kinase A (PKA), casein kinase 1 (CK1), and suppressor of fused (SUFU), which ultimately regulate the processing of glioma-associated oncogene homolog (GLI) proteins. The full-length versions of GLI1-3 proteins are transcriptional activators, while the truncated versions of GLI2-3 are transcriptional repressors. GLI proteins regulate the transcription of many genes involved in proliferation, differentiation, cell cycle regulation, stemness, epithelial-mesenchymal transition (EMT), angiogenesis, and invasiveness, as well as pathway autoregulation through PTCH1 and GLI1 .Most of the studies dealing with the role of the HH-GLI pathway in HNSCC used immunohistochemical staining as the method of choice. In most of them, only GLI1 of the three GLI proteins was stained, and its nuclear localization was associated with metastasis, poor survival, tumor size, and recurrence ,13,14,15In vitro studies on HNSCC cell lines focus mostly on the inhibition of the membrane part of the pathway, protein SMO. Cyclopamine, an SMO inhibitor, can inhibit the growth of esophageal cancer In vitro , and it GLI1 and GLI2 genes [Almost all studies examine the role of GLI1 as the main marker of the HH-GLI pathway activity, with only a few examining the roles of GLI2 and GLI3. HNSCC spheroid cultures demonstrate EMT, CSC-like phenotype, and upregulation of I2 genes . RodriguI2 genes . It is kI2 genes . LiCl prI2 genes .PTCH1, GLI1, GLI2, and GLI3 are expressed in all analyzed HNSCC cell lines. Out of the three GLI genes, GLI3 shows the strongest expression in all analyzed cell lines , GANT61 (5–25 μM), and lithium chloride (LiCl) (5–40 mM). Cyclopamine acts on the membrane part of the HH-GLI pathway, LiCl modifies the activity of a cytoplasmatic regulatory kinase GSK3β, while GANT61 inhibits GLI proteins. In four out of five cell lines, GANT61 has the strongest inhibitory effect on cell proliferation, followed by LiCl, while cyclopamine shows the weakest or no effect . The onlPTCH1 expression was determined with qRT-PCR. GANT61 treatment significantly downregulates the expression of the PTCH1 gene in three HNSCC cell lines . LiCl treatment downregulates the expression of PTCH1 in all lines except Detroit562  at 190 kDa, and the repressor form (GLI3R) at 83 kDa. The GLI3 protein was present in the full-length form in all examined cell lines, suggesting it acts as the pathway activator in HNSCC cell lines. GLI3R was found strongly expressed in two HNSCC lines, and weakly expressed in three HNSCC lines. In SCC9, SCC25, and Detroit562 cell lines, GLI3FL was downregulated after GANT61 and LiCl treatment. Interestingly, an additional, yet unidentified band around 120 kDa was detected in all five HNSCC cell lines after GANT61 treatment. In the untreated cells, the band is not detected in two lines, weakly detected in one, and strongly in two lines. Upon GANT61 treatment, it is upregulated in all cell lines, and in the case of SCC9 and A253 by LiCl treatment as well A. The GLTo test the effect of LiCl on the HH-GLI signaling pathway, phosphorylation of GSK3β was investigated by Western blot. It is known that LiCl treatment increases the inhibitory Ser9 phosphorylation of GSK3β. In all HNSCC cell lines, LiCl increased the Ser9 phosphorylated form of the GSK3β protein, while the total GSK3β levels remained unchanged. This phosphorylation affects GSK3β activity, leading to increased processing of GLI3 from full length to repressor form. This effect can be seen as reduced GLI3FL levels in all HNSCC cell lines, but the increase of GLI3R is detected only in some lines . GANT61 To test the colony forming ability of HNSCC cell lines after GANT61 or LiCl treatment, 1000 cells were plated in a 6-well and maintained for 14 days in medium containing different concentrations of GANT61 and LiCl. All the cell lines showed a dose-dependent decrease in colony forming ability after treatment with either compound. In most cell lines GANT61 was more effective, but in Detroit562 and FaDu lines, LiCl was more effective .Wound healing assay was used to examine the proliferative and migratory ability of cells A. In allIn this work we demonstrate for the first time the effect of downstream HH-GLI pathway inhibitors GANT61 and LiCl on HNSCC cell lines. It has been demonstrated previously that the HH-GLI signaling pathway is upregulated and activated in HNSCC ,14,18,20In our set of five HNSCC cell lines, the most uniformly expressed GLI protein is GLI3, while GLI2 is undetectable, and GLI1 is poorly detectable at both mRNA and protein levels. In this study we have demonstrated by the MTT assay that GANT61 and LiCl show much stronger growth inhibition of HNSCC cells than previously used cyclopamine. Different cell lines respond slightly differently, with most of them showing stronger sensitivity to GANT61 and only one line, FaDu, to LiCl. The colony forming ability of HNSCC cell lines is reduced after GANT61 or LiCl treatment, but the wound closure is affected only by LiCl and not by GANT61 treatment. Since LiCl regulates GSK3β activity, and this protein regulates many other targets except GLI  , it is lHNSSC cell lines SCC9, SCC25, A253, Detroit562, and FaDu were purchased from the ATCC  and maintained in the recommended media. A-253 was maintained in McCoy’s 5A medium , SCC9 and SCC25 in DMEM:HAM’S=1:1 (Dulbecco’s Modified Eagle Medium: Ham’s F12) medium , and FaDu and Detroit562 in EMEM  medium , all supplemented with 10% FBS . The cell lines are derived from different anatomical sites: SCC9 and SCC25 originate from the oral cavity (tongue), FaDu from the hypopharynx, Detroit562 from the metastatic site , and A253 from the salivary gland. All the cell lines are established and frequently used models of HNSCC, and were purchased as an HNSCC panel designed by the ATCC. According to the available data, all these lines are HPV-negative.3 cells/well in 96-well plates and treated with a range of concentrations of different HH-GLI pathway inhibitors: cyclopamine 1.25–10 µM , GANT61 5–25 μM , and LiCl 5–40 mM  for 24–72 h, and cell viability was measured on LabsSystems Multiskan MS microplate reader  after incubation with MTT. The treatment was done in quadruplicate for each dose, and the experiment was repeated twice.For MTT -2,5-Diphenyltetrazolium Bromide) assays, cells were plated at 2 × 10For colony forming assay, 1000 cells/well were plated in a 6-well plate, left to attach for 24 h, and then treated with 1–5 μM GANT61 or 1–10 mM LiCl. Cells were kept in culture for 2 weeks to allow for colony formation, with media with compounds changed twice per week. Cells were then washed with PBS, fixed with 4% paraformaldehyde, and stained with crystal violet to visualize the colonies. The experiments were performed in triplicate.5 cells/well were plated in 24-well plates and left for 24 h to attach. Two scratches per well were made with a 10 μL pipette tip; cells were washed with PBS  to remove floating cells and were treated with GANT61 or LiCl. Images of the scratch were taken immediately after washing with PBS and 24 h later at the same location. Eight images were taken for each treatment, and the images were processed using the MRI Wound Healing Tool plugin for FIJI [For wound healing assay, 10for FIJI  to calcuFor gene/protein extraction, cells were treated with 5 and 10 μM GANT61, or 10 and 20 mM LiCl for 24 h. Cells were collected by scraping with the cell scraper, washing in PBS, and collecting the cell pellet, which was used for either RNA or protein extraction.PTCH1, GLI1, GLI2, and GLI3 genes was measured as described previously [RPLP0 housekeeping gene.RNA was extracted from cell pellets with TRIzol reagent  per the manufacturer’s instructions. One microgram of RNA was reverse transcribed into cDNA using the High Capacity cDNA synthesis kit , and qRT-PCR was performed on the CFX-96 instrument  using SsoAdvanced SYBR Green . The expression of eviously , and the® 20 Detergent), secondary antibodies HRP (Horseradish Peroxidase)-conjugated anti-rabbit  and anti-mouse  were applied for 1h at room temperature, washed, and visualized using SuperWest Signal Pico and Femto reagents  on Uvitec Image Alliance 4.7 instrument .Total proteins were extracted by sonication in RIPA (Radioimmunoprecipitation assay buffer) buffer containing protease and phosphatase inhibitors, and the protein concentration was measured using the BCA (Bicinchonic Acid) kit ; 40 μg of protein was loaded on 7% PAA (Polyacrylamide) gel. After electrophoresis, they were transferred to a nitrocellulose membrane , blocked with 5% milk and incubated with primary antibodies overnight. Antibodies used for detection were as follows: rabbit anti-GLI1 , mouse anti-GLI2 , rabbit anti-GLI3 . Actin  was used as a loading control. After washing in TBST  ethanol and 10% (v/v) acetic acid for 1 h at room temperature followed by further fixation in 50% (v/v) methanol and 10% (v/v) acetic acid overnight at +4 °C. The gels were stained in Coomassie Blue R250  staining solution  methanol, 10% (v/v) acetic acid in water) for 4 h with gentle agitation, and afterwards destained in 50% (v/v) methanol and 10% (v/v) acetic acid. The destaining solution was changed several times until the protein bands were completely visible. The gels were stored in 5% (v/v) acetic acid at +4 °C.For immunoprecipitation (IP), proteins were extracted from GANT61-treated FaDu and A253 cell lines using TENN buffer  supplemented with protease inhibitors . Protein concentration was determined using the Pierce BCA Protein Assay Kit . Immunoprecipitation was performed using Protein G-coated Dynabeads  according to the manufacturer’s instructions . For Gli3 immunoprecipitation, 1000 μg of proteins and 5 μg of Gli3 antibody  were used for each IP sample. Samples were incubated with the Dynabead–antibody complex overnight at +4 °C. Samples were eluted with 1× loading buffer and heated for 5 min at 95 °C before electrophoresis in 7% PAA gel. After gel electrophoresis, the proteins were fixed in 50% .Normality of data distribution was tested using the D’Agostino-Pearson test. An independent samples t-test was used for comparing wound healing and gene expression between non-treated and treated cells. Two-tailed GANT61 and LiCl, downstream HH-GLI pathway inhibitors, inhibit the proliferation and colony forming capability of HNSCC cells. The upstream inhibitor cyclopamine, on the other hand, requires high doses to produce an effect on HNSCC cell lines. This suggests that the downstream components of HH-GLI signaling are activated at least partly non-canonically in HNSCC. Wound closure of HNSCC cells is not affected by GANT61 but it is reduced after LiCl treatment, suggesting that the effect of LiCl on wound closure is mediated through another pathway independent of HH-GLI. The main effector of HH-GLI signaling in HNSCC is the GLI3 protein, which is the most expressed of all three GLI proteins and is responsive to GANT61 and LiCl inhibition. LiCl increases the inhibitory Ser9 phosphorylation of GSK3β, leading to increased processing of GLI3 from the full-length form to the repressor form, and inhibiting the pathway. Therefore, downstream inhibition of HH-GLI signaling in HNSCC may be a promising therapeutic strategy.