systems biology of ewing sarcoma: a network model of ews-fli1 … 2013.pdf · 2016. 5. 16. ·...

Systems biology of Ewing sarcoma a networkmodel of EWS-FLI1 effect on proliferationand apoptosisGautier Stoll123 Didier Surdez14 Franck Tirode14 Karine Laud14

Emmanuel Barillot123 Andrei Zinovyev123 and Olivier Delattre145

1Institut Curie 26 rue drsquoUlm 75248 Paris cedex 05 France 2INSERM U900 Bioinformatique biostatistiqueet epidemiologie drsquoun systeme complexe Paris France 3Mines ParisTech Fontainebleau France 4INSERMU830 Unite de Genetique et Biologie des Cancers Paris France and 5Institut Curie Unite de genetiquesomatique Paris France

Received August 29 2012 Accepted July 10 2013

ABSTRACT

Ewing sarcoma is the second most frequent pediat-ric bone tumor In most of the patients a chromo-somal translocation leads to the expression of theEWS-FLI1 chimeric transcription factor that is themajor oncogene in this pathology Relative geneticsimplicity of Ewing sarcoma makes it particularly at-tractive for studying cancer in a systemic mannerSilencing EWS-FLI1 induces cell cycle alterationand ultimately leads to apoptosis but the exact mo-lecular mechanisms underlying this phenotype areunclear In this study a network linking EWS-FLI1to cell cycle and apoptosis phenotypes was con-structed through an original method of network re-construction Transcriptome time-series after EWS-FLI1 silencing were used to identify core modulatedgenes by an original scoring method based on fittingexpression profile dynamics curves Literature datamining was then used to connect these modulatedgenes into a network The validity of a subpart of thisnetwork was assessed by siRNART-QPCR experi-ments on four additional Ewing cell lines and con-firmed most of the links Based on the network andthe transcriptome data CUL1 was identified as a newpotential target of EWS-FLI1 Altogether using anoriginal methodology of data integration weprovide the first version of EWS-FLI1 networkmodel of cell cycle and apoptosis regulation

INTRODUCTION

Ewingrsquos sarcoma is the second most frequent pediatricbone tumor with a peak of incidence between 4 and 25

years of age In 85 of the patients a causal translocationbetween EWS and FLI1 genes is found This leads tothe expression of EWS-FLI1 chimeric transcriptionfactor (1) In most of the remaining patients alternativetranslocations between EWS and another ETS- familymember (ERG FEV ETV1 E1AF ) are detectedEwing sarcoma presents a remarkable characteristic itsoncogenesis is generally accepted to be initiated by asingle genetic event ie one of the above mentioned trans-locations Indeed EWS-FLI1 alone has been shown to beable to transform NIH3T3 fibroblasts (2) Furthermoreexpressing EWS-FLI1 in mouse mesenchymal stempro-genitor cell populations could recapitulate the diseasein vivo (34) Moreover knocking down EWS-FLI1 inEwing sarcoma cell lines slows down proliferation andinduces apoptosis in vitro (5) and in vivo (6) Finallyrescuing these two last phenotypes by re-expressing anyother gene than EWS-FLI1 could not be accomplished sofar Therefore Ewing sarcoma and EWS-FLI1 signalingcan be seen as a primarily model for understanding cancerinitiation and progression in a systemic manner EWS-FLI1 has been reported to regulate cell cycle andapoptosis at various levels For instance EWS-FLI1 canmodulate the cell cycle machinery by targeting directlyp21CDKN1A (7) Cyclin D (89) and Cyclin E (10) orindirectly through p57KIP2 (11) TGFbeta- (12) IGF-(1314) or MAPK signaling (15) The impact ofEWS-FLI1 on apoptosis can be explained for instanceby its direct effect on CASP3 (16) or indirectly throughregulating members of TNF- (17) IGF- (1314) andTGFbeta signaling (12)Nonetheless the global effect of EWS-FLI1 on cell

cycle progression and apoptosis is still poorly understoodIndeed classical approaches for elucidating the functionof a gene usually look at upstream regulators and

To whom correspondence should be addressed Tel +33 1 56 24 66 79 Fax +33 1 56 24 66 30 Email olivierdelattrecuriefr

The authors wish it to be known that in their opinion the first two authors should be regarded as joint First Authors

Nucleic Acids Research 2013 1ndash19doi101093nargkt678

The Author(s) 2013 Published by Oxford University PressThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (httpcreativecommonsorglicensesby30) whichpermits unrestricted reuse distribution and reproduction in any medium provided the original work is properly cited

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down-stream targets within a pathway missing possibleinterplays with other pathways Recent reports havestarted to address these issues by meta-analysis ofgenome-scale data to identify lists of the genes that arederegulated by EWS-FLI1 in Ewingrsquos sarcoma models(18) or linked to cell cycle regulation proliferationresponse to DNA damage and cell differentiation (19)The above mentioned publications favor the point of

view that EWS-FLI1 has a pleiotropic effect and shouldbe considered in the context of a global gene regulationnetwork This justifies the usage of a systems biologyapproach (20) ultimately such an approach produces anabstract model including deregulated genes and describinghow these genes interact with each other (21) The signal-ing network regulated by EWS-FLI1 is specific to thisdisease and can be considered as the basis for its theoret-ical description This description is possible because Ewingsarcoma is more genetically homogenous than othercancers where the choice of deregulated pathways ismore difficultA valuable source of data for systems biology

approaches is time-resolved response of perturbed experi-mental systems These data allow constructing mathemat-ical models describing time evolution of molecularnetworks and predicting their response to various perturb-ations (22) Time-series of transcriptome response tosilencingre-expressing of EWS-FLI1 were published in(23) However these experiments did not allow to followthe transcriptome response for a time period longer than afew days whereas significant transcriptome changes afterEWS-FLI1 inhibition can be observed even after 1 weekHere we took advantage of cell lines transformed with atetracycline inducible shRNA system targeting EWS-FLI1transcript (24) and collected long-term [inhibitory(17 days) and inhibitory (10 days)re-expression(7 days)]transcriptional time seriesThis article presents a network model dedicated to

Ewing sarcoma it describes EWS-FLI1 effect on prolifer-ation and apoptosis We decided to represent it through alsquogene influence networkrsquo as it is the only suitable repre-sentation for including incompletely characterized mo-lecular interactions This model was constructed in threesteps (i) Time-series data obtained in EWS-FLI1modulated cell lines were analyzed An original theoreticalmethod was developed for selecting genes modulated byEWS-FLI1 and involved in cell-cycle regulation and apop-tosis (ii) An influence network was reconstructed from theliterature connecting the above selected genes (iii)Experimental validation of a part of the regulationnetwork was performed in five Ewing cell lines Inaddition some additional transcriptional influences wereidentified by network reverse engineering using genesilencing data These influences were compared with theliterature-based network and confirmed its validity Thiscomparison also allowed to highlight EWS-FLI1 implica-tion in the regulation of the ubiquitin proteasome system(through CUL1 SKP2 ) and to identify CUL1 as anovel direct target of EWS-FLI1The detailed description of the signaling involved in

Ewing sarcoma oncogenesis should provide backgroundfor further theoretical search of combinatorial therapeutic

strategies by predictive mathematical modeling as it isdone in other cancer studies (25)

MATERIALS AND METHODS

Transcriptome time series of shRNA-inducible Ewingcell lines

Tetracycline-inducible shRNA (directed againstEWS-FLI1) clones shA673-1C and -2C (24) were used toperform a long-term inhibitory (t=0ndash17 days) and inhibi-tory (t=0ndash10 days)rescue (t=10ndash17 days) time seriesexperiments EWS-FLI1 invalidation was achieved byadding 1 mgml of doxycycline in the cell culture mediaCells were split twice a week For the inhibitory timeseries RNAs were collected at day 0 1 2 3 6 9 1113 15 17 after addition of doxycycline to the mediaFor the rescue time series doxycycline was omitted fromthe media after 10 days and RNAs were collected at day13 15 and 17 Total RNAs were isolated using the TrizolReagent (Invitrogen) at the different time points EWS-FLI1 silencing and re-expression was validated by real-time quantitative reverse transcription-PCR as previouslydescribed by Tirode et al (24) Gene expression profiles ofthe time series experiments were assessed by microarrayprofiling using Affymetrix HG-U133plus2 arrays(Affymetrix Inc Santa Clara CA) Experimental proced-ures for cRNA target synthesis and GeneChip microarraywere done according to the standard protocols describedby Affymetrix Company

Fitting non-linear response models to the time series

Points of time series were fitted by two types of curves

(i) Hyperbolic tangent

sw xeth THORN A+Btanhethax+bTHORN (a lsquoswitchrsquo with four parameters)(ii) Generalized Gaussian

puethxTHORN A+Bexp xteth THORN2a

b

(a lsquopulsersquo with five parameters)

For the temporal response of each probeset in eachclone the hyperbolic tangent was fitted in the case ofsimple inhibition of EWS-FLI1 and the generalizedGaussian in the case of inhibitionre-expression ofEWS-FLI1 The score for each fit is the ratio betweenan amplitude and a mean-squared error multiplied bya transition time penalization factor t

sc frac14

The mean-squared error is the square root of the sumof squared differences between the curve and data pointsThe amplitude is the difference between the high and lowexpression levels These levels are defined as follows

(i) For the hyperbolic tangent (lsquoswitchrsquo) the inflexionpoint of the curve define naturally a transition timeseparating the time points in a high level and a lowlevel window The two levels are simply the averagesof data points on the two windows defined above

(ii) For the generalized Gaussian (lsquopulsersquo) the two in-flection points of the curve define three time

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windows We merge the first and the last one andobtain two windows Levels are computed byaveraging as in (i)

The transition time penalization factor t is given by thefollowing formulas

(i) For the hyperbolic tangent (lsquoswitchrsquo)

frac14 exp f l=2

l

2

where f is the position of the inflection point and l thelength of the time window

(ii) For the generalized Gaussian (lsquopulsersquo)

frac14 exp f1 l=3

l

2

f2 2l=3

l

2

where f1 f2 are the two inflection points and l is thelength of the time window

If one inflection point is outside the experimental timewindow it is artificially shifted inside in order to bebetween the first and the second time points or the lastand one before last time points If there are no time pointsbetween the two inflexion points of the generalizedGaussian the inflection points are artificially shiftedaway to the closest time points If the extremum of thegeneralized Gaussian (parameter t) is outside the experi-mental time window the score is simply set to 0 SeeFigure 3C for illustrations of these fitness scores

As a result of this quantification procedure theresponse of every gene (probeset) on the Affymetrix chipcan be characterized by few parameters having clear inter-pretation switching time switching speed re-expressiontime re-expression speed and the scores for switch-likeand pulse-like model curves (Supplementary Table S1and Figure 3C for examples) All these parameters canbe used for functional characterization of a group ofgenes The curve fitting was performed in MATLABusing MATLAB Curve Fitting toolbox

Protocol for selecting genes for network reconstruction

The selection of genes and pathways were based on threesteps

(i) Selecting genes according to the fitness score intranscriptome time series experiments we selected3416 genes that have fitness score higher than agiven threshold in both inhibition and inhibitionre-expression experiments and in at least one clonefor at least one probeset (3033 probesets only inclone shA673-1C 1003 only in clone shA673-2C867 probesets in both clones 4903 probesets intotal) The thresholds were 10 lower than theminimum score value of a sample of probesetsselected by visual inspection of their time series(histograms of scores and thresholds are given inSupplementary Figure S2)

(ii) Reducing the list produced in (i) using GO (26) andBROADMSigDB (27) annotations we reduce thelist to the genes having associated GO terms lsquocellcyclersquo and lsquoapoptosisrsquo We also consider the genesselected in (i) that belong to the following BROADterms lsquocell cycle arrestrsquo lsquocell cycle checkpointrsquo lsquocellcycle pathwayrsquo lsquoapoptosisrsquo (see SupplementaryTable S1) A list of 407 genes was obtained usingthis filtering approach (a heat map of these geneexpressions in provided in Supplementary FigureS7) These genes are clearly separated in twogroups those activated on DOX treatment thoseinhibited on DOX treatment

(iii) Consider only genespathways whose effect can beassembled in an influence network among the list ofgenes of (ii) we consider only a subpart whoseeffects on proliferation or apoptosis has beenstudied enough in order to be assembled in a con-nected network (37 genes)

In parallel we selected only those gene sets that havebeen shown to be significantly enriched in GSEA analysis(with nominal P-valuelt 1) Furthermore we consideronly those pathways that have been shown to be involvedin controlling directly cell proliferation and apoptosisThese selected pathways are highlighted in red inSupplementary Tables S2ndashS5 Final results of both selec-tions methods are summarized in Table 1

Network curation framework construction of thefact sheet

This step consists in the construction of a textual descrip-tion (lsquointeraction fact-sheetrsquo) of pseudo-reactionsdescribing the influences between biological lsquoentitiesrsquogenes proteins proteins families modified proteins (egby phosphorylation) or complexes An extract of the fact-sheet is given in Table 2 The whole fact sheet is availablein Supplementary Tables S7 and S8

Network curation framework implementing the fact sheetin Cytoscape

To construct the influence network enriched with thegenes responsive to EWS-FLI1 inhibitionre-expressionfrom the fact sheet we developed a software integratedinto the BiNoM Cytoscape plugin (28) BiNoM is capableof processing the fact sheet described above in a self-con-sistent way providing an interface to the user who decideson what level of abstraction to represent the entities (in theform of a family or an individual family members) At thesecond step of the pre-processing the implicit reactionsneeded for consistent representation are added to thenetwork also under the user control The actual factssheet used for the Ewingrsquos cancer network together withpre-processing protocol is provided in the web page ofSupplementary Material (lsquoProcessing the fact sheetrsquo)This web page includes the final network provided as aCytoscape session file and a BioPAX file with all annota-tions from the fact sheet

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siRNA RT-QPCR Western Blots and ChIP procedures

Experimental procedures and references for siRNART-QPCR ChIP and Western blots as well as primersand antibodies used for these experiments are detailed inSupplementary Table S9

Network reverse engineering from siRNA silencing data

In the first step influences are inferred from siRNART-QPCR experiments For that a linear mixed modelhas been implemented in R (lme package) to determinelinear dependence between presence of siRNA (twodiscrete levels) and gene expression considering thefluctuations due to the difference between the clones andRT-QPCR measurement noise All siRNAs significantlysilenced their targets (P-value smaller than 15 107)Therefore this P-value was chosen as a threshold for iden-tifying influences All connections extracted from theliterature (Figure 6A) were confirmed by this methodIn the second step the inferred influences were

separated into necessary and non-necessary connectionsusing the sub-network from Figure 6B In that contextnon-necessary connections are links that can be explainedby a signed path in the sub-network containing at leastone intermediate node Any other connection is said to benecessaryIn the third step we applied again the concept of neces-

sary connections using the whole influence networkshown in Figure 4A as network model (see the definitionof necessary connection in supplementary Figure S3)Using this network we checked the solid arrows inFigure 6B for their necessity (the results are listed in

Table 4) Only one influence EP300 -j E2F2 remainednecessary after this test This is not surprising given thefact that the network from Figure 4A is larger than areconstructed subnetwork from Figure 6B hence itcontains more paths that can indirectly explain theinferred influences

RESULTS

The starting point of this study was the statement thatEWS-FLI1 is the central and driving force of tumorigen-esis in Ewing sarcoma To better understand long-termdownstream effects of EWS-FLI1 shA673-1C andshA673-2C tetracycline-inducible cell lines in whichEWS-FLI1 can be silenced and re-expressed were used(24) The flow chart of our approach is illustrated inFigure 1A and the causal relations between data andthe influence network is represented in Figure 1B Theprinciple was to combine transcriptome time seriesobtained in vitro with literature data mining to constructa first version of the influence network dedicated to Ewingsarcoma focused on regulation of apoptosis and prolifer-ation by EWS-FLI1

Transcriptome time series in shEWS-FLI1 induciblecell lines

A time-series experiment was performed with bothshA673-1C and shA673-2C clones by adding doxycycline(DOX) to the media from day 1 to 17 In addition arescue time-series experiment was also performed fromday 10 to 17 by withdrawing DOX from the culture

Table 1 Selected pathways

Pathways Criteria Method of selection

Tumor Necrosis Factor Some of members of TNF families including TNF receptors are negatively influenced byEWS-FLI1 in A673 cell line In addition it has been shown in that TNF pathway isregulated by EWS-FLI1 (17)

Genes selection

Transforming growthfactor beta

TGFB2 and some of TGFB receptors are negatively induced by EWS-FLI1 in A673 cellline SMAD target gene sets are enriched according to the GSEA analysis TGFBR2 hasbeen identified as a direct target of EWS-FLI1 (12)

Genes selectionGSEA

MAP kinase ERK and JNK members are negatively induced by EWS-FLI1 In addition MAPKkinases have connections to other pathways (TNF Myc) and are known to be a majorfactor affecting the cell fate decision between apoptosis and proliferation

Genes selection

IGF Although mRNA of IGF1 and IGF2 are not clearly influenced by EWS-FLI1 IGFBP3 isnegatively induced by EWS-FLI1 in A673 cell lines and have been identified as a directtarget In addition IGFBP3 is known to be a direct target of EWS-FLI1 (14)

Genes selection

NfkB One of the available NFkB pathway signatures is enriched in GSEA analysis MoreoverNFkB pathway is known to be induced by TNF In addition it has been shown thatNFkB pathway is regulated by EWS-FLI1 (17)

GSEA

c-Myc MYCBP (lsquoc-myc bind proteinrsquo a c-myc activator) is positively induced by EWS-FLI1 inA673 cell line In addition several Myc-related gene sets are enriched in GSEA analysisMyc has also been shown to be regulated by EWS-FLI1 (11)

Genes selectionGSEA

Apoptosis Many genes are influenced by EWS-FLI1 like CASP3 and CYCS In addition severalgene sets that are related to apoptosis are enriched in GSEA analysis

Genes selectionGSEA

Cell-cycle Many of the genes involved in cell-cycle machinery (like cyclins cyclin inhbitorsdegradation complexes key transcription factors) are influenced by EWS-FLI1 Inaddition targets of E2Fs and cell-cycle regulation gene sets are enriched in the GSEAanalysis In addition these genes have been identified as being directly regulated byEWS-FLI1 like p21CDKN1A (7) Cyclin D (89) and Cyclin E (10)

Genes selectionGSEA

PDGF Enriched in GSEA analysis GSEA

Arguments explaining the reason for including the pathway in network reconstruction are given together with references to publications identifyingthose pathways


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Table

2A

subsetofthefact

sheetusedto

construct

thenetwork

ReviewRef

Experim

entR

efLink

Chem

Type

Delay

Confidence

Tissue

Comments

PMID

10074428

TRAF2

(NFKB)

Influence

12h

07

TRAF2mutant

Embryonic

Kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

PMID

10074428

MAP3K14

(NFKB)

Influence

12h

07

MAP3K14mutant

Embryonic

kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

(other

nameforMAP3K14NIK

)PMID

12887914

TNFRSF1A

(TNFRSF1AR

PAIN

)Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

TNFRSF1A

(TNFRSF1ATRAF2)

Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

PMID

12887914

(TNFRSF1AR

PAIN

)

(NFKB)

Post-transcriptional

influence

07

(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

(TNFRSF1ATRAF2)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

TNFRSF1A

CTSB

Release

06

TNFR

permeablizedthe

lysosomemem

brane

release

CTSBtrueforother

cathepsin

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

CTSB

BID

Cleavage

08

Invitro

Bid

induce

apoptosisthrough

mitochondriaandCASP9

PMID

16502253

(NFKB)-jCTSB


influence

07

ThroughSPIN

2Afigure

PMID

16502253

CASP8

CTSB

Release

06

Hepatocyte

Throughlysosomerelease

PMID

16502253

CTSB

[apoptosis]

Chromatin

condensation

07

Cell-free

system

s

PMID

16502253

CTSB

BAX

Influence

04

Mutantmice

Hypotheticalconnectioncould

explain

BID

free

apoptosis

inducedbyCTSB

Titlesofthecolumnare

given

inthefirstline

Thelsquoconfidencersquoisanumber

between0and1indicatingsubjectivereliabilityoftheregulatory

connectionGenes

are

named

accordingly

toHUGO

names

ofthecomplexes

are

enclosedinto

parenthesiswithcomponentnames

separatedbycolonnames

ofthefamiliesofgenes

are

enclosedinto

parenthesiswithfamilymem

bersseparatedby

commaordefined

byawildcardforexample(N

FKB)

notifies

thefamilyconsistingofNFKB1NFKB2etc


at University C



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medium Transcriptomic profiles were generated fromthese experiments Stable and similar inhibition of EWS-FLI1 was observed in both clones on addition of DOX(Figure 2 and Supplementary Figure S1)

Scoring EWS-FLI1 regulated genes by fitting non-linearmodels to time series

At first we performed simple PCA analysis of time-seriesaiming at obtaining the dominant modes of gene expres-sion variation similarly to the work of Alter et al (29) 942microarray probesets with (i) highly correlated expressionprofile in both clones (Pearson correlation coefficientgt085) and (ii) a significant variation in both clones (geo-metrical mean variation bigger than the 95th percentile)were selected These last probesets were then used toperform the PCA The time series corresponding to thefirst principal component (explaining 57 of datavariance) for the inhibition and re-expression experimentsare shown in Figure 3A This indicates that the switch-like

(single transition) and pulse-like (double transition) modesof gene expression variation are predominant in suchEWS-FLI1 inhibition and re-expression experimentsTherefore an original method was developed to automat-ically and systematically characterize gene expressionprofiles on EWS-FLI1 inhibitionre-expression Twotime series models were considered (i) one curvedescribing the switch-like (SL single transition) profileapplied to EWS-FLI1 inhibition (DOX+) (ii) one curvedescribing pulse-like (PL double transition) profileapplied to EWS-FLI1 inhibitionre-expression (DOX+DOX) A fitness score was computed for time series ofeach probeset which defines the accuracy of the fit (theratio between estimated amplitude and the mean-squared error of the fit) Four scores were generated foreach probeset (switch-like score (SL) and a pulse-like score(PL) for both shA673-1C and -2C clones) Fitness scoredistributions are shown in Supplementary Figure S2 Athreshold for the switch-like score (tshSL=0024) and

1

2

Transcriptome me seriesin shEWS-FLI1 inducible

cell lines

Funconal characterizaon of EWS-FLI1 regulated genes Selecon of

EWS-FLI1 regulated genes involved in cell cycle or apoptosis

Scoring of EWS-FLI1 regulated genes by

fing non-linear models to me series

Construcon of an influence network around selected genes describing

EWS-FLI1 effects on cell proliferaon and apoptosis based on literature

data mining

Idenficaon of new necessary connecons in EWS-FLI1 network

siRNAQPCR experiments interpretaon

Describing EWS-FLI1 signaling

the concept of influence network

Assessing completeness of the EWS-FLI1 signaling network the concept of

necessary connecon

3

5

7

4

6

NETWORK

Transcriptome Time Series

LiteratureData Mining

siRNAQPCRexperiments

Fact sheet

Gene selecon

Processing through BiNoM

Idenfy necessary connecons

Idenfy possible transcriponal regulators


A B

Figure 1 (A) Flow chart of the article Gray rectangles are key steps of our analysis Methods and concepts are described in rounded rectangles (1)Transcriptome time-series data were obtained from shA673-1C and -2C clones after silencing or silencing and re-expressing EWS-FLI1 (2) Anoriginal method based on nonlinear curve fitting was used to perform the analysis of transcriptome time series (3) EWS-FLI1-modulated genes wereselected this list was restricted to the genes affecting proliferation and apoptosis (4) A network representation of EWS-FLI1 signaling was chosen itconsists of influences (positive or negative) between genes proteins and complexes (5) EWS-FLI1 signaling network model was reconstructed fromthe above selected genes connected by the influences known from literature (6) The notion of necessary connection was introduced it allows to refinea network model when for instance additional experimental data are provided (7) Silencing experiments were performed on several EWS-FLI1-regulated genes new necessary connections were identified and added to EWS-FLI1 signaling network (B) Causal relations between data and theinfluence network


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0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5

A673 EW7 EW24 SKNMCshA673-1C rescue

0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

CASP3

0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

E2F5

0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

250

300

350

0 5 10 15 20

CFLAR

0

25

50

75

100

125

150

0 5 10 15 20

CUL1

0

25

50

75

100

125

150

0 5 10 15 20

PARP1

0

100

200

300

400

500

600

700

0 5 10 15 20

IER3

0

100

200

300

400

500

600

700

0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

0200400600800

1000120014001600

24h 48h 72h

IER3

rela

ve

expr

essio

n le

vel

days hours

A

Figure 2 (A) RT-QPCR for a panel of EWS-FLI1-modulated genes along time series experiments in shA673-1C cells on DOX additionremoval(solid inhibition dashed grey rescue) and in four Ewing cell lines (A673 EW7 EW24 and SKNMC) on transfection with nontargeting siRNA(siCT) or EWS-FLI1-targeting siRNA (siEF1) after 24 48 or 72 h Relative expression level () for each gene to the starting point shA673-1Ccondition or to siCT conditions are displayed on the y axis Data are presented as mean values and the standard deviations (B) Western blot for apanel of EWS-FLI1-modulated genes along a time series experiment in shA673-1C cells on DOX addition and in four Ewing cell lines (A673 EW7EW24 and SKNMC) on transfection with nontargeting siRNA (siCT) or EWS-FLI1 targeting siRNA (siEF1) after 72 h For PARP western blot fulllength protein is indicated by the arrow and cleaved PARP by the arrowhead Beta-actin was used as loading control


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the pulse-like score (tshPL=094) were set using carefulmanual inspection of many individual profiles(see Materials and Methods and Supplementary FigureS2) By definition a gene was selected for furtheranalysis if both SL and PL scores were higher than theirrespective thresholds in at least one clone and for at leastone probeset Global EWS-FLI1 transcriptional responseis slightly different between the two clones fitness scoresare higher in clone shA673-1C The interest of this pro-cedure is that (i) high fitness scores can correspond to highamplitude of expression but also to small amplituderesponse that tightly fit the model curve this avoids abias in selecting highly expressed genes (ii) parametersdescribing transition time and speed are not predefinedthey are identified from the data (Figure 3CSupplementary Table S1 and Supplementary Figure S2)they are not based on a given dynamical model (likeODE) Our method is clearly different from the standardfold change-based gene selection approach as illustratedin Figure 3B Therefore genes with high fitness score werehypothesized to be potentially modulated by EWS-FLI1It is to be noted that the fitness scores (SL=0667 andPL=872) of the first principal components (Figure 3A)are substantially larger than the respective thresholdvalues (see above)

Functional characterization of EWS-FLI1 regulated genes

The characterization of EWS-FLI1 regulated genes wasbased on two approaches

In the first method GSEA method using MSigDB (27)was applied separately to the four fitness scores computedfor all probesets Enriched pathways resulting from thesefour GSEA analyses are listed in Supplementary TablesS2ndashS5

In the second method DAVID tool (3031) was appliedto the lists of modulated genes 3416 genes (4903probesets) were selected as potentially modulated byEWS-FLI1 (1426 inhibited and 1990 induced listed inSupplementary Table S1) DAVID functional annotationtool was applied to the list of modulated genes to producea list of enriched pathways based on GO KEGG andREACTOME annotations (Supplementary Table S6)

Both functional characterization methods result in iden-tification of multiple pathways potentially implicated inresponse to EWS-FLI1 inactivation As expected suchcategories as cell cycle regulation RNA processing andcell death clearly showed up We decided to focus on pro-liferation and apoptosis because in addition to ourbioinformatics analysis previous reports also clearlysupport this decision Indeed EWS-FLI1 knock-downinhibits proliferation in our cellular model and in otherEwing cell lines (5) and can also drive cells to apoptosis(1432)

Describing EWS-FLI1 signaling the concept of influencenetwork

An important objective of this study is to understand howthe genes and pathways modulated by EWS-FLI1 interact

PARP1

CUL1

EWS-FLI1

bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

MYC

E2F1

E2F2

E2F5

BEW24

siCT

siEF1

siCT

siEF1

SKNMCA673

siCT

siEF1

siCT

siEF1

EW772h

0 1 2 3 6 10 12 days

shA673-1C

dox

Figure 2 (Continued)


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with each other The above described analysis onlyallowed selecting genes whose temporal expressionprofiles can be fit to a simple switchpulse-like functionTo reconstruct a mechanistic picture of causal relationsEWS-FLI1 must be integrated in a complex regulatorynetwork where the modulated genes are connectedtogether through interactions with other intermediategenes that are not necessarily modulated by EWS-FLI1Such a gene regulation network represents a first steptoward modeling and therefore understanding the EWS-FLI1 signaling

Ideally an exhaustive representation including bio-chemical processes and phenotypic outcomes for all

genespathways should be integrated in this networkFor instance lsquocomprehensiversquo network maps of EGFRand RB signaling (3334) have been constructed includingmore than a hundred proteins and genes Howeverapplying similar approach to describing EWS-FLI1 sig-naling is not suitable Firstly the number of genespathways involved here is large (see GSEA resultsSupplementary Tables S2ndashS5) while above mentionedRB and EGFR signaling network maps describe onlyone pathway The resulting lsquocomprehensiversquo networkwould be difficult to manipulate Secondly many of theselected genespathways are poorly described and there-fore difficult to connect in a lsquocomprehensiversquo network

AQP1 E2F2

of E

WS-

FLI1

Inhi

bio

n amp

reac

va

onof

EW

S-FL

I1

CDKN1C

SL 31Tr 195 665 days

SL 08Tr 06 20 days

SL 008Tr ND

PL 432Tr 62 122 days

PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

04

0 5 10 15 20

A B

C

Switch like score6773 probesets

Fold Change5574 probesets

4409 32102364

CUL1 CFLAR

Figure 3 (A) Time series corresponding to the first principal modes of gene expression variation in EWS-FLI1 inhibition (solid line) and re-expression experiments (dashed line) (B) Comparison of two methods for selecting modulated genes one based on switch like (SL) score theother one based on fold change (FC) For both methods top 4000 probesets for each clone (shA673-1C and -2C) were selected (ranked by their SLscore or by FC between the first and the last time points) The Venn diagram compares these top scored probesets The intersection of both methodsis partial for two reasons (i) the SL score can be large for a time series tightly following the assumed model of response even if having a moderatevariance (ii) FC method is not considering intermediate time points Both CUL1 and CFLAR exhibit temporal expression responses that have agood fit to the proposed switch-like response model However only some CFLAR probesets are characterized by significant fold change values (C)Examples of curve fitting to the time series in microarray experiments AQP1 E2F2 and CDKN1C expression profiles are shown Blue curvesrepresent microarray experimental values red curves correspond to fitted functions Switch-like scores (SL) pulse-like scores (PL) and transitionsparameters (Tr) are listed under each plot SL and PL scales are not comparable as the fitting procedures are different It can be noticed that bothscores for E2F2 are smaller than those for AQP1 for two reasons the amplitude of expression variation is smaller for E2F2 and the transitionhappen at a time point closer to the limits of the time window The scores for CDKN1C are clearly lower because the expression level is less smoothIn that case transition parameters cannot be identified because the inflections points of the fitted curves are outside of the time window


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Therefore we decided to construct an influence network(35) By definition edges in the influence networkcan only represent positive or negative induction(Supplementary Figure S3) In the context of our studynodes can represent mRNAs proteins or even complexesHence this allows to integrate both well characterized aswell as poorly described biological interactions

Construction of an influence network describingEWS-FLI1 effects on cell proliferation and apoptosisbased on literature data mining

The influence network was reconstructed around theregulation of proliferation and apoptosis using EWS-FLI1-modulated genes The list of 3416 modulated genes(selected above) was shrunk to the genes known to have arole in regulation of proliferation or apoptosis accordingto GO (26) and BROADMSigDB databases (27) This listwas further reduced to 37 genes whose mechanisms of cellcycle and apoptosis regulation are clearly documented inthe literature (top probesets of Supplementary Table S1labeled by lsquoNet reconstrsquo) Enriched pathways affectingproliferationapoptosis and selected by GSEA were alsoincluded (highlighted in red in supplementary TablesS2ndashS5) This pathway (or set of genes) selection procedureis detailed in Material and Methods in lsquoProtocol of select-ing genes for network reconstructionrsquo Table 1 lists theeight pathways used for network reconstruction togetherwith the criterion used for their selection (EWS-FLI1modulated genes selected by curve fitting method andorby GSEA)The network construction was then achieved in two

steps Firstly an interaction fact sheet was generatedthis sheet is a description of annotated influences extractedfrom the literature (around 400 influences) a sub-part of itis given in Table 2 (the full table is given in SupplementaryTables S7 and S8) illustrating the formalism for interpret-ing a publication in terms of influence(s) between genesproteins Secondly a graphical representation of thenetwork extracted from the fact sheet was producedThe later step allows to handle gene families (ie E2FsIGFs) and to add implicit connections (for instanceCDK4 positively influences the (CDK4CCND) complexformation) (see Network curation framework in Materialsand Methods and Protocol 1 in the web page ofsupplementary material) The fact sheet was confrontedto the TRANSPATH database (36) and missing linkswere manually curated and included The advantage ofthis procedure is its flexibility it is easy to update thefact sheet with new publications and to produce a newversion of the network The resulting influencenetwork is shown in Figure 4A and is accessible as aCytoscape (37) session file available at httpbioinfo-outcuriefrprojectssuppmaterialssuppmat_ewing_network_paperSupp_materialNetworkSuppl_File_1_Net_1_CytoscapeSessioncys This network contains 110 nodesand 292 arrows (213 activations and 79 inhibitions)Annotations from the fact-sheet can be read usingthe BiNoM plugin (BioPAX (38) annotation file is avail-able at httpbioinfo-outcuriefrprojectssuppmaterials

suppmat_ewing_network_paperSupp_materialNetworkSuppl_File_2_Net_2_BIOPAX_Annotationowl)

This network can be seen as an organized and inter-preted literature mining (43 publications mainly reviewslisted in the fact sheet Supplementary Table S8) Itincludes schematic description of the crosstalk betweenthe following signaling pathways apoptosis signaling(through the CASP3 and cytochrome C) TNF TGFbMAPK IGF NFkB c-Myc RBE2F and other actorsof the cell-cycle regulation Many of the pathways thatwere identified in this influence network have been previ-ously described or discussed in the context of Ewingsarcoma During reconstruction of the network 9 genesregulated by EWS-FLI1 were added to the 37 genesidentified from the selection procedure (SupplementaryTable S1)

Experimental validation of EWS-FLI1 modulated genes

To assure biological significance of this Ewing sarcomanetwork a substantial number of EWS-FLI1 modulatedgenes were assessed by RT-QPCR (Figure 2A) andwestern blotting of the corresponding proteins(Figure 2B) using DOX time series experiments in theshA673-1C clone To further validate these resultssiRNA time series experiments (24 48 and 72 h) withsiEWS-FLI1 (siEF1) and control siRNA (siCT) were per-formed in four additional Ewing cell lines (A673 EW7EW24 and SKNMC) As expected cyclin D (89) andprotein kinase C beta (39) proteins (two direct EWS-FLI1 targets genes) were down-regulated in these celllines upon EWS-FLI1 silencing (Figure 2B) MYC waspreviously shown to be induced by EWS-FLI1 mostprobably through indirect mechanisms (11) This was con-firmed here at the protein level in all tested cells(Figure 2B) Down-regulation of MYC mRNA was alsoobserved upon siRNA treatment in all cell lines TheMYC variation was less obvious in the DOX-treatedshA673-1C clone probably due to the milder inhibitionof EWS-FLI1 by inducible shRNA (Figure 2A) than bysiRNA (supplementary Table S10) In addition to the pre-viously published induction of Cyclin D (89) and Cyclin E(10) by EWS-FLI1 we report here the down-regulation ofCyclin A upon EWS-FLI1 silencing (Figure 2) Amongother well described cell cycle regulators E2F1 E2F2and E2F5 were also consistently down-regulated aftersilencing of EWS-FLI1 Altogether these results empha-size the strong transcriptional effect of EWS-FLI1 onvarious cell cycle regulators Apoptosis was alsoinvestigated upon EWS-FLI1 inhibition A clear up-regu-lation of procaspase3 (mRNA and protein) was observedin all cells (except for EW7 cells) To monitor late stage ofapoptosis induction of cleaved PARP was assessed uponEWS-FLI1 inhibition No induction of apoptosis could beobserved along the time series experiment in the shA673-1C (Figure 2B arrowhead band) This was probably dueto the relatively high residual expression of EWS-FLI1(20ndash30 of original levels Figure 2) However in thetransient siRNA experiments where EWS-FLI1 wasmore efficiently knocked-down apoptosis was monitoredby induction of cleaved PARP in EW7 EW24 and


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SKNMC but not in A673 (Figure 2) It is to notice thatfull length PARP1 protein was not modulated uponsilencing of EWS-FLI1 (Figure 2B arrow band)Interestingly after EWS-FLI1 silencing the potent anti-apoptotic CFLAR protein was strongly up-regulated in

A673 but not in EW7 cells (Figure 2B) Phenotypicallythis was associated with a strong induction ofapoptosis and dramatic reduction of EW7 cell numberbut only mild effect on A673 proliferation (SupplementaryFigure S4)

A

B

Figure 4 (A) Annotated network of EWS-FLI1 effects on proliferation and apoptosis derived from literature-based fact sheet White nodes rep-resent genes or proteins gray nodes represent protein complexes EWS-FLI1 (green square) and cell cycle phasesapoptosis (octagons) represent thestarting point and the outcome phenotypes of the network Green and red arrows symbolize respectively positive and negative influence Nodes withgreen frame are induced by EWS-FLI1 according to time series expression profile and nodes with red frame are repressed The network structureshows intensive crosstalk between the pathways used for its construction up to the point that the individual pathways cannot be easily distinguished(B) Refined network including new links inferred from experimental data (thick arrows) from transcriptome time series and siRNART-QPCR


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Assessing completeness of the EWS-FLI1 signalingnetwork the concept of necessary connection

In the previous paragraphs experimental data were usedto select genes and to validate their biological implica-tions However the connections in the network wereextracted from the literature that is not always dedicatedto Ewing sarcoma Genes like IGFBP3 MYC and CyclinD are linked to EWS-FLI1 because these influences havebeen reported (891114) However several genes (E2F5SKP2 ) are modulated by EWS-FLI1 but are notdirectly linked to EWS-FLI1 (Figure 4A) Therefore thenetwork needs to be refined to match the context of Ewingsarcoma To answer this question we introduced theconcept of necessary connection between genes By defin-ition a necessary connection is such a regulatory connec-tion between two molecular entities which can be inferredfrom lsquothe datarsquo but cannot be predicted from lsquoalreadyexisting network modelrsquo From its definition a necessaryconnection always depends on (i) dataset and (ii) alreadyexisting model We provide in Supplementary Figure S3several examples of necessary connections (alwaysapplying the same definition) for various practical situ-ations For instance the connection lsquoEWS-FLI1CUL1rsquo is necessary in our context (data andnetwork) because (i) CUL1 is induced by EWS-FLI1 ac-cording to the transcriptome time series (ii) no connectionto CUL1 explains the transcriptional regulation of thisgene in the network of Figure 4A We decided to formalizethis notion of necessary connection to handle the networkmodel that can be incomplete (missing nodes and connec-tions representing indirect effects) Subsequently this def-inition was applied to all modulated genes in the networkthe resulting necessary connections are listed in Table 3Among these several necessary connections between

ubiquitin proteasome system members (CUL1 SKP1SKP2 ANAPC2) and EWS-FLI1 were identified poten-tially indicating an interesting link between this oncogeneand the protein turnover regulation in the context ofEwing sarcoma Necessary connections between EWS-FLI1 and two attractive candidates for their obviousimplication in oncogenic process the GTPase (KRAS)and the protein kinase C (PRKCB) were also identifiedusing this approach Finally a set of necessary connec-tions from EWS-FLI1 to cell cycle regulators (CDK2CDK4 CDK6) or apoptosis members (CASP3 CTSB)were highlighted To verify if these necessary connectionswere potentially direct previously published FLI1ChIPseq experiments performed on Ewing cell lines wereexamined for the presence of peaks around these targetgenes (40ndash42) A significant ChIPseq hit correspondingto a potential ETS binding site was found within theCUL1 gene Interestingly CASP3 here identified asEWS-FLI1 necessary connection was identified as adirect target of EWS-FLI1 (16) However no significantChIPseq hit could be identified in the CASP3 promoterThis may be attributed to the relatively low coverage ofthe ChIPseq data used in this study Eleven of the geneshaving a necessary connection to EWS-FLI1 with lowCHIPseq read density within their promoter regionswere selected and assessed by ChIP (Supplementary

Figure S5A and Supplementary Table S9) In agreementwith published ChIPseq data only CUL1 was identified asa direct target of EWS-FLI1 (see Supplementary FigureS5B) As indicated by the transcriptome time-series experi-ments RT-QPCR and Western blot experiments con-firmed that EWS-FLI1 induces CUL1 Indeed the levelof CUL1 is reduced to 50 on addition of DOX in theshA673-1C clone at both mRNA (Figure 2A) and proteinlevel (Figure 2B) These results were confirmed in fouradditional cell lines using siRNA time series experiments(24 48 and 72 h) and are shown in Figure 2

Identification of new necessary connections in EWS-FLI1network siRNART-QPCR experiments interpretation

The necessary connections listed in Table 3 make thenetwork compliant with the transcriptome time seriesresults To further understand EWS-FLI1 transcriptionalactivity new experiments were required We focused onthree EWS-FLI1 regulated genes FOXO1A IER3 andCFLAR These genes were selected because they partici-pate to the regulation of the cell cycle and apoptosis ma-chinery although their transcriptional regulation is not yetfully elucidated FOXO1A regulates cell cycle mainlythrough P27(kip1) (43) and is connected to apoptosis byregulation of TRAIL (44) FASL and BIM (45) IER3 is amodulator of apoptosis through TNF- or FAS-signaling(46) and MAPKERK pathway (47) CFLAR is a potentanti-apoptotic protein that share high structuralhomology with procaspase-8 but that lack caspase enzym-atic activity The anti-apoptotic effect is mainly mediatedby competitive binding to caspase 8 (48)

The first step was to validate the results obtained in thetranscriptional microarray time series on FOXO1A IER3

Table 3 Necessary connections between EWS-FLI-1 and the network

genes

Node Genes Link

ANAPC2 ANAPC2 EWS-FLI1 -j ANAPC2BTRC BTRC EWS-FLI1BTRCCASP3 CASP3 EWS-FLI1 -j CASP3CCNH CCNH EWS-FLI1CCNHCDC25A CDC25A EWS-FLI1CDC25ACDK2 CDK2 EWS-FLI1CDK2(CDK4CDK6) CDK4CDK6 EWS-FLI1 -j (CDK4CDK6)CTSB CTSB EWS-FLI1 -j CTSBCUL1 CUL1 EWS-FLI1CUL1CYCS CYCS EWS-FLI1CYCS(E2F1E2F2E2F3) E2F2 EWS-FLI1 (E2F1E2F2E2F3)(ECM) ECM1 EWS-FLI1 -j (ECM)IGF2 IGF2R EWS-FLI1 -j IGF2(RAS) KRAS EWS-FLI1 (RAS)MYCBP MYCBP EWS-FLI1MYCBP(PRKC) PRKCB EWS-FLI1 (PRKC)PTPN11 PTPN11 EWS-FLI1PTPN11RPAIN RPAIN EWS-FLI1RPAINSKP1 SKP1 EWS-FLI1 SKP1SKP2 SKP2 EWS-FLI1 SKP2TNFRSF1A TNFRSF1A EWS-FLI1 -j TNFRSF1A

The given data are the transcriptome time series the given network isthe reconstructed network based on literature These connections targetEWS-FLI1-regulated genes (identified by transcriptome time series) thathave no identified transcriptional regulators


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and CFLAR Using the same temporal conditions in anindependent experiment their expression levels weremeasured by RT-QPCR (Figure 2A) Microarrays andRT-QPCR time series exhibit similar time profiles andconfirmed that EWS-FLI1 down-regulates these genesBased on the literature mining used for the influencenetwork reconstruction (fact sheet SupplementaryTables S7 and S8) their possible regulators were identified(Figure 6A) FOXO1A is regulated by E2F1 (49) IER3 isregulated by MYC EP300 NFKB (RELA NFKB1) (50)and CFLAR by NFKB (RELA NFKB1) (51) and MYC(52) E2F2 and E2F5 were also investigated as they areboth modulated by EWS-FLI1 and share similarities withE2F1 (53)

The second step was to validate the results obtained inthe transcriptional microarray time series on these regula-tors Microarrays and RT-QPCR time series exhibitedsimilar time profiles (Figure 2A and SupplementaryFigure S6)

In the third step regulators were individually and tran-siently silenced in shA673-1C inducible cell lineExpression levels of FOXO1 IER3 CFLAR and all regu-lators were measured by RT-QPCR after each silencingexperiment (Supplementary Table S10)

All these RT-QPCR data were semi-automaticallyanalyzed by a reverse engineering method as following(see lsquoNetwork reverse engineering from siRNA silencingdatarsquo in Materials and Methods)

(i) Identification of influences from experimental data(represented by all arrows of Figure 6B) Links fromEWS-FLI1 are based on RT-QPCR time seriesother links are extracted from siRNART-QPCRexperiments

(ii) Confrontation with the literature Five out of seveninfluences were confirmed The two remaininginfluences (E2F1 -j FOXO1 and P300 -j IER3)display opposite effects as the one described bythe literature (Figure 6C) and were thereforemodified in the final version of the influencenetwork

(iii) Extraction of the necessary connections using theinfluence subnetwork of point (i) represented bysolid arrows in Figure 6B It is to notice thatsome influences cannot be interpreted Forinstance IER3 can be either directly activated byRELA or indirectly activated through a double in-hibition via P300 (RELA -j P300 -j IER3) seeFigure 6D

(iv) Filtering the necessary connections identified in (iii)using the complete network model in Figure 4A Itconsists of confronting all necessary connections ofFigure 6B with the literature mining producing theinfluence network as described in Table 4 Validityof this subnetwork is therefore confirmed with theexception of one unexplainable necessary connection(P300 -j E2F2) In case of conflict between anexperimental observation and an interactiondescribed in the literature we always used the con-nection inferred from Ewingrsquos specific experimentaldata because the original goal of this work is to

construct the network model specific to the molecu-lar context of Ewingrsquos sarcoma

The final refined model (Figure 4B) is obtained byadding all necessary connections (from transcriptometime series and siRNART-QPCR experiments) to our lit-erature-based network Altogether our results demon-strate the coherence of this influence network modeldescribing EWS-FLI1 impact on cell cycle and apoptosisImportantly successive steps allowed to identify novelplayers involved in Ewing sarcoma such as CUL1 orCFLAR or IER3

DISCUSSION

We present in this article a molecular network dedicatedto molecular mechanisms of apoptosis and cell cycle regu-lation implicated in Ewingrsquos sarcoma More specificallytranscriptome time-series of EWS-FLI1 silencing wereused to identify core nodes of this network that was sub-sequently connected using literature knowledge andrefined by experiments on Ewing cell lines For the con-struction of the network no lsquoa priorirsquo assumptions regard-ing the activity of pathways were made In this studyEWS-FLI1-modulated genes are identified because theyvary consistently along the entire time-series althoughthey may have moderate amplitude In comparison thestandard fold change-based approach focuses on thegenes showing large variability in expression Forinstance CUL1 would not have been selected based onits fold change value (Figure 3B) The influence networkis provided as a factsheet that can be visualized andmanipulated in Cytoscape environment (3754) viaBiNoM plugin (28) The advantage of this approach isits flexibility Indeed the present model is not exhaustivebut rather a coherent basis that can be constantly andeasily refined We are aware that many connections inthis model can be indirect The network is a rough ap-proximation of the hypothetically existing comprehensivenetwork of direct interactions More generally we thinkthat our method for data integration and network repre-sentation can be used for other diseases as long as thecausal genetic event(s) has(ve) been clearly identified

Biological implications

To validate the proposed network model a dozen ofEWS-FLI1 modulated transcripts and proteins werevalidated in shA673-1C cells as well as in four otherEwing cell lines These additional experiments emphasizedthe robustness of our network to describe EWS-FLI1effect on cell cycle and apoptosis in the context ofEwing sarcoma Furthermore the concept of necessaryconnection allowed to use this network for interpretingour experiments and identifying new connections Ourapproach is therefore a way to include yet poorlydescribed effects of EWS-FLI1 (which influences 20network nodes)After further experimental investigation EWS-FLI1 in-

duction of CUL1 appeared to be direct In addition thenecessary connection EWS-FLI1 induces PRKCB and


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EWS-FLI1 represses CASP3 have been recently reportedas direct regulations (1639) CASP3 is shown here to berepressed by EWS-FLI1 in Ewing sarcoma cells At thecontrary CASP3 is shown to be induced by ectopic ex-pression of EWS-FLI1 in primary murine fibroblast(MEF) (16) This highlights the critical influence of thecell background on EWS-FLI1 mechanisms of actionMEF may not be the appropriate background to investi-gate in depth EWS-FLI1 properties The notion of neces-sary connection enables to infer potential direct regulatorylinks between two proteins taking into account high-throughput data and a model of gene regulation extractedfrom the current literature Considering EWS-FLI1targets it can therefore help designing specific experiments(ChIP or luciferase reporter experiments) to confirm orinfirm direct regulationsAccording to the ENCODE histone methylation

profiles of several cell lines (55) the EWS-FLI1-boundCUL1 region appears highly H3K4me1 positive butH3K4me3 negative (Supplementary Figure 5B) H3K4monomethylation is enriched at enhancers and is generallylow at transcription start sites By contrast H3K4trimethylation is largely absent from enhancers andappears to predominate at active promoters This fitswith our data indicating that EWS-FLI1 is directenhancer of CUL1 and may be of particular interest inthe context of cancer Indeed CUL1 plays the role of

rigid scaffolding protein allowing the docking of F-boxprotein E3 ubiquitin ligases such as SKP2 or BTRC inthe SKP1-CUL1-F-box protein (SCF) complex Forinstance it was recently reported that overexpression ofCUL1 is associated with poor prognosis of patients withgastric cancer (56) Another example can be found inmelanoma where increased expression of CUL1promotes cell proliferation through regulating p27 expres-sion (57) F-box proteins are the substrate-specificitysubunits and are probably the best characterized part ofthe SCF complexes For instance in the context of Ewingsarcoma it was previously demonstrated that EWS-FLI1promotes the proteolysis of p27 protein via a Skp2-mediated mechanism (58) We confirmed here in ourtime series experiment that SKP2 is down-regulated onEWS-FLI1 inhibition Although SKP1-CUL1-SKP2complex are implicated in cell cycle regulation throughthe degradation of p21 p27 and Cyclin E other F-boxproteins (BTRC FBWO7 FBXO7 ) associated toCUL1 are also major regulators of proliferation andapoptosis [reviewed in (59)] For instance SKP1-CUL1-FBXW7 ubiquitinates Cyclin E and AURKA whereasSKP1-CUL1-FBXO7 targets the apoptosis inhibitorBIRC2 (60) SKP1-CUL1-BTRC regulates CDC25A(a G1-S phase inducer) CDC25B and WEE1 (M-phaseinducers) Interestingly the cullin-RING ubiquitin ligaseinhibitor MLN4924 was shown to trigger G2 arrest at

Table 4 siRNART-QPCR data confronted to the network each necessary connection from the network shown in Figure 5B (plain arrows) is

confronted to the global EWS-FLI1 signaling network (Figure 3A)

Type Connection Possible intermediate node Comment possible scenario

EWS-FLI1E2F1 E2F2 with E2F2E2F1 Possible scenario through cyclin and RBEWS-FLI1E2F2 P300 with p300 -j E2F2 EWS-FLI1 -j IER3 -j P300

Necessary connection identified by transcriptome time seriesappears to be non-necessary

EWS-FLI1 -j CFLAR MYC with MYC -j CFLAR EWS-FLI1MYCEWS-FLI1E2F5 E2F2 with E2F2E2F5E2F2 -j EP300 IER3 with IER3 -j EP300 E2F2 (RBL) -j MYC -j IER3IER3 -j EP300 RELA with RELA -j EP300 IER3MAPKTNFNFKB

Necessary EP300 -j E2F2 No other known transcriptionalregulation (except EWS-FLI1)

P300 -j CREBBP MYC with MYC -j CREBBP P300 -j E2F2RBL1 -j MYCIER3 -j CREBBP MYC with MYC -j CREBBP IER3MAPKMYCMYC -j CREBBP P300 with p300 -j CREBBP MYCCCND (E2F45RBL2^P)E2F45P300E2F1 -j MYC E2F5 with E2F5 -j MYC Cell cycle machinery E2F1Cycle E (E2F45RBL2^P)E2F45P300 -j MYC E2F5 with E2F5 -j MYC P300 E2F2E2F5

Post-transcriptional effect of p300 on E2F2 may be stronger thantranscriptional inhibition

E2F5 -j MYC P300 with p300 -j MYC E2F5E2F5^pP300MYC -j E2F1 E2F4 with E2F4 -j E2F1 MYCCCND (CCNDCDK) (E2F45RB^p)E2F45P300 -j E2F1 E2F4 with E2F4 -j E2F1 P300E2F4E2F1 -j NFKB1 P300 with P300 -j NFKB1 E2F1CCND3 (CCND3CDK) (E2F45RBL)E2F45P300NFKB1E2F5 E2F2 with E2F2E2F5 NFKBCCND12CCNDCDKE2F123RB^pE2F123CREBBPFOXO1 E2F1 with E2F1CREBBP CREBBP (E2F)P300 -j RELA E2F5 with E2F5 -j RELA P300 E2F2E2F5


MYC -j RELA E2F5 with E2F5 -j RELA MYCCCNE (or CCND)CCNECDKE2F45RBL^pE2F45E2F5 -j RELA P300 with p300 -j RELA E2F45 p300RELA -j CFLAR Published

For each of these connections possible transcriptional regulators are identified from the lsquofact sheetrsquo For each possible transcriptional regulator theshortest path between the source node of the connection and the regulator has been searched If the sign of influence of the found path is compatiblewith the necessary connection the path is considered as a lsquopossible scenariorsquo Connections with mention lsquonecessaryrsquo in first column are considered asnecessary related to siRNART-QPCR data and to EWS-FLI1 network (Figure 3A) ie no coherent possible scenario has been found


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subsaturating doses in several Ewing sarcoma cell linesThis arrest could only be rescued by WEE1 kinase inhib-ition or depletion (61) In addition in vivo preclinical dataemphasized the potential antitumoral activity ofMLN4924 Therefore EWS-FLI1 regulation of CUL1expression may profoundly affect SCF-mediated proteindegradation and participate to proliferation and apoptosisderegulation in Ewing sarcoma

An additional key player of oncogenesis is MYCAccording to our results MYC transcript was down-regulated by siRNA against EWS-FLI1 in all tested celllines (including shA673-1C supplementary Table S10 andFigure 2A) However milder EWS-FLI1 silencing (DOX-treated shA673-1C cells) had more subtle influence onMYC transcript (Figure 2A) though the protein levelwas clearly decreased (Figure 2B) A post-transcriptionalregulation may therefore be involved in the regulation ofMYC by EWS-FLI1 In that respect it is noteworthy thatmir145 which represses MYC (62) was significantly up-regulated in DOX-treated shA673-1C cells (63) and couldhence mediate this regulation This justifies improving ournetwork in the future including miRNA data

With the aim to experimentally validate a subpart ofour influence network regulators of IER3 CFLAR andFOXO1 were investigated Importantly most of theinfluences taken from the literature on these three geneswere confirmed using siRNART-QPCR experiments

(Figure 6B and supplementary Table S10) The influencesof P300 on IER3 and E2F1 on FOXO1 were found to berepressive (activating according to literature) Thereforethese influences were modified accordingly to our experi-mental data to fit to the context of Ewing sarcomaMore interestingly although P300 (in this study) and

MYC (in this study and in the literature) repress IER3IER3 most significant and yet unreported repressors areE2F2 and E2F5 (Figure 6B and Supplementary TableS10) This mechanism is enhanced through a synergisticmechanism of E2F2 on E2F5 (E2F2 -j IER3 andE2F2E2F5 -j IER3) Additionally a positive feed-back loop is observed between IER3 and E2F5(IER3E2F5) (Figure 6B and Supplementary TableS10) Therefore it seems that these E2Fs play a majorrole in the regulation of IER3 Because IER3 is a modu-lator of apoptosis through TNFalpha or FAS-signaling(47) the balance between its repression (through MYCE2F2 and E2F5 that are EWS-FLI1 induced and thereforedisease specific) and activation (through NFkB) may be ofparticular interest in Ewing sarcoma Indeed suppressingNFkB signaling in Ewing cell line has been shown tostrongly induce apoptosis on TNFalpha treatment (17)All cell lines but EW7 carry p53 alterations In patients

such mutations clearly define a subgroup of highly aggres-sive tumors with poor chemoresponse and overall survival(6465) Most of the results obtained in EW7 cells were

Affy

Sign

al In

tens

ity (

log2

)

No necessaryconnecon

P300 IER3

RELA

Necessaryconnecon

EWS-FLI1 CUL1

Nor

mal

ized

expr

essio

n le

vel [

]

Models Data Interpretaon

I

II

literature-based influence network

siRNA and RT-QPCRin Ewing cell-lines

99

10

101

102

103

104

105

0 5 10 15 20

CUL1 (207614_s_at)

0

100

200

300

400

siCTRL siP300 siRELA

P300 RELA IER3

days

Figure 5 Illustration of necessary and non-necessary connections within given network models and data (i) An observed influence from EWS-FLI1to CUL1 is a necessary connection because no indirect explanation (path with intermediate nodes) can be identified within the network model (ii)P300 represses IER3 but this can be explained through RELA thus P300 -j IER3 is not necessary


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ownloaded from

consistent with data from other tested cell lines except forits poor survival capacity on EWS-FLI1 knock-down(Supplementary Figure S4) However procaspase 3protein was not induced in EW7 cells on EWS-FLI1knock-down (Figure 2B) Similarly the two anti-apoptoticfactors CFLAR and IER3 were only moderately up-regulated or even repressed after silencing of EWS-FLI1in EW7 cells respectively (Figure 2A) Since EW7 is oneof the very few p53 wild-type celle line these data maypoint out to some specific p53 functions in the context ofEwing cells

Perspectives

Owing to the flexibility of our network description formatfurther versions of the network will be produced Forinstance additional genomic data such as primary tumorprofiling and ChIP-sequencing will be used to select new

pathways for completing our network Furthermoreregulated pathways such as Notch Trail hypoxia andoxidative stress regulation Wnt or Shh identified inother studies could also be included (66ndash71) Finallyfuture experiments implying additional phenotypes (suchas cell migration cellndashcell contact angiogenesis ) couldcomplete the present network

It has to be noticed that our EWS-FLI1 network is notable to reproduce all the siRNART-QPCR data indeedsome influences cannot be translated in terms of necessaryconnections like in the example of Figure 6D Thereforethis final network should be interpreted as the minimalone that reproduces the maximum amount of influencesWe can suggest two methods for solving this problem ofambiguous interpretation (i) extending experimental databy performing double-knockdown (ii) comparing data toa mathematical model applied to the whole network in a

Figure 6 (A) Transcriptional influences between EWS-FLI1 CFLAR MYC P300 E2F1 RELA IER3 and FOXO1 nodes extracted from theliterature-based influence network (B) Interpretation of experiments (siRNA transfection and RT-QPCR) in shA673-1C cells Thickness of arrowsshows the strength of the influence (values given in Supplementary Table S10) Blue arrows are based on RT-QPCR time series Plain arrowsrepresent transcriptional influences that are necessary for explaining data Dashed arrows are questionable influences that can be explained throughintermediate node The arrow EWS-FLI1 -j FOXO1 is not necessary although a recent article has identified it as a direct connection (72) (C) Thenecessary connections shown in Figure 6B have been compared with a subpart of the influence network (Figure 6A) All connections of this subparthave been confirmed although two of them display an opposite sign (D) Example of influences that cannot be interpreted as a necessary connectionbecause of ambiguity in the choice Indeed either RELA IER3 is necessary and RELA -j P300 is not or RELA-jP300 is necessary andRELA IER3 is not In this case we decided to consider both connections (RELA IER3 RELA -j P300) as non-necessary Within thischoice the set of necessary connections is interpreted as the minimal set of connections that explain the maximum amount of data with noambiguity


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quantitative way We can expect that new biological dataandor modeling results will help to enhance this networkmodel using the suggested framework of influencenetwork and the concept of necessary connections Forinstance we believe that considering more complexpatterns of expression response can be the next step inrefining the Ewingrsquos sarcoma network It may requireincreasing the number of experimentally measured timepoints

Experimental results were confronted with literatureknowledge within this network model In particular struc-tural path analysis of the influence network was carriedout to generate the Table 4 this can be considered as asimple theoretical approach To obtain a predictive modelmore sophisticated theoretical models will be constructedusing the network as already proposed in other systemsbiology approaches (73) However this task can becomplicated due to the size of networks dynamicalmodels often deal with lt50 nodes to produce robust pre-dictions For such a network there will be two types ofstrategies (i) Considering only static network properties(steady states through well-developed Flux BalanceAnalysis) (ii) Decompose the network into modules thatwill be modeled separately and then assembled into amodular network (74) More sophisticated modelingwould help to overcome the two main limitations of thepresent approach which are (i) EWS-FLI1-modulatedgenes have temporal expression profiles functionallysimilar to the dynamics of EWS-FLI1 expression and(ii) interactions between genes and proteins are repre-sented by influences (simple signed regulatory links)

The long-term goal is the construction of a theoreticalmodel that fits heterogeneous experimental data (genomictranscriptomic proteomic in cell lines and primarytumors) In other words we intend to construct a Ewingsarcoma-specific model similarly to what has been donefor liver cancer (75) Such a model should enable topropose (combination of) therapeutic strategie(s) specific-ally targeting phenotypes (such as proliferation and apop-tosis induction)

SUPPLEMENTARY DATA

Supplementary Data are available at NAR online

ACKNOWLEDGEMENTS

We thank David Gentien and Laurent Daudet for theirhelp A Zinovyev and O Delattre are considered as jointlast co-authors

FUNDING

Institut National de la Sante et de la Recherche MedicaleInstitut Curie Agence National de la Recherche [SITCONproject NR-06-BYOS-0004] Institut National du Cancer[SYBEwing project 2009-1-PLBIO-04] Ligue Nationalecontre le Cancer (Equipe labellisee and CIT program)Reseau National des Genopoles European Union(APOSYS KCK and EET pipeline projects) societe

Francaise des Cancers de lrsquoEnfant and the following asso-ciations Courir pour Mathieu Dans les pas du GeantOlivier Chape Les Bagouzamanon and les Amis deClaire The research leading to these results has receivedfunding from the European Union Seventh FrameworkProgramme (FP72007-2013) ASSET project [FP7-HEALTH-2010-259348] Funding for open accesscharge Institut Curie

Conflict of interest statement None declared

REFERENCES

1 DelattreO ZucmanJ PlougastelB DesmazeC MelotTPeterM KovarH JoubertI De JongP RouleauG et al(1992) Gene fusion with an ETS DNA-binding domain caused bychromosome translocation in human tumours Nature 359162ndash165

2 MayWA GishizkyML LessnickSL LunsfordLBLewisBC DelattreO ZucmanJ ThomasG and DennyCT(1993) Ewing sarcoma 1122 translocation produces a chimerictranscription factor that requires the DNA-binding domainencoded by FLI1 for transformation Proc Natl Acad Sci USA90 5752ndash5756

3 Castillero-TrejoY EliazerS XiangL RichardsonJA andIlariaRL (2005) Expression of the EWSFLI-1 oncogene inmurine primary bone-derived cells Results in EWSFLI-1-dependent ewing sarcoma-like tumors Cancer Res 658698ndash8705

4 RiggiN CironiL ProveroP SuvaML KaloulisK Garcia-EcheverriaC HoffmannF TrumppA and StamenkovicI(2005) Development of Ewingrsquos sarcoma from primary bonemarrow-derived mesenchymal progenitor cells Cancer Res 6511459ndash11468

5 TanakaK IwakumaT HarimayaK SatoH and IwamotoY(1997) EWS-Fli1 antisense oligodeoxynucleotide inhibitsproliferation of human Ewingrsquos sarcoma and primitiveneuroectodermal tumor cells J Clin Invest 99 239ndash247

6 Hu-LieskovanS HeidelJD BartlettDW DavisME andTricheTJ (2005) Sequence-specific knockdown of EWS-FLI1 bytargeted nonviral delivery of small interfering RNA inhibitstumor growth in a murine model of metastatic Ewingrsquos sarcomaCancer Res 65 8984ndash8992

7 NakataniF TanakaK SakimuraR MatsumotoYMatsunobuT LiX HanadaM OkadaT and IwamotoY(2003) Identification of p21WAF1CIP1 as a direct target ofEWS-Fli1 oncogenic fusion protein J Biol Chem 27815105ndash15115

8 FukumaM OkitaH HataJ and UmezawaA (2003)Upregulation of Id2 an oncogenic helix-loop-helix protein ismediated by the chimeric EWSets protein in Ewing sarcomaOncogene 22 1ndash9

9 SanchezG BittencourtD LaudK BarbierJ DelattreOAuboeufD and DutertreM (2008) Alteration of cyclin D1transcript elongation by a mutated transcription factor up-regulates the oncogenic D1b splice isoform in cancer Proc NatlAcad Sci USA 105 6004ndash6009

10 LiX TanakaK NakataniF MatsunobuT SakimuraRHanadaM OkadaT NakamuraT and IwamotoY (2005)Transactivation of cyclin E gene by EWS-Fli1 and antitumoreffects of cyclin dependent kinase inhibitor on Ewingrsquos familytumor cells Int J Cancer 116 385ndash394

11 DauphinotL De OliveiraC MelotT SevenetN ThomasVWeissmanBE and DelattreO (2001) Analysis of the expressionof cell cycle regulators in Ewing cell lines EWS-FLI-1 modulatesp57KIP2and c-Myc expression Oncogene 20 3258ndash3265

12 HahmKB (1999) Repression of the gene encoding the TGF-betatype II receptor is a major target of the EWS-FLI1 oncoproteinNat Genet 23 481

13 ScotlandiK BeniniS SartiM SerraM LolliniPLMauriciD PicciP ManaraMC and BaldiniN (1996) Insulin-


at University C



ownloaded from

like growth factor I receptor-mediated circuit in Ewingrsquos sarcomaperipheral neuroectodermal tumor a possible therapeutic targetCancer Res 56 4570ndash4574

14 PrieurA TirodeF CohenP and DelattreO (2004) EWSFLI-1silencing and gene profiling of Ewing cells reveal downstreamoncogenic pathways and a crucial role for repression of insulin-like growth factor binding protein 3 Mol Cell Biol 247275ndash7283

15 BeniniS ManaraMC CerisanoV PerdichizziSStrammielloR SerraM PicciP and ScotlandiK (2004)Contribution of MEKMAPK and PI3-K signaling pathway tothe malignant behavior of Ewingrsquos sarcoma cells therapeuticprospects Int J Cancer 108 358ndash366

16 SohnEJ LiH ReidyK BeersLF ChristensenBL andLeeSB (2010) EWSFLI1 oncogene activates caspase 3transcription and triggers apoptosis in vivo Cancer Res 701154ndash1163

17 JavelaudD WietzerbinJ DelattreO and BesanconF (2000)Induction of p21Waf1Cip1 by TNFalpha requires NF-kappaBactivity and antagonizes apoptosis in Ewing tumor cellsOncogene 19 61ndash68

18 HancockJD and LessnickSL (2008) A transcriptional profilingmeta-analysis reveals a core EWS-FLI gene expression signatureCell Cycle 7 250ndash256

19 KauerM BanJ KoflerR WalkerB DavisS MeltzerP andKovarH (2009) A molecular function map of Ewingrsquos sarcomaPloS One 4 e5415

20 KitanoH (2002) Looking beyond the details a rise in system-oriented approaches in genetics and molecular biology CurrGenet 41 1ndash10

21 Gonzalez-AnguloAM HennessyBT and MillsGB (2010)Future of personalized medicine in oncology a systems biologyapproach J Clin Oncol 28 2777ndash2783

22 AkutsuT MiyanoS and KuharaS (2000) Inferring qualitativerelations in genetic networks and metabolic pathwaysBioinformatics 16 727ndash734

23 KinseyM SmithR and LessnickSL (2006) NR0B1 is requiredfor the oncogenic phenotype mediated by EWSFLI in Ewingrsquossarcoma Mol Cancer Res 4 851ndash859

24 TirodeF Laud-DuvalK PrieurA DelormeB CharbordPand DelattreO (2007) Mesenchymal stem cell features of Ewingtumors Cancer Cell 11 421ndash429

25 SahinO FrohlichH LobkeC KorfU BurmesterSMajetyM MatternJ SchuppI ChaouiyaC ThieffryD et al(2009) Modeling ERBB receptor-regulated G1S transition to findnovel targets for de novo trastuzumab resistance BMC SystBiol 3 1

26 AshburnerM BallCA BlakeJA BotsteinD ButlerHCherryJM DavisAP DolinskiK DwightSS EppigJTet al (2000) Gene ontology tool for the unification of biologyGene Ontology Consortium Nat Genet 25 25ndash29

27 SubramanianA TamayoP MoothaVK MukherjeeSEbertBL GilletteMA PaulovichA PomeroySLGolubTR LanderES et al (2005) Gene set enrichmentanalysis a knowledge-based approach for interpreting genome-wide expression profiles Proc Natl Acad Sci USA 10215545ndash15550

28 ZinovyevA ViaraE CalzoneL and BarillotE (2008) BiNoMa Cytoscape plugin for manipulating and analyzing biologicalnetworks Bioinformatics 24 876ndash877

29 AlterO BrownPO and BotsteinD (2000) Singular valuedecomposition for genome-wide expression data processing andmodeling Proc Natl Acad Sci USA 97 10101ndash10106

30 DennisG ShermanBT HosackDA YangJ GaoWLaneHC and LempickiRA (2003) DAVID Database forannotation visualization and integrated discovery Genome Biol4 P3

31 Huang daW ShermanBT and LempickiRA (2009) Systematicand integrative analysis of large gene lists using DAVIDbioinformatics resources Nat Protoc 4 44ndash57

32 ChanskyHA Barahmand-PourF MeiQ Kahn-FarooqiWZielinska-KwiatkowskaA BlackburnM ChanskyKConradEU BrucknerJD GreenleeTK et al (2004) Targetingof EWSFLI-1 by RNA interference attenuates the tumor

phenotype of Ewingrsquos sarcoma cells in vitro J Orthop Res 22910ndash917

33 OdaK MatsuokaY FunahashiA and KitanoH (2005) Acomprehensive pathway map of epidermal growth factor receptorsignaling Mol Syst Biol 1 20050010

34 CalzoneL GelayA ZinovyevA RadvanyiF and BarillotE(2008) A comprehensive modular map of molecular interactionsin RBE2F pathway Mol Syst Biol 4 173

35 ThieffryD and ThomasR (1998) Qualitative analysis of genenetworks Pac Symp Biocomput 1998 77ndash88

36 KrullM PistorS VossN KelA ReuterI KronenbergDMichaelH SchwarzerK PotapovA ChoiC et al (2006)TRANSPATH an information resource for storing andvisualizing signaling pathways and their pathological aberrationsNucleic Acids Res 34 D546ndashD551

37 ShannonP MarkielA OzierO BaligaNS WangJTRamageD AminN SchwikowskiB and IdekerT (2003)Cytoscape a software environment for integrated models ofbiomolecular interaction networks Genome Res 13 2498ndash2504

38 DemirE CaryMP PaleyS FukudaK LemerC VastrikIWuG DrsquoEustachioP SchaeferC LucianoJ et al (2010) TheBioPAX community standard for pathway data sharing NatBiotechnol 28 935ndash942

39 SurdezD BenetkiewiczM PerrinV HanZ-Y PierronGBalletS LamoureuxF RediniF DecouvelaereA-VDaudigeos-DubusE et al (2012) Targeting the EWSR1-FLI1oncogene-induced protein kinase PKC-b abolishes ewing sarcomagrowth Cancer Res 72 4494ndash4503

40 GuillonN TirodeF BoevaV ZynovyevA BarillotE andDelattreO (2009) The oncogenic EWS-FLI1 protein bindsin vivo GGAA microsatellite sequences with potentialtranscriptional activation function PLoS One 4 e4932

41 BoevaV SurdezD GuillonN TirodeF FejesAPDelattreO and BarillotE (2010) De novo motif identificationimproves the accuracy of predicting transcription factor bindingsites in ChIP-Seq data analysis Nucleic Acids Res 38 e126

42 WeiGH BadisG BergerMF KiviojaT PalinK EngeMBonkeM JolmaA VarjosaloM GehrkeAR et al (2010)Genome-wide analysis of ETS-family DNA-binding in vitro andin vivo EMBO J 29 2147ndash2160

43 MedemaRH KopsGJ BosJL and BurgeringBM (2000)AFX-like Forkhead transcription factors mediate cell-cycleregulation by Ras and PKB through p27kip1 Nature 404782ndash787

44 ModurV NagarajanR EversBM and MilbrandtJ (2002)FOXO proteins regulate tumor necrosis factor-related apoptosisinducing ligand expression Implications for PTEN mutation inprostate cancer J Biol Chem 277 47928ndash47937

45 LabiedS KajiharaT MadureiraPA FusiL JonesMCHighamJM VarshochiR FrancisJM ZoumpoulidouGEssafiA et al (2006) Progestins regulate the expression andactivity of the forkhead transcription factor FOXO1 indifferentiating human endometrium Mol Endocrinol 20 35ndash44

46 WuMX AoZ PrasadKV WuR and SchlossmanSF (1998)IEX-1L an apoptosis inhibitor involved in NF-kappaB-mediatedcell survival Science 281 998ndash1001

47 GarciaJ YeY ArranzV LetourneuxC PezeronG andPorteuF (2002) IEX-1 a new ERK substrate involved in bothERK survival activity and ERK activation EMBO J 215151ndash5163

48 KataokaT and TschoppJ (2004) N-terminal fragment of c-FLIP(L) processed by caspase 8 specifically interacts with TRAF2and induces activation of the NF-kappaB signaling pathway MolCell Biol 24 2627ndash2636

49 NowakK KillmerK GessnerC and LutzW (2007) E2F-1regulates expression of FOXO1 and FOXO3a Biochim BiophysActa 1769 244ndash252

50 WuMX (2003) Roles of the stress-induced gene IEX-1 inregulation of cell death and oncogenesis Apoptosis 8 11ndash18

51 MicheauO LensS GaideO AlevizopoulosK and TschoppJ(2001) NF-kappaB signals induce the expression of c-FLIP MolCell Biol 21 5299ndash5305

52 RicciMS JinZ DewsM YuD Thomas-TikhonenkoADickerDT and El-DeiryWS (2004) Direct repression of FLIP


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expression by c-myc is a major determinant of TRAIL sensitivityMol Cell Biol 24 8541ndash8555

53 IaquintaPJ and LeesJA (2007) Life and death decisions by theE2F transcription factors Curr Opin Cell Biol 19 649ndash657

54 ClineMS SmootM CeramiE KuchinskyA LandysNWorkmanC ChristmasR Avila-CampiloI CreechMGrossB et al (2007) Integration of biological networks and geneexpression data using Cytoscape Nat Protoc 2 2366ndash2382

55 The ENCODE Project Consortium (2004) The ENCODE(ENCyclopedia Of DNA Elements) Project Science 306636ndash640

56 BaiJ ZhouY ChenG ZengJ DingJ TanY ZhouJ andLiG (2011) Overexpression of Cullin1 is associated with poorprognosis of patients with gastric cancer Hum Pathol 42375ndash383

57 ChenG and LiG (2010) Increased Cul1 expression promotesmelanoma cell proliferation through regulating p27 expressionInt J Oncol 37 1339ndash1344

58 MatsunobuT TanakaK NakamuraT NakataniFSakimuraR HanadaM LiX OkadaT OdaYTsuneyoshiM et al (2006) The possible role of EWS-Fli1 inevasion of senescence in Ewing family tumors Cancer Res 66803ndash811

59 FrescasD and PaganoM (2008) Deregulated proteolysis by theF-box proteins SKP2 and beta-TrCP tipping the scales of cancerNat Rev 8 438ndash449

60 ChangYF ChengCM ChangLK JongYJ and YuoCY(2006) The F-box protein Fbxo7 interacts with human inhibitorof apoptosis protein cIAP1 and promotes cIAP1 ubiquitinationBiochem Biophys Res Commun 342 1022ndash1026

61 MackintoshC Garcıa-DomınguezDJ OrdonezJL Ginel-PicardoA SmithPG SacristanMP and De AlavaE (2012)WEE1 accumulation and deregulation of S-phase proteins mediateMLN4924 potent inhibitory effect on Ewing sarcoma cellsOncogene 32 1441ndash1451

62 SachdevaM ZhuS WuF WuH WaliaV KumarSElbleR WatabeK and MoY-Y (2009) p53 represses c-Mycthrough induction of the tumor suppressor miR-145 Proc NatlAcad Sci USA 106 3207ndash3212

63 FranzettiGA Laud-DuvalK BellangerD SternMH Sastre-GarauX and DelattreO (2012) MiR-30a-5p connects EWS-FLI1and CD99 two major therapeutic targets in Ewing tumorOncogene (doi101038onc2012403 epub ahead of printSeptember 17 2012)

64 De AlavaE AntonescuCR PanizoA LeungD MeyersPAHuvosAG Pardo-MindanFJ HealeyJH and LadanyiM(2000) Prognostic impact of P53 status in Ewing sarcoma Cancer89 783ndash792

65 HuangH-Y IlleiPB ZhaoZ MazumdarM HuvosAGHealeyJH WexlerLH GorlickR MeyersP and LadanyiM(2005) Ewing sarcomas with p53 mutation or p16p14ARFhomozygous deletion a highly lethal subset associated with poorchemoresponse J Clin Oncol 23 548ndash558

66 BanJ Bennani-BaitiIM KauerM SchaeferKL PorembaCJugG SchwentnerR SmrzkaO MuehlbacherK AryeeDNet al (2008) EWS-FLI1 suppresses NOTCH-activated p53 inEwingrsquos sarcoma Cancer Res 68 7100ndash7109

67 PicardaG LamoureuxF GeffroyL DelepineP MontierTLaudK TirodeF DelattreO HeymannD and RediniF(2010) Preclinical evidence that use of TRAIL in Ewingrsquos sarcomaand osteosarcoma therapy inhibits tumor growth preventsosteolysis and increases animal survival Clin Cancer Res 162363ndash2374

68 AryeeDN NiedanS KauerM SchwentnerR Bennani-BaitiIM BanJ MuehlbacherK KreppelM WalkerRLMeltzerP et al (2010) Hypoxia modulates EWS-FLI1transcriptional signature and enhances the malignant properties ofEwingrsquos sarcoma cells in vitro Cancer Res 70 4015ndash4023

69 GrunewaldTGP DieboldI EspositoI PlehmS HauerKThielU Da Silva-ButtkusP NeffF UnlandR Muller-TidowC et al (2012) STEAP1 is associated with the invasiveand oxidative stress phenotype of Ewing tumors Mol CancerRes 10 52ndash65

70 NavarroD AgraN PestanaA AlonsoJ and Gonzalez-SanchoJM (2010) The EWSFLI1 oncogenic protein inhibitsexpression of the Wnt inhibitor DICKKOPF-1 gene andantagonizes beta-cateninTCF-mediated transcriptionCarcinogenesis 31 394ndash401

71 ZwernerJP JooJ WarnerKL ChristensenL Hu-LieskovanS TricheTJ and MayWA (2008) The EWSFLI1oncogenic transcription factor deregulates GLI1 Oncogene 273282ndash3291

72 YangL HuHM Zielinska-KwiatkowskaA and ChanskyHA(2010) FOXO1 is a direct target of EWS-Fli1 oncogenic fusionprotein in Ewingrsquos sarcoma cells Biochem Biophys ResCommun 402 129ndash134

73 SauerU HeinemannM and ZamboniN (2007) GeneticsGetting closer to the whole picture Science 316 550ndash551

74 Saez-RodriguezJ MirschelS HemenwayR KlamtSGillesED and GinkelM (2006) Visual setup of logical modelsof signaling and regulatory networks with ProMoT BMCBioinformatics 7 506

75 AlexopoulosLG Saez-RodriguezJ CosgroveBDLauffenburgerDA and SorgerPK (2010) Networks inferredfrom biochemical data reveal profound differences in toll-likereceptor and inflammatory signaling between normal andtransformed hepatocytes Mol Cell Proteomics 9 1849ndash1865


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down-stream targets within a pathway missing possibleinterplays with other pathways Recent reports havestarted to address these issues by meta-analysis ofgenome-scale data to identify lists of the genes that arederegulated by EWS-FLI1 in Ewingrsquos sarcoma models(18) or linked to cell cycle regulation proliferationresponse to DNA damage and cell differentiation (19)The above mentioned publications favor the point of

view that EWS-FLI1 has a pleiotropic effect and shouldbe considered in the context of a global gene regulationnetwork This justifies the usage of a systems biologyapproach (20) ultimately such an approach produces anabstract model including deregulated genes and describinghow these genes interact with each other (21) The signal-ing network regulated by EWS-FLI1 is specific to thisdisease and can be considered as the basis for its theoret-ical description This description is possible because Ewingsarcoma is more genetically homogenous than othercancers where the choice of deregulated pathways ismore difficultA valuable source of data for systems biology

approaches is time-resolved response of perturbed experi-mental systems These data allow constructing mathemat-ical models describing time evolution of molecularnetworks and predicting their response to various perturb-ations (22) Time-series of transcriptome response tosilencingre-expressing of EWS-FLI1 were published in(23) However these experiments did not allow to followthe transcriptome response for a time period longer than afew days whereas significant transcriptome changes afterEWS-FLI1 inhibition can be observed even after 1 weekHere we took advantage of cell lines transformed with atetracycline inducible shRNA system targeting EWS-FLI1transcript (24) and collected long-term [inhibitory(17 days) and inhibitory (10 days)re-expression(7 days)]transcriptional time seriesThis article presents a network model dedicated to

Ewing sarcoma it describes EWS-FLI1 effect on prolifer-ation and apoptosis We decided to represent it through alsquogene influence networkrsquo as it is the only suitable repre-sentation for including incompletely characterized mo-lecular interactions This model was constructed in threesteps (i) Time-series data obtained in EWS-FLI1modulated cell lines were analyzed An original theoreticalmethod was developed for selecting genes modulated byEWS-FLI1 and involved in cell-cycle regulation and apop-tosis (ii) An influence network was reconstructed from theliterature connecting the above selected genes (iii)Experimental validation of a part of the regulationnetwork was performed in five Ewing cell lines Inaddition some additional transcriptional influences wereidentified by network reverse engineering using genesilencing data These influences were compared with theliterature-based network and confirmed its validity Thiscomparison also allowed to highlight EWS-FLI1 implica-tion in the regulation of the ubiquitin proteasome system(through CUL1 SKP2 ) and to identify CUL1 as anovel direct target of EWS-FLI1The detailed description of the signaling involved in

Ewing sarcoma oncogenesis should provide backgroundfor further theoretical search of combinatorial therapeutic

strategies by predictive mathematical modeling as it isdone in other cancer studies (25)

MATERIALS AND METHODS

Transcriptome time series of shRNA-inducible Ewingcell lines

Tetracycline-inducible shRNA (directed againstEWS-FLI1) clones shA673-1C and -2C (24) were used toperform a long-term inhibitory (t=0ndash17 days) and inhibi-tory (t=0ndash10 days)rescue (t=10ndash17 days) time seriesexperiments EWS-FLI1 invalidation was achieved byadding 1 mgml of doxycycline in the cell culture mediaCells were split twice a week For the inhibitory timeseries RNAs were collected at day 0 1 2 3 6 9 1113 15 17 after addition of doxycycline to the mediaFor the rescue time series doxycycline was omitted fromthe media after 10 days and RNAs were collected at day13 15 and 17 Total RNAs were isolated using the TrizolReagent (Invitrogen) at the different time points EWS-FLI1 silencing and re-expression was validated by real-time quantitative reverse transcription-PCR as previouslydescribed by Tirode et al (24) Gene expression profiles ofthe time series experiments were assessed by microarrayprofiling using Affymetrix HG-U133plus2 arrays(Affymetrix Inc Santa Clara CA) Experimental proced-ures for cRNA target synthesis and GeneChip microarraywere done according to the standard protocols describedby Affymetrix Company

Fitting non-linear response models to the time series

Points of time series were fitted by two types of curves

(i) Hyperbolic tangent

sw xeth THORN A+Btanhethax+bTHORN (a lsquoswitchrsquo with four parameters)(ii) Generalized Gaussian

puethxTHORN A+Bexp xteth THORN2a

b

(a lsquopulsersquo with five parameters)

For the temporal response of each probeset in eachclone the hyperbolic tangent was fitted in the case ofsimple inhibition of EWS-FLI1 and the generalizedGaussian in the case of inhibitionre-expression ofEWS-FLI1 The score for each fit is the ratio betweenan amplitude and a mean-squared error multiplied bya transition time penalization factor t

sc frac14

The mean-squared error is the square root of the sumof squared differences between the curve and data pointsThe amplitude is the difference between the high and lowexpression levels These levels are defined as follows

(i) For the hyperbolic tangent (lsquoswitchrsquo) the inflexionpoint of the curve define naturally a transition timeseparating the time points in a high level and a lowlevel window The two levels are simply the averagesof data points on the two windows defined above

(ii) For the generalized Gaussian (lsquopulsersquo) the two in-flection points of the curve define three time


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frac14 exp f l=2

l

2



frac14 exp f1 l=3

l

2

f2 2l=3

l

2















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ownloaded from








RESULTS







Genes selection



Genes selectionGSEA


Genes selection


Genes selection


GSEA


Genes selectionGSEA


Genes selectionGSEA


Genes selectionGSEA




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ownloaded from

Table

2A

subsetofthefact

sheetusedto

construct

thenetwork

ReviewRef

Experim

entR

efLink

Chem

Type

Delay

Confidence

Tissue

Comments

PMID

10074428

TRAF2

(NFKB)

Influence

12h

07

TRAF2mutant

Embryonic

Kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

PMID

10074428

MAP3K14

(NFKB)

Influence

12h

07

MAP3K14mutant

Embryonic

kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

(other

nameforMAP3K14NIK

)PMID

12887914

TNFRSF1A

(TNFRSF1AR

PAIN

)Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

TNFRSF1A

(TNFRSF1ATRAF2)

Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

PMID

12887914

(TNFRSF1AR

PAIN

)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

(TNFRSF1ATRAF2)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

TNFRSF1A

CTSB

Release

06

TNFR

permeablizedthe

lysosomemem

brane

release

CTSBtrueforother

cathepsin

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

CTSB

BID

Cleavage

08

Invitro

Bid

induce

apoptosisthrough


PMID

16502253

(NFKB)-jCTSB


influence

07

ThroughSPIN

2Afigure

PMID

16502253

CASP8

CTSB

Release

06

Hepatocyte


PMID

16502253

CTSB

[apoptosis]

Chromatin

condensation

07

Cell-free

system

s

PMID

16502253

CTSB

BAX

Influence

04

Mutantmice


explain

BID

free

apoptosis

inducedbyCTSB


given

inthefirstline



connectionGenes

are

named

accordingly

toHUGO

names

ofthecomplexes

are

enclosedinto




are

enclosedinto


bersseparatedby

commaordefined


FKB)

notifies



at University C



ownloaded from





1

2


cell lines







data mining






necessary connecon

3

5

7

4

6

NETWORK




Fact sheet

Gene selecon





A B



at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

CASP3

0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

E2F5

0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

250

300

350

0 5 10 15 20

CFLAR

0

25

50

75

100

125

150

0 5 10 15 20

CUL1

0

25

50

75

100

125

150

0 5 10 15 20

PARP1

0

100

200

300

400

500

600

700

0 5 10 15 20

IER3

0

100

200

300

400

500

600

700

0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

0200400600800

1000120014001600

24h 48h 72h

IER3

rela

ve

expr

essio

n le

vel

days hours

A



(continued)

at University C



ownloaded from









PARP1

CUL1

EWS-FLI1

bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

MYC

E2F1

E2F2

E2F5

BEW24

siCT

siEF1

siCT

siEF1

SKNMCA673

siCT

siEF1

siCT

siEF1

EW772h

0 1 2 3 6 10 12 days

shA673-1C

dox



at University C



ownloaded from




AQP1 E2F2

of E

WS-

FLI1

Inhi

bio

n amp

reac

va

onof

EW

S-FL

I1

CDKN1C


SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

04

0 5 10 15 20

A B

C



4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



A

B



at University C



ownloaded from









genes

Node Genes Link




at University C



ownloaded from











DISCUSSION






at University C



ownloaded from


















at University C



ownloaded from







Affy

Sign

al In

tens

ity (

log2

)


P300 IER3

RELA

Necessaryconnecon

EWS-FLI1 CUL1

Nor

mal

ized

expr

essio

n le

vel [

]


I

II



99

10

101

102

103

104

105

0 5 10 15 20

CUL1 (207614_s_at)

0

100

200

300

400


P300 RELA IER3

days



at University C



ownloaded from


Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


ACKNOWLEDGEMENTS


FUNDING




REFERENCES















at University C



ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from




frac14 exp f l=2

l

2



frac14 exp f1 l=3

l

2

f2 2l=3

l

2















at University C



ownloaded from








RESULTS







Genes selection



Genes selectionGSEA


Genes selection


Genes selection


GSEA


Genes selectionGSEA


Genes selectionGSEA


Genes selectionGSEA




at University C



ownloaded from

Table

2A

subsetofthefact

sheetusedto

construct

thenetwork

ReviewRef

Experim

entR

efLink

Chem

Type

Delay

Confidence

Tissue

Comments

PMID

10074428

TRAF2

(NFKB)

Influence

12h

07

TRAF2mutant

Embryonic

Kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

PMID

10074428

MAP3K14

(NFKB)

Influence

12h

07

MAP3K14mutant

Embryonic

kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

(other

nameforMAP3K14NIK

)PMID

12887914

TNFRSF1A

(TNFRSF1AR

PAIN

)Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

TNFRSF1A

(TNFRSF1ATRAF2)

Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

PMID

12887914

(TNFRSF1AR

PAIN

)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

(TNFRSF1ATRAF2)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

TNFRSF1A

CTSB

Release

06

TNFR

permeablizedthe

lysosomemem

brane

release

CTSBtrueforother

cathepsin

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

CTSB

BID

Cleavage

08

Invitro

Bid

induce

apoptosisthrough


PMID

16502253

(NFKB)-jCTSB


influence

07

ThroughSPIN

2Afigure

PMID

16502253

CASP8

CTSB

Release

06

Hepatocyte


PMID

16502253

CTSB

[apoptosis]

Chromatin

condensation

07

Cell-free

system

s

PMID

16502253

CTSB

BAX

Influence

04

Mutantmice


explain

BID

free

apoptosis

inducedbyCTSB


given

inthefirstline



connectionGenes

are

named

accordingly

toHUGO

names

ofthecomplexes

are

enclosedinto




are

enclosedinto


bersseparatedby

commaordefined


FKB)

notifies



at University C



ownloaded from





1

2


cell lines







data mining






necessary connecon

3

5

7

4

6

NETWORK




Fact sheet

Gene selecon





A B



at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

CASP3

0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

E2F5

0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

250

300

350

0 5 10 15 20

CFLAR

0

25

50

75

100

125

150

0 5 10 15 20

CUL1

0

25

50

75

100

125

150

0 5 10 15 20

PARP1

0

100

200

300

400

500

600

700

0 5 10 15 20

IER3

0

100

200

300

400

500

600

700

0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

0200400600800

1000120014001600

24h 48h 72h

IER3

rela

ve

expr

essio

n le

vel

days hours

A



(continued)

at University C



ownloaded from









PARP1

CUL1

EWS-FLI1

bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

MYC

E2F1

E2F2

E2F5

BEW24

siCT

siEF1

siCT

siEF1

SKNMCA673

siCT

siEF1

siCT

siEF1

EW772h

0 1 2 3 6 10 12 days

shA673-1C

dox



at University C



ownloaded from




AQP1 E2F2

of E

WS-

FLI1

Inhi

bio

n amp

reac

va

onof

EW

S-FL

I1

CDKN1C


SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

04

0 5 10 15 20

A B

C



4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



A

B



at University C



ownloaded from









genes

Node Genes Link




at University C



ownloaded from











DISCUSSION






at University C



ownloaded from


















at University C



ownloaded from







Affy

Sign

al In

tens

ity (

log2

)


P300 IER3

RELA

Necessaryconnecon

EWS-FLI1 CUL1

Nor

mal

ized

expr

essio

n le

vel [

]


I

II



99

10

101

102

103

104

105

0 5 10 15 20

CUL1 (207614_s_at)

0

100

200

300

400


P300 RELA IER3

days



at University C



ownloaded from


Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


ACKNOWLEDGEMENTS


FUNDING




REFERENCES















at University C



ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from








RESULTS







Genes selection



Genes selectionGSEA


Genes selection


Genes selection


GSEA


Genes selectionGSEA


Genes selectionGSEA


Genes selectionGSEA




at University C



ownloaded from

Table

2A

subsetofthefact

sheetusedto

construct

thenetwork

ReviewRef

Experim

entR

efLink

Chem

Type

Delay

Confidence

Tissue

Comments

PMID

10074428

TRAF2

(NFKB)

Influence

12h

07

TRAF2mutant

Embryonic

Kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

PMID

10074428

MAP3K14

(NFKB)

Influence

12h

07

MAP3K14mutant

Embryonic

kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

(other

nameforMAP3K14NIK

)PMID

12887914

TNFRSF1A

(TNFRSF1AR

PAIN

)Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

TNFRSF1A

(TNFRSF1ATRAF2)

Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

PMID

12887914

(TNFRSF1AR

PAIN

)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

(TNFRSF1ATRAF2)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

TNFRSF1A

CTSB

Release

06

TNFR

permeablizedthe

lysosomemem

brane

release

CTSBtrueforother

cathepsin

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

CTSB

BID

Cleavage

08

Invitro

Bid

induce

apoptosisthrough


PMID

16502253

(NFKB)-jCTSB


influence

07

ThroughSPIN

2Afigure

PMID

16502253

CASP8

CTSB

Release

06

Hepatocyte


PMID

16502253

CTSB

[apoptosis]

Chromatin

condensation

07

Cell-free

system

s

PMID

16502253

CTSB

BAX

Influence

04

Mutantmice


explain

BID

free

apoptosis

inducedbyCTSB


given

inthefirstline



connectionGenes

are

named

accordingly

toHUGO

names

ofthecomplexes

are

enclosedinto




are

enclosedinto


bersseparatedby

commaordefined


FKB)

notifies



at University C



ownloaded from





1

2


cell lines







data mining






necessary connecon

3

5

7

4

6

NETWORK




Fact sheet

Gene selecon





A B



at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

CASP3

0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

E2F5

0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

250

300

350

0 5 10 15 20

CFLAR

0

25

50

75

100

125

150

0 5 10 15 20

CUL1

0

25

50

75

100

125

150

0 5 10 15 20

PARP1

0

100

200

300

400

500

600

700

0 5 10 15 20

IER3

0

100

200

300

400

500

600

700

0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

0200400600800

1000120014001600

24h 48h 72h

IER3

rela

ve

expr

essio

n le

vel

days hours

A



(continued)

at University C



ownloaded from









PARP1

CUL1

EWS-FLI1

bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

MYC

E2F1

E2F2

E2F5

BEW24

siCT

siEF1

siCT

siEF1

SKNMCA673

siCT

siEF1

siCT

siEF1

EW772h

0 1 2 3 6 10 12 days

shA673-1C

dox



at University C



ownloaded from




AQP1 E2F2

of E

WS-

FLI1

Inhi

bio

n amp

reac

va

onof

EW

S-FL

I1

CDKN1C


SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

04

0 5 10 15 20

A B

C



4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



A

B



at University C



ownloaded from









genes

Node Genes Link




at University C



ownloaded from











DISCUSSION






at University C



ownloaded from


















at University C



ownloaded from







Affy

Sign

al In

tens

ity (

log2

)


P300 IER3

RELA

Necessaryconnecon

EWS-FLI1 CUL1

Nor

mal

ized

expr

essio

n le

vel [

]


I

II



99

10

101

102

103

104

105

0 5 10 15 20

CUL1 (207614_s_at)

0

100

200

300

400


P300 RELA IER3

days



at University C



ownloaded from


Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


ACKNOWLEDGEMENTS


FUNDING




REFERENCES















at University C



ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from

Table

2A

subsetofthefact

sheetusedto

construct

thenetwork

ReviewRef

Experim

entR

efLink

Chem

Type

Delay

Confidence

Tissue

Comments

PMID

10074428

TRAF2

(NFKB)

Influence

12h

07

TRAF2mutant

Embryonic

Kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

PMID

10074428

MAP3K14

(NFKB)

Influence

12h

07

MAP3K14mutant

Embryonic

kidney

293cells

ActivationofNFKB

byTNFS18

wasobserved

24hlater

(other

nameforMAP3K14NIK

)PMID

12887914

TNFRSF1A

(TNFRSF1AR

PAIN

)Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

TNFRSF1A

(TNFRSF1ATRAF2)

Binding

08

ComplexIform

ation(other

nameforTNFRSF1A

TNF-R

1)

PMID

12887914

(TNFRSF1AR

PAIN

)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

(other

namefor

RPAIN

RIP)

PMID

12887914

(TNFRSF1ATRAF2)

(NFKB)


influence

07

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

TNFRSF1A

CTSB

Release

06

TNFR

permeablizedthe

lysosomemem

brane

release

CTSBtrueforother

cathepsin

(other

nameforTNFRSF1A

TNF-R

1)

PMID

16502253

CTSB

BID

Cleavage

08

Invitro

Bid

induce

apoptosisthrough


PMID

16502253

(NFKB)-jCTSB


influence

07

ThroughSPIN

2Afigure

PMID

16502253

CASP8

CTSB

Release

06

Hepatocyte


PMID

16502253

CTSB

[apoptosis]

Chromatin

condensation

07

Cell-free

system

s

PMID

16502253

CTSB

BAX

Influence

04

Mutantmice


explain

BID

free

apoptosis

inducedbyCTSB


given

inthefirstline



connectionGenes

are

named

accordingly

toHUGO

names

ofthecomplexes

are

enclosedinto




are

enclosedinto


bersseparatedby

commaordefined


FKB)

notifies



at University C



ownloaded from





1

2


cell lines







data mining






necessary connecon

3

5

7

4

6

NETWORK




Fact sheet

Gene selecon





A B



at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

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0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

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0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

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300

350

0 5 10 15 20

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0

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50

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0

25

50

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100

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0 5 10 15 20

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100

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300

400

500

600

700

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0

100

200

300

400

500

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0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

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1000120014001600

24h 48h 72h

IER3

rela

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expr

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vel

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A



(continued)

at University C



ownloaded from









PARP1

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0 1 2 3 6 10 12 days

shA673-1C

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at University C



ownloaded from




AQP1 E2F2

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SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

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0 5 10 15 20

A B

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4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



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at University C



ownloaded from









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Node Genes Link




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ownloaded from


















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at University C



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at University C



ownloaded from




SUPPLEMENTARY DATA


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ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from





1

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cell lines







data mining






necessary connecon

3

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NETWORK




Fact sheet

Gene selecon





A B



at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

PARP1

050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

EWS-FLI1

0

50

100

150

200

250

300

350

0 5 10 15 20

CASP3

0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

E2F5

0

25

50

75

100

125

150

0 5 10 15 20

E2F1

0

25

50

75

100

125

150

0 5 10 15 20

E2F2

0

50

100

150

200

0 5 10 15 20

MYC

0

50

100

150

200

250

300

350

0 5 10 15 20

CFLAR

0

25

50

75

100

125

150

0 5 10 15 20

CUL1

0

25

50

75

100

125

150

0 5 10 15 20

PARP1

0

100

200

300

400

500

600

700

0 5 10 15 20

IER3

0

100

200

300

400

500

600

700

0 5 10 15 20

FOXO1A

0

100

200

300

400

500

600

24h 48h 72h

FOXO1

0200400600800

1000120014001600

24h 48h 72h

IER3

rela

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expr

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days hours

A



(continued)

at University C



ownloaded from









PARP1

CUL1

EWS-FLI1

bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

MYC

E2F1

E2F2

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siCT

siEF1

SKNMCA673

siCT

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siCT

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0 1 2 3 6 10 12 days

shA673-1C

dox



at University C



ownloaded from




AQP1 E2F2

of E

WS-

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bio

n amp

reac

va

onof

EW

S-FL

I1

CDKN1C


SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

PL 019Tr ND

-04

-03

-02

-01

0

01

02

03

04

0 5 10 15 20

A B

C



4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



A

B



at University C



ownloaded from









genes

Node Genes Link




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ownloaded from











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at University C



ownloaded from


















at University C



ownloaded from







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Sign

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101

102

103

104

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0 5 10 15 20

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0

100

200

300

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at University C



ownloaded from


Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


ACKNOWLEDGEMENTS


FUNDING




REFERENCES















at University C



ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from

0

25

50

75

100

125

150

24h 48h 72h

EWS-FLI1

0

25

50

75

100

125

150

24h 48h 72h

CUL1

0

50

100

150

200

250

24h 48h 72h

CFLAR

0255075

100125150175200

24h 48h 72h

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050

100150200250300350400

24h 48h 72h

CASP3

0

25

50

75

100

125

150

24h 48h 72h

CCNA2

0

25

50

75

100

125

150

24h 48h 72h

MYC

0

25

50

75

100

125

150

24h 48h 72h

E2F1

0

50

100

150

200

24h 48h 72h

E2F2

0

25

50

75

100

125

150

24h 48h 72h

E2F5


0

25

50

75

100

125

150

0 5 10 15 20

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0

50

100

150

200

250

300

350

0 5 10 15 20

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0

25

50

75

100

125

150

0 5 10 15 20

CCNA2

0

25

50

75

100

125

150

0 5 10 15 20

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0

25

50

75

100

125

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0 5 10 15 20

E2F1

0

25

50

75

100

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150

0 5 10 15 20

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0

50

100

150

200

0 5 10 15 20

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0

50

100

150

200

250

300

350

0 5 10 15 20

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0

25

50

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0 5 10 15 20

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25

50

75

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0 5 10 15 20

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100

200

300

400

500

600

700

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0

100

200

300

400

500

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0 5 10 15 20

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0

100

200

300

400

500

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24h 48h 72h

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1000120014001600

24h 48h 72h

IER3

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(continued)

at University C



ownloaded from









PARP1

CUL1

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bACT

CFLAR

CASP3

PRKCB2

Cyclin A

Cyclin D

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E2F1

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siCT

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siCT

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0 1 2 3 6 10 12 days

shA673-1C

dox



at University C



ownloaded from




AQP1 E2F2

of E

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bio

n amp

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va

onof

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SL 08Tr 06 20 days

SL 008Tr ND


PL 4Tr 1 17 days

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-03

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0

01

02

03

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0 5 10 15 20

A B

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4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



ownloaded from



A

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ownloaded from









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Node Genes Link




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ownloaded from











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ownloaded from


















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Sign

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99

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0 5 10 15 20

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0

100

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at University C



ownloaded from


Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


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ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from









PARP1

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at University C



ownloaded from




AQP1 E2F2

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0

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A B

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4409 32102364

CUL1 CFLAR



at University C



ownloaded from










at University C



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100

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at University C



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Perspectives






at University C



ownloaded from




SUPPLEMENTARY DATA


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ownloaded from











































at University C



ownloaded from


























at University C



ownloaded from




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ownloaded from










at University C



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at University C



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ownloaded from


























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at University C



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ownloaded from


























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ownloaded from


























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ownloaded from


























at University C



ownloaded from

systems biology of ewing sarcoma: a network model of ews-fli1 … 2013.pdf · 2016. 5. 16. ·...

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