ews/fli is a master regulator of metabolic...

Metabolism

EWS/FLI is a Master Regulator of MetabolicReprogramming in Ewing SarcomaJason M. Tanner1, Claire Bensard1, Peng Wei1, Nathan M. Krah2, John C. Schell1,Jamie Gardiner3,4, Joshua Schiffman3,4, Stephen L. Lessnick5, and Jared Rutter1,6

Abstract

Ewing sarcoma is a bone malignancy driven by a translocationevent resulting in the fusion protein EWS/FLI1 (EF). EF functionsas an aberrant and oncogenic transcription factor that misregu-lates the expression of thousands of genes. Previous work hasfocused principally on determining important transcriptionaltargets of EF, as well as characterizing important regulatorypartnerships in EF-dependent transcriptional programs. Less isknown, however, about EF-dependent metabolic changes or theirrole in Ewing sarcoma biology. Therefore, themetabolic effects ofsilencing EF in Ewing sarcoma cells were determined. Metabo-lomic analyses revealed distinct separation of metabolic profilesin EF-knockdown versus control-knockdown cells.Mitochondrialstress tests demonstrated that knockdown of EF increased respi-ratory as well as glycolytic functions. Enzymes andmetabolites inseveral metabolic pathways were altered, including de novo serinesynthesis and elements of one-carbon metabolism. Furthermore,

phosphoglycerate dehydrogenase (PHGDH) was found to behighly expressed in Ewing sarcoma and correlated with worsepatient survival. PHGDH knockdown or pharmacologic inhibi-tion in vitro caused impaired proliferation and cell death. Inter-estingly, PHGDHmodulation also led to elevated histone expres-sion and methylation. These studies demonstrate that the trans-location-derived fusion protein EF is a master regulator of met-abolic reprogramming in Ewing sarcoma, diverting metabolitestoward biosynthesis. As such, these data suggest that the meta-bolic aberrations induced by EF are important contributors to theoncogenic biology of these tumors.

Implications: Thispreviouslyunexplored roleofEWS/FLI1–drivenmetabolic changes expands the understanding of Ewing sarcomabiology, and has potential to significantly inform development oftherapeutic strategies. Mol Cancer Res; 15(11); 1517–30. �2017 AACR.

IntroductionEwing sarcoma is an aggressive malignancy that most often

emerges from bone and bone-associated tissues of children andadolescents. It is the secondmost common pediatric bone tumor;second only to osteosarcoma. Surgical strategies have achievedvery good outcomes for many of these patients. But truly effectivemolecular therapies continue to evade discovery, translating toespecially poor prognosis for patients who fall victim to metas-tasis or relapse. Indeed, the rate of relapse after surgery is approx-imately 35%, and the probability of 5-year survival after relapse isapproximately 20% (1, 2). Continued investigation of themolec-ular underpinnings of this disease are thus crucial, and have thepromise of providing additional candidate therapies to thesepatients.

Ewing sarcoma tumors are uniquely devoid of recurrent muta-tions in classical oncogenes or tumor suppressors (3). Instead,oncogenesis of Ewing sarcoma is almost entirely due to the far-reaching consequences of a single genetic lesion: a translocationevent between chromosomes 11 and 22. This chromosomalrearrangement generates an in-frame fusion product merging theEWSR1 and FLI1 genes, giving rise to the oncogenic fusion proteinknown as EWS/FLI (EF). EF contains the N-terminal transcrip-tional regulatory domain of EWS and the DNA-binding domainof FLI1. Interestingly, while EF can affect known targets of FLI1 bybinding to canonical FLI1-binding sites, it also influences expres-sion of genes that are not targets of FLI1 or EWSR1 (4). Thus, EF isan aberrant transcription factor with neomorphic functions andtumorigenic consequences.

The transcriptional reprogramming driven by EF has been wellprofiled (5). In-depth study of EF targets has revealed that thismaster regulator exerts its oncogenic effects on numerous pro-cesses, contributing tomany of the hallmark capabilities of cancercells. These include: proliferation andgrowth (6), resistance to celldeath (7), immortality (8), angiogenesis (9), invasion, andmetas-tasis (7), and evading immune destruction (10). However, verylittle is known about how EF affects another characteristic featureof cancer cells—the pro-oncogenic reprogramming of cellularmetabolism.

Cancer cells characteristically adopt programs that modifysubstrate utilization and alter metabolite abundance in ways thatsupport the biosynthetic requirements of these highly prolifer-ative and resilient cells (11). Evidence increasingly indicates thatmetabolic alterations observed in cancer are not mere spectatorphenomena, but are active contributors to oncogenesis (11).Significant interrelationships exist linking cellular metabolic

1Department of Biochemistry, University of Utah School of Medicine, Salt LakeCity, Utah. 2Department of Human Genetics, University of Utah School ofMedicine, Salt Lake City, Utah. 3Department of Oncological Sciences, Universityof Utah School of Medicine, Salt Lake City, Utah. 4Huntsman Cancer Institute,University of Utah School ofMedicine, Salt LakeCity, Utah. 5Center for ChildhoodCancer & Blood Diseases, Nationwide Children's Hospital, Columbus, Ohio.6Howard Hughes Medical Institute, University of Utah, Salt Lake City, Utah.

Note: Supplementary data for this article are available at Molecular CancerResearch Online (http://mcr.aacrjournals.org/).

Corresponding Author: Jared Rutter, Department of Biochemistry, HowardHughesMedical Institute, University of Utah, Salt LakeCity, UT84112. Phone: 801-581-3340; E-mail: [email protected]

doi: 10.1158/1541-7786.MCR-17-0182

�2017 American Association for Cancer Research.

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processes to nuclear control, and vice versa (11). As a result,defining the metabolic processes undergirding cancer cells pro-vides promising new opportunities for discovery of potentiallyactionable therapeutic targets.

Currently, there is a paucity of data concerning the role of EF inalteringmetabolic processes in Ewing sarcoma.Given its relativelylow mutation burden compared with other cancers (3), Ewingsarcoma provides a unique model to study metabolic alterationsthat are important to oncogenesis. Changes in cellular metabo-lism that result from EF transcriptional misregulation likelycontribute directly to oncogenesis. Thus, studying cellular metab-olism in Ewing sarcoma promises to increase our understandingofmetabolic reprogramming in general, and informdevelopmentof novel therapeutic strategies for Ewing sarcoma specifically.With these goals in mind, we set out to investigate EF-drivenmetabolic reprogramming in Ewing sarcoma.

Materials and MethodsCell culture

Cell lines expressing EWS/FLI (A673, UTES-14-01872; seeSupplementary Fig. S2B and S2C) or EWS/ERG (TTC466) weregrown in 10%FBS/DMEM or 10% FBS/RPMI (RPMI 1640 Glu-tamax; Thermo Fisher Scientific), respectively. DMEM (Corning)contained 25 mmol/L glucose, 4 mmol/L L-glutamine, and1 mmol/L sodium pyruvate. Media were supplemented with 3mg/mL puromycin for selection of shRNA-expressing cells. 293Tcells were grown in 10% FBS/DMEM. Mycoplasma testing wasperformed as described previously (12). Population doublingswere assessed using a 3T5 growth assay as described previously(13). A673 and TTC466 lines were validated prior to experimen-tation by STR analysis (Genetica DNA Laboratories). The patient-derived cell lines A673 and TTC466 were acquired from stocksmaintained by S.L. Lessnick. UTES-14-01872 is a recently derivedcell line of Ewing sarcoma established in 2014 by J. Schiffman.

shRNA constructs, virus production, and infectionsEWS/FLI- andLuciferase-siRNA sequenceswereusedpreviously

for transcriptional profiling of Ewing sarcoma (14–16). siRNAsequences for PHGDH and PSPH were designed using the White-head Institute siRNA Selection Program (17). shRNA-encodinginserts containing these sequences were ligated into the pMKO.1pretroviral vector. siRNA sequences were: shEF, 50-ATAGAGGTGG-GAAGCTTAT-30; shPHGDH, 50-CCCTGTAGTACAGCAATAA-30;shPSPH, 50-GGCTGAAATTCTACTTTAA-30. Retrovirus was pro-duced by transfecting 293T cells with OptiMEM and Lipofecta-mine 2000, and viral supernatantwas collected at 48 and72hourspostinfection. Viral supernatant was filtered at 0.45 mm, ali-quoted, and stored at �80� C. Infections were performed byadding viral supernatant (plus 8 mg/mL polybrene) to subcon-fluent cultures. Selection in puromycin-containing media wascontinued for 3 days, and puromycin efficacy was confirmed byobserving cell death in uninfected cells. As EF is the primaryoncogenic driver of Ewing sarcoma,most Ewing sarcoma cell linesdo not tolerate its silencing well. A673 cells are known to besttolerate EF knockdown. Accordingly, we have had the mostsuccess using A673 cells, and limited success performing suchexperiments in other lines.

Mitotracker stainingA673 cells expressing shLUC or shEF were seeded onto 1.5

borosilicate chambered coverglass slides (Thermo Fisher Scien-

tific) and incubated overnight. Media were replaced with phenolred-free DMEM containing 25 mmol/L glucose, 1% Glutamax,1% sodiumpyruvate, 100nmol/LMitotracker Red FM, 200nmol/L Mitotracker Green FM, and 5 mg/mL Hoechst stain (ThermoFisher Scientific). Cells were incubated in staining medium for 10minutes, after which staining medium was removed and cellswere washed twice with phenol red-free medium. Cells wereimmediately imaged using a Zeiss Axio Observer Z1 microscope.

Images were quantified using ImageJ software. Three cells wereselected in each of 5 high-power field images for a total of 15quantified cells. Integrated density measurements were taken forMitotracker Red andMitotracker Green channels for each cell withidentical areas, and corrected total cell fluorescence (CTCF) wascalculated for each channel as described previously (18). CTCFcalculations were done as: CTCF ¼ integrated density – (area ofselected cell � mean fluorescence of background readings).

Western blottingWhole-cell extract (WCE) was prepared using RIPA buffer

containing protease inhibitor cocktail (Sigma). Total proteincontent was determined via BCA assay, and WCE was preparedfor SDS-PAGE by adding Laemmli buffer. Immunoblotting wasdone using the following antibodies: Fli1 (Abcam, 1:1,000),PHGDH (Cell Signaling Technology 1:1,000), PSAT (Abcam,1:1,000), PSPH (Abcam, 1:1,000), SHMT1 (Cell Signaling Tech-nology, 1:1,000), SHMT2 (Cell Signaling Technology, 1:1,000),TYMS (Cell Signaling Technology, 1:1,000), DHFR (Abcam,1:1,000), HK1 (Abcam, 1:1,000), H3K9me3 (Abcam, 1:1,000),H3K27me3 (Cell Signaling Technology, 1:1,000), Histone H3(Abcam, 1:5,000), a-Tubulin (Cell Signaling Technology,1:5,000), b-actin (Sigma, 1:5,000). Densitometry was performedusing ImageJ software as described previously (19).

IHCProcedures for IHC are described previously (20–22). Briefly,

paraffin was removed and tissue was subjected to high pressureantigen retrieval (Vector Unmasking Solution; Vector Laborato-ries). Sampleswere incubated in primary antibody (PHGDH,CellSignaling Technology) overnight at 4�C. Secondary antibodies,raised in donkey (Jackson ImmunoResearch), were used at adilution of 1:250 at room temperature for 1 hour. Vectastainreagents and diaminobenzidine (DAB; Vector Laboratories) wereused to develop IHC. Images were subsequently taken on a ZeissAxio Observer Z1 microscope. Tissue samples from humanpatients were obtained with patient consent and approval fromthe University of Utah Institutional Review Board.

PHGDH inhibition, 2-HG, and 5-mCThe PHGDH inhibitors NCT502 and NCT503 (Cayman

Chemical) and the inactive control molecule called PHGDH-inactive (Cayman Chemical) were reconstituted in DMSO. Finalconcentrations of inhibitors were 3.7 mmol/L, 18.5 mmol/L, and37 mmol/L for PHGDH-inactive and NCT502; and 2.5 mmol/L,12.5 mmol/L and 25 mmol/L for NCT503. These concentrationswere chosen to achieve maximal levels of inhibition at thehighest doses based on previously published IC50 data (23).

Global 5-methylcytosine levels were assessed using the Glob-al DNA Methylation Assay – LINE1 (Active Motif). D-2-hydro-xyglutarate was quantified using a colorimetric D2HG assay kit(BioVision). All procedures were carried out as indicated by themanufacturer.

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Mol Cancer Res; 15(11) November 2017 Molecular Cancer Research1518




Seahorse experimentsCells were seeded into 96-well format Seahorse analyzer plates

24 hours prior to performing Seahorse experiments. We per-formed side-by-side Mito Stress Tests and a Glycolysis Stress Testsin 96-well format using the XF96e analyzer. The Mito Stress Testwas performed in standard assay media (DMEM, 25 mmol/LGlucose, 1 mmol/L pyruvate, 2 mmol/L Glutamine, pH 7.4)with the final concentrations of drugs as follows [Oligomycin:2 mmol/L; carbonyl cyanide-4 (trifluoromethoxy) phenylhydra-zone (FCCP): 2 mmol/L; Rotenone: 0.5 mmol/L; Antimycin A: 0.5mmol/L]. Assay protocol was standard (3 measurements perphase, acute injection followed by 3-minute mixing, 0-minutewaiting, and 3-minute measuring). Data were normalized to cellnumber because morphologic changes that result from EF silenc-ing potentially confound normalization to total cellular protein(Supplementary Fig. S1D; ref. 24). The Glycolysis Stress test wasperformed in standard assay media (DMEM, 2 mmol/L gluta-mine, pH 7.4) and the following final concentrations (glucose: 10mmol/L; oligomycin: 2 mmol/L; 2-deoxy-glucose: 50 mmol/L).Results were normalized as above and analyzed inWAVE softwareand processed through the XF Mito Stress Test Report and Gly-colysis Stress TestGenerators. All statistics were generated throughGraphPad Prism 7 using unpaired two-tailed Student t tests.

Gene ontology, gene set enrichment analysis, and survivalexpression analyses

Gene ontology analysis was performed on lists of differentiallyexpressed genes from RNA-seq data (Lg2Rto: 0.585; Padj � 10).Gene lists and data are available in Supplementary Data. Meth-odology of the PANTHER gene ontology platform and gene setenrichment analysis (GSEA) are described elsewhere (25, 26).Ewing sarcoma patient survival data were compared with geneexpression by analyzing a publicly available dataset (GSE17679)with the SurvExpress online tool (27, 28). Risk groups weredefined by prognostic index, with low-risk being defined as lessthan the median prognostic index, and high-risk being defined asgreater than the median prognostic index.

RNA-Seq datasetRNA-Seq of A673 cells treated with shLUC or shEF was previ-

ously performed (16). The datasets we acquired are publiclyavailable in the NCBI BioProject database, accession #PRJNA176544, and the Sequence Read Archive (SRA059329).We reanalyzed these data to determine differential expressionanalysis using USeq (useq.sourceforge.net) version 8.8.3. Lists ofdifferentially expressed genes are provided in SupplementaryData.

MetabolomicsAll GC-MS analysis was performed with a Waters GCT Premier

mass spectrometer fittedwith an Agilent 6890 gas chromatographand a Gerstel MPS2 autosampler. Dried samples were suspendedin 40 mL of a 40mg/mLO-methoxylamine hydrochloride (MOX)in pyridine and incubated for one hour at 30�C. To autosamplervials was added 25 mL of this solution. Forty microliters of N-methyl-N-trimethylsilyltrifluoracetamide (MSTFA) was addedautomatically via the autosampler and incubated for 60 minutesat 37�C with shaking. After incubation 3 mL of a fatty acid methylester standard (FAMES) solution was added via the autosamplerthen 1 mL of the prepared sample was injected to the gas chro-matograph inlet in the split mode with the inlet temperature held

at 250�C. A 10:1 split ratio was used for analysis. The gaschromatograph had an initial temperature of 95�C for oneminutefollowed by a 40�C/minute ramp to 110�C and a hold time of 2minutes. This was followed by a second 5�C/minute ramp to250�C, a third ramp to 350�C, then a final hold time of 3minutes.A 30-m Phenomex ZB5-5 MSi column with a 5-m long guardcolumn was employed for chromatographic separation. Heliumwas used as the carrier gas at 1 mL/minute. Because of the highamounts of several metabolites, the samples were analyzed oncemore at a 10-fold dilution. Data were collected using MassLynx4.1 software (Waters). Metabolites were identified and their peakareawas recorded usingQuanLynx. This datawas transferred to anExcel spread sheet (Microsoft). Metabolite identity was estab-lished using a combination of an in-house metabolite librarydeveloped using pure purchased standards and the commerciallyavailable NIST library. Metabolomic data were further analyzedusing MetaboAnalyst 3.0 (29).

ResultsEWS/FLImisregulates numerous genes involved inmetabolism

Transcription profiling of Ewing sarcoma cells has been previ-ously performed by microarray and RNA-seq experiments aftersilencing EWS/FLI (EF) in A673 cells (14, 16, 30). We analyzeddata from a publicly available RNA-seq dataset wherein EFwas silenced by shRNA (shEF) and compared with control knock-down (shLUC). Our analysis identified 7,094 genes that aredifferentially expressed in shEF versus control shLUC cells(gene lists found as Excel files in Supplementary Data). Wedefined significant changes in expression as a fold change of�1.5-fold and a P < 0.05. After EF knockdown, 4,140 genesexhibited increased expression, while 2,954 genes were lessexpressed. Gene ontology (GO) analysis was performed on thelist of all EF-misregulated genes, revealing metabolic processes(GO: 0008152) as the second most enriched GO term (2,058genes; 33.7%; Fig. 1A). These genes were further subdivided intoprimary metabolic processes (GO: 0044238), revealing that themajority of these genes were involved in nucleobase-containingcompoundmetabolism (GO: 0006139) and protein metabolism(GO: 0019538; Fig. 1B).

To further assess whether EF significantly alters genes of met-abolic importance, gene set enrichment analysis (GSEA) wasperformed comparing our dataset of differentially expressed genesin shEF cells to gene sets from the Molecular Signatures Database(MSigDB; ref. 25). Significant correlations were observed betweendifferentially expressed genes and genes in the Hallmark-Glycol-ysis gene set and the KEGG-Pathways in Cancer gene set. (Fig. 1Cand D). Significant correlations were also observed with gene setsof Hallmark-Reactive Oxygen Species, Hallmark-Epithelial-Mes-enchymal Transition, but not in the Hallmark-Oxidative Phos-phorylation gene set (Supplementary Fig. S1A).

Knockdown of EWS/FLI causes changes incellular metabolism

To more directly study whether EF alters cellular metabolism,we performed stress tests on glycolysis and mitochondrial func-tion using the Seahorse XF96 analysis system. During the glycol-ysis stress test, addition of glucose and oligomycin caused greaterincreases of extracellular acidification rate (ECAR) in shEF versusshLUC cells (Fig. 2A). Oxygen consumption rate was not differentbetween shEF and shLUC cells throughout the glycolysis stress test(Fig. 2B). These data suggest that silencing of EF results in

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increased utilization of glucose by glycolysis (Table 1; Supple-mentary Fig. S1B). Basal ECARwasnot different between shEF andshLUC cells at the beginning of the glycolysis stress test, reflectingthe lower glucose (10 mmol/L) conditions required for a glycol-

ysis stress test (Fig. 2B). However, in the mitochondrial stress testglucose is present at typical cell culture levels (25 mmol/L),allowing us to observe that basal ECAR is higher in shEFversus shLUC cells (Fig. 2C). The apparent increase of glycolytic

A BPANTHER GO-Slim Biological Process Level 1: metabolic process (GO:0008152)

Level 2: primary metabolic process (GO:0044238)

Total # of genes: 1,581

Total # of process hits: 1,787

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1. Nucleobase-containing compound metabolic process (GO: 0006139)

2. Protein metabolic process (GO: 0019538)

Cellular process (GO:0009987)Metabolic process (GO:0008152)Developmental process (GO:0032502)Response to stimulus (GO:0050896)Localization (GO:0051179)Rhythmic process (GO:0048511 )Biological regulation (GO:0065007)Multicellular organismal process (GO:0032501)Cellular component organization or biogenesis (GO:0071840)Immune system process (GO:0002376)Biological adhesion (GO:0022610)Reproduction (GO:0000003)Locomotion (GO:0040011)Cell killing (GO:0001906)Growth (GO:0040007)

3. Lipid metabolic process (GO: 0006629)

4. Carbohydrate metabolic process (GO: 0005975)

5. Cellular amino acid metabolic process (GO: 0006520)

6. Tricarboxylic acid cycle (GO: 0006099)

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Figure 1.

EWS/FLI exerts significant effects on expression of genes important for metabolism. A, Gene ontology analysis of 5,698 EWS/FLI-regulated genes from publiclyavailable RNA-seq data (27, 37) using the PANTHER GO-Slim Biological Process annotation dataset. Threshold of significance for differential expressionwas log2(ratio) � 0.585 and Padj � 10. Of these, 1,920 (33.7%) were annotated as metabolic processes (GO: 0008152). B, Among these metabolic processes (GO:0008152), genes annotated as primary metabolic processes (GO: 0044238) were mostly involved in nucleobase-containing compound metabolism (GO: 0006139)and protein metabolism (GO: 0019538). C and D, Gene set enrichment analysis comparing a rank-ordered dataset (ranked by AdjP) of EWS/FLI knockdownRNA-seq data to the gene sets Hallmark Glycolysis and KEGG Pathways In Cancer, available from the Molecular Signatures Database (MSigDB) version 5.2.Differentially expressed genes (blue shaded region) in shEF versus shLUC correlate significantlywith genes important for glycolysis and pathways commonly alteredin cancer. NES, normalized enrichment score.

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Silencing EWS/FLI results in functional metabolic changes. A and B, Glycolysis stress tests demonstrate an increased extracellular acidification rate (ECAR) inresponse to a glucose bolus in shEF versus shLUC A673 cells. No difference was seen in oxygen consumption rate in the glycolysis stress test. C and D, BasalECAR is higher in shEF versus shLUC cells, a reflection of different basal glucose concentrations in different test media. Mito stress tests reveal increased oxygenconsumption rate (OCR) after treatment with the FCCP mitochondrial uncoupler, indicating increased maximal respiration in shEF versus shLUC A673 cells.Basal respirationwas similar between groups. E,Hexokinase 1 (HK1) was increased in A673 and UTES-14-01872 cells after EF knockdown. F,Uptake of the fluorescentglucose molecule 2-NBDG was increased in shEF versus shLUC cells, as measured by flow cytometry. G, Mitotracker red signal was elevated in shEF versus shLUCcells, suggesting increasedmitochondrialmembranepotential. A representative image fromA673 cells is shown, andquantificationwas doneon3 cells fromeachof 5high-power field images, shown as the corrected total cell fluorescence (CTCF) ratio of mitotracker red/mitotracker green (mean � SD).






rate caused by EF knockdown is surprising, and is possiblyexplained by derepression of hexokinase 1 (HK1) by shEF(Fig. 2E). Indeed, uptake of 2-deoxy-2-[(7-nitro-2,1,3-benzoxa-diazol-4-yl)amino]-D-glucose (2-NBDG; a fluorescently labeledanalogue of deoxyglucose) is elevated in shEF versus shLUCA673cells (Fig. 2F).

Tests of mitochondrial respiration showed enhanced responseto mitochondrial uncoupling by carbonyl cyanide-4 (trifluoro-methoxy) phenylhydrazone (FCCP) in shEF versus shLUC cells(Fig. 2D). These data indicate that silencing EF results in increasedmaximal respiration and spare respiratory capacity, with nochange in basal respiration, ATP production, proton leak, non-mitochondrial respiration, and coupling efficiency (Table 1; Sup-plementary Fig. S1B). To additionally assess mitochondrial respi-ratory function, cells were treated with the membrane potential-sensitive Mitotracker Red FM dye. Comparisons were made rel-ative to a membrane potential-independent mitochondrial dye(Mitotracker Green). Consistent with increased maximal respira-tion and respiratory capacity, shEF cells also had elevated mito-chondrial membrane potential compared with shLUC cells (Fig.2G). Together, these data suggest that EF increases the proportionof glucose-derived carbon that is shunted away from oxidativemetabolism consistent with a biosynthetic, pro-oncogenic met-abolic program.

EWS/FLI is required for global changes in metaboliteabundance

Global changes in metabolite abundance were assessed byperforming steady-state metabolomics analyses comparing shEFwith shLUCA673 cells. Examiningmetabolomic data byprincipalcomponents analysis revealed that EF knockdown results in adistinctmetabolic profile comparedwith control cells (Fig. 3A andB). Topologic analysis of metabolites within metabolic pathwayshighlighted several metabolic pathways that were more signifi-cantly impacted by silencing of EF (Fig. 3C; ref. 31). Pathwayssignificantly affected by EF silencing included alanine, aspartateand glutamine metabolism, and glycine, serine, and threoninemetabolism.

In addition, metabolomic profiling data were combined withtranscription profiling data to perform analyses of pathways inwhich both metabolite abundance and gene expression weresignificantly altered in shEF versus shLUC cells. This allowed usto determine which pathways were impacted the most by shEF,potentially by altering expression of enzymes and metabolites

in that pathway. Intriguingly, the enzymes of serine and glycinesynthesis were found to be significantly downregulated by EFsilencing in the RNA-seq dataset, and corresponding decreasesin serine and glycine were observed in our metabolomic dataset(Fig. 3C; Supplementary Fig. S2A).

EWS/FLI upregulates expression of serine and glycinesynthesis enzymes

The de novo serine synthesis pathway is one way by whichglycolytic intermediates can be shunted toward biosynthetic pro-cesses, and this has been implicated in many cancers (32, 33).Specifically, phosphoglycerate dehydrogenase (PHGDH) com-mits the glycolytic intermediate 3-phosphoglycerate to serine andglycine synthesis, providing important precursors for protein,lipid, and nucleic acid synthesis. Increased PHGDH has beenlinked to the oncogenic biology of other tumors (32, 34). In fact,PHGDH expression is higher in Ewing sarcoma than all othercancer cell lines in the Cancer Cell Line Encyclopedia (Supple-mentary Fig. S3A). In concordance with RNA-seq data, proteinlevels of PHGDH were decreased in shEF versus shLUC cells(Fig. 4A). Protein levels of phosphoserine aminotransferase(PSAT) and phosphoserine phosphatase (PSPH), the twoenzymes downstreamof PHGDH in the serine synthesis pathway,were also lower upon silencing of EF (Fig. 4A). Similar patterns ofexpression were observed for serine hyrdroxymethyltransferase(SHMT1 and SHMT2), which converts serine to glycine and feedsinto 1-carbon folate metabolism cycles, as well as dihydrofolatereductase (DHFR) and thymidylate synthetase (TYMS)—impor-tant enzymes in 1-carbon folate metabolism cycles (Fig. 4B;Supplementary Fig. S2D and S2E).

PHGDH is elevated in high-risk Ewing sarcoma patientsTo explore correlations between PHGDH expression and clin-

ical outcome, we used the SurvExpress tool to perform Coxsurvival analysis of a Ewing sarcoma dataset (27, 28). Patientswho were categorized as high-risk had significantly higher expres-sion of PHGDH and SHMT1/2 (Fig. 4C and D). Similarly, higherexpression of PSPH, DHFR, TYMS, MTHFD2, and SHMT2 wascorrelated with poorer survival in Ewing sarcoma patients (Sup-plementary Fig. S3B and S3C). Similar correlations were alsoobserved between expression levels and event-free survival (datanot shown).

Furthermore, PHGDH expression was assessed in histologicsamples from Ewing sarcoma patients. PHGDH staining was

Table 1. Glycolysis and mitochondrial stress tests

shLuc shEF P-value

Mito stress test: OCR (pmol/min/4 � 104 cells)Maximal respiration 154.87 � 13.60 243.46 � 26.39 0.0046Spare respiratory capacity 53.93 � 7.64 140.05 � 13.19 <0.0001Spare respiratory capacity (%) 153.75 � 8.28 237.99 � 20.39 0.0004Basal respiration 100.95 � 9.54 103.41 � 17.0 0.9002Proton leak 17.58 � 3.42 14.47 � 4.07 0.5615Non-mitochondrial respiration 28.59 � 3.86 34.41 � 4.20 0.3132ATP Production 83.37 � 8.53 88.94 � 13.93 0.7347Coupling efficiency (%) 82.58 � 3.03 86.15 � 2.51 0.3692

Glycolysis stress test: ECAR (mpH/min/4 � 104 cells)Glycolysis 43.64 � 4.71 74.95 � 5.22 <0.0001Non-glycolytic acidification 17.57 � 2.72 18.15 � 2.83 0.8832Glycolytic capacity 88.36 � 8.59 110.00 � 10.30 0.1138Glycolytic reserve 44.72 � 5.31 35.05 � 6.45 0.2533

NOTE: Data are mean � SEM.Abbreviations: ECAR, extracellular acidification rate; OCR, oxygen consumption rate.

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much higher in tumor (defined by IHC for CD99) than inadjacent normal tissue (Fig. 5A). CD99 exhibited staining alongcell surface boundaries, while PHGDH staining appeared dif-fusely cytoplasmic.

PHGDH is required for proliferation and normalchromatin modification

Inhibition of PHGDH function by NCT502 and NCT503caused growth defects and cell death in Ewing sarcoma cell lines,including the EWS/ERG-harboring TTC466 line (Fig. 5B–E). This

effect was less prominent in 293T or U2OS cells (Fig. 5F and G).This and previous data indicate that upregulated expression ofserine synthesis and 1-carbon metabolism contributes impor-tantly to the oncogenic phenotype in Ewing sarcoma (Fig. 5H).

While the major product of PHGDH is 3-phosphohydrox-ypyruvate (3-PP) from 3-phosphoglycerate (3-PG), the enzymealso produces relatively small amounts of 2-hydroxyglutarate(2-HG) from a-ketoglutarate (Fig. 6A; ref. 35). 2-HG has beenshown to be oncogenic in high amounts, typically resultingfrom neomorphic mutations of isocitrate dehydrogenase

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Figure 3.

The global profile of cellular metabolite abundance is altered by EWS/FLI. A, Principal components analysis of metabolite abundance data (assessed by GC-MS)demonstrates distinct metabolic profiles in shEF versus shLUC A673 cells. B, Hierarchical clustering of differential metabolite abundance after EF knockdownwas done by Euclidean distances and aWard statistic. Metabolite abundances cluster into two distinct groups corresponding to shEF and shLUC conditions. Serine,glycine, and 2-hydroxyglutarate are indicated by green arrowheads. C, Topological analysis of metabolite data was performed by MetaboAnalyst 3.0.Circle color represents P value and node size represents pathway impact value. Pathway impact is calculated as the cumulative percentage of centrality measures ofmetabolites notes within pathways.






enzymes (36). Interestingly, we observed that 2-HG was lowerin shEF versus shLUC cells (Fig. 6B), suggesting that EF mightbe driving elevated production of 2-HG via increased expres-sion of PHGDH.

To test whether elevated levels of 2-HG in Ewing sarcoma cellswas due to PHGDH, we assessed 2-HG levels after inhibition ofPHGDH function with NCT502. 2-HG was lower in cells treatedbyNCT502versus its inactive analogue (Fig. 6C). Importantly, thelevels of 2-HG in Ewing sarcoma cells was lower than previouslyobserved in cells harboringmutations in isocitrate dehydrogenase(IDH) enzymes (37). To test whether 2-HG generated fromPHGDH had effects on DNA methylation, we silenced PHGDHexpression and determined 5-methylcytosine (5-mC) content of

LINE-1 elements (a surrogate for bulk DNA methylation). How-ever, knockdown of EF or PHGDH had no effect on 5-mC levels(Fig. 6D). PSPH was also silenced to distinguish between serinesynthesis and 2-HG–producing functions of PHGDH, and dis-ruption of serine synthesis downstream of PHGDH also had noeffect on 5-mC levels.

To further assess whether modulation of PHGDH affects chro-matin, we immunoblotted histone proteins. This was performedwith A673 cells, as well as another more recently establishedpatient-derived EF-harboring Ewing sarcoma cell line (UTES-14-01872; Supplementary Fig. S2C). Interestingly, methylation ofhistone H3 at lysine (K) 9 and K27 was increased by PHGDHknockdown inUTES-14-01872 and TTC466 cells, but not in A673

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Figure 4.

Key enzymes of serine and glycine synthesis, and 1-carbon metabolism, are altered by silencing EWS/FLI. A, Western blots of enzymes of serine synthesisin A673 and UTES-14-01872 cells treated by shEF and shLUC. PHGDH, PSAT, and PSPH are lower in shEF versus shLUC. B, SHMT1, DHFR, and TYMSare also decreased in A673 and UTES-14-01872 cells after EF knockdown. C and D, SurvExpress analysis of overall survival of patients correlatedwith expression of PHGDH, SHMT1, and SHMT2, demonstrating higher expression correlates with poorer overall survival in Ewing sarcoma patients.CI, confidence interval.

Tanner et al.





cells (Fig. 6E and F). In addition, total histone protein wasincreased by PHGDH knockdown in two of the three cell lines.These data raise the possibility of a role for PHGDH in chromatinregulation, possibly by affecting abundance of metabolites usedas cofactors for histone-modifying enzymes.

DiscussionEF has long been recognized as the primary oncogenic driver of

Ewing sarcoma. Overexpression of EF in NIH3T3 murine fibro-blasts results in oncogenic transformation, and silencing EF

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Figure 5.

PHGDH is high in Ewing sarcomatumors, and is important for cellviability. A, IHC of CD99 and PHGDHin Ewing sarcoma tumors from aEwing sarcoma patient. Cell surfacestaining of CD99 is diagnostic forEwing sarcoma. Cytosolic staining ofPHGDH closely correlates withregions of tumor, indicating elevatedPHGDH levels in tumor versusadjacent tissue. B and C, PHGDHinhibition in A673 cells by NCT502and NCT503 causes decreased cellgrowth and eventual cell death.D and E, PHGDH inhibition alsoimpaired cell growth of TTC466 cells.F and G, High-dose treatment ofosteosarcoma cells (U2OS) and293T cells show some growth defect,but not as much as in Ewing sarcomacell lines. H, Diagrammaticrepresentation of serine/glycinesynthesis and 1-carbon metabolismpathways that are decreased by EFknockdown. Enzymes are green textand metabolites are black text. Bluearrows indicate enzymes ormetabolites that were observed asdecreased in shEF versus shLUCcells.Nucleotide and folate analogueswere not measured. Cofactors andsome intermediates are omitted forclarity.






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Figure 6.

Modulation PHGDH causes decreased 2-hydroxyglutarate, and altered expression and methylation of histones. A, Diagrammatic representation of PHGDH enzymefunction. PHGDH primarily catalyzes conversion of 3-phospho-D-glycerate (3-PG) to 3-phosphoonoxypyruvate (3-PP), but also produces a minor product viaconversion a-ketoglutarate (a-KG) to 2-hydroxyglutarate (2-HG). KEGG reaction numbers are indicated. B and C, 2-hydroxyglutarate abundance as measured byGC-MS is lower in shEF versus shLUC A673 cells. Inhibition of PHGDH by NCT-502 also decreases levels of 2-HG in A673 cells. D, LINE-1 methylation, asurrogate of bulk DNA methylation, is not different in shEF, shPHGDH, or shPSPH versus shLUC cells. E and F, In UTES-14-01872 and TTC466 cells, knockdown ofPHGDH results in increased levels of histone H3 and increasedmethylation of H3 histones at lysine 9 and 27. Thiswas not seen in A673 cells. A673 and UTES-14-01872cell lines express EWS/FLI; TTC466 cells express the EWS/ERG fusion protein. For H3K9me3 and H3K27me3, both normalization to actin and normalizationto total H3 are shown (indicated as "to actin/to total H3").

Tanner et al.





expression in patient-derived cell lines results in a loss of tumor-igenesis (5, 38, 39). However, effective therapeutic strategiesdirectly targeting transcription factors have been notoriouslyelusive. For this reason, studies of the biology of Ewing sarcomahave primarily focused on identifying important EF target genes,as well as the interacting partners EF requires to elicit its effects.Indeed, numerous EF targets have been shown tobenecessary, butnot sufficient, for oncogenesis in these tumors (6, 14, 40, 41).Unfortunately, many of these critical EF targets and partners lackenzymatic function and are themselves not susceptible to phar-macologic modulation. With this in mind, we sought to deter-mine whether EF generates an oncogenic program of cellularmetabolism bymisregulating the transcription of genes encodingkey metabolic enzymes.

By analyzing existing EF transcriptional datasets, we were ableto identify significant patterns of altered metabolic gene expres-sion (Fig. 1). Specifically, gene ontology analysis revealed signif-icant alterations in the expression of genes involved in glycolysis,nucleobase-containing metabolic processes, and protein metab-olism. Consistent with a cancerous phenotype, Ewing sarcomacells are hyperproliferative and require increased macromoleculesynthesis to sustain accelerated growth and proliferation. Inaddition, EF-driven alterations in nucleobase-containing meta-bolic processes such as (NAD salvage) could be sensitive toinhibition and therapeutic exploitation (42). The increased met-abolic demands of rapid proliferation and growth could beexploited in similar ways.

We also observed that EF knockdown resulted in increasedmaximal respiration and mitochondrial membrane potential(Fig. 2D andG). This is in line with studies of other malignancies,wherein various oncogenic processes lead to ametabolic programof aerobic glycolysis, diverting metabolites toward pathways thatare important for various kinds of macromolecule synthesis (11).As more carbon is diverted to biosynthetic pathways from gly-colysis, flux through the TCA cycle may decrease, resulting inoncogene-induced reduction in respiratory capacity, as suggestedby our data. These findings reinforce the concept that such a shiftaway from oxidative metabolism is a characteristic feature ofoncogenesis, important in all cancers, and not a spectator phe-nomenon merely correlating with oncogenic processes. Further-more, they demonstrate that EF is a driver of oncogenicmetabolicreprogramming, in addition to and as a consequence of tran-scriptional reprogramming.

Surprisingly, we also observed an increase of glycolysis inshEF versus shLUC cells (Fig. 2A and C). Such observationsappear to run contrary to the known glycolytic metabolismthat predominates in the vast majority of cancer cells. Theseobservations could be explained by transcriptional downregu-lation of genes important for glucose import or utilization.Indeed, we observed that hexokinase 1 (HK1) is increased inshEF versus shLUC cells (Fig. 2E). In such a scenario, EF mayrestrict glucose entry into glycolysis, effectively reducing thecell's ability to metabolize additional glucose (as seen in ourSeahorse analysis data). However, if this restriction is not sosevere to cause cell death by starvation, glucose uptake mayremain sufficient to fulfill cellular energy requirements. Simul-taneously, EF upregulates genes of biosynthetic pathwayssuch as de novo serine synthesis (Fig. 4A), resulting in anincreased proportion of glycolytic intermediates being shuntedinto biosynthetic pathways. Thus, it may be possible to gener-ate the net effect of oncogenic and biosynthetic reprogramming

of metabolism by increasing the proportion of glucose-derivedcarbon shunted toward biosynthetic pathways, even in theface of EF-induced downregulation of HK1. In addition, con-sidering that the putative cell of origin for Ewing sarcoma is amesenchymal stem cell, and that EF is known to repressmesenchymal features, the baseline glycolytic metabolism ofthese stem cells may provide ample flexibility to be permissiveto decreased HK1 within the context of oncogenic metabolicreprogramming induced by EF (24, 43). Thus, any decrease inglycolysis caused by EF is not likely to be pro-oncogenic orproproliferative. Instead, we believe it is merely part of aconstellation of changes, not all being programmatically con-sistent, that together contribute to transformation. Rather thanincreasing glucose utilization to provide metabolites for bio-synthetic pathways, EF could increase the proportion of glyco-lytic intermediates being diverted to pathways such as de novoserine synthesis. These and other hypotheses are a subject ofcurrent and future study.

Our findings also suggest that EF drives global shifts inmetabolite abundance in Ewing sarcoma cells (Fig. 3). Inte-grating metabolomic and transcriptomic data enabled us toidentify EF-driven upregulation of de novo serine synthesis viaPHGDH as an important candidate for further study. PHGDHis amplified in various tumors, and has been implicated pre-viously in tumor biology (32, 35). PHGDH commits theglycolytic intermediate 3-PG to de novo serine synthesis, andEF-driven upregulation of PHGDH expression appears to be animportant factor in diverting metabolites toward macromole-cule synthesis (Fig. 4A). Importantly, elevated expression ofPHGDH also correlates with a poor prognosis in Ewing sarco-ma patients, suggesting that these phenomena have importantclinical implications (Fig. 4C). Our findings are similar toprevious findings demonstrating upregulation of serine syn-thesis by other oncogenes. PHGDH, PSPH, and PSAT areupregulated in some Myc-driven lymphomas (44). In addition,oncogene-driven transcriptional misregulation in other malig-nancies (e.g., HER2, SP1, NFY, NRF2, ATF4) has been shown toincrease expression of enzymes of de novo serine synthesis (45–47). EF thus appears to generate a metabolic program ofupregulated serine synthesis similar to other malignancies.Existing transcription profiling data suggest that SP1 is anupregulated target of EF, while NFY, NRF2, and ATF4 are notdifferentially expressed after EF knockdown; HER2 may beslightly downregulated by EF (data not shown). Further studyis required to determine whether EF exerts its effects by coopt-ing any of the aforementioned regulatory factors. Moreover,existing ChIP-seq data are inconsistent on whether EF bindsdirectly at the promoter of PHGDH (48, 49). However, the factthat EF binding does appear to occur in some datasets suggeststhat it could be directly regulating expression of PHGDH, andfurther assessment of EF-directed regulation of PHGDHdeserves more in-depth study.

Downstream of de novo serine synthesis, conversion of serineto glycine is carried out by serine hydroxymethyltransferase(SHMT). This reaction is coupled to the conversion of tetra-hydrofolate to 5,10-methylenetetrahydrofolate, and is a majorprovider of 1-carbon units needed for multiple cellular func-tions (50). Our data suggest that EF is also driving upregulationof these processes, further establishing a pro-oncogenic, bio-synthetic metabolic program in these cells (Fig. 5H). It is likelythat this is also clinically relevant, as patients with high levels of






SHMT also have worse overall survival (Fig. 4D). Together,these data also raise the interesting possibility of utilizingexpression levels of metabolic enzymes such as PHGDH andSHMT to inform predictions of patient prognosis, or even usingmetabolic indices to define different patient populations.

The nature of the relationship between patient survival andexpression levels of these genes deserves further consideration.Several hypotheses exist for why increased expression of theseenzymes correlate with poorer prognosis. For instance, upregula-tion of de novo serine synthesis may contribute importantly toincreased synthesis of glycine and cysteine. Such a process wouldcontribute more amino acids to meet the biosynthetic require-ments of proliferative cancer cells. In addition, as glycine andcysteine both contribute to production of glutathione, increasedserine synthesis also potentially contributes to greater redoxbuffering. Indeed, serine metabolism has been shown to beactivated under conditions of increased ROS production, perhapscontributing to increased capacity to confront redox stress (51).Furthermore, the conversion of serine to glycine contributesimportantly to one-carbon metabolism and the folate cycle. Thede novo synthesis of adenosine, guanosine, and thymidylaterequire one-carbon units derived from serine. Thus, increasedserine synthesis could contribute to greater nucleotide synthesis ingeneral. Upregulation of SHMT1/2 and TYMS is consistent withthese hypotheses. Upregulation of such processes could permitcancer cells to become more proliferative or aggressive, thuscontributing to poorer prognosis in patients. However, as thesehypotheses have yet to be tested, more in-depth investigation isclearly needed.

Upregulation of de novo serine synthesis and 1-carbon metab-olism has far-reaching consequences for cancer cells, as fluxthrough these pathways is important for regulation of methyldonor (methionine) recycling and epigenetic states. In addition,previous work has suggested that amplified PHGDH may play arole in cancer through production of 2-HG (52). 2-HG is a knownoncometabolite when overproduced by neomorphic mutationsin IDH enzymes (36). Intriguingly, our data also suggest thatimportant links exist between de novo serine synthesis (specificallyPHGDH) and nuclear processes. We observed decreased amountsof 2-HG in shEF versus shLUC cells (Fig. 6B and C). However, theamounts of 2-HG generated by EF-induced upregulation ofPHGDH are lower than levels seen in other malignancies withIDH mutations, and 2-HG likely has less of a role in Ewingsarcoma biology. In addition, IDH mutations are rare in Ewingsarcoma (53). Somework has shown that the small amounts of 2-HG produced by PHGDH may be sufficient to promote histonemethylation, contributing to an oncogenic hypermethylation ofhistones and, possibly, DNA (52). However, our results suggestthat this is not the case in Ewing sarcoma. Rather, knockdown ofPHGDH did not affect DNAmethylation (Fig. 6D), but did causesignificant increases in histone methylation (Fig. 6E and F).Interestingly, these histone alterations were observed in EF-expressing cells (UTES-14-01872) as well as in TTC466 cells,which harbor the alternate translocation EWS/ERG (found in�10% of Ewing sarcomas). While the mechanisms responsiblefor these chromatin changes are unclear, we hypothesize thatabundance of metabolites important for the function of otherenzymes (either as substrates or cofactors) might play a role.Future efforts are aimed at testing these hypotheses and furtherelucidating the mechanisms linking metabolism and nuclearcontrol.

Taken together, our findings demonstrate that EF drives met-abolic misregulation resulting in diversion of metabolites towardbiosynthetic pathways to meet the increased demands of onco-genesis. EF induces increased expression of PHGDH, PSAT, andPSPH, shunting glycolytic intermediates into serine and glycinesynthesis, and contributing to 1-carbon metabolism throughupregulated SHMT. While most research of Ewing sarcoma haspreviously focused on transcriptional regulation, epigeneticmod-ification and other nuclear processes, little attention has beenfocused on metabolic pathways. Indeed, metabolic systems thatare co-opted or altered by the presence of EWS/FLI might includetargetable enzymes that could bemodulated by new therapies. AnEWS/FLI–specific metabolic program could provide a targetablesurrogate for EWS/FLI itself, providing a way to specificallydestroy EWS/FLI-harboring cells through pharmacologic modu-lationofmetabolic or other enzymes. Such efforts aspire to informthe development of hitherto elusive molecular targeted therapiesfor Ewing sarcoma.

Disclosure of Potential Conflicts of InterestS.L. Lessnick is an acting chiefmedical officer at Salarius Pharmaceuticals and

has ownership interest (including patents) in Salarius Pharmaceuticals in apatent on NKX2.2 and patent on GSTM4. No potential conflicts of interest weredisclosed by the other authors.

Authors' ContributionsConception and design: J.M. Tanner, J. RutterDevelopment of methodology: J.M. Tanner, P. Wei, S.L. LessnickAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): J.M. Tanner, C. Bensard, P. Wei, N.M. Krah, J.C. Schell,J.D. GardinerAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): J.M. Tanner, C. Bensard, P. Wei, N.M. Krah,S.L. LessnickWriting, review, and/or revision of the manuscript: J.M. Tanner, C. Bensard,J.D. Gardiner, J.D. Schiffman, J. RutterAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): J.M. TannerStudy supervision: J. Rutter

AcknowledgmentsThe authors would like to thank Dr. Kevin Jones, Dr. Emily Theisen, Dr.

Ranajeet Saund, and Dr. Kathleen Pishas, and all members of the Lessnick andRutter labs for many useful and thought-provoking discussions throughout thecourse of these studies.

Grant SupportJ. Rutter was supported by HHMI and NIH grant GM 110755. J. Schiffman

was supported by St. Baldrick's Foundation, Sarcoma Alliance for Researchthrough Collaboration (SARC) Career Development Award, Damon RunyonClinical Investigator Award, and the Eunice Kennedy Shriver Children'sHealth Research Career Development Award NICHD 5K12HD001410.Metabolomics analysis was performed at the Metabolomics Core Facilityat the University of Utah which is supported by 1 S10 OD016232-01, 1 S10OD021505-01, and 1 U54 DK110858-01. The authors also acknowledge thesupport of the Huntsman Cancer Institute (P30CA042014) and the Hunts-man Cancer Foundation.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received April 6, 2017; revised June 6, 2017; accepted July 14, 2017;published OnlineFirst July 18, 2017.

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Tanner et al.




2017;15:1517-1530. Published OnlineFirst July 18, 2017.Mol Cancer Res Jason M. Tanner, Claire Bensard, Peng Wei, et al. Ewing SarcomaEWS/FLI is a Master Regulator of Metabolic Reprogramming in

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