RAS effector signaling pathways such as PI3K/mTOR and ERK are frequently dysregulated in glioblastoma. While small molecule targeted therapies against these pathways have appeared promising in preclinical studies, they have been disappointing in clinical trials due to toxicity and de novo and adaptive resistance. To identify predictors of glioblastoma sensitivity to dual pathway inhibition with mTORC1/2 and MEK inhibitors, we tested these agents, alone and in combination, in a cohort of genomically characterized glioblastoma cell lines.Seven genomically characterized, patient-derived glioblastoma neurosphere cell lines were evaluated for their sensitivity to the dual mTORC1/2 kinase inhibitor sapanisertib  alone or in combination with the MEK1/2 inhibitor trametinib (GSK1120212), using assessment of proliferation and evaluation of the downstream signaling consequences of these inhibitors.Sapanisertib inhibited cell growth in neurosphere lines, but induced apoptosis only in a subset of lines, and did not completely inhibit downstream mTOR signaling via ribosomal protein S6 (RPS6). Growth sensitivity to MEK inhibitor monotherapy was observed in a subset of lines defined by loss of NF1, was predicted by an ERK-dependent expression signature, and was associated with effective phospho-RPS6 inhibition. In these lines, combined MEK/mTOR treatment further inhibited growth and induced apoptosis. Combined MEK and mTOR inhibition also led to modest antiproliferative effects in lines with intact NF1 and insensitivity to MEK inhibitor monotherapy.These data demonstrate that combined MEK/mTOR inhibition is synergistic in glioblastoma cell lines and may be more potent in NF1-deficient glioblastoma. Glioblastoma neurosphere models have active RAS signaling pathways including PI3K-mTOR and ERK.Adaptive re-activation of parallel signaling pathways is a limitation of single-agent treatment.MEK and mTORC1/2 combination treatment has synergistic effects in glioblastoma neurospheres.RAS effector signaling pathways such as PI3K/mTOR and ERK are frequently dysregulated in glioblastoma. Unfortunately, small molecule targeted therapies against these pathways have been disappointing in clinical trials for several reasons, including de novo and adaptive resistance. Here, we demonstrate that treatment with either MEK or mTORC1/2 small molecule kinase inhibitors partially reduces glioblastoma neurosphere growth, but also upregulates signaling through compensatory RAS effector pathways. Combined MEK/mTOR inhibitor treatment overcomes adaptive resistance and further inhibits neurosphere growth, particularly in lines with NF1 loss of function. These findings suggest that combined therapy could be effective for a subset of patients with glioblastoma exhibiting loss of NF1 expression, which occurs in 15–20% of glioblastoma, once optimal dosing of the combination in vivo is established.1 Among RAS effector pathways altered in GBM, aberrations in PI3K/mTOR and ERK signaling predominate, with mutations in receptor tyrosine kinases (72%), PTEN (41%), NF1 (10%), and BRAF (2%) most common.2 Despite promising preclinical data, clinical trials of targeted therapies against EGFR, mTOR, and PI3K have been disappointing in GBM.3–5 Even with demonstrable target inhibition in human subjects, development has been limited by both suboptimal antitumor efficacy and significant toxicity.7RAS signaling dysregulation occurs in the majority of glioblastoma  and thus has become a focus of avid investigation and efforts in drug development.8–11 These adaptive changes lead to sustained activation of key downstream targets such as p70-S6 kinase (p70S6K) or ribosomal protein S6 (RPS6), the activation of which can confer resistance to ERK pathway inhibitors.13 This observation has led to attempts to combine multiple RAS effector targeted therapies for GBM and other cancers.The clinical lack of efficacy of these drugs to date is likely multifactorial, but contributing causes may include activation of parallel signaling pathways that converge upon conserved substrates, or adaptive and acquired resistance after an initial response. Upregulation of compensatory RAS effector pathways in response to mTOR inhibition occurs through a variety of mechanisms, including the upregulation of ERK or WNT signaling.14–16 In GBM models, the combination can promote differentiation, inhibit clonogenic growth, and decrease in vivo tumor formation through enhanced inhibition of critical downstream targets like p70S6K.13 The antitumor effect of combination therapy appears most prominent in preclinical models of glioma with BRAF mutations or KRAS mutations, though these mutations are relatively rare in adult GBM.17–19 Similarly, the efficacy of MEKi/mTORi therapy has been demonstrated in vitro in pediatric low-grade glioma (pLGG) and in vivo in pLGG models with BRAF V600E mutations.20 Unfortunately, clinical development has been hampered by significant toxicity without notable clinical benefit in early-phase clinical trials.14–16Targeting multiple RAS effector pathways with a combination of MEK and mTOR kinase inhibitors  is a strategy that has been tested in multiple other cancers in early-phase clinical trials.NF1 encodes the GTPase-activating protein (GAP) that facilitates the switching of GTP to GDP moieties in RAS and other targets. Germline loss of the NF1 tumor suppressor gene underlies the molecular basis of neurofibromatosis type I (NF1), which is characterized by peripheral and central nervous system tumors including GBM. NF1 somatic mutations are also identified in approximately 15% of sporadic GBM, with homozygous deletion in approximately 3% and loss of NF1 expression through ubiquitination and proteasomal degradation in another 10–15%.21 Loss of NF1 and TP53 together is sufficient to induce high-grade glioma formation in mice.22 Moreover, loss of NF1 expression is associated with sensitivity to MEKi monotherapy in a subset of GBM cell lines, though this was not borne out in a clinical trial.24Identification of a biomarker-defined subset of GBM with increased susceptibility to combined RAS effector signaling blockade might increase the opportunity for the clinical efficacy of these drugs. We speculated whether the loss of NF1 could be such a genomic candidate to predict susceptibility to this combination. We hypothesized that dysregulation of multiple downstream RAS effector pathways drives treatment resistance in GBM through co-activation of mTOR and ERK signaling, and that dual inhibition of these pathways would be effective in a subset of genetically defined GBM. We tested the combination of the FDA-approved MEKi  and a mTORC1/2 kinase inhibitor currently in clinical trials for GBM  in a panel of genomically characterized GBM tumor lines, in order to identify a mutation profile that might be particularly susceptible to combined therapy.26 JHU-0879, JHH-136, JHH-520, and JHU-1016B were a gift from the Gregory Riggins laboratory and were maintained as neurosphere cultures in serum-free media.27 SkMel-103 and SkMel-113 were a gift from Dr. David Solit, Memorial Sloan Kettering Cancer Center. DBTRG-5MG was a gift from Dr. Jean Mulcahy Levy, University of Colorado, Denver. HEK293T cells were purchased from the American Type Culture Collection. All cells were maintained in appropriate media supplemented with 10% fetal bovine serum. Genomic mutations were identified by targeted sequencing (HSR-GBM1) or next-generation sequencing as previously described .27 For JH-GBM10 and JH-GBM14, we performed whole-exome sequencing and identified mutations in genes of interest. O6-methylguanine-DNA methyltransferase status is methylated for HSR-GBM1, unmethylated for JH-GBM10, and otherwise unknown. Cell line identity was verified using short tandem repeat profiling analysis and all cell lines tested negative for mycoplasma contamination except for JHU-1016B, which was treated with Plasmocin (Invivogen) and then retested negative.HSR-GBM1 (a gift from the Vescovi laboratory) and JHH-GBM10 and JHH-GBM14 (derived at Johns Hopkins University) were cultured from human GBM tissue and maintained as neurosphere cultures in serum-free neurosphere media as previously described.28 Protein concentration was determined with Pierce BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein were separated by SDS-PAGE, transferred to nitrocellulose membranes, immunoblotted with specific primary and secondary antibodies, and detected by chemiluminescence with the ECL detection reagents, Immobilon Western HRP substrate Luminol Reagent (Millipore) and Luminol Enhancer Solution (Thermo Fisher Scientific). The membranes were imaged on the ChemiDoc Touch Imaging System (Bio-Rad). Representative blots of 3–4 independent experiments are shown. Relative changes in p-RPS6 were quantitated by densitometry analysis using Image J as a function of concentration or treatment time and averaged across all blots.Cells were disrupted on ice in 1% NP40 lysis buffer or NETN buffer (Bio-Rad) as previously described.thr202/tyr204, total MEK, phospho-MEKser217/221, phospho-p70-S6 kinasethr389, phospho-RPS6ser235/236, cleaved PARP, B-actin, phospho-4EBP1thr37/46, total AKT, phospho-AKTser473, and phospho-AKTthr308 were obtained from Cell Signaling Technology, NF1 (A300-140A) from Bethyl, and Cyclin D1 from Santa Cruz Biotechnology. Trametinib and sapanisertib were purchased from SelleckChem. Drugs for in vitro studies were dissolved in DMSO to yield 10 mM or 1 mM stock solutions and stored at −20°C.Antibodies against total ERK, phospho-ERKwww.combosyn.com) as described previously.29 In brief, each dosing experiment was completed at least 3 times, and the effect at each concentration was the mean of those replicates relative to untreated control replicates. The combination index (CI) was calculated where CI less than 1 indicates synergy, CI greater than 1 indicates antagonism, and CI equal to 1 indicates additive effect.Cells were plated in triplicate per condition in 96-well plates, treated immediately with drugs as described, and incubated under standard conditions. Cell growth was quantitated using the Cell Counting Kit-8 (Dojindo) and read using an Epoch microplate spectrophotometer (BioTek). Relative survival in the presence of drug was normalized to untreated controls after background subtraction. For all experiments, at least 3 independent replicates were performed. Synergy was calculated via the Chou-Talalay method using Compusyn  with on-column DNase treatment (Qiagen) according to the manufacturer’s instructions. Reverse transcription was performed using , and qPCR was done using iQ SYBR Green Supermix (Bio-Rad) on a CFX96 Real-Time PCR System (Bio-Rad) according to the manufacturer’s instructions. Three independent experiments  were completed for each condition in each cell line. Primers for the following genes were obtained from published literature: 50 calculations, and statistical analysis were performed using GraphPad Prism, version 8. ANOVA or multiple t tests with Bonferroni correction for multiple comparisons were used to evaluate the difference between conditions. Significant differences are indicated on the figure panel for each condition.Graphing, ICc-myc amplification (IDH wild-type. One is EGFR amplified (HSR-GBM1), 1 has loss of RB1 (JHU-0879), and 3 have loss-of-function mutations in TP53 . NF1 is mutated in one allele in 3 cell lines  and in both alleles in JHH-520. Protein expression of NF1 was absent in 3 neurosphere lines , including one with wild-type NF1 on sequencing  activity was inhibited in a dose-dependent fashion , a dual mTOR1/2 kinase inhibitor, to determine sensitivity to pathway inhibition and antiproliferative effects. Cell growth was inhibited by sapanisertib in a dose-dependent fashion in all neurosphere lines, regardless of genotype  and B. T fashion . Interes fashion . Evidenc fashion . We hypo fashion . This co32 Neurosphere cultures were treated with trametinib and were variably sensitive , suggesting a higher degree of ERK dependence in those cells  and B. Tthr389, and p-4EBP1thr37/46 increased, while p-RPS6ser235/236 remained stable in the 2 cell lines most resistant to trametinib (JH-GBM10 and JH-GBM14). In cell lines with intermediate sensitivity to trametinib (HSR-GBM1 and JHU-0879), p-AKT increased, while p-S6K and p-4EBP1 remained stable, and p-RPS6 decreased in a dose-dependent fashion. This pattern was in contrast with sensitive lines , in which p-S6K remained stable or decreased after trametinib, while p-AKTser473 remained stable or increased.In order to determine whether mTOR signaling was altered in response to MEK inhibition, we measured levels of phosphorylated AKT, RPS6, and 4EBP1. We observed a dose-dependent increase in markers of mTORC1 and mTORC2 signaling after exposure to trametinib for 24 hours . SpecifiR2 = 0.775, P = .009). Sapanisertib, on the other hand, inhibited RPS6 phosphorylation in all cell lines without any correlation to the degree of growth inhibition . This finding suggests that in GBM neurospheres, RPS6 phosphorylation may predict sensitivity to MEK inhibition, but not to mTOR inhibition.We hypothesized that compensatory cross-regulation of ERK and AKT/mTOR signaling leads to sustained activity of critical downstream signaling targets such as RPS6, thereby allowing GBM cells to proliferate unabated and escape cell death. We quantified RPS6 phosphorylation changes in response to trametinib treatment and observed dose-dependent inhibition that corresponded closely with the degree of growth inhibition .30 Expression of these 4 genes was potently suppressed after exposure to trametinib in sensitive lines, whereas in insensitive lines, their expression increased , thereby conferring resistance to single-agent targeted therapy. We tested this hypothesis using combination therapy with selective inhibitors of mTOR (sapanisertib) and MEK (trametinib). We observed dose-dependent growth inhibition with combination therapy in most neurosphere lines  and B. EWe clustered cell lines into 3 groups based on their relative sensitivity to sapanisertib or trametinib monotherapy . Group 136 Several potential mechanisms for resistance have been identified, including upregulation of glutamate metabolism, WNT signaling, or ERK activity.38 In line with these observations, we demonstrated that sapanisertib, an ATP-competitive mTORC1/2 inhibitor currently being evaluated in human clinical trials, was effective at inhibiting growth in neurosphere lines, but only induced apoptosis in a subset of neurospheres and did not completely inhibit downstream targets like p-RPS6. Instead, we observed a dose-dependent increase in ERK pathway activation in most cell lines, likely secondary to loss of feedback inhibition.7 Given the inability of this mTORC1/2 inhibitor to completely inhibit downstream signaling, we evaluated dependence on multiple RAS signaling pathways in this system.The critical role of RAS effector signaling, primarily through the PI3K/mTOR pathway, has been extensively studied in GBM. Unfortunately, resistance to monotherapy with agents against EGFR or other components of the PI3K/mTOR signaling pathway has been well documented.31 Changes in expression of this signature may be a useful predictive biomarker for clinical responses, but will need to be validated in patient-derived xenograft models and prospective human trials. Notably, the MEK-dependence expression signature did not correlate with sensitivity to combination therapy, suggesting it is truly a readout of ERK dependence alone, not overall RAS dependence. In our studies, all lines sensitive to MEKi monotherapy had loss-of-functional NF1, either due to genomic mutations or by other mechanisms of expression loss. Lines with intact NF1 expression demonstrated intermediate or no sensitivity to MEKi. This finding was consistent with prior studies demonstrating that loss of NF1 may be a predictive biomarker for ERK-dependent regulation of cell proliferation.39 Combined inhibition of mTOR and MEK pathways led to a synergistic antiproliferative effect in many GBM lines, regardless of NF1 expression. We observed 2 different patterns of synergy: (1) In lines with loss of NF1, mTORi sensitized cells to the effects of MEKi, with enhanced apoptotic and antiproliferative effect. This finding is in agreement with previous data that demonstrated sensitivity in high-serum, adherent GBM lines with loss of NF123. (2) Neurosphere lines that were insensitive to MEKi monotherapy demonstrated synergy to combined MEK/mTOR inhibition, particularly at higher, biologically relevant doses of sapanisertib and trametinib. While the degree of growth inhibition in vitro was not as profound as for lines with NF1 loss, some lines with almost no sensitivity to either drug alone showed demonstrable sensitivity to the combination. These data suggest that combination therapy may produce benefit even for GBM patients without obvious genomic predictors of RAS-effector dependence.MEK inhibitor monotherapy demonstrated marked inhibition of ERK signaling in all GBM neurosphere lines tested in our studies, as expected. Only a subset of lines, however, demonstrated growth inhibition indicative of ERK pathway dependence. Our cell line models could be arranged into 3 clusters of MEKi sensitivity, independent of mTORi sensitivity: low, intermediate, and high. Growth sensitivity also correlated with altered mRNA expression of transcripts known to be associated with a MEK-dependence signature in melanoma and other ERK-dependent tumors.NF1, in GBM sensitize neurosphere models to targeted therapy with mTORi and MEKi. We hypothesized that loss of NF1 might sensitize cells to combined therapy, given preliminary data suggesting that a MEKi alone can induce growth suppression in some GBM cell lines.23 Here, we observed that loss of NF1 demonstrated a moderate correlation with sensitivity to MEKi monotherapy and combination therapy—a powerful observation given 10–15% of GBM has lost functional NF1 expression.21 Loss of neither the tumor suppressor PTEN nor TP53 correlated with sensitivity to combination therapy. Given the high frequency of RAS signaling alternations in GBM (~90%), it is possible that other mutations or amplifications not identified by our targeted sequencing are present in these GBM lines and contributed to their sensitivity.2 Additionally, our findings in this small number of neurosphere lines will need to be validated in a larger cohort.The purpose of this study was to evaluate whether identifiable genomic mutations, specifically loss of mTOR kinase inhibitors have produced a range of toxic side effects in patients in whom they have been tested, particularly when combined with MEK inhibitors in Phase I trials. Moving such a combination forward into future clinical trials may be a challenge. We nonetheless selected this particular combination given its relative specificity to explore the biological vulnerabilities of our cell line models, and further work is ongoing to optimize a dosing strategy that will be tolerable for human testing while sustaining biologically significant pathway inhibition. If dosed appropriately and with a tolerable profile of side effects, combination therapy with targeted mTOR and MEK inhibitors could potentially prove efficacious for patients with GBM regardless of genomic background.vdaa138_suppl_Supplementary_Figures_S1-S2Click here for additional data file.


Anaplastic thyroid cancer (ATC) is one of the most lethal malignancies with a median survival time of about 4 months. Currently, there is no effective treatment, and the development of new therapies is an important and urgent issue for ATC patients. YM155 is a small molecule that was identified as the top candidate in a high-throughput screen of small molecule inhibitors performed against a panel of ATC cell lines by the National Cancer Institute. However, there were no follow-up studies investigating YM155 in ATC. Here, we determined the effects of YM155 on ATC and human primary benign thyroid cell (PBTC) survival with alamarBlue assay. Our data show that YM155 inhibited proliferation of ATC cell lines while sparing normal thyroid cells, suggesting a high therapeutic window. YM155-induced DNA damage was detected by measuring phosphorylation of γ-H2AX as a marker for DNA double-strand breaks. The formamidopyrimidine-DNA glycosylase (FPG)-modified alkaline comet assay in conjunction with reactive oxygen species (ROS) assay and glutathione (GSH)/glutathione (GSSG) assay suggests that YM155-mediated oxidative stress contributes to DNA damage. In addition, we provide evidence that YM155 causes cell cycle arrest in S phase and in the G2/M transition and causes apoptosis, as seen with flow cytometry. In this study, we show for the first time the multiple effects of YM155 in ATC cells, furthering a potential therapeutic approach for ATC. Anaplastic thyroid cancer (ATC) only represents 1–2% of all thyroid cancers, however, ATC accounts for 40–50% of all thyroid cancer deaths ,4. ATC mYM155, also known as sepantronium bromide, was identified as the top candidate in a high-throughput screen of 3282 chemotherapy and small molecule inhibitors performed against a panel of ATC cell lines . HoweverIn the present study, we focused on testing the effects of YM155 in ATC cells including anti-proliferative activity, induction of DNA damage, cell cycle arrest, and apoptosis. We also tested the effect of YM155 on primary benign thyroid cells (PBTCs). Our data show that YM155 inhibited proliferation of ATC cell lines while sparing normal thyroid cells and suggests a high therapeutic window.50 for each cell line was calculated as ACT1 3.24 nM, THJ16T 5.102 nM, THJ29T 18.6433 nM, and THJ11T 73.387 nM, respectively. We also found that slow-growing PBTCs were unaffected by YM155 treatment, even when treatment was extended up to 6 days (It has been previously demonstrated that YM155 inhibits proliferation in ATC cell lines THJ16T and THJ29T [o 6 days E. Interep < 0.0001), 11.8 times more foci in THJ16T cells , and 7.1 times more in THJ29T cells . Notably, ATC cell lines ACT1 and THJ16T, which were more sensitive to YM155 in the cell viability assay, also exhibited greater increases in γ-H2AX.YM155 inhibits ATC cells growth; however, the mechanism remains unclear. Because of the fast growth of ATC cells compared to PBTCs and the difference in response to YM155, we hypothesized that YM155 may induce DNA damage in ATC cells. Phosphorylated histone H2AX (γ-H2AX) is an established surrogate marker for DNA double-strand breaks, with a tight correlation between foci per cell and the number of double-strand breaks . We measTo determine whether YM155 increases oxidative stress in ATC, we performed the formamidopyrimidine-DNA glycosylase (Fpg)-modified alkaline comet assay. Cells were treated with Fpg (formamidopyrimidine-DNA glycosylase) before electrophoresis to generate breaks at the sites of oxidative pyrimidine damage including 8-oxoguanine . As showCell cycle arrest is a self-protective mechanism following DNA damage. Here, we performed cell cycle analyses in four ATC cell lines  after YM155 or mimosine treatment. As shown in DNA damage may also induce cell programmed death (apoptosis) when the damage is extensive. ATC cells were treated with either 10 nM YM155, 100 nM YM155, or 200 nM doxorubicin and analyzed with Alexa Fluor 647 Annexin V kit to determine the proportion of live cells, apoptotic cells, and necrotic cells. The classic chemotherapeutic drug doxorubicin failed to induce apoptosis in all four ATC cell lines at 200 nM concentration. Twenty-four-hour treatment with YM155 induced apoptosis in ACT1 and THJ16T but not in THJ11T or THJ29T .BRAF or RAS genes, and drugs that target the BRAF kinase have had higher response rates than cytotoxic chemotherapy [BRAF mutation [At present, there is no effective treatment for most ATC patients. All current therapies including surgery, radiation, and chemotherapy fail to significantly prolong ATC patients’ survival. ATC remains a clinical challenge due to its highly aggressive characteristics ,3,4. Theotherapy . Targetemutation . Immunotmutation ,22. HoweTraditional approaches to drug discovery require a significant investment of time and funding. An emerging approach to developing therapies for rare or orphan cancers, such as ATC, instead exploits the multitude of established compounds that are already approved for clinical use or currently in clinical trials. YM155 is one of the most active agents selected in a previous screening of 3282 clinically approved drugs in ATC cell lines . Of the Identifying the mechanism of action of YM155 treatment is essential to predict patient response to therapy, identify synergistic drug combinations, and minimize toxicity. The proposed mechanism of YM155 as a survivin inhibitor does not fully explain the activities of YM155 in other cancer studies. A negative clinical trial of YM155 in patients with non-small cell lung cancer showed no correlation between suppression of survivin and patient response to YM155 . The tarHere, we discovered the effect of YM155 in several ATC cell lines. These ATC cell lines were chosen as the models for this study, since all cell lines included are well-characterized for identification of molecular mechanisms related to tumor biology and drug responsiveness ,28. AlthInterestingly, we also noticed that YM155 had different effects even among ATC cell lines. Cell lines ACT1 and THJ16T were very sensitive to YM155, while THJ11T and THJ29T showed limited response to YM155. In alamarBlue cell viability assay, ACT1 and THJ16T were significantly affected at 5 nM concentration of YM155, while no difference was seen in THJ11T and THJ29T until 10 nM or higher. Similar results were seen across time; an effect was more likely to be seen in THJ11T and THJ29T when extending treatment to 2 days or more. This may also help explain why ROS levels were increased in all four ATC cells with 100 nM YM155 treatment for 1 h, yet we were unable to detect a change in DNA damage in comet assay in these two cell lines.DNA damage can be caused by endogenous oxidative stress or exogenous factors such as UV light or various chemicals agents. We performed γ-H2AX staining to determine if YM155 induces damage in ATC and found an increase in foci in ACT1, THJ16T, and THJ29T but not THJ11T. Importantly, YM155 had no effect on benign thyroid cells confirmed by cell viability data, lending confidence to the potential for clinical application. Several studies have reported increased DNA damage after YM155 treatment in several cancer cell lines ,29,30,31Cell cycle arrest is one of the cellular responses to DNA damage to preserve genomic integrity. Checkpoints serve to monitor the order of events in the cell cycle and ensure that subsequent cell cycle events occur only after the completion of a prior event . DNA damProgrammed cell death (apoptosis) is a cell-controlled process triggered by extreme alterations to cellular homeostasis. Initial signaling events, such as extensive irreparable DNA damage, set off cascades to activate caspases, leading to apoptosis. Several studies have reported that YM155 induced apoptosis in cancer cells such as glioma, HeLa, and prostate cancer cells ,41,42. IAlthough the target molecules of YM155 in ATC have remained unclear, studies imply multiple synergistic downstream targets of YM155 exist beyond survivin. Here, we have confirmed and discovered previously unreported effects of YM155 in ATC. As a result, cell lines ACT1 or THJ16T represent good models to study the mechanism of action of YM155 in ATC. These results also suggest that YM155 may not be effective in all ATC patients. Further research could reveal components of the molecular signature of cell lines ACT1 or THJ16T that result in sensitivity to YM155. Similar profiles in ATC patients could allow for individualized therapy. Our lab is actively working on identifying the target of YM155 in THJ16T cells, which could then be used as a biomarker for the use of YM155 in ATC patients. These experiments will contribute to the generation of genetically engineered mouse models. Such models, with specific modifications in anaplastic thyroid cancer targets, could provide more accurate information for pre-clinical drug studies than traditional in vivo models such as the metastases mouse model. Future studies of YM155 in such models would strengthen current findings and allow for clinical studies in ATC patients.Patient-derived xenograft ATC lines THJ11T, THJ16T, and THJ29T were obtained as gifts from Dr. J. Copland  , and pat2 37 °C incubator. Fragments were filtered and collected again on day 2. The fragments were digested with 2 mL trypsin with 50 µL (28.5 U/µL) Collagenase Type 2  in an incubator for 2 h. The digested material was filtered and collected for the next step; the undigested tissue fragments were processed in the same manner one more time. The digested tissue was centrifuged at 1000 rpm for 5 min, and the pellet was added into 1 mL 1× red-blood cell lysis buffer  at room temperature for 5 min. The cells were washed twice with PBS. Finally, the cells were counted using Cellometer Mini automated cell counter  and seeded to a density of 5 × 105 cells per well in 6-well plates in 2 mL of Normal Thyroid Epithelial Cell Growth Medium  with 10% fetal bovine serum (FBS). Thyroid cell markers TG, TPO, TSHR, FOXE1, NKX2-1, and PAX8 were used to confirm cell identity, and 18S rRNA was used as an internal control. The primer sequences are shown in PBTCs were prepared following the protocol described by Wang et al.  with som3 cells per well in 96-well plates. After 16 h, cells were treated with YM155 in various concentrations for 24, 48, or 72 h. Viability of cultured cells was analyzed using alamarBlue . Cells were incubated for 1 h after stimulation with alamarBlue according to the manufacturer’s instructions. Fluorescence was read at excitation 560 nm, emission 590 nm. To calculate differences in proliferation between treated and control cells, we used the following formula for relative viability (RV):Cells were plated 4–8 × 102, 300 mM sucrose, 0.5% Triton X-100) for 7 min, washed with PBS (500 µL) for 5 min, blocked with 5% normal goat serum in PBS for 1 h, and incubated overnight at 4 °C with γ-H2AX  antibody. For visualization, cells were incubated with AlexaFluor-488-conjugated rabbit anti-mouse secondary antibody  for 1 h and shielded from light. DAPI (1 µg/mL in PBS) was used as a nuclear counterstain. A minimum of 50 cells in each experimental group were imaged using an epifluorescent Olympus BX43 microscope with cooled CCD camera, and foci were counted using the software JQuantPlus, which is a further development of JCountPro [Immunofluorescent detection of phosphorylated histone H2AX at serine 139 (γ-H2AX) foci was used as a surrogate marker for formation of double-strand DNA breaks (DSBs). ATC cells were seeded on coverslips in a 24 well plate and treated with YM155 when 80% confluent. After 24 h treatment with 10 nM YM155, cells were washed with PBS (500 µL) for 5 min then fixed with 4% paraformaldehyde for 15 min, permeabilized with permeabilization buffer  were detected using the Biovision kit (K936-250)  according to manufacturer’s protocol. Briefly, 1 × 105 cells per well were seeded in a 6-well plate overnight and treated with YM155. Cells were collected with cell scraper and then prepared and measured following the manufacturer’s protocol. The GSH/GSSG ratio was normalized to the control group.GSH, GSSG, and total glutathione were detected with the Biovision kit (K264-100) . A total of 2 × 106 cells per plate in 10 mL of culture medium at day 0. Fresh culture medium with YM155 or corresponding vehicle was added to each plate at day 1. Following treatment for 24 h, cells were harvested, fixed with cold 70% ethanol for at least 2 h at 4 °C, washed with PBS, and incubated in the dark with RNaseA (100 µg/mL) and propidium iodide (50 µg/mL)  for 30 min at room temperature. Events were detected by flow cytometry , with normalization and population selection. Data were analyzed using BD FACSDiva Software .Cells were plated in 10 cm plates at 1 × 10Cell apoptosis was detected using the Alexa Fluor 647 Annexin V kit , according to the manufacturer’s instructions.p-value less than 0.05 was considered statistically significant.All experiments were repeated at least 3 times and data are presented as mean ± standard deviation (SD) unless otherwise indicated. All statistical analyses were performed in GraphPad Prism 8 . 50 were calculated by AAT Bioquest, Inc., Sunnyvale, CA, USA. Quest Graph IC50 Calculator (v.1). Retrieved from https://www.aatbio.com/tools/ic50-calculator-v1 (accessed on 11 January 2021).ICYM155 did not harm benign thyroid cells while inhibiting growth of all four ATC cell lines at 100 nM or lower, suggesting a high therapeutic window. YM155 induced DNA damage in ACT1 and THJ16T, and oxidative stress is at least partially responsible for increases in DNA damage. YM155-induced DNA damage in ACT1 and THJ16T led to cell cycle arrest and apoptosis. YM155 could be a candidate for ATC therapy, however, it remains unclear as to why some ATC cell lines respond better to YM155 than others. We are seeking to answer this question by identifying the molecular target of YM155 in ATC. Discovering specific targets of YM155 will advance an individualized approach to ATC treatment by identifying patients likely to respond to treatment.


Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines.Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines.The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification. Disease classification provides key foundations for identification and treatment of diseases, especially for complicated diseases such as cancer . TraditiDue to the difficulties to collect clinical samples from cancer patients, cancer cell lines had been still widely used as in vitro model for exploring cancer occurrence, development, and treatments. In some cancer types, the classification analysis of cancer cell lines has been proved to be a convenient way to characterize cancer sample subtypes. For example, five subtypes of colorectal cancer were revealed by iterative clustering of 74 different colorectal cancer cell lines, reflecting the consistency with the clinical classification of colorectal cancer patients . On the In this study, we presented a systematic study on pan-cancer cell line classification based on single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database . Our stuhttps://portals.broadinstitute.org/ccle/data)  . The numFor mRNA sequence data, we used RSEM values in gene level shared by CCLE database. We used miRNA expression data from CCLE for miRNA analysis. For DNA methylation, the promoter CpG data was used for clustering analysis. And reverse phase protein array data was downloaded for protein analysis. In parallel, we downloaded segmented copy number profiles from CCLE database for CNV analysis. This SNP6.0 arrays data was used as the input data for Gistic2.0 software . Before  (1)For each omics dataset, cell lines with more than 20% features missing, and features with more than 20% cell lines missing were filtered out. (2)For each omics dataset, the missing data points were filled in using average imputations. (3)x + 1)  transformation were performed before feature selection.For mRNA and miRNA data, log2 ( (4)For mRNA and METHY data, only features in the top 5,000 in terms of variance were selected. For miRNA, RPPA and CNV data, all features were considered.For integrated multiple omics clustering, 670 cell lines from 24 cancer types with complete five omics data were used after samples alignment and deletion of cancer types with few number of cell lines.For single omics dataset, we performed hierarchical clustering with different methods and distance measurements . We usedK = 20, alpha = 0.5, t = 20, maxK = 30, pItem = 0.8, reps = 500. The silhouette coefficient, a measurement of consistency of each object within clusters, also derived using the function “silhouette_SimilarityMatrix” from the R package “CancerSubtypes”.The Similarity Network Fusion and Consensus clustering algorithm (SNF-CC), a method combining Similarity Network Fusion (SNF) and Consensus clustering (CC) together to take advantage of both for cancer type identification, was applied to integrate multiple omics data . The metThe function “pamr.listgenes” from the “pamr” R package (version 1.56.1) was used to find the most suitable clustering features for the illustration of clustering by heatmap .P-value for cancer types in each cluster. Cancer types with a P-value <10−3 were chosen as the dominant types of each cluster. The −lg represents the enrichment score that cancer types gathered in each cluster.A hypergeometric distribution was used to calculate the P-value <0.05 were selected, and were further screened according to —log2 fold-change—>1. Finally, the above DEGs were fed into enrichment analysis with GO and KEGG terms using the R package “clusterProfiler” (version 3.10.1)  among clusters using the R package “limma” (version 3.38.3) . Genes w 3.10.1) . The sigWe used the normalized mutual information (NMI), which was a measure of the interdependence between two random variables, to measure the contribution of each omics type feature. The function “rankFeaturesByNMI” in the R package “SNFtool” (version 2.3.0) were used to compute NMI . Codes ahttps://tumormap.ucsc.edu/)  . In TumoWe initially clustered cell lines based on each type of omics data, which were mRNA, miRNA, CNV, METHY and RPPA data. The optimal clustering numbers were set to 10 .In the hierarchical clustering result of 901 cell lines by mRNA , we founIn the clustering result of miRNA data of 879 cell lines , five clCNV data sorted at the chromosome arm-level for 897 cell lines were divided into 10 clusters through hierarchical clustering, four clusters mainly formed from a single cancer type  . C8 was Among the unsupervised clustering result of 755 cell lines using METHY data , there wIn hierarchical clustering of RPPA data from 854 cell lines , C4 ), pan-gynecological cluster (C18 [OV-UCEC]) and pan-squamous morphology carcinoma cluster (C5 [HNSC-ESCA]). For pan-gastrointestinal cluster (C11 [COAD/READ-STAD]), KEGG enrichment analysis results showed that these cell lines shared down-regulated in cytokine-cytokine receptor interaction and TNF signaling pathway . And celWe also observed two clusters with the same cancer type dispersed. For instance, cell lines from ALL were divided into two clusters, C4 and C8, despite the common characteristics such as Human T-cell leukemia virus 1 infection, Th17 cell differentiation, and TNF signaling pathway. The ALL cell lines in C8 were enriched in KEGG terms including up-regulated in ECM-receptor interaction, down-regulated in antigen processing and presentation pathway, while the ALL cell lines in C4 had low level of cellular senescence . GO enriIntegrated multiple omics clustering provided a global view of cancer types because it could capture both shared and complementary information from each omics data. Several cancer types which mixed together in one single omics data were divided in other single omics data or integrated omics data. For example, BRCA and SCLC were mixed together based on miRNA data, but they were separated into two distinct molecular clusters based on integrated omics data. Besides, in single omics clustering, three pan-organ system clusters were only found based on mRNA data and the pan-squamous morphology carcinoma cluster was only found based on METHY and RPPA data. But pan-gastrointestinal cluster, pan-gynecological cluster and pan-squamous morphology carcinoma clusters were simultaneously identified by integrated multiple omics clustering.The relative contribution of each omics data to the integrated clustering was computed based on the NMI value. On the basis of the top 20% statistical features from the five omics data, we found that RPPA and mRNA contributed 32.24% and 29.62% respectively, followed by METHY (16.24%) . This reWe compared the classification results of 19 cancer types shared by cancer cell lines from CCLE and patient samples from TCGA . ClusterFor hematopoietic lymphatic malignancies, the classification of cancer cell lines is more abundant than patient samples. For example, some LAML cell lines were clustered together in a group, while others were mixed with LCML cell lines into another group (LAML-LCML) in our findings. For patient samples, there is only one LAML group . The claFor most solid tumors, the classification of patient samples is generally more abundant and diverse than the corresponding cell lines. In general, patient samples with same cancer type can be divided into multiple groups, while cell lines with same cancer type are clustered in one group. For example, the samples of breast cancer were classified into three subgroups . In addition, there were a large number of BRCA samples gathered with other cancer types in a mixed cluster. Except a few of BRCA cell lines were mixed in a pan-gynecological cluster, whereas almost all BRCA cell lines were clustered in a single group. And there are similar clustering results in gastrointestinal cancer and squamous cell carcinoma. There were two pan-gastrointestinal groups and a single gastric cancer group in patient samples . While mOn the other hand, while the number of clustering groups were same, the types of clusters were different in cell lines and patient samples of some cancer types. For example, there is a pan-kidney group in the classification of patient samples. But this cluster was not in our results because there is only one cell line associated with kidney cancer, renal clear cell carcinoma (KIRC), in the CCLE database . AlthougIn general, due to the larger numbers of patients and the greater heterogeneity in TCGA samples, the classification results of patient samples are more diverse and abundant than cancer cell lines. It is clear that the existing cancer cell lines do not fully represent all the molecular types of corresponding patient samples. However, in some cancer types, the classification of patient samples and cell lines were consistent. This shows that cancer cell lines can represent primary samples to some extent.We used TumorMap web tool to visualize the landscape of pan-cancer cell lines. The same layout and four different color schemes  were used to reveal that most cancer cell lines gathered based on organ systems and histopathological similarity . More nuWe downloaded the drug susceptibility data for 24 anticancer drugs across 504 cell lines in CCLE database. We used TumorMap web tool to analyze the relationship between the drug susceptibility and the pan-cancer clustering. We divided the analysis results into four types and chose the representative drugs as examples . (1)These anticancer drugs have a strong effect on almost all cancer cell lines. For example, LUAD-LIHC (C13), GBM (C2), SARC (C6), DLBC (C12), SKCM (C15), OV-UCEC (C18) and HNSC-ESCA (C5) cell lines are sensitive to Paclitaxel, a broad-spectrum anticancer drug . (2)Almost all cell lines are not sensitive to these drugs, for example, L-685458, a gamma-secretase inhibitor . (3)Only one cell line or few cell lines are sensitive to these anticancer drugs. For example, as a BRAF inhibitor, PLX4720 has an obvious effect on some SKCM (C15) cell lines, but has no effect on other cell lines . (4)These anticancer drugs have a strong effect on many cancer cell lines, but have a weak effect on others. For example, RAF256 is a dual inhibitor of mutant BRAF and vascular endothelial growth factor receptor 2. This drug can inhibit proliferation of SKCM (C15), GBM (C2), LUAD-LIHC (C13), OV-UCEC (C18) and some COAD/READ-STAD (C11) cell lines .In this research, we provide a pan-cancer cell lines classification based on single and multiple omics data. First, unsupervised hierarchical clustering was performed using five omics data from CCLE database, involving 31 cancer types and more than 1,000 cell lines, with each omics data showing different characterization. Next, we analyzed integrated multiple omics data of pan-cancer cell lines using SNF-CC method and ultimately clustered 24 cancer types into 18 groups. Moreover, we analyzed dominant cancer types and functional enrichment of each clusters. Then, the relative contribution of each omics data were calculated. We compared the classification of cancer cell lines and patient samples. Finally, we used TumorMap web tool to illustrate the landscape of cancer cell line clusters.Our study showed that clusters were strongly influenced by organ system and cell of origin. Three pan-cancer cell line clusters: pan-gastrointestinal group, pan-gynecological group and pan-squamous morphology carcinoma group were identified by integrated multiple omics clustering simultaneously . Common Cancer cell lines have been commonly used as in vitro models of tumors in biomedical research, however, the reliability has been doubted. The comparison of molecular classification between cancer cell lines and patient samples from TCGA provided a valuable insight into the reliability of cell lines as samples. While the overall classification of cell lines and samples were quite similar, samples from cancer patients were generally more diverse and abundant than cell lines. In some types of cancer, the number of molecular groups in patient samples were more than the corresponding cancer types of cell lines, while in others, the molecular classification of patient samples matched the corresponding molecular groups of cell lines. Our study provides researchers with a widely comparison of pan-cancer cell lines and primary samples.We also presented that mRNA and proteomics data were more strongly grouped in terms of classification by cancer type than other omics data. This is meaningful for biologists and oncologists choosing what types of omics data they need for their particular analysis.In summary, we provide a novel multi-dimensional landscape of cancer cell lines, and an approach to evaluate the importance of omic features in cancer classification. This research is crucial for treating same cancer with different therapies based on molecular characteristics. The comparison of molecular classification between pan-cancer cell lines and patient samples represents a valuable resource for the reliability of available cell lines as model of tumors. With the lower cost of omics analyses and the development of high-throughput omics technologies, integrated more omics data for cancer classification could be applied in clinical diagnosis and guide personalized treatment.10.7717/peerj.9440/supp-1Figure S1Hierarchical clustering of (A) mRNA, (B) miRNA, (C) CNV, (D) METHY and (E) RPPA data. Types of cancer cell line are color-coded as shown in the right corner. The first track represents cell lines of TCGA disease. The second track represents the single omics clustering group. A bar graph was used to show cancer types and the proportion of cell lines in each cluster. The dominant cancer types were marked on the top of the bar graph.Click here for additional data file.10.7717/peerj.9440/supp-2Figure S2(A) KEGG enrichment heatmap of down-regulated genes. (B) KEGG enrichment heatmap of up-regulated genes. (C) GO biological process enrichment heatmap of down-regulated genes. (D) GO biological process enrichment heatmap of up-regulated genes. (E) GO cellular component enrichment heatmap of down-regulated genes. (F) GO cellular component enrichment heatmap of up-regulated genes. (G) GO molecular function enrichment heatmap of down-regulated genes. (H) GO molecular function enrichment heatmap of up-regulated genes. Deeper red color signifies greater enrichment score in all panels.Click here for additional data file.10.7717/peerj.9440/supp-3Figure S3Click here for additional data file.10.7717/peerj.9440/supp-4Figure S4P-value for cancer types in each cluster. The rows represent clusters, and the columns represent cancer types. The values represent the –lg of cancer types. Cancer types with –lg > 3 in each cluster were defined as dominant cancer types. All the blank cells mean the instances of P-value = 0.A hypergeometric distribution was used to calculate the Click here for additional data file.10.7717/peerj.9440/supp-5Figure S5The TumorMap layout was as described for Figure 4. Drug susceptibility for (A) Paclitaxel, (B)L-685458, (C) PLX4720 and (D) RAF265. Increasing red colors indicate increasing sensitive degree.Click here for additional data file.10.7717/peerj.9440/supp-6Table S1Click here for additional data file.10.7717/peerj.9440/supp-7Table S2Click here for additional data file.10.7717/peerj.9440/supp-8Table S3Click here for additional data file.10.7717/peerj.9440/supp-9Table S4Click here for additional data file.10.7717/peerj.9440/supp-10Table S5Click here for additional data file.10.7717/peerj.9440/supp-11Table S6Click here for additional data file.10.7717/peerj.9440/supp-12File S1Click here for additional data file.10.7717/peerj.9440/supp-13Supplemental Information 13Click here for additional data file.


Enhancer–promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information.In this work, we present a novel Sequence-based method  to predict the enhancer–promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction. The enhancer–promoter interactions (EPIs) play a critical role in gene regulation in eukaryotes. In genetics, a promoter is a region of DNA sequence upstream of a particular gene . The lenOver the past decade, many high-throughput experimental approaches, such as chromosome conformation capture-based (3C)  and its Due to the limitations of experimental approaches, the number of available experimental data of EPIs is still limited. Several computational methods have been developed to predict EPIs of the genome. Depending on the type of the input data, computational methods can mainly be divided into two categories: DNA sequence-based methods and epigenomic data-based methods. For DNA sequence-based methods, PEP  and EP2vIt is well known that transfer learning (TL), an important branch of machine learning, is widely concerned in image recognition  and natuSEPT is of great significance for three points. (i) It could be used as an alternative to the experimental methods, helping other tasks such as identifying mechanisms of SNPs from genome-wide association studies (GWAS) . (ii) Itk-mer features, and did not take into account the interactions of high-order features. Because the LS-SVM [We first compared SEPT with other state-of-the-art methods of LS-SVM , SPEID  and RIPPe LS-SVM  can onlye LS-SVM  used deee LS-SVM  is a supe LS-SVM , LS-SVM e LS-SVM  and SEPTWe designed a series of computational network architectures in feature learning phase  from the aligned sub sequences for each kernel. The motif comparison tool of Tomtom  was usedWe found a set of potentially important transcription factor binding motifs by the transfer model. For each test cell line, top five import motifs involved in EPIs are show in Additional file One important factor in SEPT is how to aggregate the labeled data of different cell lines into a source domain. We investigated the aggregation of different number of cell lines, and found that aggregating all available labeled data of cell lines as the source domain for training model can yield better performance than aggregating partial cell lines data as the source domain. In addition, there is redundancy among different cell lines. Redundancy not only slows down model training, but also damages prediction performance. Thus, how to aggregate different cell lines as the source domain data is important.How to integrate other features such as histone modifications, chromatin accessibility and DNA shape into one model for predicting EPIs is also important factors. Although we only use sequence information in this work, the results of are still better than other methods which integrate the sequence and epigenomic information. Thus, if more information related to enhancers and promoters is integrated into our SEPT, it is hope that SEPT can significantly improve the performance of EPIs prediction.Although SEPT can predict the potential EPIs in new cell line, it needs to provide the location of the enhancers and promoters in advance. Therefore, it is need to develop new methods for identifying the EPIs in specific cell line without enhancer and promoter location information.Although some deep learning methods have been developed to predict EPIs within the same cell, they cannot get a good performance for predicting EPIs in the unlabeled cell lines, due to lack of understanding of the interested cell lines. In this work, we proposed a transfer learning model to predict EPIs in interested (or “new”) cell lines. To better leverage the existing EPIs knowledge, we adopt the adversarial learning mechanism to learn useful features in the existing labeled cell lines and interesting unlabeled cell lines. Experiment results with the domain adversarial operation indicate that it is helpful to predict EPIs in new cell lines. We expect that the model could learn informative features cross domains and reveal some commonalities (common TFs) between source and target domains. By learning commonalities between the source and target domains, SEPT outperforms other state-of-the-art methods for predicting EPIs in new cell lines.Although SEPT can effectively predict EPIs in specific cell lines from enhancer and promoter sequences, it can be further improved by considering the following factors. (1) SEPT just uses the sequence information of enhancers and promoters. Integrating other experimental data such as core histone modification ChIP-seq data or DNase-seq data can improve the performance of SEPT. (2) Each cell line is treated equally in the source domain, but the contribution of different cell lines should be different for the test cell line. Determining which cell lines should be used as training data is still needed to be explored, as more and more labeled data will become available. (3) Some EPIs maybe have the cell line specificity, while others are universal across many cell lines. Thus, different samples within the same cell line should have different contribution for cross-cell prediction. Assigning a proper weight to each sample can also improve the performance of SEPT. It can be expected that SEPT can be helpful in other biological interactions prediction scenarios , 45, sucWe used the same Hi-C data as , and dowFor each positive/negative sample, sequences of enhancers are extended or cut to 3 kb flanking regions on location center of enhancers, and promoter are extended or cut to 2 kb flanking regions on location center of promoters. One-hot coding format of enhancer and promoter sequence is used as input data of model.We examined the overlapping number of positive EPIs between any two cell lines. For any two positive EPI pairs from any two cell lines, if the position of the two enhancers and the position of the two promoters both same, the two EPI pairs are considered to overlap. By comparison, positive samples of different cell lines have very little overlap  to represent each EPI pair.Domain adaptation  is definAn overview of SEPT is shown in Fig. Feature extractor consists of two convolution layers, two max-pooling layers, two dropout layers, and one recurrent long short-term memory (LSTM) layer. Domain discriminator consists of the GRL, one dense layer, one dropout layer, and the output. EPIs predictor consists of one dense layer, one dropout layer, and the output. Since informative features may differ between enhancers and promoters, we use two convolution layers, rectified linear unit (ReLU) and max-pooling layers for enhancers and promoters, respectively. Thus, the inputs are two one-hot matrixes to represent enhancer and promoter sequence, respectively. Because large number of kernels can sufficiently extract the features, and motif features of DNA sequences are short than 40 bp, so each convolution layer consists of 300 ‘kernels’ with length 40. Max-pooling layer reduces the output dimension with pool length 20, stride 20. The outputs of the two branches are concatenated into one tensor, which is input to the dropout layer with dropout rates of 0.25. The dropout layer randomly selects partial input data to next layer to avoid overfitting. The recurrent LSTM layer is used to extract feature combinations of the two branches, and the output dimension of LSTM is 100. For domain discriminator, the output of LSTM layer feeds into GRL, and a dense layer maps the learned distributed features to the domain label space. It contains 50 units with ReLU activations. The output feeds into a sigmoid unit to predict the domain probability after dropout layer with dropout rates of 0.5. For EPI predictor, the output of LSTM layer feeds into dense layer, which further maps the learned distributed features to the sample label space. It contains 100 units with ReLU activations. The output feeds into a sigmoid unit to predict the probability after dropout layer with dropout rates of 0.5.We trained SEPT for 80 epochs with mini-batches of 64 samples by back-propagation. In the training phase, source domain data were used to train the feature extractor and the EPI predictor, and both source and target domain data were used to train the feature extractor and the domain discriminator. SEPT seeks to minimize the loss of EPIs label and domain discriminator. Binary cross-entropy loss function for both EPIs label predictor and domain discriminator is used, which is minimized by stochastic gradient descent (SGD) with initialized learning rate initialized equals 0.001. In view of the two optimization objectives, SEPT learns a discriminative representation for EPI prediction and indistinguishable representation for domain prediction. The objective function of the SEPT is defined as follows:Here, It is a problem of minimax optimization, that is, we attempts to seek a saddle point of the functional The training procedure of SEPT can be described as follows: (i) Randomly separating the dataset of target domain into approximate equal two parts, one as the training data in which each sample has domain label but no EPI label, and the other as the testing data. (ii) Mixing the data from other six cell lines and randomly shuffling the data. The mixed data are used as source domain dataset in which each sample has both domain and EPIs labels. (iii) Training SEPT with the source domain data and target domain data. (iv) Evaluating the performance of SEPT with the test data in target domain.https://keras.io/) with Tensorflow as back-end [All the experiments are based on Python by using the scikit-learn machine learning library  and Keraback-end .TP is the number of correctly predicted EPIs, TN is the number of correctly predicted non-EPIs, FP is the number of incorrectly predicted EPIs and FN is the number of incorrectly predicted non-EPIs. AUC is the area under the receiver operating characteristic (ROC) curve which is the plot of the true-positive rate  as a function of false-positive rate  based on various thresholds. AUPR is the area under the precision-recall curve which is the plot of the precision as a function of recall based on various thresholds.We adopted the metrics of Accuracy, Precision, Recall, F1-score, AUC and AUPR to assess the performance of SEPT. These metrics are defined respectively as the following –53.2\doAdditional file 1. Supplementary materials.


Background: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. Sacituzumab govitecan (SG) is a novel antibody-drug-conjugate (ADC) targeting trophoblast-antigen-2 (Trop-2), a cell surface glycoprotein highly expressed in many epithelial tumors, to deliver SN-38, the active metabolite of irinotecan. This study aimed to evaluate Trop-2 expression in EOC tissues and the preclinical activity of SG against primary EOC cell lines and xenografts.Methods: Trop-2 expression was assessed in 90 formalin-fixed-paraffin-embedded tumors and nine primary tumor cell lines by immunohistochemistry (IHC) and flow cytometry, respectively. Trop-2 expression and cell viability after exposure to SG in primary tumor cell lines, non-targeting control ADC, and SG-parental antibody hRS7 were evaluated using flow-cytometry-based-assays. Antibody-dependent-cell-cytotoxicity (ADCC) against Trop-2+ and Trop-2– EOC cell lines was tested in vitro using 4 h Chromium-release-assays. In vivo activity of SG was evaluated against Trop-2+ EOC xenografts.Results: Moderate-to-strong staining was seen in 47% (42/90) of ovarian tumors by IHC while 89% (8/9) of the primary EOC cell lines overexpressed Trop-2 by flow cytometry. EOC Trop-2+ were significantly more sensitive to SG compared to control ADC (p < 0.05). Both SG and hRS7 mediated high ADCC activity against Trop-2+ cell lines. SG also induced significant bystander killing of Trop-2– tumor cells admixed with Trop-2+ EOC cells. In in vivo experiments SG treatment demonstrated impressive anti-tumor activity against chemotherapy-resistant EOC xenografts.Conclusion: SG demonstrates remarkable preclinical activity against biologically aggressive and chemotherapy-resistant EOC cell lines and a significant bystander effect against EOC cell lines with heterogenous Trop-2 expression. Clinical trials are warranted. Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy. Approximately 14,000 ovarian cancer deaths are estimated in 2019 in the United States alone . The devin vivo while potentially minimizing the side effects of highly toxic chemotherapy agents (Antibody drug conjugates (ADC) represent a novel and promising therapeutic approach for cancer patients which combine antibody targeting of differentially expressed surface proteins/receptors with a toxic payload to allow selective delivery of chemotherapeutic agents to tumor cells. ADC may optimize tumor targeting y agents , 5. One y agents .The TACSTD2 gene on chromosome 1p32, encodes the human trophoblast cell surface antigen 2 (Trop-2), a 46 kDa transmembrane glycoprotein. Many epithelial human tumors differentially express Trop-2 . While tSacituzumab govitecan or IMMU-132 (SG) is a novel ADC combining the humanized RS7 antibody targeting Trop-2 coupled to a hydrolyzable linker that allows for a time dependent release of the payload to deliver SN-38, the active metabolite of irinotecan (7-ethyl-10-hydroxycamptothecin) to tumor tissue. SN-38 is significantly more potent than irinotecan  and SG antibody is conjugated to the toxic payload with a high ratio (8:1) without affecting targeting and pharmacokinetics , 12. Thein vitro and in vivo against multiple primary EOC models and xenografts. We demonstrate for the first time that SG is highly active, both in vitro as well as in vivo, against biologically aggressive EOC and well tolerated in animals bearing EOC chemotherapy-resistant xenografts.The objective of this original research was to evaluate the expression of Trop-2 in EOC tissues and primary cell lines and to examine the preclinical anti-tumor activity of SG The study protocol was approved by the Yale Human Investigation Committee and conducted in accordance with recognized ethical guidelines. Informed written consents from cancer patients and healthy donors were obtained following institutional guidelines prior to tissue/blood collection. Fresh tumor biopsy samples, and primary EOC cell lines were established and de-identified as described previously  18–20).–20.18–20N = 90 formalin-fixed-paraffin-embedded (FFPE) tumors] represented in tissue microarray (TMA) format was used to evaluate TROP-2 expression by immunohistochemistry (IHC). Briefly, as described in previous studies 2 immunoglobulin (FITC) was used as a secondary antibody . Data were then acquired on Cell Quest software  with a FACScalibur. Mean fluorescence intensity (MFI) was evaluated using Cell Quest (BD Biosciences) and Prism 8. Cell lines with MFI >100 were determined to have 3+ expression of Trop-2 while cell lines with an MFI of 51–100 were noted to have 2+, 21–50, 1+ and 20 or less was 0.in vitro experiments. Drug-to-antibody ratio (DAR) of SG and control ADC was 6.78 and 6.84, respectively. For the in vitro experiments, the dosage of the drug was adjusted according to the DAR, in order to expose equivalent quantities of SN-38 cells treated with SG and control ADC. In in vivo experiments, SG and control ADC were dissolved in sterile 0.9% sodium chloride as a 5 mg/mL solution. hRS7 IgG (molecular weight: 150 kDa) was obtained in liquid form from Immunomedics, Inc. as a 10 mg/mL solution.SG (hRS7-CL2A-SN-38), non-targeting control ADC (h679-CL2A-SN-38), and unconjugated antibody hRS7 IgG were obtained from Immunomedics, Inc. . SG and control ADC were dissolved in sterile 0.9% sodium chloride as a 2 μM stock solution for the 51Cr) release assays to measure the cytotoxic reactivity in combination with SG, control ADC, or hRS7 against the EOC cell lines at the effector to target ratio (E:T) of 5:1 and 10:1. The release of 51Cr from target cells was measured as evidence of tumor cell lysis after exposure of the tumor cells to a concentration of 2.5 μg/mL of SG, control ADC, or hRS7. Negative controls included tumor cells incubated with PBLs in the absence of ADC. One percentage sodium dodecyl sulfate (SDS) was used to achieve complete lysis of target cells , as positive control condition. Chimeric anti-CD20 mAb rituximab (2.5 μg/mL) was also used in all bioassays as additional negative control. 51Cr-released from the target cells by cytolysis was counted using a gamma radiation counter . The percentage cytotoxicity of SG, control ADC, or hRS7 was calculated with the formula: % cytotoxicity = 100 × (E–S)/(T–S) . Results are presented as mean ± SEM.Ficoll–Hypaque-separated peripheral blood lymphocytes (PBLs) from several healthy donors, were used in standard 4-h chromium  and hRS7 at concentrations of 0.2, 0.5, 1, 2, 4 nM (adjusted according to DAR) for 12 h before being washed with culture medium to remove any unbound ADC or naked Mab. The 6-well plates were then incubated for 72 h  before being harvested and incubated with propidium iodide (2 μl of 500 μg/mL stock solution in PBS). The viable cells were then quantified using a previously characterized flow-cytometry based assay . In someBriefly, a Trop-2 negative uterine serous cell line  stably transfected with a Green Fluorescence Protein (GFP) plasmid  , was pla3, mice were randomized into four groups (six mice/group): saline control, SG, control ADC, and hRS7. SG, control ADC, and hRS7 were administered at the dose of 500 μg IV twice per week for 3 weeks. Tumors were measured twice weekly. Tumor volume was determined using the formula (A2 × B)/2, where B represented the largest tumor diameter size and A was the smaller perpendicular tumor diameter. Mice were humanely euthanized when the tumor volume reached 1.5 cm3 or when they reached the end of 60 days of follow up. The Institutional Animal Care and Use Committee (IACUC) rules were used for animal care and euthanasia in all experiments.KRCH31, a previously characterized , 22 hight-test. Tumor volume differences at specific time points were compared using an unpaired t-test. Overall survival data were analyzed using the Kaplan-Meier method. Survival curves were compared using the log-rank test. A two-sided p < 0.05 was considered to be significant . The differences in the inhibition of proliferation in the EOC cell lines after exposure to treatments were evaluated by two-tailed unpaired student nificant .A tissue microarray containing 90 ovarian high-grade serous carcinoma samples was used to semi-quantitatively analyze Trop-2 expression by IHC. Moderate-to-strong (scores 2+/3+) Trop-2 expression was seen in 47% (42/90) of the tumors . RepreseIn our study, we established nine primary EOC cell lines from fresh tumor biopsy samples as described in Methods. Stage, grade, histology and primary site of the tumors are displayed in 51Cr release assays. Both ovarian cancer cell lines were highly resistant to ADCC when treated with the isotype control antibody (Rituxan) (2 μg/mL) at E:T ratios of 5:1 and 10:1   for their sensitivity to PBL-mediated cytotoxicity in standard 4-h n + PBL) . Subsequ50 of SG, control ADC, and hRS7 for each EOC cell lines was determined. As shown in p < 0.05) (p = 0.830)  was more than 20-fold higher than SG IC50 for all the cell lines tested. To gain better insight into the mechanism of SG activity against ovarian cancer cells, in some experiments KRCH31 tumor cells were exposed to the ADC and control antibody for 24, 48, and 72 h before being harvested for Annexin V and PI staining. As representatively shown in Next, we exposed four representative primary EOC cell lines  to scalar concentrations of SG, non-targeting control ADC and hRS7 IgG. The IC < 0.05) . In cont= 0.830) . The IC5in vitro with the low/negligible Trop-2 expressing cell line . As shown in p < 0.05). No difference was seen in GFP-ARK4 cytotoxicity in monocultures.The ability of SG to induce a bystander killing effect against EOC with heterogeneous Trop-2 expression was tested by admixing KRCH31 EOC cells  In vivo experiments comparing the antitumor activity of SG, control ADC, hRS7 IgG and normal saline (Control) were conducted using the Trop-2+ chemotherapy-resistant EOC xenograft . Treatment with SG showed a statistically significant difference in tumor growth inhibition when compared to control ADC, saline and hRS7 starting at day 11 of the treatment (p < 0.05). The P-value after day 11 of the treatment remained highly significant  until all mice treated with control ADC, saline and hRS7 were euthanized (p < 0.05). The tolerability of SG treatment was monitored and no significant change in mice body weight was detected at any time point during the experiments  was not significantly affected by SG when compared to control ADC isotype. These results demonstrate the need for Trop-2 protein/receptor expression on tumor cells for the induction of SG cytotoxic activity against EOC.Accordingly, in this study we evaluated Trop-2 expression level in 90 high-grade serous ovarian carcinomas and tested for the first time the activity of SG, against nine primary ovarian cancer cell lines, with serous and clear cells histology, with differential Trop-2 expression. We report moderate-to-strong Trop-2 expression in 47% (42/90) of EOC samples evaluated by IHC, and 89% (8/9) of the primary EOC cell lines evaluated by flow cytometry. Importantly, we demonstrate high In our experiments, we also demonstrated that tumor cell killing is also potentially mediated by immune system cells . Indeed, both SG and hRS7-IgG demonstrated significant ADCC against Trop-2 positive EOC cell lines in the presence of PBL while non-targeting control ADC generated only low levels of cytotoxicity. The killing activity was Trop-2 specific as demonstrated by the negligible ADCC induced by SG against a Trop-2 negative tumor cell line (OVA14). These findings confirm and extend previous results from our group evaluating the activity of the unconjugated humanized anti-Trop-2 monoclonal antibody hRS7 .Ovarian cancer is a heterogeneous disease and accordingly, expression of Trop-2, similarly to other surface markers , may notin vivo experiments with Trop-2 + EOC xenografts established from a patient with a chemotherapy-resistant disease, we demonstrate that injections of SG twice-weekly for 3 weeks induced a statistically significant difference in KRCH31 tumor growth inhibition when compared to control ADC. Of interest, SG treatment in vivo was well tolerated, with no significant weight change documented in animals for the entire study. Taken together these results demonstrate for the first time, in vivo activity of SG against biologically aggressive and chemotherapy-resistant EOC and strongly support the use of this ADC in future clinical trials.Finally, in While the efficacy of SG in ovarian cancer patients is currently unknown, preliminary SG clinical activity has recently been reported against multiple human tumors , 16, 31.in vitro by SG, (3) SG in the presence of effector cells (NK cells) may induce significant ADCC against Trop-2 positive EOC cells, (4) SG has a significant bystander killing effect, which could aid in the treatment of tumors with heterogeneous antigen expression, and (5) chemotherapy-resistant EOC xenografts overexpressing Trop-2 are highly sensitive in vivo to SG. These preclinical results combined with recent phase I/II IMMU-132-01 basket study data demonstrating encouraging clinical activity in multiple human solid tumors resistant to chemotherapy, strongly support the design of clinical trials in Trop-2 positive recurrent EOC patients.In conclusion, our results demonstrate that (1) Trop-2 is overexpressed in ~50% of EOC, (2) primary EOC cell lines overexpressing Trop-2 are sensitive to killing The antibody-drug-conjugate sacituzumab govitecan demonstrated significant preclinical activity against primary Trop-2+ EOC cell lines and xenografts.All datasets for this study are included in the article/supplementary material.The animal study was reviewed and approved by Institutional Animal Care and Use Committee (IACUC).AS designed research. EP, SL, SP, BZ, SB, AB, LZ, AM, EB, PM, NB, GA, CH, GM, ER, D-AS, JT-R, PH, and MA performed research. PS and GS contributed new reagents and analytic tools. AS and EP wrote the paper.AS declares research funding support to Institution from Immunomedics, Inc. AM declares that she receives research funding support from Spanish Medical Oncology Society. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The poor clinical outcomes for Osteosarcoma (OS) and Ewing’s sarcoma (ES) patients underscore the urgency of developing novel therapeutic strategies for these pathologies. In this context, the emerging role of Sonic hedgehog (SHH) signaling in cancer has been critically evaluated, focusing on the potential for targeting SHH signaling as an anticancer strategy. The aims of this work were (1) to highlight and to compare a possible SHH/Gli signature between OS and ES, (2) to strengthen our knowledge concerning the role of EWS-FLI1 in the SHH signature in ES and (3) to evaluate the effect of the specific Gli inhibitor GANT61 in vivo on the growth of ES tumors using an orthotopic mice model. Our work identifies Gli1 as a promising therapeutic target in ES and demonstrates that GANT61, through inhibition of Gli1 transcriptional activity, may be a promising therapeutic strategy hindering ES tumor progression, and specifically primary tumor growth.Osteosarcoma (OS) and Ewing’s sarcoma (ES) are the most common malignant bone tumors in children and adolescents. In many cases, the prognosis remains very poor. The Sonic hedgehog (SHH) signaling pathway, strongly involved in the development of many cancers, regulate transcription via the transcriptional factors Gli1-3. In this context, RNAseq analysis of OS and ES cell lines reveals an increase of some major compounds of the SHH signaling cascade in ES cells, such as the transcriptional factor Gli1. This increase leads to an augmentation of the transcriptional response of Gli1 in ES cell lines, demonstrating a dysregulation of Gli1 signaling in ES cells and thus the rationale for targeting Gli1 in ES. The use of a preclinical model of ES demonstrates that GANT61, an inhibitor of the transcriptional factor Gli1, reduces ES primary tumor growth. In vitro experiments show that GANT61 decreases the viability of ES cell, mainly through its ability to induce caspase-3/7-dependent cell apoptosis. Taken together, these results demonstrates that GANT61 may be a promising therapeutic strategy for inhibiting the progression of primary ES tumors. Osteosarcoma (OS) and Ewing’s sarcoma (ES) are the most frequently observed primary malignant bone tumors in the second decade of life . ConventRegarding OS, whole-genome sequencing analyses demonstrate that OS are characterized by very high rates of genetic alterations with somatic mutations and copy number alterations . ConcernThe Sonic hedgehog (SHH) signaling pathway is a conserved pathway that regulates developmental processes . The celLiterature is scarce concerning the SHH pathway and primary bone sarcomas, specifically ES. Gli1 seems particularly important, as it has been identified as a direct transcriptional target of EWS-FLI1 by a mechanism independent of the SHH ligand . AlthougIn this context, the aims of this work were (1) to highlight and to compare a possible SHH/Gli signature between OS and ES, (2) to strengthen our knowledge concerning the role of EWS-FLI1 in the SHH signature in ES and (3) to evaluate the effect of the specific Gli inhibitor GANT61 in vivo on the growth of ES tumors using an orthotopic mice model.To better understand the role of the SHH pathway in the development of primary bone tumors, we first compared the gene expression of the main components of the SHH cascade in seven ES cell lines with those measured in seven OS cell lines, using RNAseq analysis. As shown in Together, these results demonstrated that the Gli1 signature identified in ES cells leads to an increased Gli transcriptional response in ES cell lines.Since Beauchamp and colleagues  describeTogether, these results, in accordance with the previous ones published by Beauchamp and colleagues , demonstAs we demonstrated that the intrinsic activation of SHH signaling was mainly due to Gli1 overexpression in response to EWS-FLI1, we then assessed the effects of the specific Gli inhibitor GANT61 on primary ES tumor growth. The efficiency of GANT61 was first evaluated by Gli-lux gene reporter assay in TC71, A673 and SKES1 ES cell lines. GANT61 decreased Gli-dependent luciferase activity in the three cell lines A. In add3 when the mice were treated with vehicle (control group) and only 1198 ± 298 mm3 when the mice were treated with 50 mg/kg of GANT61.The crucial role played by the bone microenvironment in the growth of ES tumors led us to choose to use an orthotopic mouse model to evaluate the effect of GANT61 on the growth of ES tumors. As shown in Together, these results demonstrated the ability of GANT61 (1) to block intrinsic activation of Gli signaling in ES cell lines and (2) to inhibit the primary tumor growth of ES in an orthotopic model of ES.To better understand the mechanisms that control the effects of GANT61 on ES tumor growth, we then performed in vitro experiments. Firstly, our experiments show that GANT61 significantly inhibited the viability of three ES cell lines in a dose-dependent manner A. SecondTaken together, these results demonstrate that the Gli inhibitor GANT61 reduces the viability of ES cell lines in vitro and suggest that this effect is mainly due to its ability to induce caspase-3/7-dependent apoptosis.The poor clinical outcomes for OS and ES patients underscores the urgency of developing novel therapeutic strategies for these pathologies. In this context, the role of the SHH signaling pathway in cancer has been evaluated, focusing on the potential for targeting the actors of this signaling pathway as an anti-cancer strategy . For OS,By comparing the expression of the main components of the SHH pathway among seven OS cell lines and seven ES cell lines, our work shows that the expression of Gli1, Ptch1, Ptch2 and SMO is strongly increased in the ES cell lines. Ptch1 and Ptch2 have been defined as targets of Gli ,37. ThisThe increased Gli expression and its transcriptional activity may be independent of the binding of the SHH ligand to its receptors Ptch and SMO . For exaTo our knowledge, no studies have yet evaluated the anti-tumor activities of a direct Gli1 inhibitor such as GANT61 in ES. One study using arsenic shows the rationale for targeting the SHH pathway in ES . HoweverIn the present work, we show for the first time the in vivo anti-tumor effects of a specific Gli1-2 inhibitor, GANT61 ,44, on EHuman OS  and ES  cell lines were cultured in Dulbecco’s Modified Eagle’s Medium  or Roswell Park Memorial Institute medium (Lonza) in presence of 10% fetal bovine serum  and 1% penicillin-streptomycin . The A673 ES cell line was stably transfected to integrate an inducible shRNA against EWS–FLI1 onto its genome . Treatme®RNAII . Total RNA was used for first-strand cDNA synthesis using the ThermoScript RT-PCR System . Real-time PCR was performed with a CFX 96 real-time PCR instrument  using SYBR Green Supermix reagents (Biorad). Primer sequences are provided in Total RNA from each OS and ES cell lines was extracted using NucleoSpinp-values obtained were corrected for false positives by using the independent hypothesis weighting (package IHW) multiple testing adjustment method. Genes were considered significantly differentially expressed if the log2 fold change was over 1 or less than −1 and if FDR was less than 0.05.Library preparation and sequencing were performed at Active Motif, Inc. Libraries were prepared from purified RNA using the illumina TruSeq Stranded mRNA Sample Preparation kit, and sequencing was done on the illumina NextSeq 500 as 42-nt long-paired end reads. Read mapping and fragment quantification for each gene were also performed at Active Motif. Read mapping against the human genome (GRCh38) was done using the STAR algorithm with default settings, and fragment assignment was done using feature counts with gene annotations from the Sub-read package. Only read pairs having both ends aligned with a minimum overlap of 25 base pairs, and mapping to the same chromosome and on the same strand, were counted (feature counts -p -B -C—min Overlap 25). Differential gene expression analysis was performed using the DESeq2 package. The TM  according to the manufacturer’s recommendations. Transfection efficiencies was monitored using phRLMLP-Renilla luciferase expression vector. Luciferase activity was determined using the LightSwitchTM Luciferase Assay system . The GLI-Lux construct was used as a reporter construct specifically for Gli-driven signaling .OS and ES cell were transfected with jetPEI2 apparatus .ES cell lines were seeded in a 96-well plate. After adherence, cells were treated with increasing concentrations of GANT61 for 48 h. Cells were then fixed with 1% glutaraldehyde and stained using crystal violet. Finally, the crystal violet staining was solubilized in Sorenson solution. Absorbance was measured at 570 nm with a VictorES cells were cultured and treated with or without GANT61 for 24 h. Cell apoptosis were evaluated by flow cytometry  according the FITC Annexin V Apoptosis Detection Kit I protocol (BD Biosciences). Caspase3/7 activity was measured in protein total extract using the Apo-ONE caspase 3/7 kit from Promega .Cells were lysed in lysis buffer (Sodium Sodecyl Sulfate (SDS) 1%, Tris pH 7.4 10 mM, Sodium orthovanadate 1 mM). Protein concentration was determined using the BCA Protein Assay Kit . Samples in Laemmli buffer  containing equal amounts of total protein extracts were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE). After transfer to PVDF (PolyVinyliDene Fluoride) membranes , these were probed 12 h with primary antibodies PARP and β-tubulin . Membranes were finally probed with secondary fluorescent antibodies (Li-COR IRDye). Antibody binding was visualized with the LI-COR odyssey Fc system .6 TC71 cells, leading to a rapidly growing tumor in bone tissue. Once their tumors were palpable, mice were randomly assigned to control (vehicle) or GANT61 groups. Tumor volume (V) was measured twice a week. Notably, no major changes were observed following injection of the drug GANT61 (weight loss or change in mice behavior) suggesting good tolerance to the drug.Five-week-old female Rj:NMRI nude mice  were used for in vivo experiments in accordance with the institutional guidelines of the Ethical Committee (CEEA Pays de la Loire no.06) and the authorization of the French Ministry of Agriculture and Fishery (Apafis # 8405-2017010409498904). Anesthetized mice received a paratibial injection of 1 × 10p < 0.05 were considered significant.Statistical analyses were performed using GraphPad Prism 6 software . The Mann–Whitney test was used for in vitro and in vivo analyses. Results are given as means ± SD for in vitro experiments and as means ± SEM for in vivo experiments. Results with Altogether, these results identify Gli1 as a promising therapeutic target in Ewing sarcoma and demonstrate that GANT61, through inhibition of Gli1 transcriptional activity, may be a promising therapeutic strategy hindering ES tumor progression, specifically primary tumor growth.